>>Good morning fellow nerds and welcome back to AWS Reinvent. We are live from the show floor here in Las Vegas, Nevada. My name is Savannah Peterson, joined with my fabulous co-host John Furrier. Day two keynotes are rolling. >>Yeah. What do you thinking this? This is the day where everything comes, so the core gets popped off the bottle, all the announcements start flowing out tomorrow. You hear machine learning from swee lot more in depth around AI probably. And then developers with Verner Vos, the CTO who wrote the seminal paper in in early two thousands around web service that becames. So again, just another great year of next level cloud. Big discussion of data in the keynote bulk of the time was talking about data and business intelligence, business transformation easier. Is that what people want? They want the easy button and we're gonna talk a lot about that in this segment. I'm really looking forward to this interview. >>Easy button. We all want the >>Easy, we want the easy button. >>I love that you brought up champagne. It really feels like a champagne moment for the AWS community as a whole. Being here on the floor feels a bit like the before times. I don't want to jinx it. Our next guest, Scott Castle, from Si Sense. Thank you so much for joining us. How are you feeling? How's the show for you going so far? Oh, >>This is exciting. It's really great to see the changes that are coming in aws. It's great to see the, the excitement and the activity around how we can do so much more with data, with compute, with visualization, with reporting. It's fun. >>It is very fun. I just got a note. I think you have the coolest last name of anyone we've had on the show so far, castle. Oh, thank you. I'm here for it. I'm sure no one's ever said that before, but I'm so just in case our audience isn't familiar, tell us about >>Soy Sense is an embedded analytics platform. So we're used to take the queries and the analysis that you can power off of Aurora and Redshift and everything else and bring it to the end user in the applications they already know how to use. So it's all about embedding insights into tools. >>Embedded has been a, a real theme. Nobody wants to, it's I, I keep using the analogy of multiple tabs. Nobody wants to have to leave where they are. They want it all to come in there. Yep. Now this space is older than I think everyone at this table bis been around since 1958. Yep. How do you see Siente playing a role in the evolution there of we're in a different generation of analytics? >>Yeah, I mean, BI started, as you said, 58 with Peter Lu's paper that he wrote for IBM kind of get became popular in the late eighties and early nineties. And that was Gen one bi, that was Cognos and Business Objects and Lotus 1 23 think like green and black screen days. And the way things worked back then is if you ran a business and you wanted to get insights about that business, you went to it with a big check in your hand and said, Hey, can I have a report? And they'd come back and here's a report. And it wasn't quite right. You'd go back and cycle, cycle, cycle and eventually you'd get something. And it wasn't great. It wasn't all that accurate, but it's what we had. And then that whole thing changed in about two, 2004 when self-service BI became a thing. And the whole idea was instead of going to it with a big check in your hand, how about you make your own charts? >>And that was totally transformative. Everybody started doing this and it was great. And it was all built on semantic modeling and having very fast databases and data warehouses. Here's the problem, the tools to get to those insights needed to serve both business users like you and me and also power users who could do a lot more complex analysis and transformation. And as the tools got more complicated, the barrier to entry for everyday users got higher and higher and higher to the point where now you look, look at Gartner and Forester and IDC this year. They're all reporting in the same statistic. Between 10 and 20% of knowledge workers have learned business intelligence and everybody else is just waiting in line for a data analyst or a BI analyst to get a report for them. And that's why the focus on embedded is suddenly showing up so strong because little startups have been putting analytics into their products. People are seeing, oh my, this doesn't have to be hard. It can be easy, it can be intuitive, it can be native. Well why don't I have that for my whole business? So suddenly there's a lot of focus on how do we embed analytics seamlessly? How do we embed the investments people make in machine learning in data science? How do we bring those back to the users who can actually operationalize that? Yeah. And that's what Tysons does. Yeah. >>Yeah. It's interesting. Savannah, you know, data processing used to be what the IT department used to be called back in the day data processing. Now data processing is what everyone wants to do. There's a ton of data we got, we saw the keynote this morning at Adam Lesky. There was almost a standing of vision, big applause for his announcement around ML powered forecasting with Quick Site Cube. My point is people want automation. They want to have this embedded semantic layer in where they are not having all the process of ETL or all the muck that goes on with aligning the data. All this like a lot of stuff that goes on. How do you make it easier? >>Well, to be honest, I, I would argue that they don't want that. I think they, they think they want that, cuz that feels easier. But what users actually want is they want the insight, right? When they are about to make a decision. If you have a, you have an ML powered forecast, Andy Sense has had that built in for years, now you have an ML powered forecast. You don't need it two weeks before or a week after in a report somewhere. You need it when you're about to decide do I hire more salespeople or do I put a hundred grand into a marketing program? It's putting that insight at the point of decision that's important. And you don't wanna be waiting to dig through a lot of infrastructure to find it. You just want it when you need it. What's >>The alternative from a time standpoint? So real time insight, which is what you're saying. Yep. What's the alternative? If they don't have that, what's >>The alternative? Is what we are currently seeing in the market. You hire a bunch of BI analysts and data analysts to do the work for you and you hire enough that your business users can ask questions and get answers in a timely fashion. And by the way, if you're paying attention, there's not enough data analysts in the whole world to do that. Good luck. I am >>Time to get it. I really empathize with when I, I used to work for a 3D printing startup and I can, I have just, I mean, I would call it PTSD flashbacks of standing behind our BI guy with my list of queries and things that I wanted to learn more about our e-commerce platform in our, in our marketplace and community. And it would take weeks and I mean this was only in 2012. We're not talking 1958 here. We're talking, we're talking, well, a decade in, in startup years is, is a hundred years in the rest of the world life. But I think it's really interesting. So talk to us a little bit about infused and composable analytics. Sure. And how does this relate to embedded? Yeah. >>So embedded analytics for a long time was I want to take a dashboard I built in a BI environment. I wanna lift it and shift it into some other application so it's close to the user and that is the right direction to go. But going back to that statistic about how, hey, 10 to 20% of users know how to do something with that dashboard. Well how do you reach the rest of users? Yeah. When you think about breaking that up and making it more personalized so that instead of getting a dashboard embedded in a tool, you get individual insights, you get data visualizations, you get controls, maybe it's not even actually a visualization at all. Maybe it's just a query result that influences the ordering of a list. So like if you're a csm, you have a list of accounts in your book of business, you wanna rank those by who's priorities the most likely to churn. >>Yeah. You get that. How do you get that most likely to churn? You get it from your BI system. So how, but then the question is, how do I insert that back into the application that CSM is using? So that's what we talk about when we talk about Infusion. And SI started the infusion term about two years ago and now it's being used everywhere. We see it in marketing from Click and Tableau and from Looker just recently did a whole launch on infusion. The idea is you break this up into very small digestible pieces. You put those pieces into user experiences where they're relevant and when you need them. And to do that, you need a set of APIs, SDKs, to program it. But you also need a lot of very solid building blocks so that you're not building this from scratch, you're, you're assembling it from big pieces. >>And so what we do aty sense is we've got machine learning built in. We have an LQ built in. We have a whole bunch of AI powered features, including a knowledge graph that helps users find what else they need to know. And we, we provide those to our customers as building blocks so that they can put those into their own products, make them look and feel native and get that experience. In fact, one of the things that was most interesting this last couple of couple of quarters is that we built a technology demo. We integrated SI sensee with Office 365 with Google apps for business with Slack and MS teams. We literally just threw an Nlq box into Excel and now users can go in and say, Hey, which of my sales people in the northwest region are on track to meet their quota? And they just get the table back in Excel. They can build charts of it and PowerPoint. And then when they go to their q do their QBR next week or week after that, they just hit refresh to get live data. It makes it so much more digestible. And that's the whole point of infusion. It's bigger than just, yeah. The iframe based embedding or the JavaScript embedding we used to talk about four or five years >>Ago. APIs are very key. You brought that up. That's gonna be more of the integration piece. How does embedable and composable work as more people start getting on board? It's kind of like a Yeah. A flywheel. Yes. What, how do you guys see that progression? Cause everyone's copying you. We see that, but this is a, this means it's standard. People want this. Yeah. What's next? What's the, what's that next flywheel benefit that you guys coming out with >>Composability, fundamentally, if you read the Gartner analysis, right, they, when they talk about composable, they're talking about building pre-built analytics pieces in different business units for, for different purposes. And being able to plug those together. Think of like containers and services that can, that can talk to each other. You have a composition platform that can pull it into a presentation layer. Well, the presentation layer is where I focus. And so the, so for us, composable means I'm gonna have formulas and queries and widgets and charts and everything else that my, that my end users are gonna wanna say almost minority report style. If I'm not dating myself with that, I can put this card here, I can put that chart here. I can set these filters here and I get my own personalized view. But based on all the investments my organization's made in data and governance and quality so that all that infrastructure is supporting me without me worrying much about it. >>Well that's productivity on the user side. Talk about the software angle development. Yeah. Is your low code, no code? Is there coding involved? APIs are certainly the connective tissue. What's the impact to Yeah, the >>Developer. Oh. So if you were working on a traditional legacy BI platform, it's virtually impossible because this is an architectural thing that you have to be able to do. Every single tool that can make a chart has an API to embed that chart somewhere. But that's not the point. You need the life cycle automation to create models, to modify models, to create new dashboards and charts and queries on the fly. And be able to manage the whole life cycle of that. So that in your composable application, when you say, well I want chart and I want it to go here and I want it to do this and I want it to be filtered this way you can interact with the underlying platform. And most importantly, when you want to use big pieces like, Hey, I wanna forecast revenue for the next six months. You don't want it popping down into Python and writing that yourself. >>You wanna be able to say, okay, here's my forecasting algorithm. Here are the inputs, here's the dimensions, and then go and just put it somewhere for me. And so that's what you get withy sense. And there aren't any other analytics platforms that were built to do that. We were built that way because of our architecture. We're an API first product. But more importantly, most of the legacy BI tools are legacy. They're coming from that desktop single user, self-service, BI environment. And it's a small use case for them to go embedding. And so composable is kind of out of reach without a complete rebuild. Right? But with SI senses, because our bread and butter has always been embedding, it's all architected to be API first. It's integrated for software developers with gi, but it also has all those low code and no code capabilities for business users to do the minority report style thing. And it's assemble endless components into a workable digital workspace application. >>Talk about the strategy with aws. You're here at the ecosystem, you're in the ecosystem, you're leading product and they have a strategy. We know their strategy, they have some stuff, but then the ecosystem goes faster and ends up making a better product in most of the cases. If you compare, I know they'll take me to school on that, but I, that's pretty much what we report on. Mongo's doing a great job. They have databases. So you kind of see this balance. How are you guys playing in the ecosystem? What's the, what's the feedback? What's it like? What's going on? >>AWS is actually really our best partner. And the reason why is because AWS has been clear for many, many years. They build componentry, they build services, they build infrastructure, they build Redshift, they build all these different things, but they need, they need vendors to pull it all together into something usable. And fundamentally, that's what Cient does. I mean, we didn't invent sequel, right? We didn't invent jackal or dle. These are not, these are underlying analytics technologies, but we're taking the bricks out of the briefcase. We're assembling it into something that users can actually deploy for their use cases. And so for us, AWS is perfect because they focus on the hard bits. The the underlying technologies we assemble those make them usable for customers. And we get the distribution. And of course AWS loves that. Cause it drives more compute and it drives more, more consumption. >>How much do they pay you to say that >>Keynote, >>That was a wonderful pitch. That's >>Absolutely, we always say, hey, they got a lot of, they got a lot of great goodness in the cloud, but they're not always the best at the solutions and that they're trying to bring out, and you guys are making these solutions for customers. Yeah. That resonates with what they got with Amazon. For >>Example, we, last year we did a, a technology demo with Comprehend where we put comprehend inside of a semantic model and we would compile it and then send it back to Redshift. And it takes comprehend, which is a very cool service, but you kind of gotta be a coder to use it. >>I've been hear a lot of hype about the semantic layer. What is, what is going on with that >>Semantec layer is what connects the actual data, the tables in your database with how they're connected and what they mean so that a user like you or me who's saying I wanna bar chart with revenue over time can just work with revenue and time. And the semantic layer translates between what we did and what the database knows >>About. So it speaks English and then they converts it to data language. It's >>Exactly >>Right. >>Yeah. It's facilitating the exchange of information. And, and I love this. So I like that you actually talked about it in the beginning, the knowledge map and helping people figure out what they might not know. Yeah. I, I am not a bi analyst by trade and I, I don't always know what's possible to know. Yeah. And I think it's really great that you're doing that education piece. I'm sure, especially working with AWS companies, depending on their scale, that's gotta be a big part of it. How much is the community play a role in your product development? >>It's huge because I'll tell you, one of the challenges in embedding is someone who sees an amazing experience in outreach or in seismic. And to say, I want that. And I want it to be exactly the way my product is built, but I don't wanna learn a lot. And so you, what you want do is you want to have a community of people who have already built things who can help lead the way. And our community, we launched a new version of the SES community in early 2022 and we've seen a 450% growth in the c in that community. And we've gone from an average of one response, >>450%. I just wanna put a little exclamation point on that. Yeah, yeah. That's awesome. We, >>We've tripled our organic activity. So now if you post this Tysons community, it used to be, you'd get one response maybe from us, maybe from from a customer. Now it's up to three. And it's continuing to trend up. So we're, it's >>Amazing how much people are willing to help each other. If you just get in the platform, >>Do it. It's great. I mean, business is so >>Competitive. I think it's time for the, it's time. I think it's time. Instagram challenge. The reels on John. So we have a new thing. We're gonna run by you. Okay. We just call it the bumper sticker for reinvent. Instead of calling it the Instagram reels. If we're gonna do an Instagram reel for 30 seconds, what would be your take on what's going on this year at Reinvent? What you guys are doing? What's the most important story that you would share with folks on Instagram? >>You know, I think it's really what, what's been interesting to me is the, the story with Redshift composable, sorry. No, composable, Redshift Serverless. Yeah. One of the things I've been >>Seeing, we know you're thinking about composable a lot. Yes. Right? It's, it's just, it's in there, it's in your mouth. Yeah. >>So the fact that Redshift Serverless is now kind becoming the defacto standard, it changes something for, for my customers. Cuz one of the challenges with Redshift that I've seen in, in production is if as people use it more, you gotta get more boxes. You have to manage that. The fact that serverless is now available, it's, it's the default means it now people are just seeing Redshift as a very fast, very responsive repository. And that plays right into the story I'm telling cuz I'm telling them it's not that hard to put some analysis on top of things. So for me it's, it's a, maybe it's a narrow Instagram reel, but it's an >>Important one. Yeah. And that makes it better for you because you get to embed that. Yeah. And you get access to better data. Faster data. Yeah. Higher quality, relevant, updated. >>Yep. Awesome. As it goes into that 80% of knowledge workers, they have a consumer great expectation of experience. They're expecting that five ms response time. They're not waiting 2, 3, 4, 5, 10 seconds. They're not trained on theola expectations. And so it's, it matters a lot. >>Final question for you. Five years out from now, if things progress the way they're going with more innovation around data, this front end being very usable, semantic layer kicks in, you got the Lambda and you got serverless kind of coming in, helping out along the way. What's the experience gonna look like for a user? What's it in your mind's eye? What's that user look like? What's their experience? >>I, I think it shifts almost every role in a business towards being a quantitative one. Talking about, Hey, this is what I saw. This is my hypothesis and this is what came out of it. So here's what we should do next. I, I'm really excited to see that sort of scientific method move into more functions in the business. Cuz for decades it's been the domain of a few people like me doing strategy, but now I'm seeing it in CSMs, in support people and sales engineers and line engineers. That's gonna be a big shift. Awesome. >>Thank >>You Scott. Thank you so much. This has been a fantastic session. We wish you the best at si sense. John, always pleasure to share the, the stage with you. Thank you to everybody who's attuning in, tell us your thoughts. We're always eager to hear what, what features have got you most excited. And as you know, we will be live here from Las Vegas at reinvent from the show floor 10 to six all week except for Friday. We'll give you Friday off with John Furrier. My name's Savannah Peterson. We're the cube, the the, the leader in high tech coverage.

