>> Narrator: Live from Las Vegas, it's theCube. Covering Interconnect 2017. brought to you by IBM. >> Hey welcome back everyone. We're live here in Las Vegas for IBM Interconnect 2017. Siliconangle's theCube's Exclusive coverage of IBM Interconnect 2017, I'm John Furrier. My co-host Dave Vellante. Our next two guests, Eric Herzog, Vice President of Marketing for IBM Storage. Nice to see you again, you were on yesterday. And Mark Godard, Manager of Customer Success and Partnership at Sparkcognition, a customer. Guys, welcome to theCube, good to see you again. Welcome for the first time. >> Thank you. >> Thank you. >> Okay, so we're going to talk about some stories we did yesterday, but you've got the customer here. What's the relationship, why are you guys here? >> We provide the storage platform. They use our flash technology. Spark is a professional software company. It's not a custom house, they are a software company. >> And Spark, not related to Spark OpenSource. Just the name Spark, Sparkcognition. Make sure to get that out of the way. Go ahead, continue. >> So they're a hot startup. They have a number of different use case including cybersecurity, real-time IoT, predictive analytics and a whole bunch of other things that they do. When a customer goes on premise 'cause they deliver either through a service model or on premise, when it's in their service model they use our flash and our power servers. When it's on premise they recommend here's the hardware you should use to optimize the software if the customer buys a non-premised version. They offer it both ways, but part of the reason we thought it would be interesting is they're a professional software company. A lot of the people here as you know are regular developers, in-house developers. In this case these guys are a well-funded VC startup that delivers software to the end user base. >> Tell us more about Sparkcognition. Give us the highlights. >> Yeah, appreciate it. Sparkcognition, we're a cognitive algorithms company. We do data science, machine learning, natural language processing. Kind of the whole gambit there. Working, we have three products. SparkPredict is our predictive analytic, our predictive maintenance product. SparkSecure is our network log security product. And Deep Armor is a machine learning endpoint protection product. In that you kind of hear we're in the IoT, the industrial IoT, the IIoT of things. It also, in cybersecurity we've done use cases, other machine learning use cases as well. But the predictive maintenance and cybersecurity are two most, most advanced use cases, industrial areas. So we've been around about three years. We have around 100 people. Appreciate Eric talking about how well-financed we are and how our success really is budding this far. We're happy to be here. >> John: Where are you guys located? >> We're based out of Austin, Texas. >> John: Another Austin. >> Yeah Austin, Texas. >> Dominant with Austin. >> It's always good to have financing. You can't go out of business if you don't run out of money. Talk about the industrial aspect. One of the things that is hot, it's not a mainstream here, is blockchain is the big announcement. But IoT is the big one. But industrial IoT's interesting because now you have the digitization of business as a big factor. And that data is going to be throwing off massive analog digital data now. So analog to digital, what's going on there? What are you guys doing there to help and where does the storage fit in? >> Yeah, I appreciate that. So IIoT, industrial there's obviously there's big clients there. There's value in this information. For us it's predictive maintenance is the big play. A study I read the other day by a Boston consulting group talks about how its services and applications in the industrial internet of things. There's an $80 billion market in the next five years with predictive maintenance leading the way as the most mature application there. So we're happy to be kind of riding on the front of that wave, really pushing the state of the art there. Predictive maintenance is valuable to clients because the idea is to predict failures, do optimization of resources, so to get more energy out of your wind farm, get more gas out of the ground, you name it. Having the software that can provide those solutions efficiently to clients without a lot of start up, but each new iteration. So having a product that can deliver that intellectual property efficiently is important. The whole goal is to be able to reduce maintenance costs and extend the useful life of assets. So that's what SparkPredict is our product, SparkPredict our product, Sparkcognition has been laboring to do. We have a successful deployment of 1,100 turbines with Invenergy, which is the largest wind production company in the United States. We're doing work with Duke, Nexterra, several other large electrical production companies, oil and gas companies as well. In Austin we're near Houston, we have a lot of energy production opportunity there. So predictive maintenance for us is a big play. >> So you guys did a session this week. You hosted a panel, is that right? So I mean no offense, but what we're talking about now is really even more interesting than storage. But it's a storage panel you were hosting, right? So what was the conversation like? >> The conversation around that was we had three software companies, Sparkcognition and two other software companies. Then we had a federal integrator. All of them are doing cloud delivery. So for example, one of the other software companies Medicat, delivers medical record keeping as a service to hospitals. They're doing predictive analytics and predictive maintenance, and also some cybersecurity out. So there were three professional software companies, and integrator. And in each case the issues were A, we need to be up and going all the time and the user doesn't know what storage we're using. But we can never fail because we're real time. In fact, one of the customers is the IRS. So the federal integrator, the IRS cloud runs on IBM storage. The entire IRS runs under IBM cloud. On our storage, but it's their cloud. It's their private cloud that they put together, that the integrator put together. The idea was we've got a cloud deployment. There's two key things your storage has to do. A, it needs to be resilient as heck because these guys and the other two companies on the software side if they cannot serve it as a service