(upbeat music) >> Hey, guys and girls, welcome back to New York City. Lisa Martin and John Furrier are live with theCUBE at AWS Summit 22, here in The Big Apple. We're excited to be talking about security next. James Arlen joins us, the CISO at Aiven. James, thanks so much for joining us on theCUBE today. >> Absolutely, it's good to be here. >> Tell the audience a little bit about Aiven, what you guys do, what you deliver, and what some of those differentiators are. >> Oh, Aiven. Aiven is a fantastic organization. I'm actually really lucky to work there. It's a database as a service, managed databases, all open source. And we're capital S, serious about open source. So 10 different open source database products delivered as a platform, all managed services, and the game is really about being the most performant, secure, and compliant database as a service on the market, friction free for your developers. You don't need people worrying about how to run databases. You just want to be able to say, here, take care of my data for me. And that's what we do. And that's actually the differentiator. We just take care of it for you. >> Take care of it for you, I like that. >> So they download the open source. They could do it on their own. So all the different projects are out there. >> Yeah, absolutely. >> What do you guys bringing to the table? You said the managed service, can you explain that. >> Yeah, the managed service aspect of it is, really, you could install the software yourself. You can use Postgres or Apache Kafka or any one of the products that we support. Absolutely you can do it yourself. But is that really what you do for a living, or do you develop software, or do you sell a product? So we take and do the hard work of running the systems, running the equipment. We take care of backups, high availability, all the security and compliance things around access and certifications, all of those things that are logging, all of that stuff that's actually difficult to do, well and consistently, that's all we do. >> Talk about the momentum, I see you guys were founded in what? 2016? >> Yes. >> Just in May of '22, raised $210 million in series D funding. >> Yes. >> Talk about the momentum and also from your perspective, all of the massive changes in security. >> It's very interesting to work for a company where you're building more than 100% growth year over year. It's a powers of two thing. Going from one to two, not so scary, two to four, not so scary. 512 to 1024, it's getting scary. (Lisa chuckles) 1024 to 2048, oh crap! I've been with Aiven for just almost two years now, and we are less than 70 when I started, and we're near 500 now. So, explosive growth is very interesting, but it's also that, you're growing within a reasonable burn rate boundary as well. And what that does from a security perspective, is it leaves you in the position that I had. I walked in and I was the first actual CISO. I had a team of four, I now have a team of 40. Because it turns out that like a lot of things in life, as you start unpacking problems, they're kind of fractal. You unpack the problem, you're like oh, well I did deal with that problem, but now I got another problem that I got to deal with. And so there's, it's not turtles all the way down. >> There's a lot of things going on and other authors, survive change. >> And there's fundamental problems that are still not fixed. And yet we treat them like they're fixed. And so we're doing a lot of hard work to make it so that we don't have to do hard work ongoing. >> And that's the value of the managed service. >> Yes. >> Okay, so talk about competition. Obviously, we had ETR on which is Enterprise Research Firm that we trust, we like. And we were looking at the data with the headwinds in the market, looking at the different players like got Amazon has Redshift, Snowflake, and you got Azure Sequence. I think it's called one of those products. The money that's being shifted from on premise data where the old school data warehouse like terra data and whatnot, is going first to Snowflake, then to Azure, then to AWS. Yes, so that points to snowflake being kind of like the bell of the ball if you will, in terms of from a data cloud. >> Absolutely. >> How do you compete with them? What's the pitch 'Cause that seemed to be a knee-jerk reaction from the industry. 'Cause snowflake is hot. They have a good value product. They have a smart team, Databrick is out there too. >> Yeah I mean... >> how do you guys compete against all that. >> So this is that point where you're balancing the value of a specific technology, or a specific technology vendor. And am I going to be stuck with them? So I'm tying my future to their future. With open source, I'm tying my future to the common good right. The internet runs on open source. It doesn't run on anything closed. And so I'm not hitching my wagon to something that I don't control. I'm hitching it to something where, any one of our customers could decide. I'm not getting the value I need from Aiven anymore. I need to go. And we provide you with the tools necessary, to move from our open source managed service to your own. Whether you go on-prem or you run it yourself, on a cloud service provider, move your data to you because it's your data. It's not ours. How can I hold your data? It's like weird extortion ransoming thing. >> Actually speaking, I mean enterprise, it's a big land grab 'cause with cloud you're horizontally scalable. It's a beautiful thing, open source is booming. It's going in Aiven, every day it's just escalating higher and higher. >> Absolutely. >> It is the software business. So open is open. Integration and scale seems to be the competitive advantage. >> Yeah. >> Right. So, how do you guys compete with that? Because now you got open source. How do you offer the same benefits without the lock in, or what's the switching costs? How do you guys maintain that position of not saying the same thing in Snowflake? >> Because all of the biggest data users and consumers tend to give away their data products. LinkedIn gave away their data product. Uber gave away their data product, Facebook gave away their data product. And we now use those as community solutions. So, if the product works for something the scale of LinkedIn, or something the scale of Uber. It will probably work for you too. And scale is just... >> Well Facebook and LinkedIn, they gave away the product to own the data to use against you. >> But it's the product that counts because you need to be able to manipulate data the way they manipulate data, but with yours. >> So low latency needs to work. So horizontally, scalable, fees, machine learning. That's what we're seeing. How do you make that available? Customers want on architecture? What do you recommend? Control plane, data plane, how do you think about that? >> It's interesting. There's architectural reasons to think about it in terms like that. And there's other good architectural reasons to not think about it. There's sort of this dividing line in the cloud, where your cloud service provider, takes over and provides you with the opportunity to say, I don't know. And I don't care >> As long as it's secure >> As long as it's secure absolutely. But there's sort of that water line idea, where if it's below the water line, let somebody else deal. >> What is in the table stakes? 'Cause I like that approach. I think that's a good value proposition. Store it, what boxes have to be checked? Compliance, secure, what are some of the boxes? >> You need to make sure that you've taken care of all of the same basics if you are still running it. Remember you can't absolve yourself of your duty to your customer. You're still on the hook. So, you have to have backups. You have to have access control. You have to understand who's administering it, and how and what they're doing. Good logging, good comprehension there. You have to have anomaly detection, secure operations. You have to have all those compliance check boxes. Especially if you're dealing with regulated data type like PCI data or HIPAA health data or you know what there's other countries besides the United States, there's other kinds of of compliance obligations there. So you have to make sure that you've got all that taken into account. And remember that, like I said, you can't absolve yourself with those things. You can share responsibilities. But you can't walk away from that responsibility. So you still have to make sure that you validate that your vendor knows what they're talking about. >> I wanted to ask you about the cybersecurity skills gap. So I'm kind of giving a little segue here, because you mentioned you've been with Aiven for about two years. >> Almost. >> Almost two years. You've started with a team of four. You've grown at 10X in less than two years. How have you accomplished that, considering we're seeing one of the biggest skills shortages in cyber in history. >> It's amazing, you see this show up in a lot of job Ads, where they ask for 10 years of experience in something that's existed for three years. (John Furrier laughs) And it's like okay, well if I just be logical about this I can hire somebody at less than the skill level that I need today, and bring them up to that skill level. Or I can spend the same amount of time, hoping that I'll find the magical person that has that set of skills that I need. So I can solve the problem of the skills gap by up-skilling the people that I hire. Which is strangely contrary to how this thing works. >> The other thing too, is the market's evolving so fast that, that carry up and pulling someone along, or building and growing your own so to speak is workable. >> It also really helps us with a bunch of sustainability goals. It really helps with anything that has to do with diversity and inclusion, because I can bring forward people who are never given a chance. And say, you know what? You don't have that magical ticket in life, but damn you know what you're talking about? >> It's a classic pedigree. I went to this school, I studied this degree. There's no degree if have to stop a hacker using state of the art malware. (John Furrier laughs) >> Exactly. What I do today as a job, didn't exist when I was in post-secondary at all. >> So when you hire, what do you look for? I mean obviously problem solving. What's your kind of algorithm for hiring? >> Oh, that's a really interesting question. The quickest sort of summary of it is, I'm looking for not a jerk. >> Not a jerk. >> Yeah. >> Okay. >> Because it turns out that the quality that I can't fix in a candidate, is I can't fix whether or not they're a jerk, but I can up-skill them, I can educate them. I can teach them of a part of the world that they've not had any interaction with. But if they're not going to work with the team, if they're going to be, look at me, look at me. If they're going to not have that moment of, I have this great job, and I get to work today. And that's awesome. (Lisa Martin laughs) That's what I'm trying to hire for. >> The essence of this teamwork is fundamental. >> Collaboration. >> Cooperation. >> Curiosity. >> That's the thing yeah, absolutely. >> And everybody? >> Those things, oh absolutely. Those things are really, really hard to interview for. And they're impossible to fix after the fact. So that's where you really want to put the effort. 'Cause I can teach you how to use a computer. I mean it's hard, but it's not that hard. >> Yeah, yeah, yeah. >> Well I love the current state of data management. Good overview, you guys are in the good position. We love open source. Been covering it for, since theCUBE started. It continues to redefine more and more the industry. It is the software industry. Now there's no debate about that. If people want to have that debate, that's kind of waste of time, but there are other ways that are happening. So I have to ask you. As things are going forward with innovation. Okay, if opensource is going to be the software industry. Where's the value? >> That's a fun question wow? >> Is it going to be in the community? Is it the integration? Is it the scale? If you're open and you have low switching costs... >> Yeah so, when you look at Aiven's commitment to open source, a huge part of that is our open source project office, where we contribute back to those core products, whether it's parts of the Apache Foundation, or Postgres, or whatever. We contribute to those, because we have staff who work on those products. They don't work on our stuff. They work on those. And it's like the opposite of a zero sum game. It's more like Nash equilibrium. If you ever watch that movie, "A beautiful mind." That great idea of, you don't have to have winners and losers. You can have everybody loses a little bit but everybody wins a little bit. >> Yeah and that's the open the ethos. >> And that's where it gets tied up. >> Another follow up on that. The other thing I want to get your reaction on is that, now in this modern era of open source, almost all corporations are part of projects. I mean if you're an entrepreneur and you want to get funding it's pretty simple. You start open source project. How many stars you get on GitHub guarantees it's a series C round, pretty much. So open source now has got this new thing going on, where it's not just open source folks who believe in it It's an operating model. What's the dynamic of corporations being part of the system. It used to be, oh what's the balance between corporate and influence, now it's standard. What's your reaction? >> They can do good and they can do harm. And it really comes down to why are you in it? So if you look at the example of open search, which is one of the data products that we operate in the Aiven system. That's a collaboration between Aiven. Hey we're an awesome company, but we're nowhere near the size of AWS. And AWS where we're working together on it. And I just had this conversation with one of the attendees here, where he said, "Well AWS is going to eat your story there. "You're contributing all of this "to the open search platform. "And then AWS is going to go and sell it "and they're going to make more money." And I'm like yep, they are. And I've got staff who work for the organization, who are more fulfilled because they got to deliver something that's used by millions of people. And you think about your jobs. That moment of, (sighs) I did a cool thing today. That's got a lot of value in it. >> And part of something. >> Exactly. >> As a group. >> 100%. >> Exactly. >> And we end up with a product that's used by millions. Some of it we'll capture, because we do a better job running than the AWS does, but everybody ends up winning out of the backend. Again, everybody lost a little, but everybody also won. And that's better than that whole, you have to lose so that I can win. At zero something, that doesn't work. >> I think the silo conversations are coming, what's the balance between siloing something and why that happens. And then what's going to be freely accessible for data. Because the real time information is based upon what you can access. "Hey Siri, what's the weather. "We had a guest on earlier." It says, oh that's a data query. Well, if the weather is, the data weathers stored in a database that's out here and it can't get to the response on the app. Yeah, that's not good, but the data is available. It just didn't get delivered. >> Yeah >> Exactly. >> This is an example of what people are realizing now the consequences of this data, collateral damage or economy value. >> Yeah, and it's understanding how data fits in your environment. And I don't want to get on the accountants too hard, but the accounting organizations, AICPA and ISAE and others, they haven't really done a good job of helping you understand data as an asset, or data as a liability. I hold a lot of customer data. That's a liability to me. It's going to blow up in my face. We don't talk about the income that we get from data, Google. We don't talk about the expense of regenerating that data. We talk about, well what happens if you lose it? I don't know. And we're circling the drain around fiduciary responsibility, and we know how to do this. If you own a manufacturing plant, or if you own a fleet of vehicles you understand the fiduciary duty of managing your asset. But because we can't touch it, we don't do a good job of it. >> How far do you think are people getting into the point where they actually see that asset? Because I think it's out of sight out of mind. Now there's consequences, there's now it's public companies might have to do filings. It's not like sustainability and data. Like, wait a minute, I got to deal with these things. >> It's interesting, we got this great benefit of the move to cloud computing, and the move to utility style computing. But we took away that. I got to walk around and pet my computers. Like oh! This is my good database. I'm very proud of you. Like we're missing that piece now. And when you think about the size of data centers, we become detached from that, you don't really think about, Aiven operates tens of thousands of machines. It would take entire buildings to hold them all. You don't think about it. So how do you recreate that visceral connection to your data? Well, you need to start actually thinking about it. And you need to do some of that tokenization. When was the last time you printed something out, like you get a report and happens to me all the time with security reports. Look at a security report and it's like 150 page PDF. Scroll, scroll, scroll, scroll. Print it out, stump it on the table in front of you. Oh, there's gravitas here. There's something here. Start thinking about those records, count them up, and then try to compare that to something in the real world. My wife is a school teacher, kindergarten to grade three, and tokenizing math is how they teach math to little kids. You want to count something? Here's 10 things, count them. Well, you've got 60,000 customer records, or you have 2 billion data points in your IOT database, tokenize that, what does 2 billion look like? What does $1 million look like in the form of $100 dollars bills on a pallet? >> Wow. >> Right. Tokenize that data, create that visceral connection with it, and then talk about it. >> So when you say tokenized, you mean like token as in decentralization token? >> No, I mean create like a totem or an icon of it. >> Okay, got it. >> A thing you can hold holy. If you're a token company. >> Not token as in Token economics and Crypto. >> If you're a mortgage company, take that customer record for one of your customers, print it out and hold the file. Like in a Manila folder, like it's 1963. Hold that file, and then say yes. And you're explaining to somebody and say yes, and we have 3 million of these. If we printed them all out, it would take up a room this size. >> It shows the scale. >> Right. >> Right. >> Exactly, create that connection back to the human level of interaction with data. How do you interact with a terabyte of data, but you do. >> Right. >> But once she hits upgrade from Google drive. (team laughs) >> What's a terabyte right? We don't hold that anymore. >> Right, right. >> Great conversation. >> Recreate that connection. Talk about data that way. >> The visceral connection with data. >> Follow up after this event. We'd love to dig more and love the approach. Love open source, love what you're doing there. That's a very unique approach. And it's also an alternative to some of the other vast growing plus your valuations are very high too. So you're not like a... You're not too far away from these big valuations. So congratulations. >> Absolutely. >> Yeah excellent, I'm sure there's lots of work to do, lots of strategic work to do with that round of funding. But also lots of opportunity, that it's going to open up, and we know you don't hire jerks. >> I don't >> You have a whole team of non jerks. That's pretty awesome. Especially 40 of 'em. That's impressive James.| >> It is. >> Congratulations to you on what you've accomplished in the course of the team. And thank you for sharing your insights with John and me today, we appreciate it. >> Awesome. >> Thanks very much, it's been great. >> Awesome, for John furrier, I'm Lisa Martin and you're watching theCube, live in New York city at AWS Summit NYC 22, John and I will be right back with our next segment, stick around. (upbeat music)

>> We're back at the Big Apple, theCUBE's coverage of MongoDB World 2022. Sahir Azam is here, he's the Chief Product Officer of MongoDB, and Guillermo Rauch who's the CEO of Vercel. Hot off the keynotes from this morning guys, good job. >> Thank you. >> Thank you. >> Thank you for joining us here. Thanks for having us. Guillermo when it comes to modern web development, you know the back-end, the cloud guys got to it kind of sewn up, >> you know- >> Guillermo: Forget about it. >> But all the action's in the front end, and that's where you are. Explain Vercel. >> Yeah so Vercel is the company that pioneers front-end development as serverless infrastructure. So we built Next.js which is the most popular React framework in the world. This is what front-end engineers choose to build innovative UI's, beautiful websites. Companies like Dior and GitHub and TikTok and Twitch, which we mentioned in the keynote, are powering their entire dot-coms or all of their new parts of their dot-coms with Next.js. And Vercel is the serverless platform where you can deploy frameworks like in Next.js and others like Svelte and Vue to create really fast experiences on the web. >> So I hear, so serverless, I hear that's the hot trend. You guys made some announcements today. I mean when you look at the, we have spending data with our friends at ETR right down the street. I mean it's just off the charts, whether it's Amazon, Google, Azure Functions, I mean it's just exploding. >> Sahir: Yeah, it's I think in many ways, it's a natural trend. You know, we talk a lot about, whether it be today's keynote or another industry talks you see around our industry that developers are constantly looking for ways to focus on innovation and the business logic that defines their application and as opposed to managing the plumbing, and management of infrastructure. And we've seen this happen over and over again across every layer of the stack. And so for us, you know MongoDB, we have a bit of, you know sort of a lens of a broad spectrum of the market. We certainly have you know, large enterprises that are modernizing existing kind of core systems, then we have developers all over the world who are building the next big best thing. And that's what led us to partner with Vercel is just the bleeding edge of developers building in a new way, in a much more efficient way. And we wanted to make sure we provide a data platform that fits naturally in the way they want to work. >> So explain to our audience the trade-offs of serverless, and I want to get into sort of how you've resolved that. And then I want to hear from Guillermo, what that means for developers. >> Sahir: Yeah in our case, we don't view it as an either or, there are certain workloads and definitely certain companies that will gravitate towards a more traditional database infrastructure where they're choosing the configuration of their cluster. They want full control over it. And that provides, you know, certain benefits around cost predictability or isolation or perceived benefits at least of those things. And customers will gravitate towards that. Now on the flip side, if you're building a new application or you want the ability to scale seamlessly and not have to worry about any of the plumbing, serverless is clearly the easier model. So over the long term, we certainly expect to see as a mix of things, more and more serverless workloads being built on our platform and just generally in the industry, which is why we leaned in so heavily on investing in Atlas serverless. But the flexibility to not be forced into a particular model, but to get the same database experience across your application and even switch between them is an important characteristic for us as we build going forward. >> And you stressed the cost efficiency, and not having to worry about, you know, starting cold. You've architected around that, and what does that mean for a developer? >> Guillermo: For a developer it means that you kind of get the best of both worlds, right? Like you get the best possible performance. Front-end developers are extremely sensitive to this. That's why us pioneering this concept, serverless front-end, has put us in a very privileged position because we have to deliver that really quick time to first buy, that really quick paint. So any of the old trade-offs of serverless are not accepted by the market. You have to be extremely fast. You have to be instant to deliver that front-end content. So what we talked about today for example, with the Vercel Edge network, we're removing all of the cost of that like first hit. That cold start doesn't really exist. And now we're seeing it all across the board, going into the back-end where Mongo has also gotten rid of it. >> Dave: How do you guys collaborate? What's the focus of integration specifically from, you know, an engineering resource standpoint? >> Yeah the main idea is, idea to global app in seconds, right? You have your idea. We give you the framework. We don't give you infrastructure primitives. We give you all the necessary tools to start your application. In practice this means you host it in a Git repo. You import it onto Vercel. You install the Mongo integration. Now your front-end and your data back-end are connected. And then your application just goes global in seconds. >> So, okay. So you've abstracted away the complexity of those primitives, is that correct? >> Guillermo: Absolutely. >> Do do developers ever say, "That's awesome but I'd like to get to them every now and then." Or do you not allow that? >> Definitely. We expose all the underlying APIs, and the key thing we hear is that, especially with the push for usage-based billing models, observability is of the essence. So at any time you have to be able to query, in real time, every data point that the platform is observing. We give you performance analytics in real time to see how your front-end is performing. We give you statistics about how often you're querying your back-end and so on, and your cache hit ratios. So what I talked about today in the keynote is, it's not just about throwing more compute at the problem, but the ability to use the edge to your advantage to memoize computation and reuse it across different visits. >> When we think of mission critical historically, you know, you think about going to the ATM, right? I mean a financial transaction. But Mongo is positioning for mission critical applications across a variety of industries. Do we need to rethink what mission critical means? >> I think it's all in the eye of the beholder so to speak. If you're a new business starting up, your software and your application is your entire business. So if you have a cold start latency or God forbid something actually goes down, you don't have a business. So it's just as mission critical to that founder of a new business and new technology as it is, you know, an established enterprise that's running sort of a more, you know, day-to-day application that we may all interact with. So we treat all of those scenarios with equal fervor and importance right? And many times, it's a lot of those new experiences that the become the day-to-day experiences for us globally, and are super important. And we power all of those, whether it be an established enterprise all the way to the next big startup. >> I often talk about COVID as the forced march to digital. >> Sahir: Mm-Hmm. >> Which was obviously a little bit rushed, but if you weren't in digital business, you were out of business. And so now you're seeing people step back and say, "All right, let's be more thoughtful about our digital transformation. We've got some time, we've obviously learned some things made some mistakes." It's all about the customer experience though. And that becomes mission critical right? What are you seeing Guillermo, in terms of the patterns in digital transformation now that we're sort of exiting the isolation economy? >> One thing that comes to mind is, we're seeing that it's not always predictable how fast you're going to grow in this digital economy. So we have customers in the ecommerce space, they do a drop and they're piggybacking on serverless to give them that ability to instantly scale. And they couldn't even prepare for some of these events. We see that a lot with the Web3 space and NFT drops, where they're building in such a way that they're not sensitive to this massive fluctuations in traffic. They're taking it for granted. We've put in so much work together behind the scenes to support it. But the digital native creator just, "Oh things are scaling from one second to the next like I'm hitting like 20,000 requests per second, no problem Vercel is handling it." But the amount of infrastructural work that's gone behind the scenes in support has been incredible. >> We see that in gaming all the time, you know it's really hard for a gaming company to necessarily predict where in the globe a game's going to be particularly hot. Games get super popular super fast if they're successful, it's really hard to predict. It's another vertical that's got a similar dynamic. >> So gaming, crypto, so you're saying that you're able to assist your customers in architecting so that the website doesn't crash. >> Guillermo: Absolutely. >> But at the same time, if the the business dynamic changes, they can dial down. >> Yeah. >> Right and in many ways, slow is the new down, right? And if somebody has a slow experience they're going to leave your site just as much as if it's- >> I'm out of here- >> You were down. So you know, it's really maintaining that really fast performance, that amazing customer experience. Because this is all measured, it's scientific. Like anytime there's friction in the process, you're going to lose customers. >> So obviously people are excited about your keynote, but what have they been saying? Any specific comments you can share, or questions that you got that were really interesting or? >> I'm already getting links to the apps that