>> Live from San Jose, in the heart of Silicon Valley, It's theCUBE, covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California, I'm your host, along with my cohost, James Kobielus. We're joined by Tendu Yogurtcu, she is the CTO of Syncsort. Thanks so much for coming on theCUBE, for returning to theCUBE I should say. >> Thank you Rebecca and James. It's always a pleasure to be here. >> So you've been on theCUBE before and the last time you were talking about Syncsort's growth. So can you give our viewers a company update? Where you are now? >> Absolutely, Syncsort has seen extraordinary growth within the last the last three year. We tripled our revenue, doubled our employees and expanded the product portfolio significantly. Because of this phenomenal growth that we have seen, we also embarked on a new initiative with refreshing our brand. We rebranded and this was necessitated by the fact that we have such a broad portfolio of products and we are actually showing our new brand here, articulating the value our products bring with optimizing existing infrastructure, assuring data security and availability and advancing the data by integrating into next generation analytics platforms. So it's very exciting times in terms of Syncsort's growth. >> So the last time you were on the show it was pre-GT prop PR but we were talking before the cameras were rolling and you were explaining the kinds of adoption you're seeing and what, in this new era, you're seeing from customers and hearing from customers. Can you tell our viewers a little bit about it? >> When we were discussing last time, I talked about four mega trends we are seeing and those mega trends were primarily driven by the advanced business and operation analytics. Data governance, cloud, streaming and data science, artificial intelligence. And we talked, we really made a lot of announcement and focus on the use cases around data governance. Primarily helping our customers for the GDPR Global Data Protection Regulation initiatives and how we can create that visibility in the enterprise through the data by security and lineage and delivering trust data sets. Now we are talking about cloud primarily and the keynotes, this event and our focus is around cloud, primarily driven by again the use cases, right? How the businesses are adopting to the new era. One of the challenges that we see with our enterprise customers, over 7000 customers by the way, is the ability to future-proof their applications. Because this is a very rapidly changing stack. We have seen the keynotes talking about the importance of how do you connect your existing infrastructure with the future modern, next generation platforms. How do you future-proof the platform, make a diagnostic about whether it's Amazon, Microsoft of Google Cloud. Whether it's on-premise in legacy platforms today that the data has to be available in the next generation platforms. So the challenge we are seeing is how do we keep the data fresh? How do we create that abstraction that applications are future-proofed? Because organizations, even financial services customers, banking, insurance, they now have at least one cluster running in the public cloud. And there's private implementations, hybrid becomes the new standard. So our focus and most recent announcements have been around really helping our customers with real-time resilient changes that capture, keeping the data fresh, feeding into the downstream applications with the streaming and messaging data frames, for example Kafka, Amazon Kinesis, as well as keeping the persistent stores and how to Data Lake on-premise in the cloud fresh. >> Puts you into great alignment with your partner Hortonworks so, Tendu I wonder if we are here at DataWorks, it's Hortonworks' show, if you can break out for our viewers, what is the nature, the levels of your relationship, your partnership with Hortonworks and how the Syncsort portfolio plays with HDP 3.0 with Hortonworks DataFlow and the data plan services at a high level. >> Absolutely, so we have been a longtime partner with Hortonworks and a couple of years back, we strengthened our partnership. Hortonworks is reselling Syncsort and we have actually a prescriptive solution for Hadoop and ETL onboarding in Hadoop jointly. And it's very complementary, our strategy is very complementary because what Hortonworks is trying and achieving, is creating that abstraction and future-proofing and interaction consistency around referred as this morning. Across the platform, whether it's on-premise or in the cloud or across multiple clouds. We are providing the data application layer consistency and future-proofing on top of the platform. Leveraging the tools in the platform for orchestration, integrating with HTP, certifying with Trange or HTP, all of the tools DataFlow and at last of course for lineage. >> The theme of this conference is ideas, insights and innovation and as a partner of Hortonworks, can you describe what it means for you to be at this conference? What kinds of community and deepening existing relationships, forming new ones. Can you talk about what happens here? >> This is one of the major events around data and it's DataWorks as opposed to being more specific to the Hadoop itself, right? Because stack is evolving and data challenges are evolving. For us, it means really the interactions with the customers, the organizations and the partners here. Because the dynamics of the use cases is also evolving. For example Data Lake implementations started in U.S. And we started MER European organizations moving to streaming, data streaming applications faster than U.S. >> Why is that? >> Yeah. >> Why are Europeans moving faster to streaming than we are in North America? >> I think a couple of different things might participate. The open sources really enabling organizations to move