(energetic music) >> Hi, and welcome to another Cube conversation, where we go in-depth with the thought leaders in the industry that are making significant changes to how we conduct digital business and the likelihood of success with digital business transformations. I'm Peter Burris. Every organization today has some experience with the power of analytics. But, they're also warning that the value of their analytics systems are, in part, constrained and determined by their access to core information. Some of the most important information that any business can start to utilize within their new advanced analytic systems, quite frankly, is that operational business information, that the business has been using to run the business on for years. Now, we've looked at that as silos and maybe it is. Although, partly, that's in response to the need to have good policy, good governance, and good certainty and practicably in how the system behaves and how secure it's going to be. So, the question is, how do we marry the new world of advanced analytics with the older, but, nonetheless, extremely valuable world of operational processing to create new types of value within digital business today? It's a great topic and we've got a great conversation. Tendu Yogurtcu is the CTO of Syncsort. Tendu, welcome back to The Cube! >> Hi Peter. It's great to be back here in The Cube. >> Excellent! So, look, let's start with the, let's start with a quick update on Syncsort. How are you doing, what's going on? >> Oh, it's been really exciting time at Syncsort. We have seen a tremendous growth in the last three years. We quadrupled our revenue, and also number of employees, through both organic innovation and growth, as well as through acquisitions. So, we now have 7,000 plus customers in over 100 countries, and, we still have the eight 40 Fortune 100, serving large enterprises. It's been a really great journey. >> Well, so, let's get into the specific distinction that you guys have. At Wikibon theCube, we've observed, we predicted that 1919, 2019 rather, 2019 was going to be the year that the enterprise assert itself in the cloud. We had seen a lot of developers drive cloud forward. We've seen a lot of analytics drive cloud forward. But, now as enterprises are entering into cloud in a big way, they're generating, or bringing with them, new types of challenges and issues that have to be addressed. So, when you think about where we are in the journey to more advanced analytics, better operational certainty, greater use of information, what do you think the chief challenges that customers face today are? >> Of course, as you mentioned, that everybody, every organization is trying to take advantage of the data. Data is the core. And, take advantage of the digital transformation to enable them for taking, getting more value out of their data. And, in doing so, they are moving into cloud, into hybrid cloud architectures. We have seen early implementations, starting with the data lake. Everybody started creating the centralized data hub, enabling advanced analytics and creating a data marketplace for their internal, or external clients. And, the early data lakes were for utilizing Hadoop on premise architectures. Now, we are also seeing data lakes, sometimes, expanding over hybrid or cloud architectures. The challenges that these organizations also started realizing is around, once I create this data marketplace, the access to the data, critical customer data, critical product data, >> Order data. >> Order data, is a bigger challenge than I thought that it would be in the pilot project. Because, these critical data sets, and core data sets, often in financial services, banking and insurance, and health care, are in environments, data platforms that these companies have invested over multiple decades. And, I'm not referring to that as legacy because definition of legacy changes. These environment's platforms have been holding this current critical data assets for decades successfully. So-- >> We call them high-value traditional applications. >> High-valude traditional sounds great. >> Because, they're traditional. We know what they do, and there's a certain operational certainty, and we've built up the organization around them to take care of those assets. >> But, they still are very very high-value. >> Exactly. And, making those applications and data available for next generation, next wave platforms, is becoming a challenge, for couple of different reasons. One, accessing this data. And, accessing this data, making sure the policies and the security, and the privacy around these data stores are preserved when the data is available for advanced analytics. Whether it's in the cloud or on premise deployments. >> So, before we go to the second one, I want to make sure I'm understanding that, because it seems very very important. >> Yes. >> That, what you're saying is, if I may, the data is not just the ones and the zeroes in the file. The data really start, needs to start being thought of as the policies, the governance, the security, and all the other attributes and elements, the metadata, if you will, has to be preserved as the data's getting used. >> Absolutely. And, there are challenges around that, because now you have to have skill sets to understand the data in those different types of stores. Relational data warehouses. Mainframe, IBMI, SQL, Oracle. Many different data owners, and different teams in the organization. And, then, you have to make sense of it and preserve the policies around each of these data assets, while bringing it to the new analytics environments. And, make sure that everybody's aligned with the access to privacy, and the policies, and the governance around that data. And also, mapping to metadata, to the target systems, right? That's a big challenge, because somebody who understands these data sets in a mainframe environment is not necessarily understanding the cloud data stores or the new data formats. So, how do you, kind of, bridge that gap, and map into the target-- >> And, vice-versa, right? >> Yes. >> So. >> Likewise, yes. >> So, this is where Syncsort starts getting really interesting. Because, as you noted, a lot of the folks in the mainframe world may not have the familiarity of how the cloud works, and a lot of the folks, at least from a data standpoint. >> Yes. >> And, a lot of the folks in the cloud that have been doing things with object stores and whatnot, may not, and Hadoop, may not have the knowledge of how the mainframe works. And, so, those two sides are seeing silos, but, the reality