>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service, is >> welcome back here in New York City on stew Minimum. My co host is Corey Quinn. In the keynote this morning, Warner Vogel's made some new announcements what they're doing and also brought out a couple of customers who are local and really thrilled and excited to have on the program the C i O and E V P from Finn Ra here in New York City. Steve Randall, thanks so much for joining us. You're welcome. Thank you. All right, so, you know, quite impressive. You know when when I say one of those misunderstood words out there to talk about scale and you talk about speed and you know, you were you know, I'm taking so many notes in your keynote this 1 500,000 compute note. Seven terabytes worth of new data daily with half a trillion validation checks per day, some pretty impressive scale, and therefore, you know, it's I t is not the organ that kind of sits in the basement, and the business doesn't think about it business and I t need to be in lobster. So, you know, I think most people are familiar with in Rome. But maybe give us the kind of bumper sticker as Thio What dinner is today and you know, the >> the organization. Yeah, I started it Fender and 2013. I thought I was gonna come into a typical regulator, which is, as you alluded to technologies, kind of in the basement. Not very important, not strategic. And I realized very quickly two things. Number one, The team was absolutely talented. A lot of the people that we've got on her team came from start ups and other technology companies. Atypical financial service is and the second thing is we had a major big data challenge on our hands. And so the decision to go to the cloud S I started in March 2013. By July of that year, I was already having dialogue with our board of directors about having to go to the cloud in orderto handle the data. >> Yeah, so you know, big data was supposed to be that bit flip that turned that. Oh, my God. I have so much data to Oh, yea, I can monetize and do things with their data. So give us a little bit of that, That data journey And what? That that you talk about the flywheel? The fact that you've got inside Finneran. >> Yeah. So we knew that we needed the way were running at that time on data warehouse appliances from E, M. C. And IBM. And which a data warehouse appliance. You go back 10 15 years. That was where big data was running. But those machines are vertically scalable, and when you hit the top of the scale, then you've got to buy another bigger one, which might not be available. So public cloud computing is all about horizontal scale at commodity prices to things that those those data data warehouse appliance didn't have. They were vertical and proprietary, inexpensive. And so the key thing was to come up to select the cloud vendor between Google, IBM, You know, the usual suspects and architect our applications properly so that we wouldn't be overly vendor dependent on the cloud provider and locked in if you will, and that we could have flexibility to use commodity software. So we standardized in conjunction with our move to the public cloud on open source software, which we continue today. So no proprietary software for the most part running in the cloud. And we were just very smart about architect ing our systems at that point in time to make sure that those opportunities prevailed. And the other thing I would say, this kind of the secret of our success Is it because we were such early adopters we were in the financial service industry and a regulator toe boots that we had engineering access to the cloud providers and the big, big date open source software vendors. So we actually had the engineers from eight of us and other firms coming in to help us learn how to do it, to do it right. And that's been part of our culture ever since. >> One thing that was, I guess a very welcome surprise is normally these keynotes tend to fall into almost reductive tropes where first, we're gonna have some Twitter for pet style start up talking about all the higher level stuff they're doing, and then we're gonna have a large, more serious company. Come in and talk about how we moved of'em from our data center into the cloud gay Everyone clap instead, there was it was very clear. You're using higher level, much higher level service is on top of the cloud provider. It's not just running the M somewhere else in the same way you would on premise. Was that a transitional step that you went through or did you effectively when you went all in, start leveraging those higher service is >> okay. It's a great question. And ah, differentiator for us versus a lot. A lot of the large organizations with a legacy footprint that would not be practical to rewrite. We had outsourced I t entirely in the nineties E T s and it was brought back in source in in house early in this decade. And so we had kind of a fresh, fresh environment. Fresh people, no legacy, really other than the data warehouse appliances. So we had a spring a springboard to rewrite our abs in an agile way to be fully cloud enabled. So we work with eight of us. We work with Cloudera. We work with port works with all the key vendors at that time and space to figure out how to write Ah wraps so they could take most advantage of what the cloud was offering at that time. And that continues to prevail today. >> That that's a great point because, you know so often it's that journey to cloud. But it's that application modernization, that journey. Right. So bring us in little inside there is. You know how it is. You know, what expertise did Finn Ra have there? I mean, you don't want to be building applications. It is the open stuff source. The things wasn't mature enough. How much did they have toe help work, you know, Would you call it? You know, collaboration? >> Yeah. The first year was hard because I would have, you know, every high performance database vendor, and I see a number of them here today. I'm sure they're paddling their AWS version now, but they had a a private, proprietary database version. They're saying if you want to handle the volumes that you're seeing and predicting you really need a proprietary, they wouldn't call it proprietary. But it was essentially ah, very unique solution point solution that would cause vendor dependency. And so and then and then my architects internally, we're saying, No way, Wanna go open source because that's where the innovation and evolution is gonna be fastest. And we're not gonna have vendor Lock in that decision that that took about a year to solidify. But once we went that way, we never looked back. So from that standpoint, that was a good bad, and it made sense. The other element of your question is, how How much of this did we do on our own, rely on vendors again? The kind of dirty little secret of our beginnings here is that we ll average the engineer, you know, So typically a firm would get the sales staff, right. We got the engineers we insisted on in orderto have them teach our engineers how to do these re architectures to do it right. Um and we use that because we're in the financial service industry as a regulator, right? So they viewed us as a reference herbal account that would be very valuable in their portfolio. So in many regards, that was way scratch each other's back. But ultimately, the point isn't that their engineers trained our engineers who trained other engineers. And so when I when I did the, uh um keynote at the reinvented 2016 sixteen one of my pillars of our success was way didn't rely overly on vendors. In the end, we trained 2016 1 5 to 600 of our own staff on how to do cloud architectures correctly. >> I think at this point it's very clear that you're something of an extreme outlier in that you integrate by the nature of what you do with very large financial institutions. And these historically have not been firms that have embraced the cloud with speed and enthusiasm that Fenner has. Have you found yourself as you're going in this all in on the cloud approach that you're having trouble getting some of those other larger financial firms to meet you there, or is that not really been a concern based upon fenders position with an ecosystem? >> Um, I would say that five years ago, very rare, I would say, You know, we've had a I made a conscious effort to be very loud in the process of conferences about our journey because it has helped us track talent. People are coming to work for us as a senior financial service. The regulator that wouldn't have considered it five years ago, and they're doing it because they want to be part of this experience that we're having, but it's a byproduct of being loud, and the press means that a lot of firms are saying, Well, look what Fender is doing in the cloud Let's go talk to them So we've had probably at this 50.200 firms that have come defender toe learn from our experience. We've got this two hour presentation that kind of goes through all the aspects of how to do it right, what, what to avoid, etcetera, etcetera. And, um, you know, I would say now the company's air coming into us almost universally believe it's the right direction. They're having trouble, whether it's political issues, technology dat, you name it for making the mo mentum that we've made. But unlike 45 years ago, all of them recognize that it's it's the direction to go. That's almost undisputed at this point. And you're opening comment. Yeah, we're very much an outlier. We've moved 97 plus percent of our APS 99 plus percent of our data. We are I mean, the only thing that hasn't really been moved to the cloud at this point our conscious decisions, because those applications that are gonna die on the vine in the data center or they don't make sense to move to the cloud for whatever reason. >> Okay, You've got almost all your data in the cloud and you're using open source technology. Is Cory said if I was listening to a traditional financial service company, you know, they're telling me all the reasons that for governance and compliance that they're not going to do it. So you know, why do you feel safe putting your your data in the cloud? >> Uh, well, we've looked at it. So, um, I spent my first year of Finn run 2013 early, 2014 but mostly 2013. Convincing our board of directors that moving our most critical applications to the public cloud was going to be no worse from the information security standpoint than what we're doing in our private data centers. That presentation ultimately made it to other regulators, major firms on the street industry, lobbyist groups like sifma nephi. AP got a lot of air time, and it basically made the point using logic and reasoning, that going to the cloud and doing it right not doing it wrong, but doing it right is at least is secure from a physical logical standpoint is what we were previously doing. And then we went down that route. I got the board approval in 2015. We started looking at it and realizing, Wait a minute, what we're doing here encrypting everything, using micro segmentation, we would never. And I aren't doing this in our private data center. It's more secure. And at that point in time, a lot of the analysts in our industry, like Gardner Forrester, started coming out with papers that basically said, Hey, wait a minute, this perception the cloud is not as safe is on Prem. That's wrong. And now we look at it like I can't imagine doing what we're doing now in a private data center. There's no scale. It's not a secure, etcetera, etcetera. >> And to some extent, when you're dealing with banks and start a perspective now and they say, Oh, we don't necessarily trust the cloud. Well, that's interesting. Your regulator does. In other cases, some tax authorities do. You provided tremendous value just by being as public as you have been that really starts taking the wind out of the sails of the old fear uncertainty and doubt. Arguments around cloud. >> Yeah, I mean, doubts around. It's not secure. I don't have control over it. If you do it right, those are those are manageable risks, I would argue. In some cases, you've got more risk not doing it. But I will caution everything needs to be on the condition that you've got to do it right. Sloppy migration in the cloud could make you less secure. So there there are principles that need to be followed as part of >> this. So Steve doing it right. You haven't been sitting still. One of the things that really caught my attention in the keynote was you said the last four years you've done three re architectures and what I want. Understand? You said each time you got a better price performance, you know, you do think so. How do you make sure you do it right? Yet have flexibility both in an architect standpoint, and, you know, don't you have to do a three year reserves intense for some of these? How do you make sure you have the flexibility to be able to take advantage of you? Said the innovation in automation. >> Yeah. Keep moving forward with. That's Ah, that's a deep technical question. So I'm gonna answer it simply and say that we've architected the software and hardware stack such. There's not a lot of co dependency between them, and that's natural. I t. One on one principle, but it's easier to do in the cloud, particularly within AWS, who kind of covers the whole stacks. You're not going to different vendors that aren't integrated. That helps a lot. But you also have architect it, right? And then once you do that and then you automate your software development life cycle process, it makes switching out anyone component of that stack pretty easy to do and highly automated, in some cases completely automated. And so when new service is our new versions of products, new classes of machines become available. We just slip him in, and the term I use this morning mark to market with Moore's Law. That's what we aspire to do to have the highest levels of price performance achievable at the time that it's made available. That wasn't possible previously because you would go by ah hardware kit and then you'd appreciate it for five years on your books at the end of those five years, it would get kind of have scale and reliability problems. And then you go spend tens of millions of dollars on a new kit and the whole cycle would start over again. That's not the case here. >> Machine learning something you've been dipping into. Tell us the impact, what that has and what you see. Going forward. >> It's early, but we're big believers in machine learning. And there's a lot of applications for at Venera in our various investigatory and regulatory functions. Um, again, it's early, but I'm a big believer that the that the computer stored scale, commodity costs in the public cloud could be tapped into and lever it to make Aye aye and machine learning. Achieve what everybody has been talking about it, hoping to achieve the last several decades. We're using it specifically right now in our surveillance is for market manipulation and fraud. So fraudsters coming in and manipulating prices in the stock market to take advantage of trading early days but very promising in terms of what it's delivered so far. >> Steve want to give you the final word. You know, your thank you. First of all for being vocal on this. It sounds like there's a lot of ways for people to understand and see. You know what Fenner has done and really be a you know, an early indicator. So, you know, give us a little bit. Look forward, you know what more? Where's Finn Ra going next on their journey. And what do you want to see more from, You know, Amazon and the ecosystem around them to make your life in life, your peers better. >> Yes. So some of the kind of challenges that Amazon is working with us and partnering Assan is getting Ah Maur, automated into regional fell over our our industries a little bit queasy about having everything run with a relatively tight proximity in the East Coast region. And while we replicate our data to the to the other East region, we think AIM or co production environment, like we have across the availability zones within the East, would be looked upon with Maur advocacy of that architecture. From a regulatory standpoint, that would be one another. One would be, um, one of the big objections to moving to a public cloud vendor like Amazon is the vendor dependency and so making sure that we're not overly technically dependent on them is something that I think is a shared responsibility. The view that you could go and run a single application across multiple cloud vendors. I don't think anybody has been able to successfully do that because of the differences between providers. You could run one application in one vendor and another application in another vendor. That's fine, but that doesn't really achieve the vendor dependency question and then going forward for Finn or I mean, riel beauty is if you architected your applications right without really doing any work at all, you're going to continuously get the benefits of price performance as they go forward. You're not kind of locked into a status quo, So even without doing much of any new work on our applications, we're gonna continue to get the benefits. That's probably outside of the elastic, massive scale that we take advantage of. That's probably the biggest benefit of this whole journey. >> Well, Steve Randall really appreciate >> it. >> Thank you so much for sharing the journey of All right for Cory cleanups to minimum back with lots more here from eight Summit in New York City. Thanks for watching the cue

>> live from Washington D. C. It's the Cube covering a ws public sector summit by Amazon Web services. >> Hello, everyone. Welcome back to the cubes Live coverage of a ws Public Sector summit here in our nation's capital. I'm your host, Rebecca Knight. We're joined by Raghu Rahman. He is the director of Fin Row, the Financial Industry Regulatory Authority. Thank you so much for coming on the Cube >> fighter back. Good afternoon, but happy to be here. >> So we're angry. This is the 10th annual public sector. Somebody should have said so Tell us a little bit about Finn Ra and what you do. They're >> sure Fender itself is the financial industry Regulatory authority way our private sector, not for profit institutions. Our mission is investor protection on market integrity. Way our member funded on DH. We have a member driven board board of directors and we engage in ensuring that all the stock market operations in the U. S. Capital markets play with rules. So that's the essence of who we are. >> And all of those stakeholders have a vested interest in making sure their rivals are also playing bythe. So you're here giving a presentation on fraud detection, using machine learning and artificial intelligence. That's right. What was So what were you saying? >> So, Brenda, we have a very deliberate technology strategy on We constantly keep pace with technology in order to affect our business in the best possible way, way. Always are looking for a means to get more efficient and more effective and use our funding for the best possible business value so to that, and wear completely in the cloud for a lot off our market regulation operations. All the applications are in the clouds. We, in fact, we were one of the early adopters of the cloud. From that perspective, all of our big data operations were fully operational in the cloud by 2016 itself. That was itself a two year project that we started in 40 14 then from 2016 were being working with machine language on recently. Over the past six months or so, we've been working with neural networks. So this was an opportunity for us to share what? Where we have bean, where we're coming from, where we're going with the intent that whatever we do by way of principles can be adopted by any other enterprise. We're looking to share our journey on to encourage others to adopt technology. That's really what why we do this >> and I want to dig into the presentation a little bit. But can you just set the scene for our viewers about what kinds of how big a problem fraud is with these financial institutions and how much money is on the table here? >> Well, I don't want to get you to the actual dollar figures, because each dimension off it comes up with a different aspect to it. Waken say that in full in federal, we have a full caseload year after year, decade after decade that end up with multiple millions of dollars worth of fines just on the civil cases alone. And then there are, of course, multibillion dollar worth problems that we read in the media cases going as far back as Bernie Madoff. Case is going through the different banking systems so that our various kinds of fraud across the different financial sectors, of course, we're focused on the capital markets alone. We don't do anything with regard to banking or things of that nature, But even in our own case, we franchise composed of nearly 33 100 people on all of us, engaging the fulltime task of ensuring that markets are fair for the investors on for the other participants, it's a big deal. >> So in your in your presentation, you told the story of two of your colleagues who are facing different kinds of challenges to sort to make your story come alive. Tell our viewers a little bit about about their challenges. >> We spoke about Brad, who is an expert. He's an absolute wizard when it comes to market regulation, and he's being doing this for a long time on DH What I shared with the members of the audience earlier today. Wass He can probably look ATT market, even data on probably tell you what the broker had for breakfast. >> That >> scary good on. We also shared the story about Jamie, who is in the member supervision division offender, a wicked, smart and extensive experience. So these are the kind of dedicated people that we have a fender on guy took up to Rhea life use cases sort of questions that they face. So in the case of Brad, it is always a question of Hey, we're good. But how do we get better? What is the unknown unknown there? The volume of transactions in the market keeps going up. How do we then end up with a situation where we can do effective surveillance in the market on detect the behaviors that are not off interest that are not for doctor? That might be even. Don't write manipulated. How do we make sure that way? Got it all, so to speak? That's Brad's thing. >> That idea about these? No, these unknown nun note Because we know we have no no known unknowns with the unknown unknowns are even scarier. >> Exactly. They are, and we want to shed light on that for ourselves and make sure that the markets are really fair for everybody to operate him. That is where use of the latest technologies helps us get better and better at it. To reduce the number of unknown unknowns to shed light on the entirety of market activities on toe, perform effective surveillance. So that was a just off our conversation today. How we have gotten better in the past 45 years, how machine language machine learning based technologies have helped us how artificial intelligence that we started working with specifically, neural networks have started helping us even further. >> Okay, okay. And then Jamie had a problem, too. >> In Jimmy's case. Member supervision, if you will. The problem is off a different context and character. They're still volumes of data. We still receive more than 1,000,000 individual pieces of document every year that we work with. But in her case, the important aspect of it is that it is unstructured data. It makes sense to humans. It is in plain English, but the machines, it's really difficult. So over the past two years, way have created an entirely new text analytics platform on that helps us parts through hundreds of thousands of different documents. Those could come from e mails it to come from war documents, spreadsheets, evenhanded and documents. We can go through all of those extract meaningful information, automatically summarized them, even have measures off confidence that the machine will imprint upon it to say how confident I am. I that this is off relevance to you. It will imprint that. And then it represented Jamie for her toe. Use her judgment and expertise to make a final call. One thing that we are really conscious about is way. Don't let algorithms completely take everything through. We always have a human. So we think of a I as really assistive intelligence on. We bring that to a fact for our business, >> and I think that that's a really key there, too, for the for the employees is to know that this is this is this's taking away some of their more manual, more boring tests and actually freeing them up to do the more creative, analytical problem solving >> you hit you. I think you hit that nail right on the head. All the tedious work the machine bus on. Then it leaves humans to do like you said, Absolutely the creative, the inter toe on the final judgment call. I think that's a great system. >> How much to these solutions cost way >> generally are not pricing these things individually, however overall, one of the things that we did with the cloud was actually reduce our overall cost ofthe technology. So from that perspective, we don't look at Costas, the primary driver, although many times these things do end up costing less than the prior system that we would be in. However, the benefits that offer to our clientele, the benefit that it offers to our business, to the people that are investors in the stock market, that is tremendous, and that has a lot of value for us. >> So what is next for Finneran? I mean, this is This is a really moment for so many industries in terms of the the rise of cyber threats, the end and fraud being such a huge problem. Privacy thes air the financial services industry more than, I guess maybe is equal to healthcare. This's really sensitive stuff we're talking about here. What what are some of the things that you have on the horizon? What are some of the things that you're hearing from your members? >> So all of our members treat data security really, really special on really carefully on wear, very deliberate and very conscious about how we treat the data that is interested to us way have to obligations. One is to treat it securely. The other is to extract appropriate insights from it because that's the purpose of why we're being interested with the data. Wait, take both of those dimensions very seriously. Way have an entire infrastructure organization. It's composed off experts in the field way, headed by a chief information security officer with a large team that looks at multi layered security right from the application defending itself all the way to perimeter security. We go off that we have extensive identity and access management systems. We also have an extensive program to combat insider tracks. So this type of multi layer security is what helps us keep the data secure. >> And >> every day we do notice that there are additional track factors that get exposed. So we keep ourselves on the edge in terms ofthe working with all the vendors that we partner with in working with the latest technologies to protect our data as an example, all of our data in the cloud is completely encrypted with high encryption, and it is encrypted both at rest. I'm during flight so that even in the rare case that someone has access to something is gibberish. So that's the intent of the encryption himself. So that is the extent to which we take things very seriously. >> I want to ask you to, but the technology backlash that we're seeing so much and you're you live here so you really know about the climate that does that technology industries, air facing for so long. They were our national treasure and they still are considered it all in a lot of ways. The Amazons, the Googles, the facebooks of the world. But now we have a presidential candidates calling for the break up of big tech and and they And there's been a real souring on the part of the public of concerns about privacy. How What are your thoughts? What are you seeing? What are you hearing on the ground here in D. C? >> With specifically with regard to where we operate from Infanta? We've tried not to access or use any data. That is not for regulatory purpose. Wear Very careful about it. Way don't sprawl across and crawl across social media just on a general fishing expedition. We try not to do that. All of the data that we take in store on operate technology upon we are entitled to use it for by policy are my rules are my regulation for the specific purpose off our regulator activities. We take that very seriously. We try not to access data outside off what we have need for on. So we limit ourselves to the context and that, if you look at, is really what the public is trying to tell us, don't take our data and use it in ways that we did not really authorize you to do. So So the other thing is that franchise on our profit, not for not for profit institutions. We really have absolutely no interest beyond regulatory capability to use the data. We absolutely shut it down for any other use way are not so that way. We are very clear about what our mission is. Where we use our data, why we use it and stop. >> Great. Well, Raghu, thank you so much for coming on the Cube. It's been a pleasure talking to you. >> Thank you. Thank >> you. I'm Rebecca Knight. Please stay tuned for more of the cubes. Live coverage of the es W s public Sector summit here in Washington. D c. Stay tuned. >> Oh,

