>>Thanks. Hey, everyone, welcome back to the cubes. Continuing coverage of snowflakes. Summit 22 live from Caesars Forum in Las Vegas. Lisa Martin here. I have three guests here with me. We're gonna be talking about Snowflake Ventures and the snowflakes start up Challenge. That's in its second year. I've got Harry Glaser with me. Co founder and CEO of Model Bit Start Up Challenge finalist Damon Bryan joins us as well. The CTO and co founder of Hyper Affinity. Also a startup Challenge Finalists. And Stephane Williams to my left here, VP of Corporate development and snowflake Ventures. Guys, great to have you all on this little mini panel this morning. >>Thank you. >>Thank you. >>Let's go ahead, Harry, and we'll start with you. Talk to the audience about model. But what do you guys do? And then we'll kind of unpack the snowflake. The Snowflakes challenge >>Model bit is the easiest way for data scientists to deploy machine learning models directly into Snowflake. We make use of the latest snowflake functionality called Snow Park for python that allows those models to run adjacent to the data so that machine learning models can be much more efficient and much more powerful than they were before. >>Awesome. Damon. Give us an overview of hyper affinity. >>Yes, so hyper affinity were Decision Intelligence platform. So we helped. Specifically retailers and brands make intelligent decisions through the use of their own customer, data their product data and put data science in a I into the heart of the decision makers across their business. >>Nice Step seven. Tell us about the startup challenge. We talked a little bit about it yesterday with CMO Denise Pearson, but I know it's in its second year. Give us the idea of the impetus for it, what it's all about and what these companies embody. >>Yeah, so we This is the second year that we've done it. Um, we it was really out of, um Well, it starts with snowflake Ventures when we started to invest in companies, and we quickly realised that there's there's a massive opportunity for companies to be building on top of the Lego blocks, uh, of snowflake. And so, um, open up the competition. Last year it was the inaugural competition overlay analytics one, Um, and since then, you've seen a number of different functionalities and features as part of snowflakes snow part. Being one of them native applications is a really exciting one going forward. Um, the companies can really use to accelerate their ability to kind of deliver best in class applications using best in class technology to deliver real customer outcomes and value. Um, so we've we've seen tremendous traction across the globe, 250 applicants across 50. I think 70 countries was mentioned today, so truly global in nature. And it's really exciting to see how some of the start ups are taking snowflake to to to new and interesting use cases and new personas and new industries. >>So you had 200 over 250 software companies applied for this. How did you did you narrow it down to three? >>We did. Yeah, >>you do that. >>So, behind the scenes, we had a sub judging panel, the ones you didn't see up on stage, which I was luckily part of. We had kind of very distinct evaluation criteria that we were evaluating every company across. Um and we kind of took in tranches, right? We we took the first big garden, and we kind of try to get that down to a top 50 and top 50. Then we really went into the details and we kind of across, um, myself in ventures with some of my venture partners. Um, some of the market teams, some of the product and engineering team, all kind of came together and evaluated all of these different companies to get to the top 10, which was our semifinalists and then the semi finalists, or had a chance to present in front of the group. So we get. We got to meet over Zoom along the way where they did a pitch, a five minute pitch followed by a Q and A in a similar former, I guess, to what we just went through the startup challenge live, um, to get to the top three. And then here we are today, just coming out of the competition with with With folks here on the table. >>Wow, Harry talked to us about How did you just still down what model bit is doing into five minutes over Zoom and then five minutes this morning in person? >>I think it was really fun to have that pressure test where, you know, we've only been doing this for a short time. In fact model. It's only been a company for four or five months now, and to have this process where we pitch and pitch again and pitch again and pitch again really helped us nail the one sentence value proposition, which we hadn't done previously. So in that way, very grateful to step on in the team for giving us that opportunity. >>That helps tremendously. I can imagine being a 4 to 5 months young start up and really trying to figure out I've worked with those young start ups before. Messaging is challenging the narrative. Who are we? What do we do? How are we changing or chasing the market? What are our customers saying we are? That's challenging. So this was a good opportunity for you, Damon. Would you say the same as well for hyper affinity? >>Yeah, definitely conquer. It's really helped us to shape our our value proposition early and how we speak about that. It's quite complicated stuff, data science when you're trying to get across what you do, especially in retail, that we work in. So part of what our platform does is to help them make sense of data science and Ai and implement that into commercial decisions. So you have to be really kind of snappy with how you position things. And it's really helped us to do that. We're a little bit further down the line than than these guys we've been going for three years. So we've had the benefit of working with a lot of retailers to this point to actually identify what their problems are and shape our product and our proposition towards. >>Are you primarily working with the retail industry? >>Yes, Retail and CPG? Our primary use case. We have seen any kind of consumer related industries. >>Got it. Massive changes right in retail and CPG the last couple of years, the rise of consumer expectations. It's not going to go back down, right? We're impatient. We want brands to know who we are. I want you to deliver relevant content to me that if I if I bought a tent, go back on your website, don't show me more tense. Show me things that go with that. We have this expectation. You >>just explain the whole business. But >>it's so challenging because the brothers brands have to respond to that. How do you what is the value for retailers working with hyper affinity and snowflake together. What's that powerhouse? >>Yeah, exactly. So you're exactly right. The retail landscape is changing massively. There's inflation everywhere. The pandemic really impacted what consumers really value out of shopping with retailers. And those decisions are even harder for retailers to make. So that's kind of what our platform does. It helps them to make those decisions quickly, get the power of data science or democratise it into the hands of those decision makers. Um, so our platform helps to do that. And Snowflake really underpins that. You know, the scalability of snowflake means that we can scale the data and the capability that platform in tangent with that and snowflake have been innovating a lot of things like Snow Park and then the new announcements, announcements, uni store and a native APP framework really helping us to make developments to our product as quick as snowflakes are doing it. So it's really beneficial. >>You get kind of that tailwind from snowflakes acceleration. It sounds like >>exactly that. Yeah. So as soon as we hear about new things were like, Can we use it? You know, and Snow Park in particular was music to our ears, and we actually part of private preview for that. So we've been using that while and again some of the new developments will be. I'm on the phone to my guys saying, Can we use this? Get it, get it implemented pretty quickly. So yeah, >>fantastic. Sounds like a great aligned partnership there, Harry. Talk to us a little bit about model bit and how it's enabling customers. Maybe you've got a favourite customer example at model bit plus snowflake, the power that delivers to the end user customer? >>Absolutely. I mean, as I said, it allows you to deploy the M L model directly into snowflake. But sometimes you need to use the exact same machine learning model in multiple endpoints simultaneously. For example, one of our customers uses model bit to train and deploy a lead scoring model. So you know when somebody comes into your website and they fill out the form like they want to talk to a sales person, is this gonna be a really good customer? Do we think or maybe not so great? Maybe they won't pay quite as much, and that lead scoring model actually runs on the website using model bit so that you can deploy display a custom experience to that customer we know right away. If this is an A, B, C or D lead, and therefore do we show them a salesperson contact form? Do we just put them in the marketing funnel? Based on that lead score simultaneously, the business needs to know in the back office the score of the lead so that they can do things like routed to the appropriate salesperson or update their sales forecasts for the end of the quarter. That same model also runs in the in the snowflake warehouse so that those back office systems can be powered directly off of snowflake. The fact that they're able to train and deploy one model into two production environment simultaneously and manage all that is something they can only do with bottled it. >>Lead scoring has been traditionally challenging for businesses in every industry, but it's so incredibly important, especially as consumers get pickier and pickier with. I don't want I don't want to be measured. I want to opt out. What sounds like what model but is enabling is especially alignment between sales and marketing within companies, which is That's also a big challenge at many companies face for >>us. It starts with the data scientist, right? The fact that sales and marketing may not be aligned might be an issue with the source of truth. And do we have a source of truth at this company? And so the idea that we can empower these data scientists who are creating this value in the company by giving them best in class tools and resources That's our dream. That's our mission. >>Talk to me a little bit, Harry. You said you're only 4 to 5 months old. What were the gaps in the market that you and your co founders saw and said, Guys, we've got to solve this. And Snowflake is the right partner to help us do it. >>Absolutely. We This is actually our second start up, and we started previously a data Analytics company that was somewhat successful, and it got caught up in this big wave of migration of cloud tools. So all of data tools moved and are moving from on premise tools to cloud based tools. This is really a migration. That snowflake catalyst Snowflake, of course, is the ultimate in cloud based data platforms, moving customers from on premise data warehouses to modern cloud based data clouds that dragged and pulled the rest of the industry along with it. Data Science is one of the last pieces of the data industry that really hasn't moved to the cloud yet. We were almost surprised when we got done with our last start up. We were thinking about what to do next. The data scientists were still using Jupiter notebooks locally on their laptops, and we thought, This is a big market opportunity and we're We're almost surprised it hasn't been captured yet, and we're going to get in there. >>The other thing. I think it's really interesting on your business that we haven't talked about is just the the flow of data, right? So that the data scientist is usually taking data out of a of a of a day like something like Smoke like a data platform and the security kind of breaks down because then it's one. It's two, it's three, it's five, it's 20. Its, you know, big companies just gets really big. And so I think the really interesting thing with what you guys are doing is enabling the data to stay where it's at, not copping out keeping that security, that that highly governed environment that big companies want but allowing the data science community to really unlock that value from the data, which is really, really >>cool. Wonderful for small startups like Model Bit. Because you talk to a big company, you want them to become a customer. You want them to use your data science technology. They want to see your fed ramp certification. They want to talk to your C. So we're two guys in Silicon Valley with a dream. But if we can tell them the data is staying in snowflake and you have that conversation with Snowflake all the time and you trust them were just built on top. That is an easy and very smooth way to have that conversation with the customer. >>Would you both say that there's credibility like you got street cred, especially being so so early in this stage? Harry, with the partnership with With Snowflake Damon, we'll start with you. >>Yeah, absolutely. We've been using Snowflake from day one. We leave from when we started our company, and it was a little bit of an unknown, I guess maybe 23 years ago, especially in retail. A lot of retailers using all the legacy kind of enterprise software, are really starting to adopt the cloud now with what they're doing and obviously snowflake really innovating in that area. So what we're finding is we use Snowflake to host our platform and our infrastructure. We're finding a lot of retailers doing that as well, which makes it great for when they wanted to use products like ours because of the whole data share thing. It just becomes really easy. And it really simplifies it'll and data transformation and data sharing. >>Stephane, talk about the startup challenge, the innovation that you guys have seen, and only the second year I can. I can just hear it from the two of you. And I know that the winner is back in India, but tremendous amount of of potential, like to me the last 2.5 days, the flywheel that is snowflake is getting faster and faster and more and more powerful. What are some of the things that excite you about working on the start up challenge and some of the vision going forward that it's driving. >>I think the incredible thing about Snowflake is that we really focus as a company on the data infrastructure and and we're hyper focused on enabling and incubating and encouraging partners to kind of stand on top of a best of breed platform, um, unlocked value across the different, either personas within I T organisations or industries like hypothermia is doing. And so it's it's it's really incredible to see kind of domain knowledge and subject matter expertise, able to kind of plug into best of breed underlying data infrastructure and really divide, drive, drive real meaningful outcomes for for for our customers in the community. Um, it's just been incredible to see. I mean, we just saw three today. Um, there was 250 incredible applications that past the initial. Like, do they check all the boxes and then actually, wow, they just take you to these completely different areas. You never thought that the technology would go and solve. And yet here we are talking about, you know, really interesting use cases that have partners are taking us to two >>150. Did that surprise you? And what was it last year. >>I think it was actually close to close to 2 to 40 to 50 as well, and I think it was above to 50 this year. I think that's the number that is in my head from last year, but I think it's actually above that. But the momentum is, Yeah, it's there and and again, we're gonna be back next year with the full competition, too. So >>awesome. Harry, what is what are some of the things that are next for model bed as it progresses through its early stages? >>You know, one thing I've learned and I think probably everyone at this table has internalised this lesson. Product market fit really is everything for a start up. And so for us, it's We're fortunate to have a set of early design partners who will become our customers, who we work with every day to build features, get their feedback, make sure they love the product, and the most exciting thing that happened to me here this week was one of our early design partner. Customers wanted us to completely rethink how we integrate with gets so that they can use their CI CD workflows their continuous integration that they have in their own get platform, which is advanced. They've built it over many years, and so can they back, all of model, but with their get. And it was it was one of those conversations. I know this is getting a little bit in the weeds, but it was one of those conversations that, as a founder, makes your head explode. If we can have a critical mass of those conversations and get to that product market fit, then the flywheel starts. Then the investment money comes. Then you're hiring a big team and you're off to the races. >>Awesome. Sounds like there's a lot of potential and momentum there. Damon. Last question for you is what's next for hyper affinity. Obviously you've got we talked about the street cred. >>Yeah, what's >>next for the business? >>Well, so yeah, we we've got a lot of exciting times coming up, so we're about to really fully launch our products. So we've been trading for three years with consultancy in retail analytics and data science and actually using our product before it was fully ready to launch. So we have the kind of main launch of our product and we actually starting to onboard some clients now as we speak. Um, I think the climate with regards to trying to find data, science, resources, you know, a problem across the globe. So it really helps companies like ours that allow, you know, allow retailers or whoever is to democratise the use of data science. And perhaps, you know, really help them in this current climate where they're struggling to get world class resource to enable them to do that >>right so critical stuff and take us home with your overall summary of snowflake summit. Fourth annual, nearly 10,000 people here. Huge increase from the last time we were all in person. What's your bumper sticker takeaway from Summit 22 the Startup Challenge? >>Uh, that's a big closing statement for me. It's been just the energy. It's been incredible energy, incredible excitement. I feel the the products that have been unveiled just unlock a tonne, more value and a tonne, more interesting things for companies like the model bit I profanity and all the other startups here. And to go and think about so there's there's just this incredible energy, incredible excitement, both internally, our product and engineering teams, the partners that we have spoke. I've spoken here with the event, the portfolio companies that we've invested in. And so there's there's there's just this. Yeah, incredible momentum and excitement around what we're able to do with data in today's world, powered by underlying platform, like snowflakes. >>Right? And we've heard that energy, I think, through l 30 plus guests we've had on the show since Tuesday and certainly from the two of you as well. Congratulations on being finalist. We wish you the best of luck. You have to come back next year and talk about some of the great things. More great >>things hopefully will be exhibited next year. >>Yeah, that's a good thing to look for. Guys really appreciate your time and your insights. Congratulations on another successful start up challenge. >>Thank you so much >>for Harry, Damon and Stefan. I'm Lisa Martin. You're watching the cubes. Continuing coverage of snowflakes. Summit 22 live from Vegas. Stick around. We'll be right back with a volonte and our final guest of the day. Mhm, mhm

