(upbeat music) >> Welcome everyone to theCUBE presentation of the AWS Startup Showcase, the theme here is Data as Code. This is season two, episode two of our ongoing series covering the exciting startups from the AWS ecosystem. And talk about the future of data, future of analytics, the future of development and all kind of cool stuff in Multicloud. I'm your host, John Furrier. Today we're joined by Ed Bailey, Senior Technology, Technical Evangelist at Cribl. Thanks for coming on the queue here. >> I thank you for the invitation, thrilled to be here. >> The theme of this session is the observability lake, which I love by the way I'm getting into that in a second. A breach investigation's best friend, which is a great topic. Couple of things, one, I like the breach investigation angle, but I also like this observability lake positioning, because I think this is a teaser of what's coming, more and more data usage where it's actually being applied specifically for things here, it's observability lake. So first, what is an observability lake? Why is it important? >> Why it's important is technology professionals, especially security professionals need data to make decisions. They need data to drive better decisions. They need data to understand, just to achieve understanding. And that means they need everything. They don't need what they can afford to store. They don't need not what vendor is going to let them store. They need everything. And I think as a point of the observability lake, because you couple an observability pipeline with the lake to bring your enterprise of data, to make it accessible for analytics, to be able to use it, to be able to get value from it. And I think that's one of the things that's missing right now in the enterprises. Admins are being forced to make decisions about, okay, we can't afford to keep this, we can afford to keep this, they're missing things. They're missing parts of the picture. And by bringing, able to bring it together, to be able to have your cake and eat it too, where I can get what I need and I can do it affordably is just, I think that's the future, and it just drives value for everyone. >> And it just makes a lot of sense data lake or the earlier concert, throw everything into the lake, and you can figure it out, you can query it, you can take action on it real time, you can stream it. You can do all kinds of things with it. Verb observability is important because it's the most critical thing people are doing right now for all kinds of things from QA, administration, security. So this is where the breach piece comes in. I like that's part of the talk because the breached investigation's best friend, it implies that you got the secret sourced to behind it, right? So, what is the state of the breach investigation today? What's going on with that? Because we know breaches, we see 'em out there, but like, why is this the best friend of a breach investigator? >> Well, and this is unfortunate, but typically there's an enormous delay between breach and detection. And right now, there's an IBM study, I think it's 287 days, but from the actual breach to detection and containment. It's an enormous amount of time. And the key is so when you do detect a breach, you're bringing in your instant, your response team, and typically without an observability lake, without Cribl solutions around observability pipeline, you're going to have an incomplete picture. The incident response team has to first to understand what's the scope of the breach. Is it one server? Is it three servers? Is it all the servers? You got to understand what's been compromised, what's been the end, what's the impact? How did the breach occur in the first place? And they need all the data to stitch that together, and they need it quickly. The more time it takes to get that data, the more time it takes for them to finish their analysis and contain the breach. I mean, hence the, I think about an 87, 90 days to contain a breach. And so by being able to remove the friction, by able to make it easier to achieve these goals, what shouldn't be hard, but making, by removing that friction, you speed up the containment and resolution time. Not to mention for many system administrators, they don't simply have the data because they can afford to store the data in their SIEM. Or they have to go to their backup team to get a restore which can take days. And so that's-- It's just so many obstacles to getting resolution right now. >> I mean, it's just, you're crawling through glass there, right? Because you think about it like just the timing aspect. Where is the data? Where is it stored and relevant and-- >> And do you have it at all? >> And you have it at all, and then, you know, that person doesn't work anywhere, they change jobs. I mean, who is keeping track of all this? You guys have now, this capability where you can come in and do the instrumentation with the observability lake without a lot of change to the environment, which is not the way it used to be. Used to be, buy a tool, build a platform. Cribl has a solution that eases the struggles with the enterprise. What specifically is that pain point? And what do you guys do specifically? >> Well, I'll start out with kind of example, what drew me to Cribl, so back in 2018. I'm running the Splunk team for a very large multinational. The complexity of that, we were dealing with the complexity of the data, the demands we were getting from security and operations were just an enormous issue to overcome. I had vendors come to me all the time that will solve your problems, but that means you got to move to our platform where you have to get rid of Splunk or you have to do this, and I'm losing something. And what Cribl stream brought into, was I could put it between my sources and my destinations and manage my data. And I would have flow control over the data. I don't have to lose anything. I could keep continuing use our existing analytics tools, and that sense of power and control, and I don't have to lose anything. I was like, there's something wrong here. This is too good to be true. And so what we're talking about now in terms of breach investigation, is that with Cribl stream, I can create a clone of my data to an object store. So this is in, this is almost any object store. So it can be AWS, it could be the other vendor object stores. It could be on-prem object stores. And then I can house my data, I can house all my data at the cheapest possible price. So instead of eating up my most expensive storage, I put all my data in my object store. And I only put the data I need for the detections in my SIEM. So if, and hopefully never, but if you do have a breach, lock stream has a wonderful UI that makes a trivial to then pick my data out of my object store and restore it back into my SIEM so that my IR team has to develop a complete picture of how the breach happen. What's the scope? What is their lateral movement and answer those questions. And it just, it takes the friction away. Just like you said, just no more crawling over glass. You're running to your solution. >> You mentioned object store, and you're streaming that in. You talk about the Cribble stream tool. I'm assuming there when you're streaming the pipeline stuff, but is there a schema involved? Is there database challenges? What, how do you guys look at that? I know you're vendor agnostic. I like that piece, you plug in and you leverage all the tools that are out there, Splunk, Datadog, whatever. But how about on the database side, what's the impact there? >> Well, so I'm assuming you're talking about the object store itself, so we don't have to apply the schema. We can fit the data to whichever the object store is. We structure the data so it makes it easier to understand. For example, if I want to see communications from one IP to another IP, we structure it to make it easier to see that and query that, but it is just, we're-- Yeah, it's completely vendor neutral and this makes it so simple, so simple to enable, I think-- >> So no pre-defined schema needed. >> No, not at all. And this, it made it so much easier. I think we enabled this for the enterprise. I think it took us three hours to do, and we were able to then start, I mean, start cutting our retention costs dramatically. >> Yeah, it's great when you get that kind of value, time to value critical and all the skeptics fall to the sides pretty quickly. (chuckles) I got to ask you, well, go ahead. >> So I say, I mean, previously, I would have to go to our backup team. We'd have to open up a ticket, we'd have to have a bridge, then we'd have to go through the process of pulling tape and being, it could take, you know, hours, hours if not days to restore the amount of data we needed. And just it, you know, we were able to run to our goals, and solve business problems instead of focusing on the process steps of getting things done. >> Right, so take me through the architecture here and some customer examples, 'cause you have the Cribble streaming there, observability pipeline. That's key, you mentioned that. >> Yes. >> And then they build out these observability lakes from that. So what is the impact of that? Can you share the customers that are using that solution? What are they seeing for benefits? What are some of the impact? Can you give us some specifics? >> I mean, I can't share with all the exact customer names. I can definitely give you some examples. Like referenceable conference would be TransUnion, so that I came from TransUnion. I was one of the first customers and it solved enormous number of problems for us. Autodesk is another great example. The idea that we're able to automate and data practices. I mean, just for example, what we were talking about with backups. We'd have to, you have to put a lot of time into managing your backups in your inner analytics platforms, you have to. And then you're locked into custom database schemas, you're locked into vendors. And it's also, it's still, it's expensive. So being able to spend a few hours, dramatically cut your costs, but still have the data available, and that's the key. I didn't have to make compromises, 'cause before I was having to say, okay, we're going to keep this, we're going to just drop this and hope for the best. And we just don't, we just didn't have to do that anymore. I think for the same thing for TransUnion and Autodesk, the idea that we're going to lower our cost, we're going to make it easier for our administrators to do their job and so they can spend more time on business value fundamentals, like responding to a breach. You're going to spend time working with your teams, getting value observability solutions and stop spending time on writing custom solutions using to open source tools. 