>> Live from Orlando, Florida, it's theCUBE, covering .conf 18. Brought to you by Splunk. >> We're back in Orlando, everybody, at Splunk .conf18, #splunkconf18. I'm Dave Vellante with my co-host Stu Miniman. You're watch theCUBE, the leader in live tech coverage. We like to go out to the events. We want to extract the signal from the noise. We've been documenting the ascendancy of Splunk for the last seven years, how Splunk really starts in IT operations and security, and now we hear today Splunk has aspirations to go into the line of business, but speaking of security, Gary Mikula is here. He's a senior director of cyber and information security at FINRA, and he's joined by Siddharta "Sid" Dadana, who's the director of information security engineering at FINRA. Gentlemen, welcome back to theCUBE, Gary, and Sid, first-timer, welcome on theCUBE. So, I want to start with FINRA. Why don't you explain, I mean, I think many people know what FINRA is, but explain what you guys do and, sort of, the importance of your mission. >> Sure, it's our main aspiration is to protect investors, and we do that in two ways. We actually monitor the brokers and dealers that do trades for people, but more importantly, and what precipitated our move to the Cloud was the enormous amount of data that we have to pull in daily. Every transaction on almost every US stock market has to be surveilled to ensure that people are acting properly, and we do that at the petabyte scale, and doing that with your own hardware became untenable, and so the ability to have elastic processing in the Cloud became very attractive. >> How much data are we talking about here? Is there any way you can, sort of, quantify that for us, or give us a mental picture? >> Yeah, so the example I use is, if you took every transaction that Visa has on a normal day, every Facebook like, every Facebook update, and if you took every Twitter tweet, you added them altogether, you multiplied it by 20, you would still not reach our peak on our peak day. >> (laughs) Hence, Splunk. And we'll talk about that but, Sid, what's your role, you got to architect all this stuff, the data pipeline, what do you... >> So, my role is basically to work with the webs teams, application teams to basically integrate security in the processes, how they roll out applications, how they look at data, how they use the same data that security uses for them to be able to leverage it for the webs and all the performances. >> So, your mission is to make sure security's not an afterthought, it's not a bolt-on, it's a fundamental part of the development process, so it's not thrown over the fence, "Hey, secure this application." It's built in, is that right? >> Yes. >> Okay. Gary, I wonder if you could talk about how security has changed over the last several years. You hear a lot that, well, all the spending historically has been on keeping the bad guys out the perimeter. As the perimeter disappears, things change, and the emphasis changes. Certainly, data is a bigger factor, analytics have come into play. From your perspective, what is the big change or the big changes in security? >> So, it's an interesting question. So I've been through several paradigm changes, and I don't think anyone has been as big as the move the Cloud, and... The Cloud offers so much opportunity from a cost perspective, from a processing perspective, but it also brings with it certain security concerns. And we're able to use tools like Splunk to be able to do surveillance on our AWS environments in order to give us the confidence to be able to use those services up there. And so, we now are actually looking at how we're going to secure individual AWS services before we use them, rather than looking to bring stovepipe solutions in, we're looking to leverage our AWS relationship to be able to leverage what they've built out of the box. >> Yeah, people oftentimes, Stu, talk about Cloud security like it's some binary thing. "Oh, I don't want to go the Cloud, because Cloud is dangerous" or "Cloud security is better". It's not that simple, is it? I mean, maybe the infrastructure. In fact, we heard the CIA, Stu and I were in D.C. in December, we heard the CIO of the CIA say, "The Cloud, its worse day is better than my client's server from a security perspective." But he's really talking about the infrastructure. There's so much more to security, right? >> Absolutely, and, so I agree that the Cloud gives the opportunity to be better than you are on PRAM. I think the way FINRA's rolled out, we've shown that we are more secure in the Cloud than we have been on traditional data centers, and it's because of our ability to actually monitor our whole AWS environment. Everything is API-based. We know exactly what everybody's doing. There's no shadow IT anymore, and those are all big positives. >> Yeah, I'm wondering how you've, what KPIs you look at when you look at your Splunk environment. What we hear from Splunk, you know, it's scalability, cost, performance, and then that management, the monitoring of the environment. How are they doing? How does that make your job easier? >> So, I think we still look at the same KPIs that Splunk advertises all the time, but some of the reasons, from our perspective, we kind of look at it in terms of, how much value can we give it to not just one part of the company, but how can we make it much more enhanceable part for everyone in the organization. So, the more we do that, I think that makes it a much better ROI for any organization to use a product like this one. >> You guys talk about the "shift left" movement. What is "shift left" and what is the relevance to security? >> Yeah so, "shift left" is a concept where, instead of looking at security as a bolt-on, or an add-on, or a separate entity, we're looking to leverage what are traditional DevOp tools, what are traditional SDLC pipeline roles, and we're looking at how we integrate security into that, and we use Splunk to be able to integrate collection of data into our CDCI pipelines, and it's all hands-off. So, somebody hits a button to deploy a new VPC and AWS, automatically things are monitored and into our enterprise search, I'm sorry, enterprise security SIM, and automatically being monitored. There's no hands-on that needs to be done. >> So, on a scale of one to five, thinking of a maturity model in terms of, in a DevOps context, five being, you know, the gold standard and one being you're just getting started. Where would you put FINRA on that spectrum, I mean, just subjectively? >> So, I'll never say that we're a five because I think there's always, >> You're never done. >> You're never done and there's always room for improvement, but I think we're at least a strong four. We've embraced those concepts, and we've put them into action. >> And so, I thought so, and I want to ask you from a skill standpoint how you got there. So, you've been around a long time. You had a Dev team and an Ops team before the term DevOps even came around, right? And we talk about this a lot, Stu. What did you do with the Ops guys and the Dev guys? Is it OpsDev or DevOps? Did you retrain them? Did you fire them all and hire new people? How did you go through that transition? >> Yep, that's a fair thing. I went to my CISO John Brady a couple of years ago and I told him that we were going to need to get these new skill sets in, and that I thought I had the right person in Sid to be able to head that up, and we brought in some new talent, but we also retrained the existing talent because these were really bright people, and they still had the security skills. And what Sid's been able to do is to embrace that and create a working relationship with the traditional DevOps teams so that we can integrate into their tools. >> So, it does include a little bit work even on our end to do where you kind of learn how the DevOps forces work, so you've got to do it on your own to first figure out things and then you can actually relate to the problems which they will go through and then you work through problems with them, rather than you designing up a solution and then just say, "Hey, go and implement it out." So, I think that kind of relationship has helped us and in the long run, we hope to do a bit better work. >> Yes, Sid, can you bring us in a little bit, when you look at your Splunk deployment, FINRA'S got a lot of applications, how do you get all those various applications in there? You know, Splunk talks about, you can get access to your data your way, do you find that to be the reality? >> Yes, to a certain extent, so... Let's take a step back here. So our design is much more hybrid-oriented. So, we use Splunk Cloud, but that's primarily for our indexers whereas we host our own sort of class receptor. All the data basically goes in from servers from AWS components, from on-prem, basically it flows into our Splunk Cloud indexers, and we use a role-based access management to actually give everyone access to whatever data they need to be looking at. >> Alright. The number of enhancements from 702, updates, the Cloud, Gar-Gar, is there anything that's jumped out that's going to architecturally help your team? >> So, I think one of the interesting things is the new data pipeline, and to be able to actually mangle that data before I get it into my Splunk indexers is going to be really really life-changing for us. One of the hard parts is that developers write code and they don't necessarily create logs that are event-driven. They don't have date-time stamps, they do dumps. So, I'm going to be able to actually massage that before it hits the indexers, and it's going to speed up our ability to be able to provide quick searches because the indexers won't be working on mangling that data. >> And how big of a deal is it for you? They announced yesterday the ability to scale storage and compute separately in a more granular fashion, is that a big deal for you? >> So, I actually, I remember speaking to Doug Merritt probably three years ago. >> You started this! (laughing) >> And I said, "Doug", I said, "I really think that's the direction that you need to go. You're going to have to separate those two, eventually, because we're doing a petabyte scale, we realized very early that that'd need to be done. And so, it's really really refreshing to see, because it's going to be transformative to be able to do compute-on-demand after that. Because now we can start looking at API brokers, and we can start looking at containers, and all those other things can be integrated into Splunk. >> Love having customers on like you guys, so knowledgeable. I have to ask, switch gears a little bit, I want to ask you about your security regime. We had a customer on yesterday, and it was the CISO who reported to him. He was the EVP, and he reported to the CIO. A lot of organizations say, "You know what? We want the CISO to be separate from the CIO. Cause it's like the, you know, the fox in the henhouse kind of thing. And we want that a little bit of tension in there." How do you guys approach it? What's the regime you have for... >> That is a fair question, and I've heard that from many other CISOs that have that same sort of complaint. And I think it's really organization-based. And I think, do you have the checks and balances in place? First of all, our CIO, Steve Randich, is extremely, he cares a lot about security, and he is very good at getting funding for us for initiatives to help secure the environment. But more importantly, our board of directors bring up security at every board event. They care about it, they know about it, and that permeates through the organization. So there's a checks and balances to make sure that we have the right security in place. And it's a working relationship, not adversarial at all, so, having our CISO John Brady report to Steve Randich, the CIO, has not been a hindrance. >> And I think that's a change in the last several years, because that regime that I described, which was, there was sort of a wave there, where that became common, and I think you just hit on it. When security became a board-level issue, and for every Fortune 1000, Global 2000 company, it's a board-level issue. They talk about it every board meeting. When that occurred, I think there was an epiphany of, "We need the CIO to actually be on this." And you want the CIO to be responsible for that. And the change was, it used to be, "Hey, if I fail, I get fired." And I think boards now realize that "failure" in security doesn't mean you got breached. >> Sure. >> You know. Breaches are going to happen. It's how you respond to them and, you know, how you react to them that is becoming more important. So there's much more transparency around security in our view. I wonder if you agree with that. >> I think there's transparency. And the other thing is is that you have to put the decision-making where it makes the most sense. Most of the security breaches that we're talking about are highly technical in nature, where a CIO is better able to evaluate some of those decisions, not all companies have a CEO that came from a technology train in order to be able to make those decisions. So, I think it makes more sense to have the CISO report to somebody in the technology world. >> Great, thank you for that. Now, the other question I have for you is, in terms of FINRA's experience with Splunk, did it start with SecOps and security, or was it, sort of, IT operations, or...? >> It did, it started with security. We were disenfranchised with traditional SIMs that were out there, and we decided to go with Splunk, and we made the decision that security was going to own it, but we wanted it to be a corporate asset from day one. And we worked our tails off to integrate, through brown bags, through training. So we permeated through the organization. And, on any given week, we pull about 35-40% of all of technology is using Splunk at FINRA. >> So, I'm curious as to, we heard some announcements today, I don't know if you saw them, about, you know, Splunk Next, building on that, Splunk for the line of business, the business flow, they did a nice demo there. Do you see, because security sort of was the starting point, and your mission was always to permeate the organization, do you see that continuing to other parts of the organization more aggressively now given this sort of democratization of data for the business lines, and... Will you guys be a part of that, directly? >> We hope so. We hope we are part of that change, too. I mean, the more we can use the same data for even business users that will help them, that would relieve a lot of, and they made this point again and again in the keynote, too, that, the It Ops and SecOps are already burdened enough. So, how do we make life easy for business users who actually leverage the same data? So we hope to be able to put these tools up and see if it can make any difference to business users. >> So, you guys have put a lot of emphasis on integrating with Splunk and AWS Cloud. You have a presentation later on today at .conf18 around the AWS Firehose that you have with Splunk. What's that all about? What's the AWS Firehose? How are you integrating it? Why is it important? >> So, it is streaming and it allows me to get information from AWS that's typically in something called the CloudWatch Logs, that is really difficult to be able to talk to. And I want to get it into the Splunk so I can get more value from it. And what I'm able to do is put something called a subscription filter on it, and flow that data directly into Splunk. So, Splunk worked with AWS to create this integration between the two tools, and we think we've taken it to a high level. We use it for Lambda, to grab those logs, we use it for VPC Flow Logs, we're using it for SaaS Providers, provide APIs into their data, we use it for that, and finally, we're going to be doing database activity monitoring, all leveraging this same technology. >> Love it, I mean, you guys are on the forefront of Cloud and Splunk integration, Cloud adoption, DevOps, you guys have always been great about sharing your knowledge, you know, with others, and we really appreciate you guys coming on theCUBE. Thank you. >> Thanks for having us. >> You're welcome. Alright, keep it right there, everybody. Stu and I will be back. You're watching theCUBE from .conf18, Splunk's big user conference. We'll be right back. (electronic music)

from the cube studios in Palo Alto in Boston bringing you data-driven insights from the cube and ETR this is breaking analysis with Dave vellante the ascendancy of AWS under the leadership of Andy jassy was marked by a tsunami of data and corresponding cloud services to leverage that data now those Services they mainly came in the form of Primitives I.E basic building blocks that were used by developers to create more sophisticated capabilities AWS in the 2020s being led by CEO Adam solipski will be marked by four high-level Trends in our opinion one A Rush of data that will dwarf anything we've previously seen two a doubling or even tripling down on the basic elements of cloud compute storage database security Etc three a greater emphasis on end-to-end integration of AWS services to simplify and accelerate customer adoption of cloud and four significantly deeper business integration of cloud Beyond it as an underlying element of organizational operations hello and welcome to this week's wikibon Cube insights powered by ETR in this breaking analysis we extract and analyze nuggets from John furrier's annual sit-down with the CEO of AWS we'll share data from ETR and other sources to set the context for the market and competition in cloud and we'll give you our glimpse of what to expect at re invent in 2022. now before we get into the core of our analysis Alibaba has announced earnings they always announced after the big three you know a month later and we've updated our Q3 slash November hyperscale Computing forecast for the year as seen here and we're going to spend a lot of time on this as most of you have seen the bulk of it already but suffice to say alibaba's cloud business is hitting that same macro Trend that we're seeing across the board but a more substantial slowdown than we expected and more substantial than its peers they're facing China headwinds they've been restructuring its Cloud business and it's led to significantly slower growth uh in in the you know low double digits as opposed to where we had it at 15 this puts our year-end estimates for 2022 Revenue at 161 billion still a healthy 34 growth with AWS surpassing 80 billion in 2022 Revenue now on a related note one of the big themes in Cloud that we've been reporting on is how customers are optimizing their Cloud spend it's a technique that they use and when the economy looks a little shaky and here's a graphic that we pulled from aws's website which shows the various pricing plans at a high level as you know they're much more granular than that and more sophisticated but Simplicity we'll just keep it here basically there are four levels first one here is on demand I.E pay by the drink now we're going to jump down to what we've labeled as number two spot instances that's like the right place at the right time I can use that extra capacity in the moment the third is reserved instances or RIS where I pay up front to get a discount and the fourth is sort of optimized savings plans where customers commit to a one or three year term and for a better price now you'll notice we labeled the choices in a different order than AWS presented them on its website and that's because we believe that the order that we chose is the natural progression for customers this started on demand they maybe experiment with spot instances they move to reserve instances when the cloud bill becomes too onerous and if you're large enough you lock in for one or three years okay the interesting thing is the order in which AWS presents them we believe that on-demand accounts for the majority of AWS customer spending now if you think about it those on-demand customers they're also at risk customers yeah sure there's some switching costs like egress and learning curve but many customers they have multiple clouds and they've got experience and so they're kind of already up to a learning curve and if you're not married to AWS with a longer term commitment there's less friction to switch now AWS here presents the most attractive plan from a financial perspective second after on demand and it's also the plan that makes the greatest commitment from a lock-in standpoint now In fairness to AWS it's also true that there is a trend towards subscription-based pricing and we have some data on that this chart is from an ETR drill down survey the end is 300. pay attention to the bars on the right the left side is sort of busy but the pink is subscription and you can see the trend upward the light blue is consumption based or on demand based pricing and you can see there's a steady Trend toward subscription now we'll dig into this in a later episode of Breaking analysis but we'll share with you a little some tidbits with the data that ETR provides you can select which segment is and pass or you can go up the stack Etc but so when you choose is and paths 44 of customers either prefer or are required to use on-demand pricing whereas around 40 percent of customers say they either prefer or are required to use subscription pricing again that's for is so now the further mu you move up the stack the more prominent subscription pricing becomes often with sixty percent or more for the software-based offerings that require or prefer subscription and interestingly cyber security tracks along with software at around 60 percent that that prefer subscription it's likely because as with software you're not shutting down your cyber protection on demand all right let's get into the expectations for reinvent and we're going to start with an observation in data in this 2018 book seeing digital author David michella made the point that whereas most companies apply data on the periphery of their business kind of as an add-on function successful data companies like Google and Amazon and Facebook have placed data at the core of their operations they've operationalized data and they apply machine intelligence to that foundational element why is this the fact is it's not easy to do what the internet Giants have done very very sophisticated engineering and and and cultural discipline and this brings us to reinvent 2022 in the future of cloud machine learning and AI will increasingly be infused into applications we believe the data stack and the application stack are coming together as organizations build data apps and data products data expertise is moving from the domain of Highly specialized individuals to Everyday business people and we are just at the cusp of this trend this will in our view be a massive theme of not only re invent 22 but of cloud in the 2020s the vision of data mesh We Believe jamachtagani's principles will be realized in this decade now what we'd like to do now is share with you a glimpse of the thinking of Adam solipsky from his sit down with John Furrier each year John has a one-on-one conversation with the CEO of AWS AWS he's been doing this for years and the outcome is a better understanding of the directional thinking of the leader of the number one Cloud platform so we're now going to share some direct quotes I'm going to run through them with some commentary and then bring in some ETR data to analyze the market implications here we go this is from solipsky quote I.T in general and data are moving from departments into becoming intrinsic parts of how businesses function okay we're talking here about deeper business integration let's go on to the next one quote in time we'll stop talking about people who have the word analyst we inserted data he meant data data analyst in their title rather will have hundreds of millions of people who analyze data as part of their day-to-day job most of whom will not have the word analyst anywhere in their title we're talking about graphic designers and pizza shop owners and product managers and data scientists as well he threw that in I'm going to come back to that very interesting so he's talking about here about democratizing data operationalizing data next quote customers need to be able to take an end-to-end integrated view of their entire data Journey from ingestion to storage to harmonizing the data to being able to query it doing business Intelligence and human-based Analysis and being able to collaborate and share data and we've been putting together we being Amazon together a broad Suite of tools from database to analytics to business intelligence to help customers with that and this last statement it's true Amazon has a lot of tools and you know they're beginning to become more and more integrated but again under jassy there was not a lot of emphasis on that end-to-end integrated view we believe it's clear from these statements that solipsky's customer interactions are leading him to underscore that the time has come for this capability okay continuing quote if you have data in one place you shouldn't have to move it every time you want to analyze that data couldn't agree more it would be much better if you could leave that data in place avoid all the ETL which has become a nasty three-letter word more and more we're building capabilities where you can query that data in place end quote okay this we see a lot in the marketplace Oracle with mySQL Heatwave the entire Trend toward converge database snowflake [ __ ] extending their platforms into transaction and analytics respectively and so forth a lot of the partners are are doing things as well in that vein let's go into the next quote the other phenomenon is infusing machine learning into all those capabilities yes the comments from the michelleographic come into play here infusing Ai and machine intelligence everywhere next one quote it's not a data Cloud it's not a separate Cloud it's a series of broad but integrated