>> From the SiliconANGLE media office in Boston, Massachusetts it's theCUBE. Now, here's your host Stu Miniman. >> Hi, I'm Stu Miniman and welcome to our Boston area studio. I'm happy to welcome back to the program a multi-time guest, Josh Rogers, who's the CEO of Syncsort. Josh, great to see ya. >> Great to see you. Thanks for having me. >> Alright so, Syncsort is a company that I would say is, you guys are deep in the data ocean. Data is at the center of everything. When Wikibon, when we did our predictions everything whether you're talking about cloud, whether you're talking about infrastructure, of course everything like IoT and Edge, it is at the center of it. I want you to help start off is there's this term, big iron, big data. Help explain to us what that is and what that means to both Syncsort and your customers. >> Sure yeah, so we like to talk about Syncsort as the leader in big iron to big data and it's a it's a positioning that we've chosen for the firm because we think it represents the value proposition that we bring to our customers but we also think it represents a collection of use cases that are really at the top of the agenda of CIOs today. And really we talk about it in two areas. The first is a recognition that large enterprises still run mission critical workloads on systems that they've built over the last 20, 30, 40 years. Those systems leverage mainframe computing, they leveraging IBM i or AS400 and they spent trillions of dollars building those systems and they still deliver core workloads that power their businesses. So mission number one is that these firms want to make sure that they optimize those environments. They run them as efficiently as possible. They can't go down. They've got the proper security kind of protocols around them and of course that situation's always changing as workloads grow and change on these environments. So first is how do I optimize the systems that while they may be mature, they are still mission critical. The second is a recognition that most of the critical data assets for our customers are created in these systems. These are the systems that execute the transactions and as a result have core information around the results of the firm, the firm's customers, et cetera. So second value proposition is how do I maximize the value of that data that gets produced in those systems which tends to be a focus on liberating it, making a copy of it and moving it into next generation analytic systems. And then you look at the technical requirements of that it turns out that it's hard. I'm taking data from systems that were created 50 years ago and I'm integrating it with systems that were created five years ago. And so we've got a special set of expertise and solutions that allow customers to both optimize these old systems and maximize the value data produced in those systems. >> You bring up some really good points. I've been talking the last couple of years to people about how do I really wrap my arms around my data and we're talking about a multi-cloud world and where we have pockets of information trapped. That's a challenge. So it's not just about my data center and Amazon. It's like oh wait, I've got all these SaaS deployments and I think it's probably, it's a blind spot that I had had as to sure, right, you've got companies that have let's call them legacy systems, ones that they've got a lot investment but these are mission critical, these are the ones that it is not easy to modernize them but if I can get access to the data and put this into these next generation systems it sounds like you kind of free that data and allow that to be leveraged much easier. >> That's right, that's right and we, what we try to do is focus on what are the next generation trends in data and how are they going to intersect with these older systems. And so that started as big data but it includes cloud and the multi-cloud. It includes real-time and IoT. It includes thing like Blockchain. We're really scanning the horizon for what are these kind of generational shifts in terms of how am I going to leverage data and how do we get really tight on the use cases that our customers are gonna need. So I'll integrate those new technologies with these old investments. >> Josh, I'd love to hear what you're seeing from customers. So we've talked to you at some of the big data shows. I know we've spoken to you at the Splunk shows. I felt like what we as an industry got bogged down in some of the tools for a couple of years. While Wikibon, we did the first market forecast on big data everybody was like oh, Hadoop Hadoop Hadoop and we're like well, Hadoop will catalyze a lot of things and companies will rod a lot of things but Hadoop itself will be a small piece of the market and we've started to see some consolidation in that market. So data and the value that I get out of the data is the important thing. So what are your customers focused on? How do they get from their traditional data warehouses to a more modern? What are the challenges that they're dealing with and where are you engaging with them? >> Right, sure. So I mean one of the challenges they do have is this explosion of kind of options. Am I doing things in Hadoop? What is Hadoop at this point? Which projects actually constitute Hadoop? So what repository I'm gonna use. Am I gonna use Hive? Am I gonna use something, am I gonna use MongoDB, Elastic? What are, what's the repository I'm targeting? Generally what we see is that each of those has, and a long list of additional repositories, has a role to play for the specific use case. And then how am I going to get the data there and integrate it and then get the data out and deliver insights? And that stack of technologies and tools is pretty intimidating. And so we see customers starting to coalesce around some market leaders in that space. The merger of Hortonworks and Cloudera I think was a very good thing for the industry. It just simplifies the life of the customer in terms of making decisions in confidence in that stack. It certainly simplifies our life as a partner of those firms and I think it will help accelerate maturity in that tech stack. And so I think we're starting to see pockets of maturation which I think will accelerate customers' investments in leveraging these next generation technologies. That then creates a big opportunity for us because now it's becoming real. Now I really have to get on a real-time basis my data out of my mainframe or my IBM i system into these next generation repositories and it turns out that's technically a challenge and so what we're seeing in our businesses real acceleration of our big data solutions against what I would say production-targeted workloads and projects, which is great. >> Alright, M&A, you got a always really active in this space. We've done ThinkSort for many years so we've watched some of the changes along the way. I believe you've got some news to share regarding M&A activity and there's also some recent stuff to tap in the last year. Maybe bring us up to speed. >> Sure so we've made two announcements. We made an announcement in the last few weeks and then one very recently that I'd like to share. The first is about two months ago we struck up a developmental relationship with IBM around their B2B collaboration portfolio and this product set really gives us exposure to integration styles between businesses. Historically we've been focused on integration within a business and so we really like the exposure to that. More importantly, it intersects with one of these next generational data themes around Blockchain and we believe there's a huge opportunity to help be a leader and how do you take Blockchain infrastructure and integrate it to these existing systems. So we're really excited to partner with IBM on that front. And IBM obviously is making huge investments there. >> Before we got, what's Syncsort's play there when it comes to Blockchain? We have definitely talked to IBM quite a bit about Blockchain, Hyperledger, everything going into there. So maybe give a little more color there. >> Sure, so look, we still think that production workloads on Blockchain are a few years out and we see a lot of pilot activity. So I think people are still trying to understand the specific use cases they're gonna deliver real value. But one thing is for certain, that as customers start to stand up production workloads on the Blockchain they're going to need to integrate what's happening in that new infrastructure with these traditional systems that are still managing the large majority of their transactions. And how do I add data to the Blockchain? How do I verify data on the Blockchain? How do I improve the quality of data on the Blockchain? How do I pull data off of the Blockchain? We think there's a really important role for us to play around understanding the specifics of those use cases, how they intersect with some of these legacy systems and how we provide tailored solutions that are best in class. And it's one of the reasons, it's one of the primary reasons we've struck up the relationship with IBM but also joined Hyperledger. So hopefully that gives you a little bit more context. >> That's great. >> The more recent announcement I want to make is that we've acquired a company called Eview and Eview is a terrific leader in the machine data integration space. They have a number of solutions that are complementary to what we've done with our iron string product and what we're trying to do there is support as many use cases as possible for people to maximize the value of that they can get out of machine data, particularly as it relates to older systems like mainframe and IBM