is, both sides have set up policies and governance models, and security regimes, and everything else, because it works for the workloads that are in place on each side. So, Syncsort's an interesting company, because, you guys have experience of crossing that divide. >> Absolutely. And, we see both the next phase, and the existing data platforms, as a moving, evolving target. Because, these challenges have existed 20 years ago, 10 years ago. It's just the platforms were different. The volume, the variety, complexity was different. However, Hadoop, five, ten years ago, was the next wave. Now, it's the cloud. Blockchain will be the next platform that we have to, still, kind of, adopt and make sure that we are advancing our data and creating value out of data. So, that's, accessing and preserving those policies is one challenge. And, then, the second challenge is that as you are making these data sets available for analytics, or machine learning, data science applications, deduplicating, standardizing, cleansing, making sure that you can deliver trusted data becomes a big challenge. Because, if you train the models with the bad data, if you create the models with the bad data, you have bad model, and then bad data inside. So, machine learning and artificial intelligence depends on the data, and the quality of the data. So, it's not just bringing all enterprise data for analytics. It's also making sure that the data is delivered in a trusted way. That's the big challenge. >> Yeah. Let me build on that, if I may, Tendu. Because, a lot of these tools involve black box belief in what the tool's performing. >> Correct. >> So, you really don't have a lot of visibility in the inner workings of how the algorithm is doing things. It's, you know, that's the way it is. So, in many respects, your only real visibility into the quality of the outcome of these tools is visibility into the quality of the data that's going into the building of these models. >> Correct. >> Have I got that right? >> Correct. And, in machine learning, the effect of bad data is, really, it multiplies. Because of the training of the model, as well as insights. And, with Blockchain, in the future, it will also become very critical because, once you load the data into Blockchain platform, it's immutable. So, data quality comes at a higher price, in some sense. That's another big challenge. >> Which is to say, that if you load bad data into a Blockchain, it's bad forever. >> Yes. That's very true. So, that's, obviously, another area that Syncsort, as we are accessing all of the enterprise data, delivering high-quality data, discovering and understanding the data, and delivering the duplicated standardized enriched data to the machine learning and AI pipeline, and analytics pipeline, is an area that we are focused with our products. And, a third challenge is that, as you are doing it, the speed starts mattering. Because, okay, I created the data lake or the data hub. The next big use case we started seeing is that, "Oh yeah, but I have 20 terabyte data, "only 10% is changing on a nightly basis. "So, how do I keep my data lake in sync? "Not only that, I want to keep my data lake in sync, "I also would like to feed that change data "and keep my downstream applications in sync. "I want to feed the change data to the microservices "in the cloud." That speed of delivery started really becoming a very critical requirement for the business. >> Speed, and the targeting of the delivery. >> Speed of the targeting, exactly. Because, I think the bottom line is, you really want to create an architecture that you can be agnostic. And, also be able to deliver at the speed the business is going to require at different times. Sometimes, it's near real-time, and at batch, sometimes it's real-time, and you have to feed the changes as quickly as possible to the consumer applications and the microservices in the cloud. >> Well, we've got a lot of CIO's who are starting to ask us questions about, especially, since they start thinking about Kubernetes, and Istio, and other types of platforms that are intended to facilitate the orchestration, and ultimately, the management of how these container-based applications work. And, we're starting to talk more about the idea of data assurance. Make sure the data's good. Make sure it's been high-quality. Make sure it's being taken care of. But, also make sure that it's targeted where it needs to be. Because, you don't want a situation where you spin up a new cluster, which you could do very quickly with Kubernetes. But, you haven't made the data available to that Kubernetes-based application, so that it can, actually, run. And, a lot of CIO's, and a lot of application development leaders, and a lot of business people, are now starting to think about that. "How do I make sure the data is where it needs to be, "so that the applications run when they need to run?" >> That's a great point. And, going back to your, kind of, comment around cloud, and taking advantage of cloud architectures. One of the things we have observed is organizations, for sure, looking at cloud, in terms of scalability, elasticity, and reducing costs. They did lift and shift of applications. And, not all applications can be taking advantage of cloud elasticity, then you do that. Most of these applications are created for the existing on-premise fixed architectures. So, they are not designed to take advantage of that. And, we are seeing a shift now. And, the shift is around, instead of, trying to, kind of, lift and shift existing applications. One, for new applications, let me try and adopt the technology assets, like you mentioned Kubernetes, that I can stay vendor-agnostic, for cloud vendors. But, more importantly, let me try to have some best practices in the organization. The new applications can be created to take advantage of the elasticity. Even though, they may not be running in the cloud yet. So, some organizations refer to this as cloud native, cloud first, some different terms. And, make the data. Because, the core asset here, is always the data. Make the data available, instead of going after the applications. Make the data from these existing on-premise and different platforms available for cloud. We are definitely seeing that the shift. >> Yeah, and make sure that it, and assure, that that data is high-quality, carries the policies, carries the governance, doesn't break in security models, all those other things. >> That is a big difference between how, actually, organizations ran into their Hadoop data lake implementations, versus the cloud