>> From the SiliconANGLE media office in Boston Massachusetts, it's theCUBE. Now here's your host, Dave Vellante. >> Hi everbody, this is Dave Vellante, and welcome to this special Cube conversation on a very important topic, cyber security and cyber resiliency. With me today is Stefan Voss who's the Senior Director of Product Management for Data Protection Software and Cyber Security and Compliance at Dell EMC. Stefan, thanks for coming on and helping us understand this very important topic ahead of RSA World. >> My pleasure, thanks Dave for having me. >> You're welcome, so let's talk about the environment today. We have, for years, seen back-up evolve into data protection, obviously disaster recovery is there, certainly long term retention. But increasingly, cyber resilience is part of the conversation. What are you seeing from customers? >> Yeah, definitely, we're seeing that evolution as well. It's definitely a changing market and what a perfect fit. We have to worry about right of breach, What happens when I get attacked? How can I recover? And the technologies we have, that we have for business resiliency back-up, they all apply, they all apply more than ever. But sometimes they have to be architected in a different way. So folks are very sensitive to that and they realize that they have great technologies. >> I'm glad you mentioned the focus on recovery because we have a lot of conversations on theCUBE about the CIO and how he, or she, should be communicating to the board, or the CSO, how they should be communicating to the board. That conversation has changed quite dramatically over the last 10 years. Cyber is a board-level issue. When you talk to, certainly large companies, every quarter they're talking about cyber. And not just in terms of what they're doing to keep the bad guys out but really what the processes are to respond, what the right regime is - you know, cyber security is obviously a team sport, it's not just the responsibility of the CSO or the SECOPS team, or the IT team, everybody has to be involved and be aware of it. Are you seeing that awareness at board levels within your customer base, and maybe even at smaller companies? >> 100%, I think the company size almost doesn't matter. Everybody can lose their business fairly quickly and there's one thing that NotPetya, that very bad, sort of, attack told us is that it can be very devastating. And so if we don't have a process and if we don't treat it as a team sport, we'll be uncoordinated. So, first of all, we learned that recovery is real and we need to have a recovery strategy. Doesn't mean we don't do detection, so the NIS continuum applies, but the CSOs are much more interested in the actual data recovery than they ever were before which is very interesting. And then, you know, you learn that the process is as important as the technology. So, in other words, Bob Bender - a fabulous quote from Founders Federal - you know, the notion of sweating before the game, being prepared, having a notion of a cyber recovery run book. Because the nature of the disasters are changing so, therefore, we have to think about using the same technologies in a different way. >> And I said at the open that things are shifting from just a pure back-up and recovery spectrum to much broader. The ROI is changing, people are trying to get more out of their data protection infrastructure than just insurance and, certainly, risk management and cyber resiliency and response is part of that. How is the ROI equation changing? >> Yeah, I mean, it's a very valid question. You know, we do have, people are asking for the ROI. We have to take a risk-based approach, we are mitigating risk. It's never fun to have any data protection or business resilience topology, 'cause it's incremental cost, but we do that for a reason. We need to be able to have an operational recovery strategy, a recovery strategy from a geographic disaster and, of course, now more so than ever a recovery strategy from a cyber attack. And so, therefore, we have to think about, you know, not so much the ROI but what is my risk reduction, right? By having, sort of, that process in place but also the confidence that I can get to the data that I need to recover. >> Now we're gonna get into that a little bit later when we talk about the business impact analysis. But I wanna talk about data isolation. Obviously ransomware is a hot topic today and this notion of creating an air gap. What is data isolation from your perspective? What are customers doing there? >> Yeah, I mean, I think almost every customer has a variant of data isolation. It's clear that it works, we've seen this from the NotPetya attack again that where we were, large logistics company, right, found data the domain controller on a system that underwent maintenance in Nigeria. So a system that was offline, but we don't wanna operate that way. So we wanna get the principles of isolation because we know it kind of reduces the attack surface, right, from the internal actor, from ransomware variants, you name it. All of these are, when you have stuff on the network it's theoretically fair game for the attacker. >> So that Nigeria example was basically by luck there was a system offline under maintenance that happened to be isolated? And so they were able to recover from that system? >> Absolutely. And another example was, of course, critical data that domain controller, 'cause that's what this attack happened to go after, was on tape. And so, you know, this just shows and proves that isolation works. The challenge we were running into with every customer we work with was the recovery time. Especially when you have to do selective recovery more often, you know, we wanna be able to get the benefits of online media. But also get, sort of, the benefits of isolation. >> Yeah, I mean, you don't wanna recover from tape. Tape is there as a last resort and hopefully you never have to go to it. How are customers, sort of, adopting this data isolation strategy and policy? Who's involved, what are some of the pre-requisites that they need to think about? >> Yeah, so the good thing - first thing's first, right. We have technology we know and love, so our data protection appliances where we started architecting this workflow, that we can use. So, in other words, you don't have to learn a new technology, buy something else. There's an incremental investment, yes. And then we have to think about who's involved. So that earlier point, the security folks are almost always involved, and they should be involved. Sometimes they fund the project, sometimes it comes out of IT. Right, so, this is the collaborative effort and then to the extent it's necessary, of course, you wanna have GRC - so the risk people - involved to make sure that we really focus on the most important critical assets. >> Now ahead of RSA, let's talk a little bit about what's going on in that world. There are security frameworks, Nist in particular is one, that's relatively new, I mean it's 2014 it came out, it's been revised really focusing on prevent, detect and, very importantly, respond. Something we've talked about a lot. Are people using that framework? Are they doing the self-assessments that Nist prescribes? What's your take? >> Yeah, I think they are. So, first of all, they are realizing that leaning too much left of breach, in other words hoping that we can always catch everything, sort of the eggshell perimeter, everybody understands that that's not enough. So we have to go in-depth and we also have to have a recovery strategy. And so the way I always like to break it down pragmatically is - one, what do I prioritize on? So we can always spend money on everything, but doing a business impact analysis and then maybe governing that in a tool like RSA Archer can help me be a little bit more strategic. And then, on the other end, if I can do a better job co-ordinating the data recovery along with the incident response, that will go a long way. You know and, of course, that doesn't forego any investment in the detection but it is widely adopted. >> One of the key parts about the NIS framework is understanding exposure in the supply chain where you may not have total control over one of your suppliers' policies, but yet they're embedded into your workflow. How are people handling that? Is there a high degree of awareness there? What are you seeing? >> It is absolutely, that's why product security is such an important element, and it's the number one priority for Dell Security, even above and beyond the internal security of our data center, as crazy as it sounds. Because, you know, we can do a lot of damage right in the market. So, certainly, supply chain, making sure we have robust products all along the way is something that every customer asks about all the time and it's very important. >> Let's go back to business impact analysis, we've mentioned it a couple of times now. What is a business impact analysis and how do you guys go about helping your customers conduct one? >> Yeah, I mean, let's maybe keep it to that example, let's say I go through this analysis and I find that I'm a little bit fuzzy on the recovery and that's an area I wanna invest. You know, and then I buy off on the concept that I have an isolated or cyber recovery vault on an isolated enclave onto which I can then copy data and make sure that I can get to it when I have to recover. The question then becomes, well what does business critical mean? And that's where the business impact analysis will help to say what is your business critical process - number one, number two - what are the associated applications, assets? 'Cause when you have that dependency map it makes it a lot easier to start prioritizing what applications do I put in the vault, in other words. In this specific example. And then how can I put it into financial terms to justify the investment? >> Well we were talking about ROI before, I mean really we've done actually quite a few studies looking at Global 2000 and the cost of downtime. I mean, these are real tangible metrics that, if you can reduce the amount of downtime or you can reduce the security threat, you're talking about putting money back in your pocket. Because Global 2000 organizations are losing millions and millions of dollars every year, so it is actually hard ROI. Even though some people might look at it as softer. I wanna talk about isolated data vault, you know, this notion of air gaps. What are you guys specifically doing there? Do you have solutions in that area? >> Yeah, we do. So we are using, luckily, so the concepts that we know from resiliency disaster recovery. Right, so our data protection storage which is very robust, it's very secure, it has very secure replication. So we have the mechanisms to get data into the vault, we have the mechanisms to create a read-only copy, so an immutable copy, that I can then go back into. So all of this is there, right, but the problem is how do I automate that workflow? So that's a software that we wrote that goes along with the data protection appliance sale. And what it does, it's all about ingesting that business critical data that I talked about into the secure enclave, and then rendering it into an immutable copy that I can get to when I have nowhere else to go. >> Okay, so you've got that gap, that air gap. Now, the bad guys will say 'Hey, I can get through an air gap, I can dress somebody up as a worker and put a stick in'. And so, how much awareness is there of that exposure? And I know it's maybe, you know, we're hitting the tip of the pyramid here, but still important. Can you guys help address that through, whether it's processes or product or experience? >> 100% so we have, of course, our consulting services that will then work with you on elements of physical security, or how do I lock down that remaining replication link? It's just about raising the bar for the attacker to make it more likely we'll catch them before they can get to, really, the prized assets. We're just raising the bar but, yes, those are things we do. So consulting, physical security, how do I do secure reporting out? How do I secure management going in? How do I secure that replication or synchronization link into the vault? All of these are topics that we then discuss, if they kind of deviate from the best practices and we have very good answers through our many customer arrangements. >> Stefan, let's talk about some of the specific offerings. RSA is a portfolio company in the Dell Technologies Group, it's a sister company of Dell EMC. What are you guys doing with RSA? Are you integrating with any of their specific products? Maybe you could talk about that a little bit? >> Yeah, I think, so when you think about recovery and incident response being so important, there's an obvious, right? So what RSA has found - I thought this was very interesting is that there's a lack of coordination between, typically, the security teams and the data professionals, data restoration professionals. So the more we can bridge that gap through technology, reporting, the better it is, right? So, there's a logical affinity between an incident response retainer, activity, and the data recovery solutions that we provide. That's one example, right? So every day counts, that example that I talked about NotPetya, the specific customer was losing 25 Euros every day. If I can shave off one day, it's money in the bank. Or money not out of the bank. The other area is, how do I make sure that I'm strategic about what data I protect in this way? That's the BIA Archer. And then there's some integrations we are looking at from an analytics perspective. >> Archer being the sort of governance risk and compliance, workflow, that's sort of one of the flagship products of RSA. So you integrate to that framework. And what about analytics, things like IOC, RSA NetWitness, are those products that you're integrating to or with, or leveraging in any way? >> Yeah, first off, analytics in general it's an interesting concept now we have data inside our secure enclave, right? So what if we could actually go in and give more confidence to the actual copies that we're storing there. So we have an ecosystem from an analytics perspective. We work with one specific company, we have Arrest API-based integration where we then, essentially, use them to do a vote of confidence on the copy, of the raw back up. Is it good? Are there signs that it was corrupted by malware? and so forth. So what that helps us do is be more proactive around our recovery because, I think you're about to say something - but if I knew there's something, you know, suspicious then I can start my analytics activity that much sooner. >> Well the lightbulb went off in my head. Because if I have an air gap, and I was saying before, it's necessary but insufficient. If I can run analytics on the corpus of the back up data and I can identify anomalies, I might be able to end run somebody trying to get through that air gap that I just mentioned before. Maybe it's a physical, you know, security breach. And the analytics might inform me. Is that a reasonable scenario? >> It is a reasonable scenario, though we do something slightly different. So, first of all, detection mechanisms, left of breach stuff, is what it is, we love it, we sell it, you know, we use it. But, you know, when it comes to back up they're not off-the-shelf tools we can just use and say 'Hey, why don't you scan this back up?' It doesn't typically work. So what we do is, in the vault, we have time, we have a workbench so it's almost like sending a specimen to the lab. And then we take a look at it. Are there any signs that there was data corruption that was indicative of a ransomware attack? And when there is such a scenario we say, 'You might wanna take a look at it, and do some further investigation'. That's when we then look at NetWitness or working with the security teams. But we can now be of service and say 'You might wanna look at this copy over here'. It's suspicious, there's an indicative compromise. And then take the next steps other than hoping for the best. >> You mentioned the ecosystem, you mentioned the ecosystem before. I wanna double-click on that. So, talk about the ecosystem. We've said here it's a team sport, you can't just do it alone. From a platform perspective is it open, is it API based? Maybe you can give some examples of how you're working with the ecosystem and how they're leveraging the platform. >> Yeah 100%. So, like I said, so we have, you know, our data protection appliances and that's sort of our plumbing, right, to get the data to where I want. We have the orchestration software. This is the part we're talking about. The orchestration software has Arrest API, everything's documented in Swagger. And the reason we did that is that we can do these orchestrations with third party analytics vendors, that's one use case right? So, I'm here, I have a copy here, please scan, tell me what you find and then give me an alert if you find something. The other example would be, maybe, doing a level of resiliency orchestration. Where you'd automate the recovery workflow beyond what we would have to offer. There are many examples but that is how we are enabling the ecosystem, essentially. >> You mentioned Founders Federal earlier. Is that a customer, is that a reference customer? What can you tell me about them? >> Yeah it's a reference customer and they very much saw the need for this type of protection. And, you know, we've been working with them. There's a Dell World, last year, session that we did with them. And very much the same sort of, like the quote said, focus on the process not only the product and the set of technologies, right? And, so that's how we've been partnering with them. >> The quote being 'Sweat before the game'? Founders Federal, that's a great quote. Alright, we've talked a lot about just, sort of, general terms about cyber recovery. What can you tell us, tell the audience, what makes Dell EMC cyber recovery different in the marketplace and, you know, relative to your competition? Pitch me. >> Yeah, I mean, I think it's a very unique capability. Because, one, you need a large install base and, sort of, a proven platform to even built it on, right? So when you look at the data domain technology we have a lot to work with. We have a lot of customers using it. So that's very hard to mimic. We have the orchestration software where we, I believe, are ahead of the game, right? So the orchestration software that I talked about that gets the data into the vault securely. And then our ecosystem, right? So those are really the three things. And then, of course, we have the consulting services which is also hard to mimic. To really, you know, design the process around this whole thing. But I think the ecosystem, sort of, approach is also very powerful. >> You have a big portfolio, you've got your sister company that's, sort of, well known obviously in this business. Do you also have solutions? I mean, for instance, is there an appliance as part of the portfolio that fits in here? And what is that? >> Yeah, so, you can think of this as, if I wanted to really blow it down, the two things I would buy is a data domain - it could be the smallest one - and a VxRail appliance that runs the software. And then I stick that in the vault. And then there's, sort of, that product. So you can think of it as an appliance that happens to go with the software that I talked about that does the orchestration. >> Okay, so, RSA the premier conference on cyber coming up in a couple of weeks. What have you guys got going there? Give us a little tease. >> Yeah, absolutely. So it's gonna be an awesome show and we will have a booth, and so we look forward to a lot of customer conversations. And we do have a panel. It's gonna be with Mastercard and RSA and myself. And we're really gonna take it from left of breach all the way to right of breach. >> Awesome, do you know when that panel is yet? >> It is, I think, on the 5th, I may have to check. >> Which is which day? >> I wanna say it's Wednesday. >> So it starts on the Monday, right? So that'll be day three. So check the conference schedule, I mean things change at the last minute. But that's great. Mastercard is an awesome reference customer. We've worked with them in the past and so, that's great. Stefan, thanks very much for coming to theCUBE and sharing some of your perspectives and what's coming up at RSA. It's good to have you. >> Thanks so much, Dave, I appreciate it. >> Okay, thanks for watching everybody. This is Dave Vellante from our East Cost headquarters. You're watching theCUBE.