i. And what this acquisition does is it allows us to take another step forward in terms of the value proposition that we offer our customers. One specific use case where Eview's been a leader that we're very excited about is integration with ServiceNow. And you can think of ServiceNow as kind of a next generation platform that we to date have not had integration with. This acquisition gives us that integration. It also gives us a set of technology and talent that we can put towards accelerating our overall big data plans. And so we're really excited about having the Evue team join the Syncsort family and what we can deliver for customers. >> Yeah great great. Absolutely, companies like ServiceNow and Workday, huge amounts of data there, are seeing a lot of it. Dave Alonte's been at the ServiceNow knowledge show with theCUBE for a number of years. Really interesting. Seems like this acquisition ties well in with I believe it was Vision that a year ago? >> Well so it ties in mostly with our iron string product. >> Okay. >> Now Vision contributed to the iron string product in that that gave us the expertise to deliver integration for IBM i log data into next generation analytic platforms like Splunk and Elastic. So we had built a product that was focused on delivering mainframe data in real-time to those platforms. Vision gave us both real-time capability and a huge franchise in the IBM i space. Eview builds on that and gives us additional capability in terms of delivering data to new repositories like ServiceNow. >> Great, maybe step back for a second. Give us kind of some of the speeds and feeds of Syncsort itself. Memento the company, you've been CEO for a while now. Tell us how we're doing. >> Yeah, we're doing well. We're having a record year. It's important to actually recognize that in September we celebrated our 50th anniversary. So I think we're a bit unusual in terms of our heritage. Having said that, we've never driven more innovation than we have over the last 12 months. We have tripled the size of the business over the last three years since I've been CEO. We've quadrupled the employee base. And we will continue to see I think rapid growth given the opportunity we set and we see in this big iron to big data space. >> Yeah, Josh, you talk about that. When I look at okay, a 50-year-old company. We talked about data quite a bit differently 50 years ago. What is the digital transformation today? What does that mean for Syncsort? What does that mean for your customers? Help put us in context. >> Yeah, I mean, it kind of goes back to this original positioning which is, the largest banks int he world, the largest telecommunications vendors in the world, healthcare, government, you pick the industry, they built a set of systems that they still run today over the last four or five decades. Those systems tend to produce the most important data of that enterprise, not the only data you want to analyze, but it tends to be that reference data that makes everything else, allows you to make sense of everything else. And as you think about how am I gonna analyze that data, how am I gonna maximize the value of that data there is a need to integrate the data and move it off of those platforms and into these next generation platforms. And if you look at the way a vSAN file was designed for computing requirements in 1970 it turns out it's really different than the way that you would design a file type JSON or a file for Impala. And so kind of knitting that together takes a lot of deep expertise on both sides of the equation and we uniquely have that expertise and are solving that. And what we've seen is as new technologies continue to come to market, which we refer to as the next wave, that our enterprise customer base of 7,000 customers needs a partner that can say how do I take advantage of that new technology trend in the context of the past 30, 40, 50 years of investment I've made in mission critical systems and how do I support the key integration use cases? And that's what we've determined where we can make a difference in the market is focusing on what are those use cases and how do we deliver differentiate solutions to solve 'em that help both our customers and these partners. >> Absolutely, it's always great to talk about some of the new stuff but you need to meet the customers where they are, get to that data where it is and help move it forward. Alright, Josh, why don't you give it the final words? Kind of broadly open. Big challenges, opportunities, what's exciting you as you look forward kind of the next six months? >> Yeah, so we'll continue to make investments in cloud, in data governance, in supporting real-time data streaming and in security. Those are the areas that we'll be focused on driving innovation and delivering additional capability to our customers. Some of that will come through taking technologies like Eview or like the B2B products and enhancing them for specific use cases where they intersect those things. It will also be additional investments from an acquisition perspective in those domains and you can count on Syncsort to continue to expand the value proposition that it is delivering to its customers both through new technology introductions but also through additional integration with these next generation platforms. So we're really excited I mean, we believe our strategy is working. It's led to record results in our 50th year and we think we've got many years to run with this strategy. >> Alright well Josh Rogers, CEO of Syncsort. Congratulations on the progress. New acquisition, deeper partnership with IBM and I look forward to tracking the updates. >> Thanks so much. Appreciate the opportunity. >> Alright, and thank you as always for joining. I'm Stu Miniman. Thanks for watching theCUBE. (upbeat electronic music)

>> Live from New York, it's theCUBE, covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Okay, welcome back, everyone. We're here live in New York City for CUBE NYC. This is our ninth year covering the big data ecosystem, now it's AI, machine-learning, used to be Hadoop, now it's growing, ninth year covering theCUBE here in New York City. I'm John Furrier, with Dave Vellante. Our next guest, Josh Rogers, CEO of Syncsort. I'm going back, long history in theCUBE. You guys have been on every year. Really appreciate chatting with you. Been fun to watch the evolution of Syncsort and also get the insight. Thanks for coming on, appreciate it. >> Thanks for having me. It's great to see you. >> So you guys have constantly been on this wave, and it's been fun to watch. You guys had a lot of IP in your company, and then just watching you guys kind of surf the big data wave, but also make some good decisions, made some good calls. You're always out front. You guys are on the right parts of the wave. I mean now it's cloud, you guys are doing some things. Give us a quick update. You guys got a brand refresh, so you got the new logo goin' on there. Give us a quick update on Syncsort. You got some news, you got the brand refresh. Give us a quick update. >> Sure. I'll start with the brand refresh. We refreshed the brand, and you see that in the web properties and in the messaging that we use in all of our communications. And, we did that because the value proposition of the portfolio had expanded so much, and we had gained so much more insight into some of the key use cases that we're helping customers solve that we really felt we had to do a better job of telling our story and, probably most importantly, engage with the more senior level within these organizations. What we've seen is that when you think about the largest enterprises in the world, we offer a series of solutions around two fundamental value propositions that tend to be top of mind for these executives. The first is how do I take the 20, 30, 40 years of investment in infrastructure and run that as efficiently as possible. You know, I can't make any compromises on the availability of that. I certainly have to improve my governance and secureability of that environment. But, fundamentally, I need to make sure I could run those mission-critical workloads, but I need to also save some money along the way, because what I really want to do is be a data-driven enterprise. What I really want to do is take advantage of the data that gets produced in these transactional applications that run on my AS400 or IBM I-infra environment, my mainframe environment, even in my traditional data warehouse, and make sure that I'm getting the most out of that data by analyzing it in a next-generation set of-- >> I mean one of the trends I want to get your thoughts on, Josh, cause you're kind of talking through the big, meagatrend which is infrastructure agnostic from an application standpoint. So the that's the trend with dev ops, and you guys have certainly had diverse solutions across your portfolio, but, at the end of the day, this is the abstraction layer customers want. They want to run workloads on environments that they know are in production, that work well with applications, so they almost want to view the infrastructure, or cloud, if you will, same thing, as just agnostic, but let the programmability take care of itself, under the hood, if you will. >> Right, and what we see is that people are absolutely kind of into extending and modernizing existing applications. This is in the large enterprise, and those applications and core components will still run on mainframe environments. And so, what we see in terms of use cases is how do we help customers understand how to monitor that, the performance of those applications. If I have a tier that's sitting on the cloud, but it's transacting