>>Live from San Diego, California at the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of CubeCon cloud native con 2019 in San Diego, 12,000 in attendance. I'm zoomin and my co host is John Troyer and welcome to the program, the co founder and CTO of Lacework. Vikrum. Kapore's yeah. Thank you so much for joining us that to be here. So we had your CEO on at the first cloud security show, uh, earlier this year. A security definitely, you know, it's a board level discussion from center. I can never pass up the opportunity when I have a founder on the program. Just step us back for a second kind of book. The why of Lacework. Yeah, yeah. So I think if you look at the cloud ecosystem and communities now with containers, it's very clear that it requires like a new kind of way to look at security. Like all the traditional security tools for the data center were really built for like, you know, based on network. >>And then since they can know and as you move to the cloud, you know it's very hard to take 100 bucks to the cloud. You know, even with the virtual, you know boxes, it's really not that clean and good architecture. So what we found was that, you know, you really need a new way to think about it and me think about it as really a big data problem that you collect a lot of data, you process it, you analyze it, you get people to come with compliance and governance and breach protection automatically without having them light necessarily a lot of rules. Yeah. There's a term that this show cloud native and the maturity I've heard this year is some people say when I do cloud data, that means I like bake it into Kubernetes and that means you know, I can take my database across all the environments, I can take them there. >>Does that line up with how we should think about cloud security or is it more a little bit different than that? It's a little bit different than that. And the reason being that if you do all that, then what cloud native typically would also bring with itself would be things like your VMs and containers are not long than English short learning. And like in my world, in the old world, like I've been developing for 20 years, I knew the IP address on my airways and it didn't change and I knew the port number. But now if you ask me on cloud native environments, where is my database? Like I don't know there a five instances that ain't gonna hit their head in there. So there's a lot of elasticity, dynamic stuff that comes along with a network layer is not relevant at all to like what the applications are doing. >>So you need to get into the application layer and therefore particularly becomes a little bit different in that environment. So it's kind of, you know, the fact that I can run like thousand containers for no GS in like an instance which allows me to do that also means that, you know, I have no idea where they're running and what the IPS are. And I don't know, security on IP, I do it on, no Jess, like that's really what it is. So with Lacework though, you're, you're really monitoring this a, it's a platform. It's watching in real time. All this data is coming in. So it's both analyzing the history and it's got the stuff coming in. So you have a multiple layers. I mean we're here, uh, we're here at CubeCon. Coobernetti's is kind of the engine of what's going on, but there are other layers going on here. >>There's, yeah, there's all the application code and the pods. There's a, there's a cloud underneath and you all support, you know, different public clouds and on parameter and things like that. Yeah. Can you talk a little bit about maybe what's con some of the patterns of things you are dealing with, with all those different layers and those environments? >> Yeah, so I think it's actually a very relevant question. Like if you're going to think about like, you know, Coobernetti's you know, and as you said, like nothing really guns in isolation, right? Governance has to use containers. At some level. It has to run in either, even if it's managed, it's nothing in some VM somewhere. And the VM is basically the cloud native on VMware or it's hosted on some AWS cloud account and the cloud account probably has an API access to you to be able to set these things up or unset them if an attacker gets access to that. >>So we kind of think of security as comprehensively doing across the board. Like starting from like you know, build environments to run environments where before a developer does a build, you want to do one everyday analysis and make sure you're not building something with known problems in there. So you fix them as you go. Once you deploy them you need to look at like cloud configuration and you know, buckets on Autobahn or security groups are not, you know, incorrect. And then beyond that you actually really need a breach detection system, which kind of tells you when something does go wrong. And that can't be just inside Kubernetes or just containers. You kind of have to go look at every layer because you know, I've seen it personally, like, you know, as an, you know, having to look at some of the attacks, like when an attacker gets into one layer, he'll move into any layer he wants. Like there is really no way to say, I'll isolate him in this day only. So you have to going to protect everything and you're to Derbyshire Christian across the board. Yeah, I remember >>felt like it was a couple of years ago there was a security issue inside a Coobernetti's community freaked out a little bit, but you know, ended up moving past that. What are really kind of those security risks inside where does, where does Lacework fit fit into that discussion? >>Yeah, so I think it's really around like, you know, thinking like, you know, not companies as an isolated platform but actually part of the tech stack and ecosystem and looking at holistic lacrosse. It so fundamentally some of the security concepts haven't changed. You need to make sure you don't leave those open. Right. So if I have a door open on my uh, you know, API level, well it doesn't really matter if I close it on coronaries it's going to get exploded. Whoever is also comes with its own API SOA so that you have to monitor that. Also it has its own pod and it has its own port policies. So we're going to have to figure that too. So fundamentally I think at some level it boils down to making sure you kind of work with our tech security and dev ops. You need to work together to make sure that before the deploy it, it's kind of architected the right way. >>It has the correct VPCs and the port policies and the product texture and at the same time at run time, make sure you're monitoring it so that if something happens, you know about it early versus like six months later when the data is leaving your data center and then somebody tells you it's leaving it like it's too late at that point with your customers, then you're still seeing a role for the security team in the enterprise as well. The dev ops team better not be a better be coordinated with a platform like Lacework. Can you maybe talk a little bit about the enterprise situation and I'm guessing versus a startup? There's a lot more, there's a few other requirements that are coming up. >> We see that a lot across our customers. Like fundamentally DevOps and security really have to be on the same page because at the end of the day, like you know, the way the cloud happened in the has happened, it's a very API centric world. >>Like everything I do on AWS or GCP or Azure or is to an API. So it's a developer kind of centric world. And then if I have to set up a VPC, I have to work with the dev ops for Saturday and if I have to set up security groups, I have to work for dev ops, etc. So fundamentally, if they're not on the same page, you end