>> From Las Vegas, it's theCUBE covering Open Systems. The future is crystal clear with security and SD-Wan. Brought to you by Open Systems. >> Hello everyone, I'm John Furrier here in Las Vegas for special CUBE presentation. We're at the Chandelier Bar at the Cosmopolitan Hotel, the Cosmo, on the Las Vegas strip. Part of a series of a lot of events going on. Gartner's got two events happening, But we're here as part of Open Systems. You got exclusive get-together of influencers, customers, all talking about the impact the Cloud, Secure, SD-Wan, a variety of other things. Open Systems, a very successful, Switzerland-based company expanding rapidly in the United States, a global platform and we're here with the CTO, Stefan Keller, thanks for joining me. >> Thank you for having me. >> You guys have been very successful in this, I will say, changing SD-Wan, a completely new re-imagined SD-Wan market because with the internet and Cloud, people don't want to connect to the internet anymore, they want either direct connection, they want high-secure, wide-area network connections. They want secure connections. More important than ever when you have Internet of Things, a lot of surface area, nevermind multiple headquarters or branch offices, so SD-Wan has gone from a connection, connectivity, move packets from A to B, to a fully-integrated, secure architecture that's easy to use, that can deal with mobile embedded. You guys have been successful, with almost no marketing, all word of mouth, successful product, tell us, Stefan, as the CTO, what is the most compelling thing about the technology that's been resonating with customers? >> Well, as I said, the last couple of years there was a lot of change, technology change. The requirements of our customers changed as well. With Cloud, you'll all of a sudden have traffic pattern that you didn't have before. Before, everything was static. You had just your band connectivity to the data center and there is left, towards the internet. But with SD-Wan, you now have the capability to have very complex traffic flow at the branch office, itself. So, you have a lot of logic that you put to the branch office and the challenge is now, how can you actually control all that traffic flow in a central way? Because in the end, all our customers or companies, what they want, they want to have the flexibility to use all those new technologies, be it Cloud, be it IOT, whatever. But still have the security in mind in the sense, they want to be protected, they want to be protected. You now have the branch office with a lot of new traffic patterns. How do you control that? And that's where our integrated approach of SD-Wan and security is the perfect fit. So you really have a global policy that you assign locally. >> One of the big trends that's happening now obviously, is the Cloud has grown so big and popular that the economics, you cannot ignore the economics and the value in the cloud for what you're paying. Agility, etc., we've heard that. However, validated even more than ever is on-premises. People are going to have an on-premises and Cloud or Hybrid Cloud solution. Now, IT departments and these people managing CSOS, managing all these people have to deal with the distributed, in some cases decentralized operations. The problem is there's so many vendors. They don't have the expertise so they need things as a managed service, sometimes they want to maybe choose something on premise that's deployed. So you need a diversity of choices without compromising ease of use. So the question for you is: How do you guys make that happen because this is something that you've heard people like about your product, complex, I hate the word single-pane of glass but that's been an IT term, that's essentially dashboard, central teams can use telemetry... and data but get the benefits of.. variety of environments. Why is it so successful, what is the choices for customer? Is it managed service, is that the direction? Or and odd PRAM, what's your thoughts? >> Yeah, that's a good point. In the end it's a combination because we are a managed service because, as you said, things get more complex and the talent market is challenging so it's difficult to find the right talents that can manage it. So that's where we come in as a strategic partner. We are not only in the SD-Wan market, we are also in the security market as well. So we combine security and the SD-Wan. That's what you see with all the SD-Wan vendors out there and they're very strong with SD-Wan capabilities but in order to provide security functionality they start to partner, be it with a firewall vendor, with a proxy vendor what so on. So, in the end, you as a customer, you don't deal just with one partner, all of a sudden you have four, five, or even six such partners you have to deal with. And if a managed service provider can provide a holistic approach of security and SD-Wan you have one partner you can deal with so it makes, for you, very easy. >> So a lot of peoples have say, "oh" they've been trying security, a variety, "we've seen every scheme in the book." And the easiest one was, oh, network traffic. Pack an inspection, kind of not very good. But you want to watch the bad guys move. When things are moving around, that's when you get the pattern recognition. Is there software that you guys write? How do you get that security edge? Is it watching the movement patterns, not just the packets but who has what systems, is it a variety of things? What's the underlying secret sauce for Open Systems? >> The secret sauce, well let's say, is that we are flexible to take out whatever is state of the art and put it together to a managed service in a standardized way for our customers If you look as today's companies they want to do it on their own. They may have to deal with 30, 40 different kind of vendors and components and put it together. We do that for our customers. We take state of the art technology, put it together, and make all the service of it. And the advantage is, because we have that high level of integration, we can all of a sudden, use one component for different kind of services we provide. That's the difference when you have an ecosystem like SD-Wan where you have three, four components, they don't really talk with each other, they do not have a common language. We bring that common language so that the consistent view and the consistent logic over the entire band of our customers. >> So you're the glue layer. Between all the different components. >> Right. >> Okay, so I got to ask you a question. If someone says to you "hey Stefan, this other vendor promised me all this stuff over here, some other person. I got to get current on SD-Wan." What do you think people don't know about SD, whether they should know, that might be a surprise or things that you've observed with your successful customer deployments that's a lot harder than it looks. Cause a lot of people say "oh we got that!" And it doesn't really work very well. Or is a blind spot for the CSOS team, security team, around capabilities. So you can be aspirational but you got to have the capability what are some areas that you've seen that are important for buyers to consider when architecting and then deploying and executing an SD-Wan strategy? >> I mean, When you see all those SD-Wan vendors what they say, "hey it's easy to deploy, it's zero-touch deployment." Can't be true but in the end, you have a global network you want to deploy a global policy and somehow, you have to manage that. And this is something that most of them just underestimate. You only need, really, a strategic partner who knows how to deal with it, who has the capabilities, the experience, and the know-how to deploy it easily and manage it for you So then you don't have the pain. >> Give me an example of a customer, you don't have to say their name, where the old way they did something and then the Open Systems side of it, they did it your way and watch changed, what was the impact? Did they have more efficiency with the people? Did they save time, what was some of the consequences of doing the old way versus the new way? >> The old way also then involved some kind of an MPLS network, or course, if we go with the SD-Wan approach, the really good ones convince a customer, "hey, you don't need MPLS for the application you need." For the SLA you want to have. Internet connectivity if fine and just have two or three such internet connection per location. So in the end, it was cost-saving, it was a full put agreement. Performance all of a sudden was very great and in the end they liked us because of our operational efficiency so our operations model is very efficient and helps our customers so that they can focus on their core business. >> So the applications get smarter, and then you actually saved money because, remember, it still costs a lot of money to send traffic over the network, in some cases. Okay, final question. There's a big trend towards direct connections, where do you see that going, how does that impact SD-Wan? >> I would say that's again on the security side because with SD-Wan, you have a lot of flexibility we just didn't have in the past. This means you have traffic flow all of a sudden which is not expected by many people. If you go to a single branch office, a small one, all of a sudden they have local exits, they do internet surfing, youtube video-ing, they have connections to their private data center, to their public Cloud environment, everything. So different kind of traffic pattern. And here we have just the single way, a unique approach, about a global Zone-Based Firewall. So this makes your traffic pattern all of a sudden very transparent and simple again, this helps you to control the traffic flow and to avoid any kind of leak. >> As we always say, don't send those cat videos. It still costs money to share the cat videos around. Super Content is a big part of this too, you've got all kinds of new SAS applications, talking to each other, this is another layer of abstraction that needs to be managed. That's an area you guys do? API's and applications? >> We're going in that direction, I would say we're not that far yet. We can do much more but this is the direction we have to go to. >> Final question: you come to the U.S. A lot of people are learning about you guys, If we're at a cocktail party, which we are at now, and I say "hey Stefan, bottom line me, what's the one thing about Open Systems that makes you guys great?" >> Then I'll still go back to our operational excellence. We really have a way to operate thousands of devices in a way that is so efficient and scaled very well for a huge customer base. >> Alright Stefan, thanks for coming on. Stefan Keller, CTO of Open Systems, hot start-up out of Zurich, Switzerland. A very successful company, really now exploding in the United States, expanding to Silicon Valley. We are here in Las Vegas, theCUBE coverage. Bringing all the action down here at the Open Systems influencer, expert cocktail party, here at the Chandelier Bar at the Cosmo hotel. Part of a lot of events around Gartner's events are here. Covering it all, stay with us for more after this short break. (chill electronic beats)

>> Live from Las Vegas, it's theCUBE covering AWS re:Invent 2018. (techy music) Brought to you by Amazon Web Services, Intel, and their ecosystem partners. (techy music) >> Welcome back, everyone, live coverage here of AWS re:Invent 2018, I'm John Furrier with Dave Vellante. Two sets, three days of wall-to-wall coverage. Hundreds of videos, great content. Three-hour keynote from Andy Jassy, 52,000 people here. This is where the industry now is getting together to set the agenda for the future. It's cloud-based, it's on-premise, it's all cloud all the time, our next two guests are with Amazon. Dave McCann, who's with the marketplace, and Stefan Sigg, chief R&D officer at Software AG, great to see you. >> Good being back. >> Great to come back. >> Thank you for having me. >> So, I've got to say, you know, the customer dynamic that you guys have is pretty impressive, you guys are a customer. The value creation of the cloud is pretty amazing. What's your world like these days in terms of your market? You're in Europe, you've got thousands of customers, what's the update? >> Well, I mean, we're operating worldwide, US being the biggest market, so 70 countries we're in. From a customer perspective, all over the place with our labs, and obviously, I mean the cloud and the digitalization is a whole new ballgame, and we are... Just at the time, we are reinventing ourselves for maybe the third time in order to push, to help the customers to go that transition, and our middleware expertise and our expertise that we now newly have added to in terms of IoT is just amazingly how that momentum is showing. >> I'd like to get your analysis of Andy Jassy's keynote, and I'll throw one perspective at you. >> Yeah. >> besides the IoT awesomeness that the edge is now with satellites coming-- >> Yes. >> And unlimited connectivity in the future. >> Yes. >> But it kind of points out that this new kind of software developer, new personas-- >> Oh, yeah. >> The builder, the right tool for the right job. >> Yeah. >> There's a set of services now out there that can be merchandised and bought and sold. Marketplace, which you run, software design's changing, but also consumption upon the buying side's changing. What's your analysis to that? >> Well, for us it's just... It couldn't be better, because it's now, again, that software comes into enterprises. It has been pushed aside for many years because people would just implement standard software, would implement, you know, office software, and now all of a sudden, driven by the digital transformation and stuff like IoT, there's a demand for software, building software for their own needs, not just for the back office, but you know, equipping the products with sensors, with data, and enhanced software. So, that's exactly our play, helping those customers, those enterprises to just start their software where it's necessary, and we provide the platform getting them there. >> Yeah. >> So, Dave, Software AG's almost as old as I am. Right, mainframe, you went through the client server, you dealt with the desktop, and now the cloud era. How are you helping companies like Software AG maintain their relevance, keep their infrastructure modern? How does that all work, give us some insight on that. >> So, first of all, AWS broadly is obviously working with all the world's top software companies, and if you think of it, all the large enterprises in the world are moving their applications onto the cloud, and when if you think of the average enterprise has got 1,000 applications, those 1,000 applications are woven into a lot of third party software, so as our AWS customers move onto AWS, they want to bring their software with them, and clearly we work with companies like Software AG, and these guys are modernizing and rearchitecting their software, and the launch we just did today on container marketplace, so now we've launch marketplace for containers. It's a new way of packaging your software up in a microservices model, and Software AG has already refactored 10 of their product lines onto containers, so they're modernizing, our customers are modernizing, and we're working together. >> And so, Stefan, is it a case where you say to the customer, "Run it wherever you want it," or is it more aggressive, like, "Okay, we're moving "to the cloud, you're moving with us." How does it all work, what's the customer conversation like? >> Customer conversation is, you know, customers come and they already decided their pace of going into the cloud, their, you know, maturity level going into the cloud, and for the foreseeable future, there will be a hybrid world, there will be a hybrid world. Still some pieces on-premise, new things on the cloud, application integration within the cloud, application integration from the cloud to on-premise, device integration is coming up, the integration to edge use cases-- >> Yeah. >> Very much a big topic. So, it's a rebirth of our core technology that we are now seeing-- >> Yeah. >> And we are taking our customers with us, and they take us with them. >> You know, the thing that's interesting is that the whole software building market, development or builders, and right tool for the right job, needs to have a broad set of tools available, because if you go to an IoT edge application, for instance, right, that's a complete custom build, in a way, so you don't want to have it be a one-off, just have the tools available, then it's just how you build. >> Yeah, yeah. >> You build a unique solution for the unique use case for the unique workload, use the cloud as distribution, so you need a lot of services, so this is kind of the preferred model versus buying a general purpose application and stuffing it into a use case. (chuckling) >> Well, you've got to understand that when you go to the cloud you're going to redesign a lot of your applications. It's not a simple lift and shift. In some cases it's right new, and on some occasions the developers want to use the tools they love, so you know, you guys have got, what, 10,000 customers? >> Sure. >> Call it 10,000. Those 10,000 customers have all got skills and developers, so you've probably got a million developers that understand Software AG, and they're coming onto the cloud. They want to be familiar with what they're working with. >> Yep, yeah. >> So, what I want to give, and what AWS wants to give the developer, is a consumer experience that when the developer has a project they can find the software. >> Yeah. >> And so, what we want to do is we're publishing Software AG's products right in Marketplace, and you know, yesterday we announced that we now have 200,000 customers in AWS Marketplace. Two years ago I announced for the first time that we had 100,000, so we've doubled the number of customers using marketplace in two years, and the reason is that the developers are showing up and finding the software they want-- >> Yep. >> And the more software we add, the more developers come and use Marketplace. >> It's like going to Home Depot. I need a new tool. (chuckling) You know, I need a new service, hit the catalog. This is the preferred, and with containers and Kubernetes you're seeing that explosive integration happen. People are integrating faster now because of, say, containers and Kubernetes, and with more compute, it's only more goodness to accelerate the Kubernetes and containers, so that's got to be great glue for your business. >> Well, it is just the state of the art. I mean, this virtualization technology has evolved, and now it's there with Kubernetes and Docker and containers, so that's what customers even expect us doing, yeah, and then beyond that they expect us being present in marketplaces, yeah. Like, the AWS Marketplace is the place to be. >> Yeah, it's good for-- >> That's where people are looking for us, so we better be there. >> Containers are taking off for several reasons. You know, if you're a developer, one of the compelling things about containers is consistency of deployment. You can run Kubernetes on your laptop. You can run Kubernetes up on a server. You can run Kubernetes on the cloud. So, you can develop it on your laptop, provision up on the server, and then deploy on AWS, so that consistency is very compelling to the developer. What we're doing is by putting it in Marketplace we're making it really easy, because with ECS and EKS, whether it's the Docker container model, the Kubernetes Orchestrator, we allow the developer on AWS to be well-integrated into the AWS environment. >> So, add edge into that equation, and how does that consistency flow through? What's your edge strategy in terms of developing applications? >> Well, the edge strategy is clearly providing the... At the same time, the same way we provide the platform for our usual application development, there is a huge demand for edge development. >> Yeah. >> So, for example, we have a great customer out there in Germany. They're the world market leader for paint robots. >> Yeah. >> So, obviously if you want to maintain a paint robot, it's an edge thing, yeah, so we want to make sure that the data is close to the edge, is close to the device that it can monitor and do the recognition of failures. >> The thing I want to just add to that, that you mentioned about Kubernetes and the software deployment, is that when you got Lambda, you got these services that are so fast, you can do a lot with that, so as a service you can bring that together. So, the idea of throwing more compute at it, in hundreds of milliseconds you can wrap VMs around things, you can do cool things, so almost a change of buyer behavior is built into the development process. So, that's good for your business, it's good for your business, and companies are changing their business model. So, Cisco, for instance, did a deal with you guys. A couple weeks ago we covered it. They're using EKS for all the cloud stuff, so they have their stuff on their premise, so they go, "Hey, great!" >> Yeah, so containers as a next generation of deployment is one of your choices, right? You can go SAS, you can go serverless, you can go containers, and companies are going to have all three in the mix. All of the software companies that are going to be repackaging for containers, and the other thing that we've done with containers in Marketplace is we're actually metering by the second. A lot of containers run for a very short space of time. I don't know if you know this, but 50% of containers don't run for a week. You spin them up, you shut them down. You spin them up, you shut them down, and so the consumption of the software is moving much more into pay for how much you use. >> And you're granular. >> And we're granular, so we're going to meter by the second. The vendors are typically going to price monthly and annually, or hourly, depending on what the vendor choice is, and so we're going to make it easy for that to happen, and of course, the other thing we do is that by Software AG being in Marketplace it goes on the developer's bill, developer shows up with an account. The developer just gets the Software AG software and runs it, and what makes it really easy for Software AG is that developer has a contract with AWS, but they're now using Software AG's software. >> Well, congratulations, a great opportunity. By the way, I saw the announcement about having a marketplace for machine learning, too. A lot of things happening. >> Right. >> So, the machine learning marketplace, in a way, actually leverages the same capability as the container marketplace, because if you think of it, in machine learning we're packaging up the model, or the algorithm, in a Docker container. >> Yep. >> The difference, however, is that instead of rendering the container into ECS and EKS, we actually deploy the container right into the SageMaker console, so it's a different console, and the user over there is either a data scientist-- >> Yeah. >> Or a developer, but they're going to find that packaged in a container and provision it in SageMaker and then apply the model, and you're right, we announced today... Andy announced the marketplace with machine learning with over 200 different machine learning models. >> Yeah. >> So, we had 160 container packages and we had 200 machine learning models. So, now around the world developers suddenly have access to 300 new pieces of software that they didn't have yesterday. >> I love this market, web services. Going back to the old 2001 timeframe. It's now happening, service-oriented architectures are all happening, catalogs of services, it's what it is. It's being realized right now. >> It is. >> And it's impacting and the results are obvious. The business model evolution, opportunities, not a bad thing, marketplaces of the future. You're going to be all marketplace-driven. >> AWS Marketplace right now is probably the largest live, in production infrastructure library with third party software. >> Congratulations, Dave, nice to see the success. Great to hear about these success stories there, good job. >> And you know, ultimately we've got to remember that what we're delivering is a world class experience to the customer, but a marketplace only works if we have ISVs. >> Hm... >> Yeah. >> So, I want to thank Software AG, because now all of our customers have access to their software, thank you. >> Customers win. >> Thank you. >> Thanks very much. >> It's been a pleasure. >> It's a win-win, everyone wins with the cloud. That's the best part of co-creation and the cloud scale. I'm John Furrier with Dave Vellante. Stay with us, more coverage here, day two of AWS re:Invent after this short break. Stay with us. (techy music)