with the mainframe behind the firewall, how do I get an end-to-end view of application performance? How do I take the data that ultimately gets logged in a DB2 database on the mainframe and make that available in a next-generation repository, like Hadoop, so that I can do advanced analytics? When you think about solving both the optimization and the integration challenge there, you need a lot of expertise in both sides, the old and the new, and I think that's what we uniquely offer. >> You guys done a good job with integration. I want to ask quick question on the integration piece. Is this becoming more and more table stakes, but also challenging at the same time? Integration and connecting systems together, if their stateless, is no problem, you use APIs, right, and do that, but as you start to get data that needs state information, you start to think to think about some of the challenges around different, disparate systems being distributed, but networked, in some cases, even decentralized, so distributed networking is being radically changed by the data decisions on the architecture, but also integration, call it API 2.0 or this new way to connect and integrate. >> Yeah, so what we've tried to focus on is kind of solving that piece between these older applications that run these legacy platforms and making them available to whatever the consumer is. Today, we see Kafka and in Amazon we see Kinesis as kind of key buses delivering data as a service, and so the role that we see ourselves playing and what we announced this week is an ability to track changed data, deliver it in realtime in these older systems, but deliver it to these new targets: Kafka, Kinesis, and whatever comes next. Because really that's the fundamental partner we're trying to be to our customers is we will help you solve the integration challenge between this infrastructure you've been building for 30 years and this next-generation technology that lets you get the next leg of value out of your data. >> So Jim, when you think about the evolution of this whole big data space, the early narrative in the trade press was, well, NoSQL is going to replace Oracle and DB2, and the data lake is going to replace the EDW, and unstructured data is all that matters, and so forth. And now, you look at what's really happened is the EDW is a fundamental component of making decisions and insights, and SQL is the killer app for Hadoop. And I take an example of say fraud detection, and when you think and this is where you guys sit in the middle from the standpoint of data quality, data integration, in order to do what we've done in the past 10 years take fraud detection down from well, I look at my statement a month or two later and then call the credit card company, it's now gone to a text that's instantaneous. Still some false positives, and I'm sure working on that even. So maybe you could describe that use case or any other, your favorite use case, and what your role is there in terms of taking those different data sources, integrating them, improving the data quality. >> So, I think when you think about a use case where I'm trying to improve the SLA or the responsiveness of how do manage against or detect fraud, rather than trying to detect it on a daily basis, I'm trying to detect it at transaction time. The reality is you want to leverage the existing infrastructure you have. So if you have a data warehouse that has detailed information about transaction history, maybe that's a good source. If you have an application that's running on the mainframe that's doing those transaction realtime, the ultimate answer is how do I knit together the existing infrastructure I have and embed the additional intelligence and capability I need from these new capabilities, like, for example, using Kafka, to deliver a complete solution. What we do is we help customers kind of tie that together, Specifically, we announced this integration I mentioned earlier where we can take a changed data element in a DB2 database and publish it into Kafka. That is a key requirement in delivering this real-time fraud detection if I in fact am running transactions on a mainframe, which most of the banks are. >> Without ripping and replacing >> Why would you want to rip out an application >> You don't. >> your core customer file when you can just extend it. >> And you mentioned the Cloudera 6 certification. You guys have been early on there. Maybe talk a little about that relationship, the engineering work that has to get done for you to be able to get into the press release day one. >> We just mentioned that my first time on theCUBE was in 2013, and that was on the back of our initial product release in the big data world. When we brought the initial DMX-h release to market, we knew that we needed to have deep partnerships with Cloudera and the key platform providers. I went and saw Mike Olson, I introduced myself, he was gracious enough to give me an hour, and explain what we thought we could do to help them develop more value proposition around their platform, and it's been a terrific relationship. Our architecture and our engineering and product management relationship is such that it allows us to very rapidly certify and work on their new releases, usually within a couple a days. Not only can customers take advantage of that, which is pretty unique in the industry, but we get some some visibility from Cloudera as evidenced by Tendu's quote in the press release that was released this week, which is terrific. >> Talk about your business a little bit. You guys are like a 50-year old startup. You've had this really interesting history. I remember you from when I first started in the industry following you guys. You've restructured the company, you've done some spin outs, you've done some M and A, but it seems to be working. Talk about growth and progress that you're making. >> We're the leader in the Big Iron to Big Data market. We define that as allowing customers to optimize their traditional legacy investments for cost and performance, and then we help them maximize the value of the data that get generated in those environments by integrating it with next-generation analytic environments. To do that, we need a broad set of capability. There's a lot of different ways to optimize existing infrastructure. One is capacity management, so we made an acquisition about a year ago in the capacity management space. We're allowing customers to figure out how do I make sure I've got not too much and not too little capacity. That's an example of optimization. Another area of capability is data quality. If I'm maximize the value of the data that gets produced in these older environments, it would be great that when it lands in these next-generation repositories it's as high quality as possible. We acquired Trillium about a year ago, or actually coming up >> How's that comin'? >> on two years ago and we think that's a great capability for our customers It's going terrific. We took their core data quality engine, and now it runs natively on a distributed Hadoop infrastructure. We have customers leveraging it to deliver unprecedented volume of matching, so not only breakthrough performance, but this whole notion of write once, run anywhere. I can run it on an SMP environment. I can run it on Hadoop. I can run it Hadoop in the cloud. We've seen terrific growth in that business based on our continued innovation, particularly pointing it at the big data space. >> One of the things that I'm impressed with you guys is you guys have transformed, so having a transformation message to your customers is you have a lot of credibility, but what's interesting is is that the world with containers and Kubernetes now and multi-cloud, you're seeing that you don't have to kill the legacy to bring in the new stuff. You can see you can connect systems, when you guys have done with legacy systems, look at connect the data. You don't have to kill that to bring in the new. >> Right >> You can do cloud-native, you can do some really cool things. >> Right. I think there's-- >> This rip and replace concept is kind of going away. You put containers around it too. That helps. >> Right. It's expensive and it's risky, so why do that. I think that's the realization. The reality is that when people build these mission-critical systems, they stay in place for not five years, but 25 years. The question is how do you allow the customers to leverage what they have and the investment they've made, but take advantage of the next wave, and that's what we're singularly focused on, and I think we're doing a great job of that, not just for customers, but also for these next-generation partners, which has been a lot of fun for us. >> And we also heard people doing analytics they want to have their own multi-tenent, isolated environments, which goes to don't screw this system up, if it's doing a great job on a mission-critical thing, don't bundle it, just connect it to the network, and you're good. >> And on the cloud side, we're continuing to look at our portfolio and say what capabilities will customers want to consume in a cloud-delivery model. We've been doing that in the data quality space for quite awhile. We just launched and announced over the last about three months ago capacity management as a service. You'll continue to see, both on the optimization side and on the integration side, us continuing to deliver new ways for customers to consume the capabilities they need. >> That's a key thing for you guys, integration. That's pretty much how you guys put the stake in the ground and engineer your activities around integration. >> Yeah, we start with the premise that your going to need to continue to run this older investments that you made, and you're going to need to integrate the new stuff with that. >> What's next? What's goin' on the rest of the year with you guys? >> We'll continue to invest heavily in the realtime and changed-data capture space. We think that's really interesting. We're seeing a tremendous amount of demand there. We've made a series of acquisitions in the security space. We believe that the ability to secure data in the core systems and its journey to the next-generation systems is absolutely critical, so we'll continue to invest there. And then, I'd say governance, that's an area that we think is incredibly important as people start to really take advantage of these data lakes they're building, they have to establish real governance capabilities around those. We believe we have an important role to play there. And there's other adjacencies, but those are probably the big areas we're investing in right now. >> Just continuing to move the ball down the field in the Syncsort cadence of acquisitions, organic development. Congratulations. Josh, thanks for comin' on. To John Rogers, CEO of Syncsort, here inside theCUBE. I'm John Furrier with Dave Vellante. Stay with us for more big data coverage, AI coverage, cloud coverage here. Part of CUBE NYC, we're in New York City live. We'll be right back after this short break. Stay with us. (techno music)