'Cause your engineering time is the most precious asset for any enterprise and you got to focus your engineering time on where it's needed the most. >> Yeah, and they can't underestimate the hassle and cost of ownership, of swapping out pre-existing stuff, just for the sake of having a functionality. I mean that's a big-- >> It's pain and that's a big thing about lock stream is that being vendor neutral is so important. If you want to use the Splunk universal forwarder, that's great. If you want to use Beats, that's awesome. If you want to use Fluentd, even better. If you want to use all three, you can do that too. It's the customer choice and we're saying to people, use what suits your needs. And if you want to write some of your data to elastic, that's great. Some of your data to Splunk, that's even better. Some of it to, pick your pick, fine as well or Exabeam. You have the choices to put together, put your own solutions together and put your data where you need it to be. We're not asking you only in our ecosystem to work with only our partners. We're letting you pick and choose what suits your business. >> Yeah, you know, that's the direction I was just talking about the Amazon folks around their serverless. You know, you can use any tool, you know, you can, they have that core architecture for everything, the S3 and then pick whatever you want to use. SageMaker, just that other thing. This is the new way. That's the way it has to be to be effective. How do you guys handle that? What's been the reaction from customers? Do they like, roll their eyes and doubt you guys, or can you do it? Are they skeptical? How fast can you convert 'em over? (chuckles) >> Right, and that's always the challenge. And that's, I mean, the best part of my day is talking to customers. I love hearing and feedback, what they like, what they don't and what they need. And of course I was skeptical. I didn't believe it when I first saw it because I was like this, you know, because I'm, I was used to being locked in. I was used to having to put a lot of effort, a lot of custom code, like, what do you mean? It's this easy? I believe I did the first, this is 2018, and I did our first demos, like 30 minutes in, and I cut about 1/2 million dollars out of our license in the first 30 minutes in our first demo. And I was stunned because I mean, it's like, this is easy. >> Yeah, I mean-- >> Yeah, exactly. I mean, this is, and then this is the future. And then for example, we needed to bring in so like the security team wanted to bring in a UBA solution that wasn't part of the vendor ecosystem that we were in. And I was like, not a problem. We're going to use log stream. We're going to clone a copy of our data to the UBA solution. We were able to get value from this UBA solution in weeks. What typically is a six month cycle to start getting value. And it just, it was just too easy and the best part of it. And the thing is, it just struck me was my engineers can now spend their time on delivering value instead of integrations and moving data around. >> Yeah, and also we can spend more time preventing breaches. But what's interesting is counterintuitive here is that, if you, as you add more flexibility and choice, you'd think it'd be harder to handle a breach, right? So, now let's go back to the scenario. Now you guys, say an organization has a breach, and they have the observability pipeline, They got the lake in place, your observability lake, take me through the investigation. How easy is it, what happens? How they start it, what goes on? >> So, once your SOC detects a breach, then they bring in the idea. Typically you're going to bring in your incident response team. So what we did, and this is one more way that we removed that friction, we cleaned up the glass, is we delegate to the instant response team, the ability to restore, we call it-- So if Cribl calls it replay, we play data at our object store back into your SIEM. There's a very nice UI that gives you the ability to say, "I want data from this time period, at this time period, I want it to be all the data." Or the ability to filter and say, "I want this, just this IP." For example, if I detected, okay, this IP has been breached then I'm going to pull all the data that mentions this IP and this timeframe, hit a button and it just starts. And then it's going to restore how as fast your IOPS are for your solution. And then it's back in your tool, it's back in your tool. One of the things I also want to mention is we have an amazing enrichment capability. So one of the things that we would do is we would've pipelines so as the data comes out of the object store, it hits the pipeline, and then we enrich it. We hit use GoIP information, perverse and NAS. It gets processed through threat Intel feed. So the data's already enriched and ready for the incident response people to do their job. And so it just, it bamboozle the friction of getting to the point where I can start doing my job. >> You know, at this theme, this episode for this showcase is about Data as Code. And which is, you know, we've been, I've been saying this on theCUBES for since it was being around 13 years ago, that developers are going to be dealing with data like they deal with software code, and you're starting to see, you mentioned enrichment. Where do you see Data as Code going? How relevant in it now, because we really talking about when you add machine learning in here, that has to be enriched, and iterated on too. We're talking about taking things off a branch and putting it back into the core. This is a data discussion, this isn't software, but it sounds the same. >> Right, and this is something that the irony is that, I remember first time saying it to an auditor. I was constantly going with auditors, and that's what I described is I'm going to show you the code that manages the data. This is the data's code that's going to show you how we transform it, how we secure it, where the data goes, how it's enriched. So you can see the whole story, the data life cycle in one place. And that's how we handled our orders. And I think that is enormously, you know, positive because it's so easy to be confused. It's so easy to have complexity to get in the way of progress. And by being able to represent your Data as Code, it's a step forward 'cause the amount of data and the complexity of data, it's not getting simpler, it's getting more complex. So we need to come up with better ways to handle it. >> Now you've been on both sides of the fence. You've been in the trenches as customer, now you're a supplier with Great Solution. What are people doing with this data engineering roles? Because it's not enough data engineering. I mean, 'cause if you say Data as Code, if you believe that to be true and many people do, we do. And you looked at the history of infrastructure risk code that enabled DevOps, AIOps, MLOps, DataOps, it's happening, right? So data stack ops is coming. Obviously security is huge in this. How does that data engineering role evolve? Because it just seems more and more that there's going to be a big push towards an SRE version of data, right? >> I completely agree. I was working with a customer yesterday, and I spent a large part of our conversation talking about implementing development practices for administrators. It's a new role. It's a new way to think of things 'cause traditionally your Splunk or elastic administrators is talking about operating systems and memory and talking about how to use proprietary tools in the vendor, that's just not quite the same. And so we started talking about, you need to have, you need to start getting used to code reviews. Yeah, the idea of getting used to making sure everything has a comment, was one thing I told him was like, you know, if you have a function has to have a comment, just by default, just it has to. Yeah, the standards of how you write things, how you name things all really start to matter. And also you got to start adding, considering your skillset. And this is some mean probably one of the best hire I ever made was I hired a guy with a math degree, because I needed his help to understand how do machine learning works, how to pick the best type of algorithm. And I think this is going to evolve, that you're going to be just away from the gray bearded administrator to some other gray bearded administrator with a math degree. >> It's interesting, it's a step function. You have a data engineer who's got that kind of capabilities, like what the SRA did with infrastructure. The step function of enablement, the value creation from really good data engineering, puts the democratization playback on the table, and changes, >> Thank you very much John. >> And changes that entire landscape. How do you, what's your reaction to that? >> I completely agree 'cause so operational data. So operational security data is the most volatile data in the enterprise. It changes on a whim, you have developers who change things. They don't tell you what happens, vendor doesn't tell you what happened, and so that idea, that life cycle of managing data. So the same types of standards of disciplines that database administrators have done for years is going to have, it has to filter down into the operational areas, and you need tooling that's going to give you the ability to manage that data, manage it in flight in real time, in order to drive detections, in order to drive response. All those business value things we've been talking about. >> So I got to ask you the larger role that you see with observability lakes we were talking before we came on camera live here about how exciting this kind of concept is, and you were attracted to the company because of it. I love the observability lake concept because it puts all that data in one spot, you can manage it. But you got machine learning in AI around the corner that also can help. How has all this changed in the landscape of data security and things because it makes a lot of sense, and I can only see it getting better with machine learning. >> Yeah, definitely does. >> Totally, and so the core issue, and I don't want to say, so when you talk about observability, most people have assumptions around observability is only an operational or an application support