up in like, you know, having problems. So the way we help in that environment is that we are able to get security on the DevOps team on the same page where they know security can understand what applications they can look at the behavior, they can understand, you know, what the architecture is and when they go tell dev ops to kind of, you know, there is something going on, can you help me? They can have a shared vocabulary and a language and they can talk about like things like on this part I saw access to, or you know, this website or DNS name, not that somebody in our data center went to the IP and like okay, but what does that mean the container is gone and the part's gone. >>Like what do I do with it? So I think we see that and I see, I feel longterm is really a collaboration where security brings to the table a lot of the knowhow and how to secure something. But at the same time, an actual implementation of it probably belongs in DevOps where like if you want to enforce something, you probably have to work with Kubernetes and Kubernetes API has to actually enforce it. So it kind of goes both ways. >> All right Vikram, talk to us about scale. We've talked to everything from broad scale to small scale in this environment. Give us the security aspect of that. So scale has been one of my favorite topics in the last 20 years. I've worked on this for systems and big data like at Oracle for a long time. And fundamentally what happens is that when you, when you do something on 10 PMs, you know, and you look at some alert, it's actually you know, one problem. >>But when you scale that up to like 10,000 VMs or you know, 10,000 containers and lots of users and developers doing multiple changes a day and like a billion connections now or like some of our customers do, it's no longer possible to look at like, you know, connections. It's no longer possible to look at every process. You've got to have to figure out how to deal with that problem by doing, you know, not operator processing and clustering. And that's what we do well. But at some point, scalability basically comes up when you end up having to, on any of the dimensions, having to deal with the problem where I can't, you know, as a human, I can't look at everything. So you have to kind of at that point, start investing in anomaly detection and figuring needle in the haystack problems so we can focus on them versus like, you know, one VM, something happened. All right, Vikram, really appreciate the updates. We know we're going to see lace Lacework at many of >>the cloud shows. Appreciate all the updates, everything in the Kubernetes environment. They kept doing it for John Troyer OMSU amendment back with more coverage here in just a little bit. Thanks as always for watching the cube.

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host still minimun hi I'm Stu minimun and this is a cube conversation from our Boston area studio happy to welcome to the program a first-time guest on the program but from knit scout who we've been digging into the concept of visibility without borders dr. Vikram Saxena who's with the office of the CTO from the for mention net scout thank you so much for joining us thanks to it thanks for having me all right dr. Zana before we get into kind of your role why don't you go back give us a little bit about you know your background you and I have some shared background comm we both work for some of the arms of you know Ma Bell that's right back in the day yeah you work a little bit more senior and yeah you know probably a lot more patents than I have my current count is still sure happy to do that you're right I started in 82 which was two years before the breakup of Marbella so you know and then everything started happening right around that time so yeah I started in Bell Labs you know stayed there close to 20 years did lot of the early pioneering work on packet switching before the days of internet frame relay all of that happened it was a pretty exciting time I was there building up we built up the AT&T business from scratch to a billion dollars in the IP space you know in a voice company that was always challenging so and then I moved on to do startups in the broadband space the two of them moved to the Boston area and then moved on to play the CTO role and public companies sonnez networks Tellabs and then you know came to an EPS card about five years ago yeah you know I I love talking about you know some of those incubators of innovation though I you know historically speaking just you know threw off so much technology that's right been seeing so much the media lately about you know the 50th anniversary of Apollo 11 that's so many things that came out of NASA Bell Labs was one of those places that helped inspire me to study engineering that's you know definitely got me on my career but here we are 2019 that's you're still you know working into with some of these telcos and how they're all you know dealing with this wave of cloud and yeah I know the constant change there so bring us inside you know what's your role inside net Scout that office of the CTO yes so net Scout is in the business of you know mining Network data and and what we excel at is extracting what we call actionable intelligence from network traffic which we use the term smart data but essentially my role is really to be the bridge between our technology group and the customers you know bring out understand the problems the challenges that our customers are facing and then work with the teams to build the right product to you know to fit in to the current environment okay one of our favorite things on the cube is you know talking to customers they're going through their transformation that's what you talk about the enterprise you know digital transformation that's what we think there's more than just the buzzword there yeah I've talked to financial institutions manufacturing you know you name it out there if it's a company that's not necessarily born in the cloud they are undergoing that digital transformation bring us inside you know your customer base that this telcos the service providers you know most of them have a heavy tech component to what they're doing but you know are they embracing digital transformation what what does it mean for them so you know as you said it's it's a big term that catches a lot of things but in one word if I described for the telcos it's all about agility if you look at the telco model historically it has been on a path where services get rolled out every six months year multiple years you know not exactly what we call an agile environment compared to today you know but when the cloud happened it changed the landscape because cloud not only created a new way of delivering services but also changed expectations on how fast things can happen and that created high expectations on the customer side which in turn started putting pressure on the on the telcos and and the service providers to become as agile as cloud providers and and and as you know the the network which is really the main asset of a service provider was built around platforms that were not really designed to be programmable you know so they came in with hardwired services and they would change at a very low timescale and building around that is the whole software layer of OS SPSS which over time became very monolithic very slow to change so coupling the network and the software layer created a very slow moving environment so this is what's really causing the change to go to a model where the networks can be programmable which essentially means moving from a hardware centric model to a software centric model where services can be programmed on-demand and created on the fly and maybe sometimes even under the control