>>Welcome back to NYC the Cube's coverage of Mongo DB 2022, a few thousand people here at least bigger than many people, perhaps expected, and a lot of buzz going on and we're gonna talk devs. I'm really excited to welcome back. Robbie Bellson who's the developer relations lead at Verizon and Ian Massingham. Who's the vice president of developer relations at Mongo DB Jens. Good to see you. Great >>To be here. >>Thanks having you. So Robbie, we just met a few weeks ago at the, the red hat summit in Boston and was blown away by what Verizon is doing in, in developer land. And of course, Ian, you know, Mongo it's rayon Detra is, is developers start there? Why is Mongo so developer friendly from your perspective? >>Well, it's been the ethos of MongoDB since day one. You know, back when we launched the first version of MongoDB back in 2009, we've always been about making developers lives easier. And then in 2016, we announced and released MongoDB Atlas, which is our cloud managed service for MongoDB, you know, starting with a small number of regions built on top of AWS and about 2,500 adoption events per week for MongoDB Atlas. After the first year today, MongoDB Atlas provides a managed service for MongoDB developers around the world. We're present in almost a hundred cloud regions across S DCP and Azure. And that adoption number is now running at about 25,000 developers a week. So, you know, the proof are in proof is really in the metrics. MongoDB is an incredibly popular platform for developers that wanna build data-centric applications. You just can't argue with the metrics really, >>You know, Ravi, sometimes there's an analyst who come up with these theories and one of the theories I've been spouting for a long time is that developers are gonna win the edge. And now to, to see you at Verizon building out this developer community was really exciting to me. So explain how you got this started with this journey. >>Absolutely. As you think about Verizon 5g edge or mobile edge computing portfolio, we knew from the start that developers would play a central role and not only consuming the service, but shaping the roadmap for what it means to build a 5g future. And so we started this journey back in late 20, 19 and fast forward to about a year ago with Mongo, we realized, well, wait a minute, you look at the core service offerings available at the edge. We didn't know really what to do with data. We wanted to figure it out. We wanted the vote of confidence from developers. So there I was in an apartment in Colorado racing, your open source Mongo against that in the region edge versus region, what would you see? And we saw tremendous performance improvements. It was so much faster. It's more than 40% faster for thousands and thousands of rights. And we said, well, wait a minute. There's something here. So what often starts is an organic developer, led intuition or hypothesis can really expand to a much broader go to market motion that really brings in the enterprise. And that's been our strategy from day one. Well, >>It's interesting. You talk about the performance. I, I just got off of a session talking about benchmarks in the financial services industry, you know, amazing numbers. And that's one of the hallmarks of, of Mongo is it can play in a lot of different places. So you guys both have developer relations in your title. Is that how you met some formal developer relations? >>We were a >>Program. >>Yeah, I would say that Verizon is one of the few customers that we also collaborate with on a developer relations effort. You know, it's in our mutual best interest to try to drive MongoDB consumption amongst developers using Verizon's 5g edge network and their platform. So of course we work together to help, to increase awareness of MongoDB amongst mobile developers that want to use that kind of technology. >>But so what's your story on this? >>I mean, as I, as I mentioned, everything starts with an organic developer discovery. It all started. I just cold messaged a developer advocate on Twitter and here we are at MongoDB world. It's amazing how things turn out. But one of the things that's really resonated with me as I was speaking with one of, one of your leads within your organization, they were mentioning that as Mongo DVIA developed over the years, the mantra really became, we wanna make software development easy. Yep. And that really stuck with me because from a network perspective, we wanna make networking easy. Developers are not gonna care about the internals of 5g network. In fact, they want us to abstract away those complexities so that they can focus on building their apps. So what better co-innovation opportunity than taking MongoDB, making software easy, and we make the network easy. >>So how do you think about the edge? How does you know variety? I mean, to me, you know, there's a lot of edge use cases, you know, think about the home Depot or lows. Okay, great. I can put like a little mini data center in there. That's cool. That's that's edge. Like, but when I think of Verizon, I mean, you got cell towers, you've got the far edge. How do you think about edge Robbie? >>Well, the edge is a, I believe a very ambiguous term by design. The edge is the device, the mobile device, an IOT device, right? It could be the radio towers that you mentioned. It could be in the Metro edge. The CDN, no one edge is better than the other. They're all just serving different use cases. So when we talk about the edge, we're focused on the mobile edge, which we believe is most conducive to B2B applications, a fleet of IOT devices that you can control a manufacturing plant, a fleet of ground and aerial robotics. And in doing so you can create a powerful compute mesh where you could have a private network and private mobile edge computing by way of say an AWS outpost and then public mobile edge computing by way of AWS wavelength. And why keep them separate. You could have a single compute mesh even with MongoDB. And this is something that we've been exploring. You can extend Atlas, take a cluster, leave it in the region and then use realm the mobile portfolio and spread it all across the edge. So you're creating that unified compute and data mesh together. >>So you're describing what we've been expecting is a new architecture emerging, and that's gonna probably bring new economics of new use cases, right? Where are we today in that first of all, is that a reasonable premise that this is a sort of a new architecture that's being built out and where are we in that build out? How, how do you think about the, the future of >>That? Absolutely. It's definitely early days. I think we're still trying to figure it out, but the architecture is definitely changing the idea to rip out a mobile device that was initially built and envisioned for the device and only for the device and say, well, wait a minute. Why can't it live at the edge? And ultimately become multi-tenant if that's the data volume that may be produced to each of those edge zones with hypothesis that was validated by developers that we continue to build out, but we recognize that we can't, we can't get that static. We gotta keep evolving. So one of our newest ideas as we think about, well, wait a minute, how can Mongo play in the 5g future? We started to get really clever with our 5g network APIs. And I, I think we talked about this briefly last time, 5g, programmability and network APIs have been talked about for a while, but developers haven't had a chance to really use them and our edge discovery service answering the question in this case of which database is the closest database, doesn't have to be invoked by the device anymore. You can take a thin client model and invoke it from the cloud using Atlas functions. So we're constantly permuting across the entire portfolio edge or otherwise for what it means to build at the edge. We've seen such tremendous results. >>So how does Mongo think about the edge and, and, and playing, you know, we've been wondering, okay, which database is actually gonna be positioned best for the edge? >>Well, I think if you've got an ultra low latency access network using data technology, that adds latency is probably not a great idea. So MongoDB since the very formative years of the company and product has been built with performance and scalability in mind, including things like in memory storage for the storage engine that we run as well. So really trying to match the performance characteristics of the data infrastructure with the evolution in the mobile network, I think is really fundamentally important. And that first principles build of MongoDB with performance and scalability in mind is actually really important here. >>So was that a lighter weight instance of, of Mongo or not >>Necessarily? No, not necessarily. No, no, not necessarily. We do have edge cashing with realm, the mobile databases Robbie's already mentioned, but the core database is designed from day one with those performance and scalability characteristics in mind, >>I've been playing around with this. This is kind of a, I get a lot of heat for this term, but super cloud. So super cloud, you might have data on Preem. You might have data in various clouds. You're gonna have data out at the edge. And, and you've got an abstraction that allows a developer to, to, to tap services without necessarily if, if he or she wants to go deep into the S great, but then there's a higher level of services that they can actually build for their customers. So is that a technical reality from a developer standpoint, in your view, >>We support that with the Mongo DB multi-cloud deployment model. So you can place Mongo DB, Atlas nodes in any one of the three hyperscalers that we mentioned, AWS, GCP or Azure, and you can distribute your data across nodes within a cluster that is spread across different cloud providers. So that kinds of an kind of answers the question about how you do data placement inside the MongoDB clustered environment that you run across the different providers. And then for the abstraction layer. When you say that I hear, you know, drivers ODMs the other intermediary software components that we provide to make developers more productive in manipulating data in MongoDB. This is one of the most interesting things about the technology. We're not forcing developers to learn a different dialect or language in order to interact with MongoDB. We meet them where they are by providing idiomatic interfaces to MongoDB in JavaScript in C sharp, in Python, in rust, in that in fact in 12 different pro programming languages that we support as a first party plus additional community contributed programming languages that the community have created drivers for ODMs for. So there's really that model that you've described in hypothesis exist in reality, using >>Those different Compli. It's not just a series of siloed instances in, >>In different it's the, it's the fabric essentially. Yeah. >>What, what does the Verizon developer look like? Where does that individual come from? We talked about this a little bit a few weeks ago, but I wonder if you could describe it. >>Absolutely. My view is that the Verizon or just mobile edge ecosystem in general for developers are present at this very conference. They're everywhere. They're building apps. And as Ian mentioned, those idiomatic interfaces, we need to take our network APIs, take the infrastructure that's being exposed and make sure that it's leveraging languages, frameworks, automation, tools, the likes of Terraform and beyond. We wanna meet developers where they are and build tools that are easy for them to use. And so you had talked about the super cloud. I often call it the cloud continuum. So we, we took it P abstraction by abstraction. We started with, will it work in one edge? Will it work in multiple edges, public and private? Will it work in all of the edges for a given region, public or private, will it work in multiple regions? Could it work in multi clouds? We've taken it piece by piece by piece and in doing so abstracting way, the complexity of the network, meaning developers, where they are providing those idiomatic interfaces to interact with our API. So think the edge discovery, but not in a silo within Atlas functions. So the way that we're able to converge portfolios, using tools that dev developers already use know and love just makes it that much easier. Do, >>Do you feel like I like the cloud continuum cause that's really what it is. The super cloud does the security model, how does the security model evolve with that? >>At least in the context of the mobile edge, the attack surface is a lot smaller because it's only for mobile traffic not to say that there couldn't be various configuration and human error that could be entertained by a given application experience, but it is a much more secure and also reliable environment from a failure domain perspective, there's more edge zones. So it's less conducive to a regionwide failure because there's so many more availability zones. And that goes hand in hand with security. Mm. >>Thoughts on security from your perspective, I mean, you added, you've made some announcements this week, the, the, the encryption component that you guys announced. >>Yeah. We, we issued a press release this morning about a capability called queryable encryption, which actually as we record this Mark Porter, our CTO is talking about in his keynote, and this is really the next generation of security for data stored within databases. So the trade off within field level encryption within databases has always been very hard, very, very rigid. Either you have keys stored within your database, which means that your memory, so your data is decrypted while it's resident in memory on your database engine. This allow, of course, allows you to perform query operations on that data. Or you have keys that are managed and stored in the client, which means the data is permanently OBS from the engine. And therefore you can't offload query capabilities to your data platform. You've gotta do everything in the client. So if you want 10 records, but you've got a million encrypted records, you have to pull a million encrypted records to the client, decrypt them all and see performance hit in there. Big performance hit what we've got with queryable encryption, which we announced today is the ability to keep data encrypted in memory in the engine, in the database, in the data platform, issue queries from the client, but use a technology called structural encryption to allow the database engine, to make decisions, operate queries, and find data without ever being able to see it without it ever being decrypted in the memory of the engine. So it's groundbreaking technology based on research in the field of structured encryption with a first commercial database provided to bring this to market. >>So how does the mobile edge developer think about that? I mean, you hear a lot about shifting left and not bolting on security. I mean, is this, is this an example of that? >>It certainly could be, but I think the mobile edge developer still stuck with how does this stuff even work? And I think we need to, we need to be mindful of that as we build out learning journeys. So one of my favorite moments with Mongo was an immersion day. We had hosted earlier last year where we, our, from an enterprise perspective, we're focused on BW BS, but there's nothing stopping us. You're building a B2C app based on the theme of the winner Olympics. At the time, you could take a picture of Sean White or of Nathan Chen and see that it was in fact that athlete and then overlaid on that web app was the number of medals they accrued with the little trumpeteer congratulating you for selecting that athlete. So I think it's important to build trust and drive education with developers with a more simple experience and then rapidly evolve overlaying the features that Ian just mentioned over time. >>I think one of the keys with cryptography is back to the familiar messaging for the cloud offloading heavy lifting. You actually need to make it difficult to impossible for developers to get this wrong, and you wanna make it as easy as possible for developers to deal with cryptography. And that of course is what we're trying to do with our driver technology combined with structure encryption, with query encryption. >>But Robbie, your point is lots of opportunity for education. I mean, I have to say the developers that I work with, it's, I'm, I'm in awe of how they solve problems and I, and the way they solve problems, if they don't know the answer, they figure out how to go get it. So how, how are your two communities and other communities, you know, how are they coming together to, to solve such problems and share whether it's best practices or how do I do this? >>Well, I'm not gonna lie in person. Events are a bunch of fun. And one of the easiest domain knowledge exchange opportunities, when you're all in person, you can ideate, you can whiteboard, you can brainstorm. And often those conversations are what leads to that infrastructure module that an immersion day features. And it's just amazing what in person events can do, but community groups of interest, whether it's a Twitch stream, whether it's a particular code sample, we rely heavily on digital means today to upscale the developer community, but also build on by, by means of a simple port request, introduce new features that maybe you weren't even thinking of before. >>Yeah. You know, that's a really important point because when you meet people face to face, you build a connection. And so if you ask a question, you're more likely perhaps to get an answer, or if one doesn't exist in a, in a search, you know, you, oh, Hey, we met at the, at the conference and let's collaborate on this guys. Congratulations on, on this brave new world. You're in a really interesting spot. You know, developers, developers, developers, as Steve bomber says screamed. And I was glad to see Dave was not screaming and jumping up and down on the stage like that, but, but the message still resonates. So thank you, definitely appreciate. All right, keep it right there. This is Dave ante for the cubes coverage of Mago DB world 2022 from New York city. We'll be right back.

>> Announcer: Live from Boston Massachusetts this is theCube. Covering Spark Summit East 2017, brought to you by Databricks. Now here are your hosts Dave Vellante and George Gilbert. >> Welcome back to theCube, everybody, we're here in Boston. The Cube is the worldwide leader in live tech coverage. This is Spark Summit, hashtag #SparkSummit. And Robbie Strickland is here. He's the Vice President of Engines & Pipelines, I love that title, for the Watson Data Platform at IBM Analytics, formerly with The Weather Company that was acquired by IBM. Welcome to you theCube, good to see you. >> Thank you, good to be here. >> So, it's my standing tongue-in-cheek line is the industry's changing, Dell buys EMC, IBM buys The Weather Company. [Robbie] That's right. >> Wow! That sort of says it all, right? But it was kind of a really interesting blockbuster acquisition. Great for the folks at The Weather Company, great for IBM, so give us the update. Where are we at today? >> So, it's been an interesting first year. Actually, we just hit our first anniversary of the acquisition and a lot has changed. Part of my role, new role at IBM, having come from The Weather Company, is a byproduct of the two companies bringing our best analytics work and kind of pulling those together. I don't know if we have some water but that would be great. So, (coughs) excuse me. >> Dave: So, let me chat for a bit. >> Thanks. >> Feel free to clear your throat. So, you were at IBM, the conference at the time was called IBM Insight. It was the day before the acquisition was announced and we had David Kenny on. David Kenny was the CEO of The Weather Company. And I remember we were talking, and I was like, wow, you have such an interesting business model. Off camera, I was like, what do you want to do with this company, you guys are like prime. Are you going public, you going to sell this thing, I know you have an MBA background. And he goes, "Oh, yeah, we're having fun." Next day was the announcement that IBM bought The Weather Company. I saw him later and I was like, "Aha!" >> And now he's the leader of the Watson Group. >> That's right. >> Which is part of our, The Weather Company joined The Watson Group. >> And The Cloud and analytics groups have come together in recognition that analytics and The Cloud are peanut butter and jelly. >> Robbie: That's absolutely right. >> And David's running that organization, right? >> That is absolutely right. So, it's been an exciting year, it's been an interesting year, a lot of challenges. But I think where we are now with the Watson Data Platform is a real recognition that the use dase where we want to try to make data and analytics and machine learning and operationalizing all of those, that that's not easy for people. And we need to make that easy. And our experience doing that at The Weather Company and all the challenges we ran into have informed the organization, have informed the road map and the technologies that we're using to kind of move forward on that path. >> And The Watson Data Platform was announced in, I believe, October. >> Robbie: That's right. >> You guys had a big announcement in New York City. And you took many sort of components that were viewed as individual discreet functions-- >> Robbie: That's right. >> And brought them together in a single data pipeline. Is that right? >> Robbie: That's right. >> So, maybe describe that a little bit for our audience. >> So, the vision is, you know, one of the things that's missing in the market today is the ability to easily grab data from some source, whether it's a database or a Kafka stream, or some sort of streaming data feed, which is actually something that's often overlooked. Usually you have platforms that are oriented around streaming data, data feeds, or oriented around data at rest, batch data. One of the things that we really wanted to do was sort of combine those two together because we think that's really important. So, to be able to easily acquire data at scale, bring it into a platform, orchestrate complex workflows around that, with the objective, of course, of data enrichment. Ultimately, what you want to be able to do is take those raw signals, whatever they are, and turn that into some sort of enriched data for your organization. And so, for example, we may take signals in from a mobile app, things like beacons, usage beacons on a mobile app, and turn that into a recommendation engine so we can feed real time content decisions back into a mobile platform. Well, that's really hard right now. It requires lots of custom development. It requires you to essentially stitch together your pipeline end to end. It might involve a machine learning pipeline that runs a training pipeline. It might involve, it's all batch oriented, so you land your data somewhere, you run this machine learning pipeline maybe in Spark or ADO or whatever you've got. And then the results of that get fed back into some data store that gets merged with your online application. And then you need to have a restful API or something for your application to consume that and make decisions. So, our objective was to take all of the manual work of standing up those individual pieces and build a platform where that is just, that's what it's designed to do. It's designed to orchestrate those multiple combinations of real time and batch flows. And then with a click of a button and a few configuration options, stand up a restful service on top of whatever the results are. You know, either at an interim stage or at the end of the line. >> And you guys gave an example. You actually showed a demo at the announcement. And I think it was a retail example, and you showed a lot of what would traditionally be batch processes, and then real time, a recommendation came up and completed the purchase. The inference was this is an out of the box software solution. >> Robbie: That's right. >> And that's really what you're saying you've developed. A lot of people would say, oh, it's IBM, they've cobbled together a bunch of their old products, stuck them together, put an abstraction layer on, and wrapped a bunch of services around it. I'm hearing from you-- >> That's exactly, that's just WebSphere. It's WebSphere repackaged. >> (laughing) Yeah, yeah, yeah. >> No, it's not that. So, one of the things that we're trying to do is, if you look at our cloud strategy, I mean, this is really part and parcel, I mean, the nexus of the cloud strategy is the Watson Data Platform. What we could have done is we could have said let's build a fantastic cloud and compete with Amazon or Google or Microsoft. But what we realized is that there is a certain niche there of people who want to take individual services and compose them together and build an application. Mostly on top of just raw VMs with some additional, you know, let's stitch together something with Lambda or stitch together something with SQS, or whatever it may be. Our objective was to sort of elevate that a bit, not try to compete on that level. And say, how do we bring Enterprise grade capabilities to that space. Enterprise grade data management capabilities end-to-end application development, machine learning as a first class citizen, in a cohesive experience. So that, you know, the collaboration is key. We want to be able to collaborate with business users, data scientists, data engineers, developers, API developers, the consumers of the end results of that, whether they be mobile developers or whatever. One of the things that is sort of key, I think, to the vision is that these roles that we've traditionally looked at. If you look at the way that tool sets are built, they're very targeted to specific roles. The data engineer has a tool, the data scientist has a tool. And what's been the difficult part is the boundaries between those have been very firm and the collaboration has been difficult. And so, we draw the personas as a Venn diagram. Because it's very difficult, especially if you look at a smaller company, and even sometimes larger companies, the data engineer is the data scientist. The developer who builds the mobile application is the data scientist. And then in some larger organizations, you have very large teams of data scientists that have these artificial barriers between the data scientist and the data engineer. So, how do we solve both cases? And I think the answer was for us a platform that allows for seamless collaboration where there is not these clean lines between the personas, that the tool sets easily move from one to the other. And if you're one of those hybrid people that works across lines, that the tool feels like it's one tool for you. But if you're two different teams working together, that you can easily hand off. So, that was one of the key objectives we're trying to answer. >> Definitely an innovative component of the announcement, for sure. Go ahead, George. >> So, help us sort of bracket how mature this end-to-end tool suite is in terms of how much of the pipeline it addresses. You know, from the data origin all the way to a trained model and deploying that model. Sort of what's there now, what's left to do. >> So, there are a few things we've brought to market. Probably the most significant is the data science experience. The data science experience is oriented around data science and has, as its sort of central interface, Jupyter Notebooks. Now, as well as, we brought in our studio, and those sorts of things. The idea there being that we'll start with the collaboration around data scientists. So, data scientists can use their language of choice, collaborate around data sets, save out the results of their work and have it consumed either publicly by some other group of data scientists. But the collaboration among data scientists, that was sort of step one. There's a lot of work going on that's sort of ongoing, not ready to bring to market, around how do we simplify machine learning pipelines specifically, how do we bring governance and lineage, and catalog services and those sorts of things. And then the ingest, one of the things we're working on that we have brought to market is our product called Lift which connects, as well. And that's bringing large amounts of data easily into the platform. There are a few components that have sort of been brought to market. dashDB, of course, is a key source of data clouded. So, one of the things that we're working on is some of these existing technologies that actually really play well into the eco system, trying to tie them well together. And then add the additional glue pieces. >> And some of your information management and governance components, as well. Now, maybe that is a little bit more legacy but they're proven. And I don't know if the exits and entries into those systems are as open, I don't know, but there's some capabilities there. >> Speaking of openness, that's actually a great point. If you look at the IIG suite, it's a great On-Premise suite. And one of the challenges that we've had in sort of past IBM cloud offerings is a lot of what has been the M.O. in the past is take a great On-Prem solution and just try to stand it up as a service in the cloud. Which in some cases has been successful, in other cases, less so. One of the things we're trying to look at with this platform is how do we leverage (a) open source. So that whatever you may already be running open source on, Prem or in some other provider, that it's very easy to move your workloads. So, we want to be able to say if you've got 10,000 lines of fraud detection code to map produce. You don't need to rewrite that in anything. You can just move it. And the other thing is where our existing legacy tech doesn't necessarily translate well to the cloud, our first strategy is see if there's any traction around an existing open source project that satisfies that need, and try to see if we can build on that. Where there's not, we go cloud first and we build something that's tailor made to come out. >> So, who's the first one or two customers for this platform? Is it like IBM Global Business Services where they're building the semi-custom industry apps? Or is it the very, very big and sophisticated, like banks and Telcos who are doing the same? Or have you gotten to the point where you can push it out to a much wider audience? >> That's a great question, and it's actually one that is a source of lots of conversation internally for us. If you look at where the data science experience is right now, it's a lot of individual data scientists, you know, small companies, those sorts of things coming together. And a lot of that is because some of the sophistication that we expect for Enterprise customers is not quite there yet. So, we wouldn't expect Enterprise customers to necessarily be onboarded as quickly at the moment. But if we look at sort of the, so I guess there's maybe a medium term answer and a long term answer. I think the long term answer is definitely the Enterprise customers, you know, leveraging IBM's huge entry point into all of those customers today, there's definitely a play to be made there. And one of the things that we're differentiating, we think, over an AWS or Google, is that we're trying to answer that use case in a way that they really aren't even trying to answer it right now. And so, that's one thing. The other is, you know, going beta with a launch customer that's a healthcare provider or a bank where they have all sorts of regulatory requirements, that's more complicated. And so, we are looking at, in some cases, we're looking at those banks or healthcare providers and trying to carve off a small niche use case that doesn't actually fall into the category of all those regulatory requirements. So that we can get our feet wet, get the tires kicked, those sorts of things. And in some cases we're looking for less traditional Enterprise customers to try to launch with. So, that's an active area of discussion. And one of the other key ones is The Weather Company. Trying to take The Weather Company workloads and move The Weather Company workloads. >> I want to come back to The Weather Company. When you did that deal, I was talking to one of your executives and he said, "Why do you think we did the deal?" I said, "Well, you've got 1500 data scientists, "you've got all this data, you know, it's the future." He goes, "Yeah, it's also going to be a platform "for IOT for IBM." >> Robbie: That's right. >> And I was like, "Hmmm." I get the IOT piece, how does it become a platform for IBM's IOT strategy? Is that really the case? Is that transpiring and how so? >> It's interesting because that was definitely one of the key tenets behind the acquisition. And what we've been working on so hard over the last year, as I'm sure you know, sometimes boxes and arrows on an architecture diagram and reality are more challenging. >> Dave: (laughing) Don't do that. >> And so, what we've had to do is reconcile a lot of what we built at The Weather Company, existing IBM tech, and the new things that were in flight, and try to figure out how can we fit all those pieces together. And so, it's been complicated but also good. In some cases, it's just people and expertise. And bringing those people and expertise and leaving some of the software behind. And other cases, it's actually bringing software. So, the story is, obviously, where the rubber meets the road, more complicated than what it sounds like in the press release. But the reality is we've combined those teams and they are all moving in the same direction together with various bits and pieces from the different teams. >> Okay, so, there's vision and then the road map to execute on that, and it's going to unfold over several years. >> Robbie: That's right. >> Okay, good. Stuff at the event here, I mean, what are you seeing, what's hot, what's going on with Spark? >> I think one of the interesting things with what's going on with Spark right now is a lot of the optimizations, especially things around GPUs and that. And we're pretty excited about that, being a hardware manufacturer, that's something that is interesting to us. We run our own cloud. Where some people may not be able to immediately leverage those capabilities, we're pretty excited about that. And also, we're looking at some of those, you know, taking Spark and running it on Power and those sorts of things to try to leverage the hardware improvements. So, that's one of the things we're doing. >> Alright, we have to leave it there, Robbie. Thanks very much for coming on theCube, really appreciate it. >> Thank you. >> You're welcome. Alright, keep it right there, everybody. We'll be right back with our next guest. This is theCube. We're live from Spark Summit East, hashtag #SparkSummit. Be right back. >> Narrator: Since the dawn of The Cloud, theCube.