>> Live, from Las Vegas. It's theCUBE! Covering VMworld 2018. Brought to you by VMware and it's ecosystem partners. >> Hello, everyone, welcome back to theCUBE's live coverage, here in Las Vegas, for VMworld 2018, with Day Three of three days of wall-to-wall coverage, two sets. Our ninth year of covering VMworld, we're going to have like 96 interviews, a lot of content happening, lot of updates from the entrepreneurs, from the executives, and also the partnerships. In this segment we're going to be talking Cisco and Veeam. We got Stefan Renner who's the technical director of Global Alliances for Veeam, and Chris O'Brien, Technical Marketing Director at Cisco. Programmable networks, easy-to-use backup restore, disaster recovery, all those great stuff. >> You guys just get here from Omnia? (laughing) >> Welcome to theCUBE. >> It's a good party. >> Thank you. >> Thanks for havin' us. >> Do we look like that? (laughing) >> I feel like that. (laughing) >> You know, you guys have been very successful on the Veeam side. We had Peter McKay, the co-CEO on yesterday. Cisco has been very active and relevant in programmable DevOps, or DevNetOps, as it has been called in there. So the need to make things programmable and easy, are a nice combination. You guys have a partnership. How is the Cisco/Veeam partnership going, how did it start? Take a minute to explain, how it all came together, and what's the current situation of the partnership? Well, I think from a Cisco perspective, the partnership is going great, fantastic. They were Partner of the Year. What we're hearing from our customers is they want us to solve some of their problems around how do they scale and manage their data, right? I'm from the UCS Business Unit. We see an opportunity for us to bring UCS was built on programmability, right? We have the APIs, we have those capabilities. We started out with Veeam a few, I guess 18 months ago, maybe two years ago, really focusing on some solutions around our HyperPlex platform, and we released a number of validated designs. When we do these validated designs, it's not just Cisco doing the work. We're in the labs together, we're developing the solutions. >> With Veeam. >> With Veeam. All the engineering efforts, and then obviously, as you go through and you grow that solution, you really see an opportunity where you can enhance the solution. So things like automation, we want to bring that to the table, certainly, with our partner. >> And what's your contribution on this? Obviously, Veeam's role in the solution. Are you guys doing joint validations, or joint engineering? Talk about the integration piece with Cisco, why it's important. >> If you look back, maybe it's two years, right? I took on Veeam actually three years ago, three-and-a-half years ago, and when actually, we really started to kick off the thing with Cisco. So it's a bit more than two years, I would say it's three years, right? But in these days, a couple of years back, it's more about finding a right data protection platform, where we can host Veeam on. Meaning a backup server, right? And these days, it was more about back and recovery. Well, today we talk about hyper-availability. It's not only about backing up stuff or recovering stuff, it's about providing the whole platform, the whole orchestration layer for data availability. Back in these times, three years ago, it was about finding an s3260 or a c240 server of Cisco, which fits exactly the needs we need for Veeam to run on it, right? But over the last, now, 24 months, since Cisco really started HyperFlex and going into hyperconvergency, we partner with them to make sure we have the right data protection for this kind of solution. That's what you just talked about, talking about integrations. We really invested a lot of time and efforts on both fights, it's not only Veeam development, it's also trying to see Cisco develop, to integrate into HyperFlex, to make sure we can provide the right data protection for the customer needs are. >> So talk about the high availability, I just want to talk about that for a second, 'cause I think this really highlights one, the relationship, and the desire in the market for realtime data, whether it's for developers, or for applications, to integrate. High availability is about having data available and integrating into whatever that would be, whether it's a mishmash of application development, and routing across networks. This is a huge deal, this is not like a punchline. High availability used to be, oh, we have a data center where it's fault tolerant. There's a whole another new level that that's going to. Can you just talk what that means, because backing it up and making it available means something different now. >> Yeah. >> Talk about that. >> I do agree, because again, looking back, it was really about backing up and recovering stuff. If I look back couple of years, customers were looking for a solution, that are able to pull the VM out of the v-stream data center, make sure it's stored somewhere, and they can't get it back once it's deleted, right? >> Check. >> But now, if you look at Vmworld, right, we have it at Vmworld, it's all about automation, it's about APIs being true. I can integrate this data protection platform in my centralized management interfaces, making sure I have an orchestration layer on top of it, so it's not only about backing up and recovery anymore, it's about the whole stack from end-to-end, right? Getting data from A to Z, maybe get it offsite to an S3 storage for longterm retention. So, we really went from an on-premise, very small kind of solution stack to a big solution stack, going from a VM into the cloud, and overlaying that stuff. >> Stefan, I want you to comment on this, and of course I want to get your take as well. Talk about the time aspect of it, because you mentioned, okay, I can get it back, okay, got to get the data back. When you talk about making data available, the time series or the timeframe, is critical, in some cases, latency, nanoseconds, milliseconds. This is the new normal; you guys got to make that happen. Talk about that dynamic, are customers really doing that, obviously that want it, but what are some of the examples? >> No, they are, they are. In terms of speed, like in data protection and availability, if I talk about speed I really talk about SLAs, and the RTOs, and the RPOs, so how often do I backup, how often do I have a recovery point, that's what you just talked about, and how fast can I get a data application back once it's gone, or once it's deleted, or once it's discovered an issue in the data center. Again, over the last couple of years, that really involved because in the early days customers said, you know, I want to have that, but it's luxury, right, I don't want to pay for it, it's too expensive, I can't afford that. But looking in these days, and today, even at the conference, you talk to customers that say, I need it, it's critical, I cannot live a second without my data. So this kind of RTOs requirements, they really went down from, maybe a day, which was usual ten years back, to like five minutes, ten minutes, fifteen minutes, right now. That's maybe the maximum you can really afford as a customer, and that's where the integration part comes in, and all the stuff we do with Cisco, because with integration we can actually make sure that we can cover that, and get data back in ten minutes. >> So we're really talking about a whole new way of delivering infrastructure. If I go back to the early days of UCS and conversion infrastructure, yeah, we can support a thousand VMs, and they're like, how are you going to back a thousand VMs up? And they're like, uhhhhh, well, let's see, we're workin' on that. Today, you got your take in this platform approach, it's a fundamental part of cloud, developer, DevOps, and so I wonder if you can talk about, you know, when we were at Cisco Live, the DevNet area was one of the most exciting parts of the show. And if you think about traditional enterprise companies, really, not many, I think even one, has really done a good job with developers, it's Cisco. So where do developers play, is this a platform play, really, for cloud and hybrid infrastructure? I wonder if you can talk about that, the role of developers, and how you're approaching this mindset. >> Yeah, I think from our perspective, there's no downtime window, there's no scheduled windows of downtime, right? >> It's not allowed. >> We don't have that anymore. The way that we look at our infrastructure, we certainly want it to be robust, to address latencies, issues and concerns, and what we're doing with Veeam is really tweaking that infrastructure to make that data available when it's called on, so you can consume it as a developer, as a part of the DevOps team. All of our infrastructure, as you guys probably know, are all open systems, all policy-based models. So with these APIs being available, it allows developers to consume more, if they need to scale-out these infrastructures quickly, we can do it. We're certainly playing in the DevNet space, it's growing, we have our own separate conferences. >> The network becomes more and more important, every day, I mean, at a whole 'nother level. Talk about program ability, you got to be ready for anything Veeam wants to do with you, or whatever the customer wants with respect to high availability. >> Yeah. >> And as the definition changes, you got to be enabling that. >> Totally available if you can get to it through the network. (John laughing) And we certainly carry that all the way through the UCS fabric. >> Talk about Veeam strategy, because I think there's general perception that, oh, Veeam does backup for small- and medium-sized business, that's Veeam. And we had Peter McKay on yesterday, he said, "A third of our business is SMB, a third is commercial, a third is enterprise," number one. Number two is, you guys are getting into the orchestration and management for data availability. Can you talk about the extension of Veeam, in that regard? >> I want to actually grab on your number, because we talked about, oh, we got a thousand VMs, that needs to be backed up and recover. That was a couple of years back, Today, we talk more about ten thousand VMs. Customers actually here at the booth, I talked to customer that talked about ten thousand to twenty thousand VMs that needs to be available. Now I would call a customer that hosts ten thousand VMs no longer an SMB customer, right? That's more of the enterprise, and you're right, and I guess Peter McKay said the same. I didn't actually watch the video, so hopefully, I'm inline with him, but it's really he's, for sure, going into the enterprise, making sure the products actually fit the enterprise's needs. Talking about the orchestration piece, I mentioned before, Veeam Availability Orchestrator we recently announced and released, that's certainly a step into the enterprise market because an SMB customer, even a mid-range customer, they will not invest in an orchestration layer that provides the full capabilities of fade-over secondary data centers, and all that stuff. That's certainly an enterprise play, and that's also where the company's heading to, making sure we have the right fit for the still SMB customers, and mid-range customers, because I think they are still important to the business, right? I'm not saying they're unimportant. But also having the right products, and the scale. And I think scale is actually something we going to talk about anyway, in this conversation. The right scale, to even cover that customer, ten thousand VMs, twenty-thousand VMs, they are approaching us. >> I think the other big trend that we see, and I wonder if you guys could comment, is, again, data protection, backup, used to be an afterthought, and it also used to be kind of a one-size-fits-all. So that'd mean, almost by definition, you're either under-protected or over-protected, spending too much, or too little. Today you're offering much more granularity, and the like; it's a fundamental component of the platform that you're developing, and it's extending beyond just backup. Call it data protection, there's a security component, there's a DevOps and cloud piece, there's a management piece. Maybe you guys could give us your perspectives on those trends. >> Yeah, so short comment on that one, actually, in each and every one of my sessions I speak here, I always say, once you consider to replace your storage system, or your v-stream wired man, or you consider to use HCI, make sure you include data protection immediately, on Day One of your project, because, you're completely right, the last year or so, even still now, a lot of customers I'm going to, they tell me, oh, I replaced all my infrastructure last 6 months, 8 months, and now I want the data protection. Then I get in and I say, yeah, unfortunately, what you did on your infrastructure is completely wrong for the expectations and the requirements you have in data protection. So that's exactly what to talk about, you need to bring together those projects and make sure you bring them under one hood, and talk about this from Day One. Otherwise, you might get in to a wrong direction. >> Yeah, that whole-house view of the world. >> I think, from a Cisco perspective, we really look at, we're unifying the data, we have what your intentions are, your intentions are production apps, your intentions are data protection. I think through ACI we can certainly create the application profiles to make that happen. We carry through our fabric with the UCS system, so for us, we see ourselves as flexible enough to deliver all these options, obviously there's some improvements that we can bring, you know we were talkin' earlier. But that's part of the road map, and part of the way we want to go with Veeams. >> I think one of the things I'm impressed with Cisco about, and looking at the analysis, is that the network guys have always had the keys to the kingdom. You go back to IT, you go back twenty years, if you were a network guy, you ran the show. And you had storage guys came in, they became that same kind of tier, but the network was running everything, everything was sacred. Couldn't let the network go down. It ran offices, it ran branches. And then, when the cloud came, the network now with Cloud Native, and some of the stuff going on up at the stack, makes networking skills, people who think like a networking guy, really valuable, because the data needs to be networked. So, the data's now at the application, that's where the security is, so as you guys have your Veeam, you have needs, you're moving data around, you need more in Cisco, you're going to be better for him, so this is a nice dynamic. >> We're trying to instrument it so we understand what their needs are. If you look at AppDynamics, if you look at Tetration, all these things give us more and more visibility to make the right decisions, and hopefully those will all be automated down the road so we can move as fast as the business wants to. >> Well, and I think of things, you know people talk about air gaps for ransomware, but you need more than air gaps, you need analytics that identify anomalous behavior, and the corpus of backup data has all the data there, and if you can figure out how to analyze it, you're going to have a leg up. >> As you said, that's actually a good point because ransomware, and all that stuff, like Tetration, your project to analyze the network traffic and making sure-- I actually get informed, or I take an action, once I identify ransomware attacks, that's something that we can partner up with, because it would literally mean if Cisco identifies an attack, right, they can trigger automatically a backup or a snapshot backup of the data to make sure we actually have a backup right before the attack happens. So you can see a chain of activities and potential new products, or go to marketplace in the next couple of months and years. >> A lot of opportunities. >> Because there is a lot of stuff, and a lot of potential behind those technologies. >> And there's clear visibility from a customer standpoint, that we would report here on theCUBE, that's lookin' at nanosecs and things of that nature, where at the application, whether it's a V-map, or other things. Security and data has to be centric around the app, it decouples from the network so that you're not bumping into each other, you're helping each other, you're more effective. You help them, you guys help each other. This is the new stack model, this is the way it's going. >> I would say that's all what alliances is about, right? (laughing) It's why we have alliance business, right, because no one, neither Cisco nor us, we couldn't do it on our own, we always need a partner to do that. >> Guys, thanks for comin' and sharing the partnership news. I really think, and Alan Cohen, our CUBE guest this week, said, partnerships used to be a tennis match, now it's like soccer, a lot of things going on, multiple players, certainly you know that, Cisco's been doin' a lot of that for a while. Great stuff, thanks for coming on. Final question for you guys, big takeaways from VMworld 2018 this year. Comment, what's your thoughts, third day now, lookin' back, what's the theme here, what's the big story that people need to know about? >> Just from my experience, I've had a lot of conversations around security, and bringing it to our solution, more embedded within. I'm part of the Validated Design Program, and they're asking, at least the conversations that I've had on the floor here, has really been about showcasing some of the other aspects of Cisco, what we can bring from a security perspective to protect the data. I'm certainly bringing that home. >> Awesome. >> And what are you seeing? I just can continue what he said, because the most conversations I had is around scalability and still the data growth. We've been talking about that the last couple of years, but the more data you have, and the more VMs you have, the more challenging it is to protect it. It's all about scalability and making sure you can really cover and fulfill your needs. >> Well, congratulations on your success at Veeam, the numbers don't lie. You guys are doing very well. >> Thank you. >> Congratulations on Cisco, you guys have a clear line of sight on what you guys want to do with the network. >> Thanks. >> It's great to see, thanks for comin' on. Appreciate it. >> Thank you. CUBE coverage here, live, in Las Vegas. From VMworld 2018, it's theCUBE. I'm John Furrier with Dave Vellante. Stay with us, more Day Three coverage after this short break. (techno music)