people are deploying. So the whole idea- >> Come on! >> All over the world. Yeah so it's already working I'm excited. >> So they were show they were showing off, "Look what I did" Really? >> Yeah on Twitter. >> That's amazing. >> I think from my standpoint, I got a question earlier, we were with a bunch of financial analysts and investors, and they said they've been talking to a lot of the customers in the halls. And just to see, you know, from the last time we were all in person, the number of our customers that are using multiple capabilities across this idea of a developer data platform, you know, certainly MongoDB's been a popular core database open source for a long time. But the new capabilities around search, analytics, mobile being adopted much more broadly to power these experiences is the most exciting thing from our side. >> So from 2019 to now, you're saying substantial uptick in adoption for these features? >> Yeah. And many of them are new. >> Time series as well, that's pretty new, so yeah. >> Yeah and you know, our philosophy of development at MongoDB is to get capabilities in the hands of customers early. Get that feedback to enrich and drive that product-market fit. And over the last three years especially, we've been transitioning from a single product kind of core, you know, non relational modern database to a data platform, a developer data platform that adds more and more capabilities to power these modern applications. And a lot of those were released during the pandemic. Certainly we talked about them in our virtual conferences and all the zoom meetings we had over the years. But to actually go talk to all these customers, this is the largest conference we've ever put on, and to get a sense of, wow all the amazing things they're doing with them, it's definitely a different feeling when we're all together. >> So that's interesting, when you have such a hot product, product-led growth which is what Mongo has been in, and you add these new features. They're coming from the developers who are saying, "Hey, we need this." >> Yip. >> Okay so you have a pretty high degree of confidence, but how do you know when you have product-market fit? I mean, is it adoption, usage, renewals? What's your metric? >> Yeah I think it's a mix of quantitative measures that you know, around conversion rates, the size of your funnel, the retention rate, NPS which obviously can be measured, but also just qualitative. You know when you're talking to a developer or a technology executive around what their needs are, and then you see how they actually apply it to solve a problem, it's that balance between the qualitative and the quantitative measurement of things. And you can just sort of, frankly you can feel it. You can see it in the numbers sure, but you can kind of feel that excitement, you can see that adoption and what it empowers people to do. And so to me, as a product leader, it's always a blend of those things. If you get too obsessed with purely the metrics, you can always over optimize something for the wrong reason. So you have to bring in that qualitative feedback to balance yourself out. >> Right. >> Guillermo, what's next? What do you not have that you want from Sahir and Mongo? >> So the natural next step for serverless computing is, is the Edge. So we have to auto-scale, we have to tolerate fares. We have to be avail. We have to be easy, but we have to be global. And right now we've been doing this by using a lot of techniques like caching and replication and things like this. But the future's about personalizing even more to each visitor depending on where they are. So if I'm in New York, I want to get the latest offers for New York on demand, just for me, and using AI to continue to personalize that experience. So giving the developer these tools in a way where it feels natural to build an application like this. It doesn't feel like, "Oh I'm going to do this year 10 if I make it, I'm going to do it since the very beginning." >> Dave: Okay interesting. So that says to me that I'm not going to make a round trip to the cloud necessarily for that experience. So I'm going to have some kind, Apple today, at the Worldwide Developer Conference announced the M2, right. I've been looking at the M1 Ultra, and I'm going wow look at that! And so- >> Sahir: You were talking about that new one backstage. >> I mean it's this amazing pace of Silicon development and they're focusing on the NPU and you look at what Tesla's doing. I mean it's just incredible. So you're going to have some new hardware architecture that emerges. Most of the AI that's done today is modeling in the cloud. You're going to have a real time inferencing at the Edge. So that's not going to do the round trip. There's going to be a data store there, I think it has to be. You're going to persist some of the data, maybe not all of it. So it's a whole new architecture- >> Sahir: Absolutely. >> That's developing. That sounds very disruptive. >> Sahir: Yeah. >> How do you think about that, and how does Mongo play there? Guillermo first. >> What I spent a lot of time thinking about is obviously the developer experience, giving the programmer a programming model that is natural, intuitive, and produces its great results. So if they have to think about data that's local because of regulatory reasons for example, how can we let the framework guide them to success? I'm just writing an application I deployed to the cloud and then everything else is figured out. >> Yeah or speed of light is another challenge. (Sahir and Guillermo laugh) >> How can we overcome the speed of light is our next task for sure. >> Well you're working on that aren't you? You've got the best engineers on that one. (Sahir and Guillermo laugh) >> We can solve a lot of problems, I'm not sure of that one. >> So Mongo plays in that scenario or? >> Yeah so I think, absolutely you know, we've been focused heavily on becoming the globally distributed cloud data layer. The back-end data layer that allows you to persist data to align with performance and move data where it needs to be globally or deal with data sovereignty, data nationalism that's starting to rise, but absolutely there is more data being pushed out to the Edge, to your point around processing or inference happening at the Edge. And there's going to be a globally distributed front-end layer as well, whether data and processing takes apart. And so we're focused on one, making sure the data connectivity and the layer is all connected into one unified architecture. We do that in combination with technologies that we have that do with mobility or edge distribution and synchronization of data with realm. And we do it with partnerships. We have edge partnerships with AWS and Verizon. We have partnerships with a lot of CVM players who are building out that Edge platform and making sure that MongoDB is either connected to it or just driving that synchronization back and forth. >> I call that unified experience super cloud, Robbie Belson from Verizon the cloud continuum, but that consistent experience for developers whether you're on Prim, whether you're in you know, Azure, Google, AWS, and ultimately the Edge. That's the big- >> That's where it's going. >> White space right now I'm hearing, Guillermo, right? >> I think it'll define the next generation of how software is built. And we're seeing this almost like a coalition course between some of the ideas that the Web3 developers are excited about, which is like decentralization almost to the extreme. But the Web2 also needs more decentralization, because we're seeing it with like, the data needs to be local to me, I need more privacy. I was looking at the latest encryption features in Mongo, like I think both Web2 need to incorporate more of the ideas of Web3 and vice versa to create the best possible consumer experience. Privacy matters more than ever before. Latency for conversion matters more than ever before. And regulations are changing. >> Sahir: Yeah. >> And you talked about Web3 earlier, talked about new protocols, a new distributed you know, decentralized system emerging, new hardware architectures. I really believe we really think that new economics are going to bleed back into the data center, and yeah every 15 years or so this industry gets disrupted. >> Sahir: Yeah. >> Guillermo: Absolutely. >> You know you ain't see nothing yet guys. >> We all talked about hardware becoming commoditized 10, 15 years ago- >> Yeah of course. >> We get the virtualization, and it's like nope not at all. It's actually a lot of invention happening. >> The lower the price the more the consumption. So guys thanks so much. Great conversation. >> Thank you. >> Really appreciate your time. >> Really appreciate it I enjoyed the conversation. >> All right and thanks for watching. Keep it right there. We'll be back with our next segment right after this short break. Dave Vellante for theCUBE's coverage of MongoDB World 2022. >> Man Offscreen: Clear. (clapping) >> All right wow. Don't get up. >> Sahir: Okay. >> Is that a Moonwatch? >> Sahir: It is a Speedmaster but it's that the-

>> Man Offscreen: Standby. Dave is coming you in 5, 4, 3, 2. >> We're back at the Big Apple, theCUBE's coverage of MongoDB World 2022. Sahir Azam is here, he's the Chief Product Officer of MongoDB, and Guillermo Rauch who's the CEO of Vercel. Hot off the keynotes from this morning guys, good job. >> Thank you. >> Thank you. >> Thank you for joining us here. Thanks for having us. Guillermo when it comes to modern web development, you know the back-end, the cloud guys got to it kind of sewn up, >> you know- >> Guillermo: Forget about it. >> But all the action's in the front end, and that's where you are. Explain Vercel. >> Yeah so Vercel is the company that pioneers front-end development as serverless infrastructure. So we built Next.js which is the most popular React framework in the world. This is what front-end engineers choose to build innovative UI's, beautiful websites. Companies like Dior and GitHub and TikTok and Twitch, which we mentioned in the keynote, are powering their entire dot-coms or all of their new parts of their dot-coms with Next.js. And Vercel is the serverless platform where you can deploy frameworks like in Next.js and others like Svelte and Vue to create really fast experiences on the web. >> So I hear, so serverless, I hear that's the hot trend. You guys made some announcements today. I mean when you look at the, we have spending data with our friends at ETR right down the street. I mean it's just off the charts, whether it's Amazon, Google, Azure Functions, I mean it's just exploding. >> Sahir: Yeah, it's I think in many ways, it's a natural trend. You know, we talk a lot about, whether it be today's keynote or another industry talks you see around our industry that developers are constantly looking for ways to focus on innovation and the business logic that defines their application and as opposed to managing the plumbing, and management of infrastructure. And we've seen this happen over and over again across every layer of the stack. And so for us, you know MongoDB, we have a bit of, you know sort of a lens of a broad spectrum of the market. We certainly have you know, large enterprises that are modernizing existing kind of core systems, then we have developers all over the world who are building the next big best thing. And that's what led us to partner with Vercel is just the bleeding edge of developers building in a new way, in a much more efficient way. And we wanted to make sure we provide a data platform that fits naturally in the way they want to work. >> So explain to our audience the trade-offs of serverless, and I want to get into sort of how you've resolved that. And then I want to hear from Guillermo, what that means for developers. >> Sahir: Yeah in our case, we don't view it as an either or, there are certain workloads and definitely certain companies that will gravitate towards a more traditional database infrastructure where they're choosing the configuration of their cluster. They want full control over it. And that provides, you know, certain benefits around cost predictability or isolation or perceived benefits at least of those things. And customers will gravitate towards that. Now on the flip side, if you're building a new application or you want the ability to scale seamlessly and not have to worry about any of the plumbing, serverless is clearly the easier model. So over the long term, we certainly expect to see as a mix of things, more and more serverless workloads being built on our platform and just generally in the industry, which is why we leaned in so heavily on investing in Atlas serverless. But the flexibility to not be forced into a particular model, but to get the same database experience across your application and even switch between them is an important characteristic for us as we build going forward. >> And you stressed the cost efficiency, and not having to worry about, you know, starting cold. You've architected around that, and what does that mean for a developer? >> Guillermo: For a developer it means that you kind of get the best of both worlds, right? Like you get the best possible performance. Front-end developers are extremely sensitive to this. That's why us pioneering this concept, serverless front-end, has put us in a very privileged position because we have to deliver that really quick time to first buy, that really quick paint. So any of the old trade-offs of serverless are not accepted by the market. You have to be extremely fast. You have to be instant to deliver that front-end content. So what we talked about today for example, with the Vercel Edge network, we're removing all of the cost of that like first hit. That cold start doesn't really exist. And now we're seeing it all across the board, going into the back-end where Mongo has also gotten rid of it. >> Dave: How do you guys collaborate? What's the focus of integration specifically from, you know, an engineering resource standpoint? >> Yeah the main idea is, idea to global app in seconds, right? You have your idea. We give you the framework. We don't give you infrastructure primitives. We give you all the necessary tools to start your application. In practice this means you host it in a Git repo. You import it onto Vercel. You install the Mongo integration. Now your front-end and your data back-end are connected. And then your application just goes global in seconds. >> So, okay. So you've abstracted away the complexity of those primitives, is that correct? >> Guillermo: Absolutely. >> Do do developers ever say, "That's awesome but I'd like to get to them every now and then." Or do you not allow that? >> Definitely. We expose all the underlying APIs, and the key thing we hear is that, especially with the push for usage-based billing models, observability is of the essence. So at any time you have to be able to query, in real time, every data point that the platform is observing. We give you performance analytics in real time to see how your front-end is performing. We give you statistics about how often you're querying your back-end and so on, and your cache hit ratios. So what I talked about today in the keynote is, it's not just about throwing more compute at the problem, but the ability to use the edge to your advantage to memoize computation and reuse it across different visits. >> When we think of mission critical historically, you know, you think about going to the ATM, right? I mean a financial transaction. But Mongo is positioning for mission critical applications across a variety of industries. Do we need to rethink what mission critical means? >> I think it's all in the eye of the beholder so to speak. If you're a new business starting up, your software and your application is your entire business. So if you have a cold start latency or God forbid something actually goes down, you don't have a business. So it's just as mission critical to that founder of a new business and new technology as it is, you know, an established enterprise that's running sort of a more, you know, day-to-day application that we may all interact with. So we treat all of those scenarios with equal fervor and importance right? And many times, it's a lot of those new experiences that the become the day-to-day experiences for us globally, and are super important. And we power all of those, whether it be an established enterprise all the way to the next big startup. >> I often talk about COVID as the forced march to digital. >> Sahir: Mm-Hmm. >> Which was obviously a little bit rushed, but if you weren't in digital business, you were out of business. And so now you're seeing people step back and say, "All right, let's be more thoughtful about our digital transformation. We've got some time, we've obviously learned some things made some mistakes." It's all about the customer experience though. And that becomes mission critical right? What are you seeing Guillermo, in terms of the patterns in digital transformation now that we're sort of exiting the isolation economy? >> One thing that comes to mind is, we're seeing that it's not always predictable how fast you're going to grow in this digital economy. So we have customers in the ecommerce space, they do a drop and they're piggybacking on serverless to give them that ability to instantly scale. And they couldn't even prepare for some of these events. We see that a lot with the Web3 space and NFT drops, where they're building in such a way that they're not sensitive to this massive fluctuations in traffic. They're taking it for granted. We've put in so much work together behind the scenes to support it. But the digital native creator just, "Oh things are scaling from one second to the next like I'm hitting like 20,000 requests per second, no problem Vercel is handling it." But the amount of infrastructural work that's gone behind the scenes in support has been incredible. >> We see that in gaming all the time, you know it's really hard for a gaming company to necessarily predict where in the globe a game's going to be particularly hot. Games get super popular super fast if they're successful, it's really hard to predict. It's another vertical that's got a similar dynamic. >> So gaming, crypto, so you're saying that you're able to assist your customers in architecting so that the website doesn't crash. >> Guillermo: Absolutely. >> But at the same time, if the the business dynamic changes, they can dial down. >> Yeah. >> Right and in many ways, slow is the new down, right? And if somebody has a slow experience they're going to leave your site just as much as if it's- >> I'm out of here- >> You were down. So you know, it's really maintaining that really fast performance, that amazing customer experience. Because this is all measured, it's scientific. Like anytime there's friction in the process, you're going to lose customers. >> So obviously people are excited about your keynote, but what have they been saying? Any specific comments you can share, or questions that you got that were really interesting or? >> I'm already getting links to the apps that people are deploying. So the whole idea- >> Come on! >> All over the world. Yeah so it's already working I'm excited. >> So they were show they were showing off, "Look what I did" Really? >> Yeah on Twitter. >> That's amazing. >> I think from my standpoint, I got a question earlier, we were with a bunch of financial analysts and investors, and they said they've been talking to a lot of the customers in the halls. And just to see, you know, from the last time we were all in person, the number of our customers that are using multiple capabilities across this idea of a developer data platform, you know, certainly MongoDB's been a popular core database open source for a long time. But the new capabilities around search, analytics, mobile being adopted much more broadly to power these experiences is the most exciting thing from our side. >> So from 2019 to now, you're saying substantial uptick in adoption for these features? >> Yeah. And many of them are new. >> Time series as well, that's pretty new, so yeah. >> Yeah and you know, our philosophy of development at MongoDB is to get capabilities in the hands of customers early. Get that feedback to enrich and drive that product-market fit. And over the last three years especially, we've been transitioning from a single product kind of core, you know, non relational modern database to a data platform, a developer data platform that adds more and more capabilities to power these modern applications. And a lot of those were released during the pandemic. Certainly we talked about them in our virtual conferences and all the zoom meetings we had over the years. But to actually go talk to all these customers, this is the largest conference we've ever put on, and to get a sense of, wow all the amazing things they're doing with them, it's definitely a different feeling when we're all together. >> So that's interesting, when you have such a hot product, product-led growth which is what Mongo has been in, and you add these new features. They're coming from the developers who are saying, "Hey, we need this." >> Yip. >> Okay so you have a pretty high degree of confidence, but how do you know when you have product-market fit? I mean, is it adoption, usage, renewals? What's your metric? >> Yeah I think it's a mix of quantitative measures that you know, around conversion rates, the size of your funnel, the retention rate, NPS which obviously can be measured, but also just qualitative. You know when you're talking to a developer or a technology executive around what their needs are, and then you see how they actually apply it to solve a problem, it's that balance between the qualitative and the quantitative measurement of things. And you can just sort of, frankly you can feel it. You can see it in the numbers sure, but you can kind of feel that excitement, you can see that adoption and what it empowers people to do. And so to me, as a product leader, it's always a blend of those things. If you get too obsessed with purely the metrics, you can always over optimize something for the wrong reason. So you have to bring in that qualitative feedback to balance yourself out. >> Right. >> Guillermo, what's next? What do you not have that you want from Sahir and Mongo? >> So the natural next step for serverless computing is, is the Edge. So we have to auto-scale, we have to tolerate fares. We have to be avail. We have to be easy, but we have to be global. And right now we've been doing this by using a lot of techniques like caching and replication and things like this. But the future's about personalizing even more to each visitor depending on where they are. So if I'm in New York, I want to get the latest offers for New York on demand, just for me, and using AI to continue to personalize that experience. So giving the developer these tools in a way where it feels natural to build an application like this. It doesn't feel like, "Oh I'm going to do this year 10 if I make it, I'm going to do it since the very beginning." >> Dave: Okay interesting. So that says to me that I'm not going to make a round trip to the cloud necessarily for that experience. So I'm going to have some kind, Apple today, at the Worldwide Developer Conference announced the M2, right. I've been looking at the M1 Ultra, and I'm going wow look at that! And so- >> Sahir: You were talking about that new one backstage. >> I mean it's this amazing pace of Silicon development and they're focusing on the NPU and you look at what Tesla's doing. I mean it's just incredible. So you're going to have some new hardware architecture that emerges. Most of the AI that's done today is modeling in the cloud. You're going to have a real time inferencing at the Edge. So that's not going to do the round trip. There's going to be a data store there, I think it has to be. You're going to persist some of the data, maybe not all of it. So it's a whole new architecture- >> Sahir: Absolutely. >> That's developing. That sounds very disruptive. >> Sahir: Yeah. >> How do you think about that, and how does Mongo play there? Guillermo first. >> What I spent a lot of time thinking about is obviously the developer experience, giving the programmer a programming model that is natural, intuitive, and produces its great results. So if they have to think about data that's local because of regulatory reasons for example, how can we let the framework guide them to success? I'm just writing an application I deployed to the cloud and then everything else is figured out. >> Yeah or speed of light is another challenge. (Sahir and Guillermo laugh) >> How can we overcome the speed of light is our next task for sure. >> Well you're working on that aren't you? You've got the best engineers on that one. (Sahir and Guillermo laugh) >> We can solve a lot of problems, I'm not sure of that one. >> So Mongo plays in that scenario or? >> Yeah so I think, absolutely you know, we've been focused heavily on becoming the globally distributed cloud data layer. The back-end data layer that allows you to persist data to align with performance and move data where it needs to be globally or deal with data sovereignty, data nationalism that's starting to rise, but absolutely there is more data being pushed out to the Edge, to your point around processing or inference happening at the Edge. And there's going to be a globally distributed front-end layer as well, whether data and processing takes apart. And so we're focused on one, making sure the data connectivity and the layer is all connected into one unified architecture. We do that in combination with technologies that we have that do with mobility or edge distribution and synchronization of data with realm. And we do it with partnerships. We have edge partnerships with AWS and Verizon. We have partnerships with a lot of CVM players who are building out that Edge platform and making sure that MongoDB is either connected to it or just driving that synchronization back and forth. >> I call that unified experience super cloud, Robbie Belson from Verizon the cloud continuum, but that consistent experience for developers whether you're on Prim, whether you're in you know, Azure, Google, AWS, and ultimately the Edge. That's the big- >> That's where it's going. >> White space right now I'm hearing, Guillermo, right? >> I think it'll define the next generation of how software is built. And we're seeing this almost like a coalition course between some of the ideas that the Web3 developers are excited about, which is like decentralization almost to the extreme. But the Web2 also needs more decentralization, because we're seeing it with like, the data needs to be local to me, I need more privacy. I was looking at the latest encryption features in Mongo, like I think both Web2 need to incorporate more of the ideas of Web3 and vice versa to create the best possible consumer experience. Privacy matters more than ever before. Latency for conversion matters more than ever before. And regulations are changing. >> Sahir: Yeah. >> And you talked about Web3 earlier, talked about new protocols, a new distributed you know, decentralized system emerging, new hardware architectures. I really believe we really think that new economics are going to bleed back into the data center, and yeah every 15 years or so this industry gets disrupted. >> Sahir: Yeah. >> Guillermo: Absolutely. >> You know you ain't see nothing yet guys. >> We all talked about hardware becoming commoditized 10, 15 years ago- >> Yeah of course. >> We get the virtualization, and it's like nope not at all. It's actually a lot of invention happening. >> The lower the price the more the consumption. So guys thanks so much. Great conversation. >> Thank you. >> Really appreciate your time. >> Really appreciate it I enjoyed the conversation. >> All right and thanks for watching. Keep it right there. We'll be back with our next segment right after this short break. Dave Vellante for theCUBE's coverage of MongoDB World 2022. >> Man Offscreen: Clear. (clapping) >> All right wow. Don't get up. >> Sahir: Okay. >> Is that a Moonwatch? >> Sahir: It is a Speedmaster but it's that the-