process. It's also security process. The idea that you're looking for your unknown, unknowns. This is what keeps security administrators up at night is I'm being attacked by something I don't know about. How do you find those unknown? And that's where your machine learning comes in. And that's where that you have to understand there's so many different types of machine learning algorithms, where the guy that I hired, I mean, had started educating me about the umpteen number of algorithms and how it applies to different data and how you get different value, how you have to test your data constantly. There's no such thing as the magical black box of machine learning that gives you value. You have to implement, but just like the developer practices to keep testing and over and over again, data scientists, for example. >> The best friend of a machine learning algorithm is data, right? You got to keep feeding that data, and when the data sets are baked and secure and vetted, even better, all cool. Had great stuff, great insight. Congratulations Cribl, Great Solution. Love the architecture, love the pipelining of the observability data and streaming that in to a lake. Great stuff. Give a plug for the company where you guys are at, where people can get information. I know you guys got a bunch of live feeds on YouTube, Twitch, here in theCUBE. Where else can people find you? Give the plug. >> Oh, please, please join our slack community, go to cribl.io/community. We have an amazing community. This was another thing that drew me to the company is have a large group of people who are genuinely excited about data, about managing data. If you want to try Cribl out, we have some great tool. Try Cribl tools out. We have a cloud platform, one terabyte up free data. So go to cribl.io/cloud or cribl.cloud, sign up for, you know, just never times out. You're not 30 day, it's forever up to one terabyte. Try out our new products as well, Cribl Edge. And then finally come watch Nick Decker and I, every Thursday, 2:00 PM Eastern. We have live streams on Twitter, LinkedIn and YouTube live. And so just my Twitter handle is EBA 1367. Love to have, love to chat, love to have these conversations. And also, we are hiring. >> All right, good stuff. Great team, great concepts, right? Of course, we're theCUBE here. We got our video lake coming on soon. I think I love this idea of having these video. Hey, videos data too, right? I mean, we've got to keep coming to you. >> I love it, I love videos, it's awesome. It's a great way to communicate, it's a great way to have a conversation. That's the best thing about us, having conversations. I appreciate your time. >> Thank you so much, Ed, for representing Cribl here on the Data as Code. This is season two episode two of the ongoing series covering the hottest, most exciting startups from the AWS ecosystem. Talking about the future data, I'm John Furrier, your host. Thanks for watching. >> Ed: All right, thank you. (slow upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the Cubes Coverage of 80 Bus Reinvent 2020. This is the Cube virtual. I'm your host, John Ferrier normally were there in person, a lot of great face to face, but not this year with the pandemic. We're doing a lot of remote, and he's got a great great content guest here. Rebecca Weekly, who's the senior director and senior principal engineer at for Intel's hyper scale strategy and execution. Rebecca. Thanks for coming on. A lot of great news going on around Intel on AWS. Thanks for coming on. >>Thanks for having me done. >>So Tell us first, what's your role in Intel? Because obviously compute being reimagined. It's going to the next level, and we're seeing the sea change that with Cove in 19, it's putting a lot of pressure on faster, smaller, cheaper. This is the cadence of Moore's law. This is kind of what we need. More horsepower. This is big theme of the event. What's what's your role in intel? >>Oh, well, my team looks after a joint development for product and service offerings with Intel and A W s. So we've been working with AWS for more than 14 years. Um, various projects collaborations that deliver a steady beat of infrastructure service offerings for cloud applications. So Data Analytics, ai ml high performance computing, Internet of things, you name it. We've had a project or partnership, several in those the main faces on thanks to that relationship. You know, today, customers Committee choose from over 220 different instance types on AWS global footprint. So those feature Intel processors S, P. J s ai accelerators and more, and it's been incredibly rewarding an incredibly rewarding partnership. >>You know, we've been covering Intel since silicon angle in the Cube was formed 10 years ago, and this is what we've been to every reinvent since the first one was kind of a smaller one. Intel's always had a big presence. You've always been a big partner, and we really appreciate the contribution of the industry. Um, you've been there with with Amazon. From the beginning, you've seen it grow. You've seen Amazon Web services become, ah, big important player in the enterprise. What's different this year from your perspective. >>Well, 2020 has been a challenging here for sure. I was deeply moved by the kinds of partnership that we were able to join forces on within telling a W s, uh, to really help those communities across the globe and to address all the different crisis is because it it hasn't just been one. This has been, ah, year of of multiple. Um, sometimes it feels like rolling crisis is So When the pandemic broke out in India in March of this year, there were schools that were forced to close, obviously to slow the spread of the disease. And with very little warning, a bunch of students had to find themselves in remote school out of school. Uh, so the Department of Education in India engaged career launcher, which is a partner program that we also sponsor and partner with, and it really they had to come up with a distance learning solutions very quickly, uh, that, you know, really would provide Children access to quality education while they were remote. For a long as they needed to be so Korean launcher turned to intel and to a W s. We helped design infrastructure solution to meet this challenge and really, you know, within the first, the first week, more than 100 teachers were instructing classes using that online portal, and today it serves more than 165,000 students, and it's going to accommodate more than a million over the fear. Um, to me, that's just a perfect example of how Cove it comes together with technology, Thio rapidly address a major shift in how we're approaching education in the times of the pandemic. Um, we also, you know, saw kind of a climate change set of challenges with the wildfires that occurred this year in 2020. So we worked with a partner, Roman, as well as a partner who is a partner with AWS end until and used the EEC Thio C five instances that have the second Gen Beyond available processors. And we use them to be able to help the Australian researchers who were dealing with that wildfire increase over 60 fold the number of parallel wildfire simulations that they could perform so they could do better forecasting of who needed to leave their homes how they could manage those scenarios. Um, and we also were able toe work with them on a project to actually thwart the extinction of the Tasmanian Devils. Uh, in also in Australia. So again, that was, you know, an HPC application. And basically, by moving that to the AWS cloud and leveraging those e c two instances, we were able to take their analysis time from 10 days to six hours. And that's the kind of thing that makes the cloud amazing, right? We work on technology. We hope that we get thio, empower people through that technology. But when you can deploy that technology a cloud scale and watch the world's solve problems faster, that has made, I would say 2020 unique in the positivity, right? >>Yeah. You don't wanna wish this on anyone, but that's a real upside for societal change. I mean, I love your passion on that. I think this is a super important worth calling out that the cloud and the cloud scale With that kind of compute power and differentiation, you gets faster speed to value not just horsepower, but speed to value. This is really important. And it saved lives that changes lives. You know, this is classic change. The world kind of stuff, and it really is on center stage on full display with Cove. I really appreciate, uh, you making that point? It's awesome. Now with that, I gotta ask you, as the strategist for hyper scale intel, um, this is your wheelhouse. You get the fashion for the cloud. What kind of investments are you making at Intel To make more advancements in the clock? You take a minute, Thio, share your vision and what intel is working on? >>Sure. I mean, obviously were known more for our semiconductor set of investments. But there's so much that we actually do kind of across the cloud innovation landscape, both in standards, open standards and bodies to enable people to work together across solutions across the world. But really, I mean, even with what we do with Intel Capital, right, we're investing. We've invested in a bunch of born in the cloud start up, many of whom are on top of AWS infrastructure. Uh, and I have found that to be a great source of insights, partnerships, you know, again how we can move the needle together, Thio go forward. So, in the space of autonomous learning and adopt is one of the start ups we invested in. And they've really worked to use methodologies to improve European Health Co network monitoring. So they were actually getting a ton of false positive running in their previous infrastructure, and they were able to take it down from 50 k False positive the day to 50 using again a I on top of AWS in the public cloud. Um, using obviously and a dog, you know, technology in the space of a I, um we've also seen Capsule eight, which is an amazing company that's enabling enterprisers enterprises to modernize and migrate their workloads without compromising security again, Fully born in the cloud able to run on AWS and help those customers migrate to the public cloud with security, we have found them to be an incredible partner. Um, using simple voice commands on your on your smartphone hypersonic is another one of the companies that we've invested in that lets business decision makers quickly visualized insects insight from their disparate data sources. So