(upbeat music) >> Narrator: Live from the Wynn hotel in Las Vegas, it's theCUBE, covering Magento Imagine, 2018. Brought to you by Magento. >> Welcome back to theCUBE's coverage of Magento Imagine 2018 from the Wynn, Las Vegas. I'm Lisa Martin, and I'm excited to be joined by some award finalists of the Magento Imagine Experience Awards. We have Dr. Markus Reheis, the chief marketing officer at Gabor Shoes, and Stefan Willkommer, the CEO of TechDivision, a systems integrator. Hi guys, welcome to theCUBE. >> Hi. >> Hi, nice to meet you. >> Great to have you guys here. Congratulations on being an award finalist for the best sales channel growth. We're going to talk about that in a second, but Gabor Shoes. Talk to us about what Gabor does, where you guys are located, and then we'll talk about how you're transforming the shoe space with e-Commerce. >> Gabor is a manufacturer of shoes, ladies shoes only. Our headquarters in Germany, and our main markets are in Germany and central Europe, but we do also export to 60 countries, more than 60 countries worldwide, to China, to Korea, also to the United States a little. >> Lucky me. So your customers are the retailers themselves. How many different brands do you have, different SKUs, different products? I imagine it's massive numbers. >> Yes, our main product is ladies shoes, as I said, we have several brands for the ladies shoes, Gabor, Rollingsoft for the sports shoes type, but we also offer bags, handbags, and socks and tights, but the most important is the ladies shoes for us. >> So many, many hundreds of thousands of SKUs, lots of different locations. You've been the chief marketing officer there for quite a long time. Talk to us about the opportunity that you saw, that you could give to your retailers by expanding this physical in-store shopping experience into the online world. >> Yes, we have started our online business quite late. We long time hesitated to set up our own online shop toward the end user, because it was always our philosophy that we want to be partners of our retailers. We did not want to compete with them by opening an online shop. So it was clear for us that when we start our online business, we want to have our retailers as partners on our side. And that's why we developed this Omnichannel concept that integrates the retailers. >> Omnichannel is a word that we hear a lot at events like this. It's critical for a seamless customer experience. We were talking before we went live is we're all consumers, everyday lives, we pick up a tablet or a mobile phone and we expect to be able to find whatever we want at a simple click. What was the process like of becoming partners with your retailers? Was it an obvious sell to them, that they have revenue-generating opportunities, the opportunities to reach many more customers in different regions? Or was it more of a challenging conversation to convince them? >> Yeah, let me explain a little bit about the situation. We do distribute via traditional classic brick and mortar retailers primarily. There are around 5,000 companies worldwide that buy shoes from Gabor. They have around 20,000 stores. But the situation is that there is quite a mixed structure of retailers. Many of them are small businesses, family owned businesses, and they do not have the chance to have an own online presence that is competitive. So they have to focus on their brick and mortar business. But nowadays, in Germany, we have around 30% of all shoes that are bought online, and this brings many of our customers into trouble. So it was our idea to open this marketplace to let the traditional retailer participate at the online business. >> So talk to us about, from a technology perspective, the modern technology that you needed to be able to deliver this. So, Stefan, talk to us about how TechDivision, as a long-time partner of Magento, is helping in working with Gabor shoes to enable this Omnichannel experience. >> First of all, the marketplace looks like a simple online shop. So for the end consumer, it looks like any other online shop where you simply buy just the shoes. In the background, there are a lot of processes going on, so like we have to allocate orders to stores, to inventory locations, that means the retailer, and we have to look where the inventory is. We don't want to do a lot of order splitting, of course, because we don't want to ship a lot of different packages, and you need a sophisticated solution who is capable of doing exactly that, because I mean there are a lot of processes, and of course algorithms that around, what do you need, or what do you have to have in place that you can do that like that. And Magento was offering, with the order management, exactly great product to delivering that. So this is the foundation for the whole concept, and makes it able for us, and for Gabor, to integrate the retailers really smooth. >> So how many retailers are integrated currently? >> We have started the concept just a few months ago. It was surprising that so many retailers contacted us and said, I want to be part of that system. So we have around 100 retailers with around 400 stores that still have to be connected. Currently we have 40 stores connected with the system. >> So you had retailers that were proactively reaching out to you, saying, we want to get in on this? >> Yeah. >> Wow, that must have been pretty exciting. And Stefan, you mentioned the word simple. And that's something, as buyers, we want a simple, clean experience as the consumer, but also for the retailer, right, and the supplier. Talk to us about how you're leveraging, You mentioned the Magento management software, to give your retailers this complete visibility of their inventory so that they can fulfill through the right channels. >> Yeah, first of all it is in the German market, in the German retail shoe market, a little bit simpler, maybe than in other countries. There is a couple of POS solutions, there are not too many, and we build a basic interface so they can really easily attach their inventory to our order management, or the order management of Gabor, and then we are able to utilize the different inventory, or the different stocks. That is pretty simple. There are a lot of other processes which are not really technical, it's more about contracting, so more the retailers, there is of course some training, you have to train the people, the staff, because they have to use the platform, and that way they see, okay, an order is coming in, what they have to do now, they have to create the pick list, they have to pick the stuff, pack the stuff, ship the stuff, print the label out, putting the documents in and everything, so you have to train the people and the simplicity is because they just need a simple web browser to do that. So either a tablet or a PC, that's all what they need. They don't need any other software. They don't need really other devices. Basically most of the retail stores already have these kind of devices in store. >> So they can utilize an existing POS system, or maybe an ERP system ... >> Yes. >> instead of having to replace things. So from an integration perspective, it sounds like it's a fairly... >> Yeah, they don't have to invest really money, they just have to, I mean, bring the inventory, and that can be through a flat file, or through a web service, so both possibilities are there. Right now they just need a web browser connected to the internet, that's all. >> Sounds so simple. >> Yeah, it is. >> So let's talk about, we hear the term digital transformation used everywhere, and it means different things to different organizations, depending on where they are in that digital transformation journey. When we look at commerce, commerce is becoming a center of gravity for digital transformation. Markus, talk to us about the transformation that Gabor has undergone. Where are you on this digital transformation journey? >> Still, we are really right on the beginning. We have installed, of course, digital tools to make the sales process easier, but we always thought about B2B processes. For example, we have installed a B2B online shop maybe 10 or 12 years ago, so that's an existing thing. The new thing for us is that we go towards the end user. I have a number for you. We produce around 9 million pairs of shoes every year, and still the amount what we directly sell to the end user is a very, very small amount. So we are at the beginning of this process, but we have ambitious goals, and we want to grow in the future. We started our marketplace concept in Germany, but we want to roll it out to other European countries, maybe to countries outside Europe, so there's a lot to do, a lot of opportunities for us in the coming years. >> So let's talk about the rest of 2018. Here we are in April. You're going to be adding many more retailers. What are some of the things from a technology perspective that TechDivision is going to be able to do with you, and maybe Magento, to start finding the other 8 million in opportunities that you just mentioned? >> In the end, it's not just a thing of the online store, but of course it's a thing of online marketing, so this has to be increased, definitely. Yeah, we still have a lot of things to do, onboarding the retailers, and this again is not just a technical thing, it's a lot of, Stefan already told you, it's more to do with training, and explaining the processes, that costs a lot of time. So it goes step by step, but we make good progress there. >> So last question, Stefan, for you- as the chief marketing officer, I'm a marketer myself, tell me about, from a digital marketing perspective, as consumers, and really, in the B2B space, Magento had a study on their website that said 93% of B2B buyers want to purchase online, right? So we're seeing that trend as consumerization into the business space. From a marketing perspective, there's a lot of shifting going on there, too. Big data has been a big enabler of marketing becoming a science. And being able to demonstrate to the business and influence business there. Tell me a little bit about, in the last minute or so, how are you leveraging big data and analytics, maybe even through Magento, to redefine marketing that you're doing at Gabor? >> Yeah, again, big data can help us to build customer groups, to send them individual offerings. That's a good thing about the digital business, when we have a satisfied customer, of course we can always, again, send them products, product offerings, but these offerings have to be individual, they have to be relevant for the end user. That's why artificial intelligence, for example, can help us. >> Maybe to add something, we already started using Magento BI, for example. So we are using that full commerce suite of Magento right now. We are using the areas to measuring how fast the shipment is done through a retail stores, and then adjusting the allocation to that measurement. So if a retailer is shipping faster, it's getting more likely that he's getting an order allocated next time, when an allocation run is taking place. So we are using this to get a better end user, consumer experience, by having the products earlier, or more frequent ship. >> Right. So many benefits for the businesses, the retailers, maybe repeat sales, they've got this instant purchase capability that Magento released recently, one click, get things even faster, reducing checkout time, all the things that drive up repeat business. But also the personalization front is going to be key to be able to deliver, as you said, Markus, the relevance offers that we all want. >> Exactly. The great benefit of this marketplace concept is that since we have connected the different stocks of our retailers, we can offer much more product by this way than we could do it alone. So the offering of our 3,000 styles per season gets nearly unlimited to the end user. >> Limitless. We talk about limitless commerce. Well, gentlemen, thank you so much for stopping by and having a chat with me today. We wish you good luck on the award nomination. I hear those are being given out tonight, so best of luck, and we hope to see you again on theCUBE soon. >> Thank you. >> Thanks a lot. >> We want to thank you for watching theCUBE, live from Las Vegas at Magento Imagine 2018. I'm Lisa Martin. You're watching theCUBE. Stick around, we'll be right back with our next guest. (upbeat music)

>> Announcer: From Barcelona, Spain, it's theCUBE covering Cisco Live 2018. Brought to you by Cisco, Veeam and theCUBE's ecosystem partners. >> Here in Barcelona, Spain. It's theCUBE's exclusive coverage of Cisco Live 2018 in Europe. I'm John Furrier, co-host of theCUBE, with my partner in crime this week Stu Miniman, Senior Analyst at Wikibon. Also co-host of many events across the world in terms of networking, storage, Cloud, you name it, Stu is on the developers with me. Stu, thanks. Nice seeing you. Stefan Renner is Technical Director, Global Alliances at Veeam Software is with us with Darren Williams, @MrHyperFlex, that's his Twitter handle, go check him out. HyperFlex-V at Cisco, guys welcome to theCUBE. >> Thank you. >> Also love the Twitter handle. >> Darren: I live the brand. >> You live the brand. I mean that's got some longevity to it, it's evergreen. So congratulations on that. You guys are together with Cisco Veeam, what's the story? What's going on in Europe with Cisco and Veeam? >> I would say there is a lot of stuff going on between Cisco and Veeam. Especially around the Hyperflex story, obviously is the topic of this session, right? So having integration, Hyperflex, having a good go-to-market, having a good relationship between the two companies. We just joked about how often we've been in front of cameras talking about this exact same topic. So that shows that the relationship between the two of us is really moving forward and in a good shape. >> I think we're in good shape in terms of, you think about not just my product, Hyperflex, but you look at what Veeam can do for the rest of Cisco data-centered products, and be that backup, safer hands around what we need in terms of that data protection layer. But also then, what we can add in terms of that target to be the server of choice for backups so you get the benefits of the speed, performance, and more importantly, you get quicker restores. Because that's the important bit, you need to be able to do the quick restore. >> Yeah, we usually talk about availability, right? We don't talk about backups or recovery. Even if recovery is maybe the most important part of availability, still we talk more about availability than maybe anything else. The good thing about Cisco is that the actually can deliver what we need in terms of performance, in terms of capacity, in terms of compute resources. So yeah, that's a real benefit. >> It's such an interesting time, I mean we look back at history, go back 10 years ago, maybe, or more; backup recover, that's like, "Oh, we forgot to talk about that in our RFP." Kind of bolted on, kind of retrofitted in. But now we've seen it come to the main center. But more importantly, with AI and Cloud, and all the action happening with DevOpps on premises, you hear CIOs and CXOs and developers saying, "We're data driven." >> Yeah. >> Okay, so if you're data driven, you have to be data protection driven too. So those things go hand in hand. So the question for you guys is how does a data driven organization, whether it's in the data center, all the way up to the business units, or the business processes, become data protection built in? How do they design in from day one a data protection system up and down the stack? >> Yeah, so maybe I'll start to answer that question. I think when I'm going to customers, and I fully agree on what you just said, most customers 10 years ago were focusing on getting used to platforms and getting used to org systems. It has to be an isolated project, right? Now in those days when I go to customers I tried to convince them to include data protection in every project the do in data center, because at the end, data protection is one of the core elements. >> So designing in early, at the front end? >> I say whenever you go about having a new Hyperflex system or whenever you talk about replacing your existing environment, whatever you do, right, just look into data protection, looking into your availability story. Because right now, and you mentioned that, it's about data services, right? We don't really talk about restoring of EM, we don't restore to the single file. It's about, the customer wants to have a data availability in terms of a service availability. And that includes more than just the VM, it includes more than just the single thing, right? >> Yeah. So they need to include data protection and the design of that in the whole org chart. From the beginning. >> And you're point? >> Yeah we look at it from a similar thing in terms of where you've got changes happening in terms of the way people are looking at how they want to design their applications, where they want their data to live. And that's the whole messaging around 3.0, is that multi-Cloud readiness platform. Being able to think about an application and go, "Do I want to design in the public, and house privately, "or vice versa? Do I want to house the data "of the application in a private location "and the actual application in public?" Having that being able to be transparent to a user in terms of the way they design it and then position, but also as we look at other applications, not all people on this journey are going to go, "We're going to put everything in the Cloud." They're going to look at about, maybe have a little bit in the Cloud, a little bit of the traditional apps we need to manage and protect. And it's all about that 3.0 that we've delivered the pre-multi-Cloud offering around Hyperconvergence, we've now brought the multi-Cloud element. It's giving you the choice of where you want to position things, where you want to house things, how you want to design things. And keeping it nice and simple for customers, and the agility and performance. >> Darren, some really interesting points that you just had there. When I think back to a few years ago, Hyperconverge, pretty strong in North America. But it was project based, it was like, let's take a VDI, some virtualized environment, it wasn't a Cloud discussion. >> Darren: Correct. >> Take us inside what you're seeing in Europe here, because today Hyperconverge is a lot about Cloud, how that kind of hybrid or multi-Cloud environment, so what are you hearing from your customers? >> Absolutely, and I think if you look at the, what's happened in times of Hyperconvergence up to this point it's the initial building block of this multi-Cloud. And we're seeing more and more customers now, I think the latest IDC survey, surveyed that 87% of all customers have a multi-Cloud strategy. And we're seeing now more of the ability to think of Hyperconvergence as that multi-Cloud strategy, and have that simplicity that people have done in terms of the initial thought around a simple application, how they can collapse the layers, they can now utilize that experience into the multi-Cloud experience. And we're seeing more and more of that. We've now got 2500 users around the world around Hyperflex, and about 700-800 EMEA, and the majority of those are utilizing it as private Cloud experience. They're getting the benefits of what they've had in the Cloud, and getting away from the sovereignty issues, and the shadow IT issues that they all face. They can now bring it back into their own data center. They can start small. They can spin out applications very quickly. They're getting the benefit of that Cloud message, but locally now. >> And I think that perfectly aligns with the Veeam story because as you know we are also focusing on the Cloud. We recently changed and also did some acquisitions on the Cloud, so we're also moving forward in the Cloud story and the HyperCloud area. And that's more or less what Cisco's multi-Cloud's story is also about, right? And I think one thing we should also mention here coming a bit back to how to implement and how to design such solutions as having more of a broad view on all the projects. I think one important thing for customers is the CBD Cisco has, right? And we do have CBD available to beam Cisco on the data protection layer. So we try to make it really easy for customers and for partners to design, implement and actually do the right decisions for those projects. >> Stefan, at Veeam On, of course a lot of partners, a lot of talk about the multi-Cloud, of course Veeam has a long history of VMware, but why don't you talk about Microsoft? I believe there's some things you've been doing lately with Hyper-V and the like, what's the update? >> Yeah, so obviously with Hyperflex there is Hyper-V coming, right? That's one of the bigger things coming to Hyperflex. Now for us, when we started to talk with Cisco, Cisco actually told us that Hyper-V is next and 3.0. We said that's fine for us, because as I said, we are dealing with Hyper-V like we did with VMware since a couple of years. So there is no big difference in terms of features and what we can do with Hyper-V. On the Microsoft side obviously it's around extract, which also is a big story with Cisco and Veeam, because there is a extract solution, and so we tried to get the extract fully integrated in the Veeam portfolio, and it's about effort, right? As we just talked about, making this