architectures now. Because, when initial Hadoop data lake implementations happened, it was dump all the data. And, then, "Oh, I have to deal with the data quality now." >> It was also, "Oh, those mainframe people just would, "they're so difficult to work with." Meanwhile, you're still closing the books on a monthly basis, on a quarterly basis. You're not losing orders. Your customers aren't calling you on the phone angry. And, that, at the end of the day, is what a business has to do. You have to be able to extend what you can currently do, with a digital business approach. And, if you can replace certain elements of it, okay. But, you can't end up with less functionality as you move forward in the cloud. >> Absolutely. And, it's not just mainframe. It's IBMI, it's the Oracle, it's the teledata, it's the TDZa. It's growing rapidly, in terms of the complex stuff, that data infrastructure. And, for cloud, we are seeing now, a lot of pilots are happening with the cloud data warehouses. And, trying to see if the cloud data warehouses can accommodate some of these hybrid deployments. And, also, we are seeing, there's more focus, not after the fact, but, more focus on data quality from day one. "How am I going to ensure that "I'm delivering trusted data, and populating "the cloud data stores, or delivering trusted data "to microservices in the cloud?" There's greater focus for both governance and quality. >> So, high-quality data movement, that leads to high-quality data delivery, in ways that the business can be certain that whatever derivative work is done remains high-quality. >> Absolutely. >> Tendu Yogurtcu, thank you very much for being, once again, on The Cube. It's always great to have you here. >> Thank you Peter. It's wonderful to be here! >> Tandu Yogurtcu's the CTO of Syncsort, and once again, I want to thank you very much, for participating in this cloud, or this Cube conversation. Cloud on the mind, this Cube conversation. Until next time. (upbeat electronic music)

(upbeat music) >> Hi and welcome to another Cube Conversation, where we go in depth with the thought leaders in the industry that are making significant changes to how we conduct digital business, and the likelihood of success with digital business transformations. I'm Peter Burris. Every organization today has some experience with the power of analytics, but they're also learning that the value of their analytic systems are, in part, constrained and determined by their access to core information. Some of the most important information that any business can start to utilize within their new advanced analytic systems, quite frankly, is that operational business information that the business has been using to run the business on for years. Now, we've looked at that as silos, and maybe it is, although partly that's in response to the need to have good policy, good governance, and good certainty and predictably in how the system behaves, and how secure it's going to be. So, the question is, how do we marry the new world of advanced analytics with the older, but, nonetheless, extremely valuable world of operational processing to create new types of value within digital business today? It's a great topic and we've got a great conversation. Tendü Yogurtçu is the CTO of Syncsort. Tendü, welcome back to theCube. >> Hi Peter, it's great to be back in theCube. >> Excellent. So, look, let's start with a quick update on Syncsort. How are you doing? What's going on? >> Oh, it's been really a exciting time at Syncsort. We have seen tremendous growth in the last three years. We quadrupled our revenue and also number of employees, tripled organic innovation and growth, as well as true acquisitions. So, we now have 7,000 plus customers in over 100 countries, and we still have the 84 of Fortune 100 serving large enterprises. It's been a really great journey. >> Well, so let's get into the specific distinction that you guys have. At Wikibon theCube, we've observed, we predicted that 1919, 2019, rather, 2019 was going to be the year that the enterprise asserted itself in the cloud. We had seen a lot of developers drive cloud forward, we've seen a lot of analytics drive cloud forward, but now as enterprises are entering into cloud in a big way, they're generating or bringing with them new types of challenges and issues that have to be addressed. So, when you think about where we are in the journey to more advanced analytics, better operational certainty, greater use of information, what do you think the chief challenges that customers face today are? >> Of course, as you mentioned, that everybody, every organization is trying to take advantage of the data, data is the core, and take advantage of the digital transformation to enable them for taking, getting more value out of their data. And, in doing so, they are moving into cloud, into hybrid cloud architectures. We have seen early implementations starting with the data lake. Everybody started creating this centralized data hub enabling advanced analytics and creating a data marketplace for their internal or external clients. And the early data lakes were utilizing Hadoop on on-premise architectures, now we are also seeing data lakes sometimes expanding over hybrid or cloud architectures. The challenges that these organizations also started realizing is around once I create this data marketplace, the access to the data, critical customer data, critical product data-- >> Order data. >> Order data, is a bigger challenge that I told that it will be in the pilot project because these critical data sets and core data sets often in financial services, banking, and insurance, and healthcare are environments, data platforms that these companies have invested over multiple decades. And I'm not referring to that as legacy because definition of legacy changes, these environments, platforms have been holding these critical data assets for decades successfully. >> We call them high value traditional applications because the traditional we know what they do, there's a certain operational certainty, and we've built up, you know, the organization around them to take care of those assets, but they still are very, very high value. >> Exactly, and making those applications and data available for next generation, next wave platforms is becoming a challenge for couple of different reasons. One, accessing this data, and accessing this data making sure the policies and the security and the privacy around these data stores are preserved when the data is available for advanced analytics, whether it's in the cloud or