really large unstructured data, which is the vast majority of data stored in the world that is exploding. Being able to quickly discern what should we do with this. How should we change something about our company using the power of the public cloud? I'm one of the last ones that I absolutely love to cover kind of the wide scope of the waves. That cloud is changing the innovation landscape, Um, Model mine, which is basically a company that allows people thio take decades of insights out of the mainframe data and do something with it. They actually use Amazon's cloud Service, the cloud storage service. So they were able Teoh Teik again. Mainframe data used that and be able to use Amazon's capabilities. Thio actually create, you know, meaningful insights for business users. So all of those again are really exciting. There's a bunch of information on the Intel sponsor channel with demos and videos with those customer stories and many, many, many more. Using Amazon instances built on Intel technology, >>you know that Amazon has always been in about startup born in the cloud. You mentioned that Intel has always been investing with Intel Capital, um, generations of great investments. Great call out there. Can you tell us more about what, uh, Amazon technology about the new offerings and Amazon has that's built on Intel because, as you mentioned at the top of the interview, there's been a long, long standing partnership since inception, and it continues. Can you take a minute to explain some of the offerings built on the Intel technology that Amazon's offering? >>Well, I've always happened to talk about Amazon offerings on Intel products. That's my day job. You know, really, we've spent a lot of time this year listening to our customer feedback and working with Amazon to make sure that we are delivering instances that are optimized for fastest compute, uh, better virtual memory, greater storage access, and that's really being driven by a couple of very specific workloads. So one of the first that we are introducing here it reinvents is the n five the n instant, and that's really ah, high frequency, high speed, low Leighton see network variants of what was, you know, the traditional Amazon E. C two and five. Um, it's powered by a second Gen Intel scalable processors, The Cascade late processors and really these have the highest all court turbo CPU performance from the on scalable processors in the club, with a frequency up to 4.5 gigahertz. That is really exciting for HPC work clothes, uh, for gaining for financial applications. Simulation modeling applications thes are ones where you know, automation, Um, in the automotive space in the aerospace industries, energy, Telkom, all of them can really benefit from that super low late and see high frequency. So that's really what the M five man is all about, um, on the br to others that we've introduced here today and that they are five beats and that is that can utilize up thio 60 gigabits per second of Amazon elastic block storage and really again that bandwidth and the 260 I ops that it can deliver is great for large relational databases. So the database file systems kind of workload. This is really where we are super excited. And again, this is built on Cascade Lake. The 2nd 10. Yeah, and it takes It takes advantage of many different aspects of how we're optimizing in that processor. So we were excited to partner with customers again using E. B s as well as various other solutions to ensure that data ingestion times for applications are reduced and they can see the delivery to what you were mentioning before right time to results. It's all about time to results on the last one is t three e. N. 33 e n is really the new D three instant. It's again on the Alexa Cascade Lake. We offer those for high density with high density local hard drive storage so very cost optimized but really allowing you to have significantly higher network speed and disk throughput. So very cost optimized for storage applications that seven x more storage capacity, 80% lower costs given terabytes of storage compared to the previous B two instances. So we will really find that that would be ideal for workloads in distributed and clustered file system, Big data and analytics. Of course, you need a lot of capacity on high capacity data lakes. You know, normally you want to optimize a day late for performance, but if you need tons of capacity, you need to walk that line. And I think the three and really will help you do that. And and of course, I would be absolutely remiss to not mention that last month we announced the Amazon Web Services Partnership with us on an Intel select solution, which is the first, you know, cloud Service provider to really launching until select solution there. Um, and it's an HPC space, So this is really about in high performance computing. Developers can spend weeks for months researching, you know, to manage compute storage network software configuration options. It's not a field that has gone fully cloud native by default, and those recipes air still coming together. So this is where the AWS parallel cluster solution using. It's an Intel Select solution for simulation and modeling on top of AWS. We're really excited about how it's going to make it easier for scientists and researchers like the ones I mentioned before, but also I t administrators to deploy and manage and just automatically scale those high performance computing clusters in Aws Cloud. >>Wow, that's a lot. A lot of purpose built e mean, no, you guys were really nailing. I mean, low late and see you got stories, you got density. I mean, these air use cases where there's riel workloads that require that kind of specialty and or e means beyond general purpose. Now, you're kind of the general purpose of the of the use case. This is what cloud does this is amazing. Um, final comments this year. I want to get your thoughts because you mentioned Cloud Service provider. You meant to the select program, which is an elite thing, right? Okay, we're anticipating Mawr Cloud service providers. We're expecting Mawr innovation around chips and silicon and software. This is just getting going. It feels like to me, it's just the pulse is different this year. It's faster. The cadence has changed. As a strategist, What's your final comments? Where is this all going? Because this is pretty different. Its's not what it was pre code, but I feel like this is going to continue transforming and being faster. What's your thoughts? >>Absolutely. I mean, the cloud has been one of the biggest winners in a time of, you know, incredible crisis for our world. I don't think anybody has come out of this time without understanding remote work, you know, uh, remote retail, and certainly a business transformation is inevitable and required thio deliver in a disaster recovery kind of business continuity environment. So the cloud will absolutely continue on continue to grow as we enable more and more people to come to it. Um, I personally, I couldn't be more excited than to be able Thio leverage a long term partnership, incredible strength of that insulin AWS partnership and these partnerships with key customers across the ecosystem. We do so much with SVS Os Vives s eyes MSP, you know, name your favorite flavor of acronym, uh, to help end users experience that digital transformation effectively, whatever it might be. And as we learn, we try and take those learnings into any environment. We don't care where workloads run. We care that they run best on our architecture. Er and that's really what we're designing. Thio. And when we partner between the software, the algorithm on the hardware, that's really where we enable the best and user demand and the end use their time to incite and use your time to market >>best. >>Um, so that's really what I'm most excited about. That's obviously what my team does every day. So that's of course, what I'm gonna be most excited about. Um, but that's certainly that's that's the future that you see. And I think it is a bright and rosy one. Um, you know, I I won't say things I'm not supposed to say, but certainly do be sure to tune into the Cube interview with It's on. And you know, also Chatan, who's the CEO of Havana and obviously shaken, is here at A W s, a Z. They talk about some exciting new projects in the AI face because I think that is when we talk about the software, the algorithms and the hardware coming together, the specialization of compute where it needs to go to help us move forward. But also, the complexity of managing that heterogeneity at scale on what that will take and how much more we need to do is an industry and as partners to make that happen. Um, that is the next five years of managing. You know how we are exploding and specialized hardware. I'm excited about that, >>Rebecca. Thank you for your great insight there and thanks for mentioning the Cube interviews. And we've got some great news coming. We'll be breaking that as it gets announced. The chips in the Havana labs will be great stuff. I wouldn't be remiss if I didn't call out the intel. Um, work hard, play hard philosophy. Amazon has a similar approach. You guys do sponsor the party every year replay party, which is not gonna be this year. So we're gonna miss that. I think they gonna have some goodies, as Andy Jassy says, Plan. But, um, you guys have done a great job with the chips and the performance in the cloud. And and I know you guys have a great partner. Concerts provide a customer in Amazon. It's great showcase. Congratulations. >>Thank you so much. I hope you all enjoy olive reinvents even as you adapt to New time. >>Rebecca Weekly here, senior director and senior principal engineer. Intel's hyper scale strategy and execution here in the queue breaking down the Intel partnership with a W s. Ah, lot of good stuff happening under the covers and compute. I'm John for your host of the Cube. We are the Cube. Virtual Thanks for watching