of the customers and layering on top of that changing the OS s infrastructure to make it more predictive make it more actionable and driven by advances in machine learning and artificial intelligence to make this entire environment extremely dynamic in agile so that's kind of what we are seeing in the marketplace yeah I totally agree that that agility is usually the first thing put forward I I need to be faster yeah it used to be you know faster better cheaper now like a faster faster faster I can actually help compensate for some of those other pieces there of course service riders usually you know very conscious on the cost of things there because if they can lower their cost they can usually of course make them more competitive and pass that along to their ultimate consumers you know bring us inside that you know you mentions this change to software that's going on you know there are so many waves of change going on there everything from you know you talk about IOT and edge computing yeah it's a big you know massive role at a 5g that ya even gets talked about in the general press that these days and at government states they're so you know where are you know your customers today what are some of the critical challenge they have and yeah you know where is that kind of monitoring observability that that kind of piece fit in so so good so let me give to backdrop points first of all you mentioned cost so they are always very cost-conscious trying to drive it down and the reason for that is the traditional services have been heavily commoditized you know voice texting video data they've been commoditized so the customers worn the same stuff cheaper and cheaper and cheaper all the time right so that puts a pressure on margins and reducing cost but now you the industry is at a point where I think the telcos need to grow the top line you know that's a challenge because you can always reduce cost but at some point you get to a point of diminishing returns so now I think the challenge is how do they grow their top line you know so they can become healthier again in that context and that leads to whole notion of what services they need to innovate on so it's all about once you have a programmable Network and a software that is intelligent and smart that becomes a platform for delivering new services so this is where you know you see on the enterprise side Sdn Enterprise IOT all these services are coming now using technologies of software-defined networking network function virtualization and 5g as you mentioned is the next generation of wireless technology that is coming on board right now and that opens up the possibility for the first time to new things dimensions come into play first not only a consumer centric focus which was always there but now opening it up to enterprises and businesses and IOT and secondly fixed broadband right the the the era where telcos used to either drive copper or fiber slow cumbersome takes a lot of time right and the cable guys have already done that with coaxial cable so they need to go faster and faster means use Wireless and finally with 5g you have a technology that can deliver fixed broadband which means all the high definition video voice data and other services like AR VR into the home so it's opening up a new possibility rather than having a separate fixed network and a separate wireless network for the first time they can collapse that into one common platform and go after both fixed and mobile and both consumers and enterprise force yeah we said what one of the big topics of conversation at Cisco live was at San Diego just a short time ago it was 5g and then it you know Wi-Fi six the next generation of that because I'm still going to need inside my building you know for the companies but the 5g holds the promise - give me - so much faster bandwidth so much dense for environment I guess some of the concerns I hear out there and maybe you can tell me kind of where we are and where the telcos fit in is you know 5g from a technology standpoint we understand where it is but that rollout is going to take time yes you know it's great to say you're going to have this dense and highly available thing but you know that's gonna start the same place all the previous generations all right it's the place where actually we don't have bad connectivity today it's you know it's in the urban areas it's where we have dense populations you know sometimes it's thrown out there o5g is gonna be great for edge and IOT and it's like well you know we don't have balloons and planes you know and you know the you know the towers everywhere so where are we with that rollout of 5g what side of timeframes are your customer base looking at as to where that where that goes to play so I think from what I'm seeing in the marketplace I think there is a less of a focus on building out ubiquitous coverage because you know when the focus is on consumers you need coverage because they're everywhere right but I think where they are focusing on because they want to create new revenue a new top-line growth they're focusing more on industry verticals IOT now that allows you to build out networks and pockets of air your customers are because enterprises are always focused in the top cities and you know heck top metro areas so before you make it available for consumers if you get an opportunity to build out at least in the major metropolitan area an infrastructure where you're getting paid as you're building it out because you're signing up this enterprise customers who are willing to pay for these IOT services you get paid you get to build out the infrastructure and then slowly as new applications emerge I think you can make it widely available for consumers I think the challenge on consumer side is the smart phones have been tapped out you know and and people are not going to get that excited about 5g just to use the next-gen I found right so there it has to be about new applications and services and things that people talk about always on the horizon are a are we are and think like that but they are out there they're not there today because it device has to come on board that becomes mass consumable and exciting to customers so while the industry is waiting for that to happen I think there's a great opportunity right now to turn up services for enterprise verticals in the IOT space because the devices are ready and everybody because enterprises are going through their own digital transformation they want to be in a connected world right so they're putting pressure on telcos to connect all their devices into the network and there is a monetization opportunity there so I think what the carriers are going to do is sign up verticals whether it's transportation health care so if they sign up a bunch of hospitals they're going to deploy infrastructure in that area to sign up hospitals if they're going to sign up manufacturing they're going to build their infrastructure in those areas where they're right so by that model you can build out a 5g network that is concentrated on their customer base and then get to ubiquitous coverage later when the consumer applications come yeah so I like that a lot because you know when I think back if we've learned from the sins of the past it used to be if we build it they will come let's you know dig trenches across all the highways and with as much fiber as we can and then the dot-com burst happens and we have all of this capacity that we can't give away yeah what it sounds like you're describing is really a service centric view yes I've got customers and I've got applications and I'm going to build to that and then I can build off of that yeah piece there could talk a little bit about that focus and you know where yeah where your customers