capabilities to help you manage the end-to-end life cycle of your data there you go we AWS are the cloud we're going to come back to that in a moment as well next set of comments around data very interesting here quote data governance is a huge issue really what customers need is to find the right balance of their organization between access to data and control and if you provide too much access then you're nervous that your data is going to end up in places that it shouldn't shouldn't be viewed by people who shouldn't be viewing it and you feel like you lack security around that data and by the way what happens then is people overreact and they lock it down so that almost nobody can see it it's those handcuffs there's data and asset are reliability we've talked about that for years okay very well put by solipsky but this is a gap in our in our view within AWS today and we're we're hoping that they close it at reinvent it's not easy to share data in a safe way within AWS today outside of your organization so we're going to look for that at re invent 2022. now all this leads to the following statement by solipsky quote data clean room is a really interesting area and I think there's a lot of different Industries in which clean rooms are applicable I think that clean rooms are an interesting way of enabling multiple parties to share and collaborate on the data while completely respecting each party's rights and their privacy mandate okay again this is a gap currently within AWS today in our view and we know snowflake is well down this path and databricks with Delta sharing is also on this curve so AWS has to address this and demonstrate this end-to-end data integration and the ability to safely share data in our view now let's bring in some ETR spending data to put some context around these comments with reference points in the form of AWS itself and its competitors and partners here's a chart from ETR that shows Net score or spending momentum on the x-axis an overlap or pervasiveness in the survey um sorry let me go back up the net scores on the y-axis and overlap or pervasiveness in the survey is on the x-axis so spending momentum by pervasiveness okay or should have share within the data set the table that's inserted there with the Reds and the greens that informs us to how the dots are positioned so it's Net score and then the shared ends are how the plots are determined now we've filtered the data on the three big data segments analytics database and machine learning slash Ai and we've only selected one company with fewer than 100 ends in the survey and that's databricks you'll see why in a moment the red dotted line indicates highly elevated customer spend at 40 percent now as usual snowflake outperforms all players on the y-axis with a Net score of 63 percent off the charts all three big U.S cloud players are above that line with Microsoft and AWS dominating the x-axis so very impressive that they have such spending momentum and they're so large and you see a number of other emerging data players like rafana and datadog mongodbs there in the mix and then more established players data players like Splunk and Tableau now you got Cisco who's gonna you know it's a it's a it's a adjacent to their core networking business but they're definitely into you know the analytics business then the really established players in data like Informatica IBM and Oracle all with strong presence but you'll notice in the red from the momentum standpoint now what you're going to see in a moment is we put red highlights around databricks Snowflake and AWS why let's bring that back up and we'll explain so there's no way let's bring that back up Alex if you would there's no way AWS is going to hit the brakes on innovating at the base service level what we call Primitives earlier solipsky told Furrier as much in their sit down that AWS will serve the technical user and data science Community the traditional domain of data bricks and at the same time address the end-to-end integration data sharing and business line requirements that snowflake is positioned to serve now people often ask Snowflake and databricks how will you compete with the likes of AWS and we know the answer focus on data exclusively they have their multi-cloud plays perhaps the more interesting question is how will AWS compete with the likes of Specialists like Snowflake and data bricks and the answer is depicted here in this chart AWS is going to serve both the technical and developer communities and the data science audience and through end-to-end Integrations and future services that simplify the data Journey they're going to serve the business lines as well but the Nuance is in all the other dots in the hundreds or hundreds of thousands that are not shown here and that's the AWS ecosystem you can see AWS has earned the status of the number one Cloud platform that everyone wants to partner with as they say it has over a hundred thousand partners and that ecosystem combined with these capabilities that we're discussing well perhaps behind in areas like data sharing and integrated governance can wildly succeed by offering the capabilities and leveraging its ecosystem now for their part the snowflakes of the world have to stay focused on the mission build the best products possible and develop their own ecosystems to compete and attract the Mind share of both developers and business users and that's why it's so interesting to hear solipski basically say it's not a separate Cloud it's a set of integrated Services well snowflake is in our view building a super cloud on top of AWS Azure and Google when great products meet great sales and marketing good things can happen so this will be really fun to watch what AWS announces in this area at re invent all right one other topic that solipsky talked about was the correlation between serverless and container adoption and you know I don't know if this gets into there certainly their hybrid place maybe it starts to get into their multi-cloud we'll see but we have some data on this so again we're talking about the correlation between serverless and container adoption but before we get into that let's go back to 2017 and listen to what Andy jassy said on the cube about serverless play the clip very very earliest days of AWS Jeff used to say a lot if I were starting Amazon today I'd have built it on top of AWS we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point I think the same thing is true here with Lambda which is I think if Amazon were starting today it's a given they would build it on the cloud and I think we with a lot of the applications that comprise Amazon's consumer business we would build those on on our serverless capabilities now we still have plenty of capabilities and features and functionality we need to add to to Lambda and our various serverless services so that may not be true from the get-go right now but I think if you look at the hundreds of thousands of customers who are building on top of Lambda and lots of real applications you know finra has built a good chunk of their market watch application on top of Lambda and Thompson Reuters has built you know one of their key analytics apps like people are building real serious things on top of Lambda and the pace of iteration you'll see there will increase as well and I really believe that to be true over the next year or two so years ago when Jesse gave a road map that serverless was going to be a key developer platform going forward and so lipsky referenced the correlation between serverless and containers in the Furrier sit down so we wanted to test that within the ETR data set now here's a screen grab of The View across 1300 respondents from the October ETR survey and what we've done here is we've isolated on the cloud computing segment okay so you can see right there cloud computing segment now we've taken the functions from Google AWS Lambda and Microsoft Azure functions all the serverless offerings and we've got Net score on the vertical axis we've got presence in the data set oh by the way 440 by the way is highly elevated remember that and then we've got on the horizontal axis we have the presence in the data center overlap okay that's relative to each other so remember 40 all these guys are above that 40 mark okay so you see that now what we're going to do this is just for serverless and what we're going to do is we're going to turn on containers to see the correlation and see what happens so watch what happens when we click on container boom everything moves to the right you can see all three move to the right Google drops a little bit but all the others now the the filtered end drops as well so you don't have as many people that are aggressively leaning into both but all three move to the right so watch again containers off and then containers on containers off containers on so you can see a really major correlation between containers and serverless okay so to get a better understanding of what that means I call my friend and former Cube co-host Stu miniman what he said was people generally used to think of VMS containers and serverless as distinctly different architectures but the lines are beginning to blur serverless makes things simpler for developers who don't want to worry about underlying infrastructure as solipsky and the data from ETR indicate serverless and containers are coming together but as Stu and I discussed there's a spectrum where on the left you have kind of native Cloud VMS in the middle you got AWS fargate and in the rightmost anchor is Lambda AWS Lambda now traditionally in the cloud if you wanted to use containers developers would have to build a container image they have to select and deploy the ec2 images that they or instances that they wanted to use they have to allocate a certain amount of memory and then fence off the apps in a virtual machine and then run the ec2 instances against the apps and then pay for all those ec2 resources now with AWS fargate you can run containerized apps with less infrastructure management but you still have some you know things that you can you can you can do with the with the infrastructure so with fargate what you do is you'd build the container images then you'd allocate your memory and compute resources then run the app and pay for the resources only when they're used so fargate lets you control the runtime environment while at the same time simplifying the infrastructure management you gotta you don't have to worry about isolating the app and other stuff like choosing server types and patching AWS does all that for you then there's Lambda with Lambda you don't have to worry about any of the underlying server infrastructure you're just running code AS functions so the developer spends their time worrying about the applications and the functions that you're calling the point is there's a movement and we saw in the data towards simplifying the development environment and allowing the cloud vendor AWS in this case to do more of the underlying management now some folks will still want to turn knobs and dials but increasingly we're going to see more higher level service adoption now re invent is always a fire hose of content so let's do a rapid rundown of what to expect we talked about operate optimizing data and the organization we talked about Cloud optimization there'll be a lot of talk on the show floor about best practices and customer sharing data solipsky is leading AWS into the next phase of growth and that means moving beyond I.T transformation into deeper business integration and organizational transformation not just digital transformation organizational transformation so he's leading a multi-vector strategy serving the traditional peeps who want fine-grained access to core services so we'll see continued Innovation compute storage AI Etc and simplification through integration and horizontal apps further up to stack Amazon connect is an example that's often cited now as we've reported many times databricks is moving from its stronghold realm of data science into business intelligence and analytics where snowflake is coming from its data analytics stronghold and moving into the world of data science AWS is going down a path of snowflake meet data bricks with an underlying cloud is and pass layer that puts these three companies on a very interesting trajectory and you can expect AWS to go right after the data sharing opportunity and in doing so it will have to address data governance they go hand in hand okay price performance that is a topic that will never go away and it's something that we haven't mentioned today silicon it's a it's an area we've covered extensively on breaking analysis from Nitro to graviton to the AWS acquisition of Annapurna its secret weapon new special specialized capabilities like inferential and trainium we'd expect something more at re invent maybe new graviton instances David floyer our colleague said he's expecting at some point a complete system on a chip SOC from AWS and maybe an arm-based server to eventually include high-speed cxl connections to devices and memories all to address next-gen applications data intensive applications with low power requirements and lower cost overall now of course every year Swami gives his usual update on machine learning and AI building on Amazon's years of sagemaker innovation perhaps a focus on conversational AI or a better support for vision and maybe better integration across Amazon's portfolio of you know large language models uh neural networks generative AI really infusing AI everywhere of course security always high on the list that reinvent and and Amazon even has reinforce a conference dedicated to it uh to security now here we'd like to see more on supply chain security and perhaps how AWS can help there as well as tooling to make the cio's life easier but the key so far is AWS is much more partner friendly in the security space than say for instance Microsoft traditionally so firms like OCTA and crowdstrike in Palo Alto have plenty of room to play in the AWS ecosystem we'd expect of course to hear something about ESG it's an important topic and hopefully how not only AWS is helping the environment that's important but also how they help customers save money and drive inclusion and diversity again very important topics and finally come back to it reinvent is an ecosystem event it's the Super Bowl of tech events and the ecosystem will be out in full force every tech company on the planet will have a presence and the cube will be featuring many of the partners from the serial floor as well as AWS execs and of course our own independent analysis so you'll definitely want to tune into thecube.net and check out our re invent coverage we start Monday evening and then we go wall to wall through Thursday hopefully my voice will come back we have three sets at the show and our entire team will be there so please reach out or stop by and say hello all right we're going to leave it there for today many thanks to Stu miniman and David floyer for the input to today's episode of course John Furrier for extracting the signal from the noise and a sit down with Adam solipski thanks to Alex Meyerson who was on production and manages the podcast Ken schiffman as well Kristen Martin and Cheryl Knight helped get the word out on social and of course in our newsletters Rob hoef is our editor-in-chief over at siliconangle does some great editing thank thanks to all of you remember all these episodes are available as podcasts wherever you listen you can pop in the headphones go for a walk just search breaking analysis podcast I published each week on wikibon.com at siliconangle.com or you can email me at david.valante at siliconangle.com or DM me at di vallante or please comment on our LinkedIn posts and do check out etr.ai for the best survey data in the Enterprise Tech business this is Dave vellante for the cube insights powered by ETR thanks for watching we'll see it reinvent or we'll see you next time on breaking 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>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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>>Hello, everyone. Welcome to the cubes presentation of the 80 minutes startup showcase. Season two, episode two, the theme is data as code, the future of analytics. I'm John furry, your host. We had a great day lineup for you. Fast growing startups, great lineup of companies, founders, and stories around data as code. And we're going to kick it off here with our opening keynote with Rahul Pathak VP of analytics at AWS cube alumni. Right? We'll thank you for coming on and being the opening keynote for this awesome event. >>Yeah. And it's great to see you, and it's great to be part of this event, uh, excited to, um, to help showcase some of the great innovation that startups are doing on top of AWS. >>Yeah. We last spoke at AWS reinvent and, uh, a lot's happened there, service loss of serverless as the center of the, of the action, but all these start-ups rock set Dremio Cribble monks next Liccardo, a HANA imply all doing great stuff. Data as code has a lot of traction. So a lot of still momentum going on in the marketplace. Uh, pretty exciting. >>No, it's, uh, it's awesome. I mean, I think there's so much innovation happening and you know, the, the wonderful part of working with data is that the demand for services and products that help customers drive insight from data is just skyrocketing and has no sign of no sign of slowing down. And so it's a great time to be in the data business. >>It's interesting to see the theme of the show getting traction, because you start to see data being treated almost like how developers write software, taking things out of branches, working on them, putting them back in, uh, machine learnings, uh, getting iterated on you, seeing more models, being trained differently with better insights, action ones that all kind of like working like code. And this is a whole nother way. People are reinventing their businesses. This has been a big, huge wave. What's your reaction to that? >>Uh, I think it's spot on, I mean, I think the idea of data's code and bringing some of the repeatability of processes from software development into how people built it, applications is absolutely fundamental and especially so in machine learning where you need to think about the explainability of a model, what version of the world was it trained on? When you build a better model, you need to be able to explain and reproduce it. So I think your insights are spot on and these ideas are showing up in all stages of the data work flow from ingestion to analytics to I'm out >>This next way is about modernization and going to the next level with cloud-scale. Uh, thank you so much for coming on and being the keynote presenter here for this great event. Um, I'll let you take it away. Reinventing businesses, uh, with ads analytics, right? We'll take it away. >>Okay, perfect. Well, folks, we're going to talk about, uh, um, reinventing your business with, uh, data. And if you think about it, the first wave of reinvention was really driven by the cloud. As customers were able to really transform how they thought about technology and that's well on her way. Although if you stop and think about it, I think we're only about five to 10% of the way done in terms of it span being on the cloud. So lots of work to do there, but we're seeing another wave of reinvention, which is companies reinventing their businesses with data and really using data to transform what they're doing to look for new opportunities and look for ways to operate more efficiently. And I think the past couple of years of the pandemic, it really only accelerated that trend. And so what we're seeing is, uh, you know, it's really about the survival of the most informed folks for the best data are able to react more quickly to what's happening. >>Uh, we've seen customers being able to scale up if they're in, say the delivery business or scale down, if they were in the travel business at the beginning of all of this, and then using data to be able to find new opportunities and new ways to serve customers. And so it's really foundational and we're seeing this across the board. And so, um, you know, it's great to see the innovation that's happening to help customers make sense of all of this. And our customers are really looking at ways to put data to work. It's about making better decisions, finding new efficiencies and really finding new opportunities to succeed and scale. And, um, you know, when it comes to, uh, good examples of this FINRA is a great one. You may not have heard of them, but that the U S equities regulators, all trading that happens in equities, they keep track of they're look at about 250 billion records per day. >>Uh, the examiner, I was only EMR, which is our spark and Hadoop service, and they're processing 20 terabytes of data running across tens of thousands of nodes. And they're looking for fraud and bad actors in the market. So, um, you know, huge, uh, transformation journey for FINRA over the years of customer I've gotten to work with personally since really 2013 onward. So it's been amazing to see their journey, uh, Pinterest, not a great customer. I'm sure everyone's familiar with, but, um, you know, they're about visual search and discovery and commerce, and, um, they're able to scale their daily lot searches, um, really a factor of three X or more, uh, drive down their costs. And they're using the Amazon Opus search service. And really what we're trying to do at AWS is give our customers the most comprehensive set of services for the end-to-end journey around, uh, data from ingestion to analytics and machine learning. And we will want to provide a comprehensive set of capabilities for ingestion, cataloging analytics, and then machine learning. And all of these are things that our partners and the startups that are run on us have available to them to build on as they build and deliver value for their customers. >>And, you know, the way we think about this is we want customers to be able to modernize what they're doing and their infrastructure. And we provide services for that. It's about unifying data, wherever it lives, connecting it. So the customers can build a complete picture of their customers and business. And then it's about innovation and really using machine learning to bring all of this unified data, to bear on driving new innovation and new opportunities for customers. And what we're trying to do AWS is really provide a scalable and secure cloud platform that customers and partners can build on a unifying is about connecting data. And it's also about providing well-governed access to data. So one of the big trends that we see is customers looking for the ability to make self-service data available to that customer there and use. And the key to that is good foundational governance. >>Once you can define good access controls, you then are more comfortable setting data free. And, um, uh, the other part of it is, uh, data lakes play a huge role because you need to be able to think about structured and unstructured data. In fact, about 80% of the data being generated today, uh, is unstructured. And you want to be able to connect data that's in data lakes with data that's in purpose-built data stores, whether that's databases on AWS databases, outside SAS products, uh, as well as things like data warehouses and machine learning systems, but really connecting data as key. Uh, and then, uh, innovation, uh, how can we bring to bear? And we imagine all processes with new technologies like AI and machine learning, and AI is also key to unlocking a lot of the value that's in unstructured data. If you can figure out what's in an imagine the sentiment of audio and do that in real-time that lets you then personalize and dynamically tailor experiences, all of which are super important to getting an edge, um, in, uh, in the modern marketplace. And so at AWS, we, when we think about connecting the dots across sources of data, allowing customers to use data, lakes, databases, analytics, and machine learning, we want to provide a common catalog and governance and then use these to help drive new experiences for customers and their apps and their devices. And then this, you know, in an ideal world, we'll create a closed loop. So you create a new experience. You observe our customers interact with it, that generates more data, which is a data source that feeds into the system. >>And, uh, you know, on AWS, uh, thinking about a modern data strategy, uh, really at the core is a data lakes built on us three. And I'll talk more about that in a second. Then you've got services like Athena included, lake formation for managing that data, cataloging it and querying it in place. And then you have the ability to use the right tool for the right job. And so we're big believers in purpose-built services for data because that's where you can avoid compromising on performance functionality or scale. Uh, and then as I mentioned, unification and inter interconnecting, all of that data. So if you need to move data between these systems, uh, there's well-trodden pathways that allow you to do that, and then features built into services that enable that. >>And, um, you know, some of the core ideas that guide the work that we do, um, scalable data lakes at key, um, and you know, this is really about providing arbitrarily scalable high throughput systems. It's about open format data for future-proofing. Uh, then we talk about purpose-built systems at the best possible functionality, performance, and cost. Uh, and then from a serverless perspective, this has been another big trend for us. We announced a bunch of serverless services and reinvented the goal here is to really take away the need to manage infrastructure from customers. They can really focus about driving differentiated business value, integrated governance, and then machine learning pervasively, um, not just as an end product for data scientists, but also machine learning built into data, warehouses, visualization and a database. >>And so it's scalable data lakes. Uh, data three is really the foundation for this. One of our, um, original services that AWS really the backbone of so much of what we do, uh, really unmatched your ability, availability, and scale, a huge portfolio of analytics services, uh, both that we offer, but also that our partners and customers offer and really arbitrary skin. We've got individual customers and estimator in the expert range, many in the hundreds of petabytes. And that's just growing. You know, as I mentioned, we see roughly a 10 X increase in data volume every five years. So that's a exponential increase in data volumes, Uh, from a purpose-built perspective, it's the right tool for the right job, the red shift and data warehousing Athena for querying all your data. Uh, EMR is our managed sparking to do, uh, open search for log analytics and search, and then Kinesis and Amex care for CAFCA and streaming. And that's been another big trend is, uh, real time. Data has been exploding and customers wanting to make sense of that data in real time, uh, is another big deal. >>Uh, some examples of how we're able to achieve differentiated performance and purpose-built systems. So with Redshift, um, using managed storage and it's led us and since types, uh, the three X better price performance, and what's out there available to all our customers and partners in EMR, uh, with things like spark, we're able to deliver two X performance of open source with a hundred percent compatibility, uh, almost three X and Presto, uh, with on two, which is our, um, uh, new Silicon chips on