>> Narrator: From around the globe it's theCUBE, with digital coverage of AWS reinvent 2020 special coverage sponsored by AWS global partner network. >> Hello, and welcome to theCUBE virtual with our special coverage of AWS reinvent 2020 with additional special coverage of APN partner experience. We are theCUBE virtual and I'm your host, Justin Warren. And today I'm joined by Angelos Kottas who is vice president of product marketing at elastic and he comes to us from San Francisco. Angelos , welcome to theCUBE. >> Thank you, Justin. A pleasure to join you. >> Great to have you here. Now. I've been a big fan of elastic for a while have used your products in a variety of circumstances? You're big partners of AWS and have seen quite a bit of change over the last couple of years. We were talking just before we came on air. Maybe you could talk us through what elastic is doing with AWS and a little bit about those changes that you've seen over the last >> Absolutely one period. >> Sounds good Justin. So first of all many people know elastic as the makers of elastic search. One of the most popular open source of search engines and along with elastic search we have Kibana and beats and Logstash and many people know us as the Elk Stack, right? And so clearly we have roots in the open source community and people have used us for custom applications for years and years. One of the key changes over the last few years is that we've realized that many customers were doing some of the same things with elastic. So we said, what if we really focus on end to end experiences for our three core use cases? And so we chose three use cases and built solutions around them. What is enterprise search, right? Which is how do you find information on your website in your application or in your workspace? The second is observability. So think about software development software in every industry. What about dev ops? What about performance? What about consistency and last but not least, especially you know, with some of the current transitions in digital transformation, think about security. Think about your network security, your endpoint security and how you have visibility across your entire IT ecosystem. So we've chosen those three solution areas and put significant engineering into building out that experience. How quickly can we deliver value, how pre-built can the configuration be the integrations be, the workflow, the reporting and the dashboards around those use cases. The last piece, which is very relevant for reinvent is the transition to cloud, right? So we still offer a downloadable software and many of our customers and users download the elastic stack and deploy it on-prem and hybrid cloud environments. But one of the fastest growing deployment models is in the public cloud. And of course, elastic cloud on AWS is one of our major routes to market, happy to meet many of our customers where they are, which is on AWS. >> Oh it's great to be able to have that choice I think that people can download the software try and get it, get comfortable with it but then people often find that actually running software yourself, there's quite a lot of work involved in doing that. I know that I, I've experienced that myself. Just little things like maintenance and so on. So it sounds like you're actually taking care of a lot of that for customers if they move to the cloud service. But is there anything else special about the cloud service that customers might not be that aware of? >> Well, I mean, choice is a big part of it and so it's not just do I choose cloud it's wearing cloud. So we've actually, we now run elastic cloud in over 40 regions around the world. So we can be close to you in terms of latency, and in terms of performance, in terms of data sovereignty we can be local to your environment. The other aspect it's not just how we simplify deploying elastic. You know, clearly we architect it we install it, deploy and upgraded for you. But also we have focused quite a bit on integrating cloud data sources. So with AWS, as an example, we look at all of the applications and data sources that you host on AWS. And we think about how do we get those data streams how do we get that data directly integrated into elastic. One final piece, actually which I forget sometimes it's not the technical side. It's the business side is the commercial integration, right? So we are, you know, very happy to to be listed on the AWS marketplace. We've made it easy for you to find, deploy and actually build through your AWS commercial agreements via the marketplace integration. >> Right, so easy to get started and to start using it and search is certainly something that elastic is famous for. But you mentioned observability there, a bit of a question I have around observability is, is it that just a fancy way of saying monitoring? There seems to be this, this buzzword around the place. So what do you mean when you say observability? >> So one of the key foundational principles of the elastic observability solution is that, you know you want a unified data database a unified place to store all that data. So it is stretching across logs metrics, application traces it's bringing together a common platform that lets you look at different aspects of observability. So whether you're doing end to end application traces or whether you're just collecting infrastructure logs and looking at performance metrics it's kind of across the board, even looking at things in our most recent release that just came out last week, you know expanding on user experience, monitoring and synthetics. So you can optimize web interactions and web experiences, for example. >> Right. Okay. So there's a bunch of different types of data that are involved there. I know traditionally people would silo those off into a specific customized thing just for that particular type of workload. What is it about elastic that means that you can put all of these in one place? >> Yeah. You know, one of early catchphrases for what does elastic do? What do we focus on? The value we deliver is speed, scale and relevance. And so one of the things that is famous about the elastic way of doing things is the way in which we index data on ingest and so that you can get search queries that return within milliseconds and so that performance characteristics. A second one is scale. And this is actually really key, not just for observability but right next to observability, you get security as well. We like to say, if you're going to observe you might as well protect as well. So when you expand to that universe you have not just hundreds of devices you might have thousands or tens of thousands of devices that you are ingesting information whether it's operational data, whether it's security data. So scale becomes extremely significant. How can you scale horizontally and vertically and maintain that performance even when you are in a fortune 500 scale infrastructure The last piece is relevance. And so, you know that data it's not just about knowing what to look for. It's about using things like machine learning and anomaly detection to uncover unusual patterns of behavior and proactively alerting and making that visible through notifications and through alerts that can actually integrate not just with your elastic operations but actually with third party software. Maybe you want to trigger a service now ticket or a, you know, a Slack integration and all of that is part of the elastic platform as well. >> Right? Okay. So by putting everything kind of in one place that is around what you're talking about. So we have enterprise search and then to be able to find things we're collecting all of the data that we need to find things. And then you touched on security at the beginning and we're starting to talk around security there. So I'm keen to move on to that >> (chuckles) >> By looking at all of these, these different, these signals we can hopefully then manage some of security which I know is very much front of mind for everyone over the last year. Cyber security has very much come to the forefront of everyone's thinking. >> Absolutely. And you know, we've been on the network side of security for some time. So we've had our SIM solution, you know security information event monitoring, but we made a very strategic acquisition a little over a year ago. We saw that a critical piece of visibility is also the end point. And so we partnered with end game and eventually we acquired end game to create end to end visibility on that security. So it is being able to connect, you know the path of data from your servers and network devices all the way to the end points. And an example of the power of this unified architecture is the new elastication that we introduced in beta a couple of months ago. We said, what if we had a single deployment that both does endpoint protection and does malware scanning of your endpoint devices while also ingesting data into your observability systems. And so that's kind of the power of the platform the ability to use common infrastructure common integrations, so that every use case you adopt on top of elastic, it sort of multiplies the value you're getting from using elastic as an infrastructure player. >> Alright that's a good combining a couple of different things into the one tool that you can use. I know sys who I'd spoken to are quite concerned about the proliferation of tools that they have in their environment, it seems that they've bought lots of different things but a lot of them are kind of sitting in a drawer, not really being used. And partly, it's just, we we have so many different ways of dealing with these issues. None of it's really flushed out or sorry has been fully fleshed out that we definitely know this is the one true way to solve this. So what are you hearing from customers as they start to use these security functions? What are they telling you about the way that they're managing security in their environments? >> Well, you know, we think about a few different personas in the security market, right? We think about threat hunters, for example who are looking to identify threats, we're looking at the operations team that do the cleanup that do the you know, the resolution of security threats. And we also, so there's a, you know, there's two competing terms in the security market. We have security operations in the observability world. We have dev ops, right? And, and developer, you know, the continue of developer and deployment into a dev ops role. And so we're starting to see this concept of DevSecOps, right? What if there is a unified set it's not all things to all people and that's an important thing, right? We're not trying to be, your single security vendor for all IT security needs, but instead we're saying, what if you had a security operations analyst, a thrent Hunter an executive, a CSO who's looking for, you know an overall level of threat or compliance to policy and you can bring those experiences together through the elastic security solution. >> Right? So it sounds like you you're trying to