(inspiring music) >> Welcome back to The Cube's coverage here in Las Vegas. I'm John Furrier for re:Mars coverage. Two days of live action, a lot of things happening in space, robotics, automation, and machine learning. That's Mars spelled backwards, but that's machine learning, automation, robotics and space. Got a great guest, Jens Ortmann, associate director at Boston Consulting Group, also known as BCG. Jens, welcome to The Cube. >> Thank you very much. >> So tell me what you're working on. You've got a very cool project you're working on, 'Involved'. Take us through what it is, explain what the project is. >> Yeah, so I'm part of the data science unit within BCG Gamma and I'm focusing on solving business problems for the automotive industry. What I would like to talk about is actually a small internal site project that we were building. It's a conversion rate engine, where we built an advanced analytics tool that computes the conversion rate for car dealerships, at scale. So for every single car dealer in a market, we can compute the conversion rate. >> John: What is a conversion rate? Can you explain that? >> So a conversion rate is very simple. It's actually out of the people that come into your car dealership, how many do you, as a car dealer, manage to sell a car to? >> So, what's your sell, through monthly kind of- >> Per visitor that come into, so your walk-ins. >> So, physical? >> Physical, yeah. So this was for physical stores. It's actually a key metric for sales performance for car dealerships, or for the automotive manufacturers to be aware of. >> So I'm watching here in the show floor at re:Mars, you've got the 'Just Walk Through', which is Amazon's 'take whatever you want and go', are you seeing you're getting analytics on like people coming in, you can see them, there's a drop off rate? Take me through how it works, the challenges because I don't envision like, "Oh, so they walked in and they left but they didn't leave with a car." It's not take and walk out, it's not grab and go. But the concept of using computer vision, I can imagine it being a popular thing. So how do you measure this, people coming in? >> It's actually a big challenge that we learned when we were doing this project. Traditionally, people were measuring it with like these laser sensors but the signal is very, very messy. Now when we wanted to do it at scale, we partnered with an Israeli startup called Play Sense, who aggregate mobile phone data. So we used mobile footfall data to measure how many people visit a store. So it actually is a combination of three main data sources to get to the conversion rate. One, as I mentioned, the mobile footfall data, the second one is building footprints, actual outlines of buildings that we source from the cadastral agency that we need to use it to cut out the footfall data to get the visitors. And the third one, of course, is sales that we get from the official car registration data. Then we combine those to have the key numbers. >> Is there a facial recognition involved in this? >> There's no facial recognition involved. >> So the tire kickers that come in and kick the tires and leave, but might come back. Is that factored in too, or? >> So there is a lot of pre-processing going on to really only get the signals from visitors. So filtering out people that maybe come into the store after hours, cleaning crews, people that come into the store every day, people that work there, they would be in the footfall data. So we applied some logic to identify exactly those people that are most likely actually visitors interested in buying a car >> Well everyone can relate to buying a car, obviously. I wanted you to step back and you mentioned scale. Can you scope the scale of the problem for us? How big is this observation space? What systems are involved? 'Cause when you say scale, I'm thinking all the dealerships in the aggregate. Or, is it by franchise or is it anonymous data? Can you scale the scope of this thing, or scope the scale? >> So we built this as a prototype for the German market and we used the top 10 car brands in Germany. They have around 10,000 car dealerships, for which we all have data. The actual mobile phone footprint data, it's a lot more. I think it was 30 million data points. >> Are you triangulating that? How does that mobile data work? Signal? >> So the mobile data is coming through apps. So mobile apps where you allow the app to track your location. >> Got it, okay. >> That gets anonymized and then you have these mobile data aggregators, like Play Sense. >> Got it, okay. >> That sell the data on. >> So you have to plug into a lot of systems? >> Yes. >> To make all this work. >> Yes and a lot of different data sources. >> And how easy is that? What's the challenge there? Is it cloud integration? How are you guys pulling this together? >> So we build it as a prototype initially, based on our own internal infrastructure, using basic Python and regular cloud infrastructure to process the data. >> All right, so I'm looking at my notes here. Data sets, you have a lot of data sets. What kind of analytics are you running on that? Can you share some examples? >> So I have to be careful since we filed a patent on this but a lot of the thing is actually in data processing, making sure that the data points we get are accurate and usable for this, and then differentiating between the different types of businesses that people are running. So there is on the one hand, you have the problem of outliers, basically filtering out when numbers don't make sense. On the other hand, there is a lot going on in the business itself. Like what do you do when a car dealership sells cars of multiple brands? You see only one visitor seeing cars of different brands but you see sales for two different types of brands. So this is just two examples of some of the processing that we had to implement to make this happen. >> So where can people find out information on this project? Or is it pretty much not public? Are you sharing anything publicly? >> So currently, we have held off the publication on this because we filed a patent on it. We're now about to go to market, building out a solution for the US as well, to then bring this to clients. >> What do you think about this show here at re:Mars? What's your assessment of the vibe? What's it like? Share with the folks who aren't here, what's your takeaway? >> It's really fun. It's really impressive. And it has a great, really inspiring vibe of cool innovative solutions. >> Yeah, you get the creative geniuses, you got the industrial geniuses, you got the software geniuses, all kind of coming together, and they're real people and they're here as a community. To me, the positive future vibe of this show, really is resonating in the keynotes and the energy. It's a forward thinking, positive message. And it's not marketing, this is the vibe. >> Exactly, I think it's something we really need at the moment. >> Yeah, we can solve all of the global problems by going to the moon and Mars. First the moon, then Mars. Who knows, maybe the breakthrough is there. >> People solve a lot of fundamental issues along the way that'll help in a lot of different areas as well. >> I wonder if I'll be alive when there's tourism in the moon. I was just joking with the folks earlier, "Oh yeah, I left that part on Earth, I have to go get it." Cause there's going to be a whole infrastructure there. Construction, all in good time. Okay, what's next for you guys? Tell me what's next on the project. You got a patent pending, so you're a little bit tight lipped and quiet on the secret sauce, I get that. What's next for the vision of the project? >> So this is just one example of how we can use this. Especially this footfall data set in an innovative way in the automotive industry. What we would like to look into is getting more details. Currently, we only see a single data point for a visitor. What would be interesting to understand, also, like the journey of visitors. Did they visit other car dealerships? Or, where are they from? What demographics do they come from? If you can tie that to a geographic location. And then on the business side as well, linking this for example, for companies to marketing campaigns. Does advertisement catch on? Do discounts catch on? Do they drive more people into the stores? Do they drive more sales? How does it affect conversion rate? Also, benchmark within the network, how different car dealerships are performing, how different brands are performing. And then eventually, everything is going to online. This can also be a foundation to set a baseline for online sales, which is still at the very early stages in the automotive industry. >> Yeah, I think there's a lot of reference implementations here for other applications, not just dealerships, all footfall traffic. That's interesting. The question I have for you, and the final question really before we wrap up, is the convergence of online, offline, physical, virtual. It's pretty clear we're living in a hybrid steady state right now, with all the post pandemic and the innovations pulled forward. So, having a device on me, IOT device or phone, will be a big part of things. So I'm buying online and I'm walking in, I'm one presence, virtually and physical. How do you guys see that around the corner? What's next there? Because I can see that coming together in my mind. >> It is. I mean, we can see it happen at Tesla. Tesla barely has any physical dealerships anymore, they have showrooms and do all the sales online. And I think that has a large impact on the industry at the moment. Driving the more traditional manufacturers also to think about what can be and what can be in a digital and online first world. >> Yeah, well this is happening. Well, Jens, thanks for coming on. I appreciate the commentary on re:Mars. Thanks for sharing your perspective and sharing about your project at Boston Consulting Group, also known as BCG. >> Thank you very much. >> Very reputable firm. Okay, that's the Cube coverage here at re:Mars. I'm John Furrier, your host. Two days of wall to wall coverage here. It's a great show. Machine learning, automation, robotics, and space, Mars. Of course, you got Reinvent, the big show, and at Reinforce, the security show. You got the space-software-robotics show, security. And then of course Reinvent is the big show. The Cube covers it, all three will be here. So keep watching here for more coverage. We'll be right back. (gentle inspiring music)

>>Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, or is modernized now. Yes, let's talk about that. And with me is Ravi, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >>Thank you so much. I'm so glad to be here with you. >>I asked you what the new requirements are around modern applications. I've seen some, you know, some of your comments, you gotta be flexible, distributed, multimodal, mobile edge. It, that those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >>Yeah, I think what has basically happened is that, uh, so far, uh, it's been a transition of sorts. And now we are come to a point where, uh, the tipping point and the tipping point has been, uh, uh, more because of COVID and there COVID has pushed us to a world where we are living, uh, in a sort of, uh, occasionally connected manner where our digital, uh, interactions, precede our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than, uh, in a digital manner, as opposed to sort of making a more specific human contact that has really been the, uh, sort of accelerant to this modernized. Now, as a team in this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. >>They're all sitting behind. Uh, they used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, uh, but they are all centralized still. Uh, but where our engagement happens with the data is, uh, at the edge, uh, at your point of convenience at your point of consumption, not where the data is actually sitting. So this has led to, uh, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? Uh, but it just basically comes down to the fact that the data needs to be where you are engaging with it. And that means if you are doing it on your mobile phone, or if you are sitting, uh, doing something in your body or traveling, or whether you are in a subway, whether you're in a plane or a ship, wherever the data needs to come to you, uh, and be available as opposed to every time you going to the data, which is centrally sitting in some place. >>And that is the fundamental shift in terms of how the modern architecture needs to think, uh, when they, when it comes to digital transformation and, uh, transitioning their old applications to, uh, the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Uh, otherwise people are basically waiting for that circle of death that we all know, uh, and blaming the networks and other pieces. The problem is actually, the data is not where you are engaging with. It has got to be fetched, you know, seven seas away. Um, and that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >>I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this, because date data by its very nature is distributed. It's always been distributed, but w w but distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, uh, of, uh, of a super rock solid database that can handle, you know, distributed data? Yes. >>So there are two issues that you're a little too over there with Forrest is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is, uh, like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data in one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, uh, when you have the data, you can first look at it to perform. >>Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five minutes. Again, this is a, there's a class of problem that we solve that same data. Now, eventually, without you ever having to, uh, sort of do a casting it to a different database, you can now do a solid, uh, acquire. These are classic sequel queries, which is our next magic. We are a no SQL database, but we have a full functional sequel. The sequel has been the language that has talked to data for 40 odd years successfully. Every other database has come and try to implement their own QL query language, but they've all failed only sequel as which stood the test of time of 40 odd years. >>Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is, uh, basically, uh, look at the data and any common tutorial, uh, any, uh, any which way you look at the data. All it will come, uh, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries, select star from where Canada stuff, because it's at an English level, it becomes easy to, so the same data, you didn't have to go move it to another database, do your, uh, sort of transformation of the data and all this stuff. Same day that you do this. >>Now, that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, but Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the ability to query the operational data in a different way. I'll talk budding. What was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. >>So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and find different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, uh, the database management system. And that's where the distributed, uh, platform that we have built enables us to get it to where you need the data to be, you know, in a classic way, we call it CDN in the data as in like content delivery networks. So far do static, uh, uh, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >>The first part of the, the answer to my question, are you saying you could do this without skiing with a no schema on, right? And then you can apply those techniques. >>Uh, fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read things. So because there is no schema, it is just a on document that is sitting inside. And Jason is the lingua franca of the web, as you very well know by now. So it just Jason that we manage, you can do key lookups of the Jason. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and the other sophisticated pieces of technology behind it. >>You can do searching on it, using the, um, the full textual analysis pipeline. You can do ad hoc wedding on the analytic side, and you can write your own custom logic on it using our eventing capabilities. So that's, that's what it allows because we keep the data in the native form of Jason. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing, uh, in the last 40 years because we developed various, uh, database systems and data processing systems of various points. In time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. >>We had queuing systems, all the systems, if you want to use any one of them, our answer has always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this is not one to fly instead, bring the logic to the data. So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this, >>As you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >>Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data casting because it required you to have it in seven schema in one sense at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably flooded, but it not really, uh, how do you say, um, keep to the promise that it actually meant to be? So that's why it was a swamp I need, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it, and you create different types of indexes to manage it. You distribute the index, you distribute the data you have, um, like we were discussing, you have acid semantics on top of, and when you, when you put all these things together, uh, it's, it's, it's a tough proposition, but they have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >>So you predicted the trend around multimodal and converged, uh, databases. Um, you kind of led Couchbase through that. I want to, I always ask this question because it's clearly a trend in the industry and it, it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, we have a little teeny scissors and a knife. That's not that sharp. How do you respond to that? Uh, >>A great one. Um, my answer is always, I use another analogy to tackle that, but is that, have you ever accused a smartphone of being a Swiss army knife? No. No. Nobody does that because it's actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Like as in a moment, it could be a Tom, Tom telling you all the directions, the next one, it's your PDA. >>Third one, it's a fantastic phone. Uh, four, it's a beautiful camera, which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment is a video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just taught that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, they missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app is the economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get the alert saying that today you got to leave home at eight 15 for your nine o'clock meeting. >>And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's gone there's notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place without that, you couldn't even do this simple function, uh, in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build, because half the time you're running sideline to sideline, just, you know, um, integrating data from one system to the other. >>So I love the analogy with the smartphone. I w I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? And so, so, but, but is there, is that a fair, where, in other words, those specialized databases, they say there still is a place for them, but they're getting >>Absolutely, absolutely great analogy and a great extension to the question. That's, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of the music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they haven't, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. >>Yes, it's 90% there or 80% there. It depends on your audio file mess of your, uh, I mean, you don't experience the super specialized ones do not go away. You know, there are, there are places where, uh, the specialized use cases will demand a separate system to exist, but even there that has got to be very closed. Um, how do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that, oh, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car and walk into my living room, that's same songs should continue and play in my living room speakers. Then it's a world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >>I love, I love that example too. When I was a kid, we used to go to Twitter, et cetera. And we'd to play around with, we take off the big four foot speakers. Those stores are out of business too. Absolutely. Um, now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi. >>I believe so. Uh, because I think, uh, what had happened was the relational systems. Uh, I've been where the norm, they rule the roost, if you will, for the last 40 odd years, and then gain this no sequel movement, which was almost as though a rebellion from the relational world, we all inhibited, uh, uh, because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee, they required your DBA and your data architect. And you have to call them just to add one column and stuff like that. And the world had moved on. This was the world of blogs and tweets and, uh, you know, um, mashups and, um, uh, uh, a different generation of digital behavior, digital, native people now, um, who are operating in these and the, the applications, the, the consumer facing applications. >>We are living in this world. And yet the enterprise ones were still living in the, um, in the other, the other side of the divide. So all came this solution to say that we don't need SQL. Actually, the problem was never sequel. No sequel was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations, and the inability for these, the system to scale, the relational systems were built like, uh, airplanes, which is that if, uh, San Francisco Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set in from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the alarm to somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. >>These are called vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world, uh, is make the system how it is only scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guests. I'll add one more coach to it, one more car to it. And the better part of the way we have done this year is that, and we have super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have ID only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. >>You can attach the kind of coaches we call this multi-dimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it quite. So that's the beauty of this architecture. Now, why is that important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because you would say that I cannot run this analytical Barre because then my operational workload will suffer. Then my friend, then we'll slow down millions of customers that impacted that problem. We will solve the same data in which you can do analytical buddy, an operational query because they're separated by these cars, right? As in like we, we fence the, the, the resources, so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or equity. >>And then yet you can run this analytical body, which will take a couple of minutes to run one, not impeding the other. So that's in one sense, sort of the, part of the, um, uh, problems that we have solved here is that relational versus, uh, uh, the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same quality language on top. Y it's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, uh, the internal combustion engine, uh, I think gas, uh, you says, these are the issues we really wanted to solve. Um, so solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, or that are for your shifters. >>Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So, uh, even when you feed people the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blue harder to go fast and lean back for, for it to, you know, uh, to apply a break that's, that's how we seem to define, uh, design software. Instead, we should be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, uh, and the gas bottle and the, um, and the gear shifter is by putting cul back on underneath the surface, we have completely solved, uh, the relational, uh, uh, limitations of schema, as well as scalability. >>So in, in, in that way, and by bringing back the classic acid capabilities, which is what relational systems, uh, we accounted on and being able to do that with the sequel programming language, we call it like multi-state SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up the salt in the modern times, but rather than get, um, sort of pedantic about whether it's, we have no SQL or sequel or new sequel, or, uh, you know, any of that sort of, uh, jargon, oriented debate, uh, this, these are the debates of computer science that they are actually, uh, and they were the solve and they have solved them with, uh, the latest release of $7, which we released a few months ago. >>Right, right. Last July, Ravi, we got to leave it there. I, I love the examples and the analogies. I can't wait to be face to face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >>Fantastic. Thanks for the time. And the Aboriginal Dan was, I mean, very insightful questions really appreciate it. Thank you. >>Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>> Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, is modernize now. Yes, let's talk about that. And with me is Ravi mayor him, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >> Thank you so much. I'm so glad to be here with you. >> I want to ask you what the new requirements are around modern applications. I've seen some of your comments, you got to be flexible, distributed, multimodal, mobile, edge. Those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >> Yeah, I think what has basically happened is that so far it's been a transition of sorts. And now we are come to a point where that tipping point and that tipping point has been more because of COVID and there are COVID has pushed us to a world where we are living in a in a sort of occasionally connected manner where our digital interactions precede, our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than in a digital manner, as opposed to sort of making a more specific human contact. That does really been the sort of accelerant to this modernize Now, as a team. In this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. They're all sitting behind. They used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, but they are all centralized still, but where our engagement happens with the data is at the edge at your point of convenience, at your point of consumption, not where the data is actually sitting. So this has led to, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? But it just basically comes down to the fact that the data needs to be there, if you are engaging with it. And that means if you are doing it on your mobile phone, or if you're sitting, but doing something in your while you're traveling, or whether you're in a subway, whether you're in a plane or a ship, wherever the data needs to come to you and be available, as opposed to every time you going to the data, which is centrally sitting in some place. And that is the fundamental shift in terms of how the modern architecture needs to think when they, when it comes to digital transformation and, transitioning their old applications to the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Otherwise, people are basically waiting for that circle of death that we all know, and blaming the networks and other pieces. The problem was actually, the data is not where you are engaging with it. It's got to be fetched, you know, seven sea's away. And that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >> I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this because date data by its very nature is distributed. It's always been distributed, but with the distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, of, of a super rock solid database that can handle, you know, distributed data? >> Yes. So there are two issues that you alluded little too over there. The first is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data. In one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, when you have the data, you can first look at it to perform. Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five milliseconds, this is, this is a class of problem that we solve that same data. Now, eventually, without you ever having to sort of do a casting it to a different database, you can now do solid queries. Our classic SQL queries, which is our next magic. We are a no SQL database, but we have a full functional SQL. The SQL has been the language that has talked to data for 40 odd years successfully. Every other database has come and tried to implement their own QL query language, but they've all failed only SQL has stood the test of time of 40 odd years. Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is basically a look at the data and any common editorial, any, any which way you look at the data, all it will come, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries select star from where, kind of stuff, because it's at an English level becomes easy to so the same day that you didn't have to go move it to another database, do your sort of transformation of the data and all the stuff, same day that you do this. Now that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, what Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the, your ability to query the operational data in a different way. And talk querying, what was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and apply different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, the database management system. And that's where the distributed platform that we have built enables us to get it to where you need the data to be, you know, in the classic way we call it CDN'ing the data as in like content delivery networks. So far do static, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >> And on the first part of, of the, the, the answer to my question, are you saying you could do this without scheme with a no schema on, right? And then you can apply those techniques. >> Fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read thing. So, because there is no schema, it is just a Json document that is sitting inside. And Json is the lingua franca of the web, as you very well know by now. So it just Json that we manage, you can do key value look ups of the Json. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and other sophisticated pieces of technology behind it. You can do searching on it, using the, the full textual analysis pipeline. You can do ad hoc webbing on the analytics side, and you can write your own custom logic on it using or inventing capabilities. So that's, that's what it allows because we keep the data in the native form of Json. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, bring, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing in the last 40 years, because we developed various database systems and data processing systems at various points in time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. We had queuing systems, all these systems, if you want to use any one of them are answered. It always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this, it's not going to fly instead, bring the logic to the data, right? So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this. >> But as you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >> Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data recasting because it required you to have it in seven schema in one sense at, at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably related, but it not really, how do you say keep to the promise that it actually meant to be? So that's why it was a swamp I mean, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it. And you create different types of indexes to manage it. You distribute the index, you distribute the data you have, like we were discussing, you have ACID semantics on top of, and when you, when you put all these things together, it's, it's, it's a tough proposition, but we have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >> So you predicted the trend around multimodal and converged databases. You kind of led Couchbase through that. I, I want, I always ask this question because it's clearly a trend in the industry and it, and it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, where you have have a little teeny scissors and a knife, that's not that sharp. How, how do you respond to that? >> A great one. My answer is always, I use another analogy to tackle that, and is that, have you ever accused a smartphone of being a Swiss army knife? - No. No. >> Nobody does. That because it actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Right? As in a moment, it could be a TomTom, telling you all the directions, the next one, it's your PDA. Third one. It's a fantastic phone. Four. It's a beautiful camera which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment, it's the video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just thought that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, he missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app based economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get an alert saying that today you got to leave home at >> 8: 15 for your nine o'clock meeting. And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's got this notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place. Without that, you couldn't even do this simple function in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build. Because half the time you're running sideline to sideline, just, you know, integrating data from one system to the other. >> So I love the analogy with the smartphone. I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? So, so, but, but is there, is that a fair and where, in other words, those specialized databases, they say there still is a place for them, but they're getting. >> Absolutely, absolutely great analogy and a great extension to the question. That's like, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of my music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they, I mean, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. Yes, it's 90% there or 80% there. It depends on your audio file-ness of your, I mean, your experience super specialized ones do not go away. You know, there are, there are places where the specialized use cases will demand a separate system to exist. But even there that has got to be very closed. How do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that all, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car, walk into my living room, that same songs should continue and play in my living room speakers. Then it's a connected world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >> I love, I love that example too. When I was a kid, we used to go to Tweeter, et cetera. And we used to play around with three, take home, big four foot speakers. Those stores are out of business too. Absolutely. And now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi? >> I believe so, because I think what had happened was relational systems. I've mean where the norm, they rule the roost, if you will, for the last 40 odd years and then gain this no SQL movement, which was almost as though a rebellion from the relational world, we all inhabited because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee. They required your DBA and your data architect. And you had to call them just to add one column and stuff like that. And the world had moved on. This was a world of blogs and tweets and, you know, mashups and a different generation of digital behavior, There are digital, native people now who are operating in these and the, the applications, the, the consumer facing applications. We are living in this world. And yet the enterprise ones were still living in the, in the other, the other side of the divide. So out came this solution to say that we don't need SQL. Actually the problem was never SQL. No SQL was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations and the inability for these, the system to scale, the relational systems were built like airplanes, which is that if a San Francisco, Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the allowance that you'll somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. These are all vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world is make the system horizontally scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guest. I'll add one more coach to it, one more car to it. And the better part of the way we have done this here is that, and we are super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have, I need only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. You can attach the kind of coaches we call this multidimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it, right? So that's the beauty of this architecture. Now, why is that architecture important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because, you would say that I cannot run this analytical query because then my operational workload will suffer. Then my front end, then we'll slow down millions of customers that impacted that problem. They'll solve the same data once again, do analytical query, an operational query because they're separated by these cars, right? As in like we, we, we fence the, the, the resources so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or a query. And then yet you can run this analytical query, which will take a couple of minutes to them. One, not impeding the other. So that's in one sense, sort of the part of the problems that we have solved it here is that relational versus the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same query language on top. Why? It's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, the internal combustion engine the gas, you says, these were the issues we really wanted to solve. So solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, over there your gear shifters. Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So even when you feed people, the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blow harder to go fast. And they lean back for, for it to, you know, to apply a break that's, that's how we seem to define design software. Instead, we shouldn't be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, and the gas pedal and the, and the gear shifters by putting SQL back on underneath the surface, we have completely solved the relational limitations of schema, as well as scalability. So in, in, in that way, and by bringing back the classic ACID capabilities, which is what relational systems we accounted on, and being able to do that with the SQL programming language, we call it like multi-statement SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up to solve in the modern times, rather than get sort of pedantic about whether it's we have no SQL or SQL or new SQL, or, you know, any of that sort of jargon oriented debate. This is, these are the debates of computer science that they are actually, and they were the solve, and they have solved them with the latest release of 7.0, which we released a few months ago. >> Right, right. Last July, Ravi, we got got to leave it there. I love the examples and the analogies. I can't wait to be face-to-face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >> Fantastic. Thanks for the time. And the opportunity I was, I mean, very insightful questions really appreciate it. - Thank you. >> Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Techniques to protect sensitive data have evolved over thousands of years literally. The pace of modern data protection is rapidly accelerating and presents both opportunities and threats for organizations. In particular, the amount of data stored in the cloud combined with hybrid work models, the clear and present threat of cyber crime, regulatory edicts and the ever expanding edge and associated use cases should put CXOs on notice that the time is now to rethink your data protection strategies. Hello, and welcome to this week's Wikibon theCUBE Insights powered by ETR. In this Breaking Analysis, we're going to explore the evolving world of data protection and share some information on how we see the market changing in the competitive landscape for some of the top players. Steve Kenniston AKA the Storage Alchemist shared a story with me and it was pretty clever. Way back in 4,000 BC the Sumerians invented the first system of writing. Now they used clay tokens to represent transactions at that time. Now, to prevent messing with these tokens, they sealed them in clay jars to ensure that the tokens or either data would remain secure with an accurate record, let's call it quasi immutable and lived in a clay vault. Since that time, we've seen quite an evolution in data protection. Tape, of course, was the main means of protecting data, backing data up during most of the mainframe era and that carried into client server computing, which really accentuated and underscored the issues around backup windows and challenges with RTO, Recovery Time Objective and RPO, Recovery Point Objective, and just overall recovery nightmares. Then in the 2000s data reduction made displace backup more popular and push tape into an archive last resort media data domain then EMC now Dell still sell many purpose built backup appliances as do others as a primary backup target disc base. The rise of virtualization brought more changes in backup and recovery strategies as a reduction in physical resources squeezed the one application that wasn't under utilizing compute i.e backup. And we saw the rise of Veeam, the cleverly named company that became synonymous with data protection for virtual machines. Now the cloud has created new challenges related to data sovereignty, governance latency, copy creep, expense, et cetera but more recently cyber threats have elevated data protection to become a critical adjacency to information security. Cyber resilience to specifically protect against ransomware attacks as the new trend being pushed by the vendor community as organizations are urgently looking for help with this insidious threat. Okay, so there are two major disruptors that we're going to talk about today, the cloud and cyber crime, especially around ransoming your data. Every customer is using the cloud in some way, shape or form. Around 76% are using multiple clouds that's according to a recent study by HashiCorp. We've talked extensively about skill shortages on theCUBE and data protection and security concerns are really key challenges to address given that skill shortage is a real talent gap in terms of being able to throw people at solving this problem. So what customers are doing they're either building out or they're buying, really mostly building abstraction layers to hide the underlying cloud complexity. So, what this does, the good news is it simplifies provisioning and management but it creates problems around opacity. In other words, you can't see sometimes what's going on with the data, these challenges fundamentally become data problems in our view. Things like fast, accurate, and complete backup recovery, compliance, data sovereignty, data sharing, I mentioned copy creep, cyber resiliency, privacy protections these are all challenges brought to fore by the cloud, the advantages, the pros and the cons. Now, remote workers are especially vulnerable and as clouds expand rapidly data protection technologies are struggling to keep pace. So let's talk briefly about the rapidly expanding public cloud. This chart shows worldwide revenue for the big four hyperscalers, as you can see we projected they're going to surpass $115 billion in revenue in 2021, that's up from 86 billion last year. So it's a huge market, it's growing in the 35% range. The interesting thing is last year, 80 plus billion dollars in revenue but a 100 billion dollars was spent last year by these firms in CapEx. So they're building out infrastructure for the industry. This is a gift to the balance of the industry. Now to date legacy vendors and their surrounding community have been pretty defensive around the cloud, "Oh, not everything is going to move to the cloud, it's not a zero sum game we here." And while that's all true the narrative was really kind of a defense posture and that's starting to change as large tech companies like Dell, IBM, Cisco, HPE, and others see opportunities to build on top of this infrastructure. You certainly see that with Arvind Krishna's comments at IBM, Cisco obviously leaning in from a networking and security perspective. HPE using language that is very much cloud-like with its GreenLake strategy. And of course, Dell is all over this. Let's listen to how Michael Dell is thinking about this opportunity when he was questioned on theCUBE by John Furrier about the cloud. Play the clip. >> Well, clouds are infrastructure, right? So you can have a public cloud, you can have an edge cloud, a private cloud, a Telco cloud, a hybrid cloud, multicloud, here cloud, there cloud, everywhere cloud, cloud. Yet, they'll all be there, but it's basically infrastructure. And how do you make that as easy to consume and create the flexibility that enables everything. >> Okay, so in my view, Michael nailed it, the cloud is everywhere. You have to make it easy and you have to admire the scope of his comments. We know this guy, he thinks big, right? He said enables everything. What he's basically saying is that, technology is at the point where it has the potential to touch virtually every industry, every person, every problem, everything. So let's talk about how this informs the changing world of data protection. Now, we've seen with the pandemic there's an acceleration toward digital and that has caused an escalation if you will, in the data protection mandate. So essentially what we're talking about here is the application of Michael Dell's cloud everywhere comments. You've got on-prem, private clouds, hybrid clouds, you've got public clouds across AWS, Azure, Google, Alibaba, really those big four hyperscalers. You got many clouds that are popping up all over the place, but multicloud to that HashiCorp data point, 75, 76%, and then you now see the cloud expanding out to the edge, programmable infrastructure heading out to the edge. So the opportunity here to build the data protection cloud is to have the same experiences across all these estates with automation and orchestration in that cloud, that data protection cloud if you will. So think of it as an abstraction layer that hides that underlying complexity, you log into that data protection cloud it's the same experience. So you've got backup, you've got recovery, you can handle bare-metal, you can do virtualized backups and recoveries, any cloud, any OS, out to the edge, Kubernetes and container use cases, which is an emerging data protection requirement and you've got analytics, perhaps you've got PII, Personally Identifiable Information protection in there. So the attributes of this data protection cloud, again, it abstracts the underlying cloud primitives, takes care of that. It also explodes cloud native technologies. In other words, it takes advantage of whether it's machine learning, which all the big cloud players have expertise in, new processor models things like Graviton and other services that are in the cloud natively. It doesn't just wrap it's on-prem stack in a container and shove it into the cloud, no, it actually re architects or architects around those cloud native services and it's got distributed metadata to track files and volumes and any organizational data irrespective of location. And it enables sets of services to intelligently govern in a federated governance manner while ensuring data integrity and all this is automated and orchestrated to help with the skills gap. Now, as it relates to cyber recovery, air gap solutions must be part of the portfolio, but managed outside of that data protection cloud that we just briefly described. The orchestration and the management must also be gapped if you will, otherwise, you don't have an air gap. So all of this is really a cohort to cyber security or your cybersecurity strategy and posture, but you have to be careful here because your data protection strategy could get lost in this mess. So you want to think about the data protection cloud as again, an adjacency or maybe an overlay to your cybersecurity approach, not a bolt on it's got to be fundamentally architectured from the bottom up. And yes, this is going to maybe create some overheads and some integration challenges but this is the way in which we think you should think about it. So you'll likely need a partner to do this, again, we come back to the skills gap if were seeing the rise of MSPs, managed service providers and specialist service providers, not public cloud providers, people are concerned about lock-in and that's really not their role. They're not high touch services company, probably not your technology arms dealer, excuse me, they're selling technology to these MSPs. So the MSPs, they have intimate relationships with their customers. They understand their business and specialize in architecting solutions to handle these difficult challenges. So let's take a look at some of the risk factors here and dig a little bit into the cyber threat that organizations face. This is a slide that, again, the Storage Alchemists, Steve Kenniston shared with me, it's based on a study that IBM funds with the Panama Institute, which is a firm that studies these things like cost of breaches and has for many, many, many years. The slide shows the total cost of a typical breach within each dot and on the Y-axis and the frequency in percentage terms on the horizontal axis. Now it's interesting, the top two are compromised credentials and fishing, which once again proves that bad user behavior trumps good security every time. But the point here is that the adversary's attack vectors are many and specific companies often specialize in solving these problems often with point products, which is why the slide that we showed from Optiv earlier, that messy slide looks so cluttered. So it's a huge challenge for companies, and that's why we've seen the emergence of cyber recovery solutions from virtually all the major players. Ransomware and the SolarWinds hack have made trust the number one issue for CEOs and CSOs and boards of directors, shifting CSO spending patterns are clear. Shifting largely because they're catalyzed by the work from home. But outside of the moat to endpoint security identity and access management, cloud security, the horizontal network security. So security priorities and spending are changing that's why you see the emergence of disruptors like we've covered extensively, Okta, Crowdstrike, Zscaler. And cyber resilience is top of mind and robust solutions are required and that's why companies are building cyber recovery solutions that are most often focused on the backup corpus because that's a target for the bad guys. So there is an opportunity, however to expand from just the backup corpus to all data and protect this kind of 3-2-1, or maybe it's 3-2-1-1, three copies, two backups, a backup in the cloud and one that's air gapped. So this can be extended to primary storage, copies, snaps, containers, data in motion, et cetera, to have a comprehensive data protection strategy. Customers as I said earlier, increasingly looking to manage service providers and specialists because of that skills gap and that's a big reason why automation is so important in orchestration. And automation and orchestration I'll emphasize on the air gap solutions should be separated physically and logically. All right, now let's take a look at some of the ETR data and some of the players. This is a chart that we like to show often, it's a X, Y axis, and the Y-axis is net score, which is a measure of spending momentum and the horizontal axis is market share. Now market share is an indicator of pervasiveness in the survey. It's not spending market share, it's not market share of the overall market, it's a term that ETR uses. It's essentially market share of the responses within the survey set, think of it as mind share. Okay, you've got the pure plays here on this slide in the storage category, there is no data protection or backup category so what we've done is we've isolated the pure plays or close to pure plays in backup and data protection. Notice that red line, that red line is kind of our subjective view of anything that's over that 40% line is elevated, you can see only rubric in the July survey is over that 40% line. I'll show you the ends in a moment. Smaller ends, but still rubric is the only one. Now look at Cohesity and rubric in the January, 2020. So last year pre-pandemic Cohesity and Rubrik they've come well off their peaks for net score. Look at Veeam, Veeam having studied this data for the last say 24 plus months, Veeam has been Steady Eddie. It is really always in the mid to high 30s, always shows a large shared end so it's coming up in the survey, customers are mentioning Veeam and it's got a very solid net score. It's not above that 40% line but it's hovering just below consistently, that's very impressive. Commvault has steadily been moving up. Sanjay Mirchandani has made some acquisitions, he did the Hedvig acquisition. They launched metallic that's driving cloud affinity within a Commvault large customer base so it's a good example of a legacy player, pivoting and evolving and transforming itself. Veritas continues to underperform in the ETR surveys relative to the other players. Now, for context, let's say add IBM and Dell to the chart. Now just note, this is IBM and Dell's full storage portfolio. The category in the taxonomy at ETR is all storage. Okay, this previous slide I isolated on the pure plays, but this now adds in IBM and Dell. It probably representative of where they would be, probably Dell larger on the horizontal axis than IBM, of course and you could see the spending momentum in accordingly. So you could see that in the data chart that we've inserted. So smaller ends for Rubrik and Cohesity, but still enough to pay attention, it's not like one or two when you're 20 plus, 15 plus, 25 plus you can start to pay attention to trends. Veeam again is very impressive. Its net score is solid, it's got a consistent presence in the dataset, it's clear leader here. SimpliVity is small but it's improving relative to last several surveys and we talked about Commvault. Now, I want to emphasize something that we've been hitting on for quite some time now and that's the renaissance that's coming in compute. Now we all know about Moore's law, the doubling of transistor density every two years, 18 to 24 months and that leads to a doubling of performance in that time frame. X86, that X86 curve is in the blue and if you do the math, this is expressed in trillions of operations per second. The orange line is a representative of Apple's A series culminating in the A-15 most recently, the A series is what Apple is now... It's the technology basis for what's inside, and one the new Apple laptops, which is replacing Intel. That's that orange line there we'll come back to that. So go back to the blue line for a minute. If you do the math on doubling performance every 24 months, it comes out to roughly 40% annual improvement in processing power per year. That's now moderated. So Moore's law is waning in one sense so we wrote a piece Moore's law is not dead so I'm sort of contradicting myself there, but the traditional Moore's law curve on X86 is waning. It's probably now down to around 30%, low 30s, but look at the orange line. Again, using the A series as an indicator, if you combine the CPU, the NPU, which is the neural processing unit, XPU, pick whatever PU you want, the accelerators, the DSPs, that line is growing at a 100% plus per year. It's probably more accurately around 110% a year. So there's a new industry curve occurring and it's being led by the Arm ecosystem. The other key factor there you see in a lot of use cases, a lot of consumer use cases Apple is an example but you're also seeing it in things like Tesla, Amazon with AWS Graviton, the Annapurna acquisition, building out Graviton and Nitro that's based on Arm. You can get from design to tape out in less than two years Whereas the Intel cycles we know they've been running it four to five years now, maybe Pat Gelsinger is compressing those, but Intel is behind. So, organizations that are on that orange curve are going to see faster acceleration, lower cost, lower power, et cetera. All right, so what's the tie to data protection? I'm going to leave you with this chart. Arm has introduced it's confidential compute architecture, and is ushering in a new era of security and data protection. Zero Trust is the new mandate and what Arm has done with what they call realms is create physical separation of the vulnerable components by creating essentially physical buckets to put code in and to put data in separate from the OS. Remember the OS is the most valuable entry point for hackers or one of them because it contains privileged access and it's a weak link because of things like memory leakages and vulnerabilities. And malicious code can be placed by bad guys within data in the OS and appear benign even though it's anything but. So in this architecture, all the OS does is create API calls to the realm controller. That's the only interaction. So it makes it much harder for bad actors to get access to the code and the data. And importantly, very importantly, it's an end-to-end architecture so there's protection throughout if you're pulling data from the edge and bringing it back to on-prem and the cloud you've got that end-to-end architecture and protection throughout. So the link to data protection is that backup software vendors need to be the most trusted of applications. Backup software needs to be the most trusted of applications because it's one of the most targeted areas in the cyber attack. Realms provide an end-to-end separation of data and code from the OS and is a better architectural construct to support Zero Trust and confidential computing and critical use cases like data protection/backup and other digital business apps. So our call to action is backup software vendors you can lead the charge. Arm is several years ahead at the moment, head of Intel in our view. So you got to pay attention to that, research that, we're not saying over rotate, but go investigate that. And use your relationships with Intel to accelerate its version of this architecture or ideally the industry should agree on common standards and solve this problem together. Pat Gelsinger told us in theCUBE that if it's the last thing he's going to do in his industry life he's going to solve this security problem. That's when he was at VMware. Well, Pat you're even in a better place to do it now, you don't have to solve it yourself, you can't and you know that. So while you're going about your business saving Intel, look to partner with Arm I know it sounds crazy to use these published APIs and push to collaborate on an open source architecture that addresses the cyber problem. If anyone can do it, you can. Okay, that's it for today. Remember, these episodes are all available as podcasts all you got to do is search Breaking Analysis podcast, I publish weekly on Wikibon.com and SiliconANGLE.com. Or you can reach me at dvellante on Twitter, email me at Dave.Vellante@SiliconANGLE.com. And don't forget to check out ETR.plus for all the survey and data action. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching everybody, be well and we'll see you next time. (upbeat music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> Techniques to protect sensitive data have evolved over thousands of years, literally. The pace of modern data protection is rapidly accelerating and presents both opportunities and threats for organizations. In particular, the amount of data stored in the cloud combined with hybrid work models, the clear and present threat of cyber crime, regulatory edicts, and the ever expanding edge and associated use cases should put CXOs on notice that the time is now to rethink your data protection strategies. Hello, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we're going to explore the evolving world of data protection and share some information on how we see the market changing in the competitive landscape for some of the top players. Steve Kenniston, AKA the Storage Alchemist, shared a story with me, and it was pretty clever. Way back in 4000 BC, the Sumerians invented the first system of writing. Now, they used clay tokens to represent transactions at that time. Now, to prevent messing with these tokens, they sealed them in clay jars to ensure that the tokens, i.e the data, would remain secure with an accurate record that was, let's call it quasi, immutable, and lived in a clay vault. And since that time, we've seen quite an evolution of data protection. Tape, of course, was the main means of protecting data and backing data up during most of the mainframe era. And that carried into client server computing, which really accentuated and underscored the issues around backup windows and challenges with RTO, recovery time objective and RPO recovery point objective. And just overall recovery nightmares. Then in the 2000's data reduction made disk-based backup more popular and pushed tape into an archive last resort media. Data Domain, then EMC, now Dell still sell many purpose-built backup appliances as do others as a primary backup target disc-based. The rise of virtualization brought more changes in backup and recovery strategies, as a reduction in physical resources squeezed the one application that wasn't under utilizing compute, i.e, backup. And we saw the rise of Veem, the cleverly-named company that became synonymous with data protection for virtual machines. Now, the cloud has created new challenges related to data sovereignty, governance, latency, copy creep, expense, et cetera. But more recently, cyber threats have elevated data protection to become a critical adjacency to information security. Cyber resilience to specifically protect against attacks is the new trend being pushed by the vendor community as organizations are urgently looking for help with this insidious threat. Okay, so there are two major disruptors that we're going to talk about today, the cloud and cyber crime, especially around ransoming your data. Every customer is using the cloud in some way, shape, or form. Around 76% are using multiple clouds, that's according to a recent study by Hashi Corp. We've talked extensively about skill shortages on theCUBE, and data protection and security concerns are really key challenges to address, given that skill shortage is a real talent gap in terms of being able to throw people at solving this problem. So what customers are doing, they're either building out or they're buying really mostly building abstraction layers to hide the underlying cloud complexity. So what this does... The good news is it's simplifies provisioning and management, but it creates problems around opacity. In other words, you can't see sometimes what's going on with the data. These challenges fundamentally become data problems, in our view. Things like fast, accurate, and complete backup recovery, compliance, data sovereignty, data sharing. I mentioned copy creep, cyber resiliency, privacy protections. These are all challenges brought to fore by the cloud, the advantages, the pros, and the cons. Now, remote workers are especially vulnerable. And as clouds span rapidly, data protection technologies are struggling to keep pace. So let's talk briefly about the rapidly-expanding public cloud. This chart shows worldwide revenue for the big four hyperscalers. As you can see, we projected that they're going to surpass $115 billion in revenue in 2021. That's up from 86 billion last year. So it's a huge market, it's growing in the 35% range. The interesting thing is last year, 80-plus billion dollars in revenue, but 100 billion dollars was spent last year by these firms in cap ex. So they're building out infrastructure for the industry. This is a gift to the balance of the industry. Now to date, legacy vendors and the surrounding community have been pretty defensive around the cloud. Oh, not everything's going to move to the cloud. It's not a zero sum game we hear. And while that's all true, the narrative was really kind of a defensive posture, and that's starting to change as large tech companies like Dell, IBM, Cisco, HPE, and others see opportunities to build on top of this infrastructure. You certainly see that with Arvind Krishna comments at IBM, Cisco obviously leaning in from a networking and security perspective, HPE using language that is very much cloud-like with its GreenLake strategy. And of course, Dell is all over this. Let's listen to how Michael Dell is thinking about this opportunity when he was questioned on the queue by John Furrier about the cloud. Play the clip. So in my view, Michael nailed it. The cloud is everywhere. You have to make it easy. And you have to admire the scope of his comments. We know this guy, he thinks big. He said, "Enables everything." He's basically saying is that technology is at the point where it has the potential to touch virtually every industry, every person, every problem, everything. So let's talk about how this informs the changing world of data protection. Now, we all know, we've seen with the pandemic, there's an acceleration in toward digital, and that has caused an escalation, if you will, in the data protection mandate. So essentially what we're talking about here is the application of Michael Dell's cloud everywhere comments. You've got on-prem, private clouds, hybrid clouds. You've got public clouds across AWS, Azure, Google, Alibaba. Really those are the big four hyperscalers. You got many clouds that are popping up all their place. But multi-cloud, to that Hashi Corp data point, 75, 70 6%. And then you now see the cloud expanding out to the edge, programmable infrastructure heading out to the edge. So the opportunity here to build the data protection cloud is to have the same experiences across all these estates with automation and orchestration in that cloud, that data protection cloud, if you will. So think of it as an abstraction layer that hides that underlying complexity, you log into that data protection cloud, it's the same experience. So you've got backup, you've got recovery, you can handle bare metal. You can do virtualized backups and recoveries, any cloud, any OS, out to the edge, Kubernetes and container use cases, which is an emerging data protection requirement. And you've got analytics, perhaps you've got PII, personally identifiable information protection in there. So the attributes of this data protection cloud, again, abstracts the underlying cloud primitives, takes care of that. It also explodes cloud native technologies. In other words, it takes advantage of whether it's machine learning, which all the big cloud players have expertise in, new processor models, things like graviton, and other services that are in the cloud natively. It doesn't just wrap it's on-prem stack in a container and shove it into the cloud, no. It actually re architects or architects around those cloud native services. And it's got distributed metadata to track files and volumes and any organizational data irrespective of location. And it enables sets of services to intelligently govern in a federated governance manner while ensuring data integrity. And all this is automated and an orchestrated to help with the skills gap. Now, as it relates to cyber recovery, air-gap solutions must be part of the portfolio, but managed outside of that data protection cloud that we just briefly described. The orchestration and the management must also be gaped, if you will. Otherwise, (laughs) you don't have an air gap. So all of this is really a cohort to cyber security or your cybersecurity strategy and posture, but you have to be careful here because your data protection strategy could get lost in this mess. So you want to think about the data protection cloud as again, an adjacency or maybe an overlay to your cybersecurity approach. Not a bolt on, it's got to be fundamentally architectured from the bottom up. And yes, this is going to maybe create some overheads and some integration challenges, but this is the way in which we think you should think about it. So you'll likely need a partner to do this. Again, we come back to the skill skills gap if we're seeing the rise of MSPs, managed service providers and specialist service providers. Not public cloud providers. People are concerned about lock-in, and that's really not their role. They're not high-touch services company. Probably not your technology arms dealer, (clear throat) excuse me, they're selling technology to these MSPs. So the MSPs, they have intimate relationships with their customers. They understand their business and specialize in architecting solutions to handle these difficult challenges. So let's take a look at some of the risk factors here, dig a little bit into the cyber threat that organizations face. This is a slide that, again, the Storage Alchemists, Steve Kenniston, shared with me. It's based on a study that IBM funds with the Panmore Institute, which is a firm that studies these things like cost of breaches and has for many, many, many years. The slide shows the total cost of a typical breach within each dot and on the Y axis and the frequency in percentage terms on the horizontal axis. Now, it's interesting. The top two compromise credentials and phishing, which once again proves that bad user behavior trumps good security every time. But the point here is that the adversary's attack vectors are many. And specific companies often specialize in solving these problems often with point products, which is why the slide that we showed from Optiv earlier, that messy slide, looks so cluttered. So there's a huge challenge for companies. And that's why we've seen the emergence of cyber recovery solutions from virtually all the major players. Ransomware and the solar winds hack have made trust the number one issue for CIOs and CISOs and boards of directors. Shifting CISO spending patterns are clear. They're shifting largely because they're catalyzed by the work from home. But outside of the moat to endpoint security, identity and access management, cloud security, the horizontal network security. So security priorities and spending are changing. And that's why you see the emergence of disruptors like we've covered extensively, Okta, CrowdStrike, Zscaler. And cyber resilience is top of mind, and robust solutions are required. And that's why companies are building cyber recovery solutions that are most often focused on the backup corpus because that's a target for the bad guys. So there is an opportunity, however, to expand from just the backup corpus to all data and protect this kind of 3, 2, 1, or maybe it's 3, 2, 1, 1, three copies, two backups, a backup in the cloud and one that's air gaped. So this can be extended to primary storage, copies, snaps, containers, data in motion, et cetera, to have a comprehensive data protection strategy. And customers, as I said earlier, are increasingly looking to manage service providers and specialists because of that skills gap. And that's a big reason why automation is so important in orchestration. And automation and orchestration, I'll emphasize, on the air gap solutions should be separated physically and logically. All right, now let's take a look at some of the ETR data and some of the players. This is a chart that we like to show often. It's a X-Y axis. And the Y axis is net score, which is a measure of spending momentum. And the horizontal axis is market share. Now, market share is an indicator of pervasiveness in the survey. It's not spending market share, it's not market share of the overall market, it's a term that ETR uses. It's essentially market share of the responses within the survey set. Think of it as mind share. Okay, you've got the pure plays here on this slide, in the storage category. There is no data protection or backup category. So what we've done is we've isolated the pure plays or close to pure plays in backup and data protection. Now notice that red line, that red is kind of our subjective view of anything that's over that 40% line is elevated. And you can see only Rubrik, and the July survey is over that 40% line. I'll show you the ends in a moment. Smaller ends, but still, Rubrik is the only one. Now, look at Cohesity and Rubrik in the January 2020. So last year, pre-pandemic, Cohesity and Rubrik, they've come well off their peak for net score. Look at Veeam. Veeam, having studied this data for the last say 24 hours months, Veeam has been steady Eddy. It is really always in the mid to high 30s, always shows a large shared end, so it's coming up in the survey. Customers are mentioning Veeam. And it's got a very solid net score. It's not above that 40% line, but it's hovering just below consistently. That's very impressive. Commvault has steadily been moving up. Sanjay Mirchandani has made some acquisitions. He did the Hedvig acquisition. They launched Metallic, that's driving cloud affinity within Commvault's large customer base. So it's good example of a legacy player pivoting and evolving and transforming itself. Veritas, it continues to under perform in the ETR surveys relative to the other players. Now, for context, let's add IBM and Dell to the chart. Now just note, this is IBM and Dell's full storage portfolio. The category in the taxonomy at ETR is all storage. Just previous slide, I isolated on the pure plays. But this now adds in IBM and Dell. It probably representative of where they would be. Probably Dell larger on the horizontal axis than IBM, of course. And you could see the spending momentum accordingly. So you can see that in the data chart that we've inserted. So some smaller ends for Rubrik and Cohesity. But still enough to pay attention, it's not like one or two. When you're 20-plus, 15-plus 25-plus, you can start to pay attention to trends. Veeam, again, is very impressive. It's net score is solid, it's got a consistent presence in the dataset, it's clear leader here. SimpliVity is small, but it's improving relative to last several surveys. And we talked about Convolt. Now, I want to emphasize something that we've been hitting on for quite some time now. And that's the Renaissance that's coming in compute. Now, we all know about Moore's Law, the doubling of transistor density every two years, 18 to 24 months. And that leads to a doubling of performance in that timeframe. X86, that x86 curve is in the blue. And if you do the math, this is expressed in trillions of operations per second. The orange line is representative of Apples A series, culminating in the A15, most recently. The A series is what Apple is now... Well, it's the technology basis for what's inside M1, the new Apple laptops, which is replacing Intel. That's that that orange line there, we'll come back to that. So go back to the blue line for a minute. If you do the math on doubling performance every 24 months, it comes out to roughly 40% annual improvement in processing power per year. That's now moderated. So Moore's Law is waning in one sense, so we wrote a piece Moore's Law is not dead. So I'm sort of contradicting myself there. But the traditional Moore's Law curve on x86 is waning. It's probably now down to around 30%, low 30s. But look at the orange line. Again, using the A series as an indicator, if you combine then the CPU, the NPU, which neuro processing unit, XPU, pick whatever PU you want, the accelerators, the DSPs, that line is growing at 100% plus per year. It's probably more accurately around 110% a year. So there's a new industry curve occurring, and it's being led by the Arm ecosystem. The other key factor there, and you're seeing this in a lot of use cases, a lot of consumer use cases, Apple is an example, but you're also seeing it in things like Tesla, Amazon with AWS graviton, the Annapurna acquisition, building out graviton and nitro, that's based on Arm. You can get from design to tape out in less than two years. Whereas the Intel cycles, we know, they've been running it four to five years now. Maybe Pat Gelsinger is compressing those. But Intel is behind. So organizations that are on that orange curve are going to see faster acceleration, lower cost, lower power, et cetera. All right, so what's the tie to data protection. I'm going to leave you with this chart. Arm has introduced it's confidential, compute architecture and is ushering in a new era of security and data protection. Zero trust is the new mandate. And what Arm has it's done with what they call realms is create physical separation of the vulnerable components by creating essentially physical buckets to put code in and to put data in, separate from the OS. Remember, the OS is the most valuable entry point for hackers or one of them because it contains privileged access, and it's a weak link because of things like memory leakages and vulnerabilities. And malicious code can be placed by bad guys within data in the OS and appear benign, even though it's anything but. So in this, all the OS does is create API calls to the realm controller. That's the only interaction. So it makes it much harder for bad actors to get access to the code and the data. And importantly, very importantly, it's an end-to-end architecture. So there's protection throughout. If you're pulling data from the edge and bringing it back to the on-prem or the cloud, you've got that end to end architecture and protection throughout. So the link to data protection is that backup software vendors need to be the most trusted of applications. Backup software needs to be the most trusted of applications because it's one of the most targeted areas in a cyber attack. Realms provide an end-to-end separation of data and code from the OS and it's a better architectural construct to support zero trust and confidential computing and critical use cases like data protection/backup and other digital business apps. So our call to action is backup software vendors, you can lead the charge. Arm is several years ahead at the moment, ahead of Intel, in our view. So you've got to pay attention to that, research that. We're not saying over rotate, but go investigate that. And use your relationships with Intel to accelerate its version of this architecture. Or ideally, the industry should agree on common standards and solve this problem together. Pat Gelsinger told us in theCUBE that if it's the last thing he's going to do in his industry life, he's going to solve this security problem. That's when he was at VMware. Well, Pat, you're even in a better place to do it now. You don't have to solve it yourself, you can't, and you know that. So while you're going about your business saving Intel, look to partner with Arm. I know it sounds crazy to use these published APIs and push to collaborate on an open source architecture that addresses the cyber problem. If anyone can do it, you can. Okay, that's it for today. Remember, these episodes are all available as podcasts. All you got to do is search Braking Analysis Podcast. I publish weekly on wikibond.com and siliconangle.com. Or you can reach me @dvellante on Twitter, email me at david.vellante@siliconangle.com. And don't forget to check out etr.plus for all the survey and data action. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching, everybody. Be well, and we'll see you next time. (gentle music)

(bright upbeat music) >> Hey, welcome to theCUBE's coverage of Pure Accelerate 2021. I'm Lisa Martin, please stay welcoming back one of our alumni Murli Thirumale is here, the VP & GM of the Cloud Native Business Unit at Pure Storage, Murli, welcome back. >> Lisa, it's great to be back at theCUBE, looking forward to discussion. >> Likewise, so it's been about six months or so since the Portworx acquisition by Pure Storage, give us a lay of the land, what's been going on? What are some of the successes, early wins, and some of the lessons that you've learned? >> Yeah, this is my third time being in Cloud, being a serial entrepreneur. So I've seen this movie before, and I have to say that this is really a lot of good anticipation followed by actually a lot of good stuff that has happened since, so it's been really a great ride so far. And when, let me start with the beginning, what the fundamental goal of the acquisition were, right? The couple of major goals, and then I can talk about how that integration is going. Really, I think from our viewpoint, from the Portworx viewpoint, the goal of the acquisition, from our view, was really to help turbocharge in our growth, we had really a very, very good product that was well accepted and established at customers, doing well as far as industry acceptance was concerned. And frankly, we had some great reference customers and some great installs expanding pretty well. Our issue was really how fast can we turbocharge that growth because as everybody knows, for a startup, the expensive part of an expansion is really on the go-to-market and sales side. And frankly, the timing for this was critical for us because the market had moved from the Kubernetes' market, has moved from sort of the innovator stage to the early majority stage. So from the Pure side, I think this made a lot of sense for them, because they have been looking for how they can expand their subscription models, how they can move to add more value from the array based business that there really have been a wonderful disruptor and to add more value up the stack, and that was the premise of the acquisition. One of the things that I paid a lot of attention to, as anybody does in acquisitions, is not just the strategy but really to understand if there was a culture fit between the teams, because a lot of the times acquisitions don't work because of the poor culture fit. So now let me kind of fast forward little bit and say, "Hey, what we know looking back in about six to eight months into it, how has it turning out so far?" And things have been just absolutely wonderful. Let me actually start with the culture fit, because that often is ignored and is one of the most important parts, right? The resonance in the culture between the two companies is just off the charts, right? It actually starts with what I would call a dramatic kind of customer first orientation, it's something we always had at Portworx. I always used to tell our customers with a startup you end up kind of, you buy the product, but you get the team, right? That's what happens with early stage startups, but Pure is sort of the same way, they are very focused on customer. So the customer focus is a very very useful thing that pulls us together. The second thing that's been really heartwarming to see has been really the focus on product excellence. Pure made it's dramatic entry into the market using Flash, and being the best Flash-based solution, and now they've expanded into many, many different areas. And Portworx also had a focus on product excellence, and so that has kind of moved the needle forward for both of us. And then I think the third thing is really a focus on the team winning, and not just an individual, right? And look, in these COVID times, this has been a tough year for everybody, I think it's, to some extent, even as we onboard new people, it's the culture of the team, the ability to bring new people onboard, and buy the culture, and make progress, all of that is really a function of how well the team is, 'we' is greater than 'me' type of a model, and I think that both these three values of customer first, high focus on product excellence, and the value in the team, including the resellers and the customers as part of the team, has really been the cornerstone, I think, of our success in the integration. >> That's outstanding because, like you said, this is not your first rodeo launching, coming out of stealth and launching and getting acquired, but doing so during one of the most challenging times in the last 100 years in our history while aligning cultures, I think that says a lot about the leadership on the Portworx side and the Pure side. >> I have to say, right? This is one of those amazing things, many people now that having been acquired can say this, really, most of the diligence, the transactions, all of that were done over Zoom, right? So, and then of course, everything since then is we're still in Zoom paradise. And so I think it really is a testament to the modern tools and stuff that we have that enable that. Now, let me talk a little bit about the content of what has happened, right? So strategically, I think the three areas that I think we've had huge synergy and seeing the benefits are first and foremost on the product side. A little later, I'd like to talk a little bit about some of the announcements we're making, but essentially, Pure had this outstanding core storage infrastructure product, well-known in the industry, very much Flash-oriented, part of the whole all Flash era now. And Portworx really came in with the idea of driving Kubernetes and Cloud Native workloads, which are really the majority of modern workloads. And what we found since then is that the integration of having really a more complete stack, which is really centered around what used to be an IT infrastructure of purchase, and what is in fact, for Kubernetes, a more DevOps oriented purchase. And that kind of a combination of being able to provide that combo in one package is something that we've been working very hard on in the last six months. And I'll mention some of the announcements, but we have a number of integrations with FlashArray and FlashBlade and other Pure products that we're able to highlight. So product integration for sure has been an area of some focus, but against a lot of progress. The second one is really customer synergy. I kind of described to our team when we got acquired, I said it's, for us, it's, being acquired by Pure is like strapping a rocket ship to ourselves as a small company, because we now have access to a huge customer footprint. Pure has over 8,000 customers, hugely amazingly high, almost unbelievable NPS score with customers, one of the best in the IT industry. And I think we are finding that with the deployment of containers becoming more ubiquitous, right? 80, 90% of customers in the enterprise are adopting Kubernetes and Containers. And therefore these 8,000 customers are a big huge target, they got a big target sign for both of us to be able to leverage. And so we've had a number of things that we're doing to address and use the Pure sales team to get access to them. The Pure channel of course is also part of that, Pure is 100% channel organization, which is great. So I think the synergy on the customer side with being able to have a solution that works for infrastructure and for DevOps has been a big area. In this day and age, Kubernetes is an area, for many of your listeners who are very, very familiar with Kubernetes, customers struggle, not just with day zero, but day one, day two, day three, right? It's how do you put it in production. And support, and integrating, and the use of Kubernetes and containers, putting that stack together is a big area. So support is a big area of pain for customers, and it's an area that, again, for a Portworx viewpoint, now we've expanded our footprint with a great support organization that we can bring to bear 24 by seven around the globe. Portworx is running on a lot of mission critical applications in big industries like finance and retail, and these types of things, really, support is a big area. And then the last thing I will just say is the use cases are usually synergistic, right? And we'll talk a little bit more about use cases as we go along here, but really there's legacy apps, right? In an interesting way, there's 80% of, IT spending is still on legacy apps, if you will, in that stack. However, 80% of all the new applications are being deployed on this modern app stack, right? >> Right. >> With all these open-source type of products and technologies. And most of that stack, most of the modern app stack is containerized. The 80, 85% of those applications really are where customers have chosen containers and Kubernetes as the as the mechanism to deliver those apps. And therefore Pure products like FlashBlade were very, very focused with fast recovery for these kind of modern apps, which are the stack of AI, and personalization, and all the modern digital apps. And I think those things can align well with the Portworx offering. So really around the areas of culture, customers, product synergy, support, and finally use cases, are all kind of been areas of huge progress for us. >> It also seems to me that the Portworx acquisition gives Pure a foray, a new buying center with respect to DevOps, talk to me a little bit about that as an opportunity for Pure. >> Yeah, the modern world is one where the enterprise itself has segmented into whole lot of new areas of spending and infrastructure ownership, right? And in the old days it used to be the network, storage, compute, and apps, sort of the old model of the world. And of course the app model has moved on, and then certainly there's a lot of different ways, web apps, the three tier apps, and the web apps, and so on. But the infrastructure world has morphed really into a bunch of other sub-segments, and some of it is still traditional hardware, but then even that is being cloudified, right? Because a lot of companies like Pure have taken their hardware array offerings and are offering that as a cloud-like offering where you can purchase it as a service, and in fact, Pure is offering a set of solutions called Evergreen that allow you to not even, you're just under subscription, you get your hardware refresh bundled in, very, very innovative. So you have now new buying centers coming in, in addition to the old traditional IT, there is sort of this whole, what used to be in the old ways called middleware, now has kind of morphed into this DevSecOps set of folks, right? Which is DevOps it's ITOps, and even security is a big part of that, the CISO Organization has that kind of segment. And so these buying centers often have new budgets, right? It turns out that, for example, to contrast, the Portworx budget really comes from entirely different budget, right? Our top two budget sources are usually CIO initiatives, they're not from the traditional storage budget, it comes from things like move to cloud or business transformation. And those set of folks, that set of customers, is really born in a different era, so to speak. You know, Lisa, they come, and I come from the old world, so I would say that I'm kind of more of an oldie, hopefully a Goldie, but an oldie. These folks are born in the post-DevOps, post-cloud, post-open-source world, right? They are used to brand new tools, get-ops, the way that everything's run on the cloud, it's on demand. So what we bring to Pure is really the ability to take their initiatives, which were around infrastructure, and cloudifying infrastructure to now adding two layers on top of that, right? So what Portworx adds to Pure is the access to the new automation layer of middleware. Kubernetes is nothing but really an automation of model for containers and for infrastructure now. And then the third layer is on top of us, is what I would call SaaS, the SaaSified layer, and as a service layer. And so we bring the opportunity to get those SaaS-like budgets, the DevOps budgets, and the DevOps and the SaaS kind of buyers, and together the business has very different models to it. In addition to not just a different technologies, the buying behavior is different, it's based on a consumption model, it's a subscription business. So it really is a change for new budgets, new buyers, and new financial models, which is a subscription model, which as you know, is valued much more highly by Wall Street nowadays compared to say some of the older hardware models. >> Well, Murli, when we talk about storage, we talk about data or the modern data experience. The more and more data that's being produced, the more value potentially there is for organizations, I think we saw, we learned several lessons in the last year, and one of them is that being able to glean insights from data in real-time or near real-time is, for many businesses, no longer a nice to have, it's really table stakes, it was for survival of getting through COVID, it is now in terms of identification of new business models, but it elevates the data conversation up to the C-suite, the board going, "Is our data protected? Is it secure? Can we access it?" And, "How do we deliver a modern data experience to our customers and to our internal employees?" So with that modern data experience, and maybe the elevation about conversation lengths, talk to me about some of the things that you're announcing at Accelerate with respect to Portworx. >> Yeah, so there are two sets of announcements. To be honest actually, this is a pretty exciting time for us, we're in theCUBE Cone time and the Accelerate time. And so let me kind of draw a circle around both those sets of announcements, if you will, right? So let's start perhaps with just the sets of things that we are announcing at Accelerate, right? This is kind of the first things that are coming up right now. And I'll tell you, there are some very, very exciting things that we're doing. So the majority of the announcements are centered around a release that we have called 2.8, so Portworx says, "We've been in the market now for well over five years with the product that really has been well deployed in very large global 2K enterprises." So the three or four major announcements, one of them is what I was talking about earlier, the integration of true Kubernetes applications running on Pure Storage. So we have a Cloud Native, a Native implementation of Portworx running on FlashArray and FlashBlade, where essentially when users now provision a container volume to Portworx, the storage volumes are magically created on FlashArray and FlashBlade, right? It's the idea of, without having to interface, so a DevOps engineer can deploy storage as code by provisioning volumes using Kubernetes without having to go issue a trouble ticket or a service ticket for a PureArray. And Portworx essentially access a layer between Kubernetes and the PureArray, and we allow configuration of volumes on the storage volumes of the PureArray directly. So essentially now on FlashArray, these volumes now receive the full suite of Portworx Storage Management features, including Kubernetes DR, backup, security, auto scaling, and migration. So that is a first version of this integration, right? The second one, it's, I am, is a personal favorite of mine, it's very, very exciting, right? When we came into Pure, we discovered that Pure already had this software solution called Pure as a service, it was essentially a Pure1 service that allowed for continuous call home, and log and diagnostic information, really an awesome window for customers to be able to see what their array utilization is like, complete observability, end-to-end on capacity, what's coming up, and allowed for proactive addressing of outages, or issues, or being able to kind of see it before it happen. The good news now is Portworx is integrated with Pure1, and so now customers have a unified observability stack for their Kubernetes applications using Portworx and FlashArray and FlashBlade in the Pure1 portal. So we are in the Pure1 portal now really providing end-to-end troubleshooting of issues and deployment, so very, very exciting, something that I think is a major step forward, right? >> Absolutely, well that single pane of glass is critical for management, so many companies waste a lot of time and resources managing disparate disconnected systems. And again, the last year has taught us so many businesses, there wasn't time, because there's going to be somebody right behind you that's going to be faster and more nimble, and has that single pane of glass unified view to be able to make better decisions. Last question, really, before we wrap here. >> Yeah. >> I can hear your momentum, I can feel your momentum through Zoom here. Talk to me about what's next, 'cause I know that when the acquisition happened about, we said six months or so ago, you said, "This is a small step in the Portworx journey." So what's ahead? >> Lisa, great question. I can state 10 things, but let me kind of step up a little bit at the 10,000 foot level, right? In one sense, I think no company gets to declare victory in this ongoing battle and we're just getting started. But if I had to kind of say, "What are some of the major teams that we have been part of and have been able to make happen in addition to take advantage of?" Pure obviously took advantage of the Flash wave, and they moved to all Flash, that's been a major disruptor with Pure being the lead. For Portworx, it has been really the move to containers and data management in an automated form, right? Kubernetes has become sort of not just a container orchestrator looking North, but looking southbound, is orchestrating infrastructure, we are in the throws of that revolution. But if you think about it, the other thing that's happening is all of this is in the service of, if you're a CIO, you're in the service of lines of businesses asking for a way to run their applications in a multicloud way, run their applications faster. And that is really the, as a service revolution, and it feels a little silly to almost talk about it as a service in that it's this late in the Cloud era, but the reality is that's just beginning, right? As a service revolution dramatically changed the IaaS business, the infrastructure business. But if you look at it, data services as a, data as a service is something that is what our customers are doing, so our customers are taking Pure hardware, Portworx software, and then they are building them into a platform as a service, things like databases as a service. And what we are doing, you will see some announcements from us in the second half of this year, terribly exciting, I just can't wait for it, where we're going to be actually moving forward to allow our customers to more quickly get to data services at the push of a button, so to speak, right? So- >> Excellent. >> The idea of database as a service to offer messaging as a service, search as a service, streaming as a service, and then finally some ML kind of AI as a service, these five categories of data services are what you should be expecting to see from Portworx and Pure going forward in the next half. >> Big potential there to really kick the door wide open on the total adjustable market. Well, Murli, it's been great to have you on the program, I can't wait to have you on next 'cause I know that there's so much more, like I said, I can feel your momentum through our virtual experience here. Thank you so much for joining us and giving us the lay of the land of what's been happening with the Portworx acquisition and all of the momentum and excitement that is about to come, we appreciate your time. >> Thank you, Lisa. Cheers to a great reduced COVID second half of the year. >> Oh, cheers to that. >> Yeah cheers, thanks. >> From Murli Thirumale, I'm Lisa Martin, you're watching theCUBE's coverage of Pure Accelerate. (bright upbeat music)