AWS, better price performance, about 10 to 12% better price performance, and 20% lower costs. And then, uh, all compatible source. So drop your jobs, then have them run faster and cheaper. And that translates to customer benefits for better margins for partners, uh, from a serverless perspective, this is about simplifying operations, reducing total cost of ownership and freeing customers from the need to think about capacity management. If we invent, we, uh, announced serverless redshifts EMR, uh, serverless, uh, Kinesis and Kafka, um, and these are all game changes for customers in terms of freeing our customers and partners from having to think about infrastructure and allowing them to focus on data. >>And, um, you know, when it comes to several assumptions in analytics, we've really got a very full and complete set. So, uh, whether that's around data warehousing, big data processing streaming, or cataloging or governance or visualization, we want all of our customers to have an option to run something struggles as well as if they have specialized needs, uh, uh, instances are available as well. And so, uh, really providing a comprehensive deployment model, uh, based on the customer's use cases, uh, from a governance perspective, uh, you know, like information is about easy build and management of data lakes. Uh, and this is what enables data sharing and self service. And, um, you know, with you get very granular access controls. So rule level security, uh, simple data sharing, and you can tag data. So you can tag a group of analysts in the year when you can say those only have access to the new data that's been tagged with the new tags, and it allows you to very, scaleably provide different secure views onto the same data without having to make multiple copies, another big win for customers and partners, uh, support transactions on data lakes. >>So updates and deletes. And time-travel, uh, you know, John talked about data as code and with time travel, you can look at, um, querying on different versions of data. So that's, uh, a big enabler for those types of strategies. And with blue, you're able to connect data in multiple places. So, uh, whether that's accessing data on premises in other SAS providers or, uh, clouds, uh, as well as data that's on AWS and all of this is, uh, serverless and interconnected. And, um, and really it's about plugging all of your data into the AWS ecosystem and into our partner ecosystem. So this API is all available for integration as well, but then from an AML perspective, what we're really trying to do is bring machine learning closer to data. And so with our databases and warehouses and lakes and BI tools, um, you know, we've infused machine learning throughout our, by, um, the state of the art machine running that we offer through SageMaker. >>And so you've got a ML in Aurora and Neptune for broths. Uh, you can train machine learning models from SQL, directly from Redshift and a female. You can use free inference, and then QuickSight has built in forecasting built in natural language, querying all powered by machine learning, same with anomaly detection. And here are the ideas, you know, how can we up our systems get smarter at the surface, the right insights for our customers so that they don't have to always rely on smart people asking the right questions, um, and you know, uh, really it's about bringing data back together and making it available for innovation. And, uh, thank you very much. I appreciate your attention. >>Okay. Well done reinventing the business with AWS analytics rural. That was great. Thanks for walking through that. That was awesome. I have to ask you some questions on the end-to-end view of the data. That seems to be a theme serverless, uh, in there, uh, Mel integration. Um, but then you also mentioned picking the right tool for the job. So then you've got like all these things moving on, simplify it for me right now. So from a business standpoint, how do they modernize? What's the steps that the clients are taking with analytics, what's the best practice? How do they, what's the what's the high order bit here? >>Uh, so the basic hierarchy is, you know, historically legacy systems are rigid and inflexible, and they weren't really designed for the scale of modern data or the variety of it. And so what customers are finding is they're moving to the cloud. They're moving from legacy systems with punitive licensing into more flexible, more systems. And that allows them to really think about building a decoupled, scalable future proof architecture. And so you've got the ability to combine data lakes and databases and data warehouses and connect them using common KPIs and common data protection. And that sets you up to deal with arbitrary scale and arbitrary types. And it allows you to evolve as the future changes since it makes it easy to add in a new type of engine, as we invent a better one a few years from now. Uh, and then, uh, once you've kind of got your data in a cloud and interconnected in this way, you can now build complete pictures of what's going on. You can understand all your touch points with customers. You can understand your complete supply chain, and once you can build that complete picture of your business, you can start to use analytics and machine learning to find new opportunities. So, uh, think about modernizing, moving to the cloud, setting up for the future, connecting data end to end, and then figuring out how to use that to your advantage. >>I know as you mentioned, modern data strategy gives you the best of both worlds. And you've mentioned, um, briefly, I want to get a little bit more, uh, insight from you on this. You mentioned open, open formats. One of the themes that's come out of some of the interviews, these companies we're going to be hearing from today is open source. The role opens playing. Um, how do you see that integrating in? Because again, this is just like software, right? Open, uh, open source software, open source data. It seems to be a trend. What does open look like to you? How do you see that progressing? >>Uh, it's a great question. Uh, open operates on multiple dimensions, John, as you point out, there's open data formats. These are things like JSI and our care for analytics. This allows multiple engines tend to operate on data and it'll, it, it creates option value for customers. If you're going to data in an open format, you can use it with multiple technologies and that'll be future-proofed. You don't have to migrate your data. Now, if you're thinking about using a different technology. So that's one piece now that sort of software, um, also, um, really a big enabler for innovation and for customers. And you've got things like squat arc and Presto, which are popular. And I know some of the startups, um, you know, that we're talking about as part of the showcase and use these technologies, and this allows for really the world to contribute, to innovating and these engines and moving them forward together. And we're big believers in that we've got open source services. We contribute to open-source, we support open source projects, and that's another big part of what we do. And then there's open API is things like SQL or Python. Uh, again, uh, common ways of interacting with data that are broadly adopted. And this one, again, create standardization. It makes it easier for customers to inter-operate and be flexible. And so open is really present all the way through. And it's a big part, I think, of, uh, the present and the future. >>Yeah. It's going to be fun to watch and see how that grows. It seems to be a lot of traction there. I want to ask you about, um, the other comment I thought was cool. You had the architectural slides out there. One was data lakes built on S3, and you had a theme, the glue in lake formation kind of around S3. And then you had the constellation of, you know, Kinesis SageMaker and other things around it. And you said, you know, pick the tool for the right job. And then you had the other slide on the analytics at the center and you had Redshift and all the other, other, other services around it around serverless. So one was more about the data lake with Athena glue and lake formation. The other one's about serverless. Explain that a little bit more for me, because I'm trying to understand where that fits. I get the data lake piece. Okay. Athena glue and lake formation enables it, and then you can pick and choose what you need on the serverless side. What does analytics in the center mean? >>So the idea there is that really, we wanted to talk about the fact that if you zoom into the analytics use case within analytics, everything that we offer, uh, has a serverless option for our customers. So, um, you could look at the bucket of analytics across things like Redshift or EMR or Athena, or, um, glue and league permission. You have the option to use instances or containers, but also to just not worry about infrastructure and just think declaratively about the data that you want to. >>Oh, so basically you're saying the analytics is going serverless everywhere. Talking about volumes, you mentioned 10 X volumes. Um, what are other stats? Can you share in terms of volumes? What are people seeing velocity I've seen data warehouses can't move as fast as what we're seeing in the cloud with some of your customers and how they're using data. How does the volume and velocity community have any kind of other kind of insights into those numbers? >>Yeah, I mean, I think from a stats perspective, um, you know, take Redshift, for example, customers are processing. So reading and writing, um, multiple exabytes of data there across from each shift. And, uh, you know, one of the things that we've seen in, uh, as time has progressed as, as data volumes have gone up and did a tapes have exploded, uh, you've seen data warehouses get more flexible. So we've added things like the ability to put semi-structured data and arbitrary, nested data into Redshift. Uh, we've also seen the seamless integration of data warehouses and data lakes. So, um, actually Redshift was one of the first to enable a straightforward acquiring of data. That's sitting in locally and drives as well as feed and that's managed on a stream and, uh, you know, those trends will continue. I think you'll kind of continue to see this, um, need to query data wherever it lives and, um, and, uh, allow, uh, leaks and warehouses and purpose-built stores to interconnect. >>You know, one of the things I liked about your presentation was, you know, kind of had the theme of, you know, modernize, unify, innovate, um, and we've been covering a lot of companies that have been, I won't say stumbling, but like getting to the future, some go faster than others, but they all kind of get stuck in an area that seems to be the same spot. It's the silos, breaking down the silos and get in the data lakes and kind of blending that purpose built data store. And they get stuck there because they're so used to silos and their teams, and that's kind of holding back the machine learning side of it because the machine learning can't do its job if they don't have access to all the data. And that's where we're seeing machine learning kind of being this new iterative model where the models are coming in faster. And so the silo brake busting is an issue. So what's your take on this part of the equation? >>Uh, so there's a few things I plan it. So you're absolutely right. I think that transition from some old data to interconnected data is always straightforward and it operates on a number of levels. You want to have the right technology. So, um, you know, we enable things like queries that can span multiple stores. You want to have good governance, you can connect across multiple ones. Uh, then you need to be able to get data in and out of these things and blue plays that role. So there's that interconnection on the technical side, but the other piece is also, um, you know, you want to think through, um, organizationally, how do you organize, how do you define it once data when they share it? And one of the asylees for enabling that sharing and, um, think about, um, some of the processes that need to get put in place and create the right incentives in your company to enable that data sharing. And then the foundational piece is good guardrails. You know, it's, uh, it can be scary to open data up. And, uh, the key to that is to put good governance in place where you can ensure that data can be shared and distributed while remaining protected and adhering to the privacy and compliance and security regulations that you have for that. And once you can assert that level of protection, then you can set that data free. And that's when, uh, customers really start to see the benefits of connecting all of it together, >>Right? And then we have a batch of startups here on this episode that are doing a lot of different things. Uh, some have, you know, new lake new lakes are forming observability lakes. You have CQL innovation on the front end data, tiering innovation at the data tier side, just a ton of innovation around this new data as code. How do you see as executive at AWS? You're enabling all this, um, where's the action going? Where are the white spaces? Where are the opportunities as this architecture continues to grow, um, and get traction because of the relevance of machine learning and AI and the apps are embedding data in there now as code where's the opportunities for these startups and how can they continue to grow? >>Yeah, the, I mean, the opportunity is it's amazing, John, you know, we talked a little bit about this at the beginning, but the, there is no slow down insight for the volume of data that we're generating pretty much everything that we have, whether it's a watch or a phone or the systems that we interact with are generating data and, uh, you know, customers, uh, you know, we talk a lot about the things that'll stay the same over time. And so, you know, the data volumes will continue to go up. Customers are gonna want to keep analyzing that data to make sense of it. They're going to want to be able to do it faster and more cheaply than they were yesterday. And then we're going to want to be able to make decisions and innovate, uh, in a shorter cycle and run more experiments than they were able to do. >>And so I think as long as, and they're always going to want this data to be secure and well-protected, and so I think as long as we, and the startups that we work with can continue to push on making these things better. Can I deal with more data? Can I deal with it more cheaply? Can I make it easier to get insight? And can I maintain a super high bar in security investments in these areas will just be off. Um, because, uh, the demand side of this equation is just in a great place, given what we're seeing in terms of theater and the architect for forum. >>I also love your comment about, uh, ML integration being the last leg of the equation here or less likely the journey, but you've got that enablement of the AIP solves a lot of problems. People can see benefits from good machine learning and AI is creating opportunities. Um, and also you also have mentioned the end to end with security piece. So data and security are kind of going hand in hand these days, not just the governments and the compliance stuff we're talking about security. So machine learning integration kind of connects all of this. Um, what's it all mean for the customers, >>For customers. That means that with machine learning and really enabling themselves to use machine learning, to make sense of data, they're able to find patterns that can represent new opportunities, um, quicker than ever before. And they're able to do it, uh, dynamically. So, you know, in a prior version of the world, we'd have little bit of systems and they would be relatively rigid and then we'd have to improve them. Um, with machine learning, this can be dynamic and near real time and you can customize them. So, uh, that just represents an opportunity to deepen relationships with customers and create more value and to find more efficiency in how businesses are run. So that piece is there. Um, and you know, your ideas around, uh, data's code really come into play because machine learning needs to be repeatable and explainable. And that means versioning, uh, keeping track of everything that you've done from a code and data and learning and training perspective >>And data sets are updating the machine learning. You got data sets growing, they become code modules that can be reused and, uh, interrogated, um, security okay. Is a big as a big theme data, really important security is seen as one of our top use cases. Certainly now in this day and age, we're getting a lot of, a lot of breaches and hacks coming in, being defended. It brings up the open, brings up the data as code security is a good proxy for kind of where this is going. What's your what's take on that and your reaction to that. >>So I'm, I'm security. You can, we can never invest enough. And I think one of the things that we, um, you know, guide us in AWS is security, availability, durability sort of jobs, you know, 1, 2, 3, and, um, and it operates at multiple levels. You need to protect data and rest with encryption, good key management and good practices though. You need to protect data on the wire. You need to have a good sense of what data is allowed to be seen by whom. And then you need to keep track of who did what and be able to verify and come back and prove that, uh, you know, uh, only the things that were allowed to happen actually happened. And you can actually then use machine learning on top of all of this apparatus to say, uh, you know, can I detect things that are happening that shouldn't be happening in near real time so they could put a stop to them. So I don't think any of us can ever invest enough in securing and protecting my data and our systems, and it is really fundamental or adding customer trust and it's just good business. So I think it is absolutely crucial. And we think about it all the time and are always looking for ways to raise >>Well, I really appreciate you taking the time to give the keynote final word here for the folks watching a lot of these startups that are presenting, they're doing well. Business wise, they're being used by large enterprises and people buying their products and using their services for customers are implementing more and more of the hot startups products they're relevant. What's your advice to the customer out there as they go on this journey, this new data as code this new future of analytics, what's your recommendation. >>So for customers who are out there, uh, recommend you take a look at, um, what, uh, the startups on AWS are building. I think there's tremendous innovation and energy, uh, and, um, there's really great technology being built on top of a rock solid platform. And so I encourage customers thinking about it to lean forward, to think about new technology and to embrace, uh, move to the cloud suite, modernized, you know, build a single picture of our data and, and figure out how to innovate and when >>Well, thanks for coming on. Appreciate your keynote. Thanks for the insight. And thanks for the conversation. Let's hand it off to the show. Let the show begin. >>Thank you, John pleasure, as always.

>>We'll go back to the queue. It's continuous coverage of AWS storage day. We're here in Seattle home with the Mariners home, with the Seahawks home of the Seattle storm. If you're a w NBA fan your cloud migration, according to our surveys and the ETR data that we use last year was number two initiative for it. Practitioners behind security. Welcome to this power panel on migration and transfer services. And I'm joined now by Matt Matthews. Who's the general manager of AWS transfer a family of services sitting. Roy is the GM of the snow family. And Randy boudin is the general manager of AWS data sync, gents. Welcome to good to see you. Thank you. So, Matt, you heard my narrative upfront, obviously it's top of mind for it. Pros, what are you seeing in the marketplace? >>Yeah, uh, certainly, um, many customers are currently executing on data migration strategies, uh, to the cloud. And AWS has been a primary choice for cloud storage for 15 years. Right. Um, but we still see many customers are evaluating, um, how to do their cloud migration strategies. And they're looking for, you know, um, uh, understanding what services can help them with those migrations. >>So said, well, why now? I mean, a lot of people might be feeling, you know, you got, you've got a hesitancy of taking a vaccine. What about hesitancy making a move? Maybe the best move is no movable. W why now? Why does it make sense? >>So AWS offers compelling, uh, cost savings to customers. I think with our global footprint that our 11 nines of durability are fully managed services. You're really getting the centralization benefits for the cloud, like all the resiliency and durability. And then besides that you are unlocking the on-prem data center and data store costs as well. So it's like a dual prong cost saving on both ends >>Follow up on that. If I may, I mean, again, the data was very clear cloud migration, top priority F for a lot of reasons, but at the same time migration, as you know, it's almost like a dirty word sometimes in it. So, so where do people even start? I mean, they've got so much data to migrate. How can they even handle >>That? Yeah. I'd recommend, uh, customers look at their cool and cold data. Like if they look at their backups and archives and they have not been used for long, I mean, it doesn't make sense to kind of keep them on prem, look at how you can move those and migrate those first and then slowly work your way up into like warm data and then hot data. >>Okay, great. Uh, so Randy, we know about the snow family of products. Of course, everybody's familiar with that, but what about online data migration? What can you tell us there? What's the, what are customers thinking >>About? Sure. So as you know, for many their journey to the cloud starts with data migration, right? That's right. So if you're, if you're starting that journey with, uh, an offline movement, you look to the snow family of products. If you, if you're looking for online, that's when you turn to data, sync data thinks that online data, movement, service data is it makes it fast and easy to move your data into AWS. The customers >>Figure out which services to use. Do you, how do you advise them on that? Or is it sort of word of mouth, peer to peer? How do they figure it out that that's squint through that? Yeah, >>So it comes down to a combination of things. So first is the amount of available bandwidth that you have, the amount of data that you're looking to move and the timeframe you have in which to do that. Right. So if you have a, high-speed say gigabit, uh, uh, network, uh, you can move data very quickly using data sync. If, if you have a slower network or perhaps you don't want to utilize your existing network for this purpose, then the snow family of products makes a lot of sense. Call said, that's it? Call center. That's >>My answer. Yeah, there you go. Oh, you'll >>Joke. Right. See Tam that's Chevy truck access method. You put it right on there and break it over. How about, you know, Matt, I wonder if we could talk maybe about some, some customer examples, any, any favorites that you see are ones that stand out in various industries? >>Yeah. So one of the things we're seeing is certainly getting your data to the cloud is, is important, but also customers want to migrate their applications to the cloud. And when they, when they do that, they, uh, the many applications still need ongoing data transfers from third parties, from ex partners and customers and, and whatnot. So, great example of this is, uh, FINRA and their partnership with AWS. So a FINRA is the single largest, um, uh, regulatory body for securities in the U S and they take in 335 billion market events per day, over 600,000 of their member brokers, registered brokers. So, uh, they use, um, AWS transfer family, uh, secure file transfers, uh, to get that data in an aggregated in, in S3, so they can, um, analyze it and, and, uh, really kind of, uh, understand that data so they can protect investors. So that's, that's a great example. >>So it's not just seeding the cloud, right? It's the ongoing population of it. How about, I mean, how do you guys see this shaping up the future? We all talk about storage silos. I see this as, you know, the cloud is in some ways a silo Buster. Okay. We've got all this data in the cloud now, but you know, you can not apply machine learning. There are other tooling, so what's the north star here. >>Yeah. It's really the north star of getting, you know, we want to unlock, uh, not only get the data in the cloud, but actually use it to unlock the benefits of the cloud has to offer. Right. That's really what you're getting at, aggregating all that data, uh, and using the power of the cloud to really, um, you know, harness that power to analyze the data. It's >>A big, big challenge that customers have. I mean, you guys are obsessed listening to customers, you know, w what kinds of things do you see in the future? Sid and Randy, maybe, maybe see if you can start, >>Uh, I'll start with the I'll kind of dovetail, on example, a Matthews, uh, I'll talk about a customer join, who moved 3.4 petabytes of data to the cloud joined was a streaming service provider out of Germany. They had prohibitive on-prem costs. They saved 500 K per year by moving to the cloud. And by moving to the cloud, they get much more of the data by being able to fine tune their content to local audiences and be more reactive and quicker, a reaction to business changes. So centralizing in the cloud had its benefits of access, flexibility, agility, and faster innovation, and faster time to market. Anything you'd add, right. >>Yeah, sure. So we have a customer Takara bio they're a biotech company. Uh, they're working with genome sequencing, right? So data rich information coming out of those sequencers, they're collecting and analyzing this data daily and sending it up into AWS for analysis, um, and, uh, by using data sync in order to do that, they've improved their data transfer rate by three times. And they've reduced their, uh, overhead six by 66% in terms of their process. >>Guys get, must be blown away by this. I mean, we've all sort of lived in this, so I'm prem world and you sort of lay it out infrastructure, and then you go onto the next one, but the use cases are so diverse. The industry, examples. Matt will give you the last >>Word here. Yeah, no, w w what are we looking to do? You know, we, we always want to listen to our customers, uh, but you know, collectively our, our services and working across other services, AWS, we really, uh, want to help customers not only move their data in the crowd, but also unlock the power of that data. And really, um, you know, uh, we think there's a big opportunity across their migration and transfer services to help customers choose, choose the right service, uh, based on their, where they are in their cloud migration, uh, and, and all the different things they're dealing with. >>I've said a number of times the next 10 years is not going to be like the last 10 years. It's like the cloud is growing up. You know, it's out of the infancy stage. Maybe it's an adolescent. So I don't really know exactly, but guys, thanks so much for coming to the cube and sharing your insights and information. Appreciate it. And thank you for watching everybody keep it right there. More great content from AWS storage day in Seattle.