>> 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-

>>Modernizing applications can be a complicated situation. For many folks, it's useful to have some best practices and tangible steps that can remove friction and yield some quick wins. We're now joined by couch based CTO, Ravi meam, who will cover how organizations can approach application modernization, what role the cloud plays and what you need to know about building a business case. Ravi, welcome back to the cube. Good to see you again. >>Very good to see you. Thanks for having me, Dave. >>Yes, our pleasure. Uh, according to a recent couch based digital transformation survey that you guys ran, it was about a 650 respondents, CIOs, CTOs, et cetera. The inertia of legacy technology held back according to the respondents, 82% of enterprises from modernizing their portfolios in 2021. So I wanna talk about the what and the why of modernization. Robbie, what does application modernization mean to you and why is it top of mind for organizations? >>Yeah, I think there have been multiple forces at work here for a while and they have all come to a tipping point with, uh, the pandemic and, uh, uh, it's a combination of factors and, uh, the legacy technologies were built for a different generation of applications. So it's a generational shift that we are undergoing. Uh, part of it is the, the consumption model, which is all cloud based and pay as you go kinda stuff. The other is edge is in the middle of a lot of these conversations together with, uh, the velocity variety, um, of data that you have to actually sort of consume and results that you need to produce. These were all not what the, sort of the, the infrastructure of hold on, which the applications were built on, uh, uh, stand for. So the infrastructure, the substrate requires modernization, uh, in order for the businesses to transform themselves, that's, what's going on. >>We call it digital transformation from a technology perspective, but it's businesses that are transforming, uh, the business models, uh, in front of our eyes. Uh, you know, we have seen the media go from, uh, set up boxes to streaming everywhere, um, like that every business eCommerce has changed, uh, the way we sort of, uh, do any business gaming has changed, uh, the, the banking industry, the healthcare, everything is changing, uh, in terms of the fundamental movement, if you, if you could, uh, sort of say that is to reach the consumer directly and sort of dis intermediate intermediaries. And in that process, the technologies that we had used to build the, the, you know, last previous generation of applications, no longer scale, no longer a nimble enough, uh, no longer cater to the modern, uh, the needs of the modern data and the infrastructure on which, uh, we are standing of these applications. So that's, what's driving the modernization effort. And, uh, in, in that, uh, you know, we have always started say that few years ago, that data is the new oil. Um, so that plays a very critical role in how the data silos and infrastructure that enterprises have is what's holding them back. And, uh, this whole effort is, uh, in, in, in terms of modernizing that infrastructure, uh, through the modern means of, uh, uh, the cloud computing, uh, the modern serverless architectures and microservices, and, uh, the edge and AI play play an important role in this. >>So we're gonna hear later from Amdocs, uh, about their modernization and where couch base helps and fits, but I'd love to hear your perspective as to how couch base helps organizations modernize. >>Right. I think one of the, uh, uh, fundamental things that has happened is that in the last 30, 40 odd years, the data infrastructure has sort of become, uh, a sprawl. Uh, we had built multiple systems, uh, uh, relational databases, cash is, uh, search systems, analytical systems, uh, all, uh, requiring for us to move the data, uh, from one system to the other, in order for you to get the value from those. And this is basically what we call as a data sprawl or database sprawl. And this leads to so many sort of, uh, downstream effects all the way from, uh, data not being available, uh, at the time when the engagement, uh, when the customer is engaged to data governance, security and all those issues, because the threat surface area is wide. And now you're putting all this infrastructure on the modern sort of cloud computing paradigm and, and the costs are sort of ballooning. >>And, uh, because those older infrastructures that were built, uh, when you deploy them on the cloud, uh, it, it creates its ads to the, uh, the complexity of this brawl and on top of the, the cost of this. So, uh, a system like couch base is what, um, uh, simplifies this brawl for, uh, our customers. And it is built for the modern, uh, sort of requirements of scale and performance, low latency, and the flexibility, uh, of being able to sort of not have to go through this whole sort of cycle of whenever you have to have a, a change in your application that touches your data, uh, that it, it actually creates a huge tool in those upgrades and all those life cycle having to CA carry pagers. Uh, I mean, that doesn't work anymore in these days of, I know, five, nine up times and, uh, 24 7, 365 availability of, uh, your services, uh, is so in that area is where couch base sort of helps, uh, our customers to modernize, uh, their sort of data infrastructure. >>It, uh, fuses, um, the multiple technologies that were spread across, uh, into one platform. So it gives a, a simpler programming paradigm, uh, that is one way to scale manage, administer, uh, patch, upgrade. All that mechanism is sort of not just thought through and automated, but it also sort of centralized this, uh, whole thing simplifies at the end of the day, uh, that total task of managing, uh, because that the volume of data that you have to manage now is, you know, orders of magnitude three to four orders of magnitude more than, uh, what it was just a few years ago. And, uh, so in that, uh, containing the sprawl, uh, agility of development, uh, are, are sort of, and the simplicity of deployment and management are some of the key capabilities that, uh, enterprises look to us to solve. And in that, bringing in all the way from cloud to multi-cloud to edge, uh, is how this sort of strategy evolves for enterprises. >>So square this circle for me, cuz in the panel we just had, there's a lot of agreement with what you just said, lift and shift of legacy platforms, doesn't work. Uh, it might work for the cloud vendor to get the data in the cloud, but it generally doesn't work for the customer. And you mentioned sprawl, we talked about this in the panel about, you know, data by its very nature is distributed. We talked about data mesh. There's a lot of skepticism around data mesh, but that that's cool. And you mentioned edge, so yes, I'm interested in the cloud's role here is the idea that you're actually putting all this stuff in one place. How does that fit with the edge? Maybe you could help us understand you're thinking of that and where the cloud fits. >>Yes. Um, you know, it's about, uh, centralizing a data up to a point and decentralizing it's in the magic of how you actually enable that. Um, uh, for example, just your traffic signal, your car, uh, or if you're on a cruise ship, each one is an edge, they all generate petabytes of data. And then you basically, uh, you can consume that, but if you're gonna stream all this data to a centralized place like a cloud that's, uh, you know, most of the data actually is not something that you're gonna store forever. Those are, you know, topical and that information is required at the edge. You should synthesize that information and take the noise from it and discard the signal. So that's where the edge, uh, typically the edge is not some, you know, personal device alone or uh, uh, or a IOT sensor sending data that is also, uh, sort of, uh, one, one element of the edge, but the edge is about decentralizing the cloud. >>So to say, so you can have mul your topologies of not having all your data sit in the cloud centralize someplace behind five firewalls. So when your application tries to reach that all the latency comes into place. So that's what you want to, uh, decentralize and have the data available as close to the engagement of the data with the consumer of it. So in that is the decentralization strategy where you can have multiple techologies, a three, a mesh, uh, however you choose to so that you get to get the data closest. Um, it could be a mobile device. Uh, it could be a, a smaller deployment of a server. It could be, uh, uh, a personal electronic device like watch, or it could be all the way in the IOT gateway. These are the various sort of decentralization of the data that has to happen. >>So it's about moving the data fastest. It's almost like CDN of the data is what, uh, sorry. Uh, for those it's, um, content delivery network is what CDN stands for, where we used to actually move static content in the good old days. That's what made, made our webpages faster. Now we can actually move live data that much faster by using replication technology. So when you move the data towards, towards the edge, what you're trying to do is bring that data closer, uh, to the compute where it's actually happening, as opposed to keeping the data centralized someplace back in the cloud and server and all your application logic is actually sitting on the device or on the edge. So you're constantly, uh, shoveling the data from the cloud to the edge, from edge to the cloud at the time of compute, as opposed to having it available at the time of, uh, um, the consumption of the data. >>That's where the paradigm, uh, shift is actually happening. And, uh, this basically is not about better user experience. It's also about backend networking, other costs that you can actually, uh, gain from, by not having to sort of repeatedly sort of shovel data back and forth. So that's stage strategy that, uh, enterprises are adopting. Now, this is become so to say core part of the architecture of modernization, uh, uh, in terms of where everybody can see this has to move to and, uh, our edge and mobile product, um, also plays a role in, uh, that's one of the other elements aspects of it that customers to look us, uh, look to us >>For. So it's a balance and couch base can play in both places. A lot of the data, if I heard you correctly at the edge is ephemeral, but if I want to do, you know, AI inferencing in real time, I gotta do it at the edge. I can't send it back to the cloud and, and, and do the modeling, you know, post-proces, that's not gonna work. All right, let's talk about the business case, you know, we've, we we've hit on the what and the why, but, you know, how does it get paid for companies sometimes struggle to plan for and budget appropriately for their outcomes? Yes. What do customers need to know about how do they get this past the CFO's office for, in the other business decision makers? >>I think there is an opportunity cost, uh, with the sort of lack of modernization, uh, if, uh, people are doing their classic sort of, so to say it style budgeting, uh, then it will just look like we have to modernize, uh, you know, some older infrastructure. It's not about that. It's about modernizing or making your business relevant, uh, to, uh, to the consumers, because the way consumers, uh, go about consuming your services now is very different from the way you had originally imagined and built for. And in that lies the, the, the transformation, uh, not to see this as a, it, uh, just as an it infrastructure modernization, but more from the standpoint of business transformation and, uh, the tooling that is required for this business transformation to be successful. So it requires the involvement of, um, not leaving it to just, you know, uh, uh, it oriented sort of, uh, uh, thinking of modernizing, but from the standpoint of looking at the, the, the business and what are the transformations that they need to, if they don't keep up with the Jones, they, in this digital divide, they may find themselves in the sort of either the wrong side or in the chasm. >>So I think that mindset, uh, that I was, uh, sort of in addition to sort of, uh, it pushing for this, uh, it's got to have a C-suite, uh, sponsorship understanding and, uh, sort of champion of this, then those initiatives will succeed because, uh, it's not just the technology transformation. It is accompanied by business and sort of, so to say cultural transformation inside the enterprise. >>Yeah. And it's interesting in the survey, it was very much it, you know, survey, I get that and, and the, it pros, the CIOs, et cetera, felt that, that, that, that the it organization was largely responsible for the digital strategy. And I think that was largely a function of, we just came out of the, the pandemic or Hopely coming out of the pandemic. And so they had all these tactical needs, but now you're saying step back, align with the business, make sure the C suite's involved, and that's gonna reduce the friction of, of getting this stuff paid for. >>Correct. And, you know, the, uh, this observation was also there. If you, I must have noticed that, you know, many, uh, of these sort of transf strategies, if you just leave it to like an it thing, they end up being reactive. Uh, but the proactive strategies are the one that actually, uh, succeed because they understand that this is a sort of enterprise transformation. It could be disruptive. Uh, it is what is required for the enterprise to get to the, uh, to the next level, uh, or to be, uh, in this, to be relevant in this sort of modern economy, if you would. So I think that is what, uh, what people are reacting to is the fact that this pandemic has pushed people to modernize quickly. And that may have happened as a reaction to the reality of the situation, but more and more, uh, uh, even among these strategies and more and more initiatives that people are taking, they may have sort of a longer term sort of thinking in this, uh, that requires the, uh, definitely without it's not gonna succeed and they're gonna be in the middle and they'll be, uh, in the forefront of many technology decisions that we have to make, but having a, a C-suite level sponsorship. >>In addition to that, with the impetus of what is the business transformation, this is actually going to achieve, um, those you will see will succeed a lot more because otherwise you, we see that, you know, good, good number of what 80% of these projects fail or, or, or they suffer delays or scale back or never get started, uh, because, you know, uh, the understanding of what is the business value of it is perhaps not, not clearly articulated instead, it just becomes a, a technology modernization conversation without that company benefit. >>Yeah. Got it. Okay. Uh, you guys recently announced some updates to your platform. Can you run us through the, the highlights, you know, what the customers get and, and how it relates to this conversation modernizing application strategies? >>Yes. So, uh, well, we will be, uh, releasing our couch base server 7.1. And, uh, that is what will be the sort of underneath platform for our, the couch base, uh, Capella, which is the, our DBA both, uh, have exciting innovations, um, that we would be putting out. Uh, let me just run through a few things, uh, on the, uh, uh, couch based server seven one, because there are some, uh, amazing, uh, capabilities we have introduced there. We are really excited about the opportunities. This brings couch based into play. Uh, first is we have a, uh, a brand new storage engine that we put in there, which, uh, significant significantly, uh, reduces the, uh, the cost of running couch base. Uh, with this capability, we can actually consume lot less memory and that's, that is like a 10 X improvement on this one. So from that standpoint, we are 10 X more efficient in terms of resource consumption, the expensive memory oriented resource consumption. >>This now allows couch based to sort of not just cater to those high performance, um, you know, hyperscale scenarios that we are known for, but also the more, the classic BIS oriented, uh, applications, which are not that performance sensitive, but they're more cost sensitive. So that's a huge, uh, step forward for couch base because there are a lot more, uh, opportunities where sort of, we become, uh, that much more, uh, cost efficient for enterprises to run. And this is something that, uh, many enterprises have asked for, and we know, uh, many more use cases where we would be more relevant with that innovation. And this has been a, a sort of a long journey building storage engines is, uh, you know, uh, is a very difficult Endover. And we took that on knowing that, uh, what we can achieve here would be a game changer, uh, for couch base. >>And in terms of how, uh, uh, the consolidation of multiple things that you can do in our platform just got this sort of boost of being able to do a lot more with lot less resources. In addition to that, we have done enhancements to our analytics service, uh, with, uh, the work that we have done there. Uh, it, it can sort of do a lot more, um, uh, availability, uh, of the, of, of the analytics service, uh, which, uh, will strengthens the analytics side of the product, which now allows you to run analysis O on J O uh, straight up without requiring the operational side of the, uh, the database. So you can just simply do, uh, straight off analytics stuff, because it, it, it can now, uh, give you the higher availability and disaster recovery that you would want if you're gonna depend on these, uh, systems with that, we are done over some, uh, real good work with Tableau integration, which makes it easy to visualize this, um, uh, uh, and, uh, one other important capability we introduce here is the, um, on, in the entire platform is what we call as user defined functions. >>This now allows us to write custom logic and Java script in the server couch based server. This is, this helps you write procedural logic in the middle of, uh, SQL queries, which is a humongous capability that, you know, and the classical systems process. Now, with that, we have closed the gap. If you know, how to program to sort of classical operational systems, pretty much, you have one to one equivalence of that, uh, in couch. So if you come from the good relational world, uh, it would be very easy breeze for you to understand how to program in this modern, no SQL systems, which both supports, um, uh, SQL as well as the classic asset transaction capabilities. And last, uh, we expanded the support two arm processors, and typically, uh, arm processes, at least save you quarter of, uh, your budget because of it being that much more, uh, uh, cost efficient in terms of, uh, its operational and power capabilities. >>So with that net net, uh, couch based server becomes a lot more, um, uh, cost efficient. And at the same time, it also in one, well becomes that database server, which can both handle your in memory, uh, capabilities that, that speed and hyperscale, as well as, uh, the classical use cases of being, uh, disk, uh, disoriented, uh, classical relational database use cases. Nice. So that, that, that rounds out our offering, it's been a long journey for us to get here from being the high performance, uh, low latency system to, uh, the classical database use case >>Assessment. Yeah. I mean, that's great. You got, you got memory optimization, you mentioned the, the, the, the arm base. Now you're on that curve, which is great software companies love when you get cheaper, faster hardware, uh, you making it easy to speak the language of, you know, traditional stuff. So that's awesome. Um, you and I, you mentioned, uh, Capella, you and I talked about, yes, at couch base connects Capella. You've been moving hard with your DBA strategy, how's it going? And then beyond these announcements, what's what should we look for from couch base? >>You know, uh, our fundamental, uh, mission is to make the developer experience, um, that much more easier, that much, uh, to move all the frictions that, that has existed for developers to adopt couch base. And, uh, the Capella strategy is to leverage the cloud. So you have number one, the ease of development, just bring your browsers, start to learn, develop even simple sample applications and deploy them from there. You can scale, and you can have production level deployments, that whole journey of a developer, along with the ability to sort of have your a, you know, metered billing and pay as you go, uh, uh, pricing, uh, so that it becomes easier for developers to sort of consume this and, uh, show the value of what they can build here. That is our, um, sort of journey of bringing it closer, uh, to our developers and make it simpler for them to sort of, uh, get started and build the, the mission critical applications that they have trusted to build on couch base, to become that much more simpler, faster, and easier for them. So that's the journey. So that's the kind of announcements you will see coming out in Capella. And for that this, this seven one server is, is the platform on which we, we are sort of adding those capabilities to make a Capella that much easier for developers to adopt >>Outstanding. You've been busy and it looks like you've got a lot of value. Yes. All right, we're gonna have to leave it there. Robbie, up next, we bring on the customer perspective with Amdocs. They've got a real world example of a modernization journey that they go through. They had to modernize legacy Oracle WebLogic infrastructure with a microservices architecture, and of course, couch base, keep it right there. You're watching the cube.