Cloud journey even easier for the customer, making sure we have data protection forever, or making sure we can actually use our Cloud solutions to provide the full experience in the cloud. >> So the question on European audience, I was just looking at some Twitter tweets, getting in some feedback, is, "Ask the GDPR our question." Which is basically code words for the sophistication between data protection, you know we say as you get bitten in the butt if you don't prepare. And this is one of those things where I mean literally, there's so much data out there, people can't understand their own tables. I mean, if you have accounts, how do I know a user uses a certain name in this one, I got a certain name in this database, I mean it's just a nightmare to even understand what data do you have, nevermind taking someone out of a database. >> Yeah. >> So, the challenges are massive. >> Yep. >> This is coming down and it really highlights the bigger trend is: what do I do with the data, what is my protection, what's my recovery, how do I engage in real time, GDPR issue? Talk about the GDPR issue, and then what it really is going to mean for customers going forward. >> Well, I think if you think about GDPR, and people, I've got the understanding that it's just a mere thing, it's not. It's a worldwide thing. Any data that relates to a European citizen, anywhere in the world, is covered under the GDPR. So you've got to think about the multinationals we work with, have to have this GDPR thoughts, even if they're not based in EMEA. They may house data based around a European citizen. So it's a massive thing. Now, not one person or one organization can fix GDPR. We're all part of a bigger framework. So it looks like if you look at the Hyperflex offering, having self-encrypting drives, having good data protection and replication of the data so it's protected. That protects the actual content of a record, but it doesn't solve everything around GDPR. There's no one organization that can do that. It's about having that framework of you do the right decisions around the architecture, and the data protection, you'll get in there in terms of the protection. >> Well, I mean, I'm just going to rant here and say whoever came up with GDPR doesn't know anything about databases, okay. >> Darren: Yeah. >> I mean I get the concept, but, I mean, just think about how hard it is to deal with unstructured data, and structured data in and of itself within a company. Nevermind inside a company, what's happening externally, it is a technical nightmare. And so, yeah, just hand waving, "Hey, someone came "to your website." Well, did they come in anonymously, did they login, which identity did they login on? There's no - I mean it's a nightmare. This is a huge problem. What do customers do? >> I think if you talk about GDPR it's first of all not about a single solution, right? It's not an issue of just one company, or one vendor, one solution. It goes across different databases, different applications, different software, so as you said, it's database solutions, you need to delete maybe a single table entry, which is almost impossible right now. Especially if that's ina backup, right? How are you going to do that? I think between Cisco and us, and he mentioned that one important part of GDPR is data protection itself. So the customers need to make sure they can actually promise and they can show to the government that they have a proper data protection in place, so they can showcase what does my DR plan look like? How do I recover? What is my RPO? So we can already solve those issues. >> It changes your game because, for you, it turns you into a insurance policy to a proactive; in order to do data protection you actually have to know what the data is. So it kind of creates an opportunity to say hey, this is an opportunity to say we're going to start thinking about, kind of a new e-discovery model. >> If you look at 3.0, the multi-Cloud platform, we were discussing around how Hyperconvergence started very small in certain apps. But when you actually then expand that out into the multi-Cloud, security is a major pillar. And you've got to have the security elements, and Cisco has some great security offerings in the data center and outside of the data center. They all form part of that GDPR message. But it's been baked into multi-Cloud 3.0. as a key component to allow customers that confidence. >> It's going to be a Hyperconvergence of databases. So this is coming. >> Darren: Yeah. >> So this is going to force, I think the compliance is going to be more a shot across the bow, if you will. I don't know how hardcore they're going to be enforcing it. >> It's going to be interesting in the first one. Because at the moment I think a lot of customers are thinking, "Well, we'll wait till we see "how big the fines are, and then we'll decide." >> They're going to create shell corporations in the Cayman Islands. (laughter) >> Alright, so we've talked a little bit about some of the headwinds we're facing in IT. Talk about the tailwinds. A lot of things in the Hyperflex 3.0, got 700-800 customers, what's going to drive adoption, get that into thousands of customers here in 2018? >> So I think it's the simplicity message. Customers want ease of use of technology. They want to get away from what they've had before where they've had tough times standing up applications, where they've had to invest time around different skill sets for the infrastructure, be it networking, be it storage, be it compute. Having 3 teams back leaning against each other, and change windows. So the simplicity message of Hyperflex is you can have a three node cluster up and running in 34 minutes, including the network. We're the only ones that incorporate the network into the solution, and we do it for good reason. Because when we can get predictability in performance, and we can grow the solution very, very easily. And that's the whole point of what they're doing, is they want to be able to start small, and add more nodes when required, around what applications they're going to deploy on. Our tagline is "any application, anywhere" now, and either private location or into that multi-Cloud location. Gives customers choice, and I think as we start seeing more and more customers, 700 in just under 2 years is a phenomenal amount in EMEA, and 2500 worldwide, we've had some great traction. And it's just going to get faster and faster. >> Yeah, I think a lot of customers are obviously talking about moving to the Cloud completely or at least majority of the data. So for the customers that stay for them, and I talked with some customers today, and they told me, "For us right now, we can't focus "anymore on a data center itself. "We do have much more difficult and more important "topics to talk about and to cover in our IT business "than the basic data center itself" That includes compute, that includes digitalization. So it's great to hear you can actually set up a Hyperflex system, no matter if that's Hyper-V or VM or whatever in less than an hour, right? And if I tell you now that if you add Veeam on that to provide the availability for Hyperflex environment that's also less than an hour. So if you know how to configure that you can be done in a couple of hours, and you have more or less the whole data center set up. >> You bring up a really good point. What are customers concerned about? I have to worry about my application portfolio, I have my security issue, my whole Cloud strategy piece, so, if the infrastructure piece is just invisible and I don't have to touch it, tweak it and do that, I'm going to have time to actually grow my business. >> The more integrated it is, the more easy it is to set up and to maintain and troubleshoot by the way, that's also an important thing, right? What if it doesn't work? If there is a consistent layer, a consistent way to get all this information sent to get a troubleshooting thing done, the better it is for our customers. Because again, they don't want to care anymore about what's happening in the back end. >> And that's the next challenge we're addressing, in-app product or Insight, is taking that management solution into the Cloud to make things easier for customers. And being able to take a lot of the things we have in point product into a Cloud model. So the likes of analytics, the likes of Smart Tac. Customers get fed up if when they have an issue they have to go and roll the logs up into Tac, and then go and FTP them. They get away from that, they don't need to do that in Insight. And it's all about, we're talking about the deployment of technology, well one of the fist benefits of Insight is Hyperflex. We can roll out sites without even visiting them. You just do a Cloud deployment, and a Cloud management, and it's job done. >> And this is the whole point we were kind of getting at earlier, connect back to the compliance issue, these agile like things are happening; it's throwing off data too. So now you got to organize the data, you can't protect what you don't understand. >> Correct. >> I mean that is ultimately the bottom line for what's happening here. >> Yeah, you can't protect what you don't understand, I think that's a good conclusion of the whole thing. And I think for us >> By the way when you guys use that tagline I want royalties. But it's true. (laughter) We'll get back to you on that. No, but this is a big problem. Protection is inherently assuming you know the data is. >> Stefan: Yeah. >> Darren: Yeah. >> There it is. >> That's for sure the case, and one thing we worked on and, you know, we announced it a couple of months ago, was the Veeam Ability Orchestrator, which is another layer on top of it. So he just talked about how they can deploy within the site, multiple sites of Hyperflex very easily. And for us it's about, you know, getting the customer an easy solution with all the successful recovery and failovers in areas across the data centers with the Availability Orchestrator. >> Data is the competitive advantage, data is messy if you don't control it and reign it in, of course theCUBE is doing their part and bringing the data to you guys here in theCUBE with Veeam and Cisco partnership. I'm John Furrier, Stu Miniman breaking it down here at Cisco Live in Europe 2018. Live coverage with theCUBE. Be back with more after this short break. (techno music)

>> We're back in Las Vegas at the ARIA for Fal.Con 22, CrowdStrike's big user conference. I'm Dave Vellante and you're watching the cube. Sven Krasser is here as the senior vice president and chief scientist at CrowdStrike and we're going to get a masterclass in AI for security, Sven. Thanks for coming on. Appreciate it. >> Thanks for having me. >> So I love the title. I just, I'm excited to have you on, I understand you were like employee number two or, you know, really early on >> Among the initial nine. Yeah. >> 11 years ago and I think two days you started. >> Yes. >> What was that like? You know, was that, you know, did you know George beforehand or you kind of? >> Yeah, I, I knew I knew George before, like not as well as I know him now. >> Yeah. >> And it, it sounded like a pretty good proposition about what he was having in mind. Like things security wise didn't really work that well back in the day. And we wanted to try something new, like cloud native, data driven, AI, and use that to stop, to stop breaches. So yeah, like it was very exciting. Like you go there, you have nothing there. First day, you open your laptop and you try to reinvent security. >> Yeah. So, I mean, I know he never, he talks about this. I never said we're going to be an AV company. But of course, you know, you start with antivirus and when at an endpoint and known malware, okay. But unknown malware at the time wasn't really being addressed. And if I understand it you guys brought in machine intelligence from the start. Explain that. >> That's that's right. And like, the way we, we looked at it is like, back then we said, you don't have a malware problem. You have an adversary problem. Just like recognizing that it's not malware but there's people behind it that act on objectives that you need to, that you need to counter and you don't want to run after them. You want to be ahead of them. Like that was, that was the approach, like at a very high level that we were taking and you know, now we have it a little bit more summed up and we say, we stop breaches. So like, that's, that's the end result. >> So how do you specifically leverage AI? Which parts of the portfolio, is it across the portfolio and you know, where did it start? How did it evolve? >> Yeah, we are very, we're very data driven. So we are working hard to use the, the proper tools to work with data wherever we can. And AI being one of these, these tools that we like to bring to bear. The, the cloud, the CrowdStrike security cloud at the moment we're doing about roughly 2 trillion events, with a T, per day. Like that, that volume of data, like going through our platform, that that's not something that you can, that you can work with manually, right? So we need, we need to bring the heavy machinery, like that's, that's how we're bringing AI to bear. >> 2 trillion events per day. I mean, there aren't a lot of organizations that see that many events a day. I mean, maybe, maybe some of the hyperscalers possibly. I don't know. That's a... >> Yeah. I think, I think it really allows us to get unprecedented insights into what's actually going on out there in the, in, in the landscape. And, you know, it's, it's like, it's like with a camera or a telescope, the bigger your aperture the fainter signals you can detect. And that's why like, that's why the volume is, is critical. And that's why we, that's why we from the get go, set out to build a cloud native platform so that we can actually aggregate this type of data and analyze it in one spot, basically where where everything comes together that we can draw these connections. >> Will we ever see security without humans? >> I don't, I don't think so. This, this, this notion that machine intelligence is so intelligent that it just takes these jobs over. To me it's more like a tool, right? Like these, these algorithms, they do need to learn from something they need to learn from human expertise. The way at CrowdStrike we have things set up is like our, our human teams our threat hunters, our MDR staff, our incident responders, like whatever they do, we, we are taking these insights and we're feeding them into the AI algorithms. So if there's, if there's a new type of attack and we have an incident response team on the ground and they find something, that gets leveraged put into a database and our AI can learn from that. I, I, I really like that in the keynote, Kevin Mandia actually talked to that, you know. Like get the incident responders out there, get their knowledge, bake it into products. And that that's, that's the approach that we're taking with, with with our AI. >> So in my head, I'm thinking okay, what do humans do better than machines? I mean, humans are creative, right? Machines really aren't creative, right? I mean, and adversaries are very creative. So, so I guess flip side question, what is, what does AI do? What does the machine intelligence do that that humans can't do? Is it scale? Is it just massive volumes? Help us understand what humans do well and machines do well and how they compliment each other. >> Yeah. So AI is, is very good at working with extremely large amounts of data. Again, like cloud native platform, like that's where you get this AI advantage. It can work with data that is a lot more complex like more facets of data. So we talked about XDR here at Fal.Con a lot, right? Like you get data from all these different products, from all these different angles. Like the more different facets you add to that like it becomes overwhelming for the human mind. It's just like so much complexity that a human can put together in their brain. With AI you don't have these limitations. It's just math. It's just like multiplying big matrices and you can work with a lot larger data sets, like those 2 trillion events that we do per day on the on the CrowdStrike security cloud. But also data that is a lot more complex, that has more facets, looks at the problem from different angles. That's where AI is especially useful. >> I want to ask you as a topic I haven't asked anybody this week and I've been meaning to, is, you know there's this concept of, of living off the land, right? Using your own tools against you. How are you able to detect that? Is that cuz of lateral movement or, I mean I'm sure there are many, many factors, but but how are you addressing that problem? That kind of stealthy using your tools against you? >> Yeah, so adversaries, this is, again there's motivated humans behind that. They figured if they drop a malware file on the machine that's an artifact, an indicator of compromise, right? And that can be detected. So they're avoiding dropping files on disc that could be detected or to bring their to bring their own tools. They try to work with the tools that they find on the machines. They need to act on objective though. There's something they want to accomplish. Like they're not, they're not logging in just to, you know, like do nothing. And this is where indicators of attack come in, right? Like we know what their objectives are and we're trying to capture this. We're describing this in an abstract way. What is it that they try to accomplish? That's what indicators of attack describe and when they act on these objectives then we can catch them. >> So I, I think that the the term indicators of attack, I, I, you may have coined it. I'm, I'm not sure. I think it was you announcement at, at black hat. Those indicators are not static, right? To your point, the humans on the other end are motivated. Are you a can, can AI help predict future indicators of attack maybe working with, with humans? >> Yeah, this is, this is something that we recently rolled out where we are connecting our AI intelligence to our indicator of attack framework. Where basically the AI crunches the big data and then the indicators, the, the knowledge that the AI generates, understanding the context of the situation, can feed into the indicators of attack that we're evaluating to see if an adversary is acting on a specific objective. And then if an IOA triggers, that can feed back into the AI and the AI can use that information to derive for more precise results. We have a good feedback loop between these two, these two systems and they're more tightly integrated now. >> As a, as an AI expert, I want to ask you, is is the intelligence, is AI actually artificial? Or is it, is it real? >> Well, it, it is artificial cause I guess we, we build it right? Like it's a human made. I, I think a lot of people get hung up on the term intelligent and it, it's not really intelligent in the say, in the sense that it acts on agency with, with agency like you would look at a problem, right? It's good at solving specific types of tasks and problems that we can define in ways that these algorithms work on it. But it is not the same level of creative thinking that a human brings to the problem. And this is, going back to the beginning of the conversation, this is where we like to have humans involved in the teaching of the AI. The AI connect autonomously in real time stopping threats. But there's humans that take a look at what is going on to give the AI input and feedback and, and improvements because we are up against other humans, right? You don't want to have a human kind of press the buttons of the AI until they found a way around it. But that's called adversarial machine learning. Very real threat as well. Like we are, we're looking at the problem as humans against humans. Like what, what tools do we need to bring to the battle to keep the adversaries out of our customer's networks? >> Okay. So my follow up is, but there are systems of agency for our detection is a, as an example. But your, I think your point is that that never would've been possible without humans. Is that right? Or... >> Yeah, like on, on the one hand, these systems get trained with human knowledge. On the other hand, there, there are humans that take a look at, if the systems give the right responses. Like there, there isn't like if you talk to your smart speaker, like, like for me, like I'm, I'm asking my smart speaker to turn a specific light on in my living room and it, it, half the time doesn't work, right? Like that, that wouldn't happen with a human. There's like a lot more context and understanding and humans are more robust. Like it's, it's harder to fool a human. The limitation that we humans have is complexity, complexity and volume. So we're trying to make like a peanut butter and cookie approach, a peanut butter and chocolate approach rather, where we want to use the human creativity alongside the AI, which can handle scale complexity and volume at unprecedented, unprecedented scales. >> And when you bring it out to the edge, we, we were just talking to Stefan Goldberg about IOT and extended IOT. When you think about, you know, AI, a lot of lot of AI today is modeling that's done in the cloud and then applied. But when you go out to the edge, you you're starting to see more AI inferencing and near realtime, or even real time. Will that change the equation? What's the future of, of, of AI and cyber look like? >> I think, I, I think it would be pervasively applied. So we are using it already on the edge, on our sensors, but also in the cloud, right? On the sensor, we want to be able to act very quickly on the endpoint, want to be able to act very quickly without any delay with local inflammation. Or if the system is offline for a period of time, right? So we have AI models running there. In the cloud, we have the advantage of being able to work with vast amounts of data without slowing down our customer's machines. So like models will be applied everywhere where there's data, like that's kind of the name of the game. Like let's bring, let's bring this, this type of artificial intelligence, this type of, of like refined digested expertise, wherever the data sits on the end point, in the clouds, where you have it. >> And CrowdStrike doesn't care, right? I mean, it's... >> We care about stopping the breaches. >> Yeah. But you're agnostic to the physical location of >> That, that's correct. >> The activity. So last question is, how should we as humans prepare for the future of AI in, in cyber? >> That's a, that's a good question. I, I would say like, stay, stay creative and like figure out how we can get that knowledge that you have like formalized into, into databases, right? AI, the way I look at it is an amplifier of human expertise. You do something at a small scale as a human, the AI system can do it at a big scale, right? Like it's kind of like digging with a spoon whether it's digging with an excavator, with a, with a backhoe. So I I'd say stay, stay creative and see how we can take things that we do as humans in the small scale and let's do it in the cloud, like with with large data volumes. >> Great advice, creativity, I think is, is a key. Sven, thanks so much for coming on the cube. Really appreciate your time. >> Thanks for having me. >> You're very welcome. Okay. Keep it right there. Listen, by, by the way, I meant to to tell our audience a lot of resources at siliconangle.com, thecube.net, wikibon.com, has a ton of research all available at for no charge. No, no, no password needed. Just access that. Check it out. We're live from the ARIA hotel in Las Vegas, Fal.Con 22, Dave Vellante for the cube. We'll be back after this short break. (calming xylophone music)