then no one's going to buy the software, right? Because software is the service. So for them it's critical in their own infrastructure that it be resilient. Then the second thing, it needs to be fast. You've got to meet the SLAs, right? So when you're thinking the system's integrator at the IRS, what do you think the SLAs are and they've got like 14 petabytes of all flash. >> You forgot dirt cheap. You got resilient as heck, lightning fast, and it's got to be dirt cheap, too. >> Well of course. >> They want all three, right? >> You have this panelist, so what Jenny, what were Jenny's three? Industrial ready, cloud based, and cognitive to the core. So you guys are, I'm on your website. It's cognitive this, cognitive that. You're cognitive to the core. You're presumably you're using industrial ready infrastructure and it's all cloud based, right? Talk about that a little bit, then I've got a follow up. >> To tie into what Eric is saying about the premium hardware, the cloud opportunity, for us to be able to to AI software, to be able to do machine learning models, these are very intensive applications that require massive amounts of CPU, IO, fast storage. To be able to get the value from that data quickly so that it's useful and actionable takes that premium hardware. So that's why we've done testing with flash system, with our cybersecurity product. One of the most innovative things that we did in the previous year was to move from a traditional architecture using X86, 64 where we had a cluster of eight servers there. Brought that down to one flash system array and we're able to get up to 20 times the performance doing things like analyzing, sorting, and ingesting data with our cybersecurity platform. So in that regard we were very much tied closely to the flash system product. That was a very successful use case. We offered a white paper on that. If anyone wants to read more that's available on the IBM website. >> Where do you find that, search it? >> Yeah, it's on IBM.com and it's basically how they used it to deliver software as a service. >> What do I search? >> If you search Sparkcognition IBM you'll find it on Google. >> My other question, my follow up is you talk about these IoT apps which are distributed by their very nature. Can we talk about the data flow? What are you seeing in terms of where the data flows? Everybody wants to instrument the windmill. You've got to connect it then you've got to instrument it. Where's the data going? You're doing analytics locally, you're sending data back. What are you seeing in the client base? >> Yeah, that's a great question. Those in the field use cases for the wind turbines for example, most of our clients they already have a data storage solution. We're not a data storage provider. The reason, and someone asked me this yesterday in a different conversation. They said why are wind turbines so ripe for the picking? It's because they're relatively modern assets. They were built with the sensors onboard. The data, they have been collecting the data since the invention of the modern wind turbine, they've been collecting this data. Generally it's sent in from the field at 10 minute intervals, usually stored in some sort of large data center. For our purposes though, we collect a feed off that data of the important information, run our models, store a small data set a few months, whatever we think we need to train that machine learning model and to retrain and balance that model. That's sort of an example where we're doing the analysis in a data center or in the cloud sort of out of the field. The other approach is sort of an edge analytics approach, you might have heard that term. That's usually for smaller devices where the value of the asset doesn't justify the infrastructure to relay the information and then deploy this large scale solution. So we actually are developing edge analytic solution, a version of our product as well working with a company called Flowserve, their the world's largest pump manufacturing company. To be able to say how can we add some intelligence the to these pumps that may operate near a pipeline or out in the oil field and be able to make those machines smarter even though they don't necessarily justify the robust IT infrastructure of a full wind turbine fleet. >> Is there a best practice that you guys see in terms of the storage? Because you bring out edge and the network. Great point, lot of diversity at the edge now, from industrial to people. But the data's got to be stored somewhere. I mean, is there a best practice? Is there a pattern to developing that you're seeing in terms of how people are approaching the data problem and applying algorithms to it? Just talk, do I move the data? Do I push to compute to the data? Thoughts on what you guys are seeing in terms of best practices. >> One of the other companies that was on the panel also is doing predictive modeling. They take 600 different feeds in real time then munge it for mostly for industrial markets, but mostly for the goods. So the raw goods that they need to make a machine or make a table or make the paper that is used behind us, or make the lights that are used here, they look at all that commodities and then they feed it out to all these consumers, not consumers but the companies that build these products. So for them, they need it real time so they need storage that's incredibly fast because what they're doing is they're putting out on super powerful CPUs loaded with D-ram, but you can only put so much D-ram in a server. They're building these giant clusters to analyze all this data and everything else is sitting on the flash. Then they push that out to their customers. Slightly different model from what Sparkcognition does, but a slightly similar except their taking it from 600 constant data sources in real time, 24 by seven, 365 and then feeding it back out to these manufacturing companies that are looking to buy all these commodities. >> You have "software defined" in your title. That was kind of the big buzzwords a few years ago. Everybody wanted to replicate the public cloud on prem. We think of it as programmable infrastructure, right? Set it and then you can start making API calls and set SLAs and thresholds, etc. Where are we at with software defined? Do you guys, does it resonate with you or is it just an industry buzzword? I'll start with Eric. >> For us we're the largest provider of software defined storage in the world. Hundreds