>> Welcome back to the Seaport in Boston and this is theCUBE's coverage of Red Hat Summit 2022. I'm Dave Vellante with my co-host Paul Gillin. Rob Belson is here as the Developer Relations Lead at Verizon. Robbie great to see you. Thanks for coming on theCUBE. >> Thanks for having me. >> So Verizon and developer relations. Talk about your role there. Really interesting. >> Absolutely. If you think about our mobile edge computing portfolio in Verizon 5G Edge, suddenly the developer is a more important persona than ever for actually adopting the cloud itself and adopting the mobile edge. So the question then quickly became how do we go after developers and how do we tell stories that ultimately resonate with them? And so my role has been spearheading our developer relations and experience efforts, which is all about meeting developers in the channels where they actually are, building content that resonates with them. Building out architectures that showcase how do you actually use the technology in the wild? And then ultimately creating automation assets that make their lives easier in deploying to the mobile edge. >> So, you know, telcos get a bad rap, when you're thinking it's amazing what you guys do. You put out all this capital infrastructure, big outlays. You know, we use our phones to drop a call. People like, "Ah, freaking Verizon." But it's amazing what we can actually do too. You think about the pandemic, the shift that the telcos had to go through to landlines to support home, never missed a beat. And yet at the same time you're providing all this infrastructure for people to come over the top, the cost forbid is going down, right? Your cost are going up and yet now we're doing this big 5G buildup. So I feel like there's a renaissance about to occur in edge computing that the telcos are going to lead new forms of monetization new value that you're going to be able to add, new services, new applications. The future's got to be exciting for you guys and it's going to be developer-led, isn't it? >> Absolutely. I mean it's been such an exciting time to be a part of our mobile edge computing portfolio. If you think back to late 2019 we were really asking the question with the advent of high speed 5G mobile networks, how can you drive more immersive experiences from the cloud in a cloud native way without compromising on the tools you know and love? And that's ultimately what caused us to really work with the likes of AWS and others to think about what does a mobile edge computing portfolio look like? So we started with 5G Edge with AWS Wavelength. So taking the compute and storage services you know and love in AWS and bringing it to the edge of our 4G and 5G networks. But then we start to think, well, wait a minute. Why stop at public networks? Let's think about private networks. How can we bring the cloud and private networks together? So you turn back to late 2021 we announced Verizon 5G Edge with AWS Outposts but we didn't even stop there. We said, "Well, interest's cool, but what about network APIs? We've been talking about the ability and the programmability of the 5G network but what does that actually look like to the developers? And one great example is our Edge Discovery Service. So you think about the proliferation of the edge 17 Wavelength Zones today in the US. Well, what edge is the right edge? You think about maybe the airline industry if the closest exit might be behind you absolutely applies to service discovery. So we've built an API that helps answer that seemingly basic question but is the fundamental building block for everything to workload orchestration, workload distribution. A basic network building block has become so important to some of these new sources of revenue streams, as we mentioned, but also the ability to disintermediate that purpose built hardware. You think about the future of autonomous mobile robots either ground and aerial robotics. Well, you want to make those devices as cheap as possible but you don't want to compromise on performance. And that mobile edge layer is going to become so critical for that connectivity, but also the compute itself. >> So I just kind of gave my little narrative up front about telco, but that purpose built hardware that you're talking about is exceedingly reliable. I mean, it's hardened, it's fossilized and so now as you just disaggregate that and go to a more programmable infrastructure, how are you able to and what gives you confidence that you're going to be able to maintain that reliability that I joke about? Oh, but it's so reliable. The network has amazing reliability. How are you able to maintain that? Is that just the pace of technology is now caught up, I wonder if you can explain that? >> I think it's really cool as I see reliability and sort of geo distribution as inextricably linked. So in a world where to get that best in class latency you needed to go to one place and one place only. Well, now you're creating some form of single source of failure whether it's the power, whether it's the compute itself, whether it's the networking, but with a more geo distributed footprint, particularly in the mobile edge more choices for where to deliver that immersive experience you're naturally driving an increase in reliability. But again, infra alone it's not going to do the job. You need the network APIs. So it's the convergence of the cloud and network and infra and the automation behind it that's been incredibly powerful. And as a great example, the work we've been doing in hybrid MEC the ability to converge within one single architecture, the private network, the public network, the AWS Outposts, the AWS Wavelength all in one has been such a fantastic journey and Red Hat has been a really important part in that journey. >> From the perspective of the developer when they're building a full cloud to edge application, where does Verizon pick up? Where do they start working primarily with you versus with their cloud provider? >> Absolutely. And I think you touched on a really important point. I think when you often think about the edge it's thought of as an either, or. Is it the edge? Is it the cloud? Is it both? It's an and I can't emphasize that enough. What we've seen from the customers greenfield or otherwise it's about extending an application or services that were never intended to live at the edge, to the edge itself, to deliver a more performant experience. And for certain control plane operations, metadata, backend operations analytics that can absolutely stay in the cloud itself. And so our role is to be a trusted partner in some of our enterprise customers' journeys. Of course, they can lean on the cloud provider in select cases, but we're an absolutely critical mode of support as you think about what are those architectures? How do you integrate the network APIs? And through our developer relations efforts, we've put a major role in helping to shape what those patterns really look like in the wild. >> When they're developing for 5G I mean, the availability of 5G of particularly you know, the high bandwidth 5G is pretty spotty right now. Mostly urban areas. How should they be thinking in the future developing an application roll out two years from now about where 5G will be at that point? >> Absolutely. I think one of the most important things in this case is the interoperability of our edge computing portfolio with both 4G and 5G. Whenever somebody asks me about the performance of 5G they ask how fast? Or for edge computing. It's always about benchmark. It's not an absolute value. It's always about benchmarking the performance to that next best alternative. What were you going to get if you didn't have edge computing in your back pocket? And so along that line of thought having the option to go either through 4G or 5G, having a mobile edge computing portfolio that works for both modes of connectivity even CAN-AM IoT is incredibly powerful. >> So it sounds like 4G is going to be with us for quite a while still? >> And I think it's an important part of the architecture. >> Yeah. >> Robert, tell us about the developer that's building these applications. Where does that individual come from? What's their persona? >> Oh, boy I think there's a number of different personas and flavors. I've seen everything from the startup in the back of a garage working hard to try to figure out what could I do for a next generation media and entertainment experience but also large enterprises. And I think a great area where we saw this was our 5G Edge Computing Challenge that we hosted last year. Believe it or not 100 submissions from over 22 countries, all building on Verizon 5G Edge. It was so exciting to see because so many different use cases across public safety, healthcare, media and entertainment. And what we found was that education is so important. A lot of developers have great ideas but if you don't understand the fundamentals of the infrastructure you get bogged down in networking and setting up your environment. And that's why we think that developer education is so important. We want to make it easy and in fact, the 5G Edge portfolio was designed in such a way that we'll abstract the complexities of the network away so you can focus on building your application and that's such a central theme and focus for how we approach the development. >> So what kind of services are you exposing via APIs? >> Absolutely, so first and foremost, as you think about 5G Edge with say AWS Wavelength, the infra there are APIs that are exposed by AWS to launch your infra, to patch your infrastructure, to automate your infrastructure. Specifically that Verizon has developed that's our network APIs. And a great example is our Edge Discovery Service. So think of this as like a service registry you've launched an application in all 17 edge zones. You would take that information, you would send it via API to the Edge Discovery Service so that for any mobile client say, you wake up one morning in Boston, you can ask the API or query, "Hey, what's the closest edge zone?" DNS isn't going to be able to figure it out. You need knowledge of the actual topology of the mobile network itself. So the API will answer. Let's say you take a little road trip 1,000 miles south to say Miami, Florida you ask that question again. It could change. So that's the workflow and how you would use the network API today. >> How'd you get into this? You're an engineer it's obvious how'd you stumble into this role? >> Well, yeah, I have a background in networks and distributed systems so I always knew I wanted to stay in the cloud somewhere. And there was a really unique opportunity at Verizon as the portfolio was being developed to really think about what this developer community looked like. And we built this all from scratch. If you look at say our Verizon 5G Edge Blog we launched it just along the timing of the actual GA of Wavelength. You look at our developer newsletter also around the time of the launch of Wavelength. So we've done a lot in such a short period and it's all been sort of organic, interacting with developers, working backwards from the customer. And so it's been a fairly new, but incredibly exciting journey. >> How will your data, architecture, data flow what will that look like in the future? How will that be different than it is sort of historically? >> When I think about customer workloads real time data architecture is an incredibly difficult thing to do. When you overlay the edge, admittedly, it gets more complicated. More places that produce the data, more places that consume data. How do you reconcile all of these environments? Maintain consistency? This is absolutely something we've been working on with the ecosystem at large. We're not going to solve this alone. We've looked at architecture patterns that we think are successful. And some of the things that we found that we believe are pretty cool this idea of taking that embedded mobile database, virtualizing it to the edge, even making it multi-tenant. And then you're producing data to one single source and simplifying how you organize and share data because all of the data being produced to that one location will be relevant to that topology. So Boston, as an example, Boston data being produced to that edge zone will only service Boston clients. So having a geo distributed footprint really does help data architectures, but at the core of all of this database, architectures, you need a compute environment that actually makes sense. That's performant, that's reliable. That's easy to use that you understand how to manage and that the edge doesn't make it any more difficult to manage. >> So are you building that? >> That's exactly what we're doing. So here at Red Hat Summit we've had the unique opportunity to continue to collaborate with our partners at Red Hat to think about how you actually use OpenShift in the context of hybrid MEC. So what have done is we've used OpenShift as is to extend what already exists to some of these new edge zones without adding in an additional layer of complexity that was unmanageable. >> So you use OpenShift so you don't have to cobble this together on your own as a full development environment and that's the role really that OpenShift plays here? >> That's exactly right. And we presented pieces of this at our re:Invent this past year and what we basically did is we said the edge needs to be inextricably linked with the cloud. And you want to be able to manage it from some seamless central pane of glass and using that OpenShift console is a great way. So what we did is we wanted to show a really geo-distributed footprint in action. We started with a Wavelength zone in Boston, the region in Northern Virginia, an outpost in the Texas area. We cobbled it all together in one cluster. So you had a whole compute mesh separated by thousands of miles all within a single cluster, single pane of glass. We take that and are starting to expand on the complexity of these architectures to overlay the network APIs we mentioned, to overlay multi-region support. So when we say you can use all 17 zones at once you actually can. >> So you've been talking about Wavelength and Outposts which are AWS products, but Microsoft and Google both have their distributed architectures as well. Where do you stand with those? Will you support those? Are you working with them? >> That's a great question. We have made announcements with Microsoft and Google but today I focus a lot on the work we do with AWS Wavelength and Outposts and continuing to work backwards from the customer and ultimately meet their needs. >> Yeah I mean, you got to start with an environment that the developers know that obviously a great developer community, you know, you see it at re:Invent. What was the reaction at re:Invent when you showed this from a developer community? >> Absolutely. Developers are excited and I think the best part is we're not the only ones talking about Wavelength not even AWS are the only ones talking about Wavelength. And to me from a developer ecosystem perspective that's when you know it's working. When you're not the one telling the best stories when others are evangelizing the power of your technology on your behalf that's when the ecosystem's starting to pick up. >> Speaking of making a bet on Outposts you know, it's somewhat limited today. I'll say it it's limited today in terms of we think it supports RDS and there's a few storage players. Is it your expectation that Outposts is going to be this essentially the cloud environment on your premises is that? >> That's a great question. I see it more as we want to expand customer choice more than ever and ultimately let the developers and architects decide. That's why I'm so bullish on this idea of hybrid MEC. Let's provide all of the options the most complicated geo distributed hybrid deployment you can imagine and automate it, make it easy. That way if you want to take away components of this architecture all you're doing is simplifying something that's already automated and fairly simple to begin with. So start with the largest problem to solve and then provide customers choice for what exactly meets their requirements their SLAs, their footprint, their network and work backwards from the customer. >> Exciting times ahead. Rob, thanks so much for coming on theCUBE. It's great to have you. >> Appreciate it, thanks for your time. >> Good luck. All right, thank you for watching. Keep it right there. This is Dave Vellante for Paul Gillin. We're live at Red Hat Summit 2022 from the Seaport in Boston. We'll be right back.

>>Hello everyone. And welcome to the cubes presentation of the AWS startup showcase open cloud innovations. This is season two episode one of our showcase ongoing series. We're covering very exciting startups from the AWS ecosystem. And we're going to be talking about the open source community. I'm your host, Lisa Martin. And today I'm excited to be joined by Robbie, Myra, the head of product and partner marketing at sneak. Robbie's here to talk with me about developer security for your digital transformation. Robbie, it's great to have you on the cube. >>Thanks Lisa. Nice to be here. >>So talk to me about what's going on in developer land. They're under a lot of pressure. A lot of them are building apps with open source, but what does sneak seeing from the developers lens >>From the developer's lens? There's a lot of pressure to build fast and that's probably the biggest challenge, right? We're in a world of digital transformation where everybody's trying to compete no matter what industry you're in, right on the technology and on the quality of your software or the capabilities of your software, which puts a lot of pressure on developers to build fast. That causes them to do a few things. One, it causes them to build, to develop in a way where they're doing constant iteration and so models that would have enabled a security check to come in at the end, aren't working anymore because they don't have time for those security checks. And it also causes them to do a good thing, which is to leverage other people's code when they can like open source. So they can just focus on, on their own functionality. And that's true, whether they're building new functionality or modernizing legacy applications by moving them to the cloud. >>So it's a high percentage of, of app code 80 to 90% is open source. Then that opens up. Talk to me about w where the vulnerabilities are and how you guys help customers and developers address that. >>Yeah, the vulnerabilities can be anywhere, but the key is that that point, right? If you're using open source in a typical application, 80 to 90 plus percent of the lines of code in that application are going to be open source code, their code. Somebody else wrote that you don't have a direct relationship with, and yet you own the risk that whatever they may have, whatever vulnerabilities may be in their code, you now own that risk. So what we're trying to do with sneakers, trying to do is enable developers to leverage open source, but do that securely. And then we also help them with the 10% that they rent as well, and, and do that all in one really easy environment for a developer that fits into their workflow and into their daily life. >>So security should shift left. I've had the chance to talk with a couple of, do you call them sneakers sneakers? Oh, you do a couple of sneakers recently. We've talked about security shifting lab. That's not a new concept, but I'd love to dig in more to how sneak and AWS do that. And I'm also curious if what you're doing helps. We've talked about the cybersecurity skills got for a long time. Now, just what you guys do, help address that >>It does because it's really leveraging a resource that, that is there, right? There's the number of developers worldwide is growing from, depending on who you believe for these numbers and their estimated numbers, right? But 25 million to 50 million over roughly a five-year period that's already started. So we're somewhere in the 30 now, right? Meanwhile, the security jobs, there's something like 9 million cyber security people in the world, and that's all cyber security roles. It's a much shorter, a smaller chunk that are application security folks. And there's three and a half million unfilled cybersecurity roles. So you can't get cyber security people and keep using the current model you're using. But just scale it linearly, you have to change things. And sneaks belief is the way you change things is you have the developers be part of your security solution, which means they need to have the ability to not only develop, but to develop securely. And that's our concept of developer security. We build tools and a platform that enables developers to be the first part of the security solution and enable security teams rather than individually auditing and fixing things to develop a process, govern the process, guide the development teams, but let the developers own that first step of security. And that's really how you solve that scale problem. >>When you're talking with customers, is this kind of a better together scenario, developers and security folks? Are you helping them align culturally because this is a change? >>Absolutely. I think one of the biggest misconceptions out there is that there's a tension between security and development. And I think that's because organizationally there might be right. Security is responsible for risk and developers responsible for speed of innovation and the faster you innovate, potentially there's more risk. So there might be some organizational tension, but at the human level, people understand each other, they understand the pressures that the other one's going through. They just don't have an easy way to work together. And if you can help them get that, then they, it really takes off it. The relationships form they'll build human to human programs like security champion programs and things to, to integrate the teams because they're both going after the same goal, both sides want to build awesome technology and grow in whatever market they're in. >>Right. And of course, with the need to do that at today's markets speed and scale is a great thing that you guys are doing to facilitate that collaboration. And of course the security let's kind of take a double-click now into the different integrations that sneek has with AWS services. I know there's quite a few, >>There's quite a few. The biggest one, probably the easiest one for the integrations is the native integration that we have with code pipeline. So it makes it easy for developers as they're finishing their builds and deploying to have an automatic security check that comes in, understands if there's things that need to be fixed before this really should be released, and then they can fix it and go forward. But we integrate across with our API across a lot of other services, ECR EKS code builder, so that wherever the developer is working, there's a way for us to integrate with them as they're building across their AWS development process. >>Okay. So giving them plenty of opportunity, let's dig into the platform. Talk to me about the platform, how it's really aimed at developers. You alluded to this a little bit, but I'd like to kind of take a double-click into the technology. >>Sure. That the platform, it, part of it is that idea of it we've wrapped it all as a developer tool. But the thing that makes sneak unique in this is not only we have the idea that we wanted to shift left in time, but we wanted to shift left in ownership. So the developers are primary user and we built a tool that is a developer tool that happens to do security. And we've extended that tool into a platform by enabling it to connect into the developers tools, sharing information, across different elements of what it securing. So for example, the open source that we're scanning for you and testing to find for vulnerabilities, we're also looking at the vulnerabilities in your code and where they may overlap or intersect. We can adjust priorities so that you might not need to fix something. Let's say you're using an open source, vulnerable, a package that has a vulnerability, but your code is never going to access that you don't need to fix it. >>So you can prioritize that one lower, right? Same thing with Kubernetes and containers. You may have a container vulnerability, but the way you're going to leverage the container that won't be used so we can adjust the priority to make it easy for the developer. And that's the other big thing that's different about a developer security platform than a typical security tool. A typical security tool is an audit tool it's designed to output. Here are all the things you have a problem with a developer security tool is a fixing tool. It's just defined as a, here are the problems you have developed with here's how you fix it and go back to building on that. That prioritization is a big part of that, because you can say, here's what you don't need to worry about. And then you can focus the rest of your energy on helping developers fix the problem either by giving them really good advice or automating it for them and saying, Hey, here's a button click that will generate a pull request. And your problem is this fixed. >>It must go a long way to improving developer productivity, one facilitating that speed and the agility with which they need to work, but also from a developer kind of crowd sourcing, crowd swell perspective. I imagine, talk to me about what some of the voices are, the developers that are in your community. What are some of the things that they're saying in terms of how much faster they're able to work, they're able to get those priorities established with automation so much faster? >>Well, that's the biggest thing. Is there a, the productivity gain happens because of the benefit of shift left, right? You're testing earlier. You're finding it at an earlier time when it's easier to fix, but that's because they're the ones doing it, right. If they're waiting to hand off to an auto report and then it comes back, even if somebody is, is giving them them audit faster, it's still after they've moved on. And the other way people try to solve it as well. They'll say, well, I'll take a security tool then to hand it to the developer and they can run it. But so developers are not security experts. So the tool needs to understand what they know and what they don't know, and, and working in an upload. And that's what developers generally say to us because sneak makes it easy to work, but also focuses on the fix and helps them guide them to that, to that answer. Then they're able to go much faster when we're evaluated by companies who are looking for a security solution. If the developers get involved in that evaluation, they'll choose sneak. >>So I'm curious a little bit about as, as the head of product marketing, I'm thinking customer advisory boards, things like that. What's the collaboration like between sneak and the developers to really tune and push the technology forward. I imagine it's quite collaborative, >>Quite collaborative and it's across a lot of, of spectrum. So we do have a customer advisory board and that's generally leaders, right? That's either security leaders or development leaders or operations leaders who are in that advisory board. And they're giving us input on things they need for program-wide governance or program wide adoption. We also have a developer community where we're talking directly to developers and that's where we get a lot of, Hey, here's how I could use this better as a developer. And that guides where we focus features that help developers work better, whether it's integrations with our IDs or whether it's the way we present information, help them prioritize. And then the third part is we have a lot of people using the tool because it has a free model, right? We're as a developer tool, we have a freemium model. There's a level of sneak that developers can use that they don't need to pay for. That's not a temporary trial, it's forever. If you want to use it at that level and we can observe what they're doing. So that observability gives us another insight into where folks get challenged run into, to struggles. And then we can look to address those in our roadmap as well. So, so all of that together really helps us drive the product forward. >>What is the perspective from the analyst view? You talked a little bit about the perspective from the customer. We'll get into a customer story in a bit, but I'd love to know what are the gardeners saying? >>Well, Gardner especially put us, we debuted in their magic quadrant for application security last year. And we did David as a visionary and sort of the highest part of the visionary quadrant you could get in before you crossed over into leader, which is kind of unheard of for a first time into the, into the quadrant. And the main reason for that is that they have built the way those, those magic quadrants are built is they have key capabilities and then they score companies against key capabilities and they weight those capabilities, you know, by order of importance. And Gardner has started to put some of this notion of developer security and cross cloud native application security into those key capabilities. And those tend to align really well with what sneakers. So they have a, for example, a software composition, which is sort of open source security analysis, where first, w w w where the top ranking in that, where the top ranking and container security, where the top ranking and developer enablement. So that's pulling us, they are so-so Gardner and the analyst community is seeing this same demand coming from their customers. And that's really aligning to where our vision is. >>And in terms of kind of propelling that vision forward, the voice of the customer, the voice of the analyst, aligning with what you guys are doing to kind of lead the vision going forward. I want to get into some of the intelligence before we kind of break into a customer example. Talk to me a little bit about snakes security intelligence, what the key capabilities are, and some customers that are leveraging it. Sure. >>The biggest thing is with all the developer tool wrapping that needs to be in this product than it is a developer tool. It's got a developers heart, but it has to have a security brain because it still is a security tool. There are some developer tools. We try to have little check the box capabilities of security and they'll crowdsource for vulnerabilities potentially. But if you're doing this, you need to make sure that all the vulnerabilities that could be found are in the database to be able to be found that the database is comprehensive, that it's timely. They get in very quickly that it's accurate. You don't waste time on false positives because that will turn developers off faster than anything. And that it's actionable. So when it does find something, it helps you go forward with it. And that's where sneaks really focused on. So we collect data from multiple public sources. >>We also have a fairly large proprietary research team that curates that information determines what needs to go in. Sometimes we'll adjust priorities. And we also get a lot of contributions from other sources like community contributions. Again, that big free user base of ours is giving us input academia. Open source groups are also in their social media trends. So if we see something trending on Twitter, then that'll not only get it into the database, but it'll drive prioritization. And that's a big part of what's in sneak Intel, which is the name we use for our vulnerability database. We also have a machine learning algorithm. That's constantly looking at all the code in public, in public applications and repositories. And we use that to train for our own proprietary code testing tool, but it also just gets a lot of it finds things there as well. So it brings a really good source of information that helps people make sure you're finding the vulnerabilities, you're prioritizing them correctly and fixing them. And so Amazon's one who is the, you know, one of the folks that using that tool where one of the primary sources of, of Amazon inspector for open source vulnerabilities, as well as a bunch of other security companies like rapid seven tenable and, and others. >>One of the things I was reading from, I'm always kind of looking at the differentiators and I'm sure you are as the head of product marketing and partner marketing, but it sounds like the database can, is, is a key differentiator finding vulnerabilities up to what is it? 46 days faster than competitors. >>Yeah. I mean, faster than especially public sources, which are the easier ones to, to know how you're doing against, but that's a big part of us. So when I talked about those categories, that's really what we measure ourselves against. How are we doing in terms of comprehensive? Do we have the vulnerabilities that we should have? So we have over four times the number of vulnerabilities as the next largest publicly available database, we find them faster, so timely. So that's at 46 days getting it in faster or faster than other public sources, they get into our solution and then accuracy. Again, we, it's not a stat we can test because you can't test it just from the database. You have to run the tools of our, of others in this space. And we don't have those, but making sure that you're not hitting a lot of false positives is a big part of it as well. >>Got it. Okay. And we only have a couple minutes left, but there's two more areas that I want to dig into with you just crack crack. The surface one is log four, shallow was reading. Snake says this. We were the perfect solution at the perfect time. Unpack that for me in the next minute or so. >>Yeah. And that's a bit, and it kind of wraps back to what we were talking about earlier. Everybody's using open source. If you're in the Java world, a lot of folks had logged for shell and we're using lock for shell for logging as a part of their, as a part of their applications. And so a lot of our customers, I think it was over 30%, 36% of our paying customers had the vulnerability. And you would only have the vulnerability of your Java. So it's a very large percentage of our Java using my customers had the vulnerability, but because they were using sneak, they were able, once we put it in the database, which we did the day, it was disclosed, they were able to find it and fix it very quickly. So 91% of our customers fixed that vulnerability in just two days, 98%, because this was a rolling thunder event, right. There was a vulnerability. And then there was a second vulnerability in the, in the fix. And then there was a vulnerability, even in the fix of that. So the second vulnerability that came out because everybody had been ready for it from the first time 98% picks within two days. Whereas the median number of days to generally fix a vulnerability is over two months. So really fast addressing the solution. >>So those are really impressive. And speaking of stats, I wanted to get into just really quickly a case study that really shows that lasting is one of your customer. One of your many customers, big developer community there about 3,500 developers. Give me some kind of the high level of business outcomes that at Lasagne is, is, is achieving thanks to sneaky. >>Yeah. I mean the biggest one is that almost 99% of their applications are deployed in containers. So being able to have the containers tested for vulnerabilities as they're being deployed before they're being deployed is huge for them to reduce the risk of a vulnerability. They, they had a 65% reduction in high severity container volumes a few months after using sneak across all those developers, which really reduces your, your risk profile of your, of your cloud native applications. They're obviously a big AWS user as well. So, so for them, that was the big thing. And again, it goes to that scale, right? They've got 3 3500 developers, more than 3,500 developers. If you try to go through the security team and have the security team fixing all those things, you'll just never catch up. >>Got it. Last question. Where can I get this available through the AWS market prays marketplace? You mentioned the freemium model, give folks kind of a direction on where to go. >>Yeah. So I would say if you are a, if you're someone in the security team, if you're a buyer, the AWS marketplace is a great place to go because you can probably leverage your existing spend commits with AWS. It's easy to purchase, easy billing, et cetera. If you're a developer, then there is this free version where you might go and just start using it and get comfort for it. And if you are a buyer, talk to your developers because there's a pretty good chance. Someone in your company, that's a developer is already using. Sneak will be comfortable with it. These solutions are only successful. If the developers actually use it, you can't shift left unless the developers pick it up and use it. So using the one that developers are already using is probably a good idea. >>Awesome. Robbie, this has been a great conversation, so much momentum at snake. You're the third sneaker I'd gotten to speak to you in the last month and I have, it's pretty exciting, but thanks for walking us through the technology, the capabilities, the differentiators, the voice of the customer, the voice of the analyst, we appreciate your insights and your time. And we look forward to next time we talk to you. >>Terrific. Lisa, I look forward to it as well, but there's a lot more Smith sneakers to go through before you get back to me again. I guess >>I look forward to adding to my repertoire of sneaker interviews, Ravi. Thanks so much. Thank you for Ravi Myra. I'm Lisa Martin. You're watching this cube interview as part of the AWS startup showcase. Stick around more great content coming up next.