(bright upbeat melody) >> Welcome back to Dell Technology World 2021. Dell Tech World, the virtual edition. My name is Dave Vellante. We're going to talk about the Edge. I'm very excited to invite Pierluca Chiodelli, who's the Vice President of Product Management for the Edge portfolio at Dell. And Gil Shneorson, who's the Senior Vice President, Edge portfolio also at Dell Technologies. Gentlemen, great to see you welcome to theCUBE. >> Thank you, Dave. >> Thank you, great to see you. >> Yeah, great to see you guys too. Wish we were face to face but maybe in '22. Gil, let's start with you. The Edge is very exciting, it's not really defined. It's very fragmented, but it's there. It's kind of, you know it, when you see it. What do you get excited about when you think about the Edge? >> I think of there's two elements. The first one, is that we all live at the Edge. In other words, the areas we deal with are around us everyday. When we shop, when we consume, when we drive. So it's a very physical type of activity, we know it's there. What's really exciting mostly to me is that, and you started with talking about fragmentation right off the bat. It is a great opportunity for Dell Technologies to add value. Because it's so fragmented, because it's so new, because it has developed and evolved the way it is. We see an amazing opportunity for us to add much more value than we do today and solve problems that have yet to be solved in the industry. >> And Pierluca, it's an exciting, it's almost like an infinite playground for a technologist. I mean. >> Yeah, Dave, I think that's exactly what we find out. The Edge is very exciting, there is a lot of motion especially due to the pandemic and other things. Big factor that is accelerating the innovation at the Edge but this is an inorganic acceleration and what it cause for most of our customers is also confusion, right? They need to apply multiple solutions but not very organized. So you try to solve the outcome like having the right production on your line because demand is surging. But you don't have an organic things to do that and solve the problem. So you see a lot of silos coming in for each one of the solution, and that's what Gil was referring. That's a great opportunity for us as Dell with the breadth of the portfolio we have and what our team that is a new team is focusing doing is to bring that idea to be able to consolidate multiple things at the Edge and process things at the Edge. >> We did an event. CUBE had an event called the CUBE on Cloud and Q1, we had John Rose on and the title of the segment was something like gaining the technology Edge. And we were kind of geeking out on the tech at the Edge. And my takeaway there was... We were trying to like what is Edge? It's like, well, it's the place where it makes most sense to process the data. And so that brings up a lot of challenges. There are technical challenges and there are business challenges. I wonder if we could sort of dig into those a little bit. How do you guys look at that? Maybe Gil, you want to start maybe. Maybe on the business side and then we can dig into that. >> Sure. The way things evolved. If you think about it, at the Edge is very verticalized. And because of that, they're very use case driven. And so in every industry possible, you start with some business person making a decision whether they have a need or they want to grow their business. And so for example, they will buy an applying to do fraud protection in retail or detection retail. Or they will apply an application to merit robotics and the factory need would come with its own gateway, implant, compute, and a cloud portal. And then you do it again and again, and again, every time you have a business opportunity. All of the sudden you have this proliferation of IT type equipment. At the end where it's the worst place to have it really because you don't have the right IT resources and you are in the need to protect it in a much more... In a different way that you can do in a data center. And so all of that, bring us to a point that we see an opportunity to simplify. And so not only simplification. And this is, you know, simplification or simplicity is the most important driver for any IP purchase. Things that are simple or the easiest and the most economical to operate. The next demand that we see from a customer is security. Because things are at the edge, they have a much more extended attack surface. They need to be connected to networks. They need to be connected without IT staff. So if you can simplify insecure, you can really unlock amazing value by processing data close to where it's created. Without it, we're seeing this opportunity as businesses but we can't really get to it because there are those two hurdles in front of us. >> So Pierluca. We need to you thank you for that, Gill. When you hear a lot about AI inferencing at the Edge. And if you think about AI today, much of the work is modeling. It's done in the cloud. But you're not going to be doing AI inferencing in real time in the cloud. Take the autonomous vehicle example. So that brings some technical challenges. There's obviously data challenges. I'm curious as to how you think about that. I mean, we always talk about how much data is going to be persisted. I think Tesla persists like five minutes of data, right? But some of it is going to go back. That's true. But a lot of it is going to be processed real time. And that's just really different than the way we typically think about IT. >> Yeah, absolutely. So at the Edge, especially in manufacturing, we see right now, or in other use case, it's very important to get the outcome very quickly. Now, you don't use that a deep learning model for that. You need to just understand. For example, in the computer vision use case where you take image of your production line. To your point, Dave, you not keep those image, you keep the image where you have the defect. But you need to process that AIML needs to be intelligent enough to understand that you have a defect, and send that image then to the club. So the search of the data at the Edge is a very important factor. And why you need to process data at the Edge, because as your point, you can't wait to send to the cloud and then wait, right? Tesla is a clear example of that. All the autonomous car where you need to react instantaneously to a change. But in manufacturing, for example, that is our focus for now, is for example, the robots. That if you need to optimize the robot, you need to have a immediate understanding of where the pieces are and when they need to put. And the tolerance need to be act immediately. Otherwise, you come out with the thousand of pieces that they are not in the right tolerance. So, and at the end of the day, what we see is not only the search of the need of processing AIML to the Edge, but also the need to have a new type of compute at the Edge. So in the past, was just gateway and you'd get the gateway and you send the data to the cloud. Now, it's a form of a new compute that has also GPU capability and other things to process this data. So very important. And I think that Dell, especially, we are very focused on that because is really where the customer need to extract the value. >> Thank you. And Gil, I want to get Gil to the unique value proposition to Dell and what makes you distinct. If I infer from your comments, your strategy, you said it's to simplify. And so I see two vectors there. One is to simplify at the Edge. The other is where needed connect that Edge, whether it's on prem, a public cloud, cross-cloud, that kind of simplification layer that abstracts the complex the underlying complexity. Maybe you could talk about your strategy and what makes you guys different? >> Sure. We've been talking to our... Well, we always talk to our customers. And we've been doing business at the Edge for many, many years. Let's call it coincidence that we're a very large company. We have reached, we serve our customers. So when they decide to buy something for their Edge, you know, environments, they come to us as well as other vendors. When the percentage of the time based on our market share. But when we decided to take another look at how can we be even more relevant, we started talking to a lot of them great depth. And what we discovered was the problem I talked about before. The problem of complexity, the problem of security and the problem of choice. And so our focus is to do what we do best. At the end of the day, we're an IT company and our customers for the most part are IT people. And we see them dragged more and more into Edge projects because customers need to connect Edge to the network. And they need to security, and that's how it starts. And so those worlds of IT and OT are coming together and they're coming together, applying IT best practices, which is exactly what we know how to do. And so, because of that, we think that they need to think about architecture versus unique silence solutions. Architecture that can support multiple use cases that can grow with time, consolidate more and more use cases as they grow, simplify what they do by applying tried and true or tried and true IT best practices in a secure manner. So the dealer approach would be doing that, taking a more architectural approach to the adverse as a use case. And then just like you predicted, meet the customers where they are from an application standpoint. And so we know that a lot of applications are growing and be developing on a hyper scale or public clouds. We would like to connect to those. We would like to allow them to keep working as they have, except, when they run it at the Edge. Think about environments, if can consolidate multiple workloads and not solve it for each one at the same time. And so that will be our overall approach. That's what we're working on. >> Yeah. So, okay. So in that horizontal layer, if you will, to serve many, many use cases, not just... You're not going to go a mile deep into one and be the expert at some narrow use case. You want to be that horizontal platform. Here, look, I wonder, does that call for more programmability over time of the products to really allow people to kind of design in that flexibility, if you will, build my own. Is that something that we can expect? >> Yeah, absolutely. So we spoke a little bit about this before the interview. And the things that is very important is composability, starting from a very small form factor to the cluster, and then expand to the cloud is the fundamental things. And the trend that we see. The fact that you can compose the infrastructure, starting from a small gateway that is changing in this market right up to the cloud, and be able to use the same layer that allow you to run the same application is a fundamental things. And we are working on that. We are working on this vision and our strategy is really to be able to be transparent but provide the right building block to do all the use cases that they are required. Where the data. So we, again, not only meeting the customer but meeting where the data are, what the customer wants out of those data. So that's a fundamental things. And we have project Apex. So obviously we are plugging in the project Apex. From a Edge point of view, will allow the customer to have these unique experience to go in Apex and also deploy the Edge infrastructure that is needed. We're starting right now with that. So we will touch later, but that's the first building block of that journey. >> Excellent. Let's touch now. You've got some news around Apex and what are you announcing? >> So we are very excited because as I said our team it's pretty new and it's a very important investment that Dell makes. Not only in us as a team, but as a motion. So we are announcing a reference architecture with PTC. PTC is one of the biggest company for... Actually based here in Boston for manufacturing. And reference architecture will be run on base on Apex private cloud. So the customer can go to the portal, order Apex private cloud and deploy PTC on top of that. So very important things is the first step in this journey. But it's very important steps so we want to thank you also PTC to allow us to work with them. We have other stuff as well that we are announcing. I don't know if you are familiar but we have a very unique streaming data platform. Streaming data platform that can stream multiple data collected from gateway, from every place. And that it's a need. Obviously, when you need to process data in real time, whatever is streaming. What are we doing with the new streaming data platform approach is the ability to deploy single node. So it can be very appealing for the Edge and up to three nodes. >>Awesome. That's great. So a couple of comments on that. So it was funny. We did the LiveWorx show in theCUBE a couple of years ago. PTC is a big event and it was the Edge. And I remember looking around and saying "Where's all the IT vendors?". And so that's great to see you guys leaning in like that. Pierluca, the streaming platform. Tell me more about that. What's the tech behind it? >> So the streaming data platform is a project, that we start couple of year ago, is actually start from open source Pravega. It's a very interesting technology where you can stream multiple data. It's not a traditional storage. Use a technology that can really collect thousands of different streams. And that's very important when you need to mind the data. Bring the data, the structured data in efficient that you can process them at the real time. It's very important. So there are very cool use case of that. But now, that we look at the Edge, this is make more and more tangible sense because we have a lot of partners that they're working with us, especially to extend. When you have all these sensor, you bring the data to the gateways and from the gateways, then you can use data streaming platform to collect all these streams. And then you can easily process them. So it's a very fundamental technology. We are very proud of that. As I said, our enterprise version, it's getting more and more. And now we can land these on different architecture. So it can be backed up by an unison. It can be also on different storage type now. And as I said, we looking now to bring from a what was it data center kind of structure, down to the Edge because now we can put it in a single node up to three nodes. >> It makes a lot of sense. Is this like a Kafka based thing or open source or is it something you guys built or a combination? >> It's a combination. The project is an open source project but we did that. We start this many years ago. And it works with Kafka but it's not Kafka. So it has plugging that can work with Kafka and all the other things. And it's very easy to deploy. So it's a very, very important. And the other things is the scalability of this platform. >> Yeah, so I mean, that sounds like the kind of thing you had in the labs. And you said, "Okay, this is going to be important". And then boom, all of a sudden, the market comes to you. As if you pop it right in. And then of course, the Accenture relationship. Deep, deep industry expertise. So that makes a of sense. 5G's happening. A different world the next 10 years than the last 10 years. Isn't it? >> Yep. >> What is it about manufacturing? Why did you start there? >> I can take this. We looked at where the opportunity was from two perspective. One is whether what are the opportunities to sell, Dave. And the other one obviously comes with it because there's an opportunity to have. And manufacturing today at the Edge is about 30% of the opportunity in sales. According to IDC. But more so, it's been around for the longer time. And so it's maturing, it's the most demanding. And you know, it's got very long horizons of investment. And what we did was, we figured that if we can solve problems for industry, we can then extend that and solve it for everybody else. Because this would be the toughest one to solve. And we like challenge. And then, so we decided to focus and go deep. And you said it before, well, our approach is definitely horizontal approach. We cannot take an horizontal approach without verticalizing and understanding specific needs. So nobody can avoid doing both at the same time. You need to understand. But you also want to solve it in a way that doesn't proliferate the silos. So that's our role. We will understand, but we will make it more generic. So other people can never (indistinct). >> Yeah. And David, if I can add, I think the manufacturing is also very exciting for us as a technologist, right? And Dell technology, as in the name, the technology. So it's very exciting because if I look at manufacturing, we are really in the middle of a industrial transformation. I mean, it's a new era. If you think about, nobody care in the past to connect their machinery with... That they have PLC to the network. All of these is changing because the life where we live right now, with the pandemic, with the remote working, with the fact that you need to have a much more control and be able to have predictive matter. So you're not stopping your manufacturing. Is pushing the entire manufacturing institute industry to connect these machines. And with the connectivity of these machinery, you get a lot of data. You get also a lot of challenge. For example, security. So now, that's the place where connectivity brings the IT aspect in. And the OT guys, now they starting to speak (indistinct) because now it's a more complex things, right? It's not any more computerized only to one machinery. Specifically, is the entire floor. So it's a very interesting dynamics. >> Is the connection between that programmable logic controller and the Dell solution, you mentioned to secure, better security. And I presume it's also to connect back to whatever the core or the cloud, et cetera. Is it also to do something locally? Does it improve? Is there value add that you can provide locally? And what is that value add? >> Yeah, absolutely. So the value add, as I said, if you think right in the past, right? You have a machine that probably stay in the manufacturing for 20, 25 years then you have an hardware attached to that machine that they used the POC about 11 year. The guy that he knows better about that machine, is actually not the software component of it. But he's the guy that he's been working on that machine for 15 years. Now, how you translate that knowledge to a learning algorithm that actually can do that for thousand of machine. And that's really the key, right? You need to centralize information, process those information, but not in the cloud, not in a central data center, but on the manufacturing floor. And you need to have a way to represent these things in a very simple way. So the plant manager can take action, or the guy that is responsible for the entire line, can take action immediately. And that's where the change is. It's not anymore to... Is trying to extend that knowledge to multiple machine, multiple floor, and try to get these change immediately. So that's very important. >> So the PLC doesn't become a general purpose computer, or even necessarily an Uber computer. It connects to that capability because that enables data sharing across clouds. >> That's enable the entire things. You can't do a model just with one source. You need to have multiple sources. And also think about the manufacturing is changing not only for the machinery, but people that they build new manufacturing, right? They need to be smart building. They need to have a technology for being more green, solar energy consumption. So the manufacturing itself is mean five or six different things that you need to solve. It's not just the machine. So this idea of this silos environment is starting to collapse in one. And that's why it's important for us to start from a vertical, but also in the manufacturer, you already see this will expand to multiple things. Also, smart building another thing because they need it. >> Yeah. The red guilt to your point of view. Manufacturing is like the Big Apple. If you can make it there, you can make it anywhere. And you've got adjacencies that you can take the learnings, and manufacturing, and apply them to those adjacent industries. Gil, give us the last word. >> No, usually when we talk at Dell technologies world, we talk to an ideal audience. And we're thinking this year that the way to talk about Edge, at least with the people who traditionally buy from us is expose them to the fact that they are more and more going to be responsible for every projects. And so our advice would be, our hope that they would partner with us to think ahead. Just like they do with data center with our cloud strategy. Thinking ahead as they think about their Edge and try to set up some architectural guidelines. So when they do get the request, they're ready for it. And think about what they know, think about the IP best practices that they applied. All of that is coming to them. They need to be prepared as well. And so we would like to partner with all of our customers to make them ready. And obviously help them simplify, secure, consolidated as they grow. >> Well, guys, thank you. I learned a lot today. We've made a lot of progress. You know, this is the hallmark of Dell, right? It's a very high, let me make sure I get this right. Very high do to say ratio, right? As you guys talked about doing this, a couple of couple of years ago. And you've made a lot of progress and I really appreciate you coming on theCUBE to explain this strategy. It makes a lot of sense. And so congratulations and good luck in the future. >> Thank you. >> Thank you, Dave. >> All right. And thank you for watching everybody. This is Dave Vellante for theCUBE's ongoing coverage of Dell Tech World 2021, the virtual edition. Keep it right there, I'll be right back. (closing music)

>>Welcome back to Dell Technology World. 2021. Del Tech World. The virtual edition. My name is Dave Volonte. We're gonna talk about the Edge. Very excited to invite Pierluigi Deli, who's the Vice President, Product management for the Edge portfolio. Adele and Gil Schwarzman, who is the Senior Vice President. Edge portfolio, also with Dell Technologies Gentlemen. Great to see you. Welcome to the cube. >>Thank you. Thank >>you. You see you, >>Yeah, great to see you guys to which we were face to face, but maybe maybe in 22 Gil, let's start with you. The edge is very exciting. Uh, it's, you know, not really defined, it's very fragmented, but it's there, you know, it's kind of, you know, it when you see it, what do you get excited about when you think about the edge? >>Yeah, I think uh there's two elements. The first one is that we all live at the edge. In other words, the areas we deal with our around us every day will show up um when we uh, you know, when we consume when we drive. So it's a, it's a very physical type of activity. We know it's there. What's really exciting motive to me is that you started with talking about fragmentation right on the bet. Um, it is a great opportunity for the technology is to add value um because it's so fragmented because it's so new because it has developed and evolved the way it is. We see an amazing opportunity for us to add much more value than we do today and solve problems that have yet to be solved in the industry. >>And it's an exciting, it's almost like an infinite playground for a technologist. You >>dave, I think that's exactly what we find out. The Edge is very exciting. There is a lot of motion, especially due to the pandemic and other things. Big factor that accelerate innovation at the edge but this is an inorganic acceleration and what it kills for one of the most of our customer is also confusion, right? They need to apply multiple solution but not very organized. So you try to solve the outcome like having the right production on the, on your line because demand is surging but you don't have an organic things to do that and solve the problem. So you see a lot of silence coming in for each one of the solution and that's what Gil was referring. That's a great opportunity for us as dealt with the breath of the portfolio we have and what our team that is a new team is focusing doing is to bring that idea to be able to consolidate multiple things at the edge and process things at the edge. >>We did a an event cube, had an event called the Cuban cloud and Q one and we had john Rosen and the title of segment was something like gaining the technology edge And we were kind of freaking out on, on the tech at the edge. Uh it might take away there was trying to like what is the edge? It's like, well it's the place where it makes most sense to process the data and so that brings up a lot of challenges. There are technical challenges and there are business challenges. I wonder if we could sort of dig into those a little bit. How do you guys look at that? Maybe gil you want to start maybe on the business side and then we can dig a short, right >>the way things evolved if you think about it, um, at the edge of very vertical lesson because of that they're very use case driven And so in every industry possible you start with some business person making a decision whether they have a need or they want to grow their business. And so for example they would buy an applying to do fraud protection in retail or detection retail or they will apply an application to medical robotics in the factory. And it would come with its own gateway in plant compute in a cloud portal and then you do it again and again and again every time you have a business opportunity all of the sudden you have this proliferation of I. T. Type equipment at the end where it's it's the worst place to have it really because you don't have the right I. T. Resources and you are um in the need to protect it in a much more um in a different way than you do in a data center. And so all of that brings to bring us to a point that you know we see an opportunity to simplify. Um And so not only simplification and this is you know simplification or simplicity is the most important driver for any I. T. Purchase. Um Things that are simple are the easiest that the most economical to operate the next demand that we see from a customary security because things are at the they have a much more um you know extended attack surface um they need to be connected to networks, they need to be connected without I. T. Staff. So if you can simplify insecure you can really unlock amazing value by processing data where closely to where it's created without it. You know we were seeing this opportunity as businesses but we can we get to it because there are so those two hurdles in front of us. >>So when you say thank you for that bill, when you think about, when you hear you hear a lot about AI influencing at the edge and and if you think about AI today much of the work is modeling, it's done in the cloud, but you're not going to be doing A i influencing in real time in the cloud, you know, take the autonomous vehicle example, so that brings some some technical challenges. Um, there's obviously data challenges. I'm curious as to how you think about that. I mean we always talk about how much data is going to be persisted, I think Tesla persists like five minutes of data, right? But some of it is gonna go back, that's true, but a lot of it is going to be processed real time and that's just really different than the way we typically think about. Yeah, >>absolutely. So at the Edge, especially in manufacturing, we see right now or in a uh, another use case, it's very important to get the outcome very quickly. Now. You don't use that a deep learning model for that. You need to just understand, for example, in a computer vision use case where you take the image of your production line, you actually to your point dave you not keep those image when you keep the image where you have the defect. But you need to process that. Ai Ml needs to be intelligent enough to understand that you have a defect and send that image them to the club. So the search of the data at the edge is a very important factor and why you need to process data, the Edge because your point, you can't wait to send to the cloud and I'm waiting right? Um, Tesla is a clear example of that all the autonomous car where you need to react instantaneously to change. But in manufacturing for example that is our focus for now is for example the robots that if you need to optimize the robot, you need to have a immediate understanding of where the pieces are and when they need to put in the tolerance need to be act immediately. Otherwise you come out with the thousands of pieces that they are not in the right tolerance. So at the end of the day, what we see is not only the search of the need of processing ai ml to the edge but also the need of a new type of compute at the edge. So in the past was just Gateway and you get the gate when you send the data to the cloud. Now it's a form of a new computer that come as also GPU capability and other things to process the data. So very important. And I think the Dell especially we are very focused on that because is uh is really where the customer need to extract the value. >>Thank you. And Gil I want to get into the unique value proposition to tell what makes you distinct. And it's uh I infer from your comments, your strategy you said is to simplify and so I see two vectors. There. One is to simplify at the edge. The other is to where we're needed, connect that edge, whether it's on prem public cloud across cloud, that kind of simplification layer that abstracts the complex, the underlying complexity. Uh Maybe you could talk about your strategy and what makes you guys different. >>Sure. Um We've been talking to a, well we always talk to our customers and we've been doing business at the edge for many many years. Um You know let's call it coincidental were very large company we have reached, we serve our customers so when they decide to buy something for their you know environment, they come to us as well as other vendors and we win a percentage of the time based on our market share. Um But when we decided to take another look at how can we be even more relevant? We started talking to a lot of them great depth. And what would we do we discovered was the problem I talked about before, the problem of complexity, the problem of security and the problem of you know choice. And so our focus is to do what we do best. We at the end of the day we're an I. T. Company. Um and our our customers for the most part our I. T. People and we see them dragged more and more into edge projects because customers need to connect edge to the network and they need to security and that's how it starts. And so those worlds of I. T. And OTR coming together and their coming together applying best practices which is exactly what we know how to do. And so because of that we think that they need to think about architecture versus unique silent solutions architecture can support multiple use cases that can grow with time, consolidate more and more use cases as they grow. Simplify what they do by applying you know tried and true or tried and true best practices in a secure manner. So the deal approach would be doing that taking a more architectural approach to the adverse as a use case and then just like you predicted um meet the customers where they are from an application stand book. And so we we know that a lot of applications are growing and development on a hyper scale or public clouds. We would like to connect to those. We would like to allow them to keep working as they have except when they run into the edge. Think about environments that could consolidate multiple workloads and not solve it for each one at the same time. And so that would be our overall approach. That's what we're working on. >>Yeah. Okay. So that horizontal layer, if you will uh to to to serve many many use cases, not just you're not gonna go a mile deep into one and be the expert at some narrow use case. You want to be that horizontal platform. But at the same time, look, I wonder does does that call for more program ability as we over time of the of the products to to really allow people to kind of design in that flexibility if you will build my own. Uh is that something that we can expect? >>Yeah, absolutely. So uh we spoke a little bit about this before the interview and the things that is very important is compose ability starting from a very small from factor to the cluster and then expand to