are going yeah so maybe just likely before that what I want to talk about the distributed nature of the 5g network so you mentioned edge right so one of the things that are happening when you want to deliver low latency services or high bandwidth services you need to push things closer to the edge as you know when cloud started it's more in the what we call the core you know the large data centers the hyper scale data centers where applications are are being deployed now but when you demand low latency let's say sub 15 millisecond 10 millisecond latency that has to be pushed much more closer to the customer now this is what's for saying the edge cloud deployment in 5g and then what that does is it also forces you to distribute functionality you know everything is not centralized in the core but it's distributed in the edge and the code the control plane maybe in the core but the user plane moves to the edge so that changes the entire flow of traffic and services in a 5g Network they are no longer centralized which means it becomes more challenging to be able to manage and assure these services in a highly distributed telco cloud environment which has this notion of edge and core now on top of that if you say that you know this is all about top-line growth and customer satisfaction then your focus on operationalizing these services has to change from in network centric view to a service centric view because in the past as you know when we were both in Bell Labs in AT&T you know we were pretty much you know focused on the network you know focused on the data from the network the network elements the switches and the routers and all of that and making sure that the network is healthy now that is good but it's not sufficient to guarantee that the services and the service level agreements for customers are being met so what you need to do is focus at the service layer much more so than you were doing it in the past so that changes the paradigm on what data you need to use how you want to use it and how do you stitch together this view in a highly distributed environment and do it in real-time and do it all very quickly so the customers don't see the pain if anything breaks and actually be more proactive in lot of cases be more predictive and take corrective actions before the impact services so this is the challenge and and clearly from a net Scout point of view I think we are right in the center of this hurricane and you know given the history we sort of have figured out on how to do this yeah you know the networking has a long history of we've got a lot of data we've got all of these flows and things change but right exactly as you said understanding what happened at that application that is we've been really tie to make sure it's just IT sitting on the side but IT driving that business that's my application those data flows so yeah you maybe expound a little bit more net Scouts fit there yeah and you know what why it's so critical for what customers need today yeah happy to do that so so if you look at what are the sources of data that you actually can use and and what you should use so basically they fall into three buckets what I call first is what I call infrastructure data which is all about data you get from hypervisors we switches they're telling you more about how the infrastructure is behaving where you need to add more horsepower CPU is memory storage and so on so that is very infrastructure centric the second one is from network elements you know what the DNS servers give you DHCP servers what your routers and switches are giving you the firewalls are giving you and they are also in a way telling you more about what the network elements are seeing so there's a little bit of a hybrid between infrastructure and a service layer component but the problem is that data is it's very vendor dependent it's highly fragmented across there because there's no real standards how to create this data so there is telemetry data there are sis logs and they all vendors do it what they think is best for them so the challenge then becomes on the service provider side and how do you stitch together because service is an end-to-end construct or an application it starts at a at a at a user and goes to a server and you need to be able to get that holistic view n2n so the most appropriate data that net scout feels is what we call the wire data or the traffic data is actually looking at packets themselves because they give you the most direct knowledge about how the service is behaving how it's performing and not only that you can actually predict problems as opposed to react to problems because you can trend this data you can apply machine learning to this data and be able to say what might go wrong and be able to take corrective action so we feel that extracting the right contextual information relevant implicit information timely information in a vendor independent way in a way that is universally if we available from edge to core those are the attributes of wire data and we excel in processing that at the source in real-time and converting all of that into actionable intelligence that is very analytics and automation friendly so this is our strength what that allows us to do is as they are going through this transition between 4G and 5g between physical and virtual across fixed and mobile networks you know you can go through this transition if you have it stitched together end to end view that crosses these boundaries or borders as we call it visibility without borders and in this context your operations people never lose insight into what's going on with their customer applications and behavior so they can go through this migration with confidence that they will not negatively impact their user experience by using our technology yeah you know we've thrown out these terms intelligence and automation for decades yes in our industry but if you look at these hybrid environments and all of these changes come out if an operator doesn't have tools like this they can't keep up they can go so I need to have that machine learning I have to have those tools that can help me intelligently attack these pieces otherwise there's no way I can do it yeah and one point there is you know it's like garbage in garbage out if you don't get the right data you can have the most sophisticated machine learning but it's not going to predict the right answer so the quality of data is very important just as the quality of your analytics in your algorithms so we feel that the combination of right data and the right analytics is how you're going to get advantage of you know accurate predictions and automation around that whole suite okay love that right data right information right delusion why don't want to give you right analytics I want to give you the final word final takeaways for your customers today so I think we are in a very exciting time in the industry you know 5g as a technology is a probably the first generation technology which is coming on board where there is so much focus on on things like security and and new applications and so on and and I think it's an exciting time for service providers to take advantage of this platform and then be able to use it to deliver new services and ultimately see their top lines grow which we all want in the industry because if they are successful then via suppliers you know do well you know so I think it's a pretty exciting time and and vyas net scout are happy to be in this spot right now and to see and help our customers go to go through this transition alright dr. Vikram Singh Saxena thank you so much for joining us sharing with us everything that's happening in your space and it glad to see the excitement still with the journey that you've been on thank you Stu happy to be here all right and as always check out the cubed on net for all of our content I'm Stu minimun and thanks as always for watching the cube [Music]