>>I want to welcome back to the cubes coverage. We're here at another event in person I'm John furrier, host of the cube. We've got to CNCF coop con cloud native con for in-person 2021. And we're back. It's a hybrid event and we're streaming lives on all channels, as well as all the folks watching a great guest kicking off the show here from the co-chairs from cataract coast. Is that right? Danielle Cook. Who's the vice president at Fairwinds and John Foreman director at Accenture. Thanks for coming on your co-chair. Your third co-chair is not here, but you guys are here to talk about the cloud maturity model. Pretty mature funding is flowing tons of announcements. We're going to have a startup on $200 million. They're announcing in funding and observability of all of all hot spaces. Um, so the maturity is it's the journey in the cloud native space now is crossed over to mainstream. That's the we've been telling that story for a couple of years. Now, you guys have been working on this. Tell us about the cloud maturity model you guys worked on. >>So we got together earlier this year because we, um, four of us had been working on maturity models. So Simon Forester, who is one of the co-chairs, who isn't here, he had worked on a maturity model that looked at your legacy journey, all the way to cloud native, um, myself, I had been part of the Fairwinds team working on the Kubernetes maturity model. So, and then, um, we have Robbie, who's not here. And John Foreman, who we all got together, they had worked on a maturity model and we put it together and I've been working since February to go, what is cloud native maturity and what are the stages you need to go through to achieve maturity. So put this together and now we have this great model that people can use to take them from. I have no idea what cloud native is to the steps they can take to actually be a mature organization. >>And, you know, you've made it when you have a book here. So just hold that up to the camera real quick. So you can see it. It's very much in spirit of the community, but in all seriousness, it book's great, but this is a real need. What was the pain point? What was jumping out at you guys on the problem? Was it just where people like trying to get more cloud native, they want to go move faster. It was a confusing, what were the problems you solve in? >>Well, and if anything is, if we start at the beginning, right, there was during the cloud journey DevSecOps and the Kootenays being a thing that then there's journeys to DevSecOps tributaries as well. But everything is leading to cloud native. It's about the journey to cloud native. So everybody, you know, we're taught to go John, the ecosystem's an eyesore man. If I look at, you know, landscape, >>The whole map I >>Need, it's just like in trend map, it's just so confusing what we do. So every time we go to, I revert the wheel and I get them from zero to hero. So we just put together a model instead that we can re reuse yeah. As a good reference architecture. So from that is a primary, how we built because the native trademark you have with us today. So it's a five scale model from one to five what's twice today, or how to, to, you know, what our job is getting to a five where they could optimize a really rocket rolling. >>You know, it's interesting. I love these inflection points and, you know, being a student of history and the tech business there's moments where things are the new thing, and they're really truly new things like first-time operationalized dev ops. I mean the hardcore dev ops or early adopters we've been doing that, you know, we know that, but now mainstream, like, okay, this is a real disruption in a positive way. So the transformation is happening and it's new, new roles, new, new workflows, new, uh, team formations. So there's a, it's complicated in the sense of getting it up and running so I can see the need. How can you guys share your data on where people are? Because now you have more data coming in, you have more people doing dev ops, more cloud native development, and you mentioned security shepherds shifting left. Where's the data tell you, is it, as you said, people are more like a two or more. What's the, what's the data say? >>So we've had, so part of pulling this model together was your experience at Accenture, helping clients, the Fairwinds, um, experience, helping people manage Kubernetes. And so it's from out dozens of clusters that people have managed going, okay, where are people? And they don't even know where they are. So if we provide the guidelines from them, they can read it and go, oh, I am at about two. So the data is actually anecdotal from our experiences at our different companies. Um, but we, you know, we we've made it so that you can self identify, but we've also recognized that you might be at stage two for one application, but five for another application. So just because you're on this journey, doesn't mean everything is in, >>It's not boiler plate. It's really unique to every enterprise because they everyone's different >>Journey. Put you in journey with these things. A big part of this also torn apart one to five, your clients wants to in denial, you know? So, so Mr. CX level, you are level two. We are not, there's no way we would deal with this stuff for years. You've got to be a five. No, sorry. You're too. >>So >>There's use denial also about this. People think they do a cloud-native director rolling, and I'm looking at what they're doing and go, okay, do you do workups security? And they go, what's that? I go, exactly. So we really need to peel back the onion, start from seed year out and we need to be >>All right. So I want to ask more about the, um, the process and how that relates to the themes are involved. What are some of the themes around the maturity model that you guys can share that you see that people can look at and say, how do I self identify? What's the process will come to expect? >>Well, one of the things we did when we were putting it together was we realized that there were themes coming out amongst the maturity model itself. So we realized there's a whole people layer. There's a whole policy layer process and technology. So this maturity model does not just look at, Hey, this is the tech you need to do. It looks at how you introduce cloud native to your organization. How do you take the people along with it? What policies you need to put in place the process. So we did that first and foremost, but one of the things that was super important to all of us was that security was ever present throughout it. Because as everything is shifting left, you need to be looking at security from day one and considering how it's going to happen and roll out from your developers all the way to your compliance people. Um, it's super important. And one of the themes throughout. >>So, so it would be safe to say, then that security was a catalyst for the maturity models because you gotta be mature. I mean, security, you don't fool around security. >>About the last year when I created the program for, since I worked with Cheryl Holland, from CCF, we put together the community certification, her special program. I saw a need where security was a big gap in communities. Nobody knew anything about it. They wanted to use the old rack and stack ways of doing it. They wanted to use their tray micro tombs from yesteryear, and that doesn't work anymore. You need a new set of tools for Kubernetes. It's the upgrade system. It's different way of doing things. So that knowledge is critical. So I think you're part of this again, on this journey was getting certifications out there for people to understand how to do better. Now, the next phase of that now it's how do we put all these pieces together and built this roadmap? >>Well, it's a great group. You guys have the working groups hard to pronounce the name, but, uh, it's a great effort because one of the things I'm hearing and we've been reporting this one, the Cubans looking angle is the modern software developers want speed, and they don't want to wait for the old slow groups now and security, and it are viewed as blockers and like slow things down. And so you start to see a trend where those groups could provide policy and then start putting, feeding up, uh, data models that allow the developers in real time to do their coding, to shift left and to be efficient and move on and code not be waiting for weeks or days >>Comes to play. So today is the age of Caleb's right now, get up this emerging we're only to have now where everything is code policies, code, securities, code policies, cookie figures, code. That is the place for, and then again, walk a fusion more need for a cargo office. >>Okay. What's your thoughts on that? >>So I think what's really important is enabling service ownership, right? You need the developers to be able to do security, see policy, see it live and make sure that, you know, you're not your configuration, isn't stopping the build or getting into production. So, you know, we made sure that was part of the maturity model. Like you need to be looking continuous scanning throughout checking security checking policy. What is your process? Um, and we, you know, we made that ever present so that the developers are the ones who are making sure that you're getting to Kubernetes, you're getting to cloud native and you're doing it. >>Well, the folks watching, if you don't know the cloud native landscape slide, that ecosystem slide, it's getting bigger and bigger. There's more new things emerging. You see role of software abstractions coming in, automation and AI are coming in. So it makes it very challenging if you want to jump right in lifting and shifting to the clouds, really easy check, been there, done that, but companies want to refactor their applications, not just replatform refactoring means completely taking advantage of these higher level services. So, so it's going to be hard to navigate. So I guess with all that being said, what you guys advice to people who are saying, I need the navigation. I need to have the blueprint. What do I do? How do I get involved? And how do I leverage this? >>We want people to, you can go on to get hub and check out our group and read the maturity model. You can understand it, self identify where you're at, but we want people to get involved as well. So if they're seeing something that like, actually this needs to be adjusted slightly, please join the group. The cardiograph is group. Um, you can also get copies of our book available on the show. So if you, um, if you know, you can read it and it takes you line by line in a really playful way as to where you should be at in the maturity model. >>And on top of that, if you come Thursday was Sonia book. And of course, a lot of money, one day, I promise >>You guys are good. I gotta ask, you know, the final question is like more and more, just more personal commentary. If you don't mind, as teams start to change, this is obviously causing a lot of positive transformation if done, right? So the roles and the teams are starting to change. Hearing SRS are now not just the dev ops guys provisioning they're part of the, of the scale piece, the developers shifting left, new kind of workflows, the role of certain engineers and developers now, new team formations. Why were you guys seeing that evolve? Is there any trends that you see around how people are reconfiguring their team makeup? >>I think a lot of things is going to a single panic last tonight, where I'm taking dev and ops and putting them one panel where I can see everything going on in my environment, which is very critical. So right now we're seeing a pre-training where every client wants to be able to have the holy grail of a secret credit class to drive to that. But for you to get there, there's a lot of work you've got to do overnight that will not happen. And that's where this maturity model, I think again, will enhance that ability to do that. >>There's a cultural shift happening. I mean, people are changing there's new skillsets and you know, obviously there's a lot of people who don't have the skill. So it's super important that people work with Kubernetes, get certified, use the maturity model to help them know what skills they need. >>And it's a living document too. It's not, I mean, a book and I was living book. It's going to evolve. Uh, what areas you think are going to come next? So you guys have to predict if you had to see kind of where the pieces are going. Uh, obviously with cloud, everything's getting, you know, more Lego blocks to play with more coolness you have in the, in this world. What's coming next with Sue. Do you guys see any, any, uh, forecasts or >>We're working with each one of the tag groups within the CNCF to help us build it out and come up with what is next based on their expertise in the area. So we'll see lots more coming. Um, and we hope that the maturity grows and because of something that everybody relies on and that they can use alongside the landscape and the trail map. And, um, >>It's super valuable. I think you guys need a plug for any people want to, how they join. If I want to get involved, how do I, what do I do? >>Um, you can join the Carter Garfish group. You can check us out on, get hub and see all the information there. Um, we have a slack channel within the CNCF and we have calls every other Tuesday that people can see the pools. >>Awesome. Congratulations, we'll need it. And super important as people want to navigate and start building out, you know, you've got to edge right around the corner there it's happening real fast. Data's at the edge. You got cloud at the edge. Azure, AWS, Google. I mean, they're pushing really hardcore 5g, lot changes. >>Everybody wants to cloud today. Now one client is, one is more cloud. At least both the cloud is comfortable playing everywhere. One pump wife had DevOps. >>It's distributed computing back in the modern era. Thank you so much for coming on the keep appreciating. Okay. I'm Jennifer here for cube con cloud native con 2021 in person. It's a hybrid event. We're here live on the floor show floor, bringing you all the coverage. Thanks for watching station all day. Next three days here in Los Angeles. Thanks for watching. >>Thank you.