fast. When the Data Lake initiative started, we have seen a little bit slow start in Europe but more experimentation with the Open Source Stack. And by that the more transformative use cases started really evolving. Like how do I manage interactions of the users with the remote controls as they are watching live TV, type of transformative use cases became important. And as we move to the transformative use cases, streaming is also very critical because lots of data is available and being able to keep the cloud data stores as well as on-premise data stores and downstream applications with fresh data becomes important. We in fact in early June announced that Syncsort's now's a part of Microsoft One Commercial Partner Program. With that our integrate solutions with data integration and data quality are Azure gold certified and Azure ready. We are in co-sale agreement and we are helping jointly a lot of customers, moving data and workloads to Azure and keeping those data stores close to platforms in sync. >> Right. >> So lots of exciting things, I mean there's a lot happening with the application space. There's also lots still happening connected to the governance cases that we have seen. Feeding security and IT operations data into again modern day, next generation analytics platforms is key. Whether it's Splunk, whether it's Elastic, as part of the Hadoop Stack. So we are still focused on governance as part of this multi-cloud and on-premise the cloud implementations as well. We in fact launched our Ironstream for IBMI product to help customers, not just making this state available for mainframes but also from IBMI into Splunk, Elastic and other security information and event management platforms. And today we announced work flow optimization across on-premise and multi-cloud and cloud platforms. So lots of focus across to optimize, assure and integrate portfolio of products helping customers with the business use cases. That's really our focus as we innovate organically and also acquire technologies and solutions. What are the problems we are solving and how we can help our customers with the business and operation analytics, targeting those mega trends around data governance, cloud streaming and also data science. >> What is the biggest trend do you think that is sort of driving all of these changes? As you said, the data is evolving. The use cases are evolving. What is it that is keeping your customers up at night? >> Right now it's still governance, keeping them up at night, because this evolving architecture is also making governance more complex, right? If we are looking at financial services, banking, insurance, healthcare, there are lots of existing infrastructures, mission critical data stores on mainframe IBMI in addition to this gravity of data changing and lots of data with the online businesses generated in the cloud. So how to govern that also while optimizing and making those data stores available for next generation analytics, makes the governance quite complex. So that really keeps and creates a lot of opportunity for the community, right? All of us here to address those challenges. >> Because it sounds to me, I'm hearing Splunk, Advanced Machine did it, I think of the internet of things and sensor grids. I'm hearing IBM mainframes, that's transactional data, that's your customer data and so forth. It seems like much of this data that you're describing that customers are trying to cleanse and consolidate and provide strict governance on, is absolutely essential for them to drive more artificial intelligence into end applications and mobile devices that are being used to drive the customer experience. Do you see more of your customers using your tools to massage the data sets as it were than data scientists then use to build and train their models for deployment into edge applications. Is that an emerging area where your customers are deploying Syncsort? >> Thank you for asking that question. >> It's a complex question. (laughing) But thanks for impacting it... >> It is a complex question but it's very important question. Yes and in the previous discussions, we have seen, and this morning also, Rob Thomas from IBM mentioned it as well, that machine learning and artificial intelligence data science really relies on high-quality data, right? It's 1950s anonymous computer scientist says garbage in, garbage out. >> Yeah. >> When we are using artificial intelligence and machine learning, the implications, the impact of bad data multiplies. Multiplies with the training of historical data. Multiplies with the insights that we are getting out of that. So data scientists today are still spending significant time on preparing the data for the iPipeline, and the data science pipeline, that's where we shine. Because our integrate portfolio accesses the data from all enterprise data stores and cleanses and matches and prepares that in a trusted manner for use for advanced analytics with machine learning, artificial intelligence. >> Yeah 'cause the magic of machine learning for predictive analytics is that you build a statistical model based on the most valid data set for the domain of interest. If the data is junk, then you're going to be building a junk model that will not be able to do its job. So, for want of a nail, the kingdom was lost. For want of a Syncsort, (laughing) Data cleansing and you know governance tool, the whole AI superstructure will fall down. >> Yes, yes absolutely. >> Yeah, good. >> Well thank you so much Tendu for coming on theCUBE and for giving us a lot of background and information. >> Thank you for having me, thank you. >> Good to have you. >> Always a pleasure. >> I'm Rebecca Knight for James Kobielus. We will have more from theCUBE's live coverage of DataWorks 2018 just after this. (upbeat 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.