(pensive music) >> Thank you, Jenna, it's great to see you guys and thank you for watching theCUBE's continuous coverage of AWS Storage Day. We're here at The Spheres, it's amazing venue. My name is Dave Vellante. I'm here with Mai-Lan Tomsen Bukovec who's Vice President of Block and Object Storage. Mai-Lan, always a pleasure to see you. Thanks for coming on. >> Nice to see you, Dave. >> It's pretty crazy, you know, this is kind of a hybrid event. We were in Barcelona a while ago, big hybrid event. And now it's, you know, it's hard to tell. It's almost like day-to-day what's happening with COVID and some things are permanent. I think a lot of things are becoming permanent. What are you seeing out there in terms of when you talk to customers, how are they thinking about their business, building resiliency and agility into their business in the context of COVID and beyond? >> Well, Dave, I think what we've learned today is that this is a new normal. These fluctuations that companies are having and supply and demand, in all industries all over the world. That's the new normal. And that has what, is what has driven so much more adoption of cloud in the last 12 to 18 months. And we're going to continue to see that rapid migration to the cloud because companies now know that in the course of days and months, you're, the whole world of your expectations of where your business is going and where, what your customers are going to do, that can change. And that can change not just for a year, but maybe longer than that. That's the new normal. And I think companies are realizing it and our AWS customers are seeing how important it is to accelerate moving everything to the cloud, to continue to adapt to this new normal. >> So storage historically has been, I'm going to drop a box off at the loading dock and, you know, have a nice day. And then maybe the services team is involved in, in a more intimate way, but you're involved every day. So I'm curious as to what that permanence, that new normal, some people call it the new abnormal, but it's the new normal now, what does that mean for storage? >> Dave, in the course of us sitting here over the next few minutes, we're going to have dozens of deployments go out all across our AWS storage services. That means our customers that are using our file services, our transfer services, block and object services, they're all getting improvements as we sit here and talk. That is such a fundamentally different model than the one that you talked about, which is the appliance gets dropped off at the loading dock. It takes a couple months for it to get scheduled for setup and then you have to do data migration to get the data on the new appliance. Meanwhile, we're sitting here and customers storage is just improving, under the hood and in major announcements, like what we're doing today. >> So take us through the sort of, let's go back, 'cause I remember vividly when, when S3 was announced that launched this cloud era and people would, you know, they would do a lot of experimentation of, we were storing, you know, maybe gigabytes, maybe even some terabytes back then. And, and that's evolved. What are you seeing in terms of how people are using data? What are the patterns that you're seeing today? How is that different than maybe 10 years ago? >> I think what's really unique about AWS is that we are the only provider that has been operating at scale for 15 years. And what that means is that we have customers of all sizes, terabytes, petabytes, exabytes, that are running their storage on AWS and running their applications using that storage. And so we have this really unique position of being able to observe and work with customers to develop what they need for storage. And it really breaks down to three main patterns. The first one is what I call the crown jewels, the crown jewels in the cloud. And that pattern is adopted by customers who are looking at the core mission of their business and they're saying to themselves, I actually can't scale this core mission on on-premises. And they're choosing to go to the cloud on the most important thing that their business does because they must, they have to. And so, a great example of that is FINRA, the regulatory body of the US stock exchanges, where, you know, a number of years ago, they took a look at all the data silos that were popping up across their data centers. They were looking at the rate of stock transactions going up and they're saying, we just can't keep up. Not if we want to follow the mission of being the watchdog for consumers, for transactions, for stock transactions. And so they moved that crown jewel of their application to AWS. And what's really interesting Dave, is, as you know, 'cause you've talked to many different companies, it's not technology that stops people from moving to the cloud as quick as they want to, it's culture, it's people, it's processes, it's how businesses work. And when you move the crown jewels into the cloud, you are accelerating that cultural change and that's certainly what FINRA saw. Second thing we see, is where a company will pick a few cloud pilots. We'll take a couple of applications, maybe one or a several across the organization and they'll move that as sort of a reference implementation to the cloud. And then the goal is to try to get the people who did that to generalize all the learning across the company. That is actually a really slow way to change culture. Because, as many of us know, in large organizations, you know, you have, you have some resistance to other organizations changing culture. And so that cloud pilot, while it seems like it would work, it seems logical, it's actually counter-productive to a lot of companies that want to move quickly to the cloud. And the third example is what I think of as new applications or cloud first, net new. And that pattern is where a company or a startup says all new technology initiatives are on the cloud. And we see that for companies like McDonald's, which has transformed their drive up experience by dynamically looking at location orders and providing recommendations. And we see it for the Digital Athlete, which is what the NFL has put together to dynamically take data sources and build these models that help them programmatically simulate risks to player health and put in place some ways to predict and prevent that. But those are the three patterns that we see so many customers falling into depending on what their business wants. >> I like that term, Digital Athlete, my business partner, John Furrier, coined the term tech athlete, you know, years ago on theCUBE. That third pattern seems to me, because you're right, you almost have to shock the system. If you just put your toe in the water, it's going to take too long. But it seems like that third pattern really actually de-risks it in a lot of cases, it's so it's said, people, who's going to argue, oh, the new stuff should be in the cloud. And so, that seems to me to be a very sensible way to approach that, that blocker, if you will, what are your thoughts on that? >> I think you're right, Dave. I think what it does is it allows a company to be able to see the ideas and the technology and the cultural change of cloud in different parts of the organization. And so rather than having a, one group that's supposed to generalize it across an organization, you get it decentralized and adopted by different groups and the culture change just goes faster. >> So you, you bring up decentralization and there's a, there's an emerging trend referred to as a data mesh. It was, it was coined, the term coined by Zhamak Dehghani, a very thought-provoking individual. And the concept is basically the, you know, data is decentralized, and yet we have this tendency to sort of shove it all into, you know, one box or one container, or you could say one cloud, well, the cloud is expanding, it's the cloud is, is decentralizing in many ways. So how do you see data mesh fitting in to those patterns? >> We have customers today that are taking the data mesh architectures and implementing them with AWS services. And Dave, I want to go back to the start of Amazon, when Amazon first began, we grew because the Amazon technologies were built in microservices. Fundamentally, a data mesh is about separation or abstraction of what individual components do. And so if I look at data mesh, really, you're talking about two things, you're talking about separating the data storage and the characteristics of data from the data services that interact and operate on that storage. And with data mesh, it's all about making sure that the businesses, the decentralized business model can work with that data. Now our AWS customers are putting their storage in a centralized place because it's easier to track, it's easier to view compliance and it's easier to predict growth and control costs. But, we started with building blocks and we deliberately built our storage services separate from our data services. So we have data services like Lake Formation and Glue. We have a number of these data services that our customers are using to build that customized data mesh on top of that centralized storage. So really, it's about at the end of the day, speed, it's about innovation. It's about making sure that you can decentralize and separate your data services from your storage so businesses can go faster. >> But that centralized storage is logically centralized. It might not be physically centralized, I mean, we put storage all over the world, >> Mai-Lan: That's correct. >> right? But, but we, to the developer, it looks like it's in one place. >> Mai-Lan: That's right. >> Right? And so, so that's not antithetical to the concept of a data mesh. In fact, it fits in perfectly to the point you were making. I wonder if we could talk a little bit about AWS's storage strategy and it started of course, with, with S3, and that was the focus for years and now of course EBS as well. But now we're seeing, we heard from Wayne this morning, the portfolio is expanding. The innovation is, is accelerating that flywheel that we always talk about. How would you characterize and how do you think about AWS's storage strategy per se? >> We are a dynamically and constantly evolving our AWS storage services based on what the application and the customer want. That is fundamentally what we do every day. We talked a little bit about those deployments that are happening right now, Dave. That is something, that idea of constant dynamic evolution just can't be replicated by on-premises where you buy a box and it sits in your data center for three or more years. And what's unique about us among the cloud services, is again that perspective of the 15 years where we are building applications in ways that are unique because we have more customers and we have more customers doing more things. So, you know, I've said this before. It's all about speed of innovation Dave, time and change wait for no one. And if you're a business and you're trying to transform your business and base it on a set of technologies that change rapidly, you have to use AWS services. Let's, I mean, if you look at some of the launches that we talk about today, and you think about S3's multi-region access points, that's a fundamental change for customers that want to store copies of their data in any number of different regions and get a 60% performance improvement by leveraging the technology that we've built up over, over time, leveraging the, the ability for us to route, to intelligently route a request across our network. That, and FSx for NetApp ONTAP, nobody else has these capabilities today. And it's because we are at the forefront of talking to different customers and that dynamic evolution of storage, that's the core of our strategy. >> So Andy Jassy used to say, oftentimes, AWS is misunderstood and you, you comfortable with that. So help me square this circle 'cause you talked about things you couldn't do on on-prem, and yet you mentioned the relationship with NetApp. You think, look at things like Outposts and Local Zones. So you're actually moving the cloud out to the edge, including on-prem data centers. So, so how do you think about hybrid in that context? >> For us, Dave, it always comes back to what the customer's asking for. And we were talking to customers and they were talking about their edge and what they wanted to do with it. We said, how are we going to help? And so if I just take S3 for Outposts, as an example, or EBS and Outposts, you know, we have customers like Morningstar and Morningstar wants Outposts because they are using it as a step in their journey to being on the cloud. If you take a customer like First Abu Dhabi Bank, they're using Outposts because they need data residency for their compliance requirements. And then we have other customers that are using Outposts to help, like Dish, Dish Networks, as an example, to place the storage as close as account to the applications for low latency. All of those are customer driven requirements for their architecture. For us, Dave, we think in the fullness of time, every customer and all applications are going to be on the cloud, because it makes sense and those businesses need that speed of innovation. But when we build things like our announcement today of FSx for NetApp ONTAP, we build them because customers asked us to help them with their journey to the cloud, just like we built S3 and EBS for Outposts for the same reason. >> Well, when you say over time, you're, you believe that all workloads will be on the cloud, but the cloud is, it's like the universe. I mean, it's expanding. So what's not cloud in the future? When you say on the cloud, you mean wherever you meet customers with that cloud, that includes Outposts, just the programming, it's the programmability of that model, is that correct? That's it, >> That's right. that's what you're talking about? >> In fact, our S3 and EBS Outposts customers, the way that they look at how they use Outposts, it's either as part of developing applications where they'll eventually go the cloud or taking applications that are in the cloud today in AWS regions and running them locally. And so, as you say, this definition of the cloud, you know, it, it's going to evolve over time. But the one thing that we know for sure, is that AWS storage and AWS in general is going to be there one or two steps ahead of where customers are, and deliver on what they need. >> I want to talk about block storage for a moment, if I can, you know, you guys are making some moves in that space. We heard some announcements earlier today. Some of the hardest stuff to move, whether it's cultural or maybe it's just hardened tops, maybe it's, you know, governance edicts, or those really hardcore mission critical apps and workloads, whether it's SAP stuff, Oracle, Microsoft, et cetera. You're clearly seeing that as an opportunity for your customers and in storage in some respects was a blocker previously because of whatever, latency, et cetera, then there's still some, some considerations there. How do you see those workloads eventually moving to the cloud? >> Well, they can move now. With io2 Block Express, we have the performance that those high-end applications need and it's available today. We have customers using them and they're very excited about that technology. And, you know, again, it goes back to what I just said, Dave, we had customers saying, I would like to move my highest performing applications to the cloud and this is what I need from the, from the, the storage underneath them. And that's why we built io2 Block Express and that's how we'll continue to evolve io2 Block Express. It is the first SAN technology in the cloud, but it's built on those core principles that we talked about a few minutes ago, which is dynamically evolving and capabilities that we can add on the fly and customers just get the benefit of it without the cost of migration. >> I want to ask you about, about just the storage, how you think about storage in general, because typically it's been a bucket, you know, it's a container, but it seems, I always say the next 10 years aren't going to be like the last, it seems like, you're really in the data business and you're bringing in machine intelligence, you're bringing in other database technology, this rich set of other services to apply to the data. That's now, there's a lot of data in the cloud and so we can now, whether it's build data products, build data services. So how do you think about the business in that sense? It's no longer just a place to store stuff. It's actually a place to accelerate innovation and build and monetize for your customers. How do you think about that? >> Our customers use the word foundational. Every time they talk about storage, they say for us, it's foundational, and Dave, that's because every business is a data business. Every business is making decisions now on this changing landscape in a world where the new normal means you cannot predict what's going to happen in six months, in a year. And the way that they're making those smart decisions is through data. And so they're taking the data that they have in our storage services and they're using SageMaker to build models. They're, they're using all kinds of different applications like Lake Formation and Glue to build some of the services that you're talking about around authorization and data discovery, to sit on top of the data. And they're able to leverage the data in a way that they have never been able to do before, because they have to. That's what the business world demands today, and that's what we need in the new normal. We need the flexibility and the dynamic foundational storage that we provide in AWS. >> And you think about the great data companies, those were the, you know, trillions in the market cap, their data companies, they put data at their core, but that doesn't mean they shove all the data into a centralized location. It means they have the identity access capabilities, the governance capabilities to, to enable data to be used wherever it needs to be used and, and build that future. That, exciting times we're entering here, Mai-Lan. >> We're just set the start, Dave, we're just at the start. >> Really, what ending do you think we have? So, how do you think about Amazon? It was, it's not a baby anymore. It's not even an adolescent, right? You guys are obviously major player, early adulthood, day one, day zero? (chuckles) >> Dave, we don't age ourself. I think if I look at where we're going for AWS, we are just at the start. So many companies are moving to the cloud, but we're really just at the start. And what's really exciting for us who work on AWS storage, is that when we build these storage services and these data services, we are seeing customers do things that they never thought they could do before. And it's just the beginning. >> I think the potential is unlimited. You mentioned Dish before, I mean, I see what they're doing in the cloud for Telco. I mean, Telco Transformation, that's an industry, every industry, there's a transformation scenario, a disruption scenario. Healthcare has been so reluctant for years and that's happening so quickly, I mean, COVID's certainly accelerating that. Obviously financial services have been super tech savvy, but they're looking at the Fintech saying, okay, how do we play? I mean, there isn't manufacturing with EV. >> Mai-Lan: Government. >> Government, totally. >> It's everywhere, oil and gas. >> There isn't a single industry that's not a digital industry. >> That's right. >> And there's implications for everyone. And it's not just bits and atoms anymore, the old Negroponte, although Nicholas, I think was prescient because he's, he saw this coming, it really is fundamental. Data is fundamental to every business. >> And I think you want, for all of those in different industries, you want to pick the provider where innovation and invention is in our DNA. And that is true, not just for storage, but AWS, and that is driving a lot of the changes you have today, but really what's coming in the future. >> You're right. It's the common editorial factors. It's not just the, the storage of the data. It's the ability to apply other technologies that map into your business process, that map into your organizational skill sets that drive innovation in whatever industry you're in. It's great Mai-Lan, awesome to see you. Thanks so much for coming on theCUBE. >> Great seeing you Dave, take care. >> All right, you too. And keep it right there for more action. We're going to now toss it back to Jenna, Canal and Darko in the studio. Guys, over to you. (pensive music)