>> Narrator: Live from New York City, it's theCUBE. Covering IBM Data Science For All. Brought to you by IBM. >> Welcome back to New York here on theCUBE. Along with Dave Vellante, I'm John Walls. We're Data Science For All, IBM's two day event, and we'll be here all day long wrapping up again with that panel discussion from four to five here Eastern Time, so be sure to stick around all day here on theCUBE. Joining us now is Vikram Murali, who is a program director at IBM, and Vikram thank for joining us here on theCUBE. Good to see you. >> Good to see you too. Thanks for having me. >> You bet. So, among your primary responsibilities, The Data Science Experience. So first off, if you would, share with our viewers a little bit about that. You know, the primary mission. You've had two fairly significant announcements. Updates, if you will, here over the past month or so, so share some information about that too if you would. >> Sure, so my team, we build The Data Science Experience, and our goal is for us to enable data scientist, in their path, to gain insights into data using data science techniques, mission learning, the latest and greatest open source especially, and be able to do collaboration with fellow data scientist, with data engineers, business analyst, and it's all about freedom. Giving freedom to data scientist to pick the tool of their choice, and program and code in the language of their choice. So that's the mission of Data Science Experience, when we started this. The two releases, that you mentioned, that we had in the last 45 days. There was one in September and then there was one on October 30th. Both of these releases are very significant in the mission learning space especially. We now support Scikit-Learn, XGBoost, TensorFlow libraries in Data Science Experience. We have deep integration with Horton Data Platform, which is keymark of our partnership with Hortonworks. Something that we announced back in the summer, and this last release of Data Science Experience, two days back, specifically can do authentication with Technotes with Hadoop. So now our Hadoop customers, our Horton Data Platform customers, can leverage all the goodies that we have in Data Science Experience. It's more deeply integrated with our Hadoop based environments. >> A lot of people ask me, "Okay, when IBM announces a product like Data Science Experience... You know, IBM has a lot of products in its portfolio. Are they just sort of cobbling together? You know? So exulting older products, and putting a skin on them? Or are they developing them from scratch?" How can you help us understand that? >> That's a great question, and I hear that a lot from our customers as well. Data Science Experience started off as a design first methodology. And what I mean by that is we are using IBM design to lead the charge here along with the product and development. And we are actually talking to customers, to data scientist, to data engineers, to enterprises, and we are trying to find out what problems they have in data science today and how we can best address them. So it's not about taking older products and just re-skinning them, but Data Science Experience, for example, it started of as a brand new product: completely new slate with completely new code. Now, IBM has done data science and mission learning for a very long time. We have a lot of assets like SPSS Modeler and Stats, and digital optimization. And we are re-investing in those products, and we are investing in such a way, and doing product research in such a way, not to make the old fit with the new, but in a way where it fits into the realm of collaboration. How can data scientist leverage our existing products with open source, and how we can do collaboration. So it's not just re-skinning, but it's building ground up. >> So this is really important because you say architecturally it's built from the ground up. Because, you know, given enough time and enough money, you know, smart people, you can make anything work. So the reason why this is important is you mentioned, for instance, TensorFlow. You know that down the road there's going to be some other tooling, some other open source project that's going to take hold, and your customers are going to say, "I want that." You've got to then integrate that, or you have to choose whether or not to. If it's a super heavy lift, you might not be able to do it, or do it in time to hit the market. If you architected your system to be able to accommodate that. Future proof is the term everybody uses, so have you done? How have you done that? I'm sure API's are involved, but maybe you could add some color. >> Sure. So we are and our Data Science Experience and mission learning... It is a microservices based architecture, so we are completely dockerized, and we use Kubernetes under the covers for container dockerstration. And all these are tools that are used in The Valley, across different companies, and also in products across IBM as well. So some of these legacy products that you mentioned, we are actually using some of these newer methodologies to re-architect them, and we are dockerizing them, and the microservice architecture actually helps us address issues that we have today as well as be open to development and taking newer methodologies and frameworks into consideration that may not exist today. So the microservices architecture, for example, TensorFlow is something that you brought in. So we can just pin up a docker container just for TensorFlow and attach it to our existing Data Science Experience, and it just works. Same thing with other frameworks like XGBoost, and Kross, and Scikit-Learn, all these are frameworks and libraries that are coming up in open source within the last, I would say, a year, two years, three years timeframe. Previously, integrating them into our product would have been a nightmare. We would have had to re-architect our product every time something came, but now with the microservice architecture it is very easy for us to continue with those. >> We were just talking to Daniel Hernandez a little bit about the Hortonworks relationship at high level. One of the things that I've... I mean, I've been following Hortonworks since day one when Yahoo kind of spun them out. And know those guys pretty well. And they always make a big deal out of when they do partnerships, it's deep engineering integration. And so they're very proud of that, so I want to come on to test that a little bit. Can you share with our audience the kind of integrations you've done? What you've brought to the table? What Hortonworks brought to the table? >> Yes, so Data Science Experience today can work side by side with Horton Data Platform, HDP. And we could have actually made that work about two, three months back, but, as part of our partnership that was announced back in June, we set up drawing engineering teams. We have multiple touch points every day. We call it co-development, and they have put resources in. We have put resources in, and today, especially with the release that came out on October 30th, Data Science Experience can authenticate using secure notes. That I previously mentioned, and that was a direct example of our partnership with Hortonworks. So that is phase one. Phase two and phase three is going to be deeper integration, so we are planning on making Data Science Experience and a body management pact. And so a Hortonworks customer, if you have HDP already installed, you don't have to install DSX separately. It's going to be a management pack. You just spin it up. And the third phase is