>> Announcer: Live from midtown Manhattan, it's theCUBE, covering BigData New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hello everyone, welcome back to theCUBE's special BigData NYC coverage of theCUBE here in Manhattan in New York City, we're in Hell's Kitchen. I'm John Furrier, with my cohost Jim Kobielus, whose Wikibon analyst for BigData. In conjunction with Strata Data going on right around the corner, this is our annual event where we break down the big data, the AI, the cloud, all the goodness of what's going on in big data. Our next guest is Tendu Yogurtcu who's the Chief Technology Officer at Syncsort. Great to see you again, CUBE alumni, been on multiple times. Always great to have you on, get the perspective, a CTO perspective and the Syncsort update, so good to see you. >> Good seeing you John and Jim. It's a pleasure being here too. Again the pulse of big data is in New York, and it's a great week with a lot of happening. >> I always borrow the quote from Pat Gelsinger, who's the CEO of VMware, he said on theCUBE in I think 2011, before he joined VMware as CEO he was at EMC. He said if you're not out in front of that next wave, you're driftwood. And the key to being successful is to ride the waves, and the big waves are coming in now with AI, certainly big data has been rising tide for its own bubble but now the aperture of the scale of data's larger, Syncsort has been riding the wave with us, we've been having you guys on multiple times. And it was important to the mainframe in the early days, but now Syncsort just keeps on adding more and more capabilities, and you're riding the wave, the big wave, the big data wave. What's the update now with you guys, where are you guys now in context of today's emerging data landscape? >> Absolutely. As organizations progress with their modern data architectures and building the next generation analytics platforms, leveraging machine learning, leveraging cloud elasticity, we have observed that data quality and data governance have become more critical than ever. Couple of years we have been seeing this trend, I would like to create a data lake, data as a service, and enable bigger insights from the data, and this year, really every enterprise is trying to have that trusted data set created, because data lakes are turning into data swamps, as Dave Vellante refers often (John laughs) and collection of this diverse data sets, whether it's mainframe, whether it's messaging queues, whether it's relational data warehouse environments is challenging the customers, and we can take one simple use case like Customer 360, which we have been talking for decades now, right? Yet still it's a complex problem. Everybody is trying to get that trusted single view of their customers so that they can serve the customer needs in a better way, offer better solutions and products to customers, get better insights about the customer behavior, whether leveraging deep learning, machine learning, et cetera. However, in order to do that, the data has to be in a clean, trusted, valid format, and every business is going global. You have data sets coming from Asia, from Europe, from Latin America, and many different places, in different formats and it's becoming challenge. We acquired Trillium Software in December 2016, and our vision was really to bring that world leader enterprise grade data quality into the big data environments. So last week we announced our Trillium Quality for Big Data product. This product brings unmatched capabilities of data validation, cleansing, enrichment, and matching, fuzzy matching to the data lake. We are also leveraging our Intelligent eXecution engine that we developed for data integration product, the MX8. So we are enabling the organizations to take this data quality offering, whether it's in Hadoop, MapReduce or Apache Spark, whichever computer framework it's going to be in the future. So we are very excited about that now. >> Congratulations, you mentioned the data lake being a swamp, that Dave Vellante referred to. It's interesting, because how does it become a swamp if it's a silo, right? We've seen data silos being antithesis to governance, it challenges, certainly IoT. Then you've got the complication of geopolitical borders, you mentioned that earlier. So you still got to integrate the data, you need data quality, which has been around for a while but now it's more complex. What specifically about the cleansing and the quality of the data that's more important now in the landscape now? Is it those factors, are that the drivers of the challenges today and what's the opportunity for customers, how do they figure this out? >> Complexity is because of many different factors. Some of it from being global. Every business is trying to have global presence, and the data is originating from web, from mobile, from many different data sets, and if we just take a simple address, these address formats are different in every single country. Trillium Quality for Big Data, we support over 150 postal data from different countries, and data enrichment with this data. So it becomes really complex, because you have to deal with different types of data from different countries, and the matching also becomes very