>>Mhm >>Welcome to the cube where we're celebrating the EC 2/15 birthday anniversary. My name is Dave Volonte and we're joined right now by Sandy carter, Vice President of AWS. Welcome Sandy, it's great to see you again, >>David. So great to see you too. Thanks for having me on the show today. >>Very welcome. We were last physically together. I think it was reinvent 2019. Hopefully I'll see you before 2022. But first happy birthday to EC two. I mean, it's hard to imagine back in 2006, the degree to which EC two would impact our industry. Sandy, >>I totally agree. You know, I joined a W S about 4.5 years ago in EC two and it's, it's even amazing to see what's just happened in the last 4.5 years. So I'm with you. Nobody really expected the momentum, but EC two has really shone brightly in value to our customers. >>You know, we've done the public sector summit, you know, many times. It's a great event. Things are a little different in public sector as you well know. So talk about the public sector momentum with EC two and that journey. What have you seen? >>Yeah, so it's a great question day. So I had to go back in the time vault. You know, public sector was founded in 2010 and we were actually founded by the amazon process writing a paper setting up a two pizza team, which happened to be six people. And that journey really started with a lot of our public sector customers thinking that we don't know about the cloud. So we might want to do a pilot or just look at non mission critical workloads now public sector and I know you know this day but public sector is more than just government, it has education, not for profit healthcare and now space. But everybody at that time was very skeptical. So we had to really work hard to migrate some workloads over. And one of our very first non mission critical workloads was the U. S. Navy. Um and what they did was the Navy Media Services actually moved images over to EC two. Now today that seems like oh that's pretty easy. But back then that was a big monumental reference. Um and we had to spend a lot of time on training and education to win the hearts and souls of our customers. So back then we had half of the floor and Herndon Washington, we just had a few people and that room really became a training room. We trained our reps, we trained our customers um research drive. A lot of our early adopters accounts like Nasa and jpl. And um then when cloud first came out and governments that started with the U. S. A. And we announced Govcloud, you know, things really picked up, we had migration of significant workloads. So if you think back to that S. A. P. And just moving media over um with the Navy, the Navy and S. A. P. Migrated their largest S A P E R P solution to the cloud in that time as well. Um, then we started international. Our journey continued with the UK International was UK and us was us. Then we added a P. J. And latin America and Canada. And then of course the partner team which you know, is very close to my heart. Partners today are about 73% of our overall public sector business. And it started out with some interesting small pro program SVS being very crucial to that, accelerating adoption. And then of course now the journey has continued with Covid. That has really accelerated that movement to the cloud. And we're seeing, you know, use of ec two to really help us drive by the cute power needed for A I N. M. L. And taking all that data in from IOT and computing that data. And are they are. Um, and we're really seeing that journey just continue and we see no end in sight. >>So if we can stay in the infancy and sort of the adolescent years of public sector, I mean, remember, I mean as analysts, we were really excited about, you know, the the the introduction of of of of EC two. But but there was a lot of skepticism in whatever industry, financial services, healthcare concerns about security, I presume it was similar in public sector, but I'm interested in how you you dealt with those challenges, how you you listen to folks, you know, how did you drive that leadership to where it is today? >>Yeah, you're right. The the first questions were what is the cloud? Doesn't amazon sell books? What is this clown thing? Um, what is easy to, what is easy to stand for and then what the heck is an instance? You know, way back when there was one instance, it didn't even have a name. And today of course we have over 400 instant types with different names for each one. Um and the big challenges you asked about challenges, the big challenges that we had to face. Dave were first and foremost, how do we educate? Um we had to educate our employees and then we had to educate our customers. So we created these really innovative hands on training programmes, white boarding um, sessions that we needed. They were wildly popular. So we really have to do that and then also prove security as you know. So you asked how we listen to our customers and of course we followed the amazon way we work backwards from where we were. So at that time, customers needed education. And so we started there um, data was really important. We needed to make customer or data for government more available as well. So for instance, we first started hosting the Census Bureau for instance. Um and that was all on EC two. So we had lots of early adopters and I think the early adopters around EC two really helped us to remember. I said that the UK was our international office for a while. So we had NIH we had a genomes project and the UK Ministry of Justice as well. And we had to prove security out. We had to prove how this drove a structured GovCloud and then we had to also prove it out with our partners with things like helping them get fed ramped or other certifications. I'll for that sort of thing as well. And so we really lead in those early days through that education and training. Um we lead with pilots to show the potential of the possible and we lead with that security setting those security standards and those compliance certifications, always listening to the customer, always listening to the partner, knowing how important the partners we're going to be. So for example, recovery dot gov was the first government wide system that moved to the cloud. Um the recovery transparency board was first overseeing that Recovery act spending, which included stimulus tracking website. I don't know if you remember that, but they hosted the recovery dot gov On amazon.com using EC two. And that site quickly made information available to a million visitors per hour and at that time, that was amazing. And the cost savings were significant. We also launched Govcloud. You'd asked about GovCloud earlier and that federal cloud computing strategy when the U. S. Government came out with cloud first and they had to consider what is really going to compel these federal agencies to consider cloud. They had Public-sector customers had 70 requirements for security and safety of the data that we came out with Govcloud to open up all those great opportunities. And I think Dave we continue to leave because we are customer obsessed uh you know, still supporting more security standards and compliance sort than any other provider. Um You know, now we lead with data not just data for census or images for the US Navy, but we've got now data in space and ground station and data at scale with customers like Finra who's now doing 100 billion financial transactions. Not just that one million from the early days. So it has been a heck of a ride for public sector and I love the way that the public sector team really used and leveraged the leadership principles. Re invent and simplify dive deep. Be obsessed with the customers start where they are. Um and make sure that you're always always always listening to what they need. >>You know, it's interesting just observing public sector. It's not uncommon, especially because of the certifications that some of the services, you know come out after they come out for the commercial sector. And I remember years ago when I was at I. D. C. I was kind of the steward of the public sector business. And that was a time when everybody was trying to focus in public sector on commercial off the shelf software. That was the big thing. And they want to understand, they wanted to look at commercial use cases and how they could apply them to government. And when I dug in a little bit and met with generals and like eight different agencies, I was struck by how many really smart people and the things that they were doing. And I said at the time, you know, a lot of my commercial clients could learn a lot from you. And so the reason I bring that up is because I saw the same thing with Govcloud because there was a lot of skepticism in various industries, particularly regulated industries, financial services, healthcare. And then when Govcloud hit and the CIA deal hit, people said, whoa CIA, they're like the most security conscious industry or organization in the world. And so I feel as though in a way public sector led that that breakthrough. So I'm wondering when you think about EC two today and the momentum that it has in the government, Are there similar things that you see? Where's the momentum today in public sector? >>You are right on target day? I mean that CIA was a monumental moment and that momentum with ever increasing adoption to the cloud has continued in public sector. In fact today, public sector is one of our fastest growing areas. So we've got um, you know, thousands of startups or multiple countries that were helping out today to really ignite that innovation. We have over 4000 government agencies, 9000 education agencies. Um 2000 public sector partners from all over the globe. 24,000 not for profit organizations. And what I see is the way that they're using EC two um is is leading the pack now, especially after Covid, you know, many of these folks accelerated their journey because of Covid. They got to the cloud faster and now they are doing some really things that no one else is doing like sending an outpost postbox into space or leveraging, you know robots and health care for sure. So that momentum continues today and I love that you were the champion of that you know way back when even when you were with I. D. C. >>So I want to ask you, you sort of touched on some interesting use cases, what are some of the more unusual ones and maybe breakthrough use cases that you see? >>Oh so yeah we have a couple. So one is um I mentioned it earlier but there is a robot now that is powered by IOT and EC two and the robot helps to take temperature and and readings for folks that are entering the hospital in latin America really helped during Covid, one of my favorites. It actually blew the socks off of verne or two and you know that's hard to do is a space startup called lunar outpost and they are synthesizing oxygen on mars now that's, that's driven by Ec two. That's crazy. Right? Um, we see state governments like new york, they've got this vision zero traffic and they're leveraging that to prevent accidents all through new york city. I used to live in new york city. So this is really needed. Um, and it continues like with education, we see university of Illinois and Splunk one of our partners, they created a boarding pass for students to get back to school. So I have a daughter in college. Um, and you know, it's really hard for her to prove that she's had the vaccine or that she's tested negative on the covid test. They came out with a past of this little boarding pass, just like you used to get on an airplane to get into different classes and labs and then a couple of my favorites and you guys actually filmed the Cherokee nation. So the Cherokee nation, the chief of the Cherokee nation was on our silicon um show and silicon angles show and the cube featured them And as the chief talked about how he preserves the Cherokee language. And if you remember the Cherokee language has been used to help out the US in many different ways and Presidio. One of our partners helped to create a game, a super cool game that links in with unity To help teach that next generation the language while they're playing a game and then last but not least axle three d out of the UK. Um, they're using easy to, to save lives. They've created a three D imaging process for people getting ready to get kidney transplants and they have just enhanced that taken the time frame down for months. Now today's that they can actually articulate whether the kidney transplant will work. And when I talked to roger their Ceo, they're doing R. O. L return on life's not return on investment. So those are just some of the unusual and breakthrough use cases that we see powered by E. C. To >>Sandy. I'll give you the last word. Your final closing comments. >>Well, my final closing comments are happy birthday to ec two celebrating 15 years. What a game changer and value added. It has been the early days of Ec two. Of course we're about education like what is the cloud? Why is a bookseller doing it. But um, easy to really help to create a new hub of value Now. We've got customers moving so fast with modernization using a I. M and M. L. Containers survivalists. Um, and all of these things are really changing the game and leveling it up as we increased that business connection. So I think the future is really bright. We've only just begun. We've only just begun with EC two and we've only just begun with public sector. You know, our next great moments are still left to come. >>Well, Sandy, thanks so much. Always Great to see you. Really appreciate your time. >>Thank you so much. Dave. I really appreciate it. And happy birthday again to E. C. To keep >>It right there were celebrating Ec 2's 15th birthday right back. >>Mhm.

>>Hello and welcome to this session on experiencing secure agile hybrid cloud for your absent data. My name is Andrew labor. I'm a worldwide business unit product manager, Hc I Solutions with HP and I'm joined by my teammate Jeff Corcoran, who was go to market program Solutions or HP as well. And with that let's just dive right into it. Well, everybody has absent data. They're all over the place. They're both live on your phones, your computers and the cloud and servers are everywhere, absent data are all over the place. Well, what can we really do about that from moving forward modernization of that? Well, we have expectations for personalized, instant and engaging experiences that are the benchmark of your experience, more speed and agility or more paramount than ever. You see a world where apps and data like I mentioned our live and all over the place and that data explosion is happening at the edge where 75 of data is now created in moving us from a data center too many locations and many centers of that data. We have a digital transformation that has reached only a fraction of that. And we have modern cloud experiences for speed and agility and we want to really push that into an on premise reality where data has gravity security formats and compliance that you require. You really want that data transformation that somehow remains elusive for most outside of the public cloud. We want that true private premised on premise cloud infrastructure that translates to your hybrid cloud where you already have your apps and data live in the public cloud. And so as I mentioned, 70 of the public of the apps are outside in the public cloud and we really want that to be able to be brought into the local as well. And the on premise give you more flexibility, more agility and only H P E brings the cloud experience absent data everywhere. We define that right mix for you to move your data to the local and with that we have an approach that's any cloud anywhere and we have the expertise to help you define that right mix of cloud for your enterprise. We also create modern casual platforms for innovation where we bring your non native traditional apps that are slowing you down. We bring that into a modern enabled cloud experience together with cloud data of apps to achieve that speed and agility that I mentioned, being able to create a consistent strategy for you and your infrastructure. We also consume everything as a service everywhere. We bring the modern cloud experience to you and your apps and data self service ease being able to scale up or down depending on usage and flexibility. And we also have to pay for use and all managed for you with HP. Green Lake services the market leading infrastructure as a service platform for well over a decade. We also unify that hybrid cloud estate being able to move operations to a cloud native Cloud ops process manage for you with one unified management platform. Hp Green Lake Central. This helps you manage and unify your applications across cloud native and non cloud native workloads, drive insights and control for operational excellence and we do that by defining the right mix of cloud for you with HPD Point Next services, we're able to assess applications to determine the right mix for your business objectives. Hp Point Next services, we have cloud in technology experts on hand and ready to task for you to assess your existing IT infrastructure strategy, identify trapped capital that you might not even notice is there as well as help you assess your people and teams to identify critical gaps in your cloud journey. Finally, HP Point next services capital experts can determine the right mix of cloud strategy for you. Help you move and migrate your data into that optimized for every workload. And we do that by creating modern agile platform for innovation and we achieve the speed and agility you want report folio of software defined rack optimized HP keep Reliant and H. P. S. Energy infrastructure. Using that compose Herbal cloud compose double infrastructure platform that we support through our intellectual property and through leading partner Cloud solutions and who is that? That's BM wear with cloud foundation. I am a cloud foundation is the perfect blend for HP synergy and HP. Reliant to create that universal hybrid cloud platform, both modern and traditional applications. The Cloud foundation is characterized by many tenants such as develop Already Infrastructure, which creates that automated full stack experience. To help you get ready to do your development through a PS and infrastructure. Universal platforms, a single platform virtual machines and containers as well as application focused management. To simplify your management, being able to have multiple application resources and foundation for that hybrid cloud that I described being able to extend that same software stack to the public cloud. You connect to your flavor of choice for public cloud consume. And together with HB solutions and BMR Cloud Foundation, we create that perfect platform for a consistent hybrid cloud experience from the mid market to the large enterprise customers. We are transforming that traditional I. T. To a virtualized data center. Our goal is to help you move quickly and be agile to digitally transform software defined data center supporting that hybrid infrastructure. Hp envy m where have been working together for years and we are providing a simple experience for hybrid cloud that you can create and deliver to show value instantly and continuously achieve faster innovation, consistent operation and reducing costs. And how do we do that together? Well with HB solutions from being more cloud foundation, we've revolutionized that data centre by building a single consistent hybrid cloud experience that you can see that delivers greater agility and simplicity with five times faster automation tools for building out your infrastructure in getting time to market quicker, invalidating that solution stack. Where we have end to end fully tested and validated solutions that reduce your complexity and allow you to consolidate your VMS and your containers into one environment. Seamlessly, we also integrate management. We have unique the upper management integration and automation through firmer lifecycle management. Vis a vis L C M on the VM ware side, simplify I. T and deliver more agility to your infrastructure as well as your software defined data center. And then we also have services with HB Point Next they accelerate that time to deployment using HP Green Lake and providing as a service experience that we bring that cloud to you. And we bring that with an enhanced ballistic 360° view of security that begins in the manufacturing supply chain of our servers and concludes with safeguarded end of life Decommissioning. We power that by the recently announced Gen 10 plus servers uhh peep Reliant NHP synergy and integrate that Silicon Root of Trust technology offering protection detection and recovery from attacks industry leading encryption and firmware protection. And finally all of that is brought together. Hp one view We take HP one view as the management solution which transforms all of the compute storage networking into one software defined infrastructure Through HP one View we offer a template driven approach for deploying provisioning, updating, integrating compute storage networking All together in one infrastructure. and HP one View uses those software templates single line of code. We can deploy and manage and compose all of your physical resources, require for that application or virtual host or container infrastructure. We deliver the flexibility to compose different tiers of storage as well as types of provisioning by HP One View through direct or attach fabric using cloud foundation and HPV Premera. And now I'd like to ask my coworker Jeff to dive into some customer experiences around the hybrid cloud Jeff. Take it away. >>Thanks. Andrew. I think a great way to follow up and talk about our solutions is to really look at how one of our customers is enabling this transformation. So Wedbush Security is one of the leading financial services firms in the US, providing private and institutional clients, securities brokerage wealth management, in investment banking services. The company is headquartered in los Angeles California and has about 100 offices across the United States to meet increasingly rigorous financial regulations for more resilient operations and mitigate the threat of earthquakes in the Los Angeles area and increase operational efficiencies. Wedbush was looking for transformation is looking for a change to what the way they are currently operating. And to do this, Wedbush partnered with lumen and HP to develop a new private, cloud based data center using bloomin Private cloud on VM ware Cloud foundation. This was located in lumens Dallas hosting center using HP Keep Reliant dl 33 60 jen tens to create a hyper converged, high performance infrastructure using integrated software defined networking and security. To date, Wedbush has migrated its entire production facility to this private cloud. The virtual machines support a range of business applications, including Refinitiv, Thompson, Reuters and if I ask financial systems, they're also hosting Web Bush's in house broker management tool and Microsoft, sequel server and Mongo DB. Now, how did this impact them? They were able to impact Their financial reporting by cutting that from five hours down to 58 minutes. At the same time, they are able to reduce the time that it takes to deploy these Infrastructure resources by 50%. So this allows them to deploy a modern IT infrastructure for performance, reliability and efficiency improvement. The net impact on their business Was that it reduces the analytic costs by 27%. It increases their business agility and it developed, allows them to develop new lines of business faster and increases their compliance for the new Finra financial regulations with HP Green Lake, the cloud that comes to you. Hp Green like brings that cloud experience, self serve paper use scale up and down and manage for you by HP and our partners to absent data everywhere, whether they're in the edges co locations or data centers, enabling you to free up capital most operational and financial flexibility and free up talent to accelerate what's next for you and your business with HP Green Lake customers get cloud services that our production ready, elastic for any scale With a simple experience delivered to customer locations and as little as 14 days. Now, let's take a look at how some of our customers are experiencing the benefit of HP Green Lake as the voice of Austrian business. The Austrian economic chamber delivers advocacy and support to over 500,000 companies and trade groups, thereby helping to foster the country's robust economic growth. However, a policy of fiscal prudence Led to a mandated 30 cost reduction and the chambers it service provider needed to cut costs without compromising service levels. So to do this, they turn to HP to pair a future proof compose herbal infrastructure with a consumption based support model and HP Green Lake. Now, both the internal and regional chambers offices are getting better performance and faster access to I. T. Services enabled them to focus more than ever on boosting critical Austrian economic forces in sectors. Hp is here to help you accelerate your transformation. We just talked about Green Lake. So this enables you to deploy any workload as a service and with HP Green Lake services, you can bring that cloud like speed, agility and as a service model to where your data, data and apps live today, it enables you to transform the way you do business with one experience in one operating model across your distributed clouds for apps and data at the edge in co locations and in data centers with HP Point Next services. They have conducted over 11,000. IT. projects in over 1.4 million customer interactions each and every year. HB Point Next services 15,000 plus experts and its vast ecosystem of solution partners and channel partners are uniquely able to help you at every stage of your digital transformation journey because we address some of the biggest areas of concern that can slow you down. We bring together technology and expertise to help you drive your business forward. Lastly, with HP financial services, flexibility and investment capacity are key considerations for business to drive digital transformation initiatives. In order to forge a path forward, you need access to flexible payment options that allow you to match your IT costs to usage, from helping release capital from existing infrastructures to deferring payments and providing pre owned technology to relieve capital strain. Hp financial services unlocks the value of your entire estate from edge to cloud to end user with multi vendor solutions consistently and sustainably around the world. H P E F s helps you create the financial capacity to transform your business, Y H P E. We have the experience to get you there Over 1000 successful cloud migrations. We have the expertise to help you at any stage to accelerate adoption of any cloud or financial model to help you deploy the like cloud experience for your apps and data. We're open to any cloud strategy with deep expertise across Azure AWS and google cloud. We have unbiased expertise and I p to accelerate your right mix of clouds for your enterprise and we can tie that all together with I. T. As a service from our market leading platform of HP Green Lake. After you viewed this session, we have a lot of resources that you can now use to help you continue your digital transformation and educate yourself. You'll find links here on the slide to a lot of different products and solution areas as well as social media interactions that we have to engage with you. Thank you for joining. We hope you find the sexual useful. Have a great day.