the cloud is a fundamental things and a trend that we see. The fact that you can compose the infrastructure um starting from a small gateway that is changing in this market, right up to the cloud and be able to use the same layer that allow you to run the same application is the fundamental things and we are working on that. Um we are working on this vision and our strategy is really to be able to be transparent but provide the right building block to do all the use case that they are required where the data are. So we again, not only meeting the customer but meeting where the data are, what the customer wants out of those data. So that's a fundamental things. And you know, we we have project Apex. So obviously we are plugging into the project apex from an edge point of view, will allow the customer to have this unique experience to go in Apex and also deploy the edge infrastructure that is needed. So that's that's we started right now with that. So we will touch later, but that's the first building block of that journey. >>Actually, let's touch now you've got some news around Apex and and and and talking what are you announcing? So >>we are very exciting because as I said, our team is, it's pretty new and um, it's a very important investment that Dell makes uh not only in us as a team but as a motion. Um, so we are announcing a reference architecture with PTC. PTC is the one of the biggest company for actually based here in boston uh for manufacturing and reference architecture will be run on based on apex private cloud so the customer can go to the portal, order, order apex private cloud and deploy deploy PTC on top of that. So, very important things is that the first step in this journey and but it's an important, very, very important steps. So we want to thank you also PTC to allow us to work with them. Um, we have other stuff as well that we are announcing. Um, I don't know if you are familiar but we have a very unique streaming data platform, um, streaming data platform that can stream multiple data collected from Gateway from every place. And uh it's a need obviously when you need to process data in real time, very important to have a streaming, what we're doing with the new streaming data platform approach is the ability to deploy single note. So it can be very appealing for the edge and up to free notes and last but not least gil if you want to speak about our other partnership is very important. >>Sure. Um once we started looking more in depth into manufacturing, we discover that this market is today served by combinations of um oT vendors, people who make equipment? S eyes, people who consult on integration and um and you know, a lot of SVS that make up this ecosystem and people like ourselves. And so one of the things that we decided to do is partner with accenture, accenture Industry X practice to bring our joint value to customers. We started by investing in in a five G lab. They have four industry act. So you know the usage of five G. Manufacturing industry and we will still we will expand that and work on that as a as a joint offer for our joint customers going forward. So we're really excited about this because we feel that consolidation needs to happen not only technology but also in the partnerships, we need to partner if you want to bring true value to our customers and that's the first step, >>awesome. That's great. So a couple of comments on that. So it's funny, we did the live work show in the cube a couple years ago. PTC is a big, big event and it was like it was the edge and I remember looking around saying where's all the vendors? So that's great to see you guys leaning in like that parallel to the streaming platform. Tell me more about that. What's the tech behind it? >>Uh So the streaming data platform is a project that we start a couple of years ago is actually uh start from open source Provida. Um it's uh it's a very interesting technology where you can stream multiple data, it is not a traditional storage, ah use a technology that can ah really collect thousands of different streams and that's very important when you need to mind the data, bring the data um in the structure data in a inefficient that you, you can process them at the real time. It's very important. So um there are very cool use case of that. But now that we look at the edge, this is make more and more tangible sense because we have a lot of partners that they're working with us, especially to extend when you have all this sensor, you bring the data to the gateways and from the gateways then you can use data streaming platform to collect all these dreams and then you can easily process them. So it's a very fundamental technology, we are very proud of that. Um as I said, our enterprise version uh is getting more and more and now we can land this on different architecture, so it is, it can be backed up by an Iceland. Uh it can be also on different storage type now and as I said, we're looking now to bring from a what was a data center kind of structure down to the edge because now we can put a single node up to three notes, >>it makes a lot of sense. Is this like a Kafka based thing or open source or is it something you guys built or a combination? >>It's a combination. We actually project. The project is an open source project, but we did that, we start this many years ago and um he works with Kafka, but he's not Kafka. So it's, it's a he has plugging that can work with Kafka and all the other things and, and it's very easy to deploy. So it's a very, very, very important. And the other things is the scalability of this platform. >>I mean, it sounds like the kind of thing you had in the labs and you said, OK, this is going to be important. That boom all of a sudden the market comes to you as if you pop it right in. And then of course, the accenture of relationship deep, deep industry expertise, so that makes a lot of sense. 55 Gs happening a different world the next 10 years in the last 10 years isn't it? What is it about manufacturing? Why why did you start there? >>I can take this. Um We looked at where the opportunity was from two perspectives. One is where the opportunity, what the opportunities to sell, even the other one obviously comes with it because there is an opportunity to have and manufacturing today at the edges about 30 of the opportunity in sales according to NBC but more so it's been around for the longer time and so they it's very it's maturing um it's the most demanding. Um and you know, it's got very long horizons of investment and what we did was we figured that if we can solve problems for industry we can then extend that and solving for everyone years. Because this would be the toughest one to solve and we like challenge. And so we decided to focus and go deep. You said it before? Well, our approach is definitely horizontal approach. We cannot take a horizontal approach without vertical izing and understand specific needs. So nobody can avoid doing both at the same time. You need to understand. But you also want to solve it in a way that doesn't proliferate the silos. So that's our role. We will understand what we will make it more generic so other people can never get later on >>and David, if I cannot. Uh I think the manufacturing is also very exciting for us as a technologist, right? Uh and uh Dell technology as in the name the technology. So it's very exciting because if I look at manufacturing, we we are really in the middle of a industrial transformation. I mean it's a new era. Um If you think about um nobody care in the past to connect their machinery with that the F. P. L. C. To the network. All of this is changing because the life that where we live right now with the pandemic with the remote working with the fact that you need to have a much more control and be able to have predictive matters. So you're not stopping your manufacturing is pushing the entire manufacturing instrument industry to connect this machine and with the connectivity of this machinery you get a lot of data. You get also a lot of challenge. For example security. So now that's the place where connectivity brings the I. T. Aspect in and U. T. Guys now they're starting to speak with because now it's a more complex things right? It's not any more computerized competitor eyes only to one machinery specific is the entire floor. So it's a very interesting dynamics >>is the connection between that programmable logic controller and the Dell solution is you mentioned to secure better security and I presume it's also to connect back to whatever the core or the cloud etcetera. Is it also to do you know, something locally? Does it improve? Is their value add that you can provide locally? And what is that value add? >>Absolutely. So the value, as I said, um if you think right in the past right, you have a machine that uh, probably stay in the manufacturing for 2025 years, then you have an artwork attached to that machine that it is the P. L. C. About 11 years. The guy that he knows better about that machine is actually not the software component on. But he's the guy that has been working on that machine for 15 years now. How you translate that knowledge To a learning algorithm that actually can do that 4000 of machine. And and that's really the key right. You need to centralize information, process those information but not in the cloud, not in the central data center, but on the manufacturing floor. And you need to have a way to represent these things in a very simple way. So the plant manager can take action or the or the guy that is responsible for the entire line can take action immediately. And that's where the changes is not anymore to is trying to extend that knowledge to multiple machine multiple floor and try to get this change immediately. So that's really >>so the PLC doesn't become a general purpose computer or even necessarily the Uber computer. It connects to that capability because that enables data sharing across clouds and that's >>enabled the entire things. You know, you you can't do a model just with one source. You need to have multiple sources. Um, and also think about the manufacturing is changing not only for the machinery, but people that they build new manufacturing right? They need to be smart building. They need to have a technology for being more green solar energy consumption. So the manufacturing itself is mean five or six different things that you need to solve. It's not just the machine. So this idea of the silence environment is started to collapse in one and that's why it's important for us to start from a vertical, but also in the manufacturing, you already see this will expand to multiple things. Also like smart building another thing because they need it. >>Yeah. The red guilt to your point manufacturers like the Big Apple. If you can make it there, you can make it anywhere and you've got adjacent seas, you can, you know, you can take the learnings from manufacturing and apply them to those adjacent industries. Uh, give us the last word. >>Um, look, usually when we talk at the technologies world, we talked to an I. D. Audience and we were, we're thinking this year that the way to talk about edge, at least with the people who traditionally buy from us is exposed them to the fact that they are more and more are going to be responsible for projects. And so our advice would be our hope that they would partner with us to think ahead. Just like they do with data center with their cloud strategy, think ahead as they think about their edge and try to set up some architectural guidelines. So when they do get the request, they're ready for it and think about what they think about the best practices that they applied, all of that is coming to them. They need to be prepared as well. And so we would like to partner with all of our customers to make them ready and obviously help them simplify secure, consolidate as they grow. >>Well guys, thank you, I learned a lot today. I you made a lot of progress. You know, this is the hallmark of Dell, right? It's a very high, let me make sure I get this right, very high due to say ratio right. You guys talked about doing this, you know, a couple a couple of years ago, uh, and you've made a lot of progress and I really appreciate you coming in the cube to explain the strategy. It makes a lot of sense. And so congratulations and uh, good luck in the future. >>Thank you. >>All right. And thank you for watching everybody. This is Dave Volonte for the cubes, ongoing coverage of Del Tech World 2021. The virtual edition. Keep it right there, right back, >>mm.

>> Announcer: From around the globe, it's theCUBE presenting Enterprise Digital Resilience on Hybrid and multicloud brought to you by io/tahoe >> Hello everyone, and welcome to our continuing series covering data automation brought to you by io/tahoe. Today we're going to look at how to ensure enterprise resilience for hybrid and multicloud, let's welcome in Ajay Vohora who's the CEO of io/tahoe Ajay, always good to see you again, thanks for coming on. >> Great to be back David, pleasure. >> And he's joined by Fadzi Ushewokunze, who is a global principal architect for financial services, the vertical of financial services at Red Hat. He's got deep experiences in that sector. Welcome Fadzi, good to see you. >> Thank you very much. Happy to be here. >> Fadzi, let's start with you. Look, there are a lot of views on cloud and what it is. I wonder if you could explain to us how you think about what is a hybrid cloud and how it works. >> Sure, Yeah. So, a hybrid cloud is an IT architecture that incorporates some degree of workload portability, orchestration and management across multiple clouds. Those clouds could be private clouds or public clouds or even your own data centers. And how does it all work? It's all about secure interconnectivity and on demand allocation of resources across clouds. And separate clouds can become hybrid when you're seamlessly interconnected. And it is that interconnectivity that allows the workloads to be moved and how management can be unified and orchestration can work. And how well you have these interconnections has a direct impact of how well your hybrid cloud will work. >> Okay, so well Fadzi, staying with you for a minute. So, in the early days of cloud that term private cloud was thrown around a lot. But it often just meant virtualization of an on-prem system and a network connection to the public cloud. Let's bring it forward. What, in your view does a modern hybrid cloud architecture look like? >> Sure, so, for modern hybrid clouds we see that teams or organizations need to focus on the portability of applications across clouds. That's very important, right. And when organizations build applications they need to build and deploy these applications as a small collections of independently loosely coupled services. And then have those things run on the same operating system, which means in other words, running it all Linux everywhere and building cloud native applications and being able to manage it and orchestrate these applications with platforms like Kubernetes or Red Hat OpenShift, for example. >> Okay, so, Fadzi that's definitely different from building a monolithic application that's fossilized and doesn't move. So, what are the challenges for customers, you know, to get to that modern cloud is as you've just described it as it skillsets, is it the ability to leverage things like containers? What's your View there? >> So, I mean, from what we've seen around the industry especially around financial services where I spend most of my time. We see that the first thing that we see is management, right. Now, because you have all these clouds, you know, all these applications. You have a massive array of connections, of interconnections. You also have massive array of integrations portability and resource allocation as well. And then orchestrating all those different moving pieces things like storage networks. Those are really difficult to manage, right? So, management is the first challenge. The second one is workload placement. Where do you place this cloud? How do you place these cloud native operations? Do you, what do you keep on site on prem and what do you put in the cloud? That is the other challenge. The major one, the third one is security. Security now becomes the key challenge and concern for most customers. And we're going to talk about how to address that. >> Yeah, we're definitely going to dig into that. Let's bring Ajay into the conversation. Ajay, you know, you and I have talked about this in the past. One of the big problems that virtually every company face is data fragmentation. Talk a little bit about how io/tahoe, unifies data across both traditional systems, legacy systems and it connects to these modern IT environments. >> Yeah, sure Dave. I mean, a Fadzi just nailed it there. It used to be about data, the volume of data and the different types of data, but as applications become more connected and interconnected the location of that data really matters. How we serve that data up to those apps. So, working with Red Hat and our partnership with Red Hat. Being able to inject our data discovery machine learning into these multiple different locations. whether it be an AWS or an IBM cloud or a GCP or on prem. Being able to automate that discovery and pulling that single view of where is all my data, then allows the CIO to manage cost. They can do things like, one, I keep the data where it is, on premise or in my Oracle cloud or in my IBM cloud and connect the application that needs to feed off that data. And the way in which we do that is machine learning that learns over time as it recognizes different types of data, applies policies to classify that data and brings it all together with automation. >> Right, and one of the big themes that we've talked about this on earlier episodes is really simplification, really abstracting a lot of that heavy lifting away. So, we can focus on things Ajay, as you just mentioned. I mean, Fadzi, one of the big challenges that of course we all talk about is governance across these disparate data sets. I'm curious as your thoughts how does Red Hat really think about helping customers adhere to corporate edicts and compliance regulations? Which of course are particularly acute within financial services. >> Oh yeah, yes. So, for banks and payment providers like you've just mentioned there. Insurers and many other financial services firms, you know they have to adhere to a standard such as say a PCI DSS. And in Europe you've got the GDPR, which requires stringent tracking, reporting, documentation and, you know for them to, to remain in compliance. And the way we recommend our customers to address these challenges is by having an automation strategy, right. And that type of strategy can help you to improve the security on compliance of of your organization and reduce the risk out of the business, right. And we help organizations build security and compliance from the start with our consulting services, residencies. We also offer courses that help customers to understand how to address some of these challenges. And there's also, we help organizations build security into their applications with our open source middleware offerings and even using a platform like OpenShift, because it allows you to run legacy applications and also containerized applications in a unified platform. Right, and also that provides you with, you know with the automation and the tooling that you need to continuously monitor, manage and automate the systems for security and compliance purposes. >> Ajay, anything, any color you could add to this conversation? >> Yeah, I'm pleased Fadzi brought up OpenShift. I mean we're using OpenShift to be able to take that security application of controls to the data level and it's all about context. So, understanding what data is there, being able to assess it to say, who should have access to it, which application permission should be applied to it. That's a great combination of Red Hat and io/tahoe. >> Fadzi, what about multi-cloud? Doesn't that complicate the situation even further, maybe you could talk about some of the best practices to apply automation across not only hybrid cloud, but multi-cloud as well. >> Yeah, sure, yeah. So, the right automation solution, you know can be the difference between, you know cultivating an automated enterprise or automation carries. And some of the recommendations we give our clients is to look for an automation platform that can offer the first thing is complete support. So, that means have an automation solution that provides, you know, promotes IT availability and reliability with your platform so that, you can provide enterprise grade support, including security and testing integration and clear roadmaps. The second thing is vendor interoperability in that, you are going to be integrating multiple clouds. So, you're going to need a solution that can connect to multiple clouds seamlessly, right? And with that comes the challenge of maintainability. So, you're going to need to look into a automation solution that is easy to learn or has an easy learning curve. And then, the fourth idea that we tell our customers is scalability. In the hybrid cloud space, scale is the big, big deal here. And you need to deploy an automation solution that can span across the whole enterprise in a consistent manner, right. And then also that allows you finally to integrate the multiple data centers that you have. >> So, Ajay, I mean, this is a complicated situation for if a customer has to make sure things work on AWS or Azure or Google. They're going to spend all their time doing that. What can you add to really just simplify that multi-cloud and hybrid cloud equation. >> Yeah, I can give a few customer examples here. One being a manufacturer that we've worked with to drive that simplification. And the real bonuses for them has been a reduction in cost. We worked with them late last year to bring the cost spend down by $10 million in 2021. So, they could hit that reduced budget. And, what we brought to that was the ability to deploy using OpenShift templates into their different environments, whether it was on premise or in, as you mentioned, AWS. They had GCP as well for their marketing team and across those different platforms, being able to use a template, use prebuilt scripts to get up and running and catalog and discover that data within minutes. It takes away the legacy of having teams of people having to jump on workshop calls. And I know we're all on a lot of teams zoom calls. And in these current times. They're just simply using enough hours of the day to manually perform all of this. So, yeah, working with Red Hat, applying machine learning into those templates, those little recipes that we can put that automation to work regardless which location the data's in allows us to pull that unified view together. >> Great, thank you. Fadzi, I want to come back to you. So, the early days of cloud you're in the Big Apple, you know financial services really well. Cloud was like an evil word and within financial services, and obviously that's changed, it's evolved. We talk about the pandemic has even accelerated that. And when you really dug into it, when you talk to customers about their experiences with security in the cloud, it was not that it wasn't good, it was great, whatever, but it was different. And there's always this issue of skill, lack of skills and multiple tools, set up teams. are really overburdened. But in the cloud requires, you know, new thinking you've got the shared responsibility model. You've got to obviously have specific corporate, you know requirements and compliance. So, this is even more complicated when you introduce multiple clouds. So, what are the differences that you can share from your experiences running on a sort of either on prem or on a mono cloud or, you know, versus across clouds? What, do you suggest there? >> Sure, you know, because of these complexities that you have explained here mixed configurations and the inadequate change control are the top security threats. So, human error is what we want to avoid, because as you know, as your clouds grow with complexity then you put humans in the mix. Then the rate of errors is going to increase and that is going to expose you to security threats. So, this is where automation comes in, because automation will streamline and increase the consistency of your infrastructure management also application development and even security operations to improve in your protection compliance and change control. So, you want to consistently configure resources according to a pre-approved, you know, pre-approved policies and you want to proactively maintain them in a repeatable fashion over the whole life cycle. And then, you also want to rapidly the identify system that require patches and reconfiguration and automate that process of patching and reconfiguring. So that, you don't have humans doing this type of thing, And you want to be able to easily apply patches and change assistance settings according to a pre-defined base like I explained before, you know with the pre-approved policies. And also you want ease of auditing and troubleshooting, right. And from a Red Hat perspective we provide tools that enable you to do this. We have, for example a tool called Ansible that enables you to automate data center operations and security and also deployment of applications. And also OpenShift itself, it automates most of these things and obstruct the human beings from putting their fingers and causing, you know potentially introducing errors, right. Now, in looking into the new world of multiple clouds and so forth. The differences that we're seeing here between running a single cloud or on prem is three main areas, which is control, security and compliance, right. Control here, it means if you're on premise or you have one cloud you know, in most cases you have control over your data and your applications, especially if you're on prem. However, if you're in the public cloud, there is a difference that the ownership it is still yours, but your resources are running on somebody else's or the public clouds, EWS and so forth infrastructure. So, people that are going to do these need to really, especially banks and governments need to be aware of the regulatory constraints of running those applications in the public cloud. And we also help customers rationalize some of these choices. And also on security, you will see that if you're running on premises or in a single cloud you have more control, especially if you're on prem. You can control the sensitive information that you have. However, in the cloud, that's a different situation especially from personal information of employees and things like that. You need to be really careful with that. And also again, we help you rationalize some of those choices. And then, the last one is compliance. As well, you see that if you're running on prem on single cloud, regulations come into play again, right? And if you're running on prem, you have control over that. You can document everything, you have access to everything that you need, but if you're going to go to the public cloud again, you need to think about that. We have automation and we have standards that can help you you know, address some of these challenges. >> So, that's really strong insights, Fadzi. I mean, first of all Ansible has a lot of market momentum, you know, Red Hat's done a really good job with that acquisition. Your point about repeatability is critical, because you can't scale otherwise. And then, that idea you're putting forth about control, security and compliance. It's so true, I called it the shared responsibility model. And there was a lot of misunderstanding in the early days of cloud. I mean, yeah, maybe AWS is going to physically secure the you know, the S3, but in the bucket but we saw so many misconfigurations early on. And so it's key to have partners that really understand this stuff and can share the experiences of other clients. So, this all sounds great. Ajay, you're sharp, financial background. What about the economics? You know, our survey data shows that security it's at the top of the spending priority list, but budgets are stretched thin. I mean, especially when you think about the work from home pivot and all the areas that they had to, the holes that they had to fill there, whether it was laptops, you know, new security models, et cetera. So, how to organizations pay for this? What's the business case look like in terms of maybe reducing infrastructure costs, so I can pay it forward or there's a there's a risk reduction angle. What can you share there? >> Yeah, I mean, that perspective I'd like to give here is not being multi-cloud as multi copies of an application or data. When I think back 20 years, a lot of the work in financial services I was looking at was managing copies of data that were feeding different pipelines, different applications. Now, what we're seeing at io/tahoe a lot of the work that we're doing is reducing the number of copies of that data. So that, if I've got a product lifecycle management set of data, if I'm a manufacturer I'm just going to keep that at one location. But across my different clouds, I'm going to have best of breed applications developed in-house, third parties in collaboration with my supply chain, connecting securely to that single version of the truth. What I'm not going to do is to copy that data. So, a lot of what we're seeing now is that interconnectivity using applications built on Kubernetes that are decoupled from the data source. That allows us to reduce those copies of data within that you're gaining from a security capability and resilience, because you're not leaving yourself open to those multiple copies of data. And with that come cost of storage and a cost to compute. So, what we're saying is using multi-cloud to leverage the best of what each cloud platform has to offer. And that goes all the way to Snowflake and Heroku on a cloud managed databases too. >> Well and the people cost too as well. When you think about, yes, the copy creep. But then, you know, when something goes wrong a human has to come in and figure it out. You know, you brought up Snowflake, I get this vision of the data cloud, which is, you know data. I think we're going to be rethinking Ajay, data architectures in the coming decade where data stays where it belongs, it's distributed and you're providing access. Like you said, you're separating the data from the applications. Applications as we talked about with Fadzi, much more portable. So, it's really the last 10 years it'd be different than the next 10 years ago Ajay. >> Definitely, I think the people cost reduction is used. Gone are the days where you needed to have a dozen people governing, managing byte policies to data. A lot of that repetitive work, those tasks can be in part automated. We're seen examples in insurance where reduced teams of 15 people working in the back office, trying to apply security controls, compliance down to just a couple of people who are looking at the exceptions that don't fit. And that's really important because maybe two years ago the emphasis was on regulatory compliance of data with policies such as GDPR and CCPA. Last year, very much the economic effect to reduce head counts and enterprises running lean looking to reduce that cost. This year, we can see that already some of the more proactive companies are looking at initiatives, such as net zero emissions. How they use data to understand how they can become more, have a better social impact and using data to drive that. And that's across all of their operations and supply chain. So, those regulatory compliance issues that might have been external. We see similar patterns emerging for internal initiatives that are benefiting that environment, social impact, and of course costs. >> Great perspectives. Jeff Hammerbacher once famously said, the best minds of my generation are trying to get people to click on ads and Ajay those examples that you just gave of, you know social good and moving things forward are really critical. And I think that's where data is going to have the biggest societal impact. Okay guys, great conversation. Thanks so much for coming to the program. Really appreciate your time. >> Thank you. >> Thank you so much, Dave. >> Keep it right there, for more insight and conversation around creating a resilient digital business model. You're watching theCube. (soft music)