(techno music) >> Announcer: Live, from San Francisco, it's theCUBE. Covering Flink Forward, brought to you by data Artisans. (techno music) >> Hello again everybody, this is George Gilbert, we're at the Flink Forward Conference, sponsored by data Artisans, the provider of both Apache Flink and the commercial distribution, the dA Platform that supports the productionization and operationalization of Flink, and makes it more accessible to mainstream enterprises. We're priviledged to have Kostas Tzoumas, CEO of data Artisans, with us today. Welcome Kostas. >> Thank you. Thank you George. >> So, tell us, let's start with sort of an idealized application-use case, that is in the sweet spot of Flink, and then let's talk about how that's going to broaden over time. >> Yeah, so just a little bit of an umbrella above that. So what we see very, very consistently, we see it in tech companies, and we see, so modern tech companies, and we see it in traditional enterprises that are trying to move there, is a move towards a business that runs in real time. Runs 24/7, is data-driven, so decisions are made based on data, and is software operated. So increasingly decisions are made by AI, by software, rather than someone looking at something and making a decision, yeah. So for example, some of the largest users of Apache Flink are companies like Uber, Netflix, Alibaba, Lyft, they are all working in this way. >> Can you tell us about the size of their, you know, something in terms of records per day, or cluster size, or, >> Yeah, sure. So, latest I heard, Alibaba is powering Alibaba Certs, more than a thousand nodes, terabytes of states, I'm pretty sure they will give us bigger numbers today. Netflix has reported of doing about one trillion events per day. >> George: Wow. >> On Flink. So pretty big sizes. >> So and is Netflix, I think I read, is powering their real time recommendation updates. >> They are powering a bunch of things, a bunch of applications, there's a lot of routing events internally. I think they have a talk, they had a talk definitely at the last conference, where they talk about this. And it's really a variety of use cases. It's really about building a platform, internally. And offering it to all sorts of departments in the company, be that for recommendations, be that for BI, be that for running, state of microservices, you know, all sorts of things. And we also see, the more traditional enterprise moving to this modus operandi. For example, ING is also one of our biggest partners, it's a global consumer bank based in the Netherlands, and their CEO is saying that ING is not a bank, it's a tech company that happens to have a banking license. It's a tech company that inherited a banking license. So that's how they want to operate. So what we see, is stream processing is really the enabler for this kind of business, for this kind of modern business where we interact with, in real time, they interact with the consumer in real time, they push notifications, they can change the pricing, et cetera, et cetera. So this is really the crux of stateful stream processing , for me. >> So okay, so tell us, for those who, you know, have a passing understanding of how Kafka's evolving, how Apache Spark and Structured Streaming's evolving, as distinct from, but also, Databricks. What is it about having state management that's sort of integrated, that for example, might make it easy to elastically change a cluster size by repartitioning. What can you assume about managing state internally, that makes things easier? >> Yeah, so I think really the, the sweet spot of Flink, is that if you are looking for stream process, from a stream processing engine, and for a stateful stream processing engine for that matter, Flink is the definition of this. It's the definite solution to this problem. It was created from scratch, with this in mind, it was not sort of a bolt-on on top of something else, so it's streaming from the get-go. And we have done a lot of work to make state a first-class citizen. What this means, is that in Flink programs, you can keep state that scales to terabytes, we have seen that, and you can manage this state together with your application. So Flink has this model based on check points, where you take a check point of your application and state together, and you can restart at any time from there. So it's really, the core of Flink, is around state management. >> And you manage exactly one semantics across the checkpointing? >> It's exactly once, it's application-level exactly once. We have also introduced end-to-end exactly once with Kafka. So Kafka-Flink-Kafka exactly once. So fully consistent. >> Okay so, let's drill down a little bit. What are some of the things that customers would do with an application running on a, let's say a big cluster or a couple clusters, where they want to operate both on the application logic and on the state that having it integrated you know makes much easier? >> Yeah, so it is a lot about a flipped architecture and about making operations and DevOps much, much easier. So traditionally what you would do is create, let's say a containerized stateless application and have a central centralized data store to keep all your states. What you do now, is the state becomes part of the application. So this has several benefits. It has performance benefits, it has organizational benefits in the company. >> Autonomy >> Autonomy between teams. It has, you know it gives you a lot of flexibility on what you can do with the applications, like, for example right, scaling an application. What you can do with Flink is that you have an application running with parallelism over 100 and you are getting a higher volume and you want to scale it to 500 right, so you can simply with Flink take a snapshot of the state and the application together, and then restart it at a 500 and Flink is going to resolve the state. So no need to do anything on a database. >> And then it'll reshard and Flink will reshard it. >> Will reshard and it will restart. And then one step further with the product that we have introduced, dA Platform which includes Flink, you can simply do this with one click or with one rest command. >> So, the the resharding was possible with core Flink, the Apache Flink and the dA Platform just makes it that much easier along with other operations. >> Yeah so what the dA Platform does is it gives you an API for common operational tasks, that we observed everybody that was deploying Flink at a decent scale needs to do. It abstracts, it is based on Kubernetes, but it gives you a higher-level API than Kubernetes. You can manage the application and the state together, and it gives that to you in a rest API, in a UI, et cetera. >> Okay, so in other words it's sort of like by abstracting even up from Kubernetes you might have a cluster as a first-class citizen but you're treating it almost like a single entity and then under the covers you're managing the, the things that happen across the cluster. >> So what we have in the dA Platform is a notion of a deployment which is, think of it as, I think of it as a cluster, but it's basically based on containers. So you have this notion of deployments that you can manage, (coughs) sorry, and then you have a notion of an application. And an application, is a Flink job that evolves over time. And then you have a very, you know, bird's-eye view on this. You can, when you update the code, this is the same application with updated code. You can travel through a history, you can visit the logs, and you can do common operational tasks, like as I said, rescaling, updating the code, rollbacks, replays, migrate to a new deployment target, et cetera. >> Let me ask you, outside of the big tech companies who have built much of the application management scaffolding themselves, you can democratize access to stream processing because the capabilities, you know, are not in the skill set of traditional, mainstream developers. So question, the first thing I hear from a lot of sort of newbies, or people who want to experiment, is, "Well, it's so easy to manage the state "in a shared database, even if I'm processing, "you know, continuously." Where should they make the trade-off? When is it appropriate to use a shared database? Maybe you know, for real OLTP work, and then when can you sort of scale it out and manage it integrally with the rest of the application? >> So when should we use a database and when should we use streaming, right? >> Yeah, and even if it's streaming with the embedded state. >> Yeah, that's a very good question. I think it really depends on the use case. So what we see in the market, is many enterprises start with with a use case that either doesn't scale, or it's not developer friendly enough to have these database application levels. Level separation. And then it quickly spreads out in the whole company and other teams start using it. So for example, in the work we did with ING, they started with a fraud detection application, where the idea was to load models dynamically in the application, as the data scientists are creating new models, and have a scalable fraud detection system that can handle their load. And then we have seen other teams in the company adopting processing after that. >> Okay, so that sounds like where the model becomes part of the application logic and it's a version of the application logic and then, >> The version of the model >> Is associated with the checkpoint >> Correct. >> So let me ask you then, what happens when you you're managing let's say terabytes of state across a cluster, and someone wants to query across that distributed state. Is there in Flink a query manager that, you know, knows about where all the shards are and the statistics around the shards to do a cost-based query? >> So there is a feature in Flink called queryable state that gives you the ability to do, very simple for now, queries on the state. This feature is evolving, it's in progress. And it will get more sophisticated and more production-ready over time. >> And that enables a different class of users. >> Exactly, I wouldn't, like to be frank, I wouldn't use it for complex data warehousing scenarios. That still needs a data warehouse, but you can do point queries and a few, you know, slightly more sophisticated queries. >> So this is different. This type of state would be different from like in Kafka where you can store you know the commit log for X amount of time and then replay it. This, it's in a database I assume, not in a log form and so, you have faster access. >> Exactly, and it's placed together with a log, so, you can think of the state in Flink as the materialized view of the log, at any given point in time, with various versions. >> Okay. >> And really, the way replay works is, roll back the state to a prior version and roll back the log, the input log, to that same logical time. >> Okay, so how do you see Flink spreading out, now that it's been proven in the most demanding customers, and now we have to accommodate skills, you know, where the developers and DevOps don't have quite the same distributed systems knowledge? >> Yeah, I mean we do a lot of work at data Artisans with financial services, insurance, very traditional companies, but it's definitely something that is work in progress in the sense that our product the dA Platform makes operation smarts easier. This was a common problem everywhere, this was something that tech companies solved for themselves, and we wanted to solve it for everyone else. Application development is yet another thing, and as we saw today in the last keynote, we are working together with Google and the BIM Community to bring Python, GOLD, all sorts of languages into Flink. >> Okay so that'll help at the developer level, and you're also doing work at the operations level with the platform. >> And of course there's SQL right? So Flink has Stream SQL which is standard SQL. >> And would you see, at some point, actually sort of managing the platform for customers, either on-prem or in the cloud? >> Yeah, so right now, the platform is running on Kubernetes, which means that typically the customer installs it in their clusters, in their Kubernetes clusters. Which can be either their own machines, or it can be a Kubernetes service from a cloud vendor. Moving forward I think it will be very interesting yes, to move to more hosted solutions. Make it even easier for people. >> Do you see a breakpoint or a transition between the most sophisticated customers who, either are comfortable on their own premises, or who were cloud, sort of native, from the beginning, and then sort of the rest of the mainstream? You know, what sort of applications might they move to the cloud or might coexist between on-prem and the cloud? >> Well I think it's clear that the cloud is, you know, every new business starts on the cloud, that's clear. There's a lot of enterprise that is not yet there, but there's big willingness to move there. And there's a lot of hybrid cloud solutions as well. >> Do you see mainstream customers rewriting applications because they would be so much more powerful in stream processing, or do you see them doing just new applications? >> Both, we see both. It's always easier to start with a new application, but we do see a lot of legacy applications in big companies that are not working anymore. And we see those rewritten. And very core applications, very core to the business. >> So could that be, could you be sort of the source and in an analytic processing for the continuous data and then that sort of feeds a transaction and some parameters that then feed a model? >> Yeah. >> Is that, is that a, >> Yeah. >> so in other words you could augment existing OLTP applications with analytics then inform them in real time essentially. >> Absolutely. >> Okay, 'cause that sounds like then something that people would build around what exists. >> Yeah, I mean you can do, you can think of stream processing, in a way, as transaction processing. It's not a dedicated OLTP store, but you can think of it in this flipped architecture right? Like the log is essentially the re-do log, you know, and then you create the materialized views, that's the write path, and then you have the read path, which is queryable state. This is this whole CQRS idea right? >> Yeah, Command-Query-Response. >> Exactly. >> So, this is actually interesting, and I guess this is critical, it's sort of like a new way of doing distributed databases. I know that's not the word you would choose, but it's like the derived data, managed by, sort of coming off of the state changes, then in the stream processor that goes through a single sort of append-only log, and then reading, and how do you manage consistency on the materialized views that derive data? >> Yeah, so we have seen Flink users implement that. So we have seen, you know, companies really base the complete product on the CQRS pattern. I think this is a little bit further out. Consistency-wise, Flink gives you the exactly once consistency on the write path, yeah. What we see a lot more is an architecture where there's a lot of transactional stores in the front end that are running, and then there needs to be some kind of global, of single source of truth, between all of them. And a very typical way to do that is to get these logs into a stream, and then have a Flink application that can actually scale to that. Create a single source of truth from all of these transactional stores. >> And by having, by feeding the transactional stores into this sort of hub, I presume, some cluster as a hub, and even if it's in the form of sort of a log, how can you replay it with sufficient throughput, I guess not to be a data warehouse but to, you know, have low latency for updating the derived data? And is that derived data I assume, in non-Flink products? >> Yeah, so the way it works is that, you know, you can get the change logs from the databases, you can use something like Kafka to buffer them up, and then you can use Flink for all the processing and to do the reprocessing with Flink, this is really one of the core strengths of Flink. Basically what you do is, you replay the Flink program together with the states you can get really, really high throughput reprocessing there. >> Where does the super high throughput come from? Is that because of the integration of state and logic? >> Yeah, that is because Flink is a true streaming engine. It is a high-performance streaming engine. And it manages the state, there's no tier, >> Crossing a boundary? >> no tier crossing and there's no boundary crossing when you access state. It's embedded in the Flink application. >> Okay, so that you can optimize the IO path? >> Correct. >> Okay, very, very interesting. So, it sounds like the Kafka guys, the Confluent folks, their aspirations, from the last time we talked to 'em, doesn't extend to analytics, you know, I don't know whether they want partners to do that, but it sounds like they have a similar topology, but they're, but I'm not clear how much of a first-class citizen state is, other than the log. How would you characterize the trade-offs between the two? >> Yeah, so, I mean obviously I cannot comment on Confluent, but like, what I think is that the state and the log are two very different things. You can think of the log as storage, it's a kind of hot storage because it's the most recent data but you know, you cannot query it, it's not a materialized view, right. So for me the separation is between processing state and storage. The log is is a kind of storage, so kind of message queue. State is really the active data, the real-time active data that needs to have consistency guarantees, and that's a completely different thing. >> Okay, and that's the, you're managing, it's almost like you're managing under the covers a distributed database. >> Yes, kind of. Yeah a distributed key-value store if you wish. >> Okay, okay, and then that's exposed through multiple interfaces, data stream, table. >> Data stream, table API, SQL, other languages in the future, et cetera. >> Okay, so going further down the line, how do you see the sort of use cases that are going to get you across the chasm from the big tech companies into the mainstream? >> Yeah, so we are already seeing that a lot. So we're doing a lot of work with financial services, insurance companies a lot of very traditional businesses. And it's really a lot about maintaining single source of truth, becoming more real-time in the way they interact with the outside world, and the customer, like they do see the need to transform. If we take financial services and investment banks for example, there is a big push in this industry to modernize the IT infrastructure, to get rid of legacy, to adopt modern solutions, become more real-time, et cetera. >> And so they really needed this, like the application platform, the dA Platform, because operationalizing what Netflix did isn't going to be very difficult maybe for non-tech companies. >> Yeah, I mean, you know, it's always a trade-off right, and you know for some, some companies build, some companies buy, and for many companies it's much more sensible to buy. That's why we have software products. And really, our motivation was that we worked in the open-source Flink community with all the big tech companies. We saw their successes, we saw what they built, we saw, you know, their failures. We saw everything and we decided to build this for everybody else, for everyone that, you know, is not Netflix, is not Uber, cannot hire software developers so easily, or with such good quality. >> Okay, alright, on that note, Kostas, we're going to have to end it, and to be continued, one with Stefan next, apparently. >> Nice. >> And then hopefully next year as well. >> Nice. Thank you. >> Alright, thanks Kostas. >> Thank you George. Alright, we're with Kostas Tzoumas, CEO of data Artisans, the company behind Apache Flink and now the application platform that makes Flink run for mainstream enterprises. We will be back, after this short break. (techno music)