difficult, whether it's John Furrier, J Furrier, John Currier, you have to be >> All my handles on Twitter, knowing that's about. (Tendu laughs) >> All of the handles you have. Every business is trying to have a better targeting in terms of offering product and understanding the single and one and only John Furrier as a customer. That creates a complexity, and any data management and data processing challenge, the variety of data and the speed that data is really being populated is higher than ever we have observed. >> Hold on Jim, I want to get Jim involved in this one conversation, 'cause I want to just make sure those guys can get settled in on, and adjust your microphone there. Jim, she's bringing up a good point, I want you to weigh in just to kind of add to the conversation and take it in the direction of where the automation's happening. If you look at what Tendu's saying as to complexity is going to have an opportunity in software. Machine learning, root-level cleanliness can be automated, because Facebook and others have shown that you can apply machine learning and techniques to the volume of data. No human can get at all the nuances. How is that impacting the data platforms and some of the tooling out there, in your opinion? >> Yeah well, much of the issue, one of the core issues is where do you place the data matching and data cleansing logic or execution in this distributed infrastructure. At the source, in the cloud, at the consumer level in terms of rolling up the disparate versions of data into a common view. So by acquiring a very strong, well-established reputable brand in data cleansing, Trillium, as Syncsort has done, a great service to your portfolio, to your customers. You know, Trillium is well known for offering lots of options in terms of where to configure the logic, where to deploy it within distributed hybrid architectures. Give us a sense for going forward the range of options you're going to be providing with for customers on where to place the cleansing and matching logic. How you're going to support, Syncsort, a flexible workflows in terms of curation of the data and so forth, because the curation cycle for data is critically important, the stewardship. So how do you plan to address all of that going forward in your product portfolio, Tendu? >> Thank you for asking the question, Jim, because that's exactly the challenge that we hear from our customers, especially from larger enterprise and financial services, banking and insurance. So our plan is our actually next upcoming release end of the year, is targeting very flexible deployment. Flexible deployment in the sense that you might be creating, when you understand the data and create the business rules and said what kind of matching and enrichment that you'll be performing on the data sets, you can actually have those business rules executed in the source of the data or in the data lake or switch between the source and the enterprise data lake that you are creating. That flexibility is what we are targeting, that's one area. On the data creation side, we see these percentages, 80% of data stewards' time is spent on data prep, data creation and data cleansing, and it is actually really a very high percentage. From our customers we see this still being a challenge. One area that we started investing is using the machine learning to understand the data, and using that discovery of the data capabilities we currently have to make recommendations what those business rules can be, or what kind of data validation and cleansing and matching might be required. So that's an area that we will be investing. >> Are you contemplating in terms of incorporating in your product portfolio, using machine learning to drive a sort of, the term I like to use is recommendation engine, that presents recommendations to the data stewards, human beings, about different data schemas or different ways of matching the data, different ways of, the optimal way of reconciling different versions of customer data. So is there going to be like a recommendation engine of that sort >> It's going to be >> In line with your >> That's what our plan currently recommendations so the users can opt to apply or not, or to modify them, because sometimes when you go too far with automation you still need some human intervention in making these decisions because you might be operating on a sample of data versus the full data set, and you may actually have to infuse some human understanding and insight as well. So our plan is to make as a recommendation in the first phase at least, that's what we are planning. And when we look at the portfolio of the products and our CEO Josh is actually today was also in theCUBE, part of Splunk .conf. We have acquisitions happening, we have organic innovation that's happening, and we really try to stay focused in terms of how do we create more value from your data, and how do we increase the business serviceability, whether it's with our Ironstream product, we made an announcement this week, Ironstream transaction tracing to create more visibility to application performance and more visibility to IT operations, for example when you make a payment with your mobile, you might be having problem and you want to be able