>> Announcer: From around the globe, it's theCUBE presenting Enterprise Digital Resilience on Hybrid and multicloud brought to you by io/tahoe >> Hello everyone, and welcome to our continuing series covering data automation brought to you by io/tahoe. Today we're going to look at how to ensure enterprise resilience for hybrid and multicloud, let's welcome in Ajay Vohora who's the CEO of io/tahoe Ajay, always good to see you again, thanks for coming on. >> Great to be back David, pleasure. >> And he's joined by Fadzi Ushewokunze, who is a global principal architect for financial services, the vertical of financial services at Red Hat. He's got deep experiences in that sector. Welcome Fadzi, good to see you. >> Thank you very much. Happy to be here. >> Fadzi, let's start with you. Look, there are a lot of views on cloud and what it is. I wonder if you could explain to us how you think about what is a hybrid cloud and how it works. >> Sure, Yeah. So, a hybrid cloud is an IT architecture that incorporates some degree of workload portability, orchestration and management across multiple clouds. Those clouds could be private clouds or public clouds or even your own data centers. And how does it all work? It's all about secure interconnectivity and on demand allocation of resources across clouds. And separate clouds can become hybrid when you're seamlessly interconnected. And it is that interconnectivity that allows the workloads to be moved and how management can be unified and orchestration can work. And how well you have these interconnections has a direct impact of how well your hybrid cloud will work. >> Okay, so well Fadzi, staying with you for a minute. So, in the early days of cloud that term private cloud was thrown around a lot. But it often just meant virtualization of an on-prem system and a network connection to the public cloud. Let's bring it forward. What, in your view does a modern hybrid cloud architecture look like? >> Sure, so, for modern hybrid clouds we see that teams or organizations need to focus on the portability of applications across clouds. That's very important, right. And when organizations build applications they need to build and deploy these applications as a small collections of independently loosely coupled services. And then have those things run on the same operating system, which means in other words, running it all Linux everywhere and building cloud native applications and being able to manage it and orchestrate these applications with platforms like Kubernetes or Red Hat OpenShift, for example. >> Okay, so, Fadzi that's definitely different from building a monolithic application that's fossilized and doesn't move. So, what are the challenges for customers, you know, to get to that modern cloud is as you've just described it as it skillsets, is it the ability to leverage things like containers? What's your View there? >> So, I mean, from what we've seen around the industry especially around financial services where I spend most of my time. We see that the first thing that we see is management, right. Now, because you have all these clouds, you know, all these applications. You have a massive array of connections, of interconnections. You also have massive array of integrations portability and resource allocation as well. And then orchestrating all those different moving pieces things like storage networks. Those are really difficult to manage, right? So, management is the first challenge. The second one is workload placement. Where do you place this cloud? How do you place these cloud native operations? Do you, what do you keep on site on prem and what do you put in the cloud? That is the other challenge. The major one, the third one is security. Security now becomes the key challenge and concern for most customers. And we're going to talk about how to address that. >> Yeah, we're definitely going to dig into that. Let's bring Ajay into the conversation. Ajay, you know, you and I have talked about this in the past. One of the big problems that virtually every company face is data fragmentation. Talk a little bit about how io/tahoe, unifies data across both traditional systems, legacy systems and it connects to these modern IT environments. >> Yeah, sure Dave. I mean, a Fadzi just nailed it there. It used to be about data, the volume of data and the different types of data, but as applications become more connected and interconnected the location of that data really matters. How we serve that data up to those apps. So, working with Red Hat and our partnership with Red Hat. Being able to inject our data discovery machine learning into these multiple different locations. whether it be an AWS or an IBM cloud or a GCP or on prem. Being able to automate that discovery and pulling that single view of where is all my data, then allows the CIO to manage cost. They can do things like, one, I keep the data where it is, on premise or in my Oracle cloud or in my IBM cloud and connect the application that needs to feed off that data. And the way in which we do that is machine learning that learns over time as it recognizes different types of data, applies policies to classify that data and brings it all together with automation. >> Right, and one of the big themes that we've talked about this on earlier episodes is really simplification, really abstracting a lot of that heavy lifting away. So, we can focus on things Ajay, as you just mentioned. I mean, Fadzi, one of the big challenges that of course we all talk about is governance across these disparate data sets. I'm curious as your thoughts how does Red Hat really think about helping customers adhere to corporate edicts and compliance regulations? Which of course are particularly acute within financial services. >> Oh yeah, yes. So, for banks and payment providers like you've just mentioned there. Insurers and many other financial services firms, you know they have to adhere to a standard such as say a PCI DSS. And in Europe you've got the GDPR, which requires stringent tracking, reporting, documentation and, you know for them to, to remain in compliance. And the way we recommend our customers to address these challenges is by having an automation strategy, right. And that type of strategy can help you to improve the security on compliance of of your organization and reduce the risk out of the business, right. And we help organizations build security and compliance from the start with our consulting services, residencies. We also offer courses that help customers to understand how to address some of these challenges. And there's also, we help organizations build security into their applications with our open source middleware offerings and even using a platform like OpenShift, because it allows you to run legacy applications and also containerized applications in a unified platform. Right, and also that provides you with, you know with the automation and the tooling that you need to continuously monitor, manage and automate the systems for security and compliance purposes. >> Ajay, anything, any color you could add to this conversation? >> Yeah, I'm pleased Fadzi brought up OpenShift. I mean we're using OpenShift to be able to take that security application of controls to the data level and it's all about context. So, understanding what data is there, being able to assess it to say, who should have access to it, which application permission should be applied to it. That's a great combination of Red Hat and io/tahoe. >> Fadzi, what about multi-cloud? Doesn't that complicate the situation even further, maybe you could talk about some of the best practices to apply automation across not only hybrid cloud, but multi-cloud as well. >> Yeah, sure, yeah. So, the right automation solution, you know can be the difference between, you know cultivating an automated enterprise or automation carries. And some of the recommendations we give our clients is to look for an automation platform that can offer the first thing is complete support. So, that means have an automation solution that provides, you know, promotes IT availability and reliability with your platform so that, you can provide enterprise grade support, including security and testing integration and clear roadmaps. The second thing is vendor interoperability in that, you are going to be integrating multiple clouds. So, you're going to need a solution that can connect to multiple clouds seamlessly, right? And with that comes the challenge of maintainability. So, you're going to need to look into a automation solution that is easy to learn or has an easy learning curve. And then, the fourth idea that we tell our customers is scalability. In the hybrid cloud space, scale is the big, big deal here. And you need to deploy an automation solution that can span across the whole enterprise in a consistent manner, right. And then also that allows you finally to integrate the multiple data centers that you have. >> So, Ajay, I mean, this is a complicated situation for if a customer has to make sure things work on AWS or Azure or Google. They're going to spend all their time doing that. What can you add to really just simplify that multi-cloud and hybrid cloud equation. >> Yeah, I can give a few customer examples here. One being a manufacturer that we've worked with to drive that simplification. And the real bonuses for them has been a reduction in cost. We worked with them late last year to bring the cost spend down by $10 million in 2021. So, they could hit that reduced budget. And, what we brought to that was the ability to deploy using OpenShift templates into their different environments, whether it was on premise or in, as you mentioned, AWS. They had GCP as well for their marketing team and across those different platforms, being able to use a template, use prebuilt scripts to get up and running and catalog and discover that data within minutes. It takes away the legacy of having teams of people having to jump on workshop calls. And I know we're all on a lot of teams zoom calls. And in these current times. They're just simply using enough hours of the day to manually perform all of this. So, yeah, working with Red Hat, applying machine learning into those templates, those little recipes that we can put that automation to work regardless which location the data's in allows us to pull that unified view together. >> Great, thank you. Fadzi, I want to come back to you. So, the early days of cloud you're in the Big Apple, you know financial services really well. Cloud was like an evil word and within financial services, and obviously that's changed, it's evolved. We talk about the pandemic has even accelerated that. And when you really dug into it, when you talk to customers about their experiences with security in the cloud, it was not that it wasn't good, it was great, whatever, but it was different. And there's always this issue of skill, lack of skills and multiple tools, set up teams. are really overburdened. But in the cloud requires, you know, new thinking you've got the shared responsibility model. You've got to obviously have specific corporate, you know requirements and compliance. So, this is even more complicated when you introduce multiple clouds. So, what are the differences that you can share from your experiences running on a sort of either on prem or on a mono cloud or, you know, versus across clouds? What, do you suggest there? >> Sure, you know, because of these complexities that you have explained here mixed configurations and the inadequate change control are the top security threats. So, human error is what we want to avoid, because as you know, as your clouds grow with complexity then you put humans in the mix. Then the rate of errors is going to increase and that is going to expose you to security threats. So, this is where automation comes in, because automation will streamline and increase the consistency of your infrastructure management also application development and even security operations to improve in your protection compliance and change control. So, you want to consistently configure resources according to a pre-approved, you know, pre-approved policies and you want to proactively maintain them in a repeatable fashion over the whole life cycle. And then, you also want to rapidly the identify system that require patches and reconfiguration and automate that process of patching and reconfiguring. So that, you don't have humans doing this type of thing, And you want to be able to easily apply patches and change assistance settings according to a pre-defined base like I explained before, you know with the pre-approved policies. And also you want ease of auditing and troubleshooting, right. And from a Red Hat perspective we provide tools that enable you to do this. We have, for example a tool called Ansible that enables you to automate data center operations and security and also deployment of applications. And also OpenShift itself, it automates most of these things and obstruct the human beings from putting their fingers and causing, you know potentially introducing errors, right. Now, in looking into the new world of multiple clouds and so forth. The differences that we're seeing here between running a single cloud or on prem is three main areas, which is control, security and compliance, right. Control here, it means if you're on premise or you have one cloud you know, in most cases you have control over your data and your applications, especially if you're on prem. However, if you're in the public cloud, there is a difference that the ownership it is still yours, but your resources are running on somebody else's or the public clouds, EWS and so forth infrastructure. So, people that are going to do these need to really, especially banks and governments need to be aware of the regulatory constraints of running those applications in the public cloud. And we also help customers rationalize some of these choices. And also on security, you will see that if you're running on premises or in a single cloud you have more control, especially if you're on prem. You can control the sensitive information that you have. However, in the cloud, that's a different situation especially from personal information of employees and things like that. You need to be really careful with that. And also again, we help you rationalize some of those choices. And then, the last one is compliance. As well, you see that if you're running on prem on single cloud, regulations come into play again, right? And if you're running on prem, you have control over that. You can document everything, you have access to everything that you need, but if you're going to go to the public cloud again, you need to think about that. We have automation and we have standards that can help you you know, address some of these challenges. >> So, that's really strong insights, Fadzi. I mean, first of all Ansible has a lot of market momentum, you know, Red Hat's done a really good job with that acquisition. Your point about repeatability is critical, because you can't scale otherwise. And then, that idea you're putting forth about control, security and compliance. It's so true, I called it the shared responsibility model. And there was a lot of misunderstanding in the early days of cloud. I mean, yeah, maybe AWS is going to physically secure the you know, the S3, but in the bucket but we saw so many misconfigurations early on. And so it's key to have partners that really understand this stuff and can share the experiences of other clients. So, this all sounds great. Ajay, you're sharp, financial background. What about the economics? You know, our survey data shows that security it's at the top of the spending priority list, but budgets are stretched thin. I mean, especially when you think about the work from home pivot and all the areas that they had to, the holes that they had to fill there, whether it was laptops, you know, new security models, et cetera. So, how to organizations pay for this? What's the business case look like in terms of maybe reducing infrastructure costs, so I can pay it forward or there's a there's a risk reduction angle. What can you share there? >> Yeah, I mean, that perspective I'd like to give here is not being multi-cloud as multi copies of an application or data. When I think back 20 years, a lot of the work in financial services I was looking at was managing copies of data that were feeding different pipelines, different applications. Now, what we're seeing at io/tahoe a lot of the work that we're doing is reducing the number of copies of that data. So that, if I've got a product lifecycle management set of data, if I'm a manufacturer I'm just going to keep that at one location. But across my different clouds, I'm going to have best of breed applications developed in-house, third parties in collaboration with my supply chain, connecting securely to that single version of the truth. What I'm not going to do is to copy that data. So, a lot of what we're seeing now is that interconnectivity using applications built on Kubernetes that are decoupled from the data source. That allows us to reduce those copies of data within that you're gaining from a security capability and resilience, because you're not leaving yourself open to those multiple copies of data. And with that come cost of storage and a cost to compute. So, what we're saying is using multi-cloud to leverage the best of what each cloud platform has to offer. And that goes all the way to Snowflake and Heroku on a cloud managed databases too. >> Well and the people cost too as well. When you think about, yes, the copy creep. But then, you know, when something goes wrong a human has to come in and figure it out. You know, you brought up Snowflake, I get this vision of the data cloud, which is, you know data. I think we're going to be rethinking Ajay, data architectures in the coming decade where data stays where it belongs, it's distributed and you're providing access. Like you said, you're separating the data from the applications. Applications as we talked about with Fadzi, much more portable. So, it's really the last 10 years it'd be different than the next 10 years ago Ajay. >> Definitely, I think the people cost reduction is used. Gone are the days where you needed to have a dozen people governing, managing byte policies to data. A lot of that repetitive work, those tasks can be in part automated. We're seen examples in insurance where reduced teams of 15 people working in the back office, trying to apply security controls, compliance down to just a couple of people who are looking at the exceptions that don't fit. And that's really important because maybe two years ago the emphasis was on regulatory compliance of data with policies such as GDPR and CCPA. Last year, very much the economic effect to reduce head counts and enterprises running lean looking to reduce that cost. This year, we can see that already some of the more proactive companies are looking at initiatives, such as net zero emissions. How they use data to understand how they can become more, have a better social impact and using data to drive that. And that's across all of their operations and supply chain. So, those regulatory compliance issues that might have been external. We see similar patterns emerging for internal initiatives that are benefiting that environment, social impact, and of course costs. >> Great perspectives. Jeff Hammerbacher once famously said, the best minds of my generation are trying to get people to click on ads and Ajay those examples that you just gave of, you know social good and moving things forward are really critical. And I think that's where data is going to have the biggest societal impact. Okay guys, great conversation. Thanks so much for coming to the program. Really appreciate your time. >> Thank you. >> Thank you so much, Dave. >> Keep it right there, for more insight and conversation around creating a resilient digital business model. You're watching theCube. (soft music)

>> Announcer: From around the globe, it's theCUBE presenting Enterprise Digital Resilience on Hybrid and multicloud brought to you by io/tahoe >> Hello everyone, and welcome to our continuing series covering data automation brought to you by io/tahoe. Today we're going to look at how to ensure enterprise resilience for hybrid and multicloud, let's welcome in Ajay Vohora who's the CEO of io/tahoe Ajay, always good to see you again, thanks for coming on. >> Great to be back David, pleasure. >> And he's joined by Fadzi Ushewokunze, who is a global principal architect for financial services, the vertical of financial services at Red Hat. He's got deep experiences in that sector. Welcome Fadzi, good to see you. >> Thank you very much. Happy to be here. >> Fadzi, let's start with you. Look, there are a lot of views on cloud and what it is. I wonder if you could explain to us how you think about what is a hybrid cloud and how it works. >> Sure, Yeah. So, a hybrid cloud is an IT architecture that incorporates some degree of workload portability, orchestration and management across multiple clouds. Those clouds could be private clouds or public clouds or even your own data centers. And how does it all work? It's all about secure interconnectivity and on demand allocation of resources across clouds. And separate clouds can become hybrid when you're seamlessly interconnected. And it is that interconnectivity that allows the workloads to be moved and how management can be unified and orchestration can work. And how well you have these interconnections has a direct impact of how well your hybrid cloud will work. >> Okay, so well Fadzi, staying with you for a minute. So, in the early days of cloud that term private cloud was thrown around a lot. But it often just meant virtualization of an on-prem system and a network connection to the public cloud. Let's bring it forward. What, in your view does a modern hybrid cloud architecture look like? >> Sure, so, for modern hybrid clouds we see that teams or organizations need to focus on the portability of applications across clouds. That's very important, right. And when organizations build applications they need to build and deploy these applications as a small collections of independently loosely coupled services. And then have those things run on the same operating system, which means in other words, running it all Linux everywhere and building cloud native applications and being able to manage it and orchestrate these applications with platforms like Kubernetes or Red Hat OpenShift, for example. >> Okay, so, Fadzi that's definitely different from building a monolithic application that's fossilized and doesn't move. So, what are the challenges for customers, you know, to get to that modern cloud is as you've just described it as it skillsets, is it the ability to leverage things like containers? What's your View there? >> So, I mean, from what we've seen around the industry especially around financial services where I spend most of my time. We see that the first thing that we see is management, right. Now, because you have all these clouds, you know, all these applications. You have a massive array of connections, of interconnections. You also have massive array of integrations portability and resource allocation as well. And then orchestrating all those different moving pieces things like storage networks. Those are really difficult to manage, right? So, management is the first challenge. The second one is workload placement. Where do you place this cloud? How do you place these cloud native operations? Do you, what do you keep on site on prem and what do you put in the cloud? That is the other challenge. The major one, the third one is security. Security now becomes the key challenge and concern for most customers. And we're going to talk about how to address that. >> Yeah, we're definitely going to dig into that. Let's bring Ajay into the conversation. Ajay, you know, you and I have talked about this in the past. One of the big problems that virtually every company face is data fragmentation. Talk a little bit about how io/tahoe, unifies data across both traditional systems, legacy systems and it connects to these modern IT environments. >> Yeah, sure Dave. I mean, a Fadzi just nailed it there. It used to be about data, the volume of data and the different types of data, but as applications become more connected and interconnected the location of that data really matters. How we serve that data up to those apps. So, working with Red Hat and our partnership with Red Hat. Being able to inject our data discovery machine learning into these multiple different locations. whether it be an AWS or an IBM cloud or a GCP or on prem. Being able to automate that discovery and pulling that single view of where is all my data, then allows the CIO to manage cost. They can do things like, one, I keep the data where it is, on premise or in my Oracle cloud or in my IBM cloud and connect the application that needs to feed off that data. And the way in which we do that is machine learning that learns over time as it recognizes different types of data, applies policies to classify that data and brings it all together with automation. >> Right, and one of the big themes that we've talked about this on earlier episodes is really simplification, really abstracting a lot of that heavy lifting away. So, we can focus on things Ajay, as you just mentioned. I mean, Fadzi, one of the big challenges that of course we all talk about is governance across these disparate data sets. I'm curious as your thoughts how does Red Hat really think about helping customers adhere to corporate edicts and compliance regulations? Which of course are particularly acute within financial services. >> Oh yeah, yes. So, for banks and payment providers like you've just mentioned there. Insurers and many other financial services firms, you know they have to adhere to a standard such as say a PCI DSS. And in Europe you've got the GDPR, which requires stringent tracking, reporting, documentation and, you know for them to, to remain in compliance. And the way we recommend our customers to address these challenges is by having an automation strategy, right. And that type of strategy can help you to improve the security on compliance of of your organization and reduce the risk out of the business, right. And we help organizations build security and compliance from the start with our consulting services, residencies. We also offer courses that help customers to understand how to address some of these challenges. And there's also, we help organizations build security into their applications with our open source middleware offerings and even using a platform like OpenShift, because it allows you to run legacy applications and also containerized applications in a unified platform. Right, and also that provides you with, you know with the automation and the tooling that you need to continuously monitor, manage and automate the systems for security and compliance purposes. >> Ajay, anything, any color you could add to this conversation? >> Yeah, I'm pleased Fadzi brought up OpenShift. I mean we're using OpenShift to be able to take that security application of controls to the data level and it's all about context. So, understanding what data is there, being able to assess it to say, who should have access to it, which application permission should be applied to it. That's a great combination of Red Hat and io/tahoe. >> Fadzi, what about multi-cloud? Doesn't that complicate the situation even further, maybe you could talk about some of the best practices to apply automation across not only hybrid cloud, but multi-cloud as well. >> Yeah, sure, yeah. So, the right automation solution, you know can be the difference between, you know cultivating an automated enterprise or automation carries. And some of the recommendations we give our clients is to look for an automation platform that can offer the first thing is complete support. So, that means have an automation solution that provides, you know, promotes IT availability and reliability with your platform so that, you can provide enterprise grade support, including