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

>>From around the globe. It's the queue with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hello, and welcome back to the cubes coverage of ADFS reinvent 2020 it's virtual this year because of the pandemic we can't be in person normally would do in these interviews face to face, but we're here remote. I'm your host, John furrier. We're the cube virtual and we're here with Teresa Carlson, who is the chief and heads up the public sector business, uh, for AWS and also now has industries, which is a lot of the verticals and just continues to, um, have great leadership and continues to do well in the business. I Theresa great to see you for the eighth consecutive cube interview you've been on every year and we thank you for coming on big year this year. Thanks for coming on. Great to see you. >>Thank you, John. Thank you for having me. It's hard to believe it's eight years already. Wow, go ahead. >>Well, first of all, I want to say congratulations. Um, the first year you will run, you never wavered. You always had a North star. Um, you had the Amazonia and kind of way, um, you told us what you were going to do and you did it. The CIA came on board and the dots just connected. So congratulations this year more than ever, um, during your keynote. And re-invent, even though it was virtual, um, again, you're raising the bar on the theme leadership and making use of the data two major themes this year on your keynote because of the pandemic. And just because of the cloud computing benefits are all kind of coming together. You're helping more people than ever doing a more public service with cloud when it needs it. The most. This has been a big story. Share your, your reaction to that. >>Yeah. Well, John, thank you again for having me in your coverage of reinvent. It's been three weeks of, wow. I mean, three weeks we do one hour a day three, uh, that COVID, you know, we're still, we're still not dead, right? The vaccinations are out. People are starting to, I saw on the television yesterday here in the U S the first nurse that was vaccinated. Uh, but for us, I will tell you the data side of this piece during COVID has been huge. I mean, huge. It has been, you know, our customers have always said data is golden for them, right. Uh, but during COVID, we have actually seen the use of data, just go up like crazy and not just the use of it, but, um, I will say it's multiple data lakes that are used hydrating multiple data lakes and using that data to merge. >>So if you think about economic data and health data and putting those data sets together in a way that they have deeper understanding of what's happening within their community, their state, their, their, uh, their country. So we've seen emerging of data, uh, in a big way. If you think about the vaccinations themselves, uh, John, that wouldn't have been possible to move this fast without the use of scalable compute, processing and analytics in a way like no one has ever seen it. And, uh, it's, it's, it's pretty amazing. And I don't think we'll ever go back. And also I'll just say sharing of that data has changed. Researchers are now much more open to sharing that data air cord 19 a research site that we've done has thousands of researchers on it. Now, hundreds of thousands of views on it with people sharing research about COVID and think about that. I mean, research has always been held tightly, and now we're really starting to see them open up and share that data so that we can move much faster. >>I think doing that public service with the data has always been a killer idea. We talked about national parks being kind of open for the people over the years now, super computing and data. You guys do a great job doing that, but the other area that you're getting a lot of press on and, and rightfully so is an area that I know is close to your heart, as well as our mission, which is getting people trained up on cloud computing. And you've done this for years, but this year more importantly, with all the pressure and all the need, you guys have offered, offering a huge training skills training for 29 million people globally. I saw that on the news, I saw you on doing some TV interviews on this. It's been all over the press has been getting a lot of great buzz. Can you tell me more about what that is? >>Yeah. So part of my, when I picked up bear industry business units also picked up our training and certification organization that is ran by Maureen Lonergan. I know you've had Maureen on your show before too, and then I have education, which is run by Kimma Jarris in the U S and max, uh, Peterson internationally. And we are now we've merged so that we have a model that we can teach and train around the world in a much more scalable way that this announcement was about going into 200 Kemp countries and territories training, 29 million people by 2025 free do free skills training and making that available job through multiple different programs and scaling those. So we'll take the programs we have and we'll scale those app much more rapidly. And then now we'll also look for new programs that we need to run in parallel because that's what we do. >>We have to look around corners. Also make sure that we have the right programs and, you know, I've lived, I've lived, you know, they're all amazing, but near and dear to my heart has always been our AWS educate, which we started, uh, for ages 14 and up to at the university and high school level, to be able to start to bring on those cloud skills. Then we added badging and credentialing onto that. And from there, you can go into the air Academy, which you can actually get certifications as a solution architect. Uh, but we've, we've added so many more, uh, our program restart now, which has been really, which is about training. Those who are jobless or an underserved communities and socioeconomic depressed areas. Uh, and I love that program. I told a story about an individual in Boston who had opened a training center, a gym he's a fitness trainer, and he had to close it, uh, because you know, COVID, and he went through our 12 week. >>We restart training program and now has a job with a company there in Boston. And I just love those kind of stories where you know, that you're putting people to work. And I think for us, there's thousands and thousands of jobs around the world, just in any city, if you, if you search on cloud computing jobs open, I just looked in New York when I was on CNBC. I looked in New York and there are 10,000 cloud jobs just there in New York. And I just did a quick search. So there's always jobs, and we've got to make sure that we're skilling them so they can go now fill those jobs. And that will help us close that gap. Uh, John, which we still have a big one, uh, to get all the jobs filled that are out there. >>That's a great mission. And I got to say, it's super important because one is cloud computing. There's openings for this kind of new, the new paradigm, which is now mainstream and playing out on, in real time, as, as Andy was talking about, but also the global it markets being reshaped by cloud computing. So you have the intersection of those two, which is a new skill. You can't just take it and make a cloud. You've got to bring it together. So it's a great opportunity for someone to come into the industry and level up pretty quickly. You don't have to have the 20 years of experience to do this. It's you can come in instantly level up, have a great job. >>You know, it's the one thing John, I hear all the time around the world before from like when I would go and speak with university chancellors and presidents and just professors, they would say, Hey, you know, AWS, we need you to do the micro-credentialing along the way. And this was pre COVID when they said, we need to get your students want to work while they're in school. Well now more than ever, it's important. And we also, John Luke, just in September, over 800,000 women left the workplace. That is a trend that we do not want and we can not sustain. And so doing, you know, doing programs like this virtually that you can do self paced environments, intensive environments. We want to make, we want to make these programs fit for whatever the individual needs. So it's not just a one size fits all. We want to make sure that the programs that we're providing will fit the needs of the individuals doing the training. And I, I particularly am, uh, I want to push this with their, you know, inclusion and diversity of the individuals that we need to get into the workplace, but it is pretty alarming when you see that many women leaving the workplace, you know, when a choice is being made right now, we're seeing women take the brunt of that. And we want to make sure that they have the opportunity to work virtually train themselves and get those new jobs that are out in tech. >>Well, that's one of the questions I had for you. I'll just jump to that. Now I'll get back to some of the other ones, but the customers that pivot to remote work and learning, uh, it's changing. And, you know, I was, um, riffing on an interview. Um, I think it was with one of your public sector customers, the future of work. And if you just think about the word work workforce, workplace workload work flows, the notion of work is now impacted. And you mentioned the diversity piece. This is an opportunity. So how should people think about this, uh, relearning? So we don't lose people and we actually get a net positive inbound migration to the workforce. >>You know, the flexibility I had, I did a fireside chat with Andrew Nooney. Um, he was the former CEO of PepsiCo and chairman, and is now on our Amazon board, uh, for re-invent. And she talked about, you know, being your authentic self, uh, curiosity, but one of her big points is women in the workplace. Uh, and she's gonna publish a new book soon, and it's going to be really focused on kind of equity policy, uh, areas of need that we have to focus on to make sure that we have at women being able to tackle both the home issues and being able to work and taking advantage of that plus 50%. And I would say the virtual opportunity is really fantastic, especially for, um, all levels of socioeconomic individuals, because you can work part-time full-time, you can work virtually. And I do believe while we all want to get back into the workplace. >>I think for me, I'm a social animal. I'd love to be there sitting beside you, John, you know, I think for a lot of us, we are, we kind of yearn to be back in the office, but there's also a lot that working from home, um, is, is much more achievable for them, right? Especially with childcare if school day, if it's a short day, because the schools and allowing flexibility with work is going to be really important and COVID has taught us that that is possible. My team did not miss a beat during COVID. I tell ya, it's like unbelievable. Our business, uh, has, has really kinda been on fire because public sector. And if you look at the other industries, I've picked up financial services, uh, energy and telecommunications and training and certification. These are all that had to keep going. Uh, governments were moving faster than ever. >>So our team was really busy. Um, I've had individuals asked me, well, how did you manage the downtowns? Like we didn't have any downtime. Like literally day one, we were like 24 seven and the teams were working with it pretty much every government around the world because COVID moved so quickly and all virtually. And I will have to say, John, I was really skeptical in the beginning about how is this? How, how are we going to do this? Um, but the teams really, we figured out how to operate. You know, you had to, it's a new muscle. You kind of have to build that virtual work muscle and figure out how you manage your day, how you fit things in. And then there's the point that people think you're always available because you are at home, right? So you can never, that you can't possibly not be available because you know, you're, you are sitting at home. And then there's the many times where people's cats walk across and kind of with their tail on their face. And that dog child were at REMS in with the diaper. And you know, it's all, you, you have to have grace and humor about all this. Sometimes T like you can't take everything so seriously. And perhaps we've learned that, um, work and life can blend a little bit more, right? That you can, you can have that when a lot of people, when they talk about work-life balance, now we have work-life harmony. >>You know, you and I have talked about this before. If you can tap whoever taps, the diversity of talent will always let me win the game and not just, um, diversity in terms of gender or background role. I mean, if you can tap the virtual space, you're a winner because there's talent out there that can be aggregated in, and there's no stigma associated with anything. So, you know, this is, I think Andy kinda, uh, expressed that to me. And, and he heard it in his keynote where he said, Hey, people are a square, but you can get more participation. I think that is a real positive, um, upside. And I love the perspective of this new muscle. I totally agree. You need to, you need to have that >>Square. I mean, we've, we've actually chatted. I don't know if we'll ever go back to having big rooms with people in it, because you have a voice, you have a face. And I do believe, especially for women, uh, John, who can not always speak up, it's an opportunity for them to have their own space. They ha they can have their own voice. All individuals cause centers. They have great ideas, but they don't always value them. So having, you know, when you, each person has their own square, you can actually kind of see, well, who's, who's has an opinion. Who's spoken up. Who, who do I want to call on here and ask them if they have an opinion? So I like the idea of everybody having their own space when you're having a meeting. If you have to be virtual, because you get lost in translation, especially if you have that large leader in the room and everybody else's around them, then sometimes they only kind of adhere to their voice. This is an opportunity for others to really have that pool. >>I was just, I saw a joke on Twitter from a friend that said, Hey, I run all the meetings now because I can mute people. So if someone starts talking, you're muted bye-bye. So again, this is a whole new muscle great stuff. Well, since you've, since you brought up your role, I know you have a new expanded role. Could you take a minute to explain what that is? Because I'm still not clear. I know you've been doing an amazing job. I've written about, uh, your initial successes, and now you continue to do well with public sector and believe me, I've exploding. I see it. We're reporting on it. Public service is changing with digital transformation, but these other things, what are you working on? What are the new areas? Yeah, so I >>Just passed my 10th year. I'm starting my 11th year and it's been like amazing building this public sector business. I, I, and our government customers. Wow. The innovation and education during COVID has been pretty off the charts, which I don't think I'll slow down. And then a few months ago I was asked to take on our, uh, our training and certification org and our evangelist in solution architecture org, along with the industry business units of, uh, finance, telecommunications, and energy. And then, uh, John, if you remembering June, I announced our aerospace and satellite industry business unit. So, uh, these are the ones that we have right now are very regulated. A lot of them are, you know, very closely aligned to regulated industry. Um, you know, there could be others that are not as regulated, but the ones right now, if you think about aerospace, satellite, financial services, telecommunications in, in, in energy. >>So they, for me, um, they're very, it can tell a lot of the work I've been doing in building public sector, because when I go into a country today, when my teams go in, we generally always have to work with these groups. So if you think about telecommunications, we have to go in and make sure that we're working on our networking, our connectivity, and we negotiate and work with those telco providers. Same with the energy companies, both large ones and small ones. We go in and we work to build a power purchasing agreements, you know, solar power, uh, renewable energy to power our data centers and make sure that we're giving back to the grid. So we have that partnership. And then in the financial sector, I've had our, uh, I've had all of our regulators anyway, like FINRA fed reserve. Um, I R S treasury. >>So I've already, I've always had all the regulators. So now working with the, uh, you know, the additional, the banking, the investment sector, capital markets, it's very, it's, it seems so natural if that makes sense. And now diving into the upstream and downstream stream of supply chain for both that energy and telco and what a fantastic time now for telcos with 5g. I mean, I've been saying for two or three years that I thought this would be a huge opportunity for telecommunications companies to actually look for new, uh, work streams for their customers. And I mean, edge, you know, now our connect or call centers that they can do and take advantage of that. So I'm actually really excited. Uh, John seeing seven of new opportunities and, you know, renewable the new energy, uh, startups that are out there, the things I'm seeing, power, solar, nuclear, um, and then seeing a lot of the larger energy companies take on these projects. It's a lot of fun. And, um, I'm very excited now to continue to meet those customers. I got to meet a lot during re-invent. I love their energy. Yeah. I love kind of learning about what they're looking to solve. And, and I'm also just looking forward to helping them, um, with the connections that we've already been doing in government. I think it's a really nice combination of working together. Now. >>I, I see it as, um, what you've done with public sector was take a partnership approach to an old standing industry, changed them quickly, get the transformation, build the relationships, get the successes and establish that transformation and this needed versus the organically developing, you know, stuff. That's going to be the cloud startups and whatnot. Those are going to use Amazon, but you're a transformational leader. >>John, if I could just save for a minute, if you think about re-invention, you're at re-invent and a lot of these are going through massive reinvention, uh, you know, again, 5g with telco renewables, uh, with energy and then financial services where everything is kind of moving to an online model and digital model with different types of currencies that they have to deal with. It's, it's really perfect for cloud and what we offer. So I think the opportunity, um, to dive in and really partner with these industries and aerospace and Salado. Oh my gosh. It's just, I have to say, I really do believe cloud computing is, um, the perfect kind of step forward with all these industries for reinvention and innovation, which they're all moving towards. >>Well, Theresa, you're a re-invention leader. Uh, we've covered it. And now we've got all new territory for you to work on. Um, bring your playbook, you know, people-centric partner results are charging Theresa, thank you for your time. Great to have you on. Great to see you. Wish you, we were in person in real life again soon. Thank you for coming on. >>Yeah, John, thank you. Happy holidays. I look forward to seeing you next year. >>Okay. This is the cubes coverage of AWS reinvented. We have Teresa Carlson, she heads up the public sector. She's the chief of the whole public sector, and now taking on other industries to bring that playbook, the reinvention to the industries, really a big part of the Amazon web services, vision and cultural change. That's going on with the pandemic reach rechanging and reformatting and refactoring industries. That's what's going on in the big picture and a lot of gay tech under the hood. I'm John for your host. Thanks for watching.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Welcome back to the cubes live coverage of AWS reinvent 2020 I'm Lisa Martin and I have with me a cube alumni back, please. Welcome Scott Mullins, the worldwide financial services business development leader at AWS. Scott. Welcome back. Great to have you joining us, >>Lisa. It's great to be back on the cube and to be visiting with you today from virtual re-invent 2020. >>Yes. Reinventing reinvent. The last show that I got to host in-person for the cube was reinvent last year. And here we have this three week virtual event that started last week. So lots more even going on. I think I even saw a hundred thousand or so registered, so massive event, lots of news. So walk us through some of the highlights that have been announced at reinvent this year and some of the things that you're seeing the most interest from customers in. >>Well, I think one of the big highlights is 500,000 registrants that are reinvented 50,000 attendees last year to reinvent or 50,000 or so to 500,000 re registered for the event. So that's, that's, that's worth talking about in its own. Right. But I think, you know, one of the things, and you mentioned this, you know, more re-invent three weeks, uh, this year, as opposed to the four days that we normally spend in Las Vegas together, physically, when you do, when you do it digitally, you have the ability to actually include more things and more leaders talking about things. And so when we think about the announcements that are having impacts, uh, with financial services customers specifically I'd point to a couple of things and, you know, they're obviously gonna mention Andy's keynote, but there's going to be some things that you might go wait a minute. >>I didn't even see that announcement. Uh, and then maybe I could point you and the viewers to some other, other, um, keynotes or some other sessions that were announced. So obviously I think, uh, first and foremost in Andy's keynote, uh, hybrid, uh, was something that was a very, uh, big focus for him and I for a very long time, we've had the messaging of the right tool for the right job when it comes to any of your services. I think you could alter that today to say it's the right tool for the right job at the right time and in the right place. That makes sense for you and especially for financial institutions. Um, you could look at the announcements around containers, the announcements around Amazon EKS, distro, Amazon EKS, anywhere, and then also Amazon ECS anywhere, which allows our customers to actually, uh, put AWS container technology anywhere they would like to put it. >>You could look also at the additions of the one you and two you form factors to outposts. So no longer do you have to do the, the, the large for you, uh, foreign factor for outposts, smaller outposts for smaller spaces, uh, that particular will play well in the financial service industry. You may not have necessarily as much room for a full cabinet. You could also look from the hybrid perspective in the announcement we made, um, around red hat OpenShift on AWS, all of are giving customers the ability to choose how they actually want to deploy, um, and pursue a hybrid. I'd also point to some announcements we made around management and governance in the financial services, industry governance, uh, is a very important topic. Uh, we announced the management and government lens for the AWS well architected, um, uh, program, uh, that is focused on breath practices for evolving governance for the cloud. >>It has recommended combination of AWS services integrations with our partner network and vetted reference architectures and guidance for addressing regulatory obligations as well. I'd also point to some things we made around audits. I was specifically in Steve Smith's, um, session today, he talked about AWS audit manager. That's a new tool for continually assessing areas and environments for controls or risk compliance. That includes prebuilt compliance frameworks for things like PCI DSS and GDPR, uh, two things that are very important in the financial services industry and last, but certainly not least I'd point to the announcement around the AWS audit Academy. This is training for auditors to actually be able to audit clouds from an agnostic perspective. Any cloud, not specifically AWS that's tree, uh, digital training to do that. And then also an instructor led course specifically on how to audit AWS. So some very key announcements, both from the standpoint of services, uh, as well as additional layers of helping customers in the financial services industry in regulated industries actually use our services. >>So typical, re-invent typical in a lot of news, a lot of announcements, the 500,000 Mark in terms of registering. I hadn't heard that. That's amazing. Let's talk that this has been an Andy. Jassy had an exclusive with John furrier just a couple of weeks ago before. I think it was last week, actually. And we've been talking about this acceleration of digital business transformation because of COVID we've been talking about it, the entire pandemic on the virtual cube, talking about how companies it's really about right now, surviving and thriving to be able to go forward and companies that haven't accelerated are probably in some trouble. Talk to me about how AWS has been working with your financial services customers to help them pivot and move to the cloud faster, really to not just help them survive now, but thrive in the long-term. >>Yeah. Immediately when COVID hit and it hit at different times in different, in different parts of the world. Immediately when COVID hit, we saw the conversation that we were having turning from, Hey, what's my digital strategy to immediately, what are my digital capabilities? And what that really means is what do I have the ability to do tomorrow? Because tomorrow is going to really matter. I don't have necessarily the time to plan for the next several quarters or the next several years, what can I do tomorrow to, um, really, uh, support my, my own workforce and support my own customers and the obligations I have as a financial institution. The first thing we saw people do was to try and make sure that those who financial services work can work. You can look at the adoption of Amazon workspaces, as well as our, uh, Amazon connect, uh, call centers as a service. >>As two examples there at the RBL bank in India was able to move to Amazon workspaces in just 10 days to enable its teams to actually work remotely from home. When they couldn't come into the office, you can look at Barclays. Barclays is actually a presenter at re-invent this year. They'll have a session on how they use Amazon connect, which again is our call center as a service offering to enable 25,000 contacts and our agents to work from home when they can no longer work out of the, out of their traditional contact center. The second thing we saw a financial institutions joining was making sure that customer engagements could still be meaningful when digital was the only option, um, specifically here in the U S you could look at the work that each of us did with FinTech companies like biz two X or fins Zack, or BlueVine Stripe and cabbage in support of the care act in the U S you might remember that the cares act, um, hasn't provisions for funding for small businesses. >>This small business administration had a program called the paycheck protection program, and those organizations were active in providing funding, uh, to small businesses. Uh, through that program. I'll give you an example of cabbage cabbage had previously not been an SBA lender, um, but they were able to, in two weeks build a fully automated system for small businesses to access PPP funding using Amazon text track, to extract information from documentation that those folks submitted to get alone. That reduced approval times from multiple days to about a median of four hours to actually get approval, to get funding through the PPP program. And then just four months cabbage became the second largest PPP lender. They lent over $7 billion in funding, which was twice the amount of funding that they went last year in 2019 loans. So we were happy to support organizations like cabbage and those other FinTech companies, as they help small businesses in the U S get access to funding, uh, during this critical time. >>And as we know, as you said, critical time, but really life or death for a lot of businesses. And as we continue to go through these ways, but it's interesting that you talked about that the speed of facilitation that during such unprecedented times, AWS and this massive machine was able to continue moving at full speed ahead and helping those customers to pivot. You talked about the cloud connect. I had a conversation with a guest on the queue last week about that. And, and I now think about if I have to call in a contact center and that person might be from home. So, you know, we're fortunate that the cloud computing technology and people like you and AWS, or are able to power that because it's, it's literally essential, which is probably one of the words of the year, but being able to keep the machinery going and innovate at the same time has been, make or break for a lot of businesses. >>Absolutely. And you, you look at, you know, kind of one of the last year is that I'll point to is, um, financial institutions. Uh, anti-virus, we're were very much focused on making sure that that cannot fail, that they scaled. And so you can look at the work we did with, uh, with the, with FINRA FINRA is the primary capital markets regulator here in the U S and on a daily basis frame or processes about 400 billion market events on every night to do surveillance on our markets, that when COVID hit, we had unprecedented volume and volatility in the market. And FINRA was, was, um, looking at processing, uh, anywhere from two to three times, their normal daily market volumes that's anywhere from 800 billion market events to 1.2 trillion a night. And if you look at how they were able to scale, they're actually able to scale up compute resources in AWS. We're on a nightly basis. They're able to automatically turn on and off up to a hundred thousand compute nodes in a single day. That automatic ability to scale is, is the power you're talking about. Being able to actually turn things up when you needed it and turn things down when you, when you don't need it based on the volumes. >>Well, and that's going to be something key going forward. As we know that there will be one thing I think that I always say we can count on right now is uncertainty and continued uncertainty, but we've also seen I'm calling them COVID catalysts. You know, the, what you talked about with cabbage, for example, and how that business pivoted quickly, because of the power of cloud computing and emerging technologies, what are some of the things that you think as we go into 2021 in the financial services arena, what are some of the big tech trends that you think were maybe born during COVID that are going to be critical going forward? >>Well, you know, you, you, you had Melanie Frank from capital one on cube a couple of days ago, and she was talking about, you know, their shift to cloud and what that's really enabled, and it, and she kind of sums it up nicely. She says, look, we want to give our customers experience that are real time, and that are intelligent. And you just can't do that with legacy technology. That's sitting in, you know, kind of a legacy data center. And so I think that's going to be kind of the, the, the all encompassing statement for what's happening in the financial services industry. As I mentioned, you know, organizations overnight said, okay, wait a minute, let's take that strategy. And then let's put it aside. Let's talk about capabilities. What can we do? And I think, you know, necessity is the mother of invention. Um, and when you're faced with limitations and challenges, like we all have been faced with around the world and not just in the financial services industry, it, it breeds, um, invention and the, and the desire and the need to actually meet those challenges head on, in very engineered of ways. >>And I think you're going to see more invention and specifically more invention from the established players in the financial services industry. Cloud use is not just experimental on the edges anymore. You're going to see more organizations coming out of COVID. Um, having had those experiences where they actually stood up a context center and scaled it. And, and just a matter of a few days to, to thousands of agents, you're going to find, um, organizations saying, wait a minute, we, we can do remote work. We could, we have access to things like Amazon workspaces. So I think you're, you're gonna, you're going to see that, uh, be a, be a trend. I think you're also gonna see, um, w what Lori beer said in the keynote with Andy, you know, she, she made a very, very astute statement, and I don't know if people caught it, cause it's kind of neat in the middle of her conversation. >>She said, look, we're trying to infuse analytics into everything that we do at JP Morgan. I think you're going to see more and more financial institutions looking to do that, to actually leverage the power of analytics, to power everything we do as a financial institution. So I think those, those are a couple of things that you're going to see. Um, and then, you know, looking, uh, you know, kind of around the corner, I think you're going to continue to see more re-invention within the industry. And what I mean by that is you've seen many financial institutions over the last week, uh, with, uh, re-invent making announcements, you saw bank and we towel saying, Hey, look, we are completely transforming ourselves with AWS. Uh, just a few weeks before we even saw standard charter, the same thing HSBC said, the same thing, global payments earlier in the year said the same thing. And you're going to see more and more organizations coming out and talking about these strategic decisions to reinvent everything that they do to make the financial systems of the world work. And so we're really pleased to be partnering with those organizations to make those transformations possible. We're seeing a lot of invention within the industry, and we're very pleased to be a part of the reinvention of the financial systems around the world. >>It's interesting to hear that you, you see, even the JP Morgan, some of those legacy, big houses are going to be really pivoting. They have to, to be competitive and to be able to utilize analytics, to deliver those real-time services. Because as we all know, as consumers, our patients is wearing thin these days, but I agree with you. I think there's a lot of opportunity there that innovation is exciting and there will have to be reinvention of entire industries, but I think there's a lot of silver linings there. Scott. I wish we had more time, cause I know we could keep talking, but thank you for sharing your insights on this reinvented reinvent this year. >>I appreciate it. Thank you, Lisa. It's always a pleasure to be on the cube. >>Chris Scott Mullins, I'm Lisa Martin. You're watching the cubes coverage of AWS reinvent 2020.