on-premise deployments. >> So, before you go to the second one, I want to make sure I understand that because it seems very, very important, that what you're saying is, if I may, the data is not just the ones and the zeros in the file, the data really needs to start being thought of as the policies, the governance, the security, and all the other attributes and elements, the metadata, if you will, has to be preserved as the data is getting used. >> Absolutely, and there are challenges around that because now you have to have skillsets to understand the data in those different types of stores, relational data warehouses, Mainframe, IBM i, SQL, Oracle, many different data owners and different teams in the organization, and then, you have to make sense of it and preserve the policies around each of these data assets while bringing it to the new analytics environments. And make sure that everybody is aligned with the access to privacy and the policies and the governance around that data. And also, mapping the metadata to the target systems, right? That's a big challenge because somebody who understands these data sets in a Mainframe environment is not necessarily understanding the cloud data stores or the new data formats, so how do you kind of bridge that gap and map into the target environment? >> And vice versa, right? >> Likewise, yes. >> This is where Syncsort starts getting really interesting because, as you noted, a lot of the folks in the Mainframe world may not have the familiarity of how the cloud works, and a lot of the folks, at least from a data standpoint, and a lot of folks in the cloud that have been doing things with object stores and whatnot, may not, in Hadoop, may not have the knowledge of how the Mainframe works. And so those two sides are seeing silos, but the reality is both sides have set up policies and governance models and security regimes and everything else because it works for the workloads that are in place on each side. >> Absolutely. >> So Syncsort's an interesting company because you guys have experience of crossing that divide. >> Absolutely, and we see both the next wave and existing data platforms as a moving, evolving target because these challenges have existed twenty years ago, ten years ago, it's just the platforms were different, the volume, the variety, complex was different, however, Hadoop, five, ten years ago was the next wave, now it's the cloud, blockchain will be the next platform that we have to still kind of adapt and make sure that we are advancing our data and creating value out of data. So that's accessing and preserving those policies is one challenge. And then the second challenge is that as you are making these data sets available for analytics or mission learning, data science applications, you're duplicating, standardizing, cleansing, making sure that you can deliver trusted data becomes a big challenge because if you train the models with the bad data, if you create the models with the bad data you have bad model and then bad data inside. So, mission learning and artificial intelligence depends on the data and the quality of the data, so it's not just bringing all enterprise data for analytics, it's also making sure that the data is delivered in a trusted way. That's a big challenge. >> Yeah, let me build on that if I may, Tendü, because a lot of these tools involve black box belief in what the tool's performing. >> Correct. >> So you really don't have a lot of visibility in the inner workings of how the algorithm is doing things. It's, you know, that's the way it is. So, in many respects, you're only real visibility into the quality of the outcome of these tools is visibility into the quality of data that's going into the building of these models. Have I got that right? >> Correct. And in mission learning, the effect of bad data it really multiplies because of the training of the model, as well as the insights. And with blockchain, in the future, it will also become very critical because once you load the data into blockchain platform, it's immutable. So, data quality comes at a higher price in some sense. So that's another big challenge. >> Which is to say that if you load bad data into a blockchain, it's bad forever. >> Yes, that's very true. So that's obviously another area that Syncsort, as we are accessing all of the enterprise data, delivering high quality data, discovering and understanding the data, and delivering the duplicated, standardized, enriched data to the mission learning and AI pipeline and analytics pipeline is an area that we are focused with our products. And the third challenge is that as you are doing it, the speed starts mattering because, okay, I created the data lake or the data hub, the next big use case we started seeing is that oh yeah, but I have twenty terabyte data, only ten percent is changing on a nightly basis, so how do I keep my data lake in sync? Not only that, I want to keep my data lake in sync, I also would like to feed that changed data and keep my downstream applications in sync. I want to feed the changed data to the micro services in the cloud. That speed of delivery started really becoming very critical requirement for the businesses. >> Speed and the targeting of the delivery. >> Speed of the targeting grid, exactly. Because I think the bottom line is you really want to create an architecture that you can be agnostic and also be able to deliver at the speed the business is going to require at different times. Sometimes it's near real time in a batch, sometimes it's real time and you have to feed the changes as quickly as possible to the consumer applications and the micro services in the cloud. >> Well, we've got a lot of CIOs who are starting to ask us questions about, especially as they start thinking about Kubernetes and Istio and other types of platforms that are intended to facilitate the orchestration and ultimately the management of how these container-based applications work. And we're starting to talk more about the idea of data assurance. Make sure the data is good, make sure it's high quality, make sure it's being taken care of, but also make sure that it's targeted where it needs to be, because you don't want a situation where you spin up a new cluster, which you could do very quickly with Kubernetes, but you haven't made the data available to that Kubernetes based application so that they can actually run. And a lot of CIOs and a lot of application development leaders and a lot of business people are now starting to think about that. How do I make sure the data is where it needs to