security and testing integration and clear roadmaps. The second thing is vendor interoperability in that, you are going to be integrating multiple clouds. So, you're going to need a solution that can connect to multiple clouds seamlessly, right? And with that comes the challenge of maintainability. So, you're going to need to look into a automation solution that is easy to learn or has an easy learning curve. And then, the fourth idea that we tell our customers is scalability. In the hybrid cloud space, scale is the big, big deal here. And you need to deploy an automation solution that can span across the whole enterprise in a consistent manner, right. And then also that allows you finally to integrate the multiple data centers that you have. >> So, Ajay, I mean, this is a complicated situation for if a customer has to make sure things work on AWS or Azure or Google. They're going to spend all their time doing that. What can you add to really just simplify that multi-cloud and hybrid cloud equation. >> Yeah, I can give a few customer examples here. One being a manufacturer that we've worked with to drive that simplification. And the real bonuses for them has been a reduction in cost. We worked with them late last year to bring the cost spend down by $10 million in 2021. So, they could hit that reduced budget. And, what we brought to that was the ability to deploy using OpenShift templates into their different environments, whether it was on premise or in, as you mentioned, AWS. They had GCP as well for their marketing team and across those different platforms, being able to use a template, use prebuilt scripts to get up and running and catalog and discover that data within minutes. It takes away the legacy of having teams of people having to jump on workshop calls. And I know we're all on a lot of teams zoom calls. And in these current times. They're just simply using enough hours of the day to manually perform all of this. So, yeah, working with Red Hat, applying machine learning into those templates, those little recipes that we can put that automation to work regardless which location the data's in allows us to pull that unified view together. >> Great, thank you. Fadzi, I want to come back to you. So, the early days of cloud you're in the Big Apple, you know financial services really well. Cloud was like an evil word and within financial services, and obviously that's changed, it's evolved. We talk about the pandemic has even accelerated that. And when you really dug into it, when you talk to customers about their experiences with security in the cloud, it was not that it wasn't good, it was great, whatever, but it was different. And there's always this issue of skill, lack of skills and multiple tools, set up teams. are really overburdened. But in the cloud requires, you know, new thinking you've got the shared responsibility model. You've got to obviously have specific corporate, you know requirements and compliance. So, this is even more complicated when you introduce multiple clouds. So, what are the differences that you can share from your experiences running on a sort of either on prem or on a mono cloud or, you know, versus across clouds? What, do you suggest there? >> Sure, you know, because of these complexities that you have explained here mixed configurations and the inadequate change control are the top security threats. So, human error is what we want to avoid, because as you know, as your clouds grow with complexity then you put humans in the mix. Then the rate of errors is going to increase and that is going to expose you to security threats. So, this is where automation comes in, because automation will streamline and increase the consistency of your infrastructure management also application development and even security operations to improve in your protection compliance and change control. So, you want to consistently configure resources according to a pre-approved, you know, pre-approved policies and you want to proactively maintain them in a repeatable fashion over the whole life cycle. And then, you also want to rapidly the identify system that require patches and reconfiguration and automate that process of patching and reconfiguring. So that, you don't have humans doing this type of thing, And you want to be able to easily apply patches and change assistance settings according to a pre-defined base like I explained before, you know with the pre-approved policies. And also you want ease of auditing and troubleshooting, right. And from a Red Hat perspective we provide tools that enable you to do this. We have, for example a tool called Ansible that enables you to automate data center operations and security and also deployment of applications. And also OpenShift itself, it automates most of these things and obstruct the human beings from putting their fingers and causing, you know potentially introducing errors, right. Now, in looking into the new world of multiple clouds and so forth. The differences that we're seeing here between running a single cloud or on prem is three main areas, which is control, security and compliance, right. Control here, it means if you're on premise or you have one cloud you know, in most cases you have control over your data and your applications, especially if you're on prem. However, if you're in the public cloud, there is a difference that the ownership it is still yours, but your resources are running on somebody else's or the public clouds, EWS and so forth infrastructure. So, people that are going to do these need to really, especially banks and governments need to be aware of the regulatory constraints of running those applications in the public cloud. And we also help customers rationalize some of these choices. And also on security, you will see that if you're running on premises or in a single cloud you have more control, especially if you're on prem. You can control the sensitive information that you have. However, in the cloud, that's a different situation especially from personal information of employees and things like that. You need to be really careful with that. And also again, we help you rationalize some of those choices. And then, the last one is compliance. As well, you see that if you're running on prem on single cloud, regulations come into play again, right? And if you're running on prem, you have control over that. You can document everything, you have access to everything that you need, but if you're going to go to the public cloud again, you need to think about that. We have automation and we have standards that can help you you know, address some of these challenges. >> So, that's really strong insights, Fadzi. I mean, first of all Ansible has a lot of market momentum, you know, Red Hat's done a really good job with that acquisition. Your point about repeatability is critical, because you can't scale otherwise. And then, that idea you're putting forth about control, security and compliance. It's so true, I called it the shared responsibility model. And there was a lot of misunderstanding in the early days of cloud. I mean, yeah, maybe AWS is going to physically secure the you know, the S3, but in the bucket but we saw so many misconfigurations early on. And so it's key to have partners that really understand this stuff and can share the experiences of other clients. So, this all sounds great. Ajay, you're sharp, financial background. What about the economics? You know, our survey data shows that security it's at the top of the spending priority list, but budgets are stretched thin. I mean, especially when you think about the work from home pivot and all the areas that they had to, the holes that they had to fill there, whether it was laptops, you know, new security models, et cetera. So, how to organizations pay for this? What's the business case look like in terms of maybe reducing infrastructure costs, so I can pay it forward or there's a there's a risk reduction angle. What can you share there? >> Yeah, I mean, that perspective I'd like to give here is not being multi-cloud as multi copies of an application or data. When I think back 20 years, a lot of the work in financial services I was looking at was managing copies of data that were feeding different pipelines, different applications. Now, what we're seeing at io/tahoe a lot of the work that we're doing is reducing the number of copies of that data. So that, if I've got a product lifecycle management set of data, if I'm a manufacturer I'm just going to keep that at one location. But across my different clouds, I'm going to have best of breed applications developed in-house, third parties in collaboration with my supply chain, connecting securely to that single version of the truth. What I'm not going to do is to copy that data. So, a lot of what we're seeing now is that interconnectivity using applications built on Kubernetes that are decoupled from the data source. That allows us to reduce those copies of data within that you're gaining from a security capability and resilience, because you're not leaving yourself open to those multiple copies of data. And with that come cost of storage and a cost to compute. So, what we're saying is using multi-cloud to leverage the best of what each cloud platform has to offer. And that goes all the way to Snowflake and Heroku on a cloud managed databases too. >> Well and the people cost too as well. When you think about, yes, the copy creep. But then, you know, when something goes wrong a human has to come in and figure it out. You know, you brought up Snowflake, I get this vision of the data cloud, which is, you know data. I think we're going to be rethinking Ajay, data architectures in the coming decade where data stays where it belongs, it's distributed and you're providing access. Like you said, you're separating the data from the applications. Applications as we talked about with Fadzi, much more portable. So, it's really the last 10 years it'd be different than the next 10 years ago Ajay. >> Definitely, I think the people cost reduction is used. Gone are the days where you needed to have a dozen people governing, managing byte policies to data. A lot of that repetitive work, those tasks can be in part automated. We're seen examples in insurance where reduced teams of 15 people working in the back office, trying to apply security controls, compliance down to just a couple of people who are looking at the exceptions that don't fit. And that's really important because maybe two years ago the emphasis was on regulatory compliance of data with policies such as GDPR and CCPA. Last year, very much the economic effect to reduce head counts and enterprises running lean looking to reduce that cost. This year, we can see that already some of the more proactive companies are looking at initiatives, such as net zero emissions. How they use data to understand how they can become more, have a better social impact and using data to drive that. And that's across all of their operations and supply chain. So, those regulatory compliance issues that might have been external. We see similar patterns emerging for internal initiatives that are benefiting that environment, social impact, and of course costs. >> Great perspectives. Jeff Hammerbacher once famously said, the best minds of my generation are trying to get people to click on ads and Ajay those examples that you just gave of, you know social good and moving things forward are really critical. And I think that's where data is going to have the biggest societal impact. Okay guys, great conversation. Thanks so much for coming to the program. Really appreciate your time. >> Thank you. >> Thank you so much, Dave. >> Keep it right there, for more insight and conversation around creating a resilient digital business model. You're watching theCube. (soft music)

>> Narrator: Live from New York, it's theCube! Covering AWS Global Summit 2019, brought to you by Amazon Web Services. >> Welcome back, we're here at the Javits Center in New York City for AWS Summit, I'm Stu Miniman, my cohost is Corey Quinn and happy to welcome to the program Scott Mullins, who's the head of Worldwide Financial Services Business Development with Amazon Web Services based here in The Big Apple, thanks so much for joining us. >> Thanks for having me, Stu, thanks for having me, Corey. >> All right so we had obviously financial services big location here in New York City. We just had FINRA on our program, had a great conversation about how they're using AWS for their environments, but give us a thumbnail if you will about your business, your customers and what you're seeing there. >> Sure, we're working with financial institutions all the way from the newest FinTech startups, all the way to organizations like FINRA, the largest exchanges and brokers dealers like Nasdaq, as well as insurers and the largest banks. And I've been here for five years and in that time period I actually went from being a customer speaking at the AWS Summit here in the Javits Center on stage like Steve Randich was today to watching more and more financial institutions coming forward, talking about their use in the cloud. >> Yeah before we get into technology, one of the biggest trends of moving to cloud is I'm moving from CapEx more to OpEx and oh my gosh there's uncertainty because I'm not locking in some massive contract that I'm paying up front or depreciating over five years but I've got flexibility and things are going to change. I'm curious what you're seeing as the financial pieces of how people both acquire and keep on the books what they're doing. >> Yeah it can be a little bit different, right, then what most people are used to. They're used to kind of that muscle memory and that rhythm of how you procured technology in the past and there can be a stage of adjustment, but cost isn't really the thing that people I think look to the most when it comes to cloud today, it's all about agility and FINRA is a great example. Steve has talked about over and over again over the last several years how they were able to gain such business agility and actually to do more, the fact that they're now processing 155 billion market events every night and able to run all their surveillance routines. That's really indicative of the value that people are looking for. Being able to actually get products to market faster and reducing development cycles from 18 months to three months, like Allianz, one of our customers over in Europe has been able to do. Being able to go faster I think actually trumps cost from the standpoint of what that biggest value driver that we're seeing our customers going after in financial services. >> We're starting to see such a tremendous difference as far as the people speaking at these keynotes. Once upon a time you had Netflix and folks like that on stage telling a story about how they're using cloud to achieve all these amazing things, but when you take a step back and start blinking a little bit, they fundamentally stream movies and yes, produce some awesome original content. With banks and other financial institutions if the ATM starts spitting out the wrong number, that's a different point on the spectrum of are people going to riot in the street. I'm not saying it's further along, people really like their content but it's still a different use case with a different risk profile. Getting serious companies that have world shaking impact to trust public cloud took time and we're seeing it with places like FINRA, Capital One has been very active as far as evangelizing their use of cloud. It's just been transformative. What does that look like, from being a part of that? >> Well you know it's interesting, so you know you just said it, financial services is the business of risk management. And so to get more and when you see more and more of these financial institutions coming forward and talking about their use of cloud, what that really equates to is comfort, they've got that muscle memory now, they've probably been working with us in some way, shape or form for some great period of time and so if you look at last year, you had Dean Del Vecchio from Guardian Life Insurance come out on stage at Reinvent and say to the crowd "Hey we're a 158 year old insurance company but we've now closed our data center and we're fully on AWS and we've completed the transformation of our organization". The year before you saw Goldman Sachs walk out and say "Yeah we've been working with AWS for about four years now and we're actually using them for some very interesting use cases within Goldman Sachs". And so typically what you've seen is that over the course of about a two year to sometimes a four year time period, you've got institutions that are working deeply with us, but they're not talking about it. They're gaining that muscle memory, they're putting those first use cases to begin to scale that work up and then when they're ready man, they're ready to talk about it and they're excited to talk about it. What's interesting though is today we're having this same summit that we're having here in Cape Town in Africa and we had a customer, Old Mutual, who's one of the biggest insurers there, they just started working with us in earnest back in May and they were on stage today, so you're seeing that actually beginning to happen a lot quicker, where people are building that muscle memory faster and they're much more eager to talk about it. You're going to see that trend I think continue in financial services over the next few years so I'm very excited for future summits as well as Reinvent because the stories that we're going to see are going to come faster. You're going to see more use cases that go a lot deeper in the industry and you're going to see it covering a lot more of the industry. >> It's very much not, IT is no longer what people think of in terms of Tech companies in San Francisco building products. It's banks, it's health care and these companies are transitioning to become technology companies but when your entire, as you mentioned, the entire industry becomes about risk management, it's challenging sometimes to articulate things when you're not both on the same page. I was working with a financial partner years ago at a company I worked for and okay they're a financial institution, they're ready to sign off on this but before that they'd like to tour US East one first and validate that things are as we say they are. The answer is yeah me too, sadly, you folks have never bothered to invite me to tour an active AZ, maybe next year. It's challenging to I guess meet people where they are and speak the right language, the right peace for a long time. >> And that's why you see us have a financial services team in the first place, right? Because your financial services or health care or any of the other industries, they're very unique and they have a very specific language and so we've been very focused on making sure that we speak that language that we have an understanding of what that industry entails and what's important to that industry because as you know Amazon's a very customer obsessed organization and we want to work backwards from our customers and so it's been very important for us to actually speak that language and be able to translate that to our service teams to say hey this is important to financial services and this is why, here's the context for that. I think as we've continued to see more and more financial institutions take on that technology company mindset, I'm a technology company that happens to run a bank or happens to run an exchange company or happens to run an insurance business, it's actually been easier to talk to them about the services that we offer because now they have that mindset, they're moving more towards DevOps and moving more towards agile. And so it's been really easy to actually communicate hey, here are the appropriate changes you have to make, here's how you evolve governance, here's how you address security and compliance and the different levels of resiliency that actually improve from the standpoint of using these services. >> All right so Scott, back before I did this, I worked for some large technology suppliers and there were some groups on Wall Street that have huge IT budgets and IT staffs and actually were very cutting edge in what they were building, in what they were doing and very proud of their IT knowledge, and they were like, they have some of the smartest people in the industry and they spend a ton of money because they need an edge. Talking about transactions on stock markets, if I can translate milliseconds into millions of dollars if I can act faster. So you know, those companies, how are they moving along to do the I need to build it myself and differentiate myself because of my IT versus hey I can now have access to all the services out there because you're offering them with new ones every day, but geez how do I differentiate myself if everybody can use some of these same tools. >> So that's my background as well and so you go back that and milliseconds matter, milliseconds are money, right? When it comes to trading and actually building really bespoke applications on bespoke infrastructure. So I think what we're seeing from a transitional perspective is that you still have that mindset where hey we're really good at technology, we're really good at building applications. But now it's a new toolkit, you have access to a completely new toolkit. It's almost like The Matrix, you know that scene where Neo steps into that white room and hey says "I need this" and then the shelves just show up, that's kind how it is in the cloud, you actually have the ability to leverage the latest and greatest technologies at your fingertips when you want to build and I think that's something that's been a really compelling thing for financial institutions where you don't have to wait to get infrastructure provisioned for you. Before I worked for AWS, I worked for large financial institutions as well and when we had major projects that we had to do that sometimes had a regulatory implication, we were told by our infrastructure team hey that's going to be six months before we can actually get your dev environment built so you can actually begin to develop what you need. And actually we had to respond within about thirty days and so you had a mismatch there. With the cloud you can provision infrastructure easily and you have an access to an array of services that you can use to build immediately. And that means value, that means time to market, that means time to answering questions from customers, that means really a much faster time to answering questions from regulatory agencies and so we're seeing the adoption and the embrace of those services be very large and very significant. >> It's important to make sure that the guardrails are set appropriately, especially for a risk managed firm but once you get that in place correctly, it's an incredible boost of productivity and capability, as opposed to the old crappy way of doing governance of oh it used to take six weeks to get a server in so we're going to open a ticket now whenever you want to provision an instance and it only takes four, yay we're moving faster. It feels like there's very much a right way and a wrong way to start embracing cloud technology. >> Yeah and you know human nature is to take the run book you have today and try to apply it to tomorrow and that doesn't always work because you can use that run book and you'll get down to line four and suddenly line four doesn't exist anymore because of what's happened from a technological change perspective. Yeah I think that's why things like AWS control tower and security hub, which are those guardrails, those services that we announced recently that have gone GA. We announced them a couple of weeks ago at Reinforce in Boston. Those are really interesting to financial services customers because it really begins to help automate a lot of those compliance controls and provisioning those through control tower and then monitoring those through security hub and so you've seen us focus on how do we actually make that easier for customers to do. We know that risk management, we know that governance and controls is very important in financial services. We actually offer our customers a way to look from a country specific angle, add the different countries and the rule sets and the requirements that exist in those countries and how you map those to our controls and how you map those into your own controls and all the considerations that you have, we've got them on our public website. If you went to atlas.aws right now, that's our compliance center, you could actually pick the countries you're interested in and we'll have that mapping for you. So you'll see us continue to invest in things like that to make that much easier for customers to actually deploy quickly and to evolve those governance frameworks. >> And things like with Artifact, where it's just grab whatever compliance report you need, submit it and it's done without having to go through a laborious process. It's click button, receive compliance in some cases. >> If you're not familiar with it you can go into the AWS console and you've got Artifact right there and if you need a SOC report or you need some other type of artifact, you can just download it right there through the console, yeah it's very convenient. >> Yeah so Scott you know we talked about some of the GRC pieces in place, what are you seeing trends out there kind of globally, you know GDRP was something that was on everybody's mind over the last year or so. California has new regulations that are coming in place, so anything specific in your world or just the trends that you're seeing that might impact our environments-- >> I think that the biggest trends I would point to are data analytics, data analytics, data analytics, data analytics. And on top of that obviously machine learning. You know, data is the lifeblood of financial services, it's what makes everything go. And you can look at what's happening in this space where you've got companies like Bloomberg and Refinitiv who are making their data products available on AWS so you can get B-Pipe on AWS today, you can also get the elektron platform from Refintiv and then what people are trying to do in relation to hey I want to organize my data, I want to make it much easier to actually find value in data, both either from the standpoint of regulatory reporting, as you heard Steve talk about on stage today. FINRA is building a very large data repository that they have to from the standpoint of a regulatory perspective with CAT. Broker dealers have to actually feed the CAT and so they are also worried about here in the US, how do I actually organize my data, get all the elements I have to report to CAT together and actually do that in a very efficient way. So that's a big data analytic project. Things that are helping to make that much easier are leg formations, so we came up with leg formation last year and so you've got many financial institutions that are looking at how do you make building a data leg that much easier and then how do you layer analytics on top of that, whether it's using Amazon elastic map reduce or EMR to actually run regulatory reporting jobs or how do I begin to leverage machine learning to actually make my data analytics from a standpoint of trade surveillance or fraud detection that much more enriched and actually looking for those anomalies rather than just looking for a whole bunch of false positives. So data analytics I think is what I would point to as the biggest trend and how to actually make data more useful and how to get to data insights faster. >> On the one end it seems like there's absolutely a lot of potential in this, on the other it feels in many cases with large scale data analytics, it's we have all these tools for machine learning and the rest that we can wind up passing out to you but you need to figure out what to do with them, how to make it work and it's unclear outside of a few specific use cases and I think you've alluded to a couple of those how to take in a typical business that maybe doesn't have an enormous pile of data and start applying machine learning to it in a way that makes intelligent sense. That feels right now like a storytelling failure to some extent industry wide. We're starting to see some stories emerge but it still feels a little "Gold Rush"-y to some extent. >> Yeah I would say, and my advice would be don't try to boil the ocean or don't try to boil the data leg, meaning you want to do machine learning, you've got a great amount of earnestness about that but picture use case, really hone in on what you're trying to accomplish and work backwards from that. And we offer tooling that can be really helpful in that, you know with stage maker you can train your models and you can actually make data science available to a much broader array of people than just your data scientists. And so where we see people focusing first, is where it matters to their business. So if you've got a regulatory obligation to do surveillance or fraud detection, those are great use cases to start with. How do I enhance my existing surveillance or fraud detection, so that I'm not just wading again through a sea of false positives. How do I actually reduce that workload for a human analyst using machine learning. That's a one step up and then you can go from there, you can actually continue to work deeper into the use cases and say okay how do I treat those parameters, how do I actually look for different things that I'm used to with the rules based systems. You can also look at offering more value to customers so with next best offer with Amazon Personalize, we now have encapsulated the service that we use on the amazon.com retail site as a service that we offer to customers so you don't have to build all that tooling yourself, you can actually just consume Personalize as a service to help with those personalized recommendations for customers. >> Scott, really appreciate all the updates on your customers in the financial services industry, thanks so much for joining us. >> Happy to be here guys, thanks for having me. >> All right for Corey Quinn, I'm Stu Miniman, back with more here at AWS Summit in New York City 2019, thanks as always for watching theCube.