going to be... We're going to be using YARN for resource management. YARN is very good a resource management. And for infrastructure as a service for data scientist, we can actually delegate that work to YARN. So, Hortonworks, they are putting resources into YARN, doubling down actually. And they are making changes to YARN where it will act as the resource manager not only for the Hadoop and Spark workloads, but also for Data Science Experience workloads. So that is the level of deep engineering that we are engaged with Hortonworks. >> YARN stands for yet another resource negotiator. There you go for... >> John: Thank you. >> The trivia of the day. (laughing) Okay, so... But of course, Hortonworks are big on committers. And obviously a big committer to YARN. Probably wouldn't have YARN without Hortonworks. So you mentioned that's kind of what they're bringing to the table, and you guys primarily are focused on the integration as well as some other IBM IP? >> That is true as well as the notes piece that I mentioned. We have a notes commenter. We have multiple notes commenters on our side, and that helps us as well. So all the notes is part of the HDP package. We need knowledge on our side to work with Hortonworks developers to make sure that we are contributing and making end roads into Data Science Experience. That way the integration becomes a lot more easier. And from an IBM IP perspective... So Data Science Experience already comes with a lot of packages and libraries that are open source, but IBM research has worked on a lot of these libraries. I'll give you a few examples: Brunel and PixieDust is something that our developers love. These are visualization libraries that were actually cooked up by IBM research and the open sourced. And these are prepackaged into Data Science Experience, so there is IBM IP involved and there are a lot of algorithms, mission learning algorithms, that we put in there. So that comes right out of the package. >> And you guys, the development teams, are really both in The Valley? Is that right? Or are you really distributed around the world? >> Yeah, so we are. The Data Science Experience development team is in North America between The Valley and Toronto. The Hortonworks team, they are situated about eight miles from where we are in The Valley, so there's a lot of synergy. We work very closely with them, and that's what we see in the product. >> I mean, what impact does that have? Is it... You know, you hear today, "Oh, yeah. We're a virtual organization. We have people all over the world: Eastern Europe, Brazil." How much of an impact is that? To have people so physically proximate? >> I think it has major impact. I mean IBM is a global organization, so we do have teams around the world, and we work very well. With the invent of IP telephoning, and screen-shares, and so on, yes we work. But it really helps being in the same timezone, especially working with a partner just eight miles or ten miles a way. We have a lot of interaction with them and that really helps. >> Dave: Yeah. Body language? >> Yeah. >> Yeah. You talked about problems. You talked about issues. You know, customers. What are they now? Before it was like, "First off, I want to get more data." Now they've got more data. Is it figuring out what to do with it? Finding it? Having it available? Having it accessible? Making sense of it? I mean what's the barrier right now? >> The barrier, I think for data scientist... The number one barrier continues to be data. There's a lot of data out there. Lot of data being generated, and the data is dirty. It's not clean. So number one problem that data scientist have is how do I get to clean data, and how do I access data. There are so many data repositories, data lakes, and data swamps out there. Data scientist, they don't want to be in the business of finding out how do I access data. They want to have instant access to data, and-- >> Well if you would let me interrupt you. >> Yeah? >> You say it's dirty. Give me an example. >> So it's not structured data, so data scientist-- >> John: So unstructured versus structured? >> Unstructured versus structured. And if you look at all the social media feeds that are being generated, the amount of data that is being generated, it's all unstructured data. So we need to clean up the data, and the algorithms need structured data or data in a particular format. And data scientist don't want to spend too much time in cleaning up that data. And access to data, as I mentioned. And that's where Data Science Experience comes in. Out of the box we have so many connectors available. It's very easy for customers to bring in their own connectors as well, and you have instant access to data. And as part of our partnership with Hortonworks, you don't have to bring data into Data Science Experience. The data is becoming so big. You want to leave it where it is. Instead, push analytics down to where it is. And you can do that. We can connect to remote Spark. We can push analytics down through remote Spark. All of that is possible today with Data Science Experience. The second thing that I hear from data scientist is all the open source libraries. Every day there's a new one. It's a boon and a bane as well, and the problem with that is the open source community is very vibrant, and there a lot of data science competitions, mission learning competitions that are helping move this community forward. And it's a good thing. The bad thing is data scientist like to work in silos on their laptop. How do you, from an enterprise perspective... How do you take that, and how do you move it? Scale it to an enterprise level? And that's where Data Science Experience comes in because now we provide all the tools. The tools of your choice: open source or proprietary. You have it in here, and you can easily collaborate. You can do all the work that you need with open source packages, and libraries, bring your own, and as well as collaborate with other data scientist in the enterprise. >> So, you're talking about dirty data. I mean, with Hadoop and no schema on, right? We kind of knew this problem was coming. So technology sort of got us into this problem. Can technology help us get out of it? I mean, from an architectural standpoint. When you think about dirty data, can you architect things in to help? >> Yes. So, if you look at the mission learning pipeline, the pipeline starts with ingesting data and then cleansing or cleaning that data. And then you go into creating a model, training, picking a classifier, and so on. So we have tools built into Data Science Experience, and we're working on tools, that will be coming up and down our roadmap, which will help data scientist do that themselves. I mean, they don't have to be really in depth coders or developers to do that. Python is very powerful. You can do a lot of data wrangling in Python itself, so we are enabling data scientist to do that within the platform, within Data Science Experience. >> If I look at sort of the demographics of the development teams. We were talking about Hortonworks and you guys collaborating. What are they like? I mean people picture IBM, you know like this 100 plus year old company. What's the persona of the developers in your team? >> The persona? I would say we have a very young, agile development team, and by that I mean... So we've had six releases this year in Data Science Experience. Just for the on premises side of the product, and the cloud side of the product it's got huge delivery. We have releases coming out faster than we can code. And it's not just re-architecting it every time, but it's about adding features, giving features that our customers are asking for, and not making them wait for three months, six months, one year. So