be so that the applications run when they need to run? >> That's a great point, and going back to your kind of comment around the cloud and taking advantage of cloud architectures, one of the things we have observed is organizations looking at cloud in terms of scalable elasticity and reducing costs, dated lift and shift of applications, and not all applications can be taking advantage of cloud elasticity when you do that. Most of these applications are created for the existing on premise fixed architectures, so they are not designed to take advantage of that. And we are seeing a shift now, and the shift is around instead of trying to kind of lift and shift the existing applications, one, for new applications, let me try to adopt the technology assets, like you mentioned Kubernetes, that I can stay vendor agnostic for cloud vendors, but, more importantly, let me try to have some best practices in organization that new applications can be created to take advantage of the elasticity, even though they may not be running in the cloud yet. So some organizations refer to this as cloud native, cloud first, some different terms. And make the data, because the core asset here is always the data, make the data available, instead of going after the applications, make the data from these existing on premise and different platforms available for cloud. We are definitely seeing that shift. >> Yeah, and make sure and assure that that data is high quality, carries the policies, carries the governance, doesn't break the security models, all those other things. >> That is a big difference between how actual organizations ran into their Hadoop data lake implementations versus the cloud architectures now, because when initial Hadoop data lake implementations happened, it was dump all the data. And then, oh, I have to deal with the data quality now. >> No, it was also, oh, those Mainframe people just would, they're so difficult to work with, meanwhile, you're still closing the books on a monthly basis, on a quarterly basis, you're not losing orders, your customers aren't calling you on the phone angry, and that, at the end of the day, is what business has to do. You have to be able to extend what you can currently do with a digital business approach, and if you can replace certain elements of it, okay. But you can't end up with less functionality as you move forward into the cloud. >> Absolutely, and it's not just Mainframe, it's IBM i, it's the Oracle, it's the teradata, it's the DTSA, it's growing rapidly in terms of the complexity of that data infrastructure. And for cloud, we are seeing now a lot of pilots are happening with the cloud data warehouses, and trying to see if the cloud data warehouses can accommodate some of these hybrid deployments, and also we are seeing there's more focus, not after the fact, but more focus on data quality from day one. How am I going to insure that I'm delivering trusted data and populating the cloud data stores, or delivering trusted data to micro services in the cloud. There is a greater focus for both governance and quality. >> So, high quality data movement that leads to high quality data delivery in ways that the business can be certain that whatever derivative of work is done, remains high quality. >> Absolutely. >> Tendü Yogurtçu, thank you very much for being once again on theCube, it's always great to have you here. >> Thank you, Peter, it's wonderful to be here. >> Tendü Yogurtçu is the CTO of Syncsort, and, once again, I want to thank you very much for participating in this cloud, or this Cube Conversation, cloud on the mind, this Cube Conversation. Until next time. (upbeat music)

>> 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)

>> 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 San Jose, it's theCUBE. Presenting data, Silicon Valley brought to you by Silicon Angle Media and it's ecosystem partners. >> Welcome back to theCUBE. We are live in San Jose at our event, Big Data SV. I'm Lisa Martin, my co-host is George Gilbert and we are down the street from the Strata Data Conference. We are at a really cool venue: Forager Eatery Tasting Room. Come down and join us, hang out with us, we've got a cocktail par-tay tonight. We also have an interesting briefing from our analysts on big data trends tomorrow morning. I want to welcome back to theCUBE now one of our CUBE VIP's and alumna Tendu Yogurtcu, the CTO at Syncsort, welcome back. >> Thank you. Hello Lisa, hi George, pleasure to be here. >> Yeah, it's our pleasure to have you back. So, what's going on at Syncsort, what are some of the big trends as CTO that you're seeing? >> In terms of the big trends that we are seeing, and Syncsort has grown a lot in the last 12 months, we actually doubled our revenue, it has been really an successful and organic growth path, and we have more than 7,000 customers now, so it's a great pool of customers that we are able to talk and see the trends and how they are trying to adapt to the digital disruption and make data as part of their core strategy. So data is no longer an enabler, and in all of the enterprise we are seeing data becoming the core strategy. This reflects in the four mega trends, they are all connected to enable business as well as operational analytics. Cloud is one, definitely. We are seeing more and more cloud adoption, even our financial services healthcare and banking customers are now, they have a couple of clusters running in the cloud, in public cloud, multiple workloads, hybrid seems to be the new standard, and it comes with also challenges. IT governance as well as date governance is a major challenge, and also scoping and planning for the workloads in the cloud continues to be a challenge, as well. Our general strategy for all of the product portfolio is to have our products following design wants and deploy any of our strategy. So whether it's a standalone environment on Linux or running on Hadoop or Spark, or running on Premise or in the Cloud, regardless of the Cloud provider, we are enabling the same education with no changes to run all of these environments, including hybrid. Then we are seeing the streaming trend, with the connected devices with the digital disruption and so much data being generated, being able to stream and process data on the age, with the Internet of things, and in order to address the use cases that Syncsort is focused on, we are really providing more on the Change Data Capture and near real-time and real-time data replication to the next generation analytics environments and big data environments. We launched last year