allow people to work in the way that they need to providing them the tools that suit their particular circumstance. >> That's right. That's right. I mean, in terms of how do you define success? You look at metrics like meantime to resolution, you know can we reduce the meantime to resolution or you look at law collection and how much more efficiently can you collect logs? You look at asset monitoring and what percentage of your IT infrastructure you actually have unified visibility into, you know we have one great cloud customer OALEKS group. They are a popular online marketplace, you know and they quoted to us that they had a 1900% increase in law collection, right. In terms of scope of what they are collecting logs on they reduce that MTTR by 30% for security incidents so dramatically streamlined and shortened the exposure. And then they increased asset monitoring by 35% across cloud, as well as on-prem. And I think that's the other piece is that, you know whether you deploy your security in the cloud or on-prem you are looking to secure your hybrid environment. And so being able to take data feeds from your SAS partners from your infrastructure running on AWS as well as from those endpoint devices. >> Well, it sounds like there's plenty of scope of interesting things for people to come and have a look at it, at elastic. So, Angelos, thank you so much for joining us here, please. Thank you to my guests Angelos Kottas, vice president of product marketing at elastic. You've been watching theCUBE virtual and our coverage of AWS reinvent 2020 with special coverage of APN partner experience. Make sure you check out all our coverage on your desktop laptop or on your phone, wherever you are. I've been your host, Justin Warren. And I look forward to seeing you again soon. (upbeat music)

(upbeat music) >> From San Francisco, it's theCUBE covering Conga Connect West 2018. Brought to you by Conga. >> Hey, welcome back everybody, Jeff Frick here at theCUBE, we're wrapping up a long day at Conga Connect West. The silent disco has started. If you've never done one of these, it's totally fun. You put it on, you can listen to the red, the green and the blue. >> We got three channels, that's right. >> Wow, great day today. >> Three DJs, three channels, I think you've got oldies, I've got top 40. >> I think I went EDM, I think I'm green. >> You got EDM, okay. >> I think, I know. >> I think red is oldies. >> Alright. >> So come on down to, well, it's probably too late, but-- >> Probably too late tonight. >> We're filling up the space here. >> Two more days at the Thirsty Bear. What do you have going on tomorrow entertainment-wise? >> Tomorrow, whole day of circus entertainment in the tent out back. Tomorrow night, Beats Antique which is a edgy, I think they played at Burning Man. >> Burning man. >> That's right. >> So they've got to have something going on. >> They're going to have something crazy going on. So we've got a circus tent out back, performances all day long. Open bar. >> Open bar. >> For everyone who's at Dreamforce. >> Open food. >> Food all day and by the way, we did not run out of food today. Unfortunately I heard Moscone did. (Jeff laughs) So, if you're hungry, come on down. Demo stations, solution stations. We've actually got a fire marshal in the house, so we're legal. >> Oh, did the fire marshal come on down? >> And we've got dancers right here. >> We got dancers. >> Dancers right here, he's on the red channel. >> You get the vibe. >> So the silent disco's pretty amazing 'cause you put the headphones on, only you can hear the music and you get to dance to your own beat. >> Except for your friends that have the same color. >> So you're green, I'm blue, We're all on our own. >> So we're on different beats. You get the message, it's Conga Connect West, Thirsty Bear. Free food, free drink, free entertainment and silent disco. Come on down. >> Come on down. >> Bob, great day. >> Thanks for being here. Great day today. >> Alright. Thanks for watching. >> Cheers. >> We're checking out, time to go dance, bye. (upbeat jingle)

>> Voiceover: Live from Las Vegas, it's theCUBE, covering Discover 2016, Las Vegas. Brought to you by Hewlett Packard Enterprise. Now, here are your hosts, John Furrier and Dave Vellante. >> Okay, welcome back everyone. We are here live in Las Vegas for SiliconANGLE Media's theCUBE. It's our flagship program, we go out to the events to extract the signal from the noise, we're your exclusive coverage of HP Enterprise, Discover 2016, I'm John Furrier with my co-host, Dave Vellante, extracting the signals from the noise with two great guests, Dr. Tom Bradicich, VP and General Manager of the servers and IoT systems, and Eric Starkloff, the EVP of Global Sales and Marketing at National Instruments, welcome back to theCUBE. >> Thank you. >> John: Welcome for the first time Cube alumni, welcome to theCUBE. >> Thank you. >> So we are seeing a real interesting historic announcement from HP, because not only is there an IoT announcement this morning that you are the architect of, but the twist that you're taking with IoT, is very cutting edge, kind of like I just had Google IO, and at these big conferences they always have some sort of sexy demo, that's to kind of show the customers the future, like AI, or you know, Oculus Rift goggles as the future of their application, but you actually don't have something that's futuristic, it's reality, you have a new product, around IoT, at the Edge, Edgeline, the announcements are all online. Tom, but you guys did something different. And Eric's here for a reason, we'll get to that in a second, but the announcement represents a significant bet. That you're making, and HP's making, on the future of IoT. Please share the vision, and the importance of this event. >> Well thank you, and it's great to be back here with you guys. We've looked around and we could not find anything that existed today, if you will, to satisfy the needs of this industry and our customers. So we had to create not only a new product, but a new product category. A category of products that didn't exist before, and the new Edgeline1000, and the Edgeline4000 are the first entrance into this new product category. Now, what's a new product category? Well, whoever invented the first automobile, there was not a category of automobiles. When the first automobile was invented, it created a new product category called automobiles, and today everybody has a new entry into that as well. So we're creating a new product category, called converged IoT systems. Converged IoT systems are needed to deliver the real-time insights, real-time response, and advance the business outcomes, or the engineering outcomes, or the scientific outcomes, depending on the situation of our customers. They're needed to do that. Now when you have a name, converged, that means somewhat, a synonym is integration, what did we integrate? Now, I want to tell you the three major things we integrated, one of which comes from Eric, and the fine National Instruments company, that makes this technology that we actually put in, to the single box. And I can't wait to tell you more about it, but that's what we did, a new product category, not just two new products. >> So, you guys are bringing two industries together, again, that's not only just point technologies or platforms, in tooling, you're bringing disparate kind of players together. >> Yes. >> But it's not just a partnership, it's not like shaking hands and doing a strategic partnership, so there's real meat on the bone here. Eric, talk about one, the importance of this integration of two industries, basically, coming together, converged category if you will, or industry, and what specifically is in the box or in the technology. >> Yeah, I think you hit it exactly right. I mean, everyone talks about the convergence of OT, or operational technology, and IT. And we're actually doing it together. I represent the OT side, National Instruments is a global leader. >> John: OT, it means, just for the audience? >> Operational Technology, it's basically industrial equipment, measurement equipment, the thing that is connected to the real world. Taking data and controlling the thing that is in the internet of things, or the industrial internet of things as we play. And we've been doing internet of... >> And IT is Information Technologies, we know what that is, OT is... >> I figured that one you knew, OT is Operational Technology. We've been doing IoT before it was a buzzword. Doing measurement and control systems on industrial equipment. So when we say we're making it real, this Edgeline system actually incorporates in National Instruments technology, on an industry standard called PXI. And it is a measurement and control standard that's ubiquitous in the industry, and it's used to connect to the real world, to connect to sensors, actuators, to take in image data, and temperature data and all of those things, to instrument the world, and take in huge amounts of analog data, and then apply the compute power of an Edgeline system onto that application. >> We don't talk a lot about analog data in the IT world. >> Yeah. >> Why is analog data so important, I mean it's prevalent obviously in your world. Talk a little bit more about that. >> It's the largest source of data in the world, as Tom says it's the oldest as well. Analog, of course if you think about it, the analog world is literally infinite. And it's only limited by how many things we want to measure, and how fast we measure them. And the trend in technology is more measurement points and faster. Let me give you a couple of examples of the world we live in. Our customers have acquired over the years, approximately 22 exabytes of data. We don't deal with exabytes that often, I'll give an analogy. It's streaming high definition video, continuously, for a million years, produces 22 exabytes of data. Customers like CERN, that do the Large Hadron Collider, they're a customer of ours, they take huge amounts of analog data. Every time they do an experiment, it's the equivalent of 14 million images, photographs, that they take per second. They create 25 petabytes of data each year. The importance of this and the importance of Edgeline, and we'll get into this some, is that when you have that quantity of data, you need to push processing, and compute technology, towards the edge. For two main reasons. One, is the quantity of data, doesn't lend itself, or takes up too much bandwidth, to be streaming all of it