>>Mhm Yeah, this is Dave valentin. I'd like to welcome you back to the cubes coverage of couch base connect online with the theme of this event is modernized now and one of the big announcements is Capella which of course as you all undoubtedly know is the brightest star in the constellation Auriga, which is latin for charioteer, yep, you can find that in the constellation of that constellation of the night sky in late february, early March in the northern hemisphere. So with that little tidbit, I'd like to welcome in scott Anderson to the cube, who is the senior vice president of product management and business operations. That couch base scott welcome. Good to see you. >>Thank you very much. Thanks for having me. >>That's our pleasure. So you've launched couch based cappella if I understand correctly, it's built on couch based server seven which he launched just a few months ago in the middle of the summer. Can you give us an overview of Capella? >>Yeah, absolutely. So couch based Capellas are fully managed databases. Service for enterprise applications. One of the goals of launching Capella and our databases is service offering that we just announced today is about increasing the accessibility of couch base so it's about making it easy for a developer or an enterprise to get up and running in just a few clicks in a couple of minutes Um and about making it more affordable and accessible through the development phase through the test phase, the production phase. So really it's about ease of use having the right offerings aligned to the phase of development that customers in and eventually into the production of their enterprise application leveraging capella and couch based Server seven. >>So let me ask you, I I went pretty deep with ravi on the, on the technical side and I want to understand what makes Capella different from some of the competitive offerings. Is it the sort of the fundamentals that I learned from Ravi about how you guys have have have really done an awesome focus on on on sequel but been able to maintain acid compliance deal with distributed architectural challenges and then bringing that over to database as a service. Is that the fundamental, what are some of the other differentiators? >>Yeah, that that is the fundamental, we have an amazing platform that Roddy and our core engineering team built and we've talked about that and I think Robbie mentioned that the ease of sequel and applying that to a documented oriented database, then combining some of those capabilities with the ease of use, the ability that you can get up and running, signing up for our free trial couple minutes later you've got a database endpoint that is fully managed by couch base. And so we're doing the monitoring, we're doing alerting, we have calls to action based off what events are occurring within the database environment, ensuring it's always available as well as doing kind of the mundane tasks of backup and recovery, uh scaling the environment, upgrades and so forth. So it's really about ease of use, making it um leveraging are incredibly robust, broad platform um and then making that in different consumable model for our customers and developers and getting started really easily. The other thing that we have done is really leverage the best practices over the last 10 or 11 years of some of the largest enterprises in the world using couch based for the mission critical applications. So we've codified those best practices and that's how we keep that service, high performance, always on highly available. And that's one of the core value propositions that were able to bring with Capella. It's really about management capability, global visibility of your clusters coupled with what we believe is the best no sequel database in the marketplace today. >>What about what about cost, total cost of ownership as you scale a lot of times when you scale out, you get dis economies of scale, it's kind of like, you know, you get that negative curve, uh what are you seeing? >>Yeah, we've done a third party benchmark studies which have proven out how we are able to literally scale the environment uh and continue on that curve as you add notes, you're getting that incremental performance that you would expect. The other thing that we do that's really unique within couch bases are multidimensional scaling and this allows you to place our services, things like data index, query, full text search indexes and analytics, you can co locate those on single nodes within a cluster or you can have dedicated notes for each one of those services. The reason that is important is you get work line isolation for those specific services within our cluster. The other thing that you can do is you can match the compute infrastructure to the needs of each one of those services. So some services like query are much more core, compute intensive and that allows you to have a specific instance type that is optimized for that, reducing your costs, indexes where you want very fast performance, you may want to have a higher amount of memory relative to the number of course. So that ability to mix and match the infrastructure with an existing cluster allows us to lower overall costs. That coupled with their blazing fast performance with our in memory architecture allows people to get incredible performance at scale. Um, what we've proven out in the study that I mentioned earlier is we have that linear scalability and you're able to do more for less at the end of the day, you're getting more operations per second per dollar if you want to use that as a metric. >>Got it. Thank you for that. What do customers need to think about when they want to get started with Capela? How difficult is it for people to jump in? >>It is incredibly simple. It's as simple as going to couch base dot com clicking on start your free trial, You're going to that free trial, you provide a minimal set of information for us and it's literally a few clicks and you're going to have a database endpoint within three minutes and that's really been a foundation of, of what we've been focused on over the last 6-9 months is removing any friction we can in the process because our goal is to give a tremendous user experience and get people up and running as quickly as possible. So we're really, really proud of that. And then from a paid offering perspective, we have a number of offerings which are really aligned to the needs of each customer, some individuals who want a larger cluster and they want to be able to pay for that. We've optimized service levels around that in terms of level support and the features that we think are appropriate for a dev cycle, a test cycle and then into production and lastly we will be announcing a number of promotional starter pack bundles, really trying to couple the overall service that we have with Capella with some of our expertise, so helping new users get up and running in terms of things like index definitions, what's the best way to do document design and schema within within couch base. Our end goal is to match these services and bundles with the life cycle of application development. So in my development phase what's the offering for me as I move for production readiness, what services capabilities I need and then production and the ongoing if I expand my use. So we've been really focused on, how do we get people up up and running as quickly as possible and how do we get them to production as quickly as possible at the lowest total cost? >>That's nice. That's a nice accelerant for customers. Um, so as you heard upfront, I did a little research about the name Capella. How did you choose it and why? >>Well, one thing I learned early in my career is naming is not a strong suit of mine. I leave that to John or our chief marketing officer in the overall team. Um, we all have opinions, but I trust John and we went through, I think it was over 60 names, seven rounds of debate to come up with capella, but we want to name of strength. We like the alliteration couch, basic capella together. Um, one of the little facts may have tipped it over is I believe in latin, it means little goats. So we kind of played from the barriers. Always think to jerry rice goat, greatest of all time. So that was a nice play on that also. Um, but I leave it to them and really happy with the overall name, love the liberation, Love some of the hidden meanings within that. Um, and we're really, really excited about getting going. So you wouldn't want me to pick the name. Um, I get a vote. Um, but I would say my overall influence is a little bit lower than where john's is and matt cain, who I know you spoke with previously. >>I love it, jerry rice definitely is a little go because I'm from New England. So of course tom we think tom brady is the big goat. I >>know we've, I grew up in that joe Montana era, so maybe you can take that off line after this interview. We can have our own debate, but I guess super bowl trophies or the ultimate measure at the end of the day. >>Now I've got a little stat for you. So, so Capella is also one of the 88 modern constellations as adopted by the International Astronomical Union. I. E. Not one of the ancient constellations. Pretty clever. Right. >>Exactly. >>Scott is great to have you on the cube. Thanks so much. Really, >>thank you so much. >>All right. And thank you for watching. Thank you for watching. Our pleasure. Thank you much of the cubes coverage of couch based connect 2021. Keep it right there for more great content. Mm mhm

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Hey, welcome back already, Jeffrey here with the Cube coming to you from Palo Alto studios today for our ongoing coverage of aws reinvent 2020. It's a digital event like everything else in 2020. We're excited for our next segment, so let's jump into it. We're joined in our next segment by Andrew Rafa. He is the principal and zero trust offering lead at the Light and Touche LLP. Andrew, great to see you. >>Thanks for having me. >>Absolutely. And joining him is Robbie Deval. He is the AWS cyber risk lead for Deloitte and Touche LLP. Robbie, Good to see you as well. >>Hey, Jeff, good to see you as well. >>Absolutely. So let's jump into it. You guys are all about zero trust and I know a little bit about zero trust I've been going to are safe for a number of years and I think one of the people that you like to quote analysts chase Cunningham from Forrester, who's been doing a lot of work around zero trust. But for folks that aren't really familiar with it. Andrew, why don't you give us kind of the 101? About zero trust. What is it? What's it all about? And why is it important? >>Sure thing. So is your trust is, um, it's a conceptual framework that helps organizations deal with kind of the ubiquitous nature of modern enterprise environments. Um, and then its course. Your trust commits to a risk based approach to enforcing the concept of least privileged across five key pillars those being users, workloads, data networks and devices. And the reason we're seeing is your trust really come to the forefront is because modern enterprise environments have shifted dramatically right. There is no longer a defined, clearly defined perimeter where everything on the outside is inherently considered, considered untrusted, and everything on the inside could be considered inherently trusted. There's a couple what I call macro level drivers that are, you know, changing the need for organizations to think about securing their enterprises in a more modern way. Um, the first macro level driver is really the evolving business models. So as organizations are pushing to the cloud, um, maybe expanding into into what they were considered high risk geography is dealing with M and A transactions and and further relying on 3rd and 4th parties to maintain some of their critical business operations. Um, the data and the assets by which the organization, um transact are no longer within the walls of the data center. Right? So, again, the perimeter is very much dissolved. The second, you know, macro level driver is really the shifting and evolving workforce. Um, especially given the pandemic and the need for organizations to support almost an entirely remote workforce nowadays, um, organizations, they're trying to think about how they revamp their traditional VPN technologies in order to provide connectivity to their employees into other third parties that need to get access to, uh, the enterprise. So how do we do so in a secure, scalable and reliable way and then the last kind of macro level driver is really the complexity of the I t landscape. So, you know, in legacy environment organizations on Lee had to support managed devices, and today you're seeing the proliferation of unmanaged devices, whether it be you know, B y o d devices, um, Internet of things, devices or other smart connected devices. So organizations are now, you know, have the need to provide connectivity to some of these other types of devices. But how do you do so in a way that, you know limits the risk of the expanding threat surface that you might be exposing your organization to by supporting from these connected devices? So those are some three kind of macro level drivers that are really, you know, constituting the need to think about security in a different >>way. Right? Well, I love I downloaded. You guys have, ah zero trust point of view document that that I downloaded. And I like the way that you you put real specificity around those five pillars again users, workloads, data networks and devices. And as you said, you have to take this kind of approach that it's kind of on a need to know basis. The less, you know, at kind of the minimum they need to know. But then, to do that across all of those five pillars, how hard is that to put in place? I mean, there's a There's a lot of pieces of this puzzle. Um, and I'm sure you know, we talk all the time about baking security and throughout the entire stack. How hard is it to go into a large enterprise and get them started or get them down the road on this zero trust journey? >>Yeah. So you mentioned the five pillars. And one thing that we do in our framework because we put data at the center of our framework and we do that on purpose because at the end of the day, you know, data is the center of all things. It's important for an organization to understand. You know what data it has, what the criticality of that data is, how that data should be classified and the governance around who and what should access it from a no users workloads, uh, networks and devices perspective. Um, I think one misconception is that if an organization wants to go down the path of zero trust, there's a misconception that they have to rip out and replace everything that they have today. Um, it's likely that most organizations are already doing something that fundamentally aligned to the concept of these privilege as it relates to zero trust. So it's important to kind of step back, you know, set a vision and strategy as faras What it is you're trying to protect, why you're trying to protect it. And what capability do you have in place today and take more of an incremental and iterative approach towards adoption, starting with some of your kind of lower risk use cases or lower risk parts of your environment and then implementing lessons learned along the way along the journey? Um, before enforcing, you know more of those robust controls around your critical assets or your crown jewels, if you >>will. Right? So, Robbie, I want to follow up with you, you know? And you just talked about a lot of the kind of macro trends that are driving this and clearly covert and work from anywhere is a big one. But one of the ones that you didn't mention that's coming right around the pike is five g and I o t. Right, so five g and and I o. T. We're going to see, you know, the scale and the volume and the mass of machine generated data, which is really what five g is all about, grow again exponentially. We've seen enough curves up into the right on the data growth, but we've barely scratched the surface and what's coming on? Five G and I o t. How does that work into your plans? And how should people be thinking about security around this kind of new paradigm? >>Yeah, I think that's a great question, Jeff. And as you said, you know, I UT continues to accelerate, especially with the recent investments and five G that you know pushing, pushing more and more industries and companies to adopt a coyote. Deloitte has been and, you know, helping our customers leverage a combination of these technologies cloud, Iot, TML and AI to solve their problems in the industry. For instance, uh, we've been helping restaurants automate their operations. Uh, we've helped automate some of the food safety audit processes they have, especially given the code situation that's been helping them a lot. We are currently working with companies to connect smart, wearable devices that that send the patient vital information back to the cloud. And once it's in the cloud, it goes through further processing upstream through applications and data. Let's etcetera. The way we've been implementing these solutions is largely leveraging a lot of the native services that AWS provides, like device manager that helps you onboard hundreds of devices and group them into different categories. Uh, we leveraged device Defender. That's a monitoring service for making sure that the devices are adhering to a particular security baseline. We also have implemented AWS green grass on the edge, where the device actually resides. Eso that it acts as a central gateway and a secure gateway so that all the devices are able to connect to this gateway and then ultimately connect to the cloud. One common problem we run into is ah, lot of the legacy i o t devices. They tend to communicate using insecure protocols and in clear text eso we actually had to leverage AWS lambda Function on the edge to convert these legacy protocols. Think of very secure and Q t t protocol that ultimately, you know, sense data encrypted to the cloud eso the key thing to recognize. And then the transformational shift here is, um, Cloud has the ability today to impact security off the device and the edge from the cloud using cloud native services, and that continues to grow. And that's one of the key reasons we're seeing accelerated growth and adoption of Iot devices on did you brought up a point about five G and and that's really interesting. And a recent set of investments that eight of us, for example, has been making. And they launched their AWS Waveland zones that allows you to deploy compute and storage infrastructure at the five G edge. So millions of devices they can connect securely to the computer infrastructure without ever having to leave the five g network Our go over the Internet insecurely talking to the cloud infrastructure. Uh, that allows us to actually enable our customers to process large volumes of data in a short, near real time. And also it increases the security of the architectures. Andi, I think truly, uh, this this five g combination with I o t and cloudy, I m l the are the technologies of the future that are collectively pushing us towards a a future where we're gonna Seymour smart cities that come into play driverless connected cars, etcetera. >>That's great. Now I wanna impact that a little bit more because we are here in aws re invent and I was just looking up. We had Glenn Goran 2015, introducing a W S s I O T Cloud. And it was a funny little demo. They had a little greenhouse, and you could turn on the water and open up the windows. But it's but it's a huge suite of services that you guys have at your disposal. Leveraging aws. I wonder, I guess, Andrew, if you could speak a little bit more suite of tools that you can now bring to bear when you're helping your customers go to the zero trust journey. >>Yeah, sure thing. So, um, obviously there's a significant partnership in place, and, uh, we work together, uh, pretty tremendously in the market, one of the service are one of solution offering that we've built out which we dub Delight Fortress, um is a is a concept that plays very nicely into our zero trust framework. More along the kind of horizontal components of our framework, which is really the fabric that ties it all together. Um s o the two horizontal than our framework around telemetry and analytics. A swell the automation orchestration. If I peel back the automation orchestration capability just a little bit, um, we we built this avoid fortress capability in order for organizations to kind of streamline um, some of the vulnerability management aspect of the enterprise. And so we're able through integration through AWS, Lambda and other functions, um, quickly identify cloud configuration issues and drift eso that, um, organizations cannot only, uh, quickly identify some of those issues that open up risk to the enterprise, but also in real time. Um, take some action to close down those vulnerabilities and ultimately re mediate them. Right? So it's way for, um, to have, um or kind of proactive approach to security rather than a reactive approach. Everyone knows that cloud configuration issues are likely the number one kind of threat factor for Attackers. And so we're able to not only help organizations identify those, but then closed them down in real time. >>Yeah, it's interesting because we hear that all the time. If there's a breach and if if they w s involved often it's a it's a configuration. You know, somebody left the door open basically, and and it really drives something you were talking about. Ravi is the increasing important of automation, um, and and using big data. And you talked about this kind of horizontal tele metrics and analytics because without automation, these systems are just getting too big and and crazy for people Thio manage by themselves. But more importantly, it's kind of a signal to noise issue when you just have so much traffic, right? You really need help surfacing. That signals you said so that your pro actively going after the things that matter and not being just drowned in the things that don't matter. Ravi, you're shaking your head up and down. I think you probably agree with this point. >>Yeah, yeah, Jeff and definitely agree with you. And what you're saying is truly automation is a way off dealing with problems at scale. When when you have hundreds of accounts and that spans across, you know, multiple cloud service providers, it truly becomes a challenge to establish a particular security baseline and continue to adhere to it. And you wanna have some automation capabilities in place to be able to react, you know, and respond to it in real time versus it goes down to a ticketing system and some person is having to do you know, some triaging and then somebody else is bringing in this, you know, solution that they implement. And eventually, by the time you're systems could be compromised. So ah, good way of doing this and is leveraging automation and orchestration is just a capability that enhances your operational efficiency by streamlining summed Emmanuel in repetitive tasks, there's numerous examples off what automation and orchestration could do, but from a security context. Some of the key examples are automated security operations, automated identity provisioning, automated incident response, etcetera. One particular use case that Deloitte identified and built a solution around is the identification and also the automated remediation of Cloud security. Miss Consideration. This is a common occurrence and use case we see across all our customers. So the way in the context of a double as the way we did this is we built a event driven architectures that's leveraging eight of us contribute config service that monitors the baselines of these different services. Azzan. When it detects address from the baseline, it fires often alert. That's picked up by the Cloudwatch event service that's ultimately feeding it upstream into our workflow that leverages event bridge service. From there, the workflow goes into our policy engine, which is a database that has a collection off hundreds of rules that we put together uh, compliance activities. It also matched maps back to, ah, large set of controls frameworks so that this is applicable to any industry and customer, and then, based on the violation that has occurred, are based on the mis configuration and the service. The appropriate lambda function is deployed and that Lambda is actually, uh, performing the corrective actions or the remediation actions while, you know, it might seem like a lot. But all this is happening in near real time because it is leveraging native services. And some of the key benefits that our customers see is truly the ease of implementation because it's all native services on either worse and then it can scale and, uh, cover any additional eight of those accounts as the organization continues to scale on. One key benefit is we also provide a dashboard that provides visibility into one of the top violations that are occurring in your ecosystem. How many times a particular lambda function was set off to go correct that situation. Ultimately, that that kind of view is informing. Thea Outfront processes off developing secure infrastructure as code and then also, you know, correcting the security guard rails that that might have drifted over time. Eso That's how we've been helping our customers and this particular solution that we developed. It's called the Lloyd Fortress, and it provides coverage across all the major cloud service providers. >>Yeah, that's a great summary. And I'm sure you have huge demand for that because he's mis configuration things. We hear about him all the time and I want to give you the last word for we sign off. You know, it's easy to sit on the side of the desk and say, Yeah, we got a big security and everything and you got to be thinking about security from from the time you're in, in development all the way through, obviously deployment and production and all the minutes I wonder if you could share. You know, you're on that side of the glass and you're out there doing this every day. Just a couple of you know, kind of high level thoughts about how people need to make sure they're thinking about security not only in 2020 but but really looking down the like another road. >>Yeah, yeah, sure thing. So, you know, first and foremost, it's important to align. Uh, any transformation initiative, including your trust to business objectives. Right? Don't Don't let this come off as another I t. Security project, right? Make sure that, um, you're aligning to business priorities, whether it be, you know, pushing to the cloud, uh, for scalability and efficiency, whether it's digital transformation initiative, whether it be a new consumer identity, Uh uh, an authorization, um, capability of china built. Make sure that you're aligning to those business objectives and baking in and aligning to those guiding principles of zero trust from the start. Right, Because that will ultimately help drive consensus across the various stakeholder groups within the organization. Uh, and build trust, if you will, in the zero trust journey. Um, one other thing I would say is focus on the fundamentals. Very often, organizations struggle with some. You know what we call general cyber hygiene capabilities. That being, you know, I t asset management and data classifications, data governance. Um, to really fully appreciate the benefits of zero trust. It's important to kind of get some of those table six, right? Right. So you have to understand, you know what assets you have, what the criticality of those assets are? What business processes air driven by those assets. Um, what your data criticality is how it should be classified intact throughout the ecosystem so that you could really enforce, you know, tag based policy, uh, decisions within, within the control stack. Right. And then finally, in order to really push the needle on automation orchestration, make sure that you're using technology that integrate with each other, right? So taken a p I driven approach so that you have the ability to integrate some of these heterogeneous, um, security controls and drive some level of automation and orchestration in order to enhance your your efficiency along the journey. Right. So those were just some kind of lessons learned about some of the things that we would, uh, you know, tell our clients to keep in mind as they go down the adoption journey. >>That's a great That's a great summary s So we're gonna have to leave it there. But Andrew Robbie, thank you very much for sharing your insight and and again, you know, supporting this This move to zero trust because that's really the way it's got to be as we continue to go forward. So thanks again and enjoy the rest of your reinvent. >>Yeah, absolutely. Thanks for your time. >>All right. He's Andrew. He's Robbie. I'm Jeff. You're watching the Cube from AWS reinvent 2020. Thanks for watching. See you next time.