>> Announcer: Live from Midtown Manhattan it's theCUBE. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Welcome back everyone live here in New York City this theCUBE's coverage of our fifth annual annual event that we put on ourselves in conjunction Strata Hadoop now called Strata Data. It's theCUBE and we're covering the scene here at Hadoop World going back to 2010, eight years of Coverage. I'm John Furrier co-host of theCUBE. Usually Dave Vellante is here but he's down covering the Splunk Conference and who was there yesterday was no other than Josh Rogers my next guest the CEO of Syncsort, you were with Dave Vellante yesterday and live on theCUBE in Washington, DC for the Splunk .conf kind of a Big Data Conference but it's a proprietary, branded event for themselves. This is a more industry even here at Big Data NYC that we put on. Welcome back glad you flew up on the on the Concord, the private jet. >> Early morning but it was was fine. >> No good to see you a CEO of Syncsort, you guys have been busy. For the folks watching in theCUBE community know that you've been on many times. The folks that are learning more about theCUBE every day, you guys had an interesting transformations as a company, take a minute to talk about where you've come from and where you are today. Certainly a ton of corporate development activity in your end it, as you guys are seeing the opportunities, you're moving on them. Take a minute to explain. >> So, you know it's been a great journey so far and there's a lot more work to do, but you know Syncsort is one of the first software companies, right. Founded in the late 60's today has a unparalleled franchise in the mainframe space. But over the last 10 years or so we branched out into open systems and delivered high performance data integration solutions. About 4 years ago really started to invest in the Big Data space we had a DNA around performance and scale we felt like that would be relevant in the Big Data space. We delivered a Hadoop focused product and today we focus around that product around helping customers ingest mainframe data assets into their into Hadoop clusters along with other types data. But a specific focus there. That has lead us into understanding a bigger market space that we call Big Iron to Big Data. And what we see in the marketplace is that customers are adapting. >> Just before you get in there I love that term, Big Iron Big Data you know I love Big Iron. Used to be a term for the mainframe for the younger generation out there. But you're really talking about you guys have leveraged experience with the installed base activity that scale call it batched, molded, single threaded, whatever you want to call it. But as you got into the game of Big Data you then saw other opportunities, did I get that right? You got into the game with some Hadoop, then you realize, whoa, I can do some large scale. What was that opportunity? >> The opportunity is that you know large enterprise is absolutely investing heavily in the next generation of analytic technologies in a new stack. Hadoop is a part of that, Spark is a part of that. And they're rapidly adopting these new infrastructures to drive deeper analytics to answer bigger questions and improve their business and in multiple dimensions. The opportunity we saw was that you know the ability for those enterprises to be able to integrate this new kind of architecture with the legacy architectures. So, the old architectures that were powering key applications impede key up producers of data was a challenge, there was multiple technology challenges, there's cultural challenges. And we had this kind of expertise on both sides of the house and and we found that to be unique in the marketplace. So we put a lot of effort into understanding, defining what are the challenges in that Big Iron to Big Data space that helped customers maximize their value out of these investments in next generation architectures. And we define the problem two ways, one is our two components. One is that people are generating more and more data more and more touch points and driving more and more transactions with their customers. And that's generating increased load on the compute environments and they want to figure out how do I run that, you know if I have a mainframe how to run as efficiently as possible contain my costs maximize availability and uptime. At the same time I've got all this new data that I can start to analyze but I got to get it from the area that it's produced into this next generation system. And there's a lot of challenges there. So we started to isolate, you know, what are the specific use cases the present customers challenge and deliver very different IT solutions. Overarching kind of messages around positioning is around solving the Big Iron to Big Data challenge. >> You guys had done some acquisitions and been successful, I want to talk a little bit about the ones that you like right now that happened the past year or two years. I think you've done five in the past two years. A couple key notable ones that set you up kind of give you pole position for some of these big markets, and then after we talk then I want to talk about your ecosystem opportunity. But some of the acquisitions and what's working for you? What's been the big deals? >> So the larger the larger we did in 2016 was a company called Trillium, leader in the data quality space. Long time leader in the data quality space and the opportunity we saw with Trillium was to complement our data movement integration capabilities. A natural complement, but to focus very specifically on how to drive value in this next generation architecture. Particularly in things like Hadoop. what I'd like to be able to do is apply best in class data quality routines directly in that environment. And so we, from our experience in delivering these Big Data solutions in the past, we knew that we could take a lot of technology and create really powerful solutions that were that leverage the native kind of capabilities of Hadoop but had it on a layer of you've proven technology for best in class day quality. Probably the biggest news of the last few weeks has been that we were acquired by a new private equity partner called Centerbridge Partners. In that acquisition actually acquired Syncsort and they acquired a company called Vision Solutions. And we've combined those organizations. >> John: When did that happen? >> The deal was announced July, early July and it closed in the middle of August. And vision solutions is a really interesting company. They're the leader in high availability for the IBM i market. IBM i was originally called AS/400 it's had a couple of different names and a dominant kind of market position. What we liked about that business was A. That market position four thousand customers generally large enterprise. And also you know market leading capability around data replication in real time. >> And we saw IBM. >> Migration data, disaster recovery kind of thing? >> It's DR it's high availability, it's migrations, it's also changed data capture actually. And leveraging all common technology elements there. But it also represents a market leading franchise in IBM i which is in many ways very similar to the mainframe. Run optimized for transactional systems, hard to kind of get at. >> Sounds like you're reconstructing the mainframe in the cloud. >> It's not so much that, it's the recognition that those compute systems still run the world. They still run all the transactions. >> Well, some say the cloud is a software mainframe. >> I think over time you'll see that, we don't see that our business today. There is a cloud aspect our business it's not to move this transactional applications running on those platforms into the cloud yet. Although I suspect that happens at some point. But our point, our interest was more these are the systems that are producing the world's data. And it's hard to to get. >> There are big, big power sources for data, they're not going anywhere. So we've got the expertise to source that data into these next generation systems. And that's a tricky problem for a lot of customers, and and not something. >> That a problem they have. And you guys basically cornered the market on that. >> So think about Big Iron