and hundreds and hundreds of millions of dollars every year. We don't sell any infrastructure. We just sell the raw software and they use commodity infrastructure, whatever they want: hard drives, flash drives, CPUs, anything they buy from their local reseller and then create basically high-performance arrays using that software. So they create on their own. Everything is built around automation so we automatically can replicate data, snapshot data, migrate data around from box to box, move it from on-premise to a cloud through what we call transparent cloud tiering. All of that in the software defined storage is done based on automation play. So the software defined storage allows them to if you will, build their own version of our flash system by just buying the raw software and buying flash from someone else, which is okay with us because the real value's in the software, obviously as you know. That allows them to then create infrastructure of their own, but they've got the right kind of software. They're not home brewing the software it's all built around automation. That's what we're seeing in the software defined space across a number of different industries, whether it be cloud providers, banks. We have all kinds of banks that used our software defined storage and don't buy the actual underlying storage from us, just the storage software. >> Do you, you may not have visibility in this, but getting kind of geeky on it. Do you guys adopt that sort of software defined mentality in your approach? >> Yeah, so for us software defined storage is something that we've deployed for our proof of concept evaluations. The nature of the work that we do is the solution is innovative to the point where everyone needs to have some sort of proof point for themselves before the company or the client will invest in a large scale. So software defined storage and embracing that perspective has allowed us to deploy a small scale implementation without having our own dedicated hardware, for example, at different clients. That's enabled us to spin up an instance quickly, to provision that small scale deployment, to be able to prove out results at a low cost to our client. That's where we really leverage that approach. We also have used a similar approach in the cloud where we've used multi-tenant environments to be able to support our cybersecurity product, SparkSecure in a multi-tenant cloud hosted environment which brings down delivery costs as well. It allows us to slice up that data and deliver it at a low cost. As far as our large scale physical deployments for the asset monitoring and such, we really, we generally end up with a piece of a flash system or flash storage, bare metal deployment because that speed is critical whether that's the client wants to have instant monitoring of a critical asset or they have a financial services use case where we're looking for anomalies or looking for threats in the cybersecurity landscape. Having that real-time model building and model result is very critical. So having that bare metal flash system type installation is kind of our preferred route. The only other thing I would say on that is you asked earlier about our approach. For us, the security data is very important. Most of our assets are what are called critical assets. So clients are very sensitive to the security of the data. Some are still uncomfortable with a cloud deployment. Another reason why we have an affinity for the hardware deployment with IBM. >> Why IBM? >> Our company has really deep roots with IBM. My founder Amir Hussein, was actually on the board of directors of the original IBM Watson Project as well as Manoj Saxena was the original GM of the IBM Watson program. We have just a long relationship with IBM. We have a lot of mutual interest and respect for the entity. We've also found that the products are superior in many ways. We are hardware agnostic and we're an independent advisor to our clients when it comes to how to deliver our solutions. But our professional opinion based on the testing that we've done is that IBM is a top-tier option. So we continue to prescribe that to our clients. When they feel that's appropriate they make that purchase through IBM. >> Great testimonial. Eric, excited to hear that nice testimonial for you guys? Congratulations. >> He's done several panels with us and again, part of the reason for here was A, all about IoT which they're all into. All about commo which they're all into. And to show that you can do a software as a service model even in-house. They happen to be a professional software company but if you're a giant global enterprise you may actually do software as a service to your remote branch offices which is very similar to what these guys to do other companies. This gives them an example, the other two software companies the same way, to show in-house developers if you're going to have a private cloud, not go public, you can deliver software as a service internally to your own company through the dev model and do it that way. Or you can use someone like Sparkcognition or Medicat or the other companies that we showed, Z-Power, all of which were using us to deliver their software as a service with IBM flash technology. >> Dave: And you're using Watson or Watson analytics? >> Yes, so we have done integrations with Watson for our cybersecurity product. We've also done integrations with Watson rank and retrieve using the NPL capabilities to advise the analysts both in the Predict space and in the Secure space. Sort of an advisor to say what a client user could see something happening on a turbine and say what does this mean? Using a Watson corpus. I was going to add one thing, we were talking about why IBM? IBM really has been a leader in the space of cognitive computing and they've invested in bringing and nurturing small companies and bringing up entrepreneurs in that space to build that out. So we appreciate that. I think it's important to mention that. >> All right Mark, thanks so much for joining in, the great testimonial, the great insight. Good luck with your business. Congratulations on the success startup taking names and kicking butt. Eric, great to see you again, thanks for the insight and congratulations on great, happy customers and see you again. Okay, we're watching theCube live here at Interconnect 2017. More great coverage, stay with us. There will be more after this short break. (upbeat instrumental music)