our releases are becoming a lot more frequent, and customers are loving it. And that is, in part, because of the team. The team is able to evolve. We are very agile, and we have an awesome team. That's all. It's an amazing team. >> But six releases in... >> Yes. We had immediate release in April, and since then we've had about five revisions of the release where we add lot more features to our existing releases. A lot more packages, libraries, functionality, and so on. >> So you know what monster you're creating now don't you? I mean, you know? (laughing) >> I know, we are setting expectation. >> You still have two months left in 2017. >> We do. >> We do not make frame release cycles. >> They are not, and that's the advantage of the microservices architecture. I mean, when you upgrade, a customer upgrades, right? They don't have to bring that entire system down to upgrade. You can target one particular part, one particular microservice. You componentize it, and just upgrade that particular microservice. It's become very simple, so... >> Well some of those microservices aren't so micro. >> Vikram: Yeah. Not. Yeah, so it's a balance. >> You're growing, but yeah. >> It's a balance you have to keep. Making sure that you componentize it in such a way that when you're doing an upgrade, it effects just one small piece of it, and you don't have to take everything down. >> Dave: Right. >> But, yeah, I agree with you. >> Well, it's been a busy year for you. To say the least, and I'm sure 2017-2018 is not going to slow down. So continue success. >> Vikram: Thank you. >> Wish you well with that. Vikram, thanks for being with us here on theCUBE. >> Thank you. Thanks for having me. >> You bet. >> Back with Data Science For All. Here in New York City, IBM. Coming up here on theCUBE right after this. >> Cameraman: You guys are clear. >> John: All right. That was great.

>> Narrator: Live from Las Vegas, it's theCUBE. Covering Dell EMC World 2017, brought to you by Dell EMC. >> Okay, welcome back everyone, we are live in Las Vegas for Dell EMC World 2017. This is theCUBE's eighth year of coverage of what was once EMC World, now it's Dell EMC World 2017. I'm John Furrier at SiliconANGLE, and also my cohost from SiliconANGLE, Paul Gillin. Our next guest is Vikram Bhambri, who is the Vice President of Product Management at Dell EMC. Formally with Microsoft Azure, knows cloud, knows VIPRE, knows the management, knows storage up and down, the Emerging Technologies Group, formerly of EMC. Good to see you on theCUBE again. >> Good to see you guys again. >> Okay, so Elastic Compute, this is going to be the game changer. We're so excited about one of our favorite interviews was your colleague we had on earlier. Unstructured data, object store, is becoming super valuable. And it was once the throwaway, "Yeah, store, later late ". Now with absent data driven enterprises having access to data is the value proposition that they're all driving towards. >> Absolutely. >> Where are you guys with making that happen and bringing that data to life? >> So, when I think about object storage in general, people talk about it's the S3 protocol, or it's the object protocol versus the file protocol. I think the conversation is not about that. The conversation is about data of the universe is increasing and it's increasing tremendously. We're talking about 44 zettabytes of data by 2020. You need an easier way to consume, store, that data in a meaningful way, and not only just that but being able to derive meaningful insights out of that either when the data is coming in or when the data is stored on a periodic basis being able to drive value. So having access to the data at any point of time, anywhere, is the most important aspect of it. And with ECS we've been able to actually attack the market from both sides. Whether it's talking about moving data from higher cost storage arrays or higher performance tiers down to a more accessible, more cheap storage that is available geographically, that's one market. And then also you have tons of data that's available on the tape drive but that data is so difficult to access, so not available. And if you want to go put that tape back on a actual active system the turnaround time is so long. So being able to turn all of that storage into an active storage system that's accessible all the time is the real value proposition that we have to talk about. >> Well now help me understand this because we have all these different ways to make sense of unstructured data now. We have NoSQL databases, we have JSON, we have HDFS, and we've got object storage. Where does it fit into the hierarchy of making sense of unstructured data? >> The simplest way to think about it is we talk about a data ocean, with the amount of data that's growing. Having the capability to store data that is in a global content repository. That is accessible-- >> Meaning one massive repository. >> One massive repository. And not necessarily in one data center, right? It's spread across multiple data centers, it's accessible, available with a single, global namespace, regardless of whether you're trying to access data from location A or location B. But having that data be available through a single global namespace is the key value proposition that object storage brings to bear. The other part is the economics that we're able to provide consistently better than what the public clouds are able to offer. You're talking about anywhere between 30 to 48% cheaper TCO than what public clouds are able to offer, in your own data center with all the constraints that you want to like upload to it, whether it's regular environments. Whether you're talking about country specific clouds and such, that's where it fits well together. But, exposing that same data out whether through HDFS or a file is where ECS differentiated itself from other cloud platforms. Yes, you can go to a Hadoop cluster and do a separate data processing but then you're creating more copies of the same data that you have in your primary storage. So things like that essentially help position object as the global content repository where you can just dump and forget about, about the storage needs. >> Vikram I want to ask you about the elastic cloud storage, as you mentioned, ECS, it's been around for a couple of years. You just announced a ECS lesser cloud storage, dedicated cloud. Can you tell me what that is and more about that because some people think of elastic they think Amazon, "I'll just throw it in object storage in the cloud." What are you guys doing specifically 'cause you have this hybrid offering. >> Absolutely. >> What is this about, can you explain that? >> Yeah, so if you look at, there are two extremes, or two paradigms that people are attracted by. On one side you have public clouds which give you the ease of use, you just swipe your credit card and you're in business. You don't have to worry about the infrastructure, you don't have to worry about, like, "Where my data is going to be stored?" It's just there. And then on the other side you have regular environments or you just have environments where you cannot move to public clouds so customers end up put in ECS, or other object storage for that matter, though ECS is the best. >> John: Biased, but that's okay. >> Yeah, now we are starting to see customers they're saying, "Can I have the best of both worlds? "Can I have a situation where I like the ease of use "of the public cloud but I don't want to "be in a shared bathtub environment. "I don't want to be in a public cloud environment. "I like the privacy that you are able to provide me "with this ECS in my own data center "but I don't want to take on the infrastructure management." So for those customers we have launched