our Change Data Capture, CDC, product offering with data integration, and we continue to strengthen that vision merger we had data replication, real-time data replication capabilities, and we are now seeing even Kafka database becoming a consumer of this data. Not just keeping the data lane fresh, but really publishing the changes from multiple, diverse set of sources and publishing into a Kafka database and making it available for applications and analytics in the data pipeline. So the third trend we are seeing is around data science, and if you noticed this morning's keynote was all about machine learning, artificial intelligence, deep learning, how to we make use of data science. And it was very interesting for me because we see everyone talking about the challenge of how do you prepare the data and how do you deliver the the trusted data for machine learning and artificial intelligence use and deep learning. Because if you are using bad data, and creating your models based on bad data, then the insights you get are also impacted. We definitely offer our products, both on the data integration and data quality side, to prepare the data, cleanse, match, and deliver the trusted data set for data scientists and make their life easier. Another area of focus for 2018 is can we also add supervised learning to this, because with the premium quality domain experts that we have now in Syncsort, we have a lot of domain experts in the field, we can infuse the machine learning algorithms and connect data profiling capabilities we have with the data quality capabilities recommending business rules for data scientists and helping them automate the mandate tasks with recommendations. And the last but not least trend is data governance, and data governance is almost a umbrella focus for everything we are doing at Syncsort because everything about the Cloud trend, the streaming, and the data science, and developing that next generation analytics environment for our customers depends on the data governance. It is, in fact, a business imperative, and the regulatory compliance use cases drives more importance today than governance. For example, General Data Protection Regulation in Europe, GDPR. >> Lisa: Just a few months away. >> Just a few months, May 2018, it is in the mind of every C-level executive. It's not just for European companies, but every enterprise has European data sourced in their environments. So compliance is a big driver of governance, and we look at governance in multiple aspects. Security and issuing data is available in a secure way is one aspect, and delivering the high quality data, cleansing, matching, the example Hilary Mason this morning gave in the keynote about half of what the context matters in terms of searches of her name was very interesting because you really want to deliver that high quality data in the enterprise, trust of data set, preparing that. Our Trillium Quality for big data, we launched Q4, that product is generally available now, and actually we are in production with very large deployment. So that's one area of focus. And the third area is how do you create visibility, the farm-to-table view of your data? >> Lisa: Yeah, that's the name of your talk! I love that. >> Yes, yes, thank you. So tomorrow I have a talk at 2:40, March 8th also, I'm so happy it's on the Women's Day that I'm talking-- >> Lisa: That's right, that's right! Get a farm-to-table view of your data is the name of your talk, track data lineage from source to analytics. Tell us a little bit more about that. >> It's all about creating more visibility, because for audit reasons, for understanding how many copies of my data is created, valued my data had been, and who accessed it, creating that visibility is very important. And the last couple of years, we saw everyone was focused on how do I create a data lake and make my data accessible, break the data silos, and liberate my data from multiple platforms, legacy platforms that the enterprise might have. Once that happened, everybody started worrying about how do I create consumable data set and how do I manage this data because data has been on the legacy platforms like Mainframe, IMBI series has been on relational data stores, it is in the Cloud, gravity of data originating in the Cloud is increasing, it's originating from mobile. Hadoop vendors like Hortonworks and Cloudera, they are creating visibility to what happens within the Hadoop framework. So we are deepening our integration with the Cloud Navigator, that was our announcement last week. We already have integration both with Hortonworks and Cloudera Navigator, this is one step further where we actually publish what happened to every single granular level of data at the field level with all of the transformations that data have been through outside of the cluster. So that visibility is now published to Navigator itself, we also publish it through the RESTful API, so governance is a very strong and critical initiative for all of the businesses. And we are playing into security aspect as well as data lineage and tracking aspect and the quality aspect. >> So this sounds like an extremely capable infrastructure service, so that it's trusted data. But can you sell that to an economic buyer alone, or do you go in in conjunction with anther solution like anti-money laundering for banks or, you know, what are the key things that they place enough value on that they would spend, you know, budget on it? >> Yes, absolutely. Usually the use cases might originate like anti-money laundering, which is very common, fraud detection, and it ties to getting a single view of an entity. Because in anti-money laundering, you want to understand the single view of your customer ultimately. So there is usually another solution that might be in the picture. We are providing the visibility of the data, as well as that single view of the entity, whether it's the customer view in this case or the product view in some of the use cases by delivering the matching capabilities and the cleansing capabilities, the duplication capabilities in addition to the accessing and integrating the data. >> When you go into a customer and, you know, recognizing that we still have tons of silos and we're realizing it's a lot harder to put everything in one repository, how do customers tell you they want to prioritize what they're bringing into the repository or even what do they want to work on that's continuously flowing in? >> So it depends on the business use case. And usually at the time that we are working with the customer, they selected