>> Announcer: From around the globe, it's theCUBE. With digital coverage of IBM Think 2021. Brought to you by IBM. >> John: Hello and welcome back to theCUBE's coverage of IBM Think 2021 Virtual. I'm John Furrier, your host of theCUBE. We're here with two great guests, Andrew Coward's the GM, Software Defined Networking at IBM and Caroline Chappel. Research Director, Cloud and Platform Services at Analysys Mason. Folks, thanks for coming on. Caroline, good to see you. Andrew, thanks for coming on theCUBE. >> You're welcome, it's nice to be here. >> Thank you. >> So software defined networking, love it. Software-defined data center, software defined cloud, all that has been pointing to what is now a reality which is hybrid cloud and the Edge, and soon to be multicloud. This kind of makes networking, again, at the centerpiece. This has been this way for now, at least for five hardcore years, at the center of the value proposition discussion. And certainly networking is super relevant. Why is networking now more important than ever for IBM? >> Well, to your point, I think networking is weaved into pretty much everything we touch. From Red Hat Linux for its analytics, machine learning tools, security, cloud services, and so on. And the networking business is changing very radically at the moment. We're going through kind of massive shift. Not just to the cloud, but the desegregation of networking products that, you know, you think of being very tight and integrated are actually being separated into their constituent parts. Distribution of applications and data across multiple clouds, ensuring that the products really have industry-leading capabilities, so that networking is weaved into what they do. The other thing is the scary numbers, right? But now, there's like 15 billion network-capable devices out there with general computing capabilities. And so I don't mean like really dumb things but things that are now we call smart, like a smart car. A medical center that's got applications that even your fridge now, has general compute capabilities. And all of those are expected to connect into the public or private cloud. And so how they connect, where data moves across that really on critical concern to everything that we at IBM do. >> So I have to ask you, I love the word radical change. It gets my attention for certain. What specifically are you referring to in radical change? Because, I mean, I would, I mean, I'm pretty radical that COVID has hit everybody and I think everyone woke up and never thought 100% of the workforce would be working remotely. So, you know, there is radical kind of macro conditions. What specifically though about networking would you say is radical and how does that impact the enterprise? >> Well, right. I think it's about how compute is shifting and how network has to follow. You know, we've been speaking a lot of enterprise accounts and customers. And, you know, it's through COVID and over the last year, we've seen that the ongoing migration into, not just one cloud but many clouds. But we need to think the enterprise you can stop and say, two clouds is enough to be here and to be able to do that. That's not happening. There is no limit to the number of clouds that each enterprise is going into and it's not a coordinated decision, so the radicalism is that the network guys, the cloud architects are being left to pick up the pieces and their job now is to kind of join together applications and data that might be spread in three or four different locations. And that's really, really challenging. And nobody's thinking about things like latency or connectivity, data accountability when these decisions are made. And it is kind of like the business units are allowed to make their own decisions to get it, but corporate itself then has to figure out how all this stuff works. And that's creating a lot of headaches. >> Caroline, If you could chime in on this, because this is kind of like what we're hearing. What's your thoughts? Because I mean, the platform shifting. I mean, five years ago. Oh, go move to the cloud, lift and shift. Now, the conversation is hyper-focused on cloud integration, at scale with kind of the features that enterprise really need. That's the confusion. What's your take on all this radical change? >> Well, I'd like to, to talk about another aspect of the radical change here, which I think is part of the story which is the radical change for the network itself. So the network itself is, as Andrew said, you know becoming desegregated into hardware and software and really becoming a software application if you think about it, that runs on the cloud itself. And that means you can distribute the network in a very different way, than you could in the past. And what that's really affecting is who can provide a network, how they can provide it, what services, what network services they can provide. And I think that is changing the decision points for operators, for enterprises. They're being faced with a very big choice about who do they, who will provide their connectivity services? Will it be an SD-WAN vendor? Who's not necessarily a traditional operator? Would it be a SaSS-y player that's basically just operating after the cloud. And if you look at the services themselves, there's the opportunity for enterprises to build really kind of rich, bespoke connectivity on demand and in a way that they've never had before. And I think that choice is obviously wonderful in one sense, but in another sense, it's pretty scary. And, and as Andrew said, it's not these decisions are not being taken particularly in a coordinated way. You know, you'll have your traditional network guys often very embedded with the lines of business and then you'll have the IT guys all going to the cloud. And these two parts of an enterprise don't necessarily even talk to each other in terms of how they're procuring their network services. So lot of choice, a lot of moving parts, a lot of change. And I think that's contributing to the situation we're finding ourselves in. >> So. First of all, great insight. I want to just double down on that one point around radical change, because what you just laid out is kind of the institutional lock-in or the way they've been operating things before You mentioned lines of business being embedded with the network guys. So you have radical change. So that's a disruption. So what's the disruption look like from your perspective because now you've got more choice, but it's hasn't been operationalized. What are the best practices? This is net new. Is it net new? How do I do security? This is all now new questions. So I got to ask you what's the disruption and what's it mean for the enterprise networks over the next couple of years going forward? >> Well, I think that there are a lot of disruptions but I think one of the ones that I haven't even mentioned. So I think, you know a lot of things are going to go, for example, I think that the idea of the network as being something fixed, persistent with fixed persistent connections is changing. So a lot of the enterprises I've talked to have said that their corporate networks, of course, they will need corporate networks with fixed VPNs between locations. Yeah, because they've got an awful lot of legacy they've got to support. But a lot of the new stuff that's coming along of the IOT driven stuff a lot of the changes around the edge and an operation, operational process automation and that kind of thing will actually be more on demand. We'll ask for on demand connectivity. A lot of it is will the applications themselves run on the cloud and not just on one cloud but as Andrew said on many, many distributed clouds. So you've got to think about zero trust security because you are basically spinning up these connections on demand. A lot of mobile will come in 5g. We know is going to be very important to operators in the future. So I think enterprises have got to deal with those data and security and all their best practices. We've got to shift to a much more dynamic, you know connectivity world, where they've got us to the playoffs. You know, what's the terministic on what's a network. That's just going to be on demand there when they need it and shut down when they don't. >> That's a great point. Andrew, I want you to weigh in on the IBM impact because what we just heard was application driven. That's dev ops. That's programmability. That's what we had hoped. Now you've got DevSecOps, all this is now the requirements. What's the bet on IBM side.? You got to make it happen. You got to bring the customers a solution and make it scale and be responsive to those you know, new, dynamically, flexible agile networks. >> Well, that's right. So the bet is that, you know that these applications that are being spent out there in containerize and they're being separated into these clouds and connecting those is what we as IBM have to have to do. And so kind of an example of that, kind of looking at the medical world, right? You think of an application that would today, monitor a patient. What's going on with that patient and all of the senses and so on. Well, the way we see it, the monitor itself, there might be monitoring temperature and heart rate etc. That what actually happens on that device might change moments depending on the patient's condition. That's one part of the application. Another part of that application may live in private data center. A third part of that application may live in the cloud. And depending on what's going on with that patient and what's going on with the ward and everything else. Those things may shift and move around. So, where does that data? Where's that data allowed to move to inform of what are the boundary points for that? How is the reliability, resiliency of our system guaranteed, but across many disparate parts of what's going on there. All of those things end up being a very vertically integrated solution. But fundamentally we've got a very different way, new ways of being able to react, dynamically. To both the network, the application and ultimately the unusual patient in this case and that's what kind of is the advantage of the outcome if you like for moving to this new world. >> So what are the implications then of the changes? These are massive changes for the better We're seeing that kind of innovation come from this transformational quick change. Hybrid cloud and edge is coming, you mentioned. Caroline talked about that too. What do you guys think about the implications and how enterprises specifically can prepare for these changes? >> Okay, well, I can pick that up. I think what enterprises are looking for at the moment is how do they get a holistic view of everything that's underneath them? I mean, I think the cloud providers individually are abstracting away as much of the network as they possibly can. They want it to appear to developers just as some kind of plumbing. And it's very easy now for enterprises to through API is you know, we've got a very API different world so it's very easy to say, okay I want this service and I'm just going to go through their API and connect to it. And that's why you get to the situation of multiple, multiple clouds. Now you've got this situation where you've got some companies are talking about needing 50 to 10,000 micro data centers, room closet data centers if you like ,to support some of the things that they want to do, like telemetry ,pick up telemetry from rental cars, for example. So what they really need is to look at all that connectivity, just as plumbing just as we don't worry about how electricity is being delivered to us. That's kind of how they want to do connectivity. So I think they want that view. They want that. Okay. I want to treat my network as one virtual thing. No matter how many different points of plumbing there are underneath. And it's getting to that point that I think they've really got to think about a plan for. You know, how do we get that to you? What's going to provide us with that holistic way that we can put a policy into our plumbing. And it proliferates across, you know all our applications and so on. I think that's a very difficult thing to achieve at the moment but it's certainly the way enterprises need to start thinking about things. >> Andrew, you know, when Caroline's talking, I can't help but kind of throw back to my days of the telephone closet. You know, back in the analog switches. But no, we're talking about a footprint. Radical footprint change too. You know, you need plumbing. Obviously that's a network. It's distributed. We just talked about that at the top of this interview. Now you have the plumbing, you got the footprint and data center could be in a closet, AKA, you know a couple of devices powering an edge. And the edge could be big, small, medium, extra large right? I mean, it's all now radically changed. This is reality now. what's your take on these implications and how do people prepare? >> Well, that's right. It's really the computer's generalized and it's everywhere and yes, it's in the closet. But as I say, it's also in your fridge, it's also in your medical censor and what loads and what runs on that is it's very intertwined with the network. And the lament, if you like, that network architects, the card architects have today is that they feel like they've lost control. They feel they've lost control of exactly what different business groups are doing, how these applications are playing out. And shout out to them, I guess for them is really that they need to be involved from a very early date on how these services are supposed to look. Just the latency of the patients, the data and where the data's supposed to live, where it's allowed to move to. All of those are deeply regulated and deeply controlled. And so making sure that that's aligned with how these applications will actually live and work. Even on a regular basis, sooner there has to be thought about now. An unplanned for so that we can get to the there and not trip up along the way. And then if it's bad enough now with all the different clouds, it's going to be much worse when everything can run a different workload on a minute by minute basis. Right. But that's cool. That's the world we have to find for. >> Okay. Andrew. Caroline. Thank you for your insight. Really appreciated coming on theCUBE. Thanks for coming. I really appreciate it. >> Thank you very much. >> Thank you >> Okay. This is the cube coverage of IBM Think 2021. I'm John Furrier, your host. Thanks for watching. (cheerful music playing)

>>from >>around the globe. It's the cube with >>Digital coverage of IBM think 2021 brought to you by IBM. Hello and welcome back to the cubes coverage of IBM think 2021 virtual. I'm john for your host of the cube. We're here with two great guests. Andrew cowards. GM Software defined networking at IBM and Caroline chappelle Research director, cloud and platform services at analysis mason folks. Thanks for coming on Caroline. Good to see you Andrew. Thanks for coming on. Uh, thank you. >>Welcome. Nice to >>begin. So >>software defined networking love it suffered to find data center suffered to find cloud. All that has been pointing to what is now a reality which is hybrid cloud and the edge and soon to be multi cloud. This kind of makes networking again at the center pieces has been this way for now at least for five hardcore years at the center of the value proposition and discussion and certainly networking is super relevant. Why is networking now more important than ever for IBM? >>Well, to your point, I think networking is weaved into pretty much everything we touch from red hot limits to analytics, machine learning tools, security card services and so on. And the networking business is changing very radically. At the moment we're going through a massive shit um, not just the cloud, but the desegregation of networking products that you know, you think of being very tight and integrated are actually being separated into their constituent parts, distribution of applications and data across multiple clouds, ensuring that the products really have industry leading capabilities so that networking is weaved into into what they do. Um, the other thing is, you know, this is kind of scary numbers, right? But there's now over 15 billion um, network capable devices out there with general compute capabilities. So I don't mean like really dumb things but things that are now we call smart, like a smart car, a medical center that's that's got application, even your fridge now has general compute capabilities and all of those are expected to connect into public or private cloud and so how they connect where data moves across that really on critical concern to to everything that we had IBM do. >>So I have to ask you love the word radical change, gets my attention for certain for certain um what specifically are you referring to in radical change? Because I mean I would mean I've pretty radical. The COVID has hit everybody and I think everyone woke up and never thought 100 of the workforce would be working remotely. So you know, there is radical kind of macro conditions. What specifically though about networking, would you say is radical? How is that impact enterprise >>Well, right. I think it's about how computers is shifting and how network has to follow. Um We've been speaking with lots of enterprise accounts customers and um you know, through covid and over the last year we've seen that the ongoing migration into not just one cloud, but many clouds. Um and you think that enterprises can stop saying to clouds is enough going to be here on the other there, That's not happening. There is no limit to the number of clouds that um each enterprises going into and it's not a coordinated decisions. So the radical list of this is that the network guys, the cloud architects are being left to pick up the pieces um and their job now is to kind of join together applications and data that might be spread in three or four different locations. Um and and that's really, really challenging and nobody's thinking about things like latency connectivity, um data portability when when these decisions are made. Um It's kind of like the business units are allowed to make their own decisions here. But the corporate itself then has to figure out how all this stuff works and that's creating a lot of headaches >>Carolina. If you can chime in on this because this is kind of like what we're hearing, what's your thoughts? Because I mean the platform shifting five years ago, so go move to the cloud lift and shift now. The conversation is hyper focused on cloud integration at scale with kind of the features that enterprise really need. That's that's the confusion. What's your take on all this radical change? >>Well, I'd like to talk about another aspect of the sort of radical change here, which I think is part of the story, which is the radical change for the network itself. So the network itself is, as Andrew said, becoming desegregated into hardware and software and really becoming a software application, if you think about it that runs on the cloud itself, and that means you can distribute the network in a very different way than you could in the past. And what that's really affecting is who can provide a network, how they can provide it and what services, what network services they can provide. And I think that is changing the decision points for operate for enterprises. They're being, they're being faced with a very big choice about who do they, who do they, who will provide their connectivity services, will it be an SD one and then who's not necessarily a traditional operator? Will it be a will it be a sassy player that's basically just operating out of the cloud? And if you look at the services themselves, I mean there's there's the opportunity for enterprises to build really kind of rich bespoke connectivity on demand and in in a in a way that they've never had before. Uh and I think that choice is obviously wonderful in one sense, but in another sense it's pretty scary and as and you said it's not these decisions are not being taken particularly in a coordinated way. You know, you'll have your traditional network guys often very embedded with the lines of business and then you'll have the I. T. Guys all going to the cloud and these two parts of an enterprise don't necessarily even talk to each other in terms of how they're procuring their network services. So a lot of choice, a lot of moving parts, a lot of change and I think that's that's contributing to the situation we're finding ourselves in. >>So you first great insight, I want to just double down on that one point around radical change because what you just laid out is kind of the institutional lock in or the way they've been operating things before, you mentioned lines of business being embedded with the network guys. So you have radical change, So that's a disruption. So what's the disruption look like from your perspective, because now you've got more choice, but this has been operationalized, one of the best practices. This is news that net new. How do I do security? This is all now new questions. So I gotta ask you what's the disruption and what's it mean for the enterprise networks over the next couple of years going forward? >>Well, I think that there are a lot of disruptions, but I think one of the uh and ones that I haven't even mentioned, so I think a lot of things are going to go, for example, I think that the idea of the network is being something fixed, persistent with fixed persistent connections um is changing. So a lot of enterprises I've talked to have said that uh corporate networks of course they will need corporate networks with fixed VPNS between locations because they've got an awful lot of legacy they've got to support, but a lot of the new stuff that's coming along, a lot of the IOT driven stuff, a lot of the changes around the edge and an operation operational process automation and that kind of thing will actually be be more on demand. We'll ask for on demand connectivity. A lot of it is uh it will the applications themselves run on the cloud and not just on one cloud, but as Andrew said on many, many distributed clouds. So you've got to think about zero trust security because you are basically spinning up these connections on demand, a lot of mobile will come in five G, we know is going to be very important to operators in in the future. So I think enterprises have got to deal with those data, that data and security and all their best practices have got to shift to a much more dynamic uh, connectivity world where they've got a playoff, what's deterministic and what's, what's a network that's just going to be on demand there when they need it and shut down when they don't. >>That's a great point. Andrew. I want you to weigh in on the IBM impact because what we just heard was application driven, that's devops, that's program ability, that's what we had hoped. Now you got Deb sec. Ops, all this is now the requirements. What's the bet on IBM side? You gotta gotta make it happen. You gotta bring the customers a solution and make it, make it scale and be responsive to those, you know, new dynamically flexible agile networks. >>Well, that's right. So, so the better is that these applications that are being split up there in containerized and they're being separated into these clouds and connecting those is what we as IBM has has to do. And so kind of an example of that kind of looking in the medical world, right? You think of an application that would today monitor a patient, uh what's going on with our patients in all of the senses and so on. Well, the way we see it, the monitor itself, uh that might be monitoring temperature and heart rate etcetera. That what actually happens on that device might change moment to moment depending on the patient's condition. That's one part of the application, another part of the application. They live in private data center, a third part of that application. They live in the cloud. And depending on what's going on with that patient and what's going on with the war and everything else, those things may shift and move around. So where does that data? Where's that data allowed to move to and from? And what are the boundary points for that? How is the the, the reliability resiliency of that system guaranteed across many disparate parts of what's going on there, All of those things end up being a very vertically integrated solution. But fundamentally, we've got a very different way. A new ways of being able to react dynamically to both the network, the application and ultimately, the unusual patient in this case is uh use case and that and that's what is the vanishing of the outcome, if you like, from moving to this new world. >>So, what are the implications, then, of the changes? These are massive changes for the better? Um We're seeing that kind of innovation come from this transformational change. Um Hybrid, Cloud and Edge is coming. You mentioned Caroline talked about that too. What do you guys think about the implications and how enterprises specifically can prepare for these changes? >>Okay, well, I I can pick that up. I think uh what enterprises uh looking for at the moment is how do they get a holistic view of everything that's underneath them? I mean, I think the cloud providers individually are abstracting away as much of the network as they possibly can. They want it to appear to developers just as some kind of plumbing. Um and it's very easy now for enterprises to through a P. I. S. You know, we've got a very api driven world so it's very easy to say okay I want this service and I'm just going to go through the A. P. I. And connect to it. And that's why you get to the situation of multiple multiple clouds. Now you've got, you've got this situation where you've got some, some companies are talking about needing 50-10,000 uh micro data centers, broom closet data centers if you like to support some of the things that they want to do, like telemetry to pick up telemetry from rental cars, for example. So what they really need is to look at all that connectivity just as plumbing, just as we don't worry about how electricity is being delivered to us, that's kind of how they want to do connectivity. So I think they want that view, they want that, okay, I want to treat my network as one virtual thing no matter how many different points of plumbing there are underneath. And it's getting to that point that I think they've really got to think about and plan for how do we get that view, what's going to provide us with that holistic way and we can put a policy into the into our plumbing and it it proliferates across all our applications and so on. I think that's a very difficult thing to achieve at the moment but it's certainly the way enterprises need to start thinking about things >>and you know when Caroline's talking I can't help but kind of throwback to my days of the telephone closet, you know back in the analog switches but we're talking about a footprint radical footprint change to you. You need plumbing. I'll see that's a network, it's distributed. We just talked about that the top of this interview now you have the plumbing, you've got the footprint of data center could be in a closet A. K. A. You know a couple devices powering an edge and the edge could be big small medium extra large. Right? I mean it's all now radically changed. This is reality now. What's your take on these implications and how do people prepare? >>Well that's right. It's really the computers generalist and it's everywhere and yes it's in the closet but it's also in your fridge is also a new medical sensor and what loads and what runs on that is it's very intertwined with the network and the lament if you like. That. The network architects the architects have today is that they feel like they've lost control um They feel like lost control of exactly what different business groups are doing. How these applications are playing out and shout out to them I guess for them is really that they need to be involved in the very early um date of how these services is supposed to look just the latest implications. The data where the data is supposed to live, where it's allowed to move to. All of those are deeply regulated and deeply control and so making sure that that's aligned with how these applications will actually live and work uh on the basis of something that has to be thought about now um and planned for so that we can we can get to the there and then not trip up along the way. And if it's bad enough now with all the different clouds it's going to be much worse when when everything can run a different workload on a minute by minute basis. Right? That's what that's that's the the world we have to find. >>Okay. Andrew Caroline. Thank you for your insight. Really appreciate it coming on the cube. Thanks for coming. I really appreciate it. >>Thanks very much. Okay. >>Okay. This is the Cube coverage of IBM think 2021 um, John for your host. Thanks for watching. >>Mm.