>>Hi, everyone. And thanks for joining us today for our session on the 2020 Data Drivers Award winners. I'm excited to be here today with you. I'm a lease. Bergeron, vice president, product marketing for snowflake. Thes rewards are intended to recognize companies and individuals for using snowflakes, data cloud to drive innovation and impact in their organizations. Before we start our conversations, I want to quickly congratulate all of our award winners. First in the business awards are data driver of the year is Cisco. Our machine learning master is you Nipper, Our data sharing leader is Rakuten. Our data application of the year is observed and our data for good award goes to door dash for the individual and team awards. We first have the cost. Jane, Chief Digital officer of Paccar. We have a militiamen, director of cybersecurity and data science winning our data science Manager of the Year award at Comcast for a date. A pioneer of the year. We have Faisal KP, who's our senior manager of enterprise data Services at Pizza Hut. And lastly, we have our best data team going to McKesson, led by Jimmy Herff Data and Analytics platform leader Huge congratulations to all of these winners. It was very difficult to pick them amongst amazing set of nominations. So now let's dive into our conversations. We'll start with the data driver of the year. Representing Cisco today is Robbie. I'm a month do director data platform, data and analytics. >>Let me welcome everybody to the wonderful. Within a few years before Cisco used to be a company, you know, in making the decisions partly with the data and partly with the cuts. Because, you know, the data is told in multiple places the trading is not done right and things like that. So we, you know, really understood it. You know what was a challenge in the organism? By then we defined the data strategy on we put in a few plants in place, and it is working very well. But what is more important is basically how we provide the data towards data scientists and the data community in Cisco. I'm making them available in a highly available scalable on the elastic platforms. That's where you know, snowflake came into picture really very well for arrest, along with the other data strategies that we have had in place more importantly, data. Democratization was a key. You know, you along with the simplification, something technologies involved in the past. Our clients need to be worrying, laudable the technologies involved, you know, for example, we used to manage her before we make it. Snowflake Andi Snowflake, in a solve all of these problems for us with the ease on it. Really helping enabling a data data given ordinances in our >>system. In the data sharing leaders category, Rockhampton was our winner. We have mark staying trigger VP of analytics here to share their story. I >>wanna thank Snowflake for the award, and it's an honor to be a today. The ease of use of snowflake has allowed projects to move forward innovation to move forward in a way that it simply couldn't have done on old Duke systems or or or other platforms. And I think the truth the same is true for us on a lot of the similar topics, but also in the data sharing space, data sharing is a part off innovation. Like I think, most of the tech companies we work with certainly are business partners, merchants, but also with a range of other service providers and other technology vendors, um on other companies that we strategically share data with 2 May benefit of their service or thio to allow data modeling or advanced data collaboration or strategic business deals using the data and evaluated with the data on. But I think if you look Greece snowflake, you would see a lot of time and effort money going to just establishing that data connection that often involved substantial investments in technology data pipelines, risk evaluation, hashing, encrypt encryption. Security on what we found with snowflakes sharing functionality is that we can not eliminate those concerns, but that the technology just supports the ability to share data securely easily, quickly in a way that we could never do >>previously. Now we have a really inspiring winner of the data for good award door dash with their Project Dash Initiative here to speak about their work is act shot near Engineering manager >>Thank you sports to snowflake for recognizing us for this initiative. Eso For those of you who don't know, Dash, the logistics technology platform company that connects people with the best in their cities and Project Dash, our flagship social impact program, uses the door dash logistics platform to tackle the challenges like hunger and food waste. It was launched in 2018 on over the first two years in partnership with food recovery organizations, we powered the delivery off over £2 million of surplus food from businesses to hunger relief agencies across the U. S. And Canada. Andi simply do Toko with tremendous need has a much we were ableto power. The delivery often estimated 5.8 million meals to food insecure communities and frontline workers across 48 states on the 3.5 million off. These meals have been delivered since much. We do all of our analysis for our business functions from like product development to skills and social impact in snowflake On the numbers I just provided here actually have come from Snowflake on. We have used it to provide various forms of reporting, tow our government and non profit partners on this snowflake. We can help them understand the impact, analyzed friends and ensure complaints in cases where we are supporting efforts for agencies like FEMA, our USDA onda. Lastly, our team is really excited to be recognized by snowflake for using data for good. It has reminded us to continue doubling down on our commitment to using our product and expertise to partner with communities we operated. Thank you again. >>The winner of the machine Learning Master's word is unit for Energy. Viola Sarcoma Data Innovation leader is here on behalf of unit for >>Hello, everyone, Thanks for having me here. It's really a pleasure. And we were really proud to get this award. It means a lot for you. Nipper. It's huge recognition for our effort since last couple of years assed part of our journey and also a celebration off our success now for you. Newport. It would not be possible to start looking at Advanced Analytics techniques, not having a solid data foundation in place. And that's where we invested a lot in our cloud data platform in the cloud back by snowflake. Having this platform allowed us to employ advanced analytics techniques, combining data from Markit from fundamental data, different other sources of data like weather and extracting new friends, new signals that basically help us to partly or even in some cases fully automate some trading strategy. And we believe this will be really fundamental for for the future off raiding in our company and we will definitely invest in this area in the future. >>Our data application of the year is observed. Observers recognizes the most innovative, data driven application built on Snowflake and representing observed today is their CEO, Jeremy Burton. >>Let me just echo the thanks from the other folks on the coal. I mean snowflakes, separation of storage. Compute. I can't overstate what a really big deal it is. Um, it means that we can ingest in store data. Really? For the price of Amazon s three on board, we're in a category where vendors of historically charged for volume of data ingested. So you can imagine this really represents huge savings. Um, in addition, and maybe on a more technical note, snowflakes, elastic architectures really enables us to direct queries appropriately, based on the complexity of the query. So small queries or simple queries weaken director extra small warehouses and complex queries. We can direct, you know, for Excel. Or I think even a six x l is either there are on its way. The key thing there is that users they're not sitting around waiting for results to appear regardless of the query complexity. So I mean, really? The separation storage compute on the elastic architectures is a really big deal for us. >>Turning to the data Pioneer of the Year Award, I'm excited to be here with Faisal KP, senior manager of Enterprise Data Services from Pizza Hut. >>First of all, thank you, Snowflake, for giving this wonderful person. I think it means a lot for us in terms of validating what we're doing. I think we were one of the earlier adopters of Snowflake. We saw the vision of snowflake, you know, stories. Russell's computer separation on all the goodies, right? Right from back in 2017, I believe what snowflake enabled us is to actually get the scale with very little manpower, which is needed to man the entire system. So on the Super Bowl day, we have, you know, the entire crew literally a boardroom where the right from the CME, most of the CEOs to all the folks will be sitting and watching what is happening in the system. And we have to do a lot of real time analytics during that time. So with snowflake, you know, way used the elasticity of the platform we use, you know, platform you know their solutions, like snow pipe to basically automate the data ingestion coming through various channels, from the commas, from the stores, everything simultaneously. So as soon as the program is done, you know, we can scale scale down to our normal volume, which means we can, you know, way can save a lot. Of course. So definitely it snowflake has been game changer for us in terms of how we provide real time analytics. Our systems are used by thousands off restaurants throughout the country and, you know, by hundreds of franchisees. So the scale is something we have achieved with a lot of ability and success. >>In the category of data science Manager of the Year Award, we have a mission Min, director of cybersecurity and data science at Comcast. >>So thank you for having me and thank you for this wonderful award. So one of the biggest challenges you see in this other security spaces the tremendous amount of data that we have to compute every day to find the gold haystack. So one of the big challenges we overcame with by uniting snowflake was how do we go from like my other counterparts on the panel have said Theo operational overhead of maintaining a large data store and moved to more of results driven and data focused environment. And, you know, part of that journey was really the tremendous leadership. Comcast saying, You know, we want Thio through our day to day lives by relying less on operational work and Maura on answering questions. And so you know, over the last year we've really put Snowflake at the center of our ecosystem, knowing that it's elastic platform and its ability scale infinitely have given us the ability to dream big and use it to drop five cybersecurity. And while it's traditionally used for cybersecurity, we're starting to see the benefits right away and the beauty of the snowflake. Ecos, Miss. We're now able to enable folks that not traditionally have big data skills, but they have standards, sequel skills, and they could still work in the snowflake platform. So, you know, the transition to cloud has been very powerful for us as an organization. But I think the end story, the real takeaways, by moving our secretary operation to the cloud, we're now been able to enable more people and get the results they were looking for. You know, as other people have said fast, people hate to wait. So the scale of snowflake really shines. >>Yeah. Now, let's hear from our data Executive of the year. The Cost. Jane. Chief Digital Officer Packer. >>Thank you very much, Snowflake, for this really incredible recognition and honor of the work we're doing it back. Are we began. The first step in this process was for us to develop an enterprise Great data platform in the cloud capable off managing every aspect of data at scale. This this platform includes snowflake as our analytics data warehouse amongst many other technologies that we used for ingestion of data, data processing, uh, data governance, transactional, uh, needs and others. So this platform, once developed, has really helped us leverage data across the broad pack. Our systems and applications globally very efficiently and is enabling pack are, as a result to enhance every aspect. Selfish business with data. >>Ah, big congratulations again to all of the winners of the 2020 Data Drivers Awards. Thanks so much for joining us for a great conversation. And we hope that you enjoy the rest of the data cloud summit

>>from around the globe. It's the cue with digital coverage of active EO data driven 2020. Brought to you by activity. We're back. This is the cubes coverage. Our ongoing coverage of active FiOS data driven. Of course, we've gone virtual this year. Ash. Ashutosh is here. He's the founder, president and CEO of Active Eo. Great to see you again. >>Likewise, They always always good to see you. >>We have We're in a little meet up, You and I in Boston. I always enjoy our conversations. Little did we know that, You know, a few months later, we would only be talking at this type of distance and, uh and of course, it's sad. I mean, a data driven is one of our favorite events is intimate, its customer content driven. The theme this year is you call it the next normal. Some people call it the new abnormal, the next normal. What's that all about? >>I think it's pretty pretty fascinating to see when we walked in in March, all of us were shocked by the effect of this pandemic. And for a while we all scrambled around trying to figure out How do you react to this one, and everybody reacted very differently. But most people have this tendency to think that this is going to be a pretty broom environment with lots of unknown variables, and it is important for us to try to figure out how to get a get our hands on this. By the time we came on. For six weeks into that, almost all of us have figured out this is Ah, this is not something you fight again. This is not something you wait, what, it to go away? But this is one. Did you figure out how to live in and you figured out how to work around it? And that, we believe, is the next long. It's not about trying to create a new abnormal. It's not about creating a new normal, but it's truly one that basically says that is it. That is a way, perhaps packed forward. There's a is a way to create this next normal, and you just figured out how to live with the environment, behalf and the normal outcomes of companies that have done remarkably well as a result of these actions. Fact. If you're being one of them, >>it's quite amazing isn't it? I mean, I've talked to a lot of tech companies, CEOs and their customers, and it's almost like they feel the first reaction was course they cared about their there, their employees and their broader families. Number one number two was many companies, as you know, saw a tailwind, and it initially didn't want to be seen as ambulance chasing. And then, of course, the entrepreneurial spirit kicked in and they said, Okay, we can only control what we can control and tech companies in particular just exceedingly Well, I don't think anybody really predicted that early >>on. Yeah, I, um I think of the heart, We're all human beings, and the first reaction was to take it off. Four constituencies, right? One. Take care of your family. Take it off your community, take care of your employees, take care of your customers. And that was the hardest part. The first 4 to 6 weeks was to figure out How do you do each of those four. Once you figured that part out or you figured out ways to get around to making sure you can take it off those you really found the next mom, you really start forgetting our out of continue to innovate Could, you know to support each of those four constituencies and people have done different things. I know it's amazing how, um, Cuba continues to operate As far as a user is concerned, they're all watching anymore. Yes, we don't have the wonderful desk, and we all get to chat and look in the eye. But the content of the messages asked powerful as what it waas a few months ago. So I'm sure this is how we're all going to figure out how to make through this new next normal >>and digital transformation kind of went from from push to pull. I mean, every conference you go to, they say, Well, look at uber, you know, look at Airbnb and it put up the examples you have to do this to, and then all of sudden the industry dragged you along. Some Curis esta is toe. How and and I guess the other point there is digital means data. We've said that many, many times. If you didn't have a digital strategy during the height of the lock down, you couldn't transact business and still many restaurants is still trying to figure this out, But so how did it affect you and your customers? >>Yeah, it's very interesting. And I we spend a lot of time with several of our customers were managing some of the largest I T organizations. We talk about very interesting phenomena that happened some better beginning of this year. About 20 years ago, we used to worry about this thing called the Digital Divide, those who have access the network and Internet and those who don't. And now there is this beta divide, the divide between organizations that know how to leverage, exploit and absolutely excellent the business using data and those adorable. I think we're seeing this effect so very clearly among organizations that unable to come back and address some of this stuff. And it's fascinating. Yes, we all have the examples off the lights off. People are doing delivery. People are doing retailing, but there are so many little things you're seeing organizations. And just the other day, he had a video from Century Days Is Central Data System, which is helping accelerate Cohen 19 research because it will get copies of the data faster than they would get access to data so that these are just much, much faster. Sometimes you know, several days to a few minutes. It's that that level of effect, it's not just down to some seven. You know, you almost think of it as nice to have, but it's must have life threatening stuff. Essential stuff or just addressing. Korea was running a very pretty in a wonderful article about this supercomputer in That's Doing an Aristo covert 19 and how it's figured out most of these symptoms they're able to figure out by just crunching a ton of data. And almost every one of those symptoms that the computer has predicted Supercomputer is predicted has being accurate. It's about data. It is absolutely about data, which is why I think this is a phenomenal time for companies. Toe Absolutely go change. Make this information about data exploration, data leverage, exploitation. And there's a ton of it all over all around us. >>Yeah, and and part of that digital transformation, the mandate is to really put data at the core. I mean, we've we've certainly seen this with the top market cap companies. They've got dated at the core, and and now, as they say it's it's become a A mandate. And, you know, there's been several things that we've clearly noticed. I mean, you saw the work from home required laptops and, you know, endpoint security and things of that. VD. I made a comeback, and certainly Cloud was there. But I've been struck by the reality of multi Cloud. I was kind of a multi cloud skeptic early on. >>Yeah, >>I said many times I thought it was more of a symptom than it was a strategy, but it's that's completely flipped. Ah, recently in r e t r surveys, we saw multi cloud popping up all over the place. I wonder what you're seeing when you talk to your customers and other CEOs. >>Yeah, So fascinating, though really is the first flower part of sometime in 2018. End of 2018 >>Go right, Yeah, >>the act if you'll go on world, which is a phenomenal way to completely change the way you think about the using object storage in the flower for two years that we saw about 20% of our business. By the end of two years, the beginning of this year, 20% of our business was built on never it in the cloud since March. So that was end of our almost ended the Q one. So now we just limit left you three in six months. We added 12 more percent of the business literally weeded in six months. What we did not do before for 18 months before that, right? Significantly more than what we did for a year and a half before that. And there are really three reasons and we see this old nor again, we have a large customer. We closed in January. Ironically, were deploying out of UK, a very large marketing organization. Got everything deployed, running the they're back up and beyond and a separate data center. And they had a practical problem of not being able to access the second sight literally in the middle of deployment. Mystere that customer, Did you see me Google Cloud? Because they were simply no way for them to continue protecting their data, being able to develop new applications with that data that simply had no access. So there was. This was the number one reason the inability for already physically access, but put their their employees at rest and have before the plow would be the infrastructure. That's number one, so that first of all, drove the reason for the cloud. And then there's a second reason there are practical reasons. And why some clerk platforms that good one working the other ones are not. So where, uh, some other more fuels. And so if I'm an organization that has that spans everything, I've got no power PC and X 86 machine A vm I got container platforms. I got Oracle. They got a C P. There is no single cloud platform that supports all my work loaders efficiently. It's available in all the agents I want. So inevitably I have to go at our different about barefoot. So that's a second practical visa. And then there's a strategic reason. No, when no customer what's really locked into anyone card back at least two. You're gonna go pear more likely? Three. So those are the reasons. And then, interestingly enough, have you were on a panel with as global Cee Io's and in addition to just the usual cloud providers of you all know and love inside the U. S. Across the world, in Europe, in Asia, there's a rise off the regional flower fire. See you take all this factor. So have you got absolute physical necessity? You got practical constraints of what can the club provided support the strategic reasons on why either Because I don't want to be locked into a part for better or because there is a rise off data nationalism that's going on, that people want to keep their data within the country bombs all of these reasons. But the foundations or why multiplier is almost becoming a de facto. It's impossible. What a decent size organization to assume. They were just different on one car ready. >>The big trend we're seeing, I wonder if you could comment. Is this this notion of the data life cycle of the data pipeline? It's a very complex situation for a lot of organizations, their data siloed. We hear that a lot. They have data scientists, data engineers, developers, data quality engineers, just a lot of different constituencies and lines of business. And it's kind of a mess. And so what they're trying to do is bring that together. So they've done that data. Scientists complain they spend all their time wrangling data, but but ultimately the ones that are succeeding to putting data at the core is, we've just been discussing are seeing amazing outcomes by being able to have a single version of the truth, have confidence in that data, create self serve for their for their lines of business and actually reduce the end and cycle times. It's driving your major monetization, whether that's cost cutting or revenue. And I'm curious as to what you're seeing. You guys do a lot of work. Heavy work in Dev ops and hard core database those air key components of that data Lifecycle. Yeah, you're seeing in that regard regarding that data pipeline. >>Yeah, it's a It's a phenomenal point if you really want to go back and exploit data within an organization. If you really want to be a data driven organization, the very first thing you have to do is break down the silos. Ironically, every organization has all the data required to make the decisions they want to. They just can't either get to it or it's so hard to make the silos. That is just not what trying to make it happen. And 10 years ago we set out on this mission rather than keep this individual silos of data. Why don't we flip it open and making it a pipeline, which looks like a data cloud where essentially anybody who's consuming it has access to it based on the governance rules based on the security rules that the operations people have said and based on the kind of format they want to see data. Not everyone even want to see the data in a database. Former, maybe you want the database for my convert CSP for my before you don't analytics And this idea of making data, the new infrastructure, this idea of having the operations people provide this new layer for data, it's finally come to roost. I mean, it's it's fascinating. I was the numbers last quarter. We just finished up. You do now. 45% of our customer base is uses activity or for reuse is the back of data for things that excellent. The business things that make the business move faster, more productive or you will survive. That was the mission. That was what we set out to do 10 years ago. We were talking to an analyst this morning, and now this is question off. You know, it looks like there's a team of backup data being reused, said Yeah, that's kind of what we've been saying for 10 years. Backup cannot be an insurance back up in order to your destination. It has to be something that you could use as an asset and that I think it's finally coming to the point with you can use back up a single source of truth only if you designed it right from the beginning. For that purpose, you cannot just lots of lots of ways to fake it. Make it try to pretend like you're doing it. But that was a trooper was off making date of the new infrastructure, making it a cloud, making it something that is truly an ask. And it's fascinating to see our businesses. You take any of our larger counts and the way they've gone about transforming not just basic backup. India. Yes, we are the world's glasses back up in most Kayla will be our solution. That's that's a starting point. But do we will be used after Devil applications 8, 10 times faster? Ron Analytics, 100 ex pastor. The more data you have, the more people who use data you have, the better this return makeups. >>You know, that is interesting to hear you talk about that because that has been the holy Grail of backup. Was toe go beyond insurance to actually create business value. And you're actually seeing some underlying trends We talked about that data pipeline in one of the areas that is the most interesting is in database, which was so boring for so many years. Ah, and you're seeing new workloads emerge. Take the data warehouse beyond your reporting. Never really lived up to its Ah, it's promise of 360 degree view. You mentioned analytics. That's really starting toe happen. Ah, and it's all about data John, for Used to say that your data is that is the new development kit. You call it the new infrastructure, and it's sort of the same same type of theme. So maybe some of the trends you're seeing in ah in database enoughto talk about that for a little bit and then pick your brains and some other tech like object storage is another one that we've really seen takeoff? >>Yeah. So I think our journey with object story began in 16 4017 as we started or Doctor Cloud platform in response to the user requirements, Uh, we did more like most companies have done and unfortunately continue to do to take the in print product. And then it's smooth under the cloud. And one of the things we saw was there was a fundamental difference off how the design points of flower engineering is all about what they're designed it for object story, that one of those one of those primitives fundamental stories, primitives that the cloud providers actually produced that we know really exploited. There was. It was used as a graveyard for data. It's a replacement for me, please, where data goes to die. And then we look at it really closely and say, Well, this is actually a massively scalable, very low cost storage, but it has some problems. It has an interface that you cannot use with traditional servers. Uh, it has some issues around not being able to read, modify right the data. So it feels like a consuming a lot of stories. So we're going to solve those problems because a good two years to come back with something on world that fundamentally creeds objects the lady like this massive use capable high performer disk? Yes, except it is ridiculously low cost and optimize the capacity. So this finger on world that patented has really become the foundation of how everything in our works without using CPU Ray, that is simply nothing at a lower PCO that if you wanted to basic backup, the, uh, more importantly, use that to do this a massive analytics and you don't know more data warehouse data leaks. It is not a good deal of Lake House aladi. All of these are still silent. All of these are people trying to take some data from somewhere put into one of the new construct and have it being controlled by somebody else. This is artist thing. It's just you just move the silos from some place to another place instead of creating a pipeline. If you want to really create a pipeline object story has been integral part of the pipeline, not a separate bucket by itself. And that's what we did. And same thing with databases, you know, most business, most of the critical business and I was on a daily basis, and the ability to find a way to leverage those. Move them on our leverage in terms of whichever format databases access. Which location or Saxes doesn't know how big it is. Lots of work has gone into trying to figure figure that one out. And we we had some very, very good partners in some of the largest customers who help take the journey with us. I'm pretty much all of the global 2000 accounts you see across the board, but an integral part of a process. >>You mentioned the word journey and triggered a thought. Is your discussion with Robbie, the CEO of of Seeing >>A. It was a customer years. >>Ah, and what he said. I liked what he said. He course he used the term journey. We all do. But he said, You know what? I kind of don't like that term because I want to inject the sense of urgency essentially what he was saying. I want speed, you know, journeys like Okay, kids get in the car, were in a drive across country. We're gonna make some stops. And so, while there's a journey, he also was was really trying to push the organization hard and he talked about culture. Ah, as some of the most difficult things and it goes like many. See, I said, Now the technology is almost the easy part. It's true when it works. Oh, I thought that was a great discussion that you had. What were some of your takeaways >>with thinking? Robbie's is very astute. Ah, I t executive was being around the block for so long and one of the fascinating things, but a asking this question about what's the biggest challenge was just gone through this a couple of times. What is the biggest challenge? Taking an organization as vulnerable as well known A C gate is. I mean, this is a data company. This is This is the heart of the Oliver Half the world's data is on seeing stuff. How are you today was, or company has been around for long in the middle of Silicon Valley and make it into ah into a fast growing transformation company that's responding to the newer challenges. And I thought he was going to come back with Well, you know, I gotta go to the abuses. I picked this technology that techno in. Surely that is exactly what I expected he would end up with. There's nothing through technology in this day and age when you can have an Elon Musk and send a card of Mars. It's not many technologies that we can really solve many covered 19 ism. Next one Do we gotta go solve? Well, frankly, he kid upon the one thing that matters to every company. It is the fundamental culture to create a biased of action. It's a fundamental culture where you have to come back and have a deliverable that moves the ball forward every day, every month, every quarter, as opposed to have this CDs off. Like you said, a journey that say's and we all know this right? People talk about, we're going to do this in face one. We're gonna do this and face to and good food release and face three nothing and what happens Invasive. Nobody gets a number feast. I think he did a great job of saying I fundamentally had to go change the culture that was my biggest take away, and this I've heard this so many times the most effective I D execs wait a transformation. It actually shows in the people that they have. It's not the technology, it's the people. And some. This history is replete with organizations that have done remarkably well, not by leveraging the heck out of the technology, but truly by leveraging the change in the people's mindset. And, of course, that at that point that leverages technology where a proper here. But Robbie's a insightful person, always such a They lied to talk them, said they like for him to have chosen us as a its information technology for him to go pull his data warehouses and completely transformed how I was doing manufacturing across the globe. >>You know, I want to have some color of what you just said because some key keep takeaways that from what you just said, ashes is You know, you're right when you look back at the history of the computer industry used to be very well known processes, but the technology was the big mystery and the and the big risk and you think about with Cove it were it not for Technology Way didn't know what was coming. We were inventing new processes literally every day, every week, every month. It's so technology was pretty well understood. It and enabled that. And when you when you think when we talked earlier about putting data at the core, it was interesting to hear Robbie. He basically said, Yeah, we had a big data team in the U. S. A big tainted TV in Europe. We actually organized around silos and and so you guys played a role you were very respectful about, you know, touting active video with him. You did ask him, You know what role you play, But it is interesting to hear and talk about how he had to address that both culturally. And of course, there's technology underneath to enable that unification of data that silo busting, if you will. And you guys played a role in that. >>Yeah, I always enjoy, um, conversation with folks who have taken a problem, identified what needs to be done and then just get it done. And its That's more fascinating than you. Of course, I video plays a small part in a lot of things, and we're proud to have played a small part in his big initiative, and that's true of know the thousands of customers we talk about. But it's such a fascinating story to have leaders who come back and make this transformation happen, and to understand how they went about making those decisions, how they identified where the problem with these are so hard. We all see them in our own life, right? We see there is a there's a problem, but sometimes it takes a wider don't understand. How do you identify them and what do you have to do and more importantly, actually do it? And so whenever use, whenever I get an opportunity with people like Robbie, I think understanding that there's a way to help, uh, we always make sure that we play our own small part, and we're privileged to be a part of those kinds of journeys. >>Well, I think what's interesting about activity on the company that you created is essentially that. We're talking about the democratisation of data, that whole data pipeline, that discussion, that we had the self service of that data to the lines of business and, you know, you guys clearly play a role there. The multi cloud discussion fits into that. I mean that these air all trends that are tail winds for companies that can that can help sort of you know, flattened the data globe. If you if you will, your final thoughts. >>Yeah, I know you said something that is so much at the heart of every idea Exactly that you're talking to, if they truly is. The fundamental asset that I finally end up with is an organization. The democratization of data. Where I do not lock this into another silo, another platform, another ploughed. Another application has to be part of my foundation design and therefore my ability to use each of this cloud platform for the services they provide. While I and they were to move the data to where I needed to be. That is so critical. So you almost start to think about the one possession and organization now has. And we talked about this with a group of CEOs. They might be some pretty soon. Not too far off, but data stolen asset. I might actually have our data mark data market, just like you. I was stopped working, but I can start to sell my data. You know, imagine a coup in 19. There's so many organization that have so much data, and many of them have contributed to this research because this is an existence of issue. But you can see this turning into a next level. So, yes, we've got activities, will move the data toe one level higher where it's become a foundation construct for the organization. The next part is gonna actually done. This is the one asset would actually monetize someone stuff. And it will be not too long when you need to talk about how there's this new exchange and what's the rate of data for this company? Was, is that company in the future trading options? Who knows is gonna be really interesting. >>Well, I think you're right on this notion of a data. Marketplaces is coming, and it's not not that far away, Blash. It's always great to talk to you. I hope next year a data driven weaken we could be face to face. But I mean, look, this has been we we've dealt with it. It's it's actually created opportunities for us toe to reinvent ourselves. So congratulations on the success that you've had and ah, and thank you for coming on the Cube. >>No, thank you for hosting us and always a big fan off Cube. You guys, you engage with you since early days, and it is fascinating to see how this company has grown. And it's probably many people don't even know how much you've grown behind the seats, technologies and culture that you created yourself. So it's hopefully one day we'll strict the table that I would be another side and asking of our transformation. Digital transformation of Cuban cell >>I would love to. I'd love to do that index again. And thank you, everybody for watching our continuous coverage of active fio data driven keeper Right there. We'll be back with our next guest right after this short break. >>Thank you.