ECS dedicated cloud service. And this is specifically targeted for scenarios where customers have maybe one data center, two data centers, but they want to use the full strength and the capabilities of ECS. So what we're telling them we will actually put their bought ECS in our data centers, ECS team will operate and manage that environment for the customer but they're the only dedicated customer on that cloud. So that means they have their own environment-- >> It's completely secure for their data. >> Vikram: Exactly. >> No multi tenant issues at all. >> No, and you can have either partial capabilities in our data center, or you can fully host in our data center. So you can do various permutation and combinations thus giving customers a lot of flexibility of starting with one point and moving to the other. Let's them start with a private cloud, they want to move to a hybrid version they can move that, or if they start from the hybrid and they want to go back to their own data centers they can do that as well. >> Let's change gears and talk about IoT. You guys had launched Project Nautilus, we also heard that from your boss earlier, two days ago. What is that about? Explain, specifically, what is Project Nautilus? >> So as I was mentioning earlier there is a whole universe of data that is now being generated by these IoT devices. Whether you're talking about connected cars, you're talking about wind sensors, you're talking about anything that collects a piece of data that needs to be not only stored but people want to do realtime analysis on that dataset. And today people end up using a combination of 10 different things. They're using Kafka, Speak, HDFS, Cassandra, DASH storage to build together a makeshift solution, that sort of works but doesn't really. Or you end up, like, if you're in the public cloud you'll end up using some implementation of Lambda Architecture. But the challenge there is you're storing same amount of data in a few different places, and not only that there is no consistent way of managing data, processing data that effectively. So what Project Nautilus is our attempt to essentially streamline all of that. Allow stream of data that's coming from these IoT devices to be processed realtime, or for batch, in the same solution. And then once you've done that processing you essentially push that data down to a tier, whether it's Isilon or ECS, depending on the use case that you are trying to do. So it simplifies the whole story on realtime analytics and you don't want to do it in a closed source way. What we've done is we've created this new paradigm, or new primitive called streaming storage, and we are open sourcing it, we are Project Pravega, which is in the Apache Foundation. We want the whole community, just like there is a common sense of awareness for object file we want to that same thing for streaming storage-- >> So you guys are active in open source. Explain quickly, many might not know that. Talk about that. >> So, yeah, as I mentioned Project Prevega is something we announced at Flink Forward Conference. It's a streaming storage layer which is completely open source in the Apache Foundation and we just open sourced it today. And giving customers the capability to contribute code to it, take their version, or they can do whatever they want to do, like build additional innovation on top. And the goal is to make streaming storage just like a common paradigm like everything else. And in addition we're partnering with another open source component. There is a company called data Artisans based out of Berlin, Germany, and they have a project called Flink, and we're working with them pretty closely to bring Nautilus to fruition. >> theCUBE was there by the way, we covered Flink Forward again, one of the-- >> Paul: True streaming engine. >> Very good, very big open source project. >> Yeah, we we're talking with Jeff Woodrow earlier about software defined storage, self driving storage as he calls it. >> Where does ECS fit in the self driving storage? Is this an important part of what you're doing right now or is it a different use? >> Yeah, our vision right from the beginning itself was when we built this next generation of object storage system it has to be software first. Not only software first where a customer can choose their commodity hardware to bring to bear or we an supply the commodity hardware but over time build intelligence in that layer of software so that you can pull data off smartly to other, from SSDs to more SATA based drives. Or you can bring in smarts around metadata search capabilities that we've introduced recently. Because you have now billions of billions of records that are being stored on ECS. You want ease of search of what specifically you're looking for, so we introduced metadata search capability. So making the storage system and all of the data services that were usually outside of the platform, making them be part of the code platform itself. >> Are you working with Elasticsearch? >> Yes, we are using Elasticsearch more to enable customers who want to get insights about ECS itself. And Nautilus, of course, is also going to integrate with Elasticsearch as well. >> Vikram let's wrap this up. Thank you for coming on theCUBE. Bottom line, what's the bottom line message, quickly, summarize the value proposition, why customers should be using ECS, what's the big aha moment, what's the proposition? >> I would say the value proposition is very simple. Sometimes it can be like, people talk about lots of complex terms, it's very simple. Sustainably, low cost storage, for storing a wide variety of content in a global content repository is the key value proposition. >> And used for application developers to tap into? The whole dev ops, data as code, infrastructure as code movement. >> Yeah, you start, what we have seen in the majority of the used cases customers start with one used case of archiving. And then they very quickly realize that there's, it's like a Swiss Army knife. You start with archiving then you move on to application development, more modern applications, or in the cloud native applications development. And now with IoT and Nautilus being able to leverage data from these IoT devices onto these-- >> As I said two days ago, I think this is a huge, important area for agile developers. Having access to data in less than a hundred milliseconds, from any place in the world, is going to be table steaks. >> ECS has to be, or in general, object storage, has to be part of every important conversation that is happening about digital IT transformation. >> It sounds like eventually most of the data's going to end up there. >> Absolutely. >> Okay, so I'll put ya on the spot. When are we going to be seeing data in less than a hundred milliseconds from any database anywhere in the fabric of a company for a developer to call a data ocean and give me data back from any database, from any transaction in less than a hundred milliseconds? Can we do that today? >> We can do that today, it's available today. The challenge is how quickly enterprises are adopting the technology. >> John: So they got to architect it? >> Yeah. >> They have to architect it. >> Paul: If it's all of Isilon. >> They can pull it, they can cloud pull it down from Isilon to ECS. >> True. >> Yeah. >> Speed, low latency, is the key to success. Congratulations. >> Thank you so much. >> And I love this new object store, love this tier two value proposition. It's so much more compelling for developers, certainly in cloud native. >> Vikram: Absolutely. >> Vikram, here on theCUBE, bringing you more action from Las Vegas. We'll be right back as day three coverage continues here at Dell EMC World 2017. I'm John Furrier with Paul Gillan, we'll be right back.