>> Announcer: Live from Midtown Manhattan, it's theCUBE covering Big Data New York City 2017. Brought to by SiliconANGLE Media and its ecosystem sponsors. >> Coverage to theCUBE in New York City for Big Data NYC, the hashtag is BigDataNYC. This is our fifth year doing our own event in conjunction with Strata Hadoop, now called Strata Data, used to be Hadoop World, our eighth year covering the industry, we've been there from the beginning in 2010, the beginning of this revolution. I'm John Furrier, the co-host, with Jim Kobielus, our lead analyst at Wikibon. Our next guest is Christian Rodatus, who is the CEO of Datameer. Datameer, obviously, one of the startups now evolving on the, I think, eighth year or so, roughly seven or eight years old. Great customer base, been successful blocking and tackling, just doing good business. Your shirt says show him the data. Welcome to theCUBE, Christian, appreciate it. >> So well established, I barely think of you as a startup anymore. >> It's kind of true, and actually a couple of months ago, after I took on the job, I met Mike Olson, and Datameer and Cloudera were sort of founded the same year, I believe late 2009, early 2010. Then, he told me there were two open source projects with MapReduce and Hadoop, basically, and Datameer was founded to actually enable customers to do something with it, as an entry platform to help getting data in, create the data and doing something with it. And now, if you walk the show floor, it's a completely different landscape now. >> We've had you guys on before, the founder, Stefan, has been on. Interesting migration, we've seen you guys grow from a customer base standpoint. You've come on as the CEO to kind of take it to the next level. Give us an update on what's going on at Datameer. Obviously, the shirt says "Show me the data." Show me the money kind of play there, I get that. That's where the money is, the data is where the action is. Real solutions, not pie in the sky, we're now in our eighth year of this market, so there's not a lot of tolerance for hype even though there's a lot of AI watching going on. What's going on with you guys? >> I would say, interesting enough I met with a customer, prospective customer, this morning, and this was a very typical organization. So, this is a customer that was an insurance company, and they're just about to spin up their first Hadoop cluster to actually work on customer management applications. And they are overwhelmed with what the market offers now. There's 27 open source projects, there's dozens and dozens of other different tools that try to basically, they try best of reach approaches and certain layers of the stack for specific applications, and they don't really know how to stitch this all together. And if I reflect from a customer meeting at a Canadian bank recently that has very successfully deployed applications on the data lake, like in fraud management and compliance applications and things like this, they still struggle to basically replicate the same performance and the service level agreements that they used from their old EDW that they still have in production. And so, everybody's now going out there and trying to figure out how to get value out of the data lake for the business users, right? There's a lot of approaches that these companies are trying. There's SQL-on-Hadoop that supposedly doesn't perform properly. There is other solutions like OLAP on Hadoop that tries to emulate what they've been used to from the EDWs, and we believe these are the wrong approaches, so we want to stay true to the stack and be native to the stack and offer a platform that really operates end-to-end from interesting the data into the data lake to creation, preparation of the data, and ultimately, building the data pipelines for the business users, and this is certainly something-- >> Here's more of a play for the business users now, not the data scientists and statistical modelers. I thought the data scientists were your core market. Is that not true? >> So, our primary user base as Datameer used to be like, until last week, we were the data engineers in the companies, or basically the people that built the data lake, that created the data and built these data pipelines for the business user community no matter what tool they were using. >> Jim, I want to get your thoughts on this for Christian's interest. Last year, so these guys can fix your microphone. I think you guys fix the microphone for us, his earpiece there, but I want to get a question to Chris, and I ask to redirect through you. Gartner, another analyst firm. >> Jim: I've heard of 'em. >> Not a big fan personally, but you know. >> Jim: They're still in business? >> The magic quadrant, they use that tool. Anyway, they had a good intro stat. Last year, they predicted through 2017, 60% of big data projects will fail. So, the question for both you guys is did that actually happen? I don't think it did, I'm not hearing that 60% have failed, but we are seeing the struggle around analytics and scaling analytics in a way that's like a dev ops mentality. So, thoughts on this 60% data projects fail. >> I don't know whether it's 60%, there was another statistic that said there's only 14% of Hadoop deployments, or production or something, >> They said 60, six zero. >> Or whatever. >> Define failure, I mean, you've built a data lake, and maybe you're not using it immediately for any particular application. Does that mean you've failed, or does it simply mean you haven't found the killer application yet for it? I don't know, your thoughts. >> I agree with you, it's probably not a failure to that extent. It's more like how do they, so they dump the data into it, right, they build the infrastructure, now it's about the next step data lake 2.0 to figure out how do I get value out of the data, how do I go after the right applications, how do I build a platform and tools that basically promotes the use of that data throughout the business community in a meaningful way. >> Okay, so what's going on with you guys from a product standpoint? You guys have some announcements. Let's get to some of the latest and greatest. >> Absolutely. I think we were very strong in data creation, data preparation and the entire data governance around it, and we are using, as a user interface, we are using this spreadsheet-like user interface called a workbook, it really looks like Excel, but it's not. It operates at completely different scale. It's basically an Excel spreadsheet on steroids. Our customers built a data pipeline, so this is the data engineers that we discussed before, but we also have a relatively small power user community in our client base that use that spreadsheet for deep data exploration. Now, we are lifting this to the next level, and we put up a visualization layer on top of it that runs natively in the stack, and what you get is basically a visual experience not only in the data curation process but also in deep data exploration, and this is combined with two platform technologies that we use, it's based on highly scalable distributed search in the backend engine of our product, number one. We have also adopted a columnar data store, Parquet, for our file system now. In this combination, the data exploration capabilities we bring to the market will allow power analysts to really dig deep into the data, so there's literally no limits in terms of the breadth and the depth of the data. It could be billions of rows, it could be thousands of different attributes and columns that you are looking at, and you will get a response time of sub-second as we create indices on demand as we run this through the analytic process. >> With these fast queries and visualization, do you also have the ability to do semantic data virtualization roll-ups across multi-cloud or multi-cluster? >> Yeah, absolutely. We, also there's a second trend that we discussed right before we started the live transmission here. Things are also moving into the cloud, so what we are seeing right now is the EDW's not going away, the on prem is data lake, that prevail, right, and now they are thinking about moving certain workload types into the cloud, and we understand ourselves as a platform play that builds a data fabric that really ties all these data assets together, and it enables business. >> On the trends, we weren't on camera, we'll bring it up here, the impact of cloud to the data world. You've seen this movie before, you have extensive experience in this space going back to the origination, you'd say Teradata. When it was the classic, old-school data warehouse. And then, great purpose, great growth, massive value creation. Enter the Hadoop kind of disruption. Hadoop evolved from batch to do ranking stuff, and then tried to, it was a hammer that turned into a lawnmower, right? Then they started going down the path, and really, it wasn't workable for what people were looking at, but everyone was still trying to be the Teradata of whatever. Fast forward, so things have evolved and things are starting to shake out, same picture of data warehouse-like stuff, now you got cloud. It seems to be changing the nature of what it will become in the future. What's your perspective on that evolution? What's different about now and what's same about now that's, from the old days? What's the similarities of the old-school, and what's different that people are missing? >> I think it's a lot related to cloud, just in general. It is extremely important to fast adoptions throughout the organization, to get performance, and service-level agreements without customers. This is where we clearly can help, and we give them a user experience that is meaningful and that resembles what they were used to from the old EDW world, right? That's number one. Number two, and this comes back to a question to 60% fail, or why is it failing or working. I think there's a lot of really interesting projects out, and our customers are betting big time on the data lake projects whether it being on premise or in the cloud. And we work with HSBC, for instance, in the United Kingdom. They've got 32 data lake projects throughout the organization, and I spoke to one of these-- >> Not 32 data lakes, 32 projects that involve tapping into the data lake. >> 32 projects that involve various data lakes. >> Okay. (chuckling) >> And I spoke to one of the chief data officers there, and they said they are data center infrastructure just by having kick-started these projects will explode. And they're not in the business of operating all the hardware and things like this, and so, a major bank like them, they made an announcement recently, a public announcement, you can read about it, started moving the data assets into the cloud. This is clearly happening at rapid pace, and it will change the paradigm in terms of breathability and being able to satisfy peak workload requirements as they come up, when you run a compliance report at quota end or something like this, so this will certainly help with adoption and creating business value for our customers. >> We talk about all the time real-time, and there's so many examples of how data science has changed the game. I mean, I was talking about, from a cyber perspective, how data science helped capture Bin Laden to how I can get increased sales to better user experience on devices. Having real-time access to data, and you put in some quick data science around things, really helps things in the edge. What's your view on real-time? Obviously, that's super important, you got to kind of get your house in order in terms of base data hygiene and foundational work, building blocks. At the end of the day, the real-time seems to be super hot right now. >> Real-time is a relative term, right, so there's certainly applications like IOT applications, or machine data that you analyze that require real-time access. I would call it right-time, so what's the increment of data load that is required for certain applications? We are certainly not a real-time application yet. We can possibly load data through Kafka and stream data through Kafka, but in general, we are still a batch-oriented platform. We can do. >> Which, by the way, is not going away any time soon. It's like super important. >> No, it's not going away at all, right. It can do many batches at relatively frequent increments, which is usually enough for what our customers demand from our platform today, but we're certainly looking at more streaming types of capability as we move this forward. >> What do the customer architectures look like? Because you brought up the good point, we talk about this all the time, batch versus real-time. They're not mutually exclusive, obviously, good architectures would argue that you decouple them, obviously will have a good software elements all through the life cycle of data. >> Through the stack. >> And have the stack, and the stack's only going to get more robust. Your customers, what's the main value that you guys provide them, the problem that you're solving today and the benefits to them? >> Absolutely, so our true value is that there's no breakages in the stack. We enter, and we can basically satisfy all requirements from interesting the data, from blending and integrating the data, preparing the data, building the data pipelines, and analyzing the data. And all this we do in a highly secure and governed environment, so if you stitch it together, as a customer, the customer this morning asked me, "Whom do you compete with?" I keep getting this question all the time, and we really compete with two things. We compete with build-your-own, which customers still opt to do nowadays, while our things are really point and click and highly automated, and we compete with a combination of different products. You need to have at least three to four different products to be able to do what we do, but then you get security breaks, you get lack of data lineage and data governance through the process, and this is the biggest value that we can bring to the table. And secondly now with visual exploration, we offer capability that literally nobody has in the marketplace, where we give power users the capability to explore with blazing fast response times, billion rows of data in a very free-form type of exploration process. >> Are there more power users now than there were when you started as a company? It seemed like tools like Datameer have brought people into the sort of power user camp, just simply by the virtue of having access to your tool. What are your thoughts there? >> Absolutely, it's definitely growing, and you see also different companies exploiting their capability in different ways. You might find insurance or financial services customers that have a very sophisticated capability building in that area, and you might see 1,000 to 2,000 users that do deep data exploration, and other companies are starting out with a couple of dozen and then evolving it as they go. >> Christian, I got to ask you as the new CEO of Datameer, obviously going to the next level, you guys have been successful. We were commenting yesterday on theCUBE about, we've been covering this for eight years in depth in terms of CUBE coverage, we've seen the waves come and go of hype, but now there's not a lot of tolerance for hype. You guys are one of the companies, I will say, that stay to your knitting, you didn't overplay your hand. You've certainly rode the hype like everyone else did, but your solution is very specific on value, and so, you didn't overplay your hand, the company didn't really overplay their hand, in my opinion. But now, there's really the hand is value. >> Absolutely. >> As the new CEO, you got to kind of put a little shiny new toy on there, and you know, rub the, keep the car lookin' shiny and everything looking good with cutting edge stuff, the same time scaling up what's been working. The question is what are you doubling down on, and what are you investing in to keep that innovation going? >> There's really three things, and you're very much right, so this has become a mature company. We've grown with our customer base, our enterprise features and capabilities are second to none in the marketplace, this is what our customers achieve, and now, the three investment areas that we are putting together and where we are doubling down is really visual exploration as I outlined before. Number two, hybrid cloud architectures, we don't believe the customers move their entire stack right into the cloud. There's a few that are going to do this and that are looking into these things, but we will, we believe in the idea that they will still have to EDW their on premise data lake and some workload capabilities in the cloud which will be growing, so this is investment area number two. Number three is the entire concept of data curation for machine learning. This is something where we've released a plug-in earlier in the year for TensorFlow where we can basically build data pipelines for machine learning applications. This is still very small. We see some interest from customers, but it's growing interest. >> It's a directionally correct kind of vector, you're looking and say, it's a good sign, let's kick the tires on that and play around. >> Absolutely. >> 'Cause machine learning's got to learn, too. You got to learn from somewhere. >> And quite frankly, deep learning, machine learning tools for the rest of us, there aren't really all that many for the rest of us power users, they're going to have to come along and get really super visual in terms of enabling visual modular development and tuning of these models. What are your thoughts there in terms of going forward about a visualization layer to make machine learning and deep learning developers more productive? >> That is an area where we will not engage in a way. We will stick with our platform play where we focus on building the data pipelines into those tools. >> Jim: Gotcha. >> In the last area where we invest is ecosystem integration, so we think with our visual explorer backend that is built on search and on a Parquet file format is, or columnar store, is really a key differentiator in feeding or building data pipelines into the incumbent BRE ecosystems and accelerating those as well. We've currently prototypes running where we can basically give the same performance and depth of analytic capability to some of the existing BI tools that are out there. >> What are some the ecosystem partners do you guys have? I know partnering is a big part of what you guys have done. Can you name a few? >> I mean, the biggest one-- >> Everybody, Switzerland. >> No, not really. We are focused on staying true to our stack and how we can provide value to our customers, so we work actively and very important on our cloud strategy with Microsoft and Amazon AWS in evolving our cloud strategy. We've started working with various BI vendors throughout that you know about, right, and we definitely have a play also with some of the big SIs and IBM is a more popular one. >> So, BI guys mostly on the tool visualization side. You said you were a pipeline. >> On tool and visualization side, right. We have very effective integration for our data pipelines into the BI tools today we support TD for Tableau, we have a native integration. >> Why compete there, just be a service provider. >> Absolutely, and we have more and better technology come up to even accelerate those tools as well in our big data stuff. >> You're focused, you're scaling, final word I'll give to you for the segment. Share with the folks that are a Datameer customer or have not yet become a customer, what's the outlook, what's the new Datameer look like under your leadership? What should they expect? >> Yeah, absolutely, so I think they can expect utmost predictability, the way how we roll out the division and how we build our product in the next couple of releases. The next five, six months are critical for us. We have launched Visual Explorer here at the conference. We're going to launch our native cloud solution probably middle of November to the customer base. So, these are the big milestones that will help us for our next fiscal year and provide really great value to our customers, and that's what they can expect, predictability, a very solid product, all the enterprise-grade features they need and require for what they do. And if you look at it, we are really enterprise play, and the customer base that we have is very demanding and challenging, and we want to keep up and deliver a capability that is relevant for them and helps them create values from the data lakes. >> Christian Rodatus, technology enthusiast, passionate, now CEO of Datameer. Great to have you on theCUBE, thanks for sharing. >> Thanks so much. >> And we'll be following your progress. Datameer here inside theCUBE live coverage, hashtag BigDataNYC, our fifth year doing our own event here in conjunction with Strata Data, formerly Strata Hadoop, Hadoop World, eight years covering this space. I'm John Furrier with Jim Kobielus here inside theCUBE. More after this short break. >> Christian: Thank you. (upbeat electronic music)