to trace back to the back end, which is usually a legacy mainframe environment, or whether you are populating the data lake and you want to keep the data in sync and fresh with the data source, and apply the change as a CDC, or whether you are making that data from raw data set to more consumable data by creating the trusted, high quality data set. We are very much focused on creating more value and bigger insights out of the data sets. >> And Josh'll be on tomorrow, so folks watching, we're going to get the business perspective. I have some pointed questions I'm going to ask him, but I'll take one of the questions I was going to ask him but I want to get your response from a technical perspective as CTO. As Syncsort continues your journey, you keep on adding more and more things, it's been quite impressive, you guys done a great job, >> Tendu: Thank you. >> We enjoy covering the success there, watching you guys really evolve. What is the value proposition for Syncsort today, technically? If you go in, talk to a customer, and prospective new customer, why Syncsort, what's the enabling value that you're providing under the hood, technically for customers? >> We are enabling our customers to access and integrate data sets in a trusted manner. So we are ultimately liberating the data from all of the enterprise data stores, and making that data consumable in a trusted manner. And everything we provide in that data management stack, is about making data available, making data accessible and integrated the modern data architecture, bridging the gap between those legacy environments and the modern data architecture. And it becomes really a big challenge because this is a cross-platform play. It is not a single environment that enterprises are working with. Hadoop is real now, right? Hadoop is in the center of data warehouse architecture, and whether it's on-premise or in the cloud, there is also a big trend about the cloud. >> And certainly batch, they own the batch thing. >> Yeah, and as part of that, it becomes very important to be able to leverage the existing data assets in the enterprise, and that requires an understanding of the legacy data stores, and existing infrastructure, and existing data warehouse attributes. >> John: And you guys say you provide that. >> We provide that and that's our baby and provide that in enterprise grade manner. >> Hold on Jim, one second, just let her finish the thought. Okay, so given that, okay, cool you got that out there. What's the problem that you're solving for customers today? What's the big problem in the enterprise and in the data world today that you address? >> I want to have a single view of my data, and whether that data is originating on the mobile or that data is originating on the mainframe, or in the legacy data warehouse, and we provide that single view in a trusted manner. >> When you mentioned Ironstream, that reminded me that one of the core things that we're seeing in Wikibon in terms of, IT operations is increasingly being automated through AI, some call it AI ops and whatnot, we're going deeper on the research there. Ironstream, by bringing mainframe and transactional data, like the use case you brought in was IT operations data, into a data lake alongside machine data that you might source from the internet of things and so forth. Seem to me that that's a great enabler potentially for Syncsort if it wished to play your solutions or position them into IT operations as an enabler, leveraging your machine learning investments to build more automated anomaly detection and remediation into your capabilities. What are your thoughts? Is that where you're going or do you see it as an opportunity, AI for IT ops, for Syncsort going forward? >> Absolutely. We target use cases around IT operations and application performance. We integrate with Splunk ITSI, and we also provide this data available in the big data analytics platforms. So those are really application performance and IT operations are the main uses cases we target, and as part of the advanced analytics platform, for example, we can correlate that data set with other machine data that's originating in other platforms in the enterprise. Nobody's looking at what's happening on mainframe or what's happening in my Hadoop cluster or what's happening on my VMware environment, right. They want to correlate the data that's closed platform, and that's one of the biggest values we bring, whether it's on the machine data, or on the application data. >> Yeah, that's quite a differentiator for you. >> Tendu, thanks for coming on theCUBE, great to see you. Congratulations on your success. Thanks for sharing. >> Thank you. >> Okay, CUBE coverage here in BigData NYC, exclusive coverage of our event, BigData NYC, in conjunction with Strata Hadoop right around the corner. This is our annual event for SiliconANGLE, and theCUBE and Wikibon. I'm John Furrier, with Jim Kobielus, who's our analyst at Wikibon on big data. Peter Burris has been on theCUBE, he's here as well. Big three days of wall-to-wall coverage on what's happening in the data world. This is theCUBE, thanks for watching, be right back with more after this short break.

>> 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)