>> Announcer: From around the globe, it's theCUBE. With digital coverage of IBM Think 2021. Brought to you by IBM. >> John: Hello and welcome back to theCUBE's coverage of IBM Think 2021 Virtual. I'm John Furrier, your host of theCUBE. We're here with two great guests, Andrew Coward's the GM, Software Defined Networking at IBM and Caroline Chappel. Research Director, Cloud and Platform Services at Analysys Mason. Folks, thanks for coming on. Caroline, good to see you. Andrew, thanks for coming on theCUBE. >> You're welcome, it's nice to be here. >> Thank you. >> So software defined networking, love it. Software-defined data center, software defined cloud, all that has been pointing to what is now a reality which is hybrid cloud and the Edge, and soon to be multicloud. This kind of makes networking, again, at the centerpiece. This has been this way for now, at least for five hardcore years, at the center of the value proposition discussion. And certainly networking is super relevant. Why is networking now more important than ever for IBM? >> Well, to your point, I think networking is weaved into pretty much everything we touch. From Red Hat Linux for its analytics, machine learning tools, security, cloud services, and so on. And the networking business is changing very radically at the moment. We're going through kind of massive shift. Not just to the cloud, but the desegregation of networking products that, you know, you think of being very tight and integrated are actually being separated into their constituent parts. Distribution of applications and data across multiple clouds, ensuring that the products really have industry-leading capabilities, so that networking is weaved into what they do. The other thing is the scary numbers, right? But now, there's like 15 billion network-capable devices out there with general computing capabilities. And so I don't mean like really dumb things but things that are now we call smart, like a smart car. A medical center that's got applications that even your fridge now, has general compute capabilities. And all of those are expected to connect into the public or private cloud. And so how they connect, where data moves across that really on critical concern to everything that we at IBM do. >> So I have to ask you, I love the word radical change. It gets my attention for certain. What specifically are you referring to in radical change? Because, I mean, I would, I mean, I'm pretty radical that COVID has hit everybody and I think everyone woke up and never thought 100% of the workforce would be working remotely. So, you know, there is radical kind of macro conditions. What specifically though about networking would you say is radical and how does that impact the enterprise? >> Well, right. I think it's about how compute is shifting and how network has to follow. You know, we've been speaking a lot of enterprise accounts and customers. And, you know, it's through COVID and over the last year, we've seen that the ongoing migration into, not just one cloud but many clouds. But we need to think the enterprise you can stop and say, two clouds is enough to be here and to be able to do that. That's not happening. There is no limit to the number of clouds that each enterprise is going into and it's not a coordinated decision, so the radicalism is that the network guys, the cloud architects are being left to pick up the pieces and their job now is to kind of join together applications and data that might be spread in three or four different locations. And that's really, really challenging. And nobody's thinking about things like latency or connectivity, data accountability when these decisions are made. And it is kind of like the business units are allowed to make their own decisions to get it, but corporate itself then has to figure out how all this stuff works. And that's creating a lot of headaches. >> Caroline, If you could chime in on this, because this is kind of like what we're hearing. What's your thoughts? Because I mean, the platform shifting. I mean, five years ago. Oh, go move to the cloud, lift and shift. Now, the conversation is hyper-focused on cloud integration, at scale with kind of the features that enterprise really need. That's the confusion. What's your take on all this radical change? >> Well, I'd like to, to talk about another aspect of the radical change here, which I think is part of the story which is the radical change for the network itself. So the network itself is, as Andrew said, you know becoming desegregated into hardware and software and really becoming a software application if you think about it, that runs on the cloud itself. And that means you can distribute the network in a very different way, than you could in the past. And what that's really affecting is who can provide a network, how they can provide it, what services, what network services they can provide. And I think that is changing the decision points for operators, for enterprises. They're being faced with a very big choice about who do they, who will provide their connectivity services? Will it be an SD-WAN vendor? Who's not necessarily a traditional operator? Would it be a SaSS-y player that's basically just operating after the cloud. And if you look at the services themselves, there's the opportunity for enterprises to build really kind of rich, bespoke connectivity on demand and in a way that they've never had before. And I think that choice is obviously wonderful in one sense, but in another sense, it's pretty scary. And, and as Andrew said, it's not these decisions are not being taken particularly in a coordinated way. You know, you'll have your traditional network guys often very embedded with the lines of business and then you'll have the IT guys all going to the cloud. And these two parts of an enterprise don't necessarily even talk to each other in terms of how they're procuring their network services. So lot of choice, a lot of moving parts, a lot of change. And I think that's contributing to the situation we're finding ourselves in. >> So. First of all, great insight. I want to just double down on that one point around radical change, because what you just laid out is kind of the institutional lock-in or the way they've been operating things before You mentioned lines of business being embedded with the network guys. So you have radical change. So that's a disruption. So what's the disruption look like from your perspective because now you've got more choice, but it's hasn't been operationalized. What are the best practices? This is net new. Is it net new? How do I do security? This is all now new questions. So I got to ask you what's the disruption and what's it mean for the enterprise networks over the next couple of years going forward? >> Well, I think that there are a lot of disruptions but I think one of the ones that I haven't even mentioned. So I think, you know a lot of things are going to go, for example, I think that the idea of the network as being something fixed, persistent with fixed persistent connections is changing. So a lot of the enterprises I've talked to have said that their corporate networks, of course, they will need corporate networks with fixed VPNs between locations. Yeah, because they've got an awful lot of legacy they've got to support. But a lot of the new stuff that's coming along of the IOT driven stuff a lot of the changes around the edge and an operation, operational process automation and that kind of thing will actually be more on demand. We'll ask for on demand connectivity. A lot of it is will the applications themselves run on the cloud and not just on one cloud but as Andrew said on many, many distributed clouds. So you've got to think about zero trust security because you are basically spinning up these connections on demand. A lot of mobile will come in 5g. We know is going to be very important to operators in the future. So I think enterprises have got to deal with those data and security and all their best practices. We've got to shift to a much more dynamic, you know connectivity world, where they've got us to the playoffs. You know, what's the terministic on what's a network. That's just going to be on demand there when they need it and shut down when they don't. >> That's a great point. Andrew, I want you to weigh in on the IBM impact because what we just heard was application driven. That's dev ops. That's programmability. That's what we had hoped. Now you've got DevSecOps, all this is now the requirements. What's the bet on IBM side.? You got to make it happen. You got to bring the customers a solution and make it scale and be responsive to those you know, new, dynamically, flexible agile networks. >> Well, that's right. So the bet is that, you know that these applications that are being spent out there in containerize and they're being separated into these clouds and connecting those is what we as IBM have to have to do. And so kind of an example of that, kind of looking at the medical world, right? You think of an application that would today, monitor a patient. What's going on with that patient and all of the senses and so on. Well, the way we see it, the monitor itself, there might be monitoring temperature and heart rate etc. That what actually happens on that device might change moments depending on the patient's condition. That's one part of the application. Another part of that application may live in private data center. A third part of that application may live in the cloud. And depending on what's going on with that patient and what's going on with the ward and everything else. Those things may shift and move around. So, where does that data? Where's that data allowed to move to inform of what are the boundary points for that? How is the reliability, resiliency of our system guaranteed, but across many disparate parts of what's going on there. All of those things end up being a very vertically integrated solution. But fundamentally we've got a very different way, new ways of being able to react, dynamically. To both the network, the application and ultimately the unusual patient in this case and that's what kind of is the advantage of the outcome if you like for moving to this new world. >> So what are the implications then of the changes? These are massive changes for the better We're seeing that kind of innovation come from this transformational quick change. Hybrid cloud and edge is coming, you mentioned. Caroline talked about that too. What do you guys think about the implications and how enterprises specifically can prepare for these changes? >> Okay, well, I can pick that up. I think what enterprises are looking for at the moment is how do they get a holistic view of everything that's underneath them? I mean, I think the cloud providers individually are abstracting away as much of the network as they possibly can. They want it to appear to developers just as some kind of plumbing. And it's very easy now for enterprises to through API is you know, we've got a very API different world so it's very easy to say, okay I want this service and I'm just going to go through their API and connect to it. And that's why you get to the situation of multiple, multiple clouds. Now you've got this situation where you've got some companies are talking about needing 50 to 10,000 micro data centers, room closet data centers if you like ,to support some of the things that they want to do, like telemetry ,pick up telemetry from rental cars, for example. So what they really need is to look at all that connectivity, just as plumbing just as we don't worry about how electricity is being delivered to us. That's kind of how they want to do connectivity. So I think they want that view. They want that. Okay. I want to treat my network as one virtual thing. No matter how many different points of plumbing there are underneath. And it's getting to that point that I think they've really got to think about a plan for. You know, how do we get that to you? What's going to provide us with that holistic way that we can put a policy into our plumbing. And it proliferates across, you know all our applications and so on. I think that's a very difficult thing to achieve at the moment but it's certainly the way enterprises need to start thinking about things. >> Andrew, you know, when Caroline's talking, I can't help but kind of throw back to my days of the telephone closet. You know, back in the analog switches. But no, we're talking about a footprint. Radical footprint change too. You know, you need plumbing. Obviously that's a network. It's distributed. We just talked about that at the top of this interview. Now you have the plumbing, you got the footprint and data center could be in a closet, AKA, you know a couple of devices powering an edge. And the edge could be big, small, medium, extra large right? I mean, it's all now radically changed. This is reality now. what's your take on these implications and how do people prepare? >> Well, that's right. It's really the computer's generalized and it's everywhere and yes, it's in the closet. But as I say, it's also in your fridge, it's also in your medical censor and what loads and what runs on that is it's very intertwined with the network. And the lament, if you like, that network architects, the card architects have today is that they feel like they've lost control. They feel they've lost control of exactly what different business groups are doing, how these applications are playing out. And shout out to them, I guess for them is really that they need to be involved from a very early date on how these services are supposed to look. Just the latency of the patients, the data and where the data's supposed to live, where it's allowed to move to. All of those are deeply regulated and deeply controlled. And so making sure that that's aligned with how these applications will actually live and work. Even on a regular basis, sooner there has to be thought about now. An unplanned for so that we can get to the there and not trip up along the way. And then if it's bad enough now with all the different clouds, it's going to be much worse when everything can run a different workload on a minute by minute basis. Right. But that's cool. That's the world we have to find for. >> Okay. Andrew. Caroline. Thank you for your insight. Really appreciated coming on theCUBE. Thanks for coming. I really appreciate it. >> Thank you very much. >> Thank you >> Okay. This is the cube coverage of IBM Think 2021. I'm John Furrier, your host. Thanks for watching. (cheerful music playing)

>> From around the globe, it's theCUBE, with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We are celebrating Exascale Day. 10, 18, I think it's the second year of celebrating Exascale Day, and we're really excited to have our next guest and talk about kind of what this type of compute scale enables, and really look a little bit further down the road at some big issues, big problems and big opportunities that this is going to open up. And I'm really excited to get in this conversation with our next guest. He is Bryan Pijanowski the Professor of Landscape and Soundscape Ecology at Purdue University. Bryan, great to meet you. >> Great to be here. >> So, in getting ready for this conversation, I just watched your TED Talk, and I just loved one of the quotes. I actually got one of quote from it that's basically saying you are exploring the world through sound. I just would love to get a little deeper perspective on that, because that's such a unique way to think about things and you really dig into it and explain why this is such an important way to enjoy the world, to absorb the world and think about the world. >> Yeah, that's right Jeff. So the way I see it, sound is kind of like a universal variable. It exists all around us. And you can't even find a place on earth where there's no sound, where it's completely silent. Sound is a signal of something that's happening. And we can use that information in ways to allow us to understand the earth. Just thinking about all the different kinds of sounds that exist around us on a daily basis. I hear the birds, I hear the insects, but there's just a lot more than that. It's mammals and some cases, a lot of reptiles. And then when you begin thinking outside the biological system, you begin to hear rain, wind, thunder. And then there's the sounds that we make, sounds of traffic, the sounds of church bells. All of this is information, some of it's symbolic, some of it's telling me something about change. As an ecologist that's what I'm interested in, how is the earth changing? >> That's great and then you guys set up at Purdue, the Purdue Center for Global Soundscapes. Tell us a little bit about the mission and some of the work that you guys do. >> Well, our mission is really to use sound as a lens to study the earth, but to capture it in ways that are meaningful and to bring that back to the public to tell them a story about how the earth kind of exists. There's an incredible awe of nature that we all experience when we go out and listen into to the wild spaces of the earth. I've gone to the Eastern Steppes of Mongolian, I've climbed towers in the Paleotropics of Borneo and listened at night. And ask the question, how are these sounds different? And what is a grassland really supposed to sound like, without humans around? So we use that information and bring it back and analyze it as a means to understand how the earth is changing and really what the biological community is all about, and how things like climate change are altering our spaces, our wild spaces. I'm also interested in the role that people play and producing sound and also using sound. So getting back to Mongolia, we have a new NSF funded project where we're going to be studying herders and the ways in which they use sonic practices. They use a lot of sounds as information sources about how the environment is changing, but also how they relate back to place and to heritage a special sounds that resonate, the sounds of a river, for example, are the resonance patterns that they tune their throat to that pay homage to their parents that were born at the side of that river. There's these special connections that people have with place through sound. And so that's another thing that we're trying to do. In really simple terms, I want to go out and, what I call it sounds rather simple, record the earth-- >> Right. >> What does that mean? I want to go to every major biome and conduct a research study there. I want to know what does a grassland sound like? What is a coral reef sound like? A kelp forest and the oceans, a desert, and then capture that as baseline and use that information-- >> Yeah. >> For scientific purposes >> Now, there's so much to unpack there Bryan. First off is just kind of the foundational role that sound plays in our lives that you've outlined in great detail and you talked about it's the first sense that's really activated as we get consciousness, even before we're born right? We hear the sounds of our mother's heartbeat and her voice. And even the last sense that goes at the end a lot of times, in this really intimate relationship, as you just said, that the sounds represent in terms of our history. We don't have to look any further than a favorite song that can instantly transport you, almost like a time machine to a particular place in time. Very, very cool. Now, it's really interesting that what you're doing now is taking advantage of new technology and just kind of a new angle to capture sound in a way that we haven't done before. I think you said you have sound listening devices oftentimes in a single location for a year. You're not only capturing sound, the right sound is changes in air pressure, so that you're getting changes in air pressure, you're getting vibration, which is kind of a whole different level of data. And then to be able to collect that for a whole year and then start to try to figure out a baseline which is pretty simple to understand, but you're talking about this chorus. I love your phrase, a chorus, because that sound is made up of a bunch of individual inputs. And now trying to kind of go under the covers to figure out what is that baseline actually composed of. And you talk about a bunch of really interesting particular animals and species that combine to create this chorus that now you know is a baseline. How did you use to do that before? I think it's funny one of your research papers, you reach out to the great bird followers and bird listeners, 'cause as you said, that's the easiest way or the most prolific way for people to identify birds. So please help us in a crowdsource way try to identify all the pieces that make this beautiful chorus, that is the soundscape for a particular area. >> Right, yeah, that's right. It really does take a team of scientists and engineers and even folks in the social sciences and the humanities to really begin to put all of these pieces together. Experts in many fields are extremely valuable. They've got great ears because that's the tools that they use to go out and identify birds or insects or amphibians. What we don't have are generalists that go out and can tell you what everything sounds like. And I'll tell you that will probably never ever happen. That's just way too much, we have millions of species that exist on this planet. And we just don't have a specific catalog of what everything sounds like, it's just not possible or doable. So I need to go out and discover and bring those discoveries back that help us to understand nature and understand how the earth is changing. I can't wait for us to eventually develop that catalog. So we're trying to develop techniques and tools and approaches that allow us to develop this electronic catalog. Like you're saying this chorus, and it doesn't necessarily have to be a species specific chorus, it can be a chorus of all these different kind of sounds that we think relate back to this kind of animal or that kind of animal based upon the animals instrument-- >> Right, great. >> And this is the sound. >> Now again, you know, keep it to the exascale theme, right? You're collecting a lot of data and you mentioned in one of the pieces I've dug up, that your longest study in a single location is 17 years. You've got over 4 million recordings. And I think you said over 230 years if you wanted to listen to them all back to back. I mean, this is a huge, a big data problem in terms of the massive amount of data that you have and need to run through an analysis. >> Yeah, that's right. We're collecting 48,000 data points per second. So that's 48 kilohertz. And then so you multiply everything and then you have a sense of how many data points you actually have to put them all together. When you're listening to a sound file over 10 minutes, you have hundreds of sounds that exist in them. Oftentimes you just don't know what they are, but you can more or less put some kind of measure on all of them and then begin to summarize them over space and time and try to understand it from a perspective of really science. >> Right, right. And then I just love to get your take as you progress down this kind of identification road, we're all very familiar with copyright infringement hits on YouTube or social media or whatever, when it picks up on some sound and the technology is actually really sophisticated to pick up some of those sound signatures. But to your point, it's a lot easier to compare against the known and to search for that known. Then when you've got this kind of undefined chorus that said we do know that there can be great analysis done that we've seen AI and ML applied, especially in the surveillance side on the video-- >> Right. >> With video that it can actually do a lot of computation and a lot of extracting signal from the noise, if you will. As you look down the road on the compute side for the algorithms that you guys are trying to build with the human input of people that know what you're listening to, what kind of opportunities do you see and where are we on that journey where you can get more leverage out of some of these technology tools? >> Well, I think what we're doing right now is developing the methodological needs, kind of describe what it is we need to move into that new space, which is going to require these computational, that computational infrastructure. So, for example, we have a study right now where we're trying to identify certain kinds of mosquitoes (chuckling) a vector-borne mosquitoes, and our estimates is that we need about maybe 900 to 1200 specific recordings per species to be able to put it into something like a convolutional neural network to be able to extract out the information, and look at the patterns and data, to be able to say indeed this is the species that we're interested in. So what we're going to need and in the future here is really a lot of information that allow us to kind of train these neural networks and help us identify what's in the sound files. As you can imagine the computational infrastructure needed to do that for data storage and CPU, GPU is going to be truly amazing. >> Right, right. So I want to get your take on another topic. And again the basis of your research is really all bound around the biodiversity crisis right? That's from the kind of-- >> Yeah. >> The thing that's started it and now you're using sound as a way to measure baseline and talk about loss of species, reduced abundancies and rampant expansion of invasive species as part of your report. But I'd love to get your take on cities. And how do you think cities fit the future? Clearly, it's an efficient way to get a lot of people together. There's a huge migration of people-- >> Right. >> To cities, but one of your themes in your Ted Talk is reconnecting with nature-- >> Yeah. >> Because we're in cities, but there's this paradox right? Because you don't want people living in nature can be a little bit disruptive. So is it better to kind of get them all in a tip of a peninsula in San Francisco or-- >> Yeah. >> But then do they lose that connection that's so important. >> Yeah. >> I just love to get your take on cities and the impacts that they're have on your core research. >> Yeah, I mean, it truly is a paradox as you just described it. We're living in a concrete jungle surrounded by not a lot of nature, really, honestly, occasional bird species that tend to be fairly limited, selected for limited environments. So many people just don't get out into the wild. But visiting national parks certainly is one of those kinds of experience that people oftentimes have. But I'll just say that it's getting out there and truly listening and feeling this emotional feeling, psychological feeling that wraps around you, it's a solitude. It's just you and nature and there's just no one around. >> Right. >> And that's when it really truly sinks in, that you're a part