and Big Data as these two components, being able to source data and make a productive using these next generation analytics systems, and also be able to run those existing systems as you know efficiently as possible. >> All right, so how do you talk to customers and I've asked this question before so I just ask again, oh, Syncsort now you got vision you guys are just a bunch of old mainframe guys. What do you know about cloud native? A lot of the hipsters and the young guns out there might not know about some of the things you're doing on the cutting edge, because even though you have the power base of these old big systems, we're just throwing off massive amounts of data that aren't going anywhere. You still are integrated into some cutting edge. Talk about that, that narrative, and how you. >> So I mean the folks that we target. >> I used cloud only as an example. Shiny, cool, new toys. >> Organizations we target and our customers and prospects, and generally we we serve large enterprise. You know large complex global enterprises. They are making significant investments in Hadoop and Splunk and these next generation environments. We approach them and say we believe to get full value out of your investments in these next generation technologies, it would be helpful if you had your most critical data assets available. And that's hard, and we can help you do that. And we can help you do that in a number of ways that you won't be able to find anywhere else. That includes features in our products, it includes experts on the ground. And what we're seeing is there's a huge demand because, you know, Hadoop is really kind of you can see it in the Cloudera and Hortonworks results and the scale of revenue. This is a you know a real foundational component data management this point. Enterprises are embracing it. If they can't solve that integration challenge between the systems that produce all the data and, you know, where they want to analyze the data There's a there's a big value gap. And we think we're uniquely positioned to be able to do that, one because we've got the technical expertise, two, they're all our customers at this point, we have six thousand customers. >> You guys have executed very well. I just got to say you guys are just slowly taking territory down you and you got a great strategy, get into a business, you don't overplay your hand or get over your skis, whatever you want to call it. And you figure it out and see if was a fit. If it is, grab it, if not, you move on. So also you guys have relationships so we're talking about your ecosystem. What is your ecosystem and what is your partner strategy? >> I'll talk a little bit about the overall strategy and I'll talk about how partners fit into that. Our strategy is to identify specific use cases that are common and challenging in our customer set, that fall within this Big Iron to Big Data umbrella. It's then to deliver a solution that is highly differentiated. Now, the third piece of that is to partner very closely with you know the emerging platform vendors in the in the Big Data space. And the reason for that is we're solving an integration challenge for them. Like Cloudera, like Hortonworks, like Splunk. We launched a relationship with Calibra in the middle the year. We just announced our relationship. >> Yeah, for them the benefits of them is they don't do the heavy lifting you've got that covered. >> We can we can solve a lot of pain points they have getting their platforms setup. >> That's hard to replicate on their end, it's not like they're going to go build it. >> Cloudera and Hortonworks, they don't have mainframe skills. They don't understand how to go access >> Classic partnering example. >> But that the other pieces is we do real engineering work with these partnerships. So we build, we write code to integrate and add value to platforms. >> It's not a Barney deal, it's not an optical deal. >> Absolutely. >> Any jazz is critical in the VM world of some of the deals he's been done in the industry referring to his deal, that's seems to be back in vogue thank God, that people going to say they're going to do a deal and they back it with actually following through. What about other partnerships, how else, how you looking at partnering? So, pretty much, where it fits in your business, are people coming to you, are you going to them? >> We certainly have people coming to us. The the key thing, the number one driver is customers. You know, as we understand use cases, as customers introduce us to new challenges that they are facing, we will not just look at how do we solve it, but and what are the other platforms that we're integrating with, and if we believe we can add unique value to that partner we'll approach that partner. >> Let's talk customers, give me some customer use cases that you're working on right now, that you think are notable worth highlighting. >> Sure so we do a lot in the in the financial services space. You know we have a number of customers >> Where there's mainframes. >> Where there's a lot of mainframes, but it's not just in financial services. Here's an interesting one, was insurance company and they were looking at how to transition their mainframe archive strategy. So they have regulations around how long they have to keep data, they had been using traditional mainframe archive technology, very expensive on annual basis and also unflexible. They didn't have access to. >> And performance too. At the end of the day don't forget performance >> They want performance, this was more of an archive use case and what they really wanted was an ability both access the data and also lower the cost of storing the data for the required time from a regulation perspective. And so they made the decision that they wanted to store it in the cloud, they want to store it in S3. There's a complicated data movement there, there's a complicated data translation process there and you need to understand the mainframe and you need to understand AWS and S3 and all those components, and we had all those pieces and all that expertise and were able to solve that. So we're doing that with a few different customers now. But that's just an example of, you know, there's a great ROI, there's a lot more business flexibility then there's a modernization aspect to it that's very attractive. >> Well, great to hear from you today. I'm glad you made it up here, again you were in DC yesterday thanks for coming in, checking out to shows you're certainly pounding the pavement as they say in New York, to quote New Yorker phrase. What's new for you guys, what's coming out? More acquisitions happening? what's the outlook for Syncsort? >> So were were always active on the M&A front. We certainly have a pipeline of activities and there's a lot of different you know interesting spaces, adjacencies that we're exploring right now. There's nothing that I can really talk about there >> Can you talk about the categories you're looking at? >> Sure you know, things around metadata management, things around real-time data movement, cloud opportunities. There's there's some interesting opportunities in the artificial intelligence, machine learning space. Those are all >> Deep learning. >> Deep learning, those are all interesting spaces for us to think about. Security and other space is interesting. So we're pretty active in a lot of adjacencies >> Classic adjacent markets that you're looking at. So you take one step at a time, slow. >> But then we try to innovate on, you know, after the catch, so we did three announcements this week. Transaction tracing for Ironstream and a kind of refresh of data quality for Hadoop approach. So we'll continue to innovate on the organic setup as well. >> Final question the whole private equity thing. So that's done, so they put a big bag of money in there and brought the two companies together. Is there structural changes, management changes, you're the Syncsort CEO is there a new co name? >> The combined companies will operate under the Syncsort name, I'll serve as the CEO. >> Syncsort is the remaining name and you guys now have another company under it. >> Yes, that's right. >> And cash they put in, probably a boatload of cash for corporate development. >> The announcement the announced deal value was $1.2 billion a little over $1.2 billion. >> So you get a checkbook and looking to buy companies? >> We are we're going to continue, as I said yesterday, to Dave, you know I like to believe that we proved the hypothesis were in about the second inning. Can't wait to keep playing the game. >> It's interesting just, real quick while I got you in here, we got a break coming up for the guys. Private equity move is a good move in this transitional markets, you and I have talked about this in the past off-camera. It's a great thing to do, is take, if you're public and you're not really knocking it out of the park. Kill the 90 day shot clock, go private, there seems to be a lot of movement there. Retool and then re-emerge stronger. >> We've never been public, but I will say, the Centerbridge team has been terrific. A lot of resources there and certainly we do talk we're still very quarterly focused, but I think we've got a great partner and look forward to continue. >> The waves are coming, the big waves are coming so get your big surfboard out, we say in California. Josh, thanks for spending the time. Josh Rogers, CEO Syncsort here on theCUBE. More live coverage in New York after this break. Stay with us for our day two of three days of coverage of Big Data NYC 2017. Our event that we hold every year here in conjunction with Hadoop World right around the corner. I'm John Furrier, we'll be right back.