that top priority use case. The risk here, and the anti-money laundering, or for insurance companies, we are seeing a trend, for example, building the data marketplace, as that tantalize data marketplace concept. So depending on the business case, many of our insurance customers in US, for example, they are creating the data marketplace and they are working with near real-time and microbatches. In Europe, Europe seems to be a bit ahead of the game in some cases, like Hadoop production was slow but certainly they went right into the streaming use cases. We are seeing more directly streaming and keeping it fresh and more utilization of the Kafka and messaging frameworks and database. >> And in that case, where they're sort of skipping the batch-oriented approach, how do they keep track of history? >> It's still, in most of the cases, microbatches, and the metadata is still associated with the data. So there is an analysis of the historical what happened to that data. The tools, like ours and the vendors coming to picture, to keep track, of that basically. >> So, in other words, by knowing what happened operationally to the data, that paints a picture of a history. >> Exactly, exactly. >> Interesting. >> And for the governance we usually also partner, for example, we partner with Collibra data platform, we partnered with ASG for creating that business rules and technical metadata and providing to the business users, not just to the IT data infrastructure, and on the Hadoop side we partner with Cloudera and Hortonworks very closely to complete that picture for the customer, because nobody is just interested in what happened to the data in Hadoop or in Mainframe or in my relational data warehouse, they are really trying to see what's happening on Premise, in the Cloud, multiple clusters, traditional environments, legacy systems, and trying to get that big picture view. >> So on that, enabling a business to have that, we'll say in marketing, 360 degree view of data, knowing that there's so much potential for data to be analyzed to drive business decisions that might open up new business models, new revenue streams, increase profit, what are you seeing as a CTO of Syncsort when you go in to meet with a customer, data silos, when you're talking to a Chief Data Officer, what's the cultural, I guess, not shift but really journey that they have to go on to start opening up other organizations of the business, to have access to data so they really have that broader, 360 degree view? What's that cultural challenge that they have to, journey that they have to go on? >> Yes, Chief Data Officers are actually very good partners for us, because usually Chief Data Officers are trying to break the silos of data and make sure that the data is liberated for the business use cases. Still most of the time the infrastructure and the cluster, whether it's the deployment in the Cloud versus on Premise, it's owned by the IT infrastructure. And the lines of business are really the consumers and the clients of that. CDO, in that sense, almost mitigates and connects to those line of businesses with the IT infrastructure with the same goals for the business, right? They have to worry about the compliance, they have to worry about creating multiple copies of data, they have to worry about the security of the data and availability of the data, so CDOs actually help. So we are actually very good partners with the CDOs in that sense, and we also usually have IT infrastructure owner in the room when we are talking with our customers because they have a big stake. They are like the gatekeepers of the data to make sure that it is accessed by the right... By the right folks in the business. >> Sounds like maybe they're in the role of like, good cop bad cop or maybe mediator. Well Tendu, I wish we had more time. Thanks so much for coming back to theCUBE and, like you said, you're speaking tomorrow at Strata Conference on International Women's Day: Get a farm-to-table view of your data. Love the title. >> Thank you. >> Good luck tomorrow, and we look forward to seeing you back on theCUBE. >> Thank you, I look forward to coming back and letting you know about more exciting both organic innovations and acquisitions. >> Alright, we look forward to that. We want to thank you for watching theCUBE, I'm Lisa Martin with my co-host George Gilbert. We are live at our event Big Data SV in San Jose. Come down and visit us, stick around, and we will be right back with our next guest after a short break. >> Tendu: Thank you. (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)

>> Man's Voiceover: Live, from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017, brought to you by Hortonworks. (upbeat music) >> Welcome back to theCUBE, we are live at Day One of the DataWorks Summit, we've had a great day here, I'm surprised that we still have our voices left. I'm Lisa Martin, with my co-host George Gilbert. We have been talking with great innovators today across this great community, folks from Hortonworks, of course, IBM, partners, now I'd like to welcome back to theCube, who was here this morning in the green shoes, the CTO of Hortonworks, Scott Gnau, welcome back Scott! >> Great to be here yet again. >> Yet again! And we have another CTO, we've got CTO corner over here, with CUBE Alumni and the CTO of SyncSort, Tendu Yogurtcu Welcome back to theCUBE both of you >> Pleasure to be here, thank you. >> So, guys, what's new with the partnership? I know that syncsort, you have 87%, or 87 of the Fortune 100 companies are customers. Scott, 60 of the Fortune 100 companies are customers of Hortonworks. Talk to us about the partnership that you have with syncsort, what's new, what's going on there? >> You know there's always something new in our partnership. We launched our partnership, what a year and a half ago or so? >> Yes. And it was really built on the foundation of helping our customers get time to value very quickly, right and leveraging our mutual strengths. And we've been back on theCUBE a couple of times and we continue to have new things to talk about whether it be new customer successes or new feature functionalities or new integration of our technology. And so it's not just something that's static and sitting still, but it's a partnership that was had a great foundation in value and continues to grow. And, ya know, with some of the latest moves that I'm sure Tendu will bring us up to speed on that Syncsort has made, customers who have jumped on the bandwagon with us together are able to get much more benefit than originally they even