>> live from New York. It's the Q covering AWS Global Summit 2019. Brought to you by Amazon Web service is >> Hi and welcome to New York City, The Big Apple. I'm stupid and my co host for today is Cory Quinn, and this is eight of us. Summit New York City. It is one of the regional events that they have, but these regional events are actually tend to be bigger and more exciting than >> many companies. You know, big events not say that companies don't do good shows, but if you look, we've got 11,500 people in attendance over 120 seconds over 125. Sponsoring partners here in the ecosystem just had Werner Vogels up on stage. A number of the customers such a fin ra and Gordon, who we will have on the program on good energy, a local show it is free to attend Cory. Before >> we get into the technology, though, there's a little bit of a protest going on. Here is actually the second Amazon show in a row that this was was that Amazon re Mars, where a protester talking about I believe it was >> something around about chickens in Whole Foods. Basically, she got really close to the richest man in the world. But the protest here, it's outside, it's going and it's about ice and border control was actually a very well organized protest. Security had to take many of them out for the first least half hour of the of the keynote. Warner stopped a few times and said, Look, I'll be happy to talk to you after, >> but please let me finish. I thought he handled it, respectively. But what? What was your take? >> Very much so. And it's, I think it's an issue with There aren't too many people you'd want associate with on the other side of it, Kids in Cages is not something anyone sensible wants to endorse. The challenge that I continually have, I think, is that it's easy to have these conversations. Now is not the time. Okay, great. Typically, it's difficult to get big companies to say, and now is the time for us to address this and anything outside of very carefully worded statements. So I empathize. I really do. I mean, as a speaker myself, it's terrifying to me the idea that I could go up and have to have that level of conversation and a suddenly interrupted by people yelling at me. It's gotta be nerve wracking. Speaking to 10,000 people on its own is not easy, and having to carry that forward with something that effectively comes down to a morality question is it's gotta be tough. I have sympathy for people going through this on work on Amazon, and it's I don't know that there's a great answer right now. >> So, Cory, I know you know You are not >> deep in the government space, but you were at the public sector show there and there's always this discussion as you know Well, you're supplying the technology. While Amazon might not be providing, you know, bummers and, you know, guns. They are providing the technology underneath. Facial recognition causes a lot of concern, you know, rightfully so that make sure we understand this thing. Security products in the light. So, you know, when you have the Department of Defense and Border Control as your clients, they do open themselves up for some criticism, >> right? At some point, you have to wonder who you do business with versus who don't do business with and the historical approach. Well, as long as there are sanctions or laws preventing us from doing business with someone, we'll be open to all comers. I some level I find that incredibly compelling. In practice, the world is messy. If things were that black and white, we wouldn't have these social media content, moderation issues. It would be a very different story with a very different narrative. >> Yeah, definitely. Amazon as a whole has a platform, and they have relationships. You know, Jeff Bezos has met with, you know, the highest levels of power in this country. They've got Jay Carney. The foot was part of the Obama administration helping with policy. So absolutely with great to see Amazon, you know, take a strong statement and you know, for good is something that we're hugely a part of and therefore way want to see all the suppliers you know, having a dialogue and helping to move this >> for you. And I think the lesson that we take from it, too, is that there are multiple ways to agitate for change and protest. One is to disrupt the keynote, and I understand that it gets attention and it's valuable. But you could do that, or you can have a seat at the table and start lobbying for change, either internally or with stakeholders. But you need to it. There's a bunch of different paths to get there, and I think that I don't blame anyone who's protesting today, and I don't blame anyone who chose not to. >> All right, So let's let's let's talk now about some of the content. So, Cory lutely, you know that there there's in the Amazon ecosystem. Every day we wake up and there were multiple new announcements. A matter of fact. We're always saying, Oh my gosh, how do I keep up with all of the things happening there? Well, one of the ways we keep up with it is reading last week in a VWs, which is your newsletter. I'll do the shameless plug, you know, for much. Appreciate your telling my story, Cory, But Amazon Cloudwatch Container Insight, Amazon event bridge. You know, new developer kids fluent bid, you know, talking about the momentum of the company security databases on you know, the general adoption overall, you know, quick take for me as I love to hear you know, Werner up there talking about applications. It's not purely Oh, everything's going to live in the cloud and it'll be sun shines in unicorns and rainbows. But we understand that there's challenges here, your data and how we manage that requires, you know, >> a broad ecosystem that was the event bridge is something I would >> definitely want a drilling on because from a serverless environment, not just one thing, it's lots of different things. And how do we play between all of them? But since you do sort through and sift through all of these announcements, give us a date. It was there anything new here? Did you already know all of this because it's in your R S s feed Newsletters are you know what did grab you? >> Surprisingly, it turns out, in the weeks with you have, obviously reinvent is just a firehose torrent that no human being can wind up consuming. And you see a few releases in Santa Clara and a few in New York. But I thought I knew most of things that were coming out, and I did. I missed one that I just noticed. About two minutes we went on the air called cloudwatch anomaly detection the idea is that it uses machine learning. So someone check that off the business card of the bingo card. And at that point, you take all the cloudwatch logs and start running machine learning and look for anomalies discrepancies. In the rest it uses machine learning. But rather than go figure out what it's for, it's applied to a very specific problem and those of the A. I am l products. I like the best where it's we're solving a problem with your data for you. But riding guard rails as opposed to step one, hire $2,000,000 worth of data. Scientists Step two. We're still working on that. >> All right, so court cloudwatch actually e saw the event bridge that I mentioned, which is that event ecosystem around Lambda uh, Deepak, who we're going to have on the program that said that it was the learnings from cloudwatch that helped them to build. This may be for audience. Just give us cloudwatch. There's a lot of different products under that. Give us what you hear from your customers. You know where cloudwatch fits and, you >> know, let's start at the beginning For those who are fortune enough never to have used it. Cloudwatch is AWS is internal monitoring solution. It gathers metrics, it gathers logs, it presents them in different ways. And it has interesting bill impacts as a cloud economist. I see it an awful lot where every time you the monitoring company, walk around the Expo hall, you'll trip over 40 of those. They're all gathering their data on the infrastructure from Bob Watch and interpreting that. Now you're paying for the monitoring company and you're paying for the FBI charges against it. And I was sort of frozen in amber, more or less for a good five years or so. I wrote a bit of a hit piece late last year and had some fascinating conversations afterwards, and it hasn't aged well, they're really coming to the fore with a lot of enhancements that are valuable on it. The problem is, there's a tremendous amount of data. How do you get a signal from it? How do you look at actionable things? If you're running 10,000 instances, you're not looking at individual metrics for individualism. You care about aggregates, but you also care about observe ability. You care about drilling down into things Bernard talked about X rays distributed tracing framework today, and I think we're rapidly seeing across the board that it all ties back to events. Watch events is what's driving a lot of things like >> Event Bridge >> and the idea of an event centric architecture is really what we're trying to see Software's evolving into. >> Yeah, it's one of those things, you know, when you >> talk, you know that server list term out, their events are at the center of them. And how do I get some standardization across the industry? There's some open source groups that are trying to insert themselves and give some flexibility here. You know, when I want understand from Vin, Fridge says, Okay, it's Lambda and their ecosystem. But is this going to be a lame the only ecosystem or, well, this lay the ground work so that, yes, there are other clouds out there. You know what azure has other environment? Will this eventually be able to extend beyond this, or is this a Amazon proprietary system? Do you have any insight there? >> It's a great question. I would argue that I guess one of the taking a step back for a second. It would have to be almost irrelevant in some cases. When you start looking at server this lock in, it's not the fact that who there's this magic system only in one provider that will take my crappy code and run it for me. It's tied into the entire event ecosystem. It's tied into a bunch of primitives that do not translate very well. Now, inherently by looking What event bridge is in the fact that anyone who wants to be integrated into their applications, you absolutely could wind up with a deep native integration coming from another large, hyper scale pop provider? The only question is, will >> you great, great point. I know when I've talked to some of the surveillance ecosystem, it's that skills on understanding, you know, each environment because today, doing A W S versus doing azure, there's still a lot of difference, is there? Sure I could learn >> it, But yeah, and one of the things that I think is fascinating to is we've seen a couple attempts of this before from other start ups that are doing very similar things in open stores or trying to do something themselves. But one of the things that change this tremendously here is that this is a double us doing, that it doesn't matter what they do, what ridiculous name they give it when they want something. World generally tends to sit up and notice, just by sheer virtue of its scale and the fact that it's already built out. And you don't have to build the infrastructure yourself to run these things. If anything has a chance to start driving a cohesive standard around this, it's something coming from someone like Amazon. >> Yeah, absolutely. All right, Cory, you know, database is always a hot topic. Latest stat from Werner is, I believe it was 150,000 databases migrated. You called and >> said, Hey, why's amazon dot com on there? Jeff Faris like, Well, they have a choice. And of course, Amazon would point out they were using >> a traditional database for a long time and now have >> completely unplug the last in a >> long time. But they finally got off of a database that was produced by a law firm, and I understand the reasons behind that. But I was talking with people afterwards. Amazon does have a choice. Do they use, and if AWS wants to win them over to use their service is they have to sell them just like any other customer. And that's why it's on that slide as a customer. Now, if you're not in the ecosystem like some of us are, it looks a little disjointed of weight. C successfully sold yourself and put yourself on the slide. Well, okay, >> yes, it was actually so so the biggest thing I learned at the Amazon remarks show when >> you talk about all the fulfillment centers in the robotics and machine, learning almost everything underneath there it's got eight of us. Service is underneath it, So absolutely, it is one company. But yes, Amazon is the biggest customer of AWS. But that doesn't mean that there isn't somewhere, you know. You know, I still haven't gotten the word if they're absolutely 100% on that WS because we expect that there's some 400 sitting in the back ground running >> one of those financial service things. Maybe they finally micro did that one >> that's rather building in AWS 400. >> All right, Cory, what else you know either from the key note or from your general observations about Amazon that you want to share? >> I want to say that it's very clear that Amazon is getting an awful lot of practice at putting these events on and just tracking it here. Two year, Not just the venue. Logistics, which Okay, great. Get a bunch of people in a conference room, have a conversation. Do Aquino throw him out the end. But the way they're pacing the chinos, the way they're doing narratives, the customer stories that are getting up on stage are a lot less challenging. But then they were in years past. Where people get on stage, they seem more comfortable. It's very clear that a number of Amazon exacts not just here but another. Summits have been paying serious attention to how to speak publicly to 10,000 people once it's its own unique skill. >> Yeah, and you gotta like that, You know that. You know, the two first customers that they put on which will have on financial service is, of course, a big presence here in New York City. Gord Ash has their headquarters, you know, just a few blocks uptown from good, deep stories. Isn't you know, there there's that mixed that they did a good job. I thought of kind of cloud 10 >> one because still many customers are very early on that journey. We're not all cloud native, you know, run by the developers and everything there. But, you know, good looks of technology and the new pieces for those people that have been in a while, but still, you know, welcoming and embracing for how to get started >> and the stories we're moving up the stack to. It's not. >> We had a bunch of the >> EMS, and we put them in a different place. Okay, which is great news. Everyone starts there. But now the stories are moving into running serious regulated workloads with higher level of service is And that's great, because it's also not the far extreme Twitter for pets. We built this toy project last week when someone else fell through. And now we have to give this talk. It's very clearly something large enterprises. >> Yeah. So, Corey, last thing I want to ask you is you remember in the early days, you know, that public cloud? Oh, it was It was cheap and easy to use today. They have 200 instance types up there. You know, What does that mean for customers? You know you are a cloud economist. So need your official opinion diagnosis. >> I think it reduces the question, too, before you buy a bunch of reserve businesses. Are you on the right instance? Types. And the answer is almost certainly not just based on statistics alone. So now it's a constant state of indecision. It's rooted in an epic game of battleship between two Amazon S. V. V S. And I really hope one of the winds already so we can stop getting additional instance dives every couple of months. But so far no luck. >> So in your your your perfect world, you know what the announcement reinvented, fixes the problem. >> That's a really good question. I think that fundamentally, I don't I don't And I don't think I have any customers who care what type of incidents they're running on. They want certain resource levels. They want certain performance characteristics. But whatever you call that does not matter to them and having to commit to, though what you picked for 1 to 3 years, that's a problem. You don't have to. You can go on demand, but you're leaving 30% of the day. >> Yeah, and I love that point is actually taken notes fin rot. I want to talk to them because they say they've been three major re architectures in four years. So therefore, how did they make sure that they get the latest price performance but still get, you know, good, good economics on the outdated >> regulatory authority? I just assume they get there with audit threats when it comes time >> for renegotiating. >> All right. You're Cory Quinn. I am stupid. I mean, we have a full day here of water wall coverage from eight of US. Summit, New York City. Thank you so much for watching.

>> live from New York. It's the Q covering AWS Global Summit 2019. Brought to you by Amazon Web service is >> Hi and welcome to New York City, The Big Apple. I'm stupid and my co host for today is Cory Quinn, and this is eight of us. Summit New York City. It is one of the regional events that they have, but these regional events are actually tend to be bigger and more exciting than many companies. You know, big events not, you know, say that companies don't do good shows, but if you look, we've got 11,500 people in attendance over 120 seconds over 125. Sponsoring partners here in the ecosystem just had Werner Vogels up on stage. A number of the customers such a fin ra and Gordon, who we will have on the program on good energy, a local show it is free to attend Cory. Before we get into the technology, though, there's a little bit of a protest going on. Here is actually the second Amazon show in a row. That this was was an Amazon re Mars, where a protester talking about I believe it was something around about chickens in Whole Foods. Basically, she got really close to the richest man in the world. But the protest here, it's outside, it's going and it's about ice and border control was actually a very well organized protest. Security had to take many of them out for the first least half hour of the of the keynote. Warner stopped a few times and said, Look, I'll be happy to talk to you after, but please let me finish. I thought he handled it, respectively. But what was your take? >> Very much so. And it's, I think it's an issue with There aren't too many people you'd want to associate with. On the other side of it, kids in Cages is not something anyone sensible wants to endorse. The challenge that I continually have, I think, is that it's easy to have these conversations. Now is not the time. Okay, great. Typically, it's difficult to get big companies to say, and now is the time for us to address this in anything outside of very carefully worded statements. So I empathize. I really do. I mean, as a speaker myself, it's terrifying to me the idea that I could >> go up and >> have to have that level of conversation and a suddenly interrupted by people yelling at me. It's gotta be nerve wracking. Speaking to 10,000 people on its own is not easy, and having to carry that forward with something that effectively comes down to a morality question is it's gotta be tough. I have sympathy for people going through this on work on Amazon, and it's I don't know that there's a great answer right now. >> So, Cory, I know you know You are not deep in the government space, but you were at the public sector show there and there's always this discussion as you know Well, you're supplying the technology. While Amazon might not be providing, you know, bummers and, you know, guns. They are providing the technology underneath. Facial recognition causes a lot of concern. You rightfully so that make sure we understand this thing security products and the like. So you know, when you have the Department of Defense and Border Control as your clients, they do open themselves up >> for some criticism, right? At some point you have to wonder who you do business with versus who don't do business with and the historical approach. Well, as long as there are sanctions or laws preventing us from doing business with someone, we'll be open to all comers. I some level I find that incredibly compelling. In practice, the world is messy. If things were that black and white, he wouldn't have the social media content, moderation issues. It would be a very different story with a very different narrative. >> Yeah, definitely. Amazon as a whole has a platform, and they have relationships. You know, Jeff Bezos has met with, you know, the highest levels of power in this country. They've got a carny. The foot was part of the Obama administration helping with policy. So absolutely with great to see Amazon, you know, take a strong puff statement and you know, for good is something that we're hugely a part of and therefore way want to see all the suppliers you know, having a dialogue and helping to move this >> for you. I think the lesson that we take from it, too, is that there are multiple ways to agitate for change and protest. One is to disrupt the keynote, and I understand that it gets attention and it's valuable But you could do that, or you can have a seat at the table and start lobbying for change, either internally or with stakeholders. But you need to it. There's a bunch of different paths to get there, and I think that I don't blame anyone who's protesting today, and I don't blame anyone who chose not to. >> All right, So let's let's let's talk now about some of the content. So Cory lutely, you know, there there's in the Amazon ecosystem. Every day we wake up and there were multiple new announcements. A matter of fact. We're always saying, Oh my gosh, how do I keep up with all of the things happening there? Well, one of the ways we keep up with it is reading last week in a VWs, which is your newsletter. I'll do the shameless plug, you know, for a much appreciated by telling my story. Cory. But Amazon Cloudwatch Container Insight, Amazon Event Bridge. You know, new developer kids fluent bit, you know, talking about the momentum of the company security databases on you know, the general adoption overall, you know, quick take for me as I love to hear you know, Werner up there talking about applications. It's not purely Oh, everything's going to live in the cloud and it'll be sun shines and unicorns and rainbows. But we understand that there's challenges here, your data and how we manage that requires, you know, a broad ecosystem that was the event bridge is something I would definitely want drilling on because from a serverless environment, not just one thing, it's lots of different things. And how do we play between all of them? But since you do sort through and sift through all of these announcements, give us a date. It was there anything new here? Did you already know all of this because it's in your R S s feed newsletters? What did grab you? >> Surprisingly, it turns out, in the weeks with you have, obviously reinvent is just a firehose torrent that no human being can wind up consuming. And you see a few releases in Santa Clara and a few in New York. But I thought I knew most of things that were coming out, and I did. I missed one that I just noticed. About two minutes we went on the air called cloudwatch anomaly detection. The idea is that it uses machine learning. So someone check that off the business card of the bingo card. And at that point, you take all the cloudwatch logs and start running machine learning and look for anomalies discrepancies. In the rest it uses machine learning. But rather than go figure out what it's for, it's applied to a very specific problem and those of the A. I am l products. I like the best where it's we're solving a problem with your data for you. But riding guard rails as opposed to step one, hire $2,000,000 worth of data. Scientists Step two. We're still working on that. >> All right, so court cloudwatch Actually, you saw the event bridge that I mentioned, which is that event ecosystem around Lambda uh, Deepak, who we're going to have on the program that said that it was the learnings from cloudwatch that helped them to build. This may be for audience. Just give us cloudwatch. There's a lot of different products under that. Give us what you hear from your customers. You know, we're cloudwatch fits and, you >> know, let's start at the beginning for those who are fortune enough never to have used it. Cloudwatch is AWS is internal monitoring solution. It gathers metrics. It gathers logs, it presents them in different ways. And it has interesting bill impacts as a cloud economist. I see it an awful lot where every time you, the monitoring company, walk around the Expo Hall, you'll trip over 40 of those. They're all gathering their data on the infrastructure from Bob Watch and interpreting that. Now you're paying for the monitoring company and you're paying for the FBI charges against it. And it was sort of frozen in amber, more or less for a good five years or so. I wrote a bit of a hit piece late last year and had some fascinating conversations afterwards, and it hasn't aged well, they're really coming to the floor with a lot of enhancements that are valuable on it. The problem is, there's a tremendous amount of data. How do you get signal from it? How do you look at actionable things? If you're running 10,000 instances, you're not looking at individual metrics or individualist. You care about aggregates, but you also care about observe ability. You care about drilling down into things. Burner talked about X Rays distributed tracing framework today, and I think we're rapidly seeing across the board that it all ties back to events. Cloudwatch events is what's driving a lot of things like Event Bridge and the idea of a defense centric architecture is really what we're trying to see Software's evolving into. >> Yeah, it's one of those things, you know, when you talk, you know that server lis term out, their events are at the center of them. And how do I get some standardization across the industry? There's open source groups that are trying to insert themselves and give some flexibility here. You know, when I want understand from Ben, Fridge says, Okay, it's Lambda and their ecosystem. But is this going to be a lame the only ecosystem? Or will this lay the ground work so that, yes, there are other clouds out there? You know what azure has other environment? Will this eventually be able to extend beyond this for? Is this a Amazon proprietary system? You have any insight there? >> It's a great question. I would argue that I guess one of the taking a step back for a second. It would have to be almost irrelevant In some cases when you start looking at server this lock in, it's not the fact that who there's this magic system only in one provider that will take my crappy code and run it for me. It's tied into the entire event ecosystem. It's tied into a bunch of primitives that do not translate very well. Now, inherently by looking. What event bridge is in the fact that anyone who wants to be integrated into their applications, you absolutely could wind up with a deep native integration coming from another large, hyper scale pop provider? The only question is, will >> you great, great point. I know when I've talked to some of the server lis ecosystem. It's that skills on understanding, you know, each environment because today, doing A W S versus doing azure, there's still a lot of differences there. Sure, I could learn it, but >> yeah, and one of the things that I think is fascinating to is we've seen a couple attempts of this before from other start ups that are doing very similar things in open stores or trying to do something themselves. But one of the things that change this tremendously here is it this is AWS doing that? It doesn't matter what they do, what ridiculous name they give it when they want something. World generally tends to sit up and notice, just by sheer virtue of its scale and the fact that it's already built out. And you don't have to build the infrastructure, help to run these things. If anything has a chance to start driving a cohesive standard around this, it's something coming from someone like Amazon. >> Yeah, absolutely. All right, Cory, you know, database is always a hot topic. Latest stat from Warner is I believe it was 150,000 databases migrated. You know, you called and said, Hey, why is amazon dot com on there? Jeff Faris like, Well, they have a choice. And of course, Amazon would point out they were using a traditional database for a long time and now have completely unplug the last in a >> long time. But they finally got off of a database that was produced by a law firm, and I understand the reasons behind that. But I was talking with people afterwards. Amazon does have a choice. Do they use, And if AWS wants to win them over to use their service is they have to sell them just like any other customer. And that's why it's on that slide as a customer. Now, if you're not in the ecosystem like some of us are, it looks a little disjointed of weight. So successfully sold yourself and put yourself on the slide. Okay, >> Yes, it was actually. So so. The biggest thing I learned at the Amazon remarks show when you talk about all the fulfillment centers in the robotics in machine, learning almost everything underneath there it's got eight of us. Service is underneath it. So absolutely, it is one company. But yes, Amazon is the biggest customer of AWS. But that doesn't mean that there isn't somewhere, you know. You know, I still haven't gotten the word if they're absolutely 100% on that, because we expect that there's some 400 sitting in the back ground running one of those financial service things. Maybe they finally micro did that one >> that's building in AWS 400. >> All right, Cory, what else you know either from the key note or from your general observations about Amazon that you want to share, >> I I want to say that it's very clear that Amazon is getting an awful lot of practice at putting these events on and just tracking a year to year, not just the venue. Logistics, which, Okay, great. Get a bunch of people in a conference room, have a conversation. Do Aquino throw him out the end. But the way they're pacing the Gino's, the way they're doing narratives. The customer stories that are getting up on stage are a lot less challenging. But then they were in years past. Where people get on stage, they seem more comfortable. It's very clear that a number of Amazon exacts not just here but another. Summits have been paying serious attention to how to speak publicly to 10,000 people at once. It's its own unique skill. >> Yeah, and you gotta like that, You know that. You know, the two first customers that they put on which will have on financial service is, of course, a big presence here in New York City. Gord Ash has their headquarters, you know, just a few blocks uptown from good, deep stories. Isn't you know, there there's that mixed that they did a good job. I thought of kind of cloud 101 because still many customers are very early on that journey. We're not all cloud native, you know, run by the developers and everything there. But, you know, good looks of technology and the new pieces for those people that have been in a while, But still, you know, welcoming and embracing offer how to get started >> and the stories we're moving up the stack to. It's not >> We had a bunch of B. >> M s and we put them in a different place. >> Hey, >> which is great news. Everyone starts there. But now the stories are moving into running serious regulated workloads with higher level of service is And that's great because it's also not the far extreme Twitter for pets. We built this toy project last week when someone else fell through. And now we have to give this talk. It's very clearly something large enterprises. >> Yeah. So, Corey, last thing I want to ask you is you remember in the early days, you know that public cloud? Oh, it was It was cheap and easy to use today. They have 200 instance types up there, you know? What does that mean for customers. You know you are a cloud economist. So need your official opinion diagnosis. >> I think it reduces the question, too, before you buy a bunch of reserve businesses. Are you on the right instance? Types. And the answer is almost certainly not just based on statistics alone. So now it's a state of indecision. It's rooted in an epic game of battleship between two Amazon s VVS, and I really hope one of the winds already so we can stop getting additional instance dives every couple of months. But so far no luck. >> So in your your your perfect world, you know what the announcement reinvented, fixes the problem. >> That's a really good question. I think that fundamentally, I don't I don't And I don't think I have any customers who care what type of incidents they're running on. They want certain resource levels. They want certain performance characteristics. But whatever you call that does not matter to them and having to commit to, though what you picked for 1 to 3 years, that's a problem. You don't have to. You can go on demand, but you're leaving 30% of the day. >> Yeah, and I love that point is actually taken. Notes fin rot. I want to talk to them because they say they've done three major re architectures in four years. So therefore, how did they make sure that they get the latest price performance but still get you no good? Good economics on the outdated >> regulatory authority? I just assume they get there with audit threats when it comes time for >> renegotiating. All right. You're Cory Quinn. I am stupid. I mean, we have a full day here of world Wall coverage from eight of US. Summit, New York City. Thank you so much for watching.