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey welcome back everybody. Jeff Frick here with theCUBE. Coming to you today from our Palo Alto studios. It's COVID is still going on. So, there's still no shows, but the good news is we've got the technology we can reach out to the community, and bring them in from far, far away. So today joining us from Virginia across the country is Dave Scott. He is the director of Product Management for Veritas, Dave, great to see you. >> Thanks Jeff, great to be here. >> Absolutely. So let's jump into it. You guys have been about backup and recovery for years and years and years, but oh my goodness, how the landscape continues to evolve between, you know, public cloud and you know, all the things happening with Amazon and Google, and Microsoft. And then now, of course big push for Hybrid. And, you know, we're the workloads, and kind of application centric infrastructure. You guys still got a backup and secure all this things. I wonder if you can give us a little bit of your perspective on, you know, kind of the increasing complexity of the computing environment, has all these different kind of pieces of the puzzle, are kind of gaining traction at the same time. >> Yeah, absolutely. I mean, I'm on the compliance side of the company. So I'm more on looking after requirements around collection of content preparation for litigation, making sure you're adhering to compliance regulations in different parts of the world. And, I mean that's a constantly evolving space. One of the, so basically the products I look after are Enterprise Vaults, Enterprise Vault.cloud, and eDiscovery platform. And, as you say, I mean, one of the biggest challenges is that customers are starting to move, you know, customers are looking for flexibility in how they deploy our solutions. We've had a product in market with enterprise vault for about 20 years. And so, we have a lot of customers that have a lot of data on premise, and now they're starting, you know, they've got cloud mandates, they want to move that content to the cloud. So we have gotten very aggressive at building out our SaaS, archiving solution, Enterprise Vault.cloud. But we also provide other options. Like if you want to move enterprise vault from your data center on premise, to your tenant in Azure, Amazon, we fully support that. In fact, we're taking advantage of cloud services to make that a much more viable option for our customers. >> So let's get into the regulation and the compliance, 'cause that's a big piece of the motivation beyond just, you know, making sure that the business can recover, that the regulation and compliance thing is huge. You know, the GDPR, which has been around now for a couple of years, California protection act. And I think what I find interesting from your perspective is you have this kind of crazy sea of regulations that are different by country, by industry, by data type, and they're evolving all the time. So, that's got to be a relatively complex little grid you got to keep track of. >> Yeah, it makes the job interesting. But it also is a huge competitive advantage for us. We have a team that researches data privacy regulations around the world, and it's been a competitive advantage in that we can be incredibly nimble in creating a new policy. We had some opportunities come up in Turkey, there's a regulation there that mirrors GDPR called KVKK or KVKK I think they call it locally. And it's, a joke that it's kind of like GDPR, but with jail time for noncompliance. So there's a lot more motivation on the part of an IT department, to make sure they're meeting that requirement. But it has to do with dealing with, you know, data privacy again, and ensuring the safety of the continent. That's proliferating throughout the world. You mentioned California Consumer Privacy Act, many other States are starting to follow what the California Consumer Privacy Act. And I'm sure, it won't be long before we have a data privacy act in the US, that's nationwide instead of at the state level. In other industries that we serve, like the financial services industry. There's, you know, there's always been a lot of regulation around SEC and FINRA in the US, that's spreading to other countries now, you know, MiFID II in the European union has been huge. And that dictates you need to capture all voice conversations, all text conversations, instant messages, everything that goes on between a broker and the end customer, has to be captured, has to be supervised, and has to be maintained on warm storage. So that's a great segment for us as well. That's an area we play very well in. >> So it's interesting. 'Cause in preparing for this, I saw some of the recent announcements around the concept of data supervision. So I think a lot of people are familiar with backup and recovery, and continuity, but specifically data supervision. What does that really mean? How is that different than kind of traditional backup and recovery, and what are some of the really key features or attributes to make that a successful platform? >> Yeah, no, it is really outside of the realm of backup and recovery. Archiving is very different from backup and recovery. And then archiving is about preserving the communication, and being able to monitor that communication, for the purposes of meeting compliance regulations. So, in the case of our solution, Veritas advanced supervision, It sounds a bit big brotherish, if I'm being honest, but it is a requirement for the financial service community that you sample a subset of those communications looking for violations. So you're looking for insider trading, you're looking for money laundering. In some companies, at the HR departments, or even just trying to ensure that their employees are being compliant. And so you may sample a subset of content. But it's absolutely required within the financial services community. And we're starting to see a lot of other industries, you know, leveraging this technology just to ensure compliance with different regulations, or compliance with their own internal policies. Ensuring a safe work place, ensuring that there's not any sexual harassment, or that type of thing going on through office communications. So it is a way of just monitoring your employees communications. >> So it's while I remember when, when people used to talk about messaging, and kind of the generic sense, like I could never understand why, you know, it's an email, it's a text. I mean, little did I know that every single application is now installed on every single device that I have, has a messaging app, you know, has a direct messaging feature. So, I mean the complexity and, and I guess the, the variability in the communication methods, across all these applications and, you know, probably more than half of them, that most of us work on are SaaS as well, really adds a ton of complexity to the challenge that you were just talking about. >> Oh, absolutely. I mean, I'm old. You know, when I started, all of my communications were on a Microsoft mail server, all my files were in the file, you know, the server room down the hall. Now I've got about 20 different ways to communicate on my phone. And, the fragmentation of communication does make that job a lot more, more challenging. You know, now you need to take a voice conversation, convert it to text. With COVID and with, you know, the dawn of telemedicine, or at least the rapid growth, and telemessaging, telemedicine sorry. There is a whole new potential market for this kind of supervision tool. Now you can capture every doctor patient interaction that takes place over Zoom or over a Team's video, transcribe that content, and there's a wealth of value in that conversation. Not only can you tell if the doctor is responding to the patient, if the interaction is positive or negative, is the doctor helping to calm the patient down? Do they have good quality of interaction? That sort of thing. And so there's incredible value in capturing those communications, so you can learn from the... you know learn best practices, I guess. And then, feed that into a broader data lake, and correlate the interaction with patient outcomes, who are your great doctors? who are your, you know, that type of thing. So that's an area that we're very excited about going forward. >> Wow, that's pretty interesting. I never kind of thought that through, because I would have assumed that, you know, kind of most of the calls for this type of data were based on some type of a litigation. You know, it was some type of an ask or a request, that I was going to ask you, now how does that actually work within the context of this sea of data, that you have. Is it usually around a specific individual, who's got some issues and you're kind of looking at their ecosystem of communications, or is it more of a pattern, or is it potentially more of a keyword type of thing that's triggering, You know, kind of this forensics into this tremendous amount of data that's in all these enterprises. >> Yeah, it's a little bit of everything. Like, so first of all, we have the ability to capture a lot of different native content sources. But we also leverage partners to bring in other content sources. We can capture over 80 different content sources, all the, you know, instant messaging, social media, of course email, but even voice communications and video communications. And to answer your question as far as litigation, I mean, it really depends on the incident right? in the past, in the old days, any kind of litigation resulted in a fire drill where you're trying to find every scrap of evidence, every piece of information related to the case. By being a little bit proactive and capturing your email, and your communication streams into immutable storage in an archive, you're ready for that litigation event. And you've already indexed that content, you've already classified that content. So you can find the needle in the haystack. You can find the relevant content to prove your innocence, or at least to comply with the request for information. Now that has also led to solving similar issues for public sector. US federal, with the Freedom of Information Act. They're getting all kinds of requests for right now for COVID related communications. And that could be related to lawsuits. it could be related to just information around how stimulus funds are being spent. And they've got to respond to these requests very, very quickly. Our team came up with a COVID-19 classification policy, where we can actually weed out the communications related to COVID-19. To allow those federal agencies, and even state and local agencies, to quickly respond to those types of requests. So that's been an exciting area for us. And then there's still the SEC requirements to monitor broker dealers and conversations with end users, to ensure they're not doing anything, they shouldn't be doing like insider trading, >> Right. Which is so different, than kind of a post event, you know, kind of forensics investigation, and then collecting that data. So I'm curious, you know, how often are you having to update policies and update, you know, kind of the sniffers and the intelligence that goes behind the monitoring to trigger a flag, And then that just go into their own internal kind of compliance reg and set off a whole another chain of events? I would imagine. >> Yeah. I mean, there's a lot of things we can do with our classification policies. And like, in the case of the COVID policy, we just kind of crowd source that internally, and created a policy, in about a week, really. That we, you know, we shaped the basic policy and then kept refining it, refining it, testing it. And we were able to go from start to finish, and have it publicly available within about a week and a half. It was really a great effort. And we have that kind of ability to be very nimble, to react to different types of regulations as they become, you know, get out there. And then there's also a constant refining of even data privacy for every country that we support. You know, we have data privacy regulations for the entire European union and for most countries around the world, obviously the US, Canada, Australia, and so on. And, you can always make those policies better. So we've introduced feedback loops where our customers can give us feedback on what works and what isn't working, and we can tweak the policies as needed. But it is a great way to respond to whatever's going on in the world, to help our customer base, which, you know, is largely the financial verticals, the public sector verticals, but even healthcare is becoming more important for us. >> So Dave, I wonder if there's some other use cases that people aren't thinking about, where you guys have seen value in this type of analytics. >> Yeah, I mean, definitely the one thing that I think is just starting to emerge as the value that's inherent in communications. So I mentioned earlier the telemedicine idea, and, you know, can you learn from doctor-patient interactions if you're capturing them over telemedicine vehicles, you know again, Video Chat, Zoom, and that sort of thing. But similarly, if you've captured communications for a long time, as many of our customers have, what can you do with that data? And how can it feed into a broader data lake to give you new insights? So for example, if you want to gauge whether a major deal is about to close, you know, you can rely on your sales reps to populate the CRM and give you an indication it's 10% complete, it's 50% complete, whatever. But you're dependent on all the games that salespeople play. It would be far better to look at the pattern of a traditional deal Closing. You know, first you start out with one person at your company talking, to one person at the target customer that leads to meetings, that leads to calendar invites, that leads to emails being sent back and forth. You can look at the time of response, how quickly does the target customer respond to the sales rep? How often are they interacting? How many people are they interacting with? Is it spanning different GEOS? Is it spanning different groups within the company? Are there certain documents being sent back and forth, like, a quote for example. All of this can give you a higher confidence that that deal is going to close, or that deals failing. You don't really know. You can also look at historical data, and compare the current account manager to his predecessor. You know, does the current account manager interact with his customer as much as the former rep did? And is there a correlation in their effectiveness? You know, based on kind of their interactions, and their just basic skills. So I think that's an exciting field, and it shines a new light on the data that you have to collect, to comply with regulations, the data you have to collect for litigation and other reasons. Now there's other value there. >> Right. That's a fascinating story. So the reason that you guys would be involved in this, is because you're sitting on, you're sitting all that comms data, because you have to, for the regulation. I mean, what you're describing sounds like a perfect, you know, kind of sales force. Plug in. >> Absolutely. >> With a much richer dataset, versus as you said, relying on the sales person to input the sales force, information which would require them to remember their password, which gets reset every three weeks. So the chances of that are pretty slim. (laughs) >> Yeah. There's a fact, I think I've read a stat recently that about, you know, only 10% of information is actually captured in a CRM. You know, contact information and that sort of thing. But if you're looking at their emails, if you're looking at their phone calls and their texts, and that sort of thing, you get a rich set of data on contacts and people that you're interacting with at a target customer, and, you know, sales. More than any other job, I think sales has high turnover. And so you need that record of, you know, off the counter. One account rep leaves, you don't want to lose all their contacts and start over again. You want a smooth handover to the next person. >> Right. >> If you capture all that content from their communications into CRM, you're in great shape. >> Dave, I want to get your take on something that's happening now, because you're so dialed into policy, and policy and regulations, which are such a giant determinant of what people can and should and should not do, with data. When you take something like COVID and the conversations about people going back to work, and contact tracing. To me it's like, Wow! You know, it's kind of this privacy clash against HIPAA, and, you know, that's medical information. And yet it's like this particular disease has been deemed such that it kind of falls outside the traditional, you know, kind of HIPAA rules. They're not going to test me for any other ailments before I come in the door at work, but they, you know, eventually we're going to be scanning people. So, you know, the levels of complexity and dynamicism, if that's even a word, around something like that, that's even a one off, within a specific, you know, kind of medical data is got to be, you know, I guess, interesting and challenging, but from a policy perspective and an actual handling of that information, that's got to be a crazy challenge. >> Yeah. I mean, we do expect that COVID it's going to lead to all kinds of litigation and Freedom of Information Act request. And that's a big reason why we saw the importance very quickly, that we need a classification policy to highlight that content. So what we can do in this case is we can, first of all, identify where that content is stored. We have a product called data insight that can monitor your file system and quickly locate. If you've got a document that includes, you know, patient data or anything related to COVID-19, we can find that. And now as we bring in the communications, we can flag communications, as we archive them and say, this is related to COVID-19. Then when a litigation happens, you can look, you can do a quick search and you can filter on the COVID-19 tag. And the people you're concerned with, and the date range you're concerned with, you can easily pull in all of the communications, all of the file content, anything related to COVID-19. And this is huge for, again, for public sector, where there are subject to finance, you know, sorry, Freedom of Information Act request. But it's also going to affect every company, because like, it's going to be litigation around, when a company decided that they would work from home, and did they wait too long. You know, and did someone get sick because they weren't aggressive enough. There's going to be frivolous lawsuits, there's going to be more tangible lawsuits, and, there's going to be all kinds of activity around how stimulus funds were spent and that sort of thing. So, yeah. That's a great example of a case where you've got to find the content quickly and respond to requests very quickly. Classification go a long way there. >> Yeah. That's the lawyers have hardly gotten involved in this COVID thing yet. And, to your point, it's going to be both frivolous as well as justified. And did people come back too early? Did they take the right steps? It's going to be messy and sloppy, but it sounds like you're in a good position to help people get through it. So, you know, just kind of your final thoughts you've been in this business for a long time. The rate and pace of change is only increasing the complexity of veracity, stealing some good, old, big data words. Velocity of the data is only increasing, the sources are growing exponentially. You know, as you kind of sit back and reflect obviously, a lot of exciting stuff ahead, but what do you think about what gets you up in the morning beyond just continuing to race to keep up with the neverending see of changing regulatory environment? >> Yeah, that's a great question. I mean, I think we have a great portfolio that can really help us react to change, and to take advantage of some of these new trends. And that is exciting, like telemedicine, the changes that come with COVID-19, what we could do for telemedicine rating doctors gauging their performance. We could do the same sort of thing for tele-education. You know, like I have two kids that have had, you know, homeschooling for the last three months, and, probably are going to face that in the fall. And, there might be some needs to just rate how the teachers are doing, how well are the classes interacting, and what can we learn from best practices there. So I think that's interesting and interesting space as well. But what keeps me going is the fact that we've got market leading products in archiving, eDiscovery, and supervision. We're putting a lot of new energy into those solutions. They've been around a long time. We've been archiving since 1998 I think, and doing supervision and discovery for 20 years. And, it's strange, the market's still there, it's still expanding, it's still growing. And, it's kind of just keeping up to change and, trying to find better ways of surfacing the relevant human communications content that said that's kind of the key to the job, I think. >> Right. Well yeah, Finding that signal amongst the noise is going to get increasingly... >> Exactly. >> More difficult than has been kind of a recurring theme here over the last 12 weeks or 15 weeks, or however long it's been. As you know, this kind of light switch moment on digital transformation is no longer, when are we going to get to it, or we're going to do a POC or let's experiment a little bit, you know, here and there it's, you know, ready, set, go. Whether you're ready or not, whether that's a kindergarten teacher, that's never taught online, a high school teacher running a big business. So nothing but a great opportunity. (laughs) >> Absolutely. >> All right. >> Absolutely. I mean, it's a very, a changing world and lots of opportunity comes with that. >> All right. Well Dave, thank you for sharing your insight, obviously regulation compliance, and I like that, you know, data supervision is not just backup and recovery is much, much bigger opportunity, in a lot higher value activity. So congrats to you and the team. And thanks for the update. >> All right. Thank you, Jeff. Thanks for the time. >> All right. He's Dave and I'm Jeff. You're watching theCUBE. Thanks for watching, we'll see you next time. (upbeat music)