>>from the Cube Studios >>in Palo Alto and >>Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hey, welcome back here. Ready? Jeff Frick here with the Cube. We're doing a special presentation today really talking about AI and making ai really with two companies that are right in the heart of the Dell EMC as well as Intel. So we're excited to have a couple Cube alumni back on the program. Haven't seen him in a little while. First off from Intel. Lisa Spelman. She is the corporate VP and GM for the Xeon Group in Jersey on and Memory Group. Great to see you, Lisa. >>Good to see you again, too. >>And we've got Ravi Pinter. Conte. He is the SBP server product management, also from Dell Technologies. Ravi, great to see you as well. >>Good to see you on beast. Of course, >>yes. So let's jump into it. So, yesterday, Robbie, you guys announced a bunch of new kind of ai based solutions where if you can take us through that >>Absolutely so one of the things we did Jeff was we said it's not good enough for us to have a point product. But we talked about hope, the tour of products, more importantly, everything from our workstation side to the server to these storage elements and things that we're doing with VM Ware, for example. Beyond that, we're also obviously pleased with everything we're doing on bringing the right set off validated configurations and reference architectures and ready solutions so that the customer really doesn't have to go ahead and do the due diligence. Are figuring out how the various integration points are coming for us in making a solution possible. Obviously, all this is based on the great partnership we have with Intel on using not just their, you know, super cues, but FPG's as well. >>That's great. So, Lisa, I wonder, you know, I think a lot of people you know, obviously everybody knows Intel for your CPU is, but I don't think they recognize kind of all the other stuff that can wrap around the core CPU to add value around a particular solution. Set or problems. That's what If you could tell us a little bit more about Z on family and what you guys are doing in the data center with this kind of new interesting thing called AI and machine learning. >>Yeah. Um, so thanks, Jeff and Ravi. It's, um, amazing. The way to see that artificial intelligence applications are just growing in their pervasiveness. And you see it taking it out across all sorts of industries. And it's actually being built into just about every application that is coming down the pipe. And so if you think about meeting toe, have your hardware foundation able to support that. That's where we're seeing a lot of the customer interest come in. And not just a first Xeon, but, like Robbie said on the whole portfolio and how the system and solution configuration come together. So we're approaching it from a total view of being able to move all that data, store all of that data and cross us all of that data and providing options along that entire pipeline that move, um, and within that on Z on. Specifically, we've really set that as our cornerstone foundation for AI. If it's the most deployed solution and data center CPU around the world and every single application is going to have artificial intelligence in it, it makes sense that you would have artificial intelligence acceleration built into the actual hardware so that customers get a better experience right out of the box, regardless of which industry they're in or which specialized function they might be focusing on. >>It's really it's really wild, right? Cause in process, right, you always move through your next point of failure. So, you know, having all these kind of accelerants and the ways that you can carve off parts of the workload part of the intelligence that you can optimize betters is so important as you said Lisa and also Rocket and the solution side. Nobody wants General Ai just for ai sake. It's a nice word. Interesting science experiment. But it's really in the applied. A world is. We're starting to see the value in the application of this stuff, and I wonder you have a customer. You want to highlight Absalon, tell us a little bit about their journey and what you guys did with them. >>Great, sure. I mean, if you didn't start looking at Epsilon there in the market in the marketing business, and one of the crucial things for them is to ensure that they're able to provide the right data. Based on that analysis, there run on? What is it that the customer is looking for? And they can't wait for a period of time, but they need to be doing that in the near real time basis, and that's what excellent does. And what really blew my mind was the fact that they actually service are send out close to 100 billion messages. Again, it's 100 billion messages a year. And so you can imagine the amount of data that they're analyzing, which is in petabytes of data, and they need to do real time. And that's all possible because of the kind of analytics we have driven into the power It silver's, you know, using the latest of the Intel Intel Xeon processor couple with some of the technologies from the BGS side, which again I love them to go back in and analyze this data and service to the customers very rapidly. >>You know, it's funny. I think Mark Tech is kind of an under appreciated ah world of ai and, you know, in machine to machine execution, right, That's the amount of transactions go through when you load a webpage on your site that actually ideas who you are you know, puts puts a marketplace together, sells time on that or a spot on that ad and then lets people in is a really sophisticated, as you said in massive amounts of data going through the interesting stuff. If it's done right, it's magic. And if it's done, not right, then people get pissed off. You gotta have. You gotta have use our tools. >>You got it. I mean, this is where I talked about, you know, it can be garbage in garbage out if you don't really act on the right data. Right. So that is where I think it becomes important. But also, if you don't do it in a timely fashion, but you don't service up the right content at the right time. You miss the opportunity to go ahead and grab attention, >>right? Right. Lisa kind of back to you. Um, you know, there's all kinds of open source stuff that's happening also in the in the AI and machine learning world. So we hear things about tense or flow and and all these different libraries. How are you guys, you know, kind of embracing that world as you look at ai and kind of the development. We've been at it for a while. You guys are involved in everything from autonomous vehicles to the Mar Tech. Is we discussed? How are you making sure that these things were using all the available resources to optimize the solutions? >>Yeah, I think you and Robbie we're just hitting on some of those examples of how many ways people have figured out how to apply AI now. So maybe at first it was really driven by just image recognition and image tagging. But now you see so much work being driven in recommendation engines and an object detection for much more industrial use cases, not just consumer enjoyment and also those things you mentioned and hit on where the personalization is a really fine line you walk between. How do you make an experience feel good? Personalized versus creepy personalized is a real challenge and opportunity across so many industries. And so open source like you mentioned, is a great place for that foundation because it gives people the tools to build upon. And I think our strategy is really a stack strategy that starts first with delivering the best hardware for artificial intelligence and again the other is the foundation for that. But we also have, you know, Milat type processing for out of the Edge. And then we have all the way through to very custom specific accelerators into the data center, then on top about the optimized software, which is going into each of those frameworks and doing the work so that the framework recognizes the specific acceleration we built into the CPU. Whether that steel boost or recognizes the capabilities that sit in that accelerator silicon, and then once we've done that software layer and this is where we have the opportunity for a lot of partnership is the ecosystem and the solutions work that Robbie started off by talking about. So Ai isn't, um, it's not easy for everyone. It has a lot of value, but it takes work to extract that value. And so partnerships within the ecosystem to make sure that I see these are taking those optimization is building them in and fundamentally can deliver to customers. Reliable solution is the last leg of that of that strategy, but it really is one of the most important because without it you get a lot of really good benchmark results but not a lot of good, happy customer, >>right? I'm just curious, Lee says, because you kind of sit in the catbird seat. You guys at the core, you know, kind of under all the layers running data centers run these workloads. How >>do you see >>kind of the evolution of machine learning and ai from kind of the early days, where with science projects and and really smart people on mahogany row versus now people are talking about trying to get it to, like a citizen developer, but really a citizen data science and, you know, in exposing in the power of AI to business leaders or business executioners. Analysts, if you will, so they can apply it to their day to day world in their day to day life. How do you see that kind of evolving? Because you not only in it early, but you get to see some of the stuff coming down the road in design, find wins and reference architectures. How should people think about this evolution? >>It really is one of those things where if you step back from the fundamentals of AI, they've actually been around for 50 or more years. It's just that the changes in the amount of computing capability that's available, the network capacity that's available and the fundamental efficiency that I t and infrastructure managers and get out of their cloud architectures as allowed for this pervasiveness to evolve. And I think that's been the big tipping point that pushed people over this fear. Of course, I went through the same thing that cloud did where you had maybe every business leader or CEO saying Hey, get me a cloud and I'll figure out what for later give me some AI will get a week and make it work, But we're through those initial use pieces and starting to see a business value derived from from those deployments. And I think some of the most exciting areas are in the medical services field and just the amount, especially if you think of the environment we're in right now. The amount of efficiency and in some cases, reduction in human contact that you could require for diagnostics and just customer tracking and ability, ability to follow their entire patient History is really powerful and represents the next wave and care and how we scale our limited resource of doctors nurses technician. And the point we're making of what's coming next is where you start to see even more mass personalization and recommendations in that way that feel very not spooky to people but actually comforting. And they take value from them because it allows them to immediately act. Robbie reference to the speed at which you have to utilize the data. When people get immediately act more efficiently. They're generally happier with the service. So we see so much opportunity and we're continuing to address across, you know, again that hardware, software and solution stack so we can stay a step ahead of our customers, >>Right? That's great, Ravi. I want to give you the final word because you guys have to put the solutions together, it actually delivering to the customer. So not only, you know the hardware and the software, but any other kind of ecosystem components that you have to bring together. So I wonder if you can talk about that approach and how you know it's it's really the solution. At the end of the day, not specs, not speeds and feeds. That's not really what people care about. It's really a good solution. >>Yeah, three like Jeff, because end of the day I mean, it's like this. Most of us probably use the A team to retry money, but we really don't know what really sits behind 80 and my point being that you really care at that particular point in time to be able to put a radio do machine and get your dollar bills out, for example. Likewise, when you start looking at what the customer really needs to know, what Lisa hit upon is actually right. I mean what they're looking for. And you said this on the whole solution side house. To our our mantra to this is very simple. We want to make sure that we use the right basic building blocks, ensuring that we bring the right solutions using three things the right products which essentially means that we need to use the right partners to get the right processes in GPU Xen. But then >>we get >>to the next level by ensuring that we can actually do things we can either provide no ready solutions are validated reference architectures being that you have the sausage making process that you now don't need to have the customer go through, right? In a way. We have done the cooking and we provide a recipe book and you just go through the ingredient process of peering does and then off your off right to go get your solution done. And finally, the final stages there might be helped that customers still need in terms of services. That's something else Dell technology provides. And the whole idea is that customers want to go out and have them help deploying the solutions. We can also do that we're services. So that's probably the way we approach our data. The way we approach, you know, providing the building blocks are using the right technologies from our partners, then making sure that we have the right solutions that our customers can look at. And finally, they need deployment. Help weaken due their services. >>Well, Robbie, Lisa, thanks for taking a few minutes. That was a great tee up, Rob, because I think we're gonna go to a customer a couple of customer interviews enjoying that nice meal that you prepared with that combination of hardware, software, services and support. So thank you for your time and a great to catch up. All right, let's go and run the tape. Hi, Jeff. I wanted to talk about two examples of collaboration that we have with the partners that have yielded Ah, really examples of ah put through HPC and AI activities. So the first example that I wanted to cover is within your AHMAD team up in Canada with that team. We collaborated with Intel on a tuning of algorithm and code in order to accelerate the mapping of the human brain. So we have a cluster down here in Texas called Zenith based on Z on and obtain memory on. And we were able to that customer with the three of us are friends and Intel the norm, our team on the Dell HPC on data innovation, injuring team to go and accelerate the mapping of the human brain. So imagine patients playing video games or doing all sorts of activities that help understand how the brain sends the signal in order to trigger a response of the nervous system. And it's not only good, good way to map the human brain, but think about what you can get with that type of information in order to help cure Alzheimer's or dementia down the road. So this is really something I'm passionate about. Is using technology to help all of us on all of those that are suffering from those really tough diseases? Yeah, yeah, way >>boil. I'm a project manager for the project, and the idea is actually to scan six participants really intensively in both the memory scanner and the G scanner and see if we can use human brain data to get closer to something called Generalized Intelligence. What we have in the AI world, the systems that are mathematically computational, built often they do one task really, really well, but they struggle with other tasks. Really good example. This is video games. Artificial neural nets can often outperform humans and video games, but they don't really play in a natural way. Artificial neural net. Playing Mario Brothers The way that it beats the system is by actually kind of gliding its way through as quickly as possible. And it doesn't like collect pennies. For example, if you play Mary Brothers as a child, you know that collecting those coins is part of your game. And so the idea is to get artificial neural nets to behave more like humans. So like we have Transfer of knowledge is just something that humans do really, really well and very naturally. It doesn't take 50,000 examples for a child to know the difference between a dog and a hot dog when you eat when you play with. But an artificial neural net can often take massive computational power and many examples before it understands >>that video games are awesome, because when you do video game, you're doing a vision task instant. You're also doing a >>lot of planning and strategy thinking, but >>you're also taking decisions you several times a second, and we record that we try to see. Can we from brain activity predict >>what people were doing? We can break almost 90% accuracy with this type of architecture. >>Yeah, yeah, >>Use I was the lead posts. Talk on this collaboration with Dell and Intel. She's trying to work on a model called Graph Convolution Neural nets. >>We have being involved like two computing systems to compare it, like how the performance >>was voting for The lab relies on both servers that we have internally here, so I have a GPU server, but what we really rely on is compute Canada and Compute Canada is just not powerful enough to be able to run the models that he was trying to run so it would take her days. Weeks it would crash, would have to wait in line. Dell was visiting, and I was invited into the meeting very kindly, and they >>told us that they started working with a new >>type of hardware to train our neural nets. >>Dell's using traditional CPU use, pairing it with a new >>type off memory developed by Intel. Which thing? They also >>their new CPU architectures and really optimized to do deep learning. So all of that sounds great because we had this problem. We run out of memory, >>the innovation lab having access to experts to help answer questions immediately. That's not something to gate. >>We were able to train the attic snatch within 20 minutes. But before we do the same thing, all the GPU we need to wait almost three hours to each one simple way we >>were able to train the short original neural net. Dell has been really great cause anytime we need more memory, we send an email, Dell says. Yeah, sure, no problem. We'll extended how much memory do you need? It's been really simple from our end, and I think it's really great to be at the edge of science and technology. We're not just doing the same old. We're pushing the boundaries. Like often. We don't know where we're going to be in six months. In the big data world computing power makes a big difference. >>Yeah, yeah, yeah, yeah. The second example I'd like to cover is the one that will call the data accelerator. That's a publisher that we have with the University of Cambridge, England. There we partnered with Intel on Cambridge, and we built up at the time the number one Io 500 storage solution on. And it's pretty amazing because it was built on standard building blocks, power edge servers until Xeon processors some envy me drives from our partners and Intel. And what we did is we. Both of this system with a very, very smart and elaborate suffering code that gives an ultra fast performance for our customers, are looking for a front and fast scratch to their HPC storage solutions. We're also very mindful that this innovation is great for others to leverage, so the suffering Could will soon be available on Get Hub on. And, as I said, this was number one on the Iot 500 was initially released >>within Cambridge with always out of focus on opening up our technologies to UK industry, where we can encourage UK companies to take advantage of advanced research computing technologies way have many customers in the fields of automotive gas life sciences find our systems really help them accelerate their product development process. Manage Poor Khalidiya. I'm the director of research computing at Cambridge University. Yeah, we are a research computing cloud provider, but the emphasis is on the consulting on the processes around how to exploit that technology rather than the better results. Our value is in how we help businesses use advanced computing resources rather than the provision. Those results we see increasingly more and more data being produced across a wide range of verticals, life sciences, astronomy, manufacturing. So the data accelerators that was created as a component within our data center compute environment. Data processing is becoming more and more central element within research computing. We're getting very large data sets, traditional spinning disk file systems can't keep up and we find applications being slowed down due to a lack of data, So the data accelerator was born to take advantage of new solid state storage devices. I tried to work out how we can have a a staging mechanism for keeping your data on spinning disk when it's not required pre staging it on fast envy any stories? Devices so that can feed the applications at the rate quiet for maximum performance. So we have the highest AI capability available anywhere in the UK, where we match II compute performance Very high stories performance Because for AI, high performance storage is a key element to get the performance up. Currently, the data accelerated is the fastest HPC storage system in the world way are able to obtain 500 gigabytes a second read write with AI ops up in the 20 million range. We provide advanced computing technologies allow some of the brightest minds in the world really pushed scientific and medical research. We enable some of the greatest academics in the world to make tomorrow's discoveries. Yeah, yeah, yeah. >>Alright, Welcome back, Jeff Frick here and we're excited for this next segment. We're joined by Jeremy Raider. He is the GM digital transformation and scale solutions for Intel Corporation. Jeremy, great to see you. Hey, thanks for having me. I love I love the flowers in the backyard. I thought maybe you ran over to the Japanese, the Japanese garden or the Rose Garden, Right To very beautiful places to visit in Portland. >>Yeah. You know, you only get him for a couple. Ah, couple weeks here, so we get the timing just right. >>Excellent. All right, so let's jump into it. Really? And in this conversation really is all about making Ai Riel. Um, and you guys are working with Dell and you're working with not only Dell, right? There's the hardware and software, but a lot of these smaller a solution provider. So what is some of the key attributes that that needs to make ai riel for your customers out there? >>Yeah, so, you know, it's a it's a complex space. So when you can bring the best of the intel portfolio, which is which is expanding a lot, you know, it's not just the few anymore you're getting into Memory technologies, network technologies and kind of a little less known as how many resources we have focused on the software side of things optimizing frameworks and optimizing, and in these key ingredients and libraries that you can stitch into that portfolio to really get more performance in value, out of your machine learning and deep learning space. And so you know what we've really done here with Dell? It has started to bring a bunch of that portfolio together with Dell's capabilities, and then bring in that ai's V partner, that software vendor where we can really take and stitch and bring the most value out of that broad portfolio, ultimately using using the complexity of what it takes to deploy an AI capability. So a lot going on. They're bringing kind of the three legged stool of the software vendor hardware vendor dental into the mix, and you get a really strong outcome, >>right? So before we get to the solutions piece, let's stick a little bit into the Intel world. And I don't know if a lot of people are aware that obviously you guys make CPUs and you've been making great CPIs forever. But there's a whole lot more stuff that you've added, you know, kind of around the core CPU. If you will in terms of of actual libraries and ways to really optimize the seond processors to operate in an AI world. I wonder if you can kind of take us a little bit below the surface on how that works. What are some of the examples of things you can do to get more from your Gambira Intel processors for ai specific applications of workloads? >>Yeah, well, you know, there's a ton of software optimization that goes into this. You know that having the great CPU is definitely step one. But ultimately you want to get down into the libraries like tensor flow. We have data analytics, acceleration libraries. You know, that really allows you to get kind of again under the covers a little bit and look at it. How do we have to get the most out of the kinds of capabilities that are ultimately used in machine learning in deep learning capabilities, and then bring that forward and trying and enable that with our software vendors so that they can take advantage of those acceleration components and ultimately, you know, move from, you know, less training time or could be a the cost factor. But those are the kind of capabilities we want to expose to software vendors do these kinds of partnerships. >>Okay. Ah, and that's terrific. And I do think that's a big part of the story that a lot of people are probably not as aware of that. There are a lot of these optimization opportunities that you guys have been leveraging for a while. So shifting gears a little bit, right? AI and machine learning is all about the data. And in doing a little research for this, I found actually you on stage talking about some company that had, like, 350 of road off, 315 petabytes of data, 140,000 sources of those data. And I think probably not great quote of six months access time to get that's right and actually work with it. And the company you're referencing was intel. So you guys know a lot about debt data, managing data, everything from your manufacturing, and obviously supporting a global organization for I t and run and ah, a lot of complexity and secrets and good stuff. So you know what have you guys leveraged as intel in the way you work with data and getting a good data pipeline. That's enabling you to kind of put that into these other solutions that you're providing to the customers, >>right? Well, it is, You know, it's absolutely a journey, and it doesn't happen overnight, and that's what we've you know. We've seen it at Intel on We see it with many of our customers that are on the same journey that we've been on. And so you know, this idea of building that pipeline it really starts with what kind of problems that you're trying to solve. What are the big issues that are holding you back that company where you see that competitive advantage that you're trying to get to? And then ultimately, how do you build the structure to enable the right kind of pipeline of that data? Because that's that's what machine learning and deep learning is that data journey. So really a lot of focus around you know how we can understand those business challenges bring forward those kinds of capabilities along the way through to where we structure our entire company around those assets and then ultimately some of the partnerships that we're gonna be talking about these companies that are out there to help us really squeeze the most out of that data as quickly as possible because otherwise it goes stale real fast, sits on the shelf and you're not getting that value out of right. So, yeah, we've been on the journey. It's Ah, it's a long journey, but ultimately we could take a lot of those those kind of learnings and we can apply them to our silicon technology. The software optimization is that we're doing and ultimately, how we talk to our enterprise customers about how they can solve overcome some of the same challenges that we did. >>Well, let's talk about some of those challenges specifically because, you know, I think part of the the challenge is that kind of knocked big data, if you will in Hadoop, if you will kind of off the rails. Little bit was there's a whole lot that goes into it. Besides just doing the analysis, there's a lot of data practice data collection, data organization, a whole bunch of things that have to happen before. You can actually start to do the sexy stuff of AI. So you know, what are some of those challenges. How are you helping people get over kind of these baby steps before they can really get into the deep end of the pool? >>Yeah, well, you know, one is you have to have the resource is so you know, do you even have the resource is if you can acquire those Resource is can you keep them interested in the kind of work that you're doing? So that's a big challenge on and actually will talk about how that fits into some of the partnerships that we've been establishing in the ecosystem. It's also you get stuck in this poc do loop, right? You finally get those resource is and they start to get access to that data that we talked about. It start to play out some scenarios, a theorize a little bit. Maybe they show you some really interesting value, but it never seems to make its way into a full production mode. And I think that is a challenge that has faced so many enterprises that are stuck in that loop. And so that's where we look at who's out there in the ecosystem that can help more readily move through that whole process of the evaluation that proved the r a y, the POC and ultimately move that thing that capability into production mode as quickly as possible that you know that to me is one of those fundamental aspects of if you're stuck in the POC. Nothing's happening from this. This is not helping your company. We want to move things more quickly, >>right? Right. And let's just talk about some of these companies that you guys are working with that you've got some reference architectures is data robot a Grid dynamics H 20 just down the road in Antigua. So a lot of the companies we've worked with with Cube and I think you know another part that's interesting. It again we can learn from kind of old days of big data is kind of generalized. Ai versus solution specific. Ai and I think you know where there's a real opportunity is not AI for a sake, but really it's got to be applied to a specific solution, a specific problem so that you have, you know, better chatbots, better customer service experience, you know, better something. So when you were working with these folks and trying to design solutions or some of the opportunities that you saw to work with some of these folks to now have an applied a application slash solution versus just kind of AI for ai's sake. >>Yeah. I mean, that could be anything from fraud, detection and financial services, or even taking a step back and looking more horizontally like back to that data challenge. If if you're stuck at the AI built a fantastic Data lake, but I haven't been able to pull anything back out of it, who are some of the companies that are out there that can help overcome some of those big data challenges and ultimately get you to where you know, you don't have a data scientist spending 60% of their time on data acquisition pre processing? That's not where we want them, right? We want them on building out that next theory. We want them on looking at the next business challenge. We want them on selecting the right models, but ultimately they have to do that as quickly as possible so that they can move that that capability forward into the next phase. So, really, it's about that that connection of looking at those those problems or challenges in the whole pipeline. And these companies like data robot in H 20 quasi. Oh, they're all addressing specific challenges in the end to end. That's why they've kind of bubbled up as ones that we want to continue to collaborate with, because it can help enterprises overcome those issues more fast. You know more readily. >>Great. Well, Jeremy, thanks for taking a few minutes and giving us the Intel side of the story. Um, it's a great company has been around forever. I worked there many, many moons ago. That's Ah, that's a story for another time, but really appreciate it and I'll interview you will go there. Alright, so super. Thanks a lot. So he's Jeremy. I'm Jeff Frick. So now it's time to go ahead and jump into the crowd chat. It's crowdchat dot net slash make ai real. Um, we'll see you in the chat. And thanks for watching