>> From Tavern on the Green in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day. Brought to you by Veritas. >> Hi everybody, welcome back to Tavern on the Green. We're in the heart of Central Park in New York City, the Big Apple. My name is Dave Vellante and you're watching theCUBE, the leader in live tech coverage. We're here at the Veritas Solution Day #VtasVision. Veritas used to have a big main tent day where they brought in all the customers. Now they're going out, belly-to-belly, 20 cities. Dave Raffo is here, he's the editorial director for TechTarget Storage. Somebody who follows this space very closely. David good to see you, welcome to theCUBE. >> Yeah, it's great to be on theCUBE. I always hear and watch you guys but never been on before. >> Well you're now an alum, I got to get him a sticker. So, we were talking about VMworld just now, and that show, last two years, one of the hottest topics anyway, was cloud, multi-cloud, Kubernetes of course was a hot topic. But, data protection was right up there. Why, in your view, is data protection such a hot topic right now? >> Well there's a lot of changes going on. First of all, couple years ago it was backup, nobody calls it backup anymore right. The whole market is changing. Data protection, you have newer guys like Cohesity and Ruberik, would come out with a, you know, architecture. They're basically, from scratch, they built scale-out and that's changing the way people look at data protection. You have all of the data protection guys, the Dell EMC, CommVault, Veeam, they're all kind of changing a little. And Veritas, the old guys, have been doing it forever. And now they're changing the way that they're reacting to the competition. The cloud is becoming a major force in where data lives, and you have to protect that. So there's a lot of changes going on in the market. >> Yeah I was talking to a Gartner analyst recently, he said they're data suggested about 2/3 of the customers that they talk to, within the next, I think, 18 months, are going to change they're backup approach or reconsider how they do backup or data protection as it were, as you just said. What do you think is driving that? I mean, people cite digital transformation they cite cloud, they cite big data, all the buzz words. You know, where there's smoke, there's fire, I guess. But what are your thoughts? >> Yeah, it's a little bit of all of those things, because the IT infrastructure is changing, virtualization containers, everything, every architectural change changes the way you protect and manage your data, right. So, we're seeing a lot of those changes, and now people are reacting to it and everybody's figuring out still how to use the cloud and where the data is going to live. So then, you know, how do you protect that data? >> And of course, when you listen to vendors talk, data protection, backup, recovery, it's very sexy when you talk to the customers they're just, oftentimes, drinking from the fire hose, right. Just trying to solve the next problem that they have. But what are you hearing from the customers? TechTarget obviously has a big community. You guys do a lot of events. You talk personally to a lot of customers, particularly when there are new announcements. And what does the landscape look like to you? >> So they're all, you know like I said, everybody's looking at the cloud. They're looking at all these, how they're going to use these things. They're not sure yet, but they want data protection, data management that will kind of fit in no matter which direction they go. It's kind of, you know, we know we're looking at where we're going to be in five years and now we want to know how we're going to protect, how we're going to manage our data, how we're going to use it, move it from cloud to cloud. So, you know, it's kind of like, it's a lot of positioning going on now. A lot of planing for the future. And they're trying to figure out what's the best way they're going to be able to do all this stuff. >> Yeah, so, you know the hot thing, it used to be, like you said, backup. And then of course, people said backup is one thing, recovery is everything. You know, so it was the old bromide, my friend Fred Moore, I think coined that term, back in the old storage tech days. But when you think about cloud, and you think about the different cloud suppliers, they've all got different approaches, they're different walled gardens, essentially. And they've got different processes for at least replicating, backing up data. Where do you see customers, in terms of having that sort of single abstraction layer, the single data protection philosophy or strategy and set of products for multi-cloud? >> Well, where they are is they're not there, and they're, you know, far from it, but that's where they want to be. So, that's where a lot of the vendor positioning is going. A lot of the customers are looking to do that. But another thing that's changing it is, you know, people aren't using Oracle, SQL databases all the time anymore either. They're using the NoSQL MongoDB. So that change, you know, you need different products for that too. So, the whole, almost every type of product, hyper-converged is changing backup. So, you know, all these technologies are changing the way people actually are going to protect their data. >> So you look at the guys with the big install base, obviously Veritas is one, guys like IBM, certainly CommVault and there are others that have large install bases. And the new guys, the upstarts, they're licking their chops to go after them. What do you see as, let's take Veritas as an example, the vulnerabilities and the strengths of a company like that? >> So the vulnerabilities of an old company that's been around forever is that, the newer guys are coming with a clean sheet of paper and coming up and developing their products around technologies that didn't exist when NetBackup was created, right. So the strength is that, for Veritas, they have huge install base. They have all the products, technology they need. They have a lot of engineers so they can get to the board, drawing board, and figure it out and add stuff. And what they're trying to do is build around NetBackup saying all these companies are using NetBackup, so let's expand that, let's build archiving in, let's build, you know, copy data protect, copy data management into that. Let's build encryption, all of that, into NetBackup. You know, appliances, they're going farther, farther and farther into appliances. Seems like nobody wants to just buy backup software, and backup hardware as separate, which they were forever. So you know, we're seeing the integration there. >> Well that brings up another good point, is you know, for years, backup's been kind of one size fits all. So that meant you were either over protected, or under protected. It was maybe an after thought, a bolt-on, you put in applications, put it in a server, an application on top of it. You know, install Linux, maybe some Oracle databases. All of the a sudden, oh, we got to back this thing up. And increasingly, people are saying, hey, I don't want to just pay for insurance, I'd like to get more value. And so, you're hearing a lot of talk about governance, certainly security, ransomware is now a big topic, analytics. What are you seeing, in terms of, some of those additional value, those value adds beyond that, is it still just insurance, or are we seeing incremental value to customers? >> Yeah, well I think everybody wants incremental value. They have the data, now it's not just, like you said, insurance. It's like how is this going to, how am I going to use this data? How's it going to help my business? So, the analytics is a big thing that everybody's trying to get in. You know, primary and secondary storage everybody's adding analytics. AI, how we use AI, machine learning. You know, how we're going to back up data from the edge into and out of thing. What are we going to do with all this data? How are we going to collect it, centralize it, and then use it for our business purposes? So there's, you know, it's a wide open field. Remember it used to be, people would say backup, nobody ever changes their backup, nobody wants to change backup. Now surveys are saying within the next two years or so, more than 50% of people are looking to either add a backup product, or just change out their whole backup infrastructure. >> Well that was the interesting about when, you know, the ascendancy of Data Domain, as you recall, you were following the company back then. The beauty of that architecture was, you don't have to change your backup processes. And now, that's maybe a challenge for a company like that. Where people are, because of digital, because of cloud, they're actually looking to change their backup processes. Not unlike, although there are differences, but a similar wave, remember the early days of virtualization, you had, you're loosing physical resources, so you had to rethink backup. Are you seeing similar trends today, with cloud, and digital? >> Yeah, the cloud, containers, microservices, things like that, you know, how do you protect that data? You know people, some people are still struggling with virtualization, you know, like, there's so many more VMs being created so quickly, and that you know, a lot of the backup products still haven't caught up to that. So, I mean Veeam has made an awful great business around dealing with VM backup, right? >> Right. >> Where was everybody else before that? Nobody else could do it. >> We storage guys, we're like the cockroaches of the industry. We're just this, storage just doesn't seem to die. You know the joke is, there's a hundred people in storage and 99 seats. But you've been following it for a long time. Yeah, you see all the hot topics like cloud and multi-cloud and digital transformation. Are you surprised at the amount of venture capital over the last, you know, four or five years, that has flooded in to storage, that continues to flood in to storage? And you see some notable successes, sure some failures, but even those failures, you're seeing the CEOs come out and sell to new companies and you're seeing the rise of a lot of these startups and a lot of these unicorns. Does it surprise you, or is that kind of your expectation? >> Well, I mean, like you said, that's the way it's always been in storage. When you look at storage compared to networking and compute, how many startups are there in those other areas. Very few, but storage keeps getting funded. A couple of years ago, I used to joke, if you said I do Flash, people would just throw hundreds of millions of dollars at you, then it was cloud. There always seem to be like a hot topic, a hot spot, that you can get money from VCs. And there's always four or five, at least, storage vendors who are in that space. >> Yeah, the cloud, the storage cloud AI blockchain company is really the next unicorn right? >> Right, yeah, if you know the right buzz words you can get money. And there's never just one right, there's always a couple in that same area and then one or two make it. >> Yeah, or, and or, if you've done before, right, you're seeing that a lot. I mean, you see what the guys like, for instance at Datrium are doing. Brian Biles he did it a Data Domain, and now he's, they just did a giant raise. >> Qumulo. >> Yeah, you know, Qumulo, for sure. Obviously the Cohesity are sort of well known, in terms of how they've done giant raises. So there's massive amount of capital now pouring in, much of which will go into innovation. It's kind of, it's engineering and it's you know, go to market and marketing. So, you know, no doubt, that that innovation curve will continue. I guess you can't bet against data growth. >> Right, you know, yeah, right, everybody knows data is going to grow. They're saying it's the new oil, right. Data is the big thing. The interesting thing with the funding stuff now is the, not the new companies, but the companies that have been around a little bit, and it's now time for them to start showing revenue. And where in the past it was easier for them to get money, now it seems a little tougher for those guys. So, you know, we could see more companies go away without getting bought up or go public but-- >> Okay, great. Dave, thanks very much for coming on theCUBE. >> Alright. >> It was great to have you. >> Thanks for having me on. >> Alright keep it right there everybody. We'll be back with our next guest. You're watching theCUBE from Veritas Vision in Central Park. We'll be right back. (theCUBE theme music)

>> Live from New York, it's theCUBE covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. (techy music) >> Welcome back to the Big Apple, everybody. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with my cohost Peter Burris, and this is our week-long coverage of CUBENYC. It used to be, really, a big data theme. It sort of evolved into data, AI, machine learning. Ronan Schwartz is here, he's the senior vice president and general manager of cloud, big data, and data integration at data integration company Informatica. Great to see you again, Ronan, thanks so much for coming on. >> Thanks for inviting me, it's a good, warm day in New York. >> Yeah, the storm is coming and... Well, speaking of storms, the data center is booming. Data is this, you know, crescendo of storms (chuckles) have occurred, and you guys are at the center of that. It's been a tailwind for your business. Give us the update, how's business these days? >> So, we finished Q2 in a great, great success, the best Q2 that we ever had, and the third quarter looks just as promising, so I think the short answer is that we are seeing the strong demand for data, for technologies that supports data. We're seeing more users, new use cases, and definitely a huge growth in need to support... To support data, big data, data in the cloud, and so on, so I think very, very good Q2 and it looks like Q3's going to be just as good, if not better. >> That's great, so there's been a decades-long conversation, of course, about data, the value of data, but more often than not over the history of recent history, when I say recent I mean let's say 20 years on, data's been a problem for people. It's been expensive, how do you manage it, when do you delete it? It's sort of this nasty thing that people have to deal with. Fast forward to 2010, the whole Hadoop movement, all of a sudden data's the new oil, data's... You know, which Peter, of course, disagrees with for many reasons. >> No, it's... >> We don't have to get into it. >> It's subtlety. >> It's a subtlety, but you're right about it, and well, maybe if we have time we can talk about that, but the bromide of... But really focused attention on data and the importance of data and the value of data, and that was really a big contribution that Hadoop made. There were a lot of misconceptions. "Oh, we don't need the data warehouse anymore. "Oh, we don't need old," you know, "legacy databases." Of course none of those are true. Those are fundamental components of people's big data strategy, but talk about the importance of data and where Informatica fits. >> In a way, if I look into the same history that you described, and Informatica have definitely been a player through this history. We divide it into three eras. The first one is when data was like this thing that sits below the application, that used the application to feed the data in and if you want to see the data you go through the application, you see the data. We sometimes call that as Data 1.0. Data 2.0 was the time that companies, including Informatica, kind of froze and been able to give you a single view of the data across multiple systems, across your organization, and so on, because we're Informatica we have the ETL with data quality, even with master data management, kind of came into play and allowed an organization to actually build analytics as a system, to build single view as a system, et cetera. I think what is happening, and Hadoop was definitely a trigger, but I would say the cloud is just as big of a trigger as the big data technologies, and definitely everything that's happening right now with Spark and the processing power, et cetera, is contributing to that. This is the time of the Data 3.0 when data is actually in the center. It's not a single application like it was in the Data 2.0. It's not this thing below the application in Data 1.0. Data is in the center and everything else is just basically have to be connected to the data, and I think it's an amazing time. A big part of digitalization is the fact that the data is actually there. It's the most important asset the organization has. >> Yeah, so I want to follow up on something. So, last night we had a session Peter hosted on the future of AI, and he made the point, I said earlier data's the new oil. I said you disagreed, there's a nuance there. You made the point last night that oil, I can put oil in my car, I can put oil in my house, I can't do both. Data is the new currency, people said, "Well, I can spend a dollar or I can spend "a dollar on sports tickets, I can't do both." Data's different in that... >> It doesn't follow the economics of scarcity, and I think that's one of the main drivers here. As you talk about 1.0, 2.0, and 3.0, 1.0 it's locked in the application, 2.0 it's locked in a model, 3.0 now we're opening it up so that the same data can be shared, it can be evolved, it can be copied, it can be easily transformed, but their big issue is we have to sustain overall coherence of it. Security has to remain in place, we have to avoid corruption. Talk to us about some of the new demands given, especially that we've got this, more data but more users of that data. As we think about evidence-based management, where are we going to ensure that all of those new claims from all of those new users against those data sources can be satisfied? >> So, first, I truly like... This is a big nuance, it's not a small one. (laughs) The fact that you have better idea actually means that you do a lot of things better. It doesn't mean that you do one thing better and you cannot do the other. >> Right. I agree 100%, I actually contribute that for two things. One is more users, and the other thing is more ways to use the data, so the fact that you have better data, more data, big data, et cetera, actually means that your analytics is going to be better, right, but it actually means that if you are looking into hyperautomation and AI and machine learning and so on, suddenly this is possible to do because you have this data foundation that is big enough to actually support machine learning processes, and I think we're just in the beginning of that. I think we're going to see data being used for more and more use cases. We're in the integration business and in the data management business, and we're seeing, within what our customers are asking us to support, this huge growth in the number of patterns of how they want the data to be available, how they want to bring data into different places, into different users, so all of that is truly supporting what you just mentioned. I think if you look into the Data 2.0 timeframe, it was the time that a single team that is very, very strong with the right tools can actually handle the organization needs. In what you described, suddenly self-service. Can every group consume the data? Can I get the data in both batch and realtime? Can I get the data in a massive amount as well as in small chunks? These are all becoming very, very central. >> And very use case, but also user and context, you know, we think about time, dependent, and one of the biggest challenges that we have is to liberate the data in the context of the multiple different organization uses, and one of the biggest challenges that customers have, or that any enterprise has, and again, evidence-based management, nice trend, a lot of it's going to happen, but the familiarity with data is still something that's not, let's say broadly diffused, and a lot of the tools for ensuring that people can be made familiar, can discover, can reuse, can apply data, are modestly endowed today, so talk about some of these new tools that are going to make it easier to discover, capture, catalog, sustain these data assets? >> Yeah, and I think you're absolutely right, and if this is such a critical asset, and data is, and we're actually looking into more user consuming the data in more ways, it actually automatically create a bottleneck in how do I find the data, how do I identify the data that I need, and how am I making this available in the right place at the right time? In general, it looks like a problem that is almost unsolvable, like I got more data, more users, more patterns, nobody have their budget tripled or quadrupled just to be able to consume it. How do you address that, and I think Informatica very early have identified this growing need, and we have invested in a product that we call the enterprise data catalog, and it's actually... The concept of a catalog or a metadata repository, a place that you can actually identify all the data that exists, is not necessarily a new concept-- >> No, it's been around for years. >> Yes, but doing it in an enterprise-unified way is unique, and I think if you look into what we're trying to basically empower any user to do I basically, you know, we all using Google. You type something and you find it. If you're trying to find data in the organization in a similar way, it's a much harder task, and basically the catalog and Informatica unified, enterprise-unified catalog is doing that, leveraging a lot of machine learning and AI behind the scenes to basically make this search possible, make basically the identification of the data possible, the curation of the data possible, and basically empowering every user to find the data that he wants, see recommendation for other data that can work with it, and then basically consume the data in the way that he wants. I totally think that this will change the way IT is functioning. It is actually an amazing bridge between IT and the business. If there is one place that you can search all your data, suddenly the whole interface between IT and the business is changing, and Informatica's actually leading this change. >> So, the catalog gives you line-of-sight on all, (clears throat) all those data sources, what's the challenge in terms of creating a catalog and making it performant and useful? >> I think there are a few levels of the challenge. I chose the word enterprise-unified intelligent catalog deliberately, and I think each one of them is kind of representing a different challenge. The first challenge is the unified. There is technical metadata, this is the mapping and the processes that move data from one place to the other, then there is business metadata. These are the definition the business is using, and then there is the operational metadata as well, as well as the physical location and so on. Unifying all of them so that you can actually connect and see them in one place is a unique challenge that at this stage we have already completely addressed. The second one is enterprise, and when talking about enterprise metadata it means that you want all of your applications, you want application in the cloud, you want your cloud environment, your big data environment. You want, actually, your APIs, you want your integration environment. You want to be able to collect all of this metadata across the enterprise, so unified all the types, enterprise is the second one. The third challenge is actually the most exciting one, is how can you leverage intelligence so it's not limited by the human factor, by the amount of people that you have to actually put the data together, right? >> Mm-hm. >> And today we're using a very, very sophisticated, interesting logarithm to run on the metadata and be able to tell you that even though you don't know how the data got from here to here, it actually did get from here to here. >> Mm-hm. >> It's a dotted line, maybe somebody copied it, maybe something else happened, but the data is so similar that we can actually tell you it came from one place. >> So, actually, let me see, because I think there's... I don't think you missed a step, but let me reveal a step that's in there. One of the key issues in the enterprise side of things is to reveal how data's being used. The value of data is tied to its context, and having catalogs that can do, as you said, the unified, but also the metadata becomes part of how it's used makes that opportunity, that ability to then create audit trails and create lineage possible. >> You're absolutely right, and I think it actually is one of the most important things, is to see where the data came from and what steps did it go to. >> Right. >> There's also one other very interesting value of lineage that I think sometimes people tend to ignore is who else is using it? >> Right. >> Who else is consuming it, because that is actually, like, a very good indicator of how good the data is or how common the data is. The ability to actually leverage and create this lineage is a mandatory thing. The ability to create lineage that is inferred, and not actually specifically defined, is also very, very interesting, but we're now doing, like, things that are, I think, really exciting. For example, let's say that a user is looking into a data field in one source and he is actually identifying that this is a certain, specific ID that his organization is using. Now we're able to actually automatically understand that this field actually exists in 700 places, and actually, leverage the intelligence that he just gave us and actually ask him, "Do you want it to be automatically updated everywhere? "Do you want to do it in a step-by-step, guided way?" And this is how you actually scale to handle the massive amount of data, and this is how organizations are going to learn more and more and get the data to be better and better the more they work with the data. >> Now, Ronan, you have hard news this week, right? Why don't you update us on what you've announced? >> So, I think in the context for our discussion, Informatica announced here, actually today, this morning in Strata, a few very exciting news that are actually helping the customer go into this data journey. The first one is basically supporting data across, big data across multi-clouds. The ability to basically leverage all of these great tools, including the catalog, including the big data management, including data quality, data governance, and so on, on AWS, on Azure, on GCP, basically without any effort needed. We're even going further and we're empowering our user to use it in a serverless mode where we're actually allowing them full control over the resources that are being consumed. This is really, really critical because this is actually allowing them to do more with the data in a lower cost. I think the last part of the news that is really exciting is we added a lot, a lot of functionality around our Spark processing and the capabilities of the things that you can do so that the developers, the AI and machine learning can use their stuff, but at the same time we actually empower business users to do more than they ever did before. So, kind of being able to expand the amount of users that can access the data, wanting a more sophisticated way, and wanting a very simple but still very powerful way, I think this is kind of the summary of the news. >> And just a quick followup on that. If I understand it, it's your full complement of functionality across these clouds, is that right? You're not neutering... (chuckles) >> That is absolutely correct, yes, and we are seeing, definitely within our customers, a growing choice to decide to focus their big data efforts in the cloud, it makes a lot of sense. The ability to scale up and down in the cloud is significantly superior, but also the ability to give more users access in the cloud is typically easier, so I think Informatica have chosen as the market we're focusing on enterprise cloud data management. We talked a lot about data management. This is a lot about the cloud, the cloud part of it, and it's basically a very, very focused effort in optimizing things across clouds. >> Cloud is critical, obviously. That's how a lot of people want to do business. They want to do business in a cloud-like fashion, whether it's on-prem or off-prem. A lot of people want things to be off-prem. Cloud's important because it's where innovation is happening, and scale. Ronan, thanks so much for coming on theCUBE today. >> Yeah, thank you very much and I did learn something, oil is not one of the terms that I'm going to use for data in the future. >> Makes you think about that, right? >> I'm going to use something different, yes. >> It's good, and I also... My other takeaway is, in that context, being able to use data in multiple places. Usage is a proportional relationship between usage and value, so thanks for that. >> Excellent. >> Happy to be here. >> And thank you, everybody, for watching. We will be right back right after this short break. You're watching theCUBE at #CUBENYC, we'll be right back. (techy music)

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)