back to central, to cloud, or centralized storage locations. The other one that's very, very important is latency. In the applications that we serve, you often need to make a decision in microseconds. And that means that the processing needs to be done, literally the speed of light is a limiting factor, the processing must be done on the edge, at the thing itself. >> So basically you need a data center at the edge. >> A great way to say it. >> A great way to say it. And this data, or big analog data as we love to call it, is things like particulates, motion, acceleration, voltage, light, sound, location, such as GPS, as well as many other things like vibration and moisture. That is the data that is pent up in things. In the internet of things. And Eric's company National Instruments, can extract that data, digitize it, make it ones and zeroes, and put it into the IT world where we can compute it and gain these insights and actions. So we really have a seminal moment here. We really have the OT industry represented by Eric, connecting with the IT industry, in the same box, literally in the same product in the box, not just a partnership as you pointed out. In fact it's quite a moment, I think we should have a photo op here, shaking hands, two industries coming together. >> So you talk about this new product category. What are the parameters of a new product category? You gave an example of an automobile, okay, but nobody had ever seen one before, but now you're bringing together sort of two worlds. What defines the parameters of a product category, such that it warrants a new category? >> Well, in general, never been done before, and accomplishes something that's not been done before, so that would be more general. But very specifically, this new product, EL1000 and EL4000, creates a new product category because this is an industry first. Never before have we taken data acquisition and capture technology from National Instruments, and data control technology from National Instruments, put that in the same box as deep compute. Deep x86 compute. What do I mean by deep? 64 xeon cores. As you said, a piece of the data center. But that's not all we converged. We took Enterprise Class systems management, something that HP has done very well for many, many years. We've taken the Hewlett Packard Enterprise iLo lights-out technology, converged that as well. In addition we put storage in there. 10s of terabytes of storage can be at the edge. So by this combination of things, that did exist before, the elements of course, by that combination of things, we've created this new product category. >> And is there a data store out there as well? A database? >> Oh yes, now since we have, this is the profundity of what I said, lies in the fact that because we have so many cores, so close to the acquisition of the data, from National Instruments, we can run virtually any application that runs on an x86 server. So, and I'm not exaggerating, thousands. Thousands of databases. Machine learning. Manageability, insight, visualization of data. Data capture tools, that all run on servers and workstations, now run at the edge. Again, that's never been done before, in the sense that at the edge today, are very weak processing. Very weak, and you can't just run an unmodified app, at that level. >> And in terms of the value chain, National Instruments is a supplier to this new product category? Is that the right way to think about it? >> An ingredient, a solution ingredient but just like we are, number one, but we are both reselling the product together. >> Dave: Okay. >> So we've jointly, collaboratively, developed this together. >> So it's engineers and engineers getting together, building the product. >> Exactly. His engineers, mine, we worked extremely close, and produced this beauty. >> We had a conversation yesterday, argument about the iPhone, I was saying hey, this was a game-changing category, if you will, because it was a computer that had software that could make phone calls. Versus the other guys, who had a phone, that could do text messages and do email. With a browser. >> Tom: With that converged product. >> So this would be similar, if I may, and you can correct me if I'm wrong, I want you to correct me and clarify, what you're saying is, you guys essentially looked at the edge differently, saying let's build the data center, at the edge, in theory or in concept here, in a little concept, but in theory, the power of a data center, that happens to do edge stuff. >> Tom: That's right. >> Is that accurate? >> I think it's very accurate. Let me make a point and let you respond. >> Okay. >> Neapolitan ice cream has three flavors. Chocolate, vanilla, strawberry, all in one box. That's what we did with this Edgeline. What's the value of that? Well, you can carry it, you can store it, you can serve it more conveniently, with everything together. You could have separate boxes, of chocolate, vanilla, and strawberry, that existed, right, but coming together, that convergence is key. We did that with deep compute, with data capture and control, and then systems management and Enterprise class device and systems management. And I'd like to explain why this is a product. Why would you use this product, you know, as well. Before I continue though, I want to get to the seven reasons why you would use this. And we'll go fast. But seven reasons why. But would you like to add anything about the definition of the conversion? >> Yeah, I was going to just give a little perspective, from an OT and an industrial OT kind of perspective. This world has generally lived in a silo away from IT. >> Mm-hmm. >> It's been proprietary networking standards, not been connected to the rest of the enterprise. That's the huge opportunity when we talk about the IoT, or the industrial IT, is connecting that to the rest of the enterprise. Let me give you an example. One of our customers is Duke Energy. They've implemented an online monitoring system for all of their power generation plants. They have 2,000 of our devices called CompactRIO, that connect to 30,000 sensors across all of their generation plants, getting real-time monitoring, predictive analytics, predictive failure, and it needs to have processing close to the edge, that latency issue I mentioned? They need to basically be able to do deep processing and potentially shut down a machine. Immediately if it's an a condition that warrants so. The importance here is that as those things are brought online, into IT infrastructure, the importance of deep compute, and the importance of the security and the capability that HPE has, becomes critical to our customers in the industrial internet of things. >> Well, I want to push back and just kind of play devil's advocate, and kind of poke holes in your thesis, if I can. >> Eric: Sure thing. >> So you got the probes and all the sensors and all the analog stuff that's been going on for you know, years and years, powering and instrumentation. You've got the box. So okay, I'm a customer. I have other stuff I might put in there, so I don't want to just rely on just your two stuff. Your technologies. So how do you deal with the corner case of I might have my own different devices, it's connected through IT, is that just a requirement on your end, or is that... How do you deal with the multi-vendor thing? >> It has to be an open standard. And there's two elements of open standard in this product, I'll let Tom come in on one, but one of them is, the actual IO standard, that connects to the physical world, we said it's something called PXI. National Instruments is a major vendor within this PXI market, but it is an open standard, there are 70 different vendors, thousands of products, so that part of it in connecting to the physical world, is built on an open standard, and the rest of the platform is as well. >> Indeed. Can I go back to your metaphor of the smartphone that you held up? There are times even today, but it's getting less and less, that people still carry around a camera. Or a second phone. Or a music player. Or the Beats headphones, et cetera, right? There's still time for that. So to answer your question, it's not a replacement for everything. But very frankly, the vision is over time, just like the smartphone, and the app store, more and more will get converged into this platform. So it's an introduction of a platform, we've done the inaugural convergence of the aforementioned data capture, high compute, management, storage, and we'll continue to add more and more, again, just like the smartphone analogy. And there will still be peripheral solutions around, to address your point. >> But your multi-vendor strategy if I get this right, doesn't prevent you, doesn't foreclose the customer's benefits in any way, so they connect through IT, they're connected into the box and benefits. You changed, they're just not converged inside the box. >> At this point. But I'm getting calls regularly, and you may too, Eric, of other vendors saying, I want in. I would like to relate that conceptually to the app store. Third party apps are being produced all the time that go onto this platform. And it's pretty exciting. >> And before you get to your seven killer attributes, what's the business model? So you guys have jointly engineered this product, you're jointly selling it through your channels, >> Eric: Yes. >> If you have a large customer like GE for example, who just sort of made the public commitment to HPE infrastructure. How will you guys "split the booty," so to speak? (laughter) >> Well we are actually, as Tom said we are doing reselling, we'll be reselling this through our channel, but I think one of the key things is bringing together our mutual expertise. Because when we talk about convergence of OT and IT, it's also bringing together the engineering expertise of our two companies. We really understand acquiring data from the real world, controlling industrial systems. HPE is the world leader in IT technology. And so, we'll be working together and mutually with customers to bring those two perspectives together, and we see huge opportunity in that. >> Yeah, okay so it's engineering. You guys are primarily a channel company anyway, so. >> Actually, I can make it frankly real simple, knowing that if we go back to the Neapolitan ice cream, and we reference National Instruments as chocolate, they have all the contact with the chocolate vendor, the chocolate customers if you will. We have all the vanilla. So we can go in and then pull