>> Narrator: Live from Washington D.C., it's theCUBE. Covering Dotcom 2017. Brought to you by Splunk. >> And welcome back to the nation's capital. The Cube, continuing our coverage of Dotcom 2017. At Splunk's annual get together and coming to Washington D.C. for the first time. Huge success, 7,000 plus attendees, 65 countries. I forget the millions of miles. Was it three million miles traveling? >> Let's see, was it three million? It was 30 million. >> Maybe 30 million. >> Yeah. It's a big number. >> 30 million miles. Dave Vellante and John Walls here on theCUBE. I'd say off to a roaring start here, to say the least. Josh Rogers joins us, he's the CEO of Syncsort. And Josh, good to have you on theCUBE. Good to you see sir. >> Thanks sir. Thanks for having me. >> Good week for you, big week for you. Couple of announcements that you made here recently. Go ahead and share with us a little bit about those. >> Sure, so we made two announcements yesterday. The first is a new product, it's called Transaction Tracing, it's an add on to our Ironstream product. Ironstream is a solution that delivers mainframe machine data to Splunk Enterprise, and has integration points on the security and on the IT service intelligence components within Splunk. What Transaction Tracing does, the new product introduction, is it adds additional capabilities to understand and trace a transaction that could begin on a mobile device and follow it all the way through the multiple hops it will take to ultimately transact against a mainframe. And when that transaction hits the mainframe, there's several things that you want to understand. One is, you want to understand how is is performing, how is it affecting my mainframe environment. Is it causing problems in other places? And you want to be able to look at that transaction, or that application, as a service. And so you want to be able to track that whole service end to end. And so what we've done with Transaction Tracing is created an ability for Splunk customers to be able to surface all of that data, collate it together, and get a unified view of both how the service is behaving, the performance that characteristics it's delivering to the customers that are utilizing the service, and then the impacts that it's having on the mainframe. All of which are, core components of understanding how you're IT operations are performing. And kind of all about what Splunk is supporting. We're just adding on additional capabilities for Splunk customers. >> So I wonder if I could follow up on Transaction Tracing. So I remember about 20 years ago, David Floyer did a piece of research, when we were working together at a former company, and I was struck at the time by the number of subsequent transactions that had to occur just to get an outcome of a check process. >> Right, right, right. >> I mean it was like some orders of magnitude >> Right. >> greater. Add to that mobile transactions, I can't imagine with all the internet traffic and other activities going on, now add to that big data, and security, and fraud detection, and all the other things that we're doing with the data. The number of ancillary transactions >> Right. >> has got to be enormous. Hence the need presumably for Transaction Tracing. >> Absolutely. >> So maybe talk about the market need, and why Syncsort? You would think doesn't the mainframe have all this stuff integrated into it? Maybe talk about that. >> Yeah sure, so I think one of the things to understand is that the mainframe compute volumes continue to go up. I think people just tend to think about mainframes as a environment that perhaps isn't growing, but in fact, it is growing. And one of the key drivers is this new transaction workload that is driven in part my mobile, and other devices. And so what you have if you're running a mainframe is I'm experiencing increase in my transaction workloads, I need to figure out how to kind of support that. But I also have a lot more characteristics I care about, security, performance, et cetera. And so I need deeper analytics. And of course, they are difficult systems. You need to understand the mainframe, you need to understand how KICKS and DB2 interact and support a transaction. But you also need to understand kind of this next generation analytic environment, how can I leverage that to actually get the insight I want. And that's really what we call, it's an example of, a big iron to big data challenge. And so what Syncsort's been incredibly focused on is helping customers understand the very specific use cases that are included in that big iron to big data space, and providing very differentiated solutions with very deep differentiation to solve those specific use cases. And Transaction Tracing is a good example of that. It sounds fairly narrow, but it's incredibly important if you're a bank and you want to give your customers an ability to kind of check account balances, interact with you in a way that they haven't in the past. >> Well, it's one of those things that we talk about you know depth apps, in depth apps, this is a depth app. >> Right. >> Alright, okay. And then in terms of the Splunk relationship, where does that fit in, and what are the swim lanes between you and Splunk? >> Well we view Splunk as a key platform in the world today for kind of understanding IT operations and security. We view them as incredibly powerful from a platform perspective. And we also view them as a partner that we can add value to. That we can provide access to data that enrich their platform and allows their customers to get more value of it, and that we can do that in a unique way. And so we have a very close relationship with Splunk. And that's not just at a go to market level, it's also at a product management and engineering level. We work very closely to make sure that our products integrate well with Splunk. So we've got deep integration with IT service intelligence, we've got deep integration with enterprise security, and we'll continue to drive deeper integration into the Splunk platform. So when a customer comes across a scenario where they want to ingest mainframe data, they can be assured that they will get no better product on the marketplace than Syncsort Ironstream and associated modules, in terms of both how it will perform on its own, but also how it will integrate with Splunk. >> So that deep integration something that's always interesting to us on theCUBE. Lot of times you see Barney deals. Barney, I love you, you love me, let's do a press release. And so one of the ways in which we measure, or try to measure, the intensity of the integration is the engineering that's involved. So I wonder if you could, sort of double click on that. >> Sure. >> Is it kind of just making sure you're familiar with the APIs? Are you actually doing integration and engineering on both sides? Maybe you could talk about that. >> Well, so I'll talk about our integration with enterprise, security, and IT service intelligence. >> Dave: Great. >> And those are, you can think of those as specific applications to support deep analytics. And these are Splunk offerings. Deep analytics around those two areas of confidence. Such that a user can rapidly build a set of dashboards that would allow them to answer the questions you want to answer if you're focused on IT service intelligence or understanding security. Fundamentally they're data models. They've gone out and mapped what are all the data elements that you need, what's the structure that you need of that data model, to be able to answer the questions that a security minded analyst would want to answer. That allows you to, if you map the data sources into those data models, that would allow you to rapidly build those to that dashboards that support those types of roles on the enterprise. What we've done is taken the very large amount of mainframe machine data that gets produced, generally it's an SMF record, so there's 260 types of SMF records, each one has its subtype. We've mapped it into those two data models that Splunk has created. Nobody else has done that. And what that does is it allows those customers to get a complete end to end view of how can I rapidly enhance my IT service intelligence application, or my enterprise security application with mainframe data. Which just happens to run my most sensitive applications and most voluminous applications, from a transaction perspective in my enterprise. So we thing that deep integration is a really powerful capability, and it's just an example of where we like to go deeper with our partners than what we see other companies doing. >> You know when you talked about the mobile environment a little while ago, and complexities and that, I'm always just kind of curious. With everybody talk about what that does in terms of when you're harvesting data and now you're in a non-stationary environment. And that comes with it a whole different set of characteristics and challenges. I mean, what layer of complexity do you take on when you all of a sudden you can be anywhere and feeding data at any time from any machine. >> Sure, well I mean what it creates is a lot more interaction points. So I probably interact with my bank a lot more today than I did 10 years ago, 'cause I don't have to find an ATM, or go by a branch, >> John: You never walk into a branch. >> And I did this over the weekend. I had to kind of transfer some money, right. So I just transferred it and I was in Colorado hiking, and I transferred funds between accounts. And then later on the golf course I did a wire, literally. >> John: You didn't have to transfer money on the golf course for a reason, did you? >> No, no, no, those were unrelated events. >> Just making sure. >> Lost a few, Josh? >> But that type of interaction. So you get more frequent interaction, which creates an operational challenge. Particularly when you think about the mainframe and how customers pay for that, right. They pay for it based on how much CPU they use on a monthly basis. And so what we want to do is help customers run that system as efficiently as possible. It also creates a massive analytic opportunity, because now I have a lot more