of this place, this marvelous place called earth. And so there are very few people that have had that experience. And so as I've gone to some of these places, I say to myself I need to bring this back. I need to tell the story, tell the story of the awe of nature, because it truly is an amazing place. Even if you just close your eyes and listen. >> Right, right. >> And it, the dawn chorus in the morning in every place tells me so much about that place. It tells me about all the animals that exist there. The nighttime tells me so much too. As a scientist that's spent most of his career kind of going out and working during the day, there's so much happening at night. Matter of fact-- >> Right. >> There's more sounds at night than there were during the day. So there is a need for us to experience nature and we don't do that. And we're not aware of these crises that are happening all over the planet. I do go to places and I listen, and I can tell you I'm listening for things that I think should be there. You can listen and you can hear the gaps, the gaps and that in that chorus, and you think what should be there-- >> Right. >> And then why isn't it there? And that's where I really want to be able to dig deep into my sound files and start to explore that more fully. >> It's great, it's great, I mean, I just love the whole concept of, and you identified it in the moment you're in the tent, the thunderstorm came by, it's really just kind of changing your lens. It's really twisting your lens, changing your focus, because that sound is there, right? It's been there all along, it's just, do you tune it in or do you tune it out? Do you pay attention? Do not pay attention is an active process or a passive process and like-- >> Right. >> I love that perspective. And I want to shift gears a little bit, 'cause another big environmental thing, and you mentioned it quite frequently is feeding the world's growing population and feeding it-- >> Yeah. >> In an efficient way. And anytime you see kind of factory farming applied to a lot of things you wonder is it sustainable, and then all the issues that come from kind of single output production whether that's pigs or coffee or whatever and the susceptibility to disease and this and that. So I wonder if you could share your thoughts on, based on your research, what needs to change to successfully and without too much destruction feed this ever increasing population? >> Yeah, I mean, that's one of the grand challenges. I mean, society is facing so many at the moment. In the next 20 years or so, 30 years, we're going to add another 2 billion people to the planet, and how do we feed all of them? How do we feed them well and equitably across the globe? I don't know how to do that. But I'll tell you that our crops and the ecosystem that supports the food production needs the animals and the trees and the microbes for the ecosystem to function. We have many of our crops that are pollinated by birds and insects and other animals, seeds need to be dispersed. And so we need the rest of life to exist and thrive for us to thrive too. It's not an either, it's not them or us, it has to be all of us together on this planet working together. We have to find solutions. And again, it's me going out to some of these places and bringing it back and saying, you have to listen, you have to listen to these places-- >> Right. >> They're truly a marvelous. >> So I know most of your listening devices are in remote areas and not necessarily in urban areas, but I'm curious, do you have any in urban areas? And if so, how has that signature changed since COVID? I just got to ask, (Bryan chuckling) because we went to this-- >> Yeah. >> Light switch moment in the middle of March, human activity slowed down-- >> Yeah. >> In a way that no one could have forecast ever on a single event, globally which is just fascinating. And you think of the amount of airplanes that were not flying and trains that we're not moving and people not moving. Did you have any any data or have you been able to collect data or see data as the impact of that? Not only directly in wherever the sensors are, but a kind of a second order impact because of the lack of pollution and the other kind of human activity that just went down. I mean, certainly a lot of memes (Bryan chuckling) on social media of all the animals-- >> Yeah. >> Come back into the city. But I'm just curious if you have any data in the observation? >> Yeah, we're part of actually a global study, there's couple of hundred of us that are contributing our data to what we call the Silent Cities project. It's being coordinated out of Europe right now. So we placed our sensors out in different areas, actually around West Lafayette area here in Indiana, near road crossings and that sort of thing to be able to kind of capture that information. We have had in this area here now, the 17 year study. So we do have studies that get into areas that tend to be fairly urban. So we do have a lot of information. I tell you, I don't need my sensors to tell me something that I already know and you suspect is true. Our cities were quiet, much quieter during the COVID situation. And it's continued to kind of get a little bit louder, as we've kind of released some of the policies that put us into our homes. And so yes, there is a major change. Now there have been a couple of studies that just come out that are pretty interesting. One, which was in San Francisco looking at the white-crowned sparrow. And they looked at historical data that went back something like 20 years. And they found that the birds in the cities were singing a much softer, 30% softer. >> Really? >> And they, yeah, and they would lower their frequencies. So the way sound works is that if you lower your frequencies that sound can travel farther. And so the males can now hear themselves twice as far just due to the fact that our cities are quieter. So it does have an impact on animals, truly it does. There was some studies back in 2001, during  the September, the 9/11 crisis as well, where people are going out and kind of looking at data, acoustic data, and discovering that things were much quieter. I'd be very interested to look at some of the data we have in our oceans, to what extent are oceans quieter. Our oceans sadly are the loudest part of this planet. It's really noisy, sound travels, five times farther. Generally the noise is lower frequencies, and we have lots of ships that are all over the planet and in our oceans. So I'd really be interested in those kinds of studies as well, to what extent is it impacting and helping our friends in the oceans. >> Right, right, well, I was just going to ask you that question because I think a lot of people clearly understand sound in the air that surrounds us, but you talk a lot about sound in ocean, and sound as an indicator of ocean health, and again, this concept of a chorus. And I think everybody's probably familiar with the sounds of the humpback whale right? He got very popular and we've all seen and heard that. But you're doing a lot of research, as you said, in oceans and in water. And I wonder if you can, again, kind of provide a little bit more color around that, because I don't think you people, maybe we're just not that tuned into it, think of the ocean or water as a rich sound environment especially to the degree as you're talking about where you can actually start to really understand what's going on. >> Yeah, I mean, some of us think that sound in the oceans is probably more important to animals than on land, on the terrestrial side. Sound helps animals to navigate through complex waterways and find food resources. You can only use site so far underwater especially when it gets to be kind of dark, once you get down to certain levels. So there many of us think that sound is probably going to be an important component to measuring the status of health in our oceans. >> It's great. Well, Bryan, I really enjoyed this conversation. I've really enjoyed your Ted Talk, and now I've got a bunch of research papers I want to dig into a little bit more as well. >> Okay.(chuckling) >> It's a fascinating topic, but I think the most important thing that you talked about extensively in your Ted Talk is really just taking a minute to take a step back from the individual perspective, appreciate what's around us, hear, that information and I think there's a real direct correlation to the power of exascale, to the power of hearing this data, processing this data, and putting intelligence on that data, understanding that data in a good way, in a positive way, in a delightful way, spiritual way, even that we couldn't do before, or we just weren't paying attention like with what you know is on your phone please-- >> Yeah, really. >> It's all around you. It's been there a whole time. >> Yeah. (both chuckling) >> Yeah, Jeff, I really encourage your viewers to count it, just go out and listen. As we say, go out and listen and join the mission. >> I love it, and you can get started by going to the Center for Global Soundscapes and you have a beautiful landscape. I had it going earlier this morning while I was digging through some of the research of Bryan. (Bryan chuckling) Thank you very much (Bryan murmurs) and really enjoyed the conversation best to you-- >> Okay. >> And your team and your continued success. >> Alright, thank you. >> Alright, thank you. All right, he's Bryan-- >> Goodbye. >> I'm Jeff, you're watching theCUBE. (Bryan chuckling) for continuing coverage of Exascale Day. Thanks for watching. We'll see you next time. (calm ambient music)

>>from around the globe. It's the Cube covering HP Discover Virtual experience brought to you by HP. >>Hi. And welcome back to the Cube's coverage of HP Discover 2020. The virtual experience. I'm stew Minimum and happy to welcome back to the program. One of our cube alumni, Partha Narasimha He is the chief technology officer of Aruba. Which, of course, Aruba is an H p e company. Partha. Thanks so much for joining us. Thank you. Alright, so HP Discover is a big event. But, you know, for the networking people, of course, Aruba has its own event atmosphere, which happened, you know, just ahead of Discover you gave a keynote there some news there that we'll talk about. But, you know, just, you know, we bring our audience up to speed a little bit about, you know, the role of the networking inside of HP with Aruba. >>And so you know, when everybody's primary focus is networking and security. Uh, we really have expanded in the past few years and scope of the problems that we work on, what we call the intelligent edge and we define the edge is where people are, where the action is And how do you think about the kinds of experiences that end users care about? In addition to just connecting security into their absence of data on the upside, but also about the experiences in the physical world? And then there are these stakeholders that care about in an efficiency, productivity and so on. So the intelligent edge is it includes networking and security, but it really focuses on people and businesses. Hooks. >>Yeah, that's great. You know, Often we talk. It's, you know, the business outcomes that matter and experiences. It's you know so much about people. The current global pandemic absolutely has put a real focus on people. Um, you know, from a networking standpoint, of course, everybody's working from home a lot more. Um, you know, VPN services need to be considered. And you know what? I'm curious the impact from your business and your customers as to what's happening. >>I think this is where you know the focus on on the intelligent edge on people's experiences and business outcomes. Uh, is actually it was being having now, with the presence of endemic, if you're defining and go back to the definition of the edge as where people are and where the action is in the last 2 to 3 months. A lot of that exists in people's hearts in all of our homes. That's where that's where I am right now. And that's where we all of being, Um, so what does that mean for where the edges? And so we kind of see at least three phases in here, where right now we're focused on business continuity, Which is how do you know enable employees to continue to stay productive and connect securely to be enterprise data? Perhaps, but also, when you know some of the subsides, how do you bring people back safely? Go into physical spaces? And that's what we call business recovery. And then, as I talk to customers, you know, there's there's a spectrum of opinions about what is going to stake. And, you know, if you call that the new normal Andi, when an image goes out of our in our lives, it doesn't look like they're all going back to everywhere in January of Apple Siri. And so what is the new normal? And how do you how does a painful that's that's really what we're focused. >>Yeah, it's so important right now. Parts you know, when you look at enterprise is rapidly adjusting to to ah, situations is not necessarily what we think of. Of course, in the last few months, we've had to move very fast to be able to enable the workforce. Uh, I would love to hear what you're hearing from CEOs that there and customers and, you know, how are you helping them react to things you know much faster than they might have. >>So a lot of the business part unity. Actually, the focus is on 19 right? Because you know, how do you deploy technologies are actually leverage the technologies that have already been deployed in order to allow employees to stay productive from their homes? And there's been a spike in demand from for work from home solutions. Believe it or not, Aruba had we have built a solution for the remote access point way back in 2005 or 2006 when there was a different endemic, a time as a business continuity solution. But given the intensity of how this pandemic is affected all of our lives there was a strike and demand for work from home solutions not just from a connectivity and security perspective, but also every employee is home eyes very different, right? Based on the speed of your Internet connection. How many other people are home? How many other devices are connecting to the home network and what else is happening in the three Netflix streams running in parallel? And in this case, in that kind of environment, how do we now provide some visibility for key help employees getting there? So while we built a remote access point as a security solution, we ended up realizing that would be really solved. For the end user was a better user experience where they just see the same network that they see in the office. They see that home but more again, helping I t get some visibility and maybe troubleshoot some issues employees might have. So all three of these have been integral components of that solution and, you know, it became even more front and center well, when the wind and make it in terms of the site. >>Yeah, you know, when I think about security, has really, you know, in the last five years or so escalated only to the C suite. But the board level for constant consideration, has Has the current situation really raise the visibility of networking, you know, to the C suite. >>It has essentially, you know, the focus Until now. Could be in that. Okay, I my all of my employees get into the office and also and create an environment within the office building that allows for collaboration that allows for seamless connectivity and security. Um, but the pace at which we all have to go to this work from home situation, what's the time? And I was so shocked. I have to respond quickly on day after this phone, quickly where, you know, they could have done with a few dozen office buildings to now thousands of employees homes, and so >>be bigger. >>The all of the effort that we put in to create that solution earlier now pick off because we were already but this for for this situation. Even though, you know, we all live in interesting times, and I never want to see this again in my lifetime. But the fact that you know we had a focus on it for the last 15 years or so made it ready for us. But more importantly, as we look at, you know, as the business community face was about I p the business recovery, which is which is probably very a starting point right now. How do we now bring people back safely into lying to physical spaces? Now the stakeholders are you know, that set is expanding, right? Whether it's, you know, maybe we have the steam called the crisis management that is looking at Okay, how do I now manage Not just the crisis, but bringing people back in facilities is important, because if things space has to get rearranged in order to make certain density or spacing objectives, so they know they have some interest in there marketing. If you're a retailer, you know, hospitality and so on, they get interested in it. So there's a lot of other stakeholders now the lie on the infrastructure that I be has deployed primarily for security and security. Now that same infrastructure, it's gonna go benefit other stakeholders so that you get a competitive advantage in the business recovery face. Like if I'm able to safely brain a lot of my key employees that are required to be in physical spaces back in while addressing all of their concerns about the health and the safety associated with the recovery. That definitely gives me a competitive. And I believe that the solutions and Aruba has provided to I t. Until now are now. There's a There's a spark like Chinese on it because a lot of people, a lot of other stakeholders, could benefit from that infrastructure. That is already, >>Yeah, there's a lot of conversation going on in the industry about what things look like post endemic. And, you know, while there is still obviously a lot of uncertainties, we really think there will be some hybrid modes going on. So, you know, work from home might not stay permanent. But many companies, we're talking about being more flexible. So how does that impact? You know what you're offering? Cause, you know, I think about, you know, from the enterprise. You know, I needed a certain density. Now I need to think about Okay. How do I make sure that whether you're in the office or working remotely that I can have you participate and have the same kind of experience wherever you >>are? And, you know, this is again this is where we rely on the network infrastructure, right? Because if you dig ah, connected with the network that enables mobility is secure and is always available, it drives participation. That participation leverages net flow data to provide visibility toe into physical space. Right, And you think about even the recovery face. And we see, actually, three achieves three interesting scenarios and organizations with customers on how the network can help them in the recovery face. And it points to dispensing requirements are how do I reduce density so that I get some level of increased distancing amongst my employees. So that way you can look at naked data and figure out okay where the hearts thoughts are in terms of people density and can go make changes to those to try and lower that meet my internal guidelines or public safety guidelines to shared spaces are also medium of transmission, you know, off this particular virus or disease, and so shakes faces like conference route cafeteria tables and others that we can again use the network to figure out usage of those and potentially provide guidelines to cleaning crews to pay more attention to certain spaces in favor of others necessarily seeing the same level of usage on three. If in the unfortunate situation that some individual becomes a person of interest, we can quickly figure out all of the spaces that they have seen in the past. A certain window, including who else they could have overlap are being close to within that space, right? So at least you're not relying purely on human memory for contact tracing, there's a certain level of additional data that can be used to enhance a refresh human memory. That is really what we see happening in the business recovering. But you made a good point on what is the new normal. Because as we again after customers, I'm trying to gauge what is going to stick beyond the beyond the recovery feet, striking that you fast forward, let's say, a year from now we have a vaccine and the viruses control. We are going to go back to everywhere before the widest entered our lives in and on. The common opinion seems to be that some things are here to stay, and you look at work from home. You made a reference to that. You know, a lot of our customers do believe that there is gonna be an increased amount of work from home that stays with us, even even after the biases off all of our lives. That again, the special things we built for the business continuity continues forward. Even as you know, some of you know we start to get back into physical spaces security again. It's paramount rate the home, essentially as far as I t is concern is an uncontrolled in mind because they just don't have control over many things that happen in brackets homes. And so how do we bring in a layer of visibility and some degree of control in an environment that is inherently not subject to that level through the same way that that an office building can be? And those are the kinds of things that we're looking at. When we talk to higher education customers, for example, they are looking at plans for them. You know this upcoming fall semester, or for the next I can make your off running the classrooms at 30% occupancy. So if you had 100 students sign up for a class physically in the classroom, they only want to have the respondents and the other 70 could be on campus, but they're all dialed in remotely online past. But how do you manage this process or which study people get to be in the classroom learning way? And we believe that a lot of these work, the work flows and interesting use cases that directly address the intelligent edge are gonna become important as we get into that. >>Alright. And I'm glad you talked about the intelligent edge. So your keynote that you gave that atmosphere was accelerating innovation at the edge. And you have the tough task of being right before the Space X Speaker two. So give us a little bit our audience a little bit about you know, the innovation. How should we be thinking about the edge? >>So atmosphere visitor Two weeks ago, we announced the manage services platform on the SP for sharp and it's a little bit of a play because we really believe that we're building solutions that have 1/6 sense in in sensing what end users looking for what stakeholders looking for when problems show up and how do we quickly resolve that right, So that degree of focus on our data driven AI operations was key in us starting to coin the term DSP on it services platform. So it really looks at addressing not just connectivity connect and protect. We're also analyze and act because the telemetry data coming out of the network is really the same data that is that helps with the business recovery. But we won't actually bubble that up and put it into a common daily that helps us deliver a better connection and connectivity and security services, but also enable all of these experiences and outcomes at the edge. And so the CSP or then Ed services platform was the key announcement atmosphere this year. And we see that as the the foundation on which everything that we're gonna do starting now is going to get >>Yeah, I'm curious. You know, we talked about some of the the things that have been accelerated due to the current situation. After that respond work from home in the and the like. When you look at edge environment, is that something that you know? Is that something you see people, you know, accelerating them or they pausing them. Is it just kind of happening at the same pace any data or sense that you have from users right now. >>So edge is going to become even more important that we used to focus on the edge. We had the focus on edge for a while, but uncle, now until the pandemic it us. The assumption is always that people are going to show up in physical spaces and then let's focus on the experiences of the hour. I believe with the pandemic coming in, some of the power of choice has shifted towards the towards the user of the person in choosing whether they want to consume a particular service experience by going to a physical space or by staying at home and doing it on the radio. I look at the past few weeks, we've had a few. Both parties were used to me in person, and now we are doing it over a zoom or other video conferencing policies. So that choice moving over to the person means that we can't just assume that people are going to show up in physical spaces and then focus only on okay once they show up, what can I do about the experience of the outcomes? The focus on edge is now shifting to where we have even before, we have even look at enabling the technologies and experiences that entice people that convince people come into the physical space and consume that service on. But our experience and that means that the scope of what we do at the intelligent edge actually is gonna increase is gonna whiten. And that's the reason why it was timely that we were working on the ed Services platform. Even, you know, it started working on it long before the panoramic ever showed up. But that focus is now putting us in the right place. You know, from a competitive perspective, leverage all of the technologies that people so far, the package it up together to offer our customers something that is far beyond just connectivity and security. >>Great final question I have for you. You're talking about these, you know, not necessarily in person. Experience is here. We have, you know, did the Discover virtual experience give our audience just a little bit as to what they expect from Ruba and what you want people to take away from the Discover virtual experience when it comes to Aruba and networking at hp, >>right? And you know, the common way we kind of when we talked to some customers is always an association off of the Aruba brand todo a wireless lan. And you know what time in the past five years is being part of Hewlett Packard Enterprise? We've kind of expanded that into other networking and some some of the security functions that we also. But more importantly, I encourage everybody to go look at some of the technologies that we packaged together as orderly it services platform and how they can help. Our customers are not just like we are also all of the other stakeholders within their organizations, but to create that compelling experiences and outcomes at the edge. >>Excellent. Well, thank you so much for joining us. Appreciate all the updates. >>Thanks. Thanks for having me. >>Alright. Stay tuned for more coverage. HP discover virtual experience. I'm Stew Minimum. And thank you for watching the Cube. >>Yeah, yeah, yeah.