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AI has the potential to profoundly change our lives with Dell Technologies. AI is easy to adopt, easy to manage and easy to scale. And there's nothing artificial about that. Yeah, yeah, from >>the Cube Studios in Palo Alto and Boston >>connecting with >>thought leaders all around the world. This is a cube conversation. Hi, I'm Stew Minimum. And welcome to this special launch with our friends at Dell Technologies. We're gonna be talking about AI and the reality of making artificial intelligence real happy to welcome to the program. Two of our Cube alumni Rob, depending 90. He's the senior vice president of server product management and very Pellegrino vice president, data centric workloads and solutions in high performance computing, both with Dell Technologies. Thank you both for joining thanks to you. So you know, is the industry we watch? You know, the AI has been this huge buzz word, but one of things I've actually liked about one of the differences about what I see when I listen to the vendor community talking about AI versus what I saw too much in the big data world is you know, it used to be, you know Oh, there was the opportunity. And data is so important. Yes, that's really But it was. It was a very wonky conversation. And the promise and the translation of what has been to the real world didn't necessarily always connect and We saw many of the big data solutions, you know, failed over time with AI on. And I've seen this in meetings from Dell talking about, you know, the business outcomes in general overall in i t. But you know how ai is helping make things real. So maybe we can start there for another product announcements and things we're gonna get into. But Robbie Interior talk to us a little bit about you know, the customers that you've been seeing in the impact that AI is having on their business. >>Sure, Teoh, I'll take us a job in it. A couple of things. For example, if you start looking at, uh, you know, the autonomous vehicles industry of the manufacturing industry where people are building better tools for anything they need to do on their manufacturing both. For example, uh, this is a good example of where that honors makers and stuff you've got Xeon ut It's actually a world war balcony. Now it is using our whole product suite right from the hardware and software to do multiple iterations off, ensuring that the software and the hardware come together pretty seamlessly and more importantly, ingesting, you know, probably tens of petabytes of data to ensure that we've got the right. They're training and gardens in place. So that's a great example of how we are helping some of our customers today in ensuring that we can really meet is really in terms of moving away from just a morning scenario in something that customers are able to use like today. >>Well, if I can have one more, Ah Yanai, one of our core and more partners than just customers in Italy in the energy sector have been been really, really driving innovation with us. We just deployed a pretty large 8000 accelerator cluster with them, which is the largest commercial cluster in the world. And where they're focusing on is the digital transformation and the development of energy sources. And it's really important not be an age. You know, the plan. It's not getting younger, and we have to be really careful about the type of energies that we utilize to do what we do every day on they put a lot of innovation. We've helped set up the right solution for them, and we'll talk some more about what they've done with that cluster. Later, during our chat, but it is one of the example that is tangible with the appointment that is being used to help there. >>Great. Well, we love starting with some of the customer stories. Really glad we're gonna be able to share some of those, you know, actual here from some of the customers a little bit later in this launch. But, Robbie, you know, maybe give us a little bit as to what you're hearing from customers. You know, the overall climate in AI. You know, obviously you know, so many challenges facing, you know, people today. But you know, specifically around ai, what are some of the hurdles that they might need to overcome Be able to make ai. Really? >>I think the two important pieces I can choose to number one as much as we talk about AI machine learning. One of the biggest challenges that customers have today is ensuring that they have the right amount and the right quality of data to go out and do the analytics percent. Because if you don't do it, it's giggle garbage in garbage out. So the one of the biggest challenges our customers have today is ensuring that they have the most pristine data to go back on, and that takes quite a bit of an effort. Number two. A lot of times, I think one of the challenges they also have is having the right skill set to go out and have the execution phase of the AI pod. You know, work done. And I think those are the two big challenges we hear off. And that doesn't seem to be changing in the very near term, given the very fact that nothing Forbes recently had an article that said that less than 15% off, our customers probably are using AI machine learning today so that talks to the challenges and the opportunities ahead for me. All right, >>So, Ravi, give us the news. Tell us the updates from Dell Technologies how you're helping customers with AI today, >>going back to one of the challenges, as I mentioned, which is not having the right skin set. One of the things we are doing at Dell Technologies is making sure that we provide them not just the product but also the ready solutions that we're working with. For example, Tier and his team. We're also working on validated and things are called reference architectures. The whole idea behind this is we want to take the guesswork out for our customers and actually go ahead and destroying things that we have already tested to ensure that the integration is right. There's rightsizing attributes, so they know exactly the kind of a product that would pick up our not worry about me in time and the resources needed you get to that particular location. So those are probably the two of the biggest things we're doing to help our customers make the right decision and execute seamlessly and on time. >>Excellent. So teary, maybe give us a little bit of a broader look as to, you know, Dell's part participation in the overall ecosystem when it comes to what's happening in AI on and you know why is this a unique time for what's happening in the in the industry? >>Yeah, I mean, I think we all live it. I mean, I'm right here in my home, and I'm trying to ensure that the business continues to operate, and it's important to make sure that we're also there for our customers, right? The fight against covered 19 is eyes changing what's happening around the quarantines, etcetera. So Dell, as a participant not only in the AI the world that we live in on enabling AI is also a participant in all of the community's s. So we've recently joined the covered 19 High Performance Computing Consortium on. We also made a lot of resources available to researchers and scientists leveraging AI in order to make progress towards you're and potentially the vaccine against Corbyn. 19 examples are we have our own supercomputers in the lab here in Austin, Texas, and we've given access to some of our partners. T. Gen. Is one example. The beginning of our chat I mentioned and I So not only did they have barely deport the cluster with us earlier this year that could 19 started hitting, so they've done what's the right thing to do for community and humanity is they made the resource available to scientists in Europe on tack just down the road here, which had the largest I can't make supercomputer that we deployed with them to. Ai's doing exactly the same thing. So this is one of the real examples that are very timely, and it's it's it's happening right now we hadn't planned for it. A booth there with our customers, the other pieces. This is probably going to be a trend, but healthcare is going through and version of data you mentioned in the beginning. You're talking about 2.3000 exabytes, about 3000 times the content of the Library of Congress. It's incredible, and that data is useless. I mean, it's great we can We can put that on our great ice on storage, but you can also see it as an opportunity to get business value out of it. That's going to be we're a lot more resource is with AI so a lot happening here. That's that's really if I can get into more of the science of it because it's healthcare, because it's the industry we see now that our family members at the M. Ware, part of the Dell Technologies Portfolio, are getting even more relevance in the discussion. The industry is based on virtualization, and the M ware is the number one virtualization solution for the industry. So now we're trying to weave in the reality in the I T environment with the new nodes of AI and data science and HPC. So you will see the VM Ware just added kubernetes control plane. This fear Andi were leveraging that to have a very flexible environment On one side, we can do some data science on the other side. We can go back to running some enterprise class hardware class software on top of it. So this is is great. And we're capitalizing on it with validates solutions, validated design on. And I think that's going to be adding a lot of ah power in the hands of our customers and always based on their feedback. And they asked back, >>Yeah, I may ask you just to build on that interesting comment that you made on we're actually looking at very shortly will be talking about how we're gonna have the ability to, for example, read or V Sphere and Allah servers begin. That essentially means that we're going to cut down the time our customers need to go ahead and deploy on their sites. >>Yeah, excellent. Definitely been, you know, very strong feedback from the community. We did videos around some of the B sphere seven launch, you know, theory. You know, we actually had done an interview with you. Ah, while back at your big lab, Jeff Frick. Otto, See the supercomputers behind what you were doing. Maybe bring us in a little bit inside as who? You know, some of the new pieces that help enable AI. You know, it often gets lost on the industry. You know, it's like, Oh, yeah, well, we've got the best hardware to accelerate or enable these kind of workloads. So, you know, bring us in its But what, You know, the engineering solution sets that are helping toe make this a reality >>of today. Yeah, and truly still you've been there. You've seen the engineers in the lab, and that's more than AI being real. That that is double real because we spend a lot of time analyzing workloads customer needs. We have a lot of PhD engineers in there, and what we're working on right now is kind of the next wave of HPC enablement Azaz. We all know the consumption model or the way that we want to have access to resources is evolving from something that is directly in front of us. 1 to 1 ratio to when virtualization became more prevalent. We had a one to many ratio on genes historically have been allocated on a per user. Or sometimes it is study modified view to have more than one user GP. But with the addition of big confusion to the VM our portfolio and be treated not being part of these fear. We're building up a GPU as a service solutions through a VM ware validated design that we are launching, and that's gonna give them flexibility. And the key here is flexibility. We have the ability, as you know, with the VM Ware environment, to bring in also some security, some flexibility through moving the workloads. And let's be honest with some ties into cloud models on, we have our own set of partners. We all know that the big players in the industry to But that's all about flexibility and giving our customers what they need and what they expect in the world. But really, >>Yeah, Ravi, I guess that brings us to ah, you know, one of the key pieces we need to look at here is how do we manage across all of these environments? Uh, and you know, how does AI fit into this whole discussion between what Dell and VM ware doing things like v Sphere, you know, put pulling in new workloads >>stew, actually a couple of things. So there's really nothing artificial about the real intelligence that comes through with all that foolish intelligence we're working out. And so one of the crucial things I think we need to, you know, ensure that we talk about is it's not just about the fact that it's a problem. So here are our stories there, but I think the crucial thing is we're looking at it from an end to end perspective from everything from ensuring that we have direct workstations, right servers, the storage, making sure that is well protected and all the way to working with an ecosystem of software renders. So first and foremost, that's the whole integration piece, making sure they realized people system. But more importantly, it's also ensuring that we help our customers by taking the guess work out again. I can't emphasize the fact that there are customers who are looking at different aliens off entry, for example, somebody will be looking at an F G. A. Everybody looking at GP use. API is probably, as you know, are great because they're price points and normal. Or should I say that our needs our lot lesser than the GP use? But on the flip side, there's a need for them to have a set of folks who can actually program right. It is why it's called the no programming programmable gate arrays of Saas fee programmable. My point being in all this, it's important that we actually provide dried end to end perspective, making sure that we're able to show the integration, show the value and also provide the options, because it's really not a cookie cutter approach of where you can take a particular solution and think that it will put the needs of every single customer. He doesn't even happen in the same industry, for that matter. So the flexibility that we provide all the way to the services is truly our attempt. At Dell Technologies, you get the entire gamut of solutions available for the customer to go out and pick and choose what says their needs the best. >>Alright, well, Ravi interior Thank you so much for the update. So we're gonna turn it over to actually hear from some of your customers. Talk about the power of ai. You're from their viewpoint, how real these solutions are becoming. Love the plan words there about, you know, enabling really artificial intelligence. Thanks so much for joining after the customers looking forward to the VM Ware discussion, we want to >>put robots into the world's dullest, deadliest and dirtiest jobs. We think that if we can have machines doing the work that put people at risk than we can allow people to do better work. Dell Technologies is the foundation for a lot of the >>work that we've done here. Every single piece of software that we developed is simulated dozens >>or hundreds of thousands of times. And having reliable compute infrastructure is critical for this. Yeah, yeah, A lot of technology has >>matured to actually do something really useful that can be used by non >>experts. We try to predict one system fails. We try to predict the >>business impatience things into images. On the end of the day, it's that >>now we have machines that learn how to speak a language from from zero. Yeah, everything >>we do really, at Epsilon centered around data and our ability >>to get the right message to >>the right person at the right >>time. We apply machine learning and artificial intelligence. So in real time you can adjust those campaigns to ensure that you're getting the most optimized message theme. >>It is a joint venture between Well, cars on the Amir are your progress is automated driving on Advanced Driver Assistance Systems Centre is really based on safety on how we can actually make lives better for you. Typically gets warned on distracted in cars. If you can take those kind of situations away, it will bring the accidents down about 70 to 80%. So what I appreciate it with Dell Technologies is the overall solution that they have to live in being able to deliver the full package. That has been a major differentiator compared to your competitors. >>Yeah. Yeah, alright, welcome back to help us dig into this discussion and happy to welcome to the program Chris Facade. He is the senior vice president and general manager of the B sphere business and just Simon, chief technologist for the High performance computing group, both of them with VM ware. Gentlemen, thanks so much for joining. Thank >>you for having us. >>All right, Krish. When vm Ware made the bit fusion acquisition. Everybody was looking the You know what this will do for space Force? GPU is we're talking about things like AI and ML. So bring us up to speed. As to you know, the news today is the what being worth doing with fusion. Yeah. >>Today we have a big announcement. I'm excited to announce that, you know, we're taking the next big step in the AI ML and more than application strategy. With the launch off bit fusion, we're just now being fully integrated with VCF. They're in black home, and we'll be releasing this very shortly to the market. As you said when we acquire institution A year ago, we had a showcase that's capable days as part of the animal event. And at that time we laid out a strategy that part of our institution as the cornerstone off our capabilities in the black home in the Iot space. Since then, we have had many customers take a look at the technology and we have had feedback from them as well as from partners and analysts. And the feedback has been tremendous. >>Excellent. Well, Chris, what does this then mean for customers? You know What's the value proposition that diffusion brings the VC? Yeah, >>if you look at our customers, they are in the midst of a big ah journey in digital transformation. And basically, what that means is customers are building a ton of applications and most of those applications some kind of data analytics or machine learning embedded in it. And what this is doing is that in the harbor and infrastructure industry, this is driving a lot of innovation. So you see the advent off a lot off specialized? Absolutely. There's custom a six FPs. And of course, the views being used to accelerate the special algorithms that these AI ml type applications need. And unfortunately, customer environment. Most of these specialized accelerators uh um bare metal kind of set up, but they're not taking advantage off optimization and everything that it brings to that. Also, with fusion launched today, we are essentially doing the accelerator space. What we need to compute several years ago and that is essentially bringing organization to the accelerators. But we take it one step further, which is, you know, we use the customers the ability to pull these accelerators and essentially going to be couple it from the server so you can have a pool of these accelerators sitting in the network. And customers are able to then target their workloads and share the accelerators get better utilization by a lot of past improvements and, in essence, have a smaller pool that they can use for a whole bunch of different applications across the enterprise. That is a huge angle for our customers. And that's the tremendous positive feedback that we get getting both from customers as well. >>Excellent. Well, I'm glad we've got Josh here to dig into some of the thesis before we get to you. They got Chris. Uh, part of this announcement is the partnership of VM Ware in Dell. So tell us about what the partnership is in the solutions for for this long. Yeah. >>We have been working with the Dell in the in the AI and ML space for a long time. We have ah, good partnership there. This just takes the partnership to the next level and we will have ah, execution solution. Support in some of the key. I am el targeted words like the sea for 1 40 the r 7 40 Those are the centers that would be partnering with them on and providing solutions. >>Excellent. Eso John. You know, we've watched for a long time. You know, various technologies. Oh, it's not a fit for virtualized environment. And then, you know, VM Ware does does what it does. Make sure you know, performance is there. And make sure all the options there bring us inside a little bit. You know what this solution means for leveraging GPS? Yeah. So actually, before I before us, answer that question. Let me say that the the fusion acquisition and the diffusion technology fits into a larger strategy at VM Ware around AI and ML. That I think matches pretty nicely the overall Dell strategy as well, in the sense that we are really focused on delivering AI ml capabilities or the ability for our customers to run their am ai and ml workloads from edge before the cloud. And that means running it on CPU or running it on hardware accelerators like like G fuse. Whatever is really required by the customer in this specific case, we're quite excited about using technology as it really allows us. As Chris was describing to extend our capabilities especially in the deep learning space where GPU accelerators are critically important. And so what this technology really brings to the table is the ability to, as Chris was outlining, to pull those resources those hardware resource together and then allow organizations to drive up the utilization of those GP Resource is through that pooling and also increase the degree of sharing that we support that supported for the customer. Okay, Jeff, take us in a little bit further as how you know the mechanisms of diffusion work. Sure, Yeah, that's a great question. So think of it this way. There there is a client component that we're using a server component. The server component is running on a machine that actually has the physical GPU is installed in it. The client machine, which is running the bit fusion client software, is where the user of the data scientist is actually running their machine machine learning application. But there's no GPU actually in that host. And what is happening with fusion technology is that it is essentially intercepting the cuda calls that are being made by that machine learning app, patience and promoting those protocols over to the bit fusion server and then injecting them into the local GPU on the server. So it's actually, you know, we call it into a position in the ability that remote these protocols, but it's actually much more sophisticated than that. There are a lot of underlying capabilities that are being deployed in terms of optimization who takes maximum advantage of the the networking link that sits between the client machine and the server machine. But given all of that, once we've done it with diffusion, it's now possible for the data scientist. Either consume multiple GP use for single GPU use or even fractional defuse across that Internet using the using technology. Okay, maybe it would help illustrate some of these technologies. If you got a couple of customers, Sure, so one example would be a retail customer. I'm thinking of who is. Actually it's ah, grocery chain. That is the flowing, ah, large number of video cameras into their to their stores in order to do things like, um, watch for pilfering, uh, identify when storage store shelves could be restocked and even looking for cases where, for example, maybe a customer has fallen down in denial on someone needs to go and help those multiple video streams and then multiple app patients that are being run that part are consuming the data from those video streams and doing analytics and ml on them would be perfectly suited for this type of environment where you would like to be ableto have these multiple independent applications running but having them be able to efficiently share the hardware resources of the GP use. Another example would be retailers who are deploying ml Howard Check out registers who helped reduce fraud customers who are buying, buying things with, uh, fake barcodes, for example. So in that case, you would not necessarily want to employ a single dedicated GPU for every single check out line. Instead, what you would prefer to do is have a full set of resource. Is that each inference operation that's occurring within each one of those check out lines could then consume collectively. That would be two examples of the use of this kind of pull in technology. Okay, great. So, Josh, a lot last question for you is this technology is this only for use and anything else. You can give us a little bit of a look forward to as to what we should be expecting from the big fusion technology. Yeah. So currently, the target is specifically NVIDIA GPU use with Cuda. The team, actually even prior to acquisition, had done some work on enablement of PJs and also had done some work on open CL, which is more open standard for a device that so what you will see over time is an expansion of the diffusion capabilities to embrace devices like PJs. The domain specific a six that first was referring to earlier will roll out over time. But we are starting with the NVIDIA GPU, which totally makes sense, since that is the primary hardware acceleration and for deep learning currently excellent. Well, John and Chris, thank you so much for the updates to the audience. If you're watching this live, please throwing the crowd chat and ask your questions. This faith, If you're watching this on demand, you can also go to crowdchat dot net slash make ai really to be able to see the conversation that we had. Thanks so much for joining. >>Thank you very much. >>Thank you. Managing your data center requires around the clock. 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