each other that way, and then go in and pull this way, right? So that's one way as this market develops. And that's going to very powerful because indeed, the more we talk about when it used to be separated, before today, the more we're expressing that also separate customers. That the other guy does not know. And that's the key here in this relationship. >> So talk about the trend we're hearing here at the show, I mean it's been around in IT for a long time. But more now with the agility, the DevOps and cloud and everything. End to end management. Because that seems to be the table stakes. Do you address any of that in the announcement, is it part, does it fit right in? >> Absolutely, because, when we take, and we shift left, this is one of our monikers, we shift left. The data center and the cloud is on the right, and we're shifting left the data center class capabilities, out to the edge. That's why we call it shift left. And we meet, our partner National Instruments is already there, and an expert and a leader. As we shift left, we're also shifting with it, the manageability capabilities and the software that runs the management. Whether it be infrastructure, I mean I can do virtualization at the edge now, with a very popular virtualization package, I can do remote desktops like the Citrix company, the VMware company, these technologies and databases that come from our own Vertica database, that come from PTC, a great partner, with again, operations technology. Things that were running already in the data center now, get to run there. >> So you bring the benefit to the IT guy, out to the edge, to management, and Eric, you get the benefit of connecting into IT, to bring that data benefits into the business processes. >> Exactly. And as the industrial internet of things scales to billions of machines that have monitoring, and online monitoring capability, that's critical. Right, it has to be manageable. You have to be able to have these IT capabilities in order to manage such a diverse set of assets. >> Well, the big data group can basically validate that, and the whole big data thesis is, moving data where it needs to be, and having data about physical analog stuff, assets, can come in and surface more insight. >> Exactly. The biggest data of all. >> And vice versa. >> Yup. >> All right, we've got to get to the significant seven, we only have a few minutes left. >> All right. Oh yeah. >> Hit us. >> Yeah, yeah. And we're cliffhanging here on that one. But let me go through them real quick. So the question is, why wouldn't I just, you know, rudimentary collect the data, do some rudimentary analytics, send it all up to the cloud. In fact you hear that today a lot, pop-up. Censored cloud, censored cloud. Who doesn't have a cloud today? Every time you turn around, somebody's got a cloud, please send me all your data. We do that, and we do that well. We have Helion, we have the Microsoft Azure IoT cloud, we do that well. But my point is, there's a world out there. And it can be as high as 40 to 50 percent of the market, IDC is quoted as suggesting 40 percent of the data collected at the edge, by for example National Instruments, will be processed at the edge. Not sent, necessarily back to the data center or cloud, okay. With that background, there are seven reasons to not send all the data, back to the cloud. That doesn't mean you can't or you shouldn't, it just means you don't have to. There are seven reasons to compute at the edge. With an Edgeline system. Ready? >> Dave: Ready. >> We're going to go fast. And there'll be a test on this, so. >> I'm writing it down. >> Number one is latency, Eric already talked about that. How fast do you want your turnaround time? How fast would you like to know your asset's going to catch on fire? How fast would you like to know when the future autonomous car, that there's a little girl playing in the road, as opposed to a plastic bag being blown against the road, and are you going to rely on the latency of going all the way to the cloud and back, which by the way may be dropped, it's not only slow, but you ever try to make a phone call recently, and it not work, right? So you get that point. So that's latency one. You need to time to incite, time to response. Number one of seven, I'll go real quick. Number two of seven is bandwidth. If you're going to send all this big analog data, the oldest, the fastest, and the biggest of all big data, all back, you need tremendous bandwidth. And sometimes it doesn't exist, or, as some of our mutual customers tell us, it exists but I don't want to use it all for edge data coming back. That's two of seven. Three of seven is cost. If you're going to use the bandwidth, you've got to pay for it. Even if you have money to pay for it, you might not want to, so again that's three, let's go to four. (coughs) Excuse me. Number four of seven is threats. If you're going to send all the data across sites, you have threats. It doesn't mean we can't handle the threats, in fact we have the best security in the industry, with our Aruba security, ClearPass, we have ArcSight, we have Volt. We have several things. But the point is, again, it just exposes it to more threats. I've had customers say, we don't want it exposed. Anyway, that's four. Let's move on to five, is duplication. If you're going to collect all the data, and then send it all back, you're going to duplicate at the edge, you're going to duplicate not all things, but some things, both. All right, so duplication. And here we're coming up to number six. Number six is corruption. Not hostile corruption, but just package dropped. Data gets corrupt. The longer you have it in motion, e.g. back to the cloud, right, the longer it is as well. So you have corruption, you can avoid. And number three, I'm sorry, number seven, here we go with number seven. Not to send all the data back, is what we call policies and compliance, geo-fencing, I've had a customer say, I am not allowed to send all the data to these data centers or to my data scientists, because I can't leave country borders. I can't go over the ocean, as well. Now again, all these seven, create a market for us, so we can solve these seven, or at least significantly ameliorate the issues by computing at the edge with the Edgeline systems. >> Great. Eric, I want to get your final thoughts here, and as we wind down the segment. You're from the ops side, ops technologies, this is your world, it's not new to you, this edge stuff, it's been there, been there, done that, it is IoT for you, right? So you've seen the evolution of your industry. For the folks that are in IT, that HP is going to be approaching with this new category, and this new shift left, what does it mean? Share your color behind, and reasoning and reality check, on the viability. >> Sure. >> And relevance. >> Yeah, I think that there are some significant things that are driving this change. The rise of software capability, connecting these previously siloed, unconnected assets to the rest of the world, is a fundamental shift. And the cost point of acquisition technology has come down the point where we literally have a better, more compelling economic case to be made, for the online monitoring of more and more machine-type data. That example I gave of Duke Energy? Ten years ago they evaluated online monitoring, and it wasn't economical, to implement that type of a system. Today it is, and it's actually very, very compelling to their business, in terms of scheduled downtime, maintenance cost, it's a compelling value proposition. And the final one is as we deliver more analytics capability to the edge, I believe that's going to create opportunity that we don't even really, completely envision yet. And this deep computing, that the Edgeline systems have, is going to enable us to do an analysis at the edge, that we've previously never done. And I think that's going to create whole new opportunities. >> So based on your expert opinion, talk to the IT guys watching, viability, and ability to do this, what's the... Because some people are a little nervous, will the parachute open? I mean, it's a huge endeavor for an IT company to instrument the edge of their business, it's the cutting, bleeding edge, literally. What's the viability, the outcome, is it possible? >> It's here now. It is here now, I mean this announcement kind of codifies it in a new product category, but it's here now, and it's inevitable. >> Final word, your thoughts. >> Tom: I agree. >> Proud papa, you're like a proud papa now, you got your baby out there. >> It's great. But the more I tell you how wonderful the EL1000, EL4000 is, it's like my mother calling me handsome. Therefore I want to point the audience to Flowserve. F-L-O-W, S-E-R-V-E. They're one of our customers using Edgeline, and National Instruments equipment, so you can find that video online as well. They'll tell us about really the value here, and it's really powerful to hear from a customer. >> John: And availability is... >> Right now we have EL1000s and EL4000s in the hands of our customers, doing evaluations, at the end of the summer... >> John: Pre-announcement, not general availability. >> Right, general availability is not yet, but we'll have that at the end of the summer, and we can do limited availability as we call it, depending on the demand, and how we roll it out, so. >> How big the customer base is, in relevance to the... Now, is this the old boon shot box, just a quick final question. >> Tom: It is not, no. >> Really? >> We are leveraging some high-performance, low-power technology, that Intel has just announced, I'd like to shout out to that partner. They just announced and launched... Diane Bryant did her keynote to launch the new xeon, E3, low-power high-performance xeon, and it was streamed, her keynote, on the Edgeline compute engine. That's actually going into the Edgeline, that compute blade is going into the Edgeline. She streamed with it, we're pretty excited about that as well. >> Tom and Eric, thanks so much for sharing the big news, and of course congratulations, new category. >> Thank you. >> Let's see how this plays out, we'll be watching, got to get the draft picks in for this new sports league, we're calling it, like IoT, the edge, of course we're theCUBE, we're living at the edge, all the time, we're at the edge of HPE Discovery. Have one more day tomorrow, but again, three days of coverage. You're watching theCUBE, I'm John Furrier with Dave Vellante, we'll be right back. (electronic music)