data that I can start to analyze to understand trends, because I have more touchpoints. But the trick is I've got to get that data into a repository and into an analytic environment that can handle that data. And that's where I think Splunk creates such an interesting opportunity. And what we're trying to do is just add value to that, make it easy for customers to leverage all of their data. Does that make sense? >> Yeah. >> It does. How 'about the government marketplace? We're here in the District. You guys have an announcement around new partners. >> Yes. >> Maybe talk about the importance of government, and what you do in there. >> Sure, so we signed a distribution relationship with Carahsoft, also a big Splunk partner. And that is going to allow government customers to more easily take advantage of Ironstream and Transaction Tracing in these used cases. The federal government is a enormous market opportunity, it's also a big mainframe environment. There's a lot of government core, government applications, that still run on mainframe environments. In fact, I would tell you most do. IRS, Social Security, CIA, and other agencies. And so we think giving ourselves an easy route to market for these customers is a great opportunity for us, it's also a good opportunity for Splunk's customers who are in the government, 'cause they can go and buy additional capabilities that are relevant to their environment through the same partners that they've been working with Splunk. >> But is there a difference with how you deal with public and private sector then? I mean, governance and compliance, and all those things. I would assume you have different hurdles. >> They're different contract vehicles, which have different kind of requirements in them. And that's one of the values that we get with the Carahsoft relationship, is just giving us access to those various contract vehicles. Yeah. >> Talk to me a little bit about life. I mean, you've always been a private company. But you're you don't have the 90 day shot clock, you have new owners, what's the objective, maybe talk about that sort of the patience of the capital, what your priorities are with regard to these owners. Maybe discuss that a little bit. >> Yeah, sure. So just to give a little background in early July we announced and in mid August we closed a transaction whereby Centerbridge Partners acquired Syncsort and another company, Vision Solutions, from our previous owner, Clearlake Capital. And we combined the companies under the Syncsort umbrella, and myself and our leadership team is going to take the company forward. So the 90 day shot clock, I would say definitely we still care about the 90 day shot clock. We are very focused on growing this business and doing that in a consistent way on a quarterly basis. I guess the difference is I get to talk to my investors every day rather than once a quarter. But they've been great partners. The Centerbridge guys have a lot of resources, they've been incredibly helpful in helping us start to think through kind of the strategies, some of the integration work we're doing with Vision. But we think there's an opportunity to build a big business. We employed a dual strategy of organic growth focused largely in the big iron to big data spaces, as described earlier, combined with MNA. And you know, over the last 24 months we've tripled the size of Syncsort. So it's grown 3X-- >> So you are growing, that was one of my questions, were you growing. >> And in revenue, >> Substantially. >> we've doubled in employees. >> So, say that again. >> We've tripled revenue. >> You've tripled revenue. Double head count. >> And double head count. >> Okay, so you've increased profitability in theory then. >> So, and we will continue to run the same play. We're seeing acceleration in our organic place, but focus on the big iron to big data market. And we also believe there are additional data management capabilities that are relevant to our customers, that we can acquire and help point towards that big iron to big data play. And so we'll continue to look at various spaces that are interesting adjacencies that are relevant to our customers. >> And some of that revenue growth obviously is through acquisition. >> Josh: Right. >> Right, and so when you think about, you know it used to be the classic private equity play was to suck all the money out of the company, leave the carcass for somebody else to deal with. It seems like there's a new thinking. Not seems like, there is a new thinking here. Invest, acquire, increase the value, the money guys are realizing wow this, there's a lot more money to be made. >> Absolutely. I definitely-- >> The technology business. >> We have an eye towards profitable growth. But we are absolutely making investments. And as you get larger scale you can make meaningful investments in these specific areas that can help deliver really great innovation to customers. And Transaction Tracing is an example of that. And certainly I can give you others. But for sure, we are trying to build value. This is not a traditional kind of private equity play. And I also think that private equity is generally understanding there's an opportunity to create value after the catch, if you will, in the tech industry. And I was looking at an analysis last week that financial investors, private equity, for the first time ever will do more deals in technology than strategics, in 2017. And so I think that's a statement that says that there's certainly an opportunity to create long term sustained value in a private equity backed kind of model. And I think to some extent, Syncsort's been pioneering that. With a dual approach on organic growth, and on additional acquisitions. >> Well, and you've seen it, coming out of the down turn, or sort of in the down turn, a lot of these public companies were struggling. >> Right. >> I mean you certainly saw with Dell, BMC, Riverbed, Infor, all examples of private equity where there's investment going on and I think a longer term vision. >> Right. >> With some, as a I call, patient capital. Syncsort is obviously part of that. Syncsort, actually interesting, when it spun out its storage business, you know as a successful company. Catalogic is doing its thing. So Syncsort was able to monetize that. And then really focus on the core knitting. >> Yeah. >> And then figure out where in the big data space that you can make money. Which, not a lot of people were making money in the big data space. So, that's good, congratulations on that. >> I like to tell folks that we've had a really good run, but it's really the first couple of innings. The Centerbridge team is going to be incredibly supportive, and I can't wait to get started on the next leg of the journey. I think there's going to be a lot more innovation to come and I'm looking forward to it. >> Dave: Great. >> So, you're in the middle of the game. We appreciate the time here. Good luck with that, the long term plan down the road. I hope the show's going well for you. >> It's going great. >> And it's good seeing you. >> Great, thanks John. >> Thanks, Josh. >> See you Dave. >> Josh Rogers from Syncsort with us today here. Syncsort, rather, here on theCUBE. Back with more Washington D.C., theCUBE live at Dotcom 2017, right after this. (upbeat music)

>> Announcer: Live from San Jose, California, it's The Cube covering Big Data Silicon Valley 2017. (innovative music) >> Welcome back, everyone, Live in Silicon Valley is The Cube's coverage of Big Data SV, our event in Silicon Valley in conjunction with our Big Data NYC for New York City. Every year, twice a year, we get our event going around Strata Hadoop in conjunction with those guys. I'm John Furrier with SiliconANGLE with George Gilbert, our Wikibon (mumbles). Our next guest is Josh Rogers, the CEO of Syncsort, but on many times, Cube alumni, that firm that acquired Trillium, which we talked about yesterday. Welcome back to The Cube, good to see you. >> Good to see you, how are ya? >> So Syncsort is just one of those companies that's really interesting. We were talking about this. I want to get your thoughts on this because I'm not sure if it was in the plan or not, or really ingenius moves by you guys on the manager's side, but Legacy Business, lockdown legacy environments, like the mainframe, and then transform into a modern data company. Was that part of the plan or kind of on purpose by accident? Or what's-- >> Part of the plan. You think about what we've been doing for the last 40 years. We had specific capabilities around managing data at scale and around helping customers who process that data to give more value out of it through analytics, and we've just continually moved through the various kind of generations of technology to apply that same discipline in new environments and big data is frankly been a terrific opportunity for us to apply that same technical and talented DNA in that new environment. It's kind of been running the same game plan. (talking over each other) >> You guys have a good execution, but I think one of the things we were point out, and this is one of those things where, certainly, I live in Palo Alto in Silicon Valley. We love innovation. We love all the shiny, new toys, but you get tempted to go after something really compelling, cool, and relevant, and then go, "Whoa, I forgot about locking down "some of the legacy data stuff," and then you're kind of working down and you guys took a different approach. You going in to the trends from a solid foundation. That's a different execution approach and, like you said, by design, so that's working. >> Yeah, it's definitely working and I think it's also kind of focused on an element that maybe is under-reported, which is a lot of these legacy systems aren't going away, and so one of the big challenges-- >> And this is for record, by the way. >> Right (talking over each other). How do I integrate those legacy environments with these next-generation environments and to do that you have to have expertise on both side, and so one of the things I think we've done a good job is developing that big data expertise and then turning around and saying we can solve