live from Barcelona Spain it's the cube covering Cisco live 2020 rot to you by Cisco and its ecosystem partners welcome back over 17,000 in attendance here for Cisco live 2020 in Barcelona ops 2 min and my co-host is Dave Volante and to help us to dig in to of course one of the most important topic of the day of course that security we're thrilled to have back a distinguished engineer Francisco one of our cube alumni TK kia nini TK thanks so much for joining us ideal man good good all right so TK it's 2020 it's a new decade we know the bad actors are still out there they're there the the question always is you know it used to be you know how do you keep ahead of them then I've here Dave say many times well you know it's not you know when it's it's not if it's when you know you probably already have been ok you know compromise before so it gives latest so you know what you're seeing out there what you're talking to customers about in this important space yeah it's it's kind of an innovation spiral you know we we innovate we make it harder for them and then they innovate they make it harder for us right and round and round we go that's been going on for for many years I think I think the most significant changes that have happened recently have to deal with not essentially their objectives but how they go about their objectives and Defenders topologies have changed greatly instead of just your standard Enterprise you now have you know hybrid multi cloud and all these new technologies so while while all that innovation happens you know they get a little clever and they find weaknesses and round and round we go so we talked a lot about the sort of changing profile of the the threat actors gone from hacktivists to criminals now is a huge business and nation-states even what's that profile looked like today and how has that changed over the last decade or so you know that's pretty much stayed the same you know bad guys are bad guys at some point in time you know just how how they go about their business their techniques they're having to like I said innovate around you know we make it harder for them they you know on Monday we're safe on Tuesday we're not you know and then on Wednesday it switches again so so it talked about kind of this multi-cloud environment when we talked to customers it's like well I want the developer to be able to build their application and not really have to think too much underneath it that that has to have some unique challenges we know security we knew long ago well I just go to the cloud it doesn't mean they take care of it some things are there some things they're gonna remind you now you need to make sure you set certain things otherwise you could be there but how do we make sure that Security's baked in everywhere and is up as a practice that everybody's doing well I mean again some of the practices hold true no matter what the environment I think the big thing was cognitive is in back in the day when when you looked at an old legacy data center you were apart sort of administrator in your part detective most people don't even know what's running on there that's not true in cloud native environments some some llamó file some some declaration it's it's just exactly what production should look like right and then the machines instantiate production so you're doing things that machine scale forces the human scale people to be explicit and and for me I mean that's that's a breath of fresh air because once you're explicit then you take the mystery out of what you're protecting how about in terms of how you detect threats right phishing for credentials has become a huge deal but not just you know kicking down the door or smashing a window yeah using your your own credentials to get inside of your network so how is that affected the way in which you detect yeah it's it's a big deal you know a lot of a lot of technology has a dual use and what I mean by that is network cryptology you know that that whole crypto on the network has made us safer for us to compute over unsecure networks and unfortunately it works just as well for the bad guys so you know all of their malicious activity is now private to so it you know for us we just have to invent new ways of detecting direct inspection for instance I think is a thing of the past I mean we just can't depend on it anymore we have to have tools of inference and not only that but it's it's gave rise in a lot of innovation on behavioral science and as you say you know it's it's not that the attacker is breaking into your network anymore they're logging in okay what do you do then right Alice Alice's account it's not gonna set off the triggers so you have to say you know when did Alice start to behave differently you know she's working in accounting why is she playing around with the source code repository that's that's a different thing right yeah automation is such a big trend you know how do we make sure that automation doesn't leave us more vulnerable that's rarity because we need to be able automate we've gone beyond human scale for most of these configurations that's exactly right and and how do how do we I always say just with security automation in particular just because you can automate something doesn't mean you should and and you really have to go back and have practices you know you could argue that that this thing is just a you know machine scale automation you could do math on a legal pad or you can use a computer to do it right what so apply that to production if you mechanized something like order entry or whatever you're you're you're automating part of your business use threat modeling you use the standard threat modeling like you would your code the network is code now right and storage is code and everything is code so you know just automate your testing do your threat modeling do all that stuff please do not automate for your attacker matrix is here I want to go back to the Alice problem because you're talking about before you have to use inference so Alice's is in the network and you're observing her moves every day and then okay something anomalous occurs maybe she's doing something that normally she wouldn't do so you've got to have her profile in her actions sort of observed documented stored the data has got to be there and at the same time you want to make sure it's always that balance of putting handcuffs on people and you know versus allowing them to you know do their job and be productive at the same time as well you don't want to let the bad guys know that you know that alice is doing something that she didn't be doing is actually not Alice so all that complexity how are you dealing with it and what's the data model look like doing it machines help let's say that machines can help us you know you and I we have only so many sense organs and the cognitive brain can only store so many so much state machines really help us extend that and so you know looking at not three dimensions of change but 7000 dimensions have changed right something in the machine is going to say there's an outlier here that's interesting and you can get another machine to say that's that's interesting maybe I should focus on that you build these analytical pipelines so that at the end of it you know they may argue with each other all the way to the end but at the end you have a very high fidelity indicator that might be at the protocol level it might be at the behavioral level it might be seven days back or thirty days back all these temporal and spatial dimensions it's really cheap to do it with a machine yeah and if we could stay on that for a second so it I've tried to understand I know that's a high level example but is it best practice to have the Machine take action or is it is it an augmentation and I know it depends on the use case but but how is that sort of playing out again you have to do all of this safely okay a lot of things that machines do don't return back to human scale stuff that returns back to human scale that humans understand that is as useful so for instance if machines you know find out all these types of in assertions even in medical you know right now if if you've got so much telemetry going into the medical field say the machine tells you you have three weeks to live I mean you better explain what the heck you know how you came about that assertion it's the same with security you know if I'm gonna say look we're gonna quarantine your machine or we're gonna reimage this machine it's not I'm not like picking movies for you or the next song you might listen to this is high stakes and so when you do things like that your analytics needs to have what it's called entailment you have to explain what it is how you got to that assertion that's become incredibly important in how we measure our effectiveness in in doing analytics that's interesting because because you're using a lot of machine intelligence to do this and a lot of AI is blackbox you're saying you cannot endure that blackbox problem in security yeah that black box is is is very dangerous you know I you know personally I feel that you know things that should be open sourced this type of technology it's so advanced that the developer needs to understand that the tester needs to understand that certainly the customer needs to understand it you need to publish papers and be very very transparent with this domain because if it is in fact you know black box and it's given the authority to automate something like you know shut down the power or do things like that that's when things really start to get dangerous so good TK what wonder you know give us the latest on stealthWatch there you know Cisco's positioning when it when it comes to everything we've been talking about here you know stealthWatch again is it's been in market for quite some time it's actually been in market since 2001 and when I when I look back and see how much has changed you know how we've had to keep up with the market and again it's not just the algorithms rewrite for detection it's the environments have changed right but when did when did multi-cloud happen so so operating again cusp it's not that stealthWatch wants to go their customers are going there and they want the stealthWatch function across their digital business and so you know we've had to make advancements on the changing topology we've had to make advancements because of things like dark data you know the the network's opaque now right we have to have a lot of inference so we've just you know kept up and stayed ahead of it you know we've been spending a lot of time talking to developer communities and there's a lot of open-source tooling out there that that's helping enable developers specifically in security space you were talking about open-source earlier how does what you've been doing the stealthWatch intersect with that yeah that's always interesting too because there's been sort of a shift in in let's call them the cool kids right the cool kids um they want everything is code right so it's not about what's on glass or you know a single pane of glass anymore it's it's what stealth watches code right what's your router as code look at definitely definite is basically Cisco as code and it's beautiful because that is infrastructure as code I mean that is the future and so all the products not just stealthWatch have beautiful api's and that's that's really exciting I've been saying for a while now it's do you I think you agree is that that is a big differentiator for Cisco I think you you're one of the few if not the only large established player and the enterprise that has figured out that sort of infrastructure is code play others have tried and are sort of getting there but start-stop you use a term that really cool is like living off the land you know bear bear grylls like the guy who lives off the land so and and and threat actors are doing that now they're using your own installed software and tooling to hack you and steal from you how were you dealing with that problem yeah it's a tough one and like I said you know much respect the the adversary is talented and they're patient they're well funded okay that's that's where it starts and so you know why why bring why bring an interpreter to a host when there's already one there right why right all this complicated software distribution when I can just use yours and so that's that's where the play the game starts and and the most advanced threats aren't leaving footprints because the footprints are already there you know they'll get on a machine and behaviorally they'll check the cache to see what's hot and what's hot in the cache means that behaviorally it's a path they can go they're not cutting a new trail most of the time right so living off the land is not only the tools that they're using the automation your automation they're using against you but it's also behavioral and so that that makes it you know it makes it harder it's it impossible no can we make it harder for them yes so yeah I'm having fun and I've been doing this for over twenty five years every week it's something new well it's a hard problem you're attacking and you know Robert Herjavec who came of the cube sort of opened my eyes and you think about what are we securing we're securing everything I mean a critical infrastructure were essentially assured securing the entire global economy and he said something that really struck me since there's an 86 trillion dollar economy we spend point zero one four percent on securing that economy and that's nothing now of course he's an entrepreneur and he's pimping for his his business but it's true we are barely scratching the surface of this problem yeah and it's changing I mean it's changing it could it be better yes it is changing his board awareness well you know 20 years ago they then write me to a dinner party they you know what does your husband do I'd say you know cyber security or something they'd roll their eyes and change the subject now they asked me the same question so oh you know my computer's running really slow right these are not this is everyone I'm worried about a life hack yeah how do I protect myself or what about these company us the bank I mean that's guys a dinner table company every party so now now you know I just make something up I don't do cybersecurity I just I you know a tort or a jipner's you've been in this business forever I can't remember have I ever asked you the superhero question what is that your favorite superhero that's a tough one there's all the security guys I know they like always dreamed about saving the world [Laughter] you're my superhero man I love what you do I think you're a great asset for Cisco and Cisco's customers really Thanks TK give us a final word if people want to you know find out more about about what Cisco's doing read more what you're working on what's some of the best resources I have to go to do you know just drop by the web pages I mean everything's published out that like I said even even for the super nerdy you know we publish all our our lars security analytics papers I think we're over 50 papers published in the last 12 years TK thank you so much always a pleasure to catch it all right yeah I think they've traveled thank you so much for de Villante um Stu Mittleman John Fourier is also in the house we will be back with lots more coverage here from Cisco live 20/20 in Barcelona thanks for watching the keys

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum, World 2019 brought to you by VM Wear and its ecosystem partners. >> Welcome back. We're here! Mosconi North for VM World 2019 10th Year of the Cube covering VM World. I'm stupid and my co host is John Troyer. And welcome to the program to guest from Del Technologies. Sitting to my right is Tender, my Mondal, who's the director of storage solutions and sitting to his right is David when the senior director of server, product planning and management also with Dell. Gentlemen, thanks so much for joining us. All right, so we've got server and storage and talk about something that we've been talking about for a while on the server side been delivered for a bit and on the storage side is now rolling out. So everybody's favorite topic. Nonviolent till memory express or envy me as it rolls off the tongue storage class memory, or SCM and lots of other things, you know, down there, really helping a big, transformational wave that, you know, we really changes how our applications interact with the infrastructure channel, you know, bring us up to date on the latest. >> Sure on, let's start where you ended. We're seeing explosion off applications, right? And in fact, in mornings, keynote. Bad girl singer had a stocky speaks. There are 352 million enterprise applications today. On it will be 792 million in three years. Now, as the applications are growing exponentially, we cannot keep growing the infrastructure at that rate, So N v m e is the way we can consolidate it. Ah, lot off the infrastructure. If we can think about in tow and envy, Emmy starting from the server in fear me off our fabric through the stories area down, toe the back end with envy Emmy necessities. This actually can put together a great platform where you can consulate it. Ah, lot off the applications and delivering the high performance low latency that will need while meeting video surfaced level objectives so we can go over a little bit off the details, but I think it all starts from envy me over fabric coming from the server to the story, Ari. So probably like that's the fourth step we need to consider >> David. Do You know, I love this discussion when we get to talk at the application later because, you know, Flash changed the market a lot. You know, it's like, you know, much better energy, and it's much faster, Anything. But you know, this inflection point that we're talking about for application modernization, you know, envy me is one of those enablers there and something they know your team's been working on >> for a while. Yeah, actually, on the power each side we've been, You know, we've been embracing the benefits of enemy for quite some so many years now, right? We start out by introducing enemy in our 12 generations servers, you know, frontloaded hot, serviceable drives. And then, of course, we branch out from there on in today, you know, Ah, a lot of the servers from a Polish family all support enemy devices. So the benefit there is really giving customer choices in terms of what kind of storage kind of cheering they wanted, you know, for the applications needs. Right now, one of things that's great about, you know, enemy over fabric is it's more than just a flash storage itself. It's about enabling the standards, you know, across the host across the data fire Break down to the storage really to deliver on the overall performance that you know the applications of needs and buy, you know, improving I ops and lower late, Easy overall, from a server perspective, this just means that we're releasing more CPU cycles back into the application so that they can run different types of workloads. And for us, this is this is a great story from power. Just was from Power Macs and coming together to enable this Emmy, Emmy or fabric. >> You know, I'm I'm I'm kind of slow about some of these things, but if you kind of squint at the history and, you know, we went from the PC revolution and then we had, you know, we had Sands and raise right and we had we had centralized toward shared storage last couple of years, a lot of interest and stale right hyper converged. And you had a You had a lot of pizza boxes with the storage right there. It's I mean, I now think right and I'm following the threat, I think which is now that where we now can have ah, Iraq with again a fabric and and again, now we can We can focus on our envy me storage over our envy me over fabric driven, solid state storage somewhere below my servers that are that are doing handling compute somewhere else. Is that that the future we're headed towards now >> Yes. I mean, everything has its place. But to give you the perspective, right? It's not just, I mean coming down to the storage area, but how This is enough bling, the future storage as well. And the storage class memory is the perfect example. And as Defeat said, let's take power, Max, as an example, right. Eso in power Max, you can It is like entrant, envy me ready like you get envy emi over Fabrica de front end But then we have n v m E s s trees in the back end. The thing is now it is also the N v m e is enabling technologies like stories class memory which is bringing in very high performance, very less latency Latency is going down in the order off like tents off microseconds. Now this is as close as you can get. Tow the like Dedham with persistent story. However, you need a balance. This is like order of magnitude are costlier. Now you got bar Max. What we're doing in terms of first, it's envy me. Done right? What do you mean by that? You have, like, Marty controller architectures that can actually do this level of parallel processing and our concurrency. And then we have bought, like, ECM for storage, class, memory and envy, Emmy essences. And we're doing intelligent tearing best on the built in mission learning engine that we have. And it is looking at 40 million data sets. Really time to decide. Like which sort of walk lords should go on this same drives which should go on and the M. E s estates. And on top of it, you add quality of service. So this platform gives you are service level objectives. You can choose from diamond, platinum, gold, silver or bronze, and you can consulate it. Ah, lot off those 352 million different types of applications on this area guaranteeing you are going to meet all off your SL s, no matter what type of applications they were consolidated into. >> Okay, I'm wonder if you could boast. You know bring us into what this means for VM wear customers and break it into two pieces. One is kind of a traditional virtualized shop. And secondly, you know, spend a lot of time in the keynote this morning talking about the cloud native containerized, you know, type of environment. Will there be any difference from from both of your world? >> Yeah, absolutely. I'm glad you brought that up because, you know, from from our perspective, right, what we've seen with the enablement of enemy platforms. You know, John, you brought up a very interesting point, right? It seems like you know, past couple years, we went from moving storage onto the host and now would envy me with fabric. We're actually taking the storage away from the host again. Right? And that's exactly true, because, you know, the first, the first statement you brought up stew. It's about how flash enabled different applications to run better on the host. What? We see that still right? And so what enemy? You know, we see the lower response time enabling our customers Thio run more jobs and more v ems per server. That's one aspect of it. You know, we've seen his benefit a lot of our platform today or using various different applications and solutions, and you talk about the ex rail that's a visa and story for Del. You Talk about Visa and ready notes for customers who want to build it themselves. Right platforms enabled would envy me back playing enemies. Storage allows them to use enemy or SAS sata whatever they want. But the point is, here is that when they're using every me flash, for instance, and I'll talk a little bit about the power climaxed with this all flash, uh, me back plane in a case in the study that we did with V San application running, oh ltp type of workload, we saw the response time with every me over traditional SAS, you know, from our competitors improved by 56% right, which means that from that same particular solution build out, we were able to add 44% more of'em on the platform. Now, at the same time, we increase the overall orders per minute by roughly over 600,000. Oh, pm's for that type of, uh, benchmark over our nearest competitors so that right there is the benefit that we see from my virtual eyes from, Ah, being where perspective >> on. I'll add from the storage perspective in two ways. In fact, in last vehement in a MIA, we demonstrated in tow and envy, EMI over five break up with special build off this fear supporting Envy me over fabric and stories. Class memory with envy Me drives what it gives you a regular like this fear best environment is that you have the ability to move your PM's around like the applications where the highest performance and Latin's is critical. It will be on those special service levels and special like de testers. In fact, that demonstration was like ECM did a store, and in P m E Sense media does so in the same fabric with in Bar Mexican moved things around, whether it's like regular Fibre Channel or CNN and then the other part. I want to add in the morning like we saw the announcement that now communities is built in or will be built in with the years Excite platform, right and you're sexy is bread and butter off all the storage customers that we have now with like when you consider those, uh, those things built in under this fear black from Think about, like how many applications? How many actualized workloads you can run, where that it's on premise or humor. Cloud on AWS. All of those consolidation, as well as like the performance needs while reducing your footprint does the benefit of the V M R R shops. But the PM admits are going to see from the storage site >> again. I'm not following the parts, but what kind of we're not talking about a couple of megabytes here anymore, Right? What size of parts are shipping these days? So >> So, from our perspective, up to 77 gigabyte actually start. Seven terabytes drives are available on the markets today for Envy Me Now, whether customer by those drives, you know, it depends on economic factor. But yeah, it's something that's in this available from Dell >> so on. I'll act to what David said so far in CM drives 750 gig to 1.5. Articulate a C M drives on Dwell ported often drives that will be available in the power Max Acela's 15 terabyte envy EMI assistants. So this is the capacity we're talking about. And again the Latin's is at the application level, like from the storage like you're going to see, like, less than 300 microsecond. That's the power we are bringing in with this technology to the market. >> Give >> us a >> little look forward we talked about, you know, envy me has been shipping for a bit on the servers now, really rolling out on the storage side, I saw there's a lot of started from the space. You know, one recent acquisition got guts and people talking. What? What should we be looking for from both of you over kind of the next 6 to 12 months. >> So over next to a next 6 to 12 months, he will see a lot of innovation in this case from the storage site where wth e order of magnitude. I mean, the one single Ari, I mean, today it supports, say, like, 10 million I offs less than 500 microsecond latency. Ah, I cannot give you the exact details, but within like, a short time, these numbers are going to go up by more than, like, 50%. Latency is goingto get reduced. The troop would will be driving will actually like more than double s o. You see, like a lot of these innovations and kind of like evolution in terms off the drive capacities both from the CME, drives perspective. Envy me, assess these. Those will continue to expand, leading to foster performance. Better consolidation, Uh, for all the workloads. >> Yeah, from our perspective, I mean, you know, data growth is gonna continue. We all know that, And for us, it's like designing systems based on what the customers need, what the applications needs, right. And that's why we have different types of storage available today. So for us, you know, while we're doing a lot of things from a direct attached storage perspective, customers continue to have a need for share storage. EMI over fabric just provides a better know intense story for us, really from a Power edge and Power Macs perspective. But in the future, you asked what we're going to do. Well, we see the need to probably decouple stories, class memory from the host again. And really, what's preventing us from doing today? It's really having the right fabric in place to be able to deliver to that performance level that applications needs. MM evil fabrics, fibre Channel Ethernet ice, scuzzy or I'm sorry, Infinite Band, whatever. These are some of the things that you know we're looking forward to in the future to make that that lead. All >> right, well, it's really been great to see technology that I know the people that build your products have been excited about for many years. But rolling out into the real world deployment for customers that will transform what they're doing. So for John Troyer, I'm still Minuteman back with lots more coverage here from Be enrolled 2019. Thanks for watching the Cube.