that challenge for you, and obviously, the big iron, the big data solutions we bring to market are a perfect example of that, but there's additional solutions that we can provide customers, and we'll talk more about those in a few-- >> Talk about the Trillium acquisition. I want to just, you take a minute to describe that you bought a company called Trillium. What is it, just take a minute to explain what it is and why is it relevant? >> Trillium is a really special company. They are the independent leader in data quality and have been for many years. They've been in the top-right of the gartner magic quadrant for more than a decade, and really, when you look at large, complex, global enterprises, they are the kind of gold-standard in data quality, and when I say data quality, what I mean is an ability to take a dataset, understand the issues with that dataset, and then establish business rules to improve the quality of that data so you can actually trust that data. Obviously that's relevant in a near-adjacency to the data movement and transformation that Syncsort's been known for for so long. What's interesting about it is you think about the development and the maturity of big data environments, specifically Hadoop, you know, people have a desire to obviously do analytics in that data and implicit in that is the ability to trust that data and the way you get there is being able to apply profiling equality rules in that environment, and that's an underserved market today. When we thought about the Trillium acquisition, it was partly, "Hey, this is a great firm "that has so much respect and the space, "and so much talented capability, a powerful capability "and market-leading data quality talent, "but also, we have an ability to apply it "in this next generation environment "much like we did on the ETL and data movement space." And I think that the industry is at a point where enterprises are realizing, "I'm going to need to apply the same "data management disciplines to make use of my data "in my next generation analytics environment "that I did in my data warehouse environment." Obviously, there's different technologies involved. There's different types of data involved. But those disciplines don't go away and being able to improve the quality and be able to kind of build integrity in your datasets is critical, and Trillium is best in market capabilities in that respect. >> Josh, you were telling us earlier about sort of the strategy of knocking down the pins one by one as, you know, it's become clear that we sort of took, first the archive from the data warehouse, and then ETL off-loaded, now progressively more of the business intelligence. What are some of the, besides data quality, what are some of the other functions you have to-- >> There's the whole notion of metadata management, right? And that's incredibly important to support a number of key business initiatives that people want to leverage. There's different styles of movement of data so a thing you'll hear a lot about is change data capture, right, so if I'm moving datasets from source systems into my Hadoop environment, I can move the whole set, but how do I move the incremental changes on a ongoing basis at the speed of business. There's notions of master data management, right? So how do I make sure that I understand and have a gold kind of standard of reference data that I can use to try my own analytic capabilities, and then of course, there's all the analytics that people want to do both in terms of visualization and predictive analytics, but you can think about all these is various engines that I need to apply the data to get maximum value. And it's not so much that these engines aren't important anymore. It's I can now apply them in a different environment that gives me a lot more flexibility, a lot more scale, a better cost structure, and an ability to kind of harness broader datasets. And so that's really our strategy is bring those engines to this new environment. There's two ways to do that. One is build it from scratch, which is kind of a long process to get it right when you're thinking about complex, global, large enterprise requirements. The other is to take existing, tested, proven, best-in-market engines and integrate it deeply in this environment and that's the strategy we've taken. We think that offers a much faster time to value for customers to be able to maximize their investments in this next generation analytics infrastructure. >> So who shares that vision and sort of where are we in the race? >> I think we're fairly unique in our approach of taking that approach. There's certainly other large platform players. They have a broad (mumbles) ability and I think they're working on, "How do I kind of take that architecture and make it relevant?" It ends up creating a co-generation approach. I think that approach has limitations, and I think if you think about taking the core engine and integrate it deeply within the Hadoop ecosystem and Hadoop capabilities, you get a faster time to market and a more manageable solution going forward, and also one that gives you kind of a future pre-shoot from underlying changes that we'll continue to see in the Hadoop component, sort of the big data components, I guess is a better articulation. >> Josh, what's the take on the show this year and the trends, (mumbles) will become a machine learning, and I've seen that. You guys look at your execution plan. What's the landscape happening out there in the show this year? I mean, we're starting to see more business outcome conversations about machine-learning in AI. It's really putting pressure on the companies, and certainly IOT in the cloud-growth as a forcing function. Do you see the same thing? What's your thoughts? >> So machine-learning's a really powerful capability and I think as it relates to the data integration kind of space, there's a lot of benefit to be had. Think about quality. If I have to establish a set of business rules to improve the quality of my data, wouldn't it be great if those little rules could learn as they actually process datasets and see how they change over time, so there's really interesting opportunities there. We're seeing a lot of adoption of cloud. More and more customers are looking at "How do I live in a world where I've got a piece "of my operations on premise, "I've got a piece of operations in cloud, "manage those together and gradually "probably shift more into cloud over time." So I'm doing a lot of work in that space. There's some basic fundamental recognitions that have happened, which is, if I stand up a Hadoop cluster, I am going to have to buy a series of tools to make to get value out of that data in that cluster. That's a good step forward in my perspective because this notion of I'm going to stand up a team off-shore and they're just going to build all these things. >> Cost of ownership goes through the roof. >> Yeah, so I think the industry's moved past this concept of "I make an investment in Hadoop. "I don't need additional solutions." >> It highlights something that we were talking about at Google Next last week about enterprise-ready, and I want to get your thoughts 'cause you guys have a lot of experience, something that's, get in your wheelhouse, how you guys have attacked the market's been pretty impressive and not obvious, and on paper, it looks pretty boring, but you're doing great! I mean, you've done the right strategy, it works. Mainframe, locking in the mainframe, system of record. We've talked this on The Cube. Lots of videos going back three years, but enterprise-ready is a term now that's forcing people, even the best at Google, to be like like, look in the mirror and saying, "Wait a minute. "We have a blind spot." Best tech doesn't always win. You've got table steps; you've got SLAs; you've got mission data quality. One piece of bad data that should be clean could really screw up something. So what's your thoughts on enterprise-ready right now? >> I think that people are recognizing that to get a payoff on a lot of these investments in next generation analytic infrastructure, they're going to need to build, run mission-critical workloads there and take on mission-critical kind of business initiatives and prove out the value. To do that you have to be able to manage the environment, achieve the up-times, have the reliability resiliency that, quite frankly, we've been delivering for four years, and so I think that's another kind of point in our value proposition that frankly seems to be so unique, which is hey, we've been doing this for thousands of customers, the most sophisticated-- >> What are one of the ones that are going to be fatal flaws for people if they don't pay attention to? >> Well, security is huge. I think the manageability, right. So look, if I have to upgrade 25 components in my Hadoop cluster to get to the next version and I need to upgrade all the tools, I've got to have a way to do that that allows me to not only get to the next level of capability that the vendors are providing, but also to do that in a way that doesn't maybe bring down all these mission-critical workloads that have to be 24 by seven. Those pieces are really important and having both the experience and understanding of what that means, and also being able to invest the engineering resources to be able to-- >> And don't forget the sales force. You've got the DNA and the people on the streets. Josh, thanks for coming to The Cube, really appreciate it, great insight. You guys have, just to give you a compliment, great strategy, and again, good execution on your side and as you guys, you're in new territory. Every time we talk to you, you're entering in something new every time, so great to see you. Syncsort here inside The Cube. Always back at sharing commentary on what's going on in the marketplace: AI machine-learning with the table stakes in the enterprise security and what not, still critical for execution and again, IOT is really forcing the function of (mumbles). You've got to focus on the data. Thanks so much. I'm (mumbles). We'll be back with more live coverage after this break. (upbeat innovative music)