intended. >> Let me talk about some of the things actually happening with Syncsort and with the partnership. Thank you Scott. And Trillium acquisition has been transformative for us really. We have achieved quite a lot within the last six months. Delivering joint solutions between our data integration, DMX-h, and Trillium data quality and profiling portfolio and that was kind of our first step very much focused on the data governance. We are going to have data quality for Data Lake product available later this year and this week actually we will be announcing our partnership with Collibra data governance platform basically making business rules and technical meta data available through the Collibra dashboards for data scientists. And in terms of our joint solution and joint offering for data warehouse optimization and the bundle that we launched early February of this year that's in production, a large complex production deployment's already happened. Our customers access all their data all enterprise data including legacy data, warehouse, new data sources as well as legacy main frame in the data lake so we will be announcing again in a week or so change in the capture capabilities from legacy data storage into Hadoop keeping that data fresh and giving more choices to our customers in terms of populating the data lake as well as use cases like archiving data into cloud. >> Tendu, let me try and unpack what was a very dense, in a good way, lot of content. Sticking my foot in my mouth every 30 seconds (laughter) >> Scott Voiceover: I think he called you dense. (laughter) >> So help us visualize a scenario where you have maybe DMX-h bringing data in you might have changed it at capture coming from a live data base >> Tendu Voiceover: Yes. and you've got the data quality at work as well. Help us picture how much faster and higher fidelity the data flow might be relative to >> Sure, absolutely. So, our bundle and our joint solution with Hortonworks really focuses on business use cases. And one of those use cases is enterprise data warehouse optimization where we make all data, all enterprise data accessible in the data lake. Now, if you are an insurance company managing claims or you are building a data as a service, Hadoop is a service architecture, there are multiple ways that you can keep that data fresh in the data lake. And you can have changed it at capture by basically taking snap-shots of the data and comparing in the data lake which is a viable method of doing it. But, as the data volumes are growing and the real time analytics requirements of the business are growing we recognize our customers are also looking for alternative ways that they can actually capture the change in real time when the change is just like less than 10% of the data, original data set and keep the data fresh in the data lake. So that enables faster analytics, real time analytics, as well as in the case that if you are doing something from on-premise to the cloud or archiving data, it also saves on the resources like the network bandwidth and overall resource efficiency. Now, while we are doing this, obviously we are accessing the data and the data goes through our processing engines. What Trillium brings to the table is the unmatched capabilities that are on profiling that data, getting better understanding of that data. So we will be focused on delivering products around that because as we understand data we can also help our customers to create the business rules, to cleanse that data, and preserve the fidelity of the data and integrity of the data. >> So, with the change data capture it sounds like near real time, you're capturing changes in near real time, could that serve as a streaming solution that then is also populating the history as well? >> Absolutely. We can go through streaming or message cues. We also offer more efficient proprietary ways of streaming the data to the Hadoop. >> So the, I assume the message cues refers to, probably Kafka and then your own optimized solution for sort of maximum performance, lowest latency. >> Yes, we can do either true Kafka cues which is very efficient as well. We can also go through proprietary methods. >> So, Scott, help us understand then now the governance capabilities that, um I'm having a senior moment (laughter) I'm getting too many of these! (laughter) Help us understand the governance capabilities that Syncsort's adding to the, sort of mix with the data warehouse optimization package and how it relates to what you're doing. >> Yeah, right. So what we talked about even again this morning, right the whole notion of the value of open squared, right open source and open ecosystem. And I think this is clearly an open ecosystem kind of play. So we've done a lot of work since we initially launched the partnership and through the different product releases where our engineering teams and the Syncsort teams have done some very good low-level integration of our mutual technologies so that the Syncsort tool can exploit those horizontal core services like Yarn for multi tendency and workload management and of course Atlas for data governance. So as then the Syncsort team adds feature functionality on the outside of that tool that simply accrete's to the benefit of what we've built together. And so that's why I say customers who started down this journey with us together are now going to get the benefit of additional options from that ecosystem that they can plug in additional feature functionality. And at the same time we're really thrilled because, and we've talked about this on many times right, the whole notion of governance and meta data management in the big data space is a big deal. And so the fact that we're able to come to the table with an open source solution to create common meta data tagging that then gets utilized by multiple different applications I think creates extreme value for the industry and frankly for our customers because now, regardless of the application they choose, or the applications that they choose, they can at least have that common trusted infrastructure where all of that information is tagged and it stays with the data through the data's life cycle. >> So you're partnership sounds very very symbiotic, that there's changes made on one side that reflect the other. Give us an example of where is your common customer, and this might not be, well, they're all over the place, who has got an enterprise data warehouse, are you finding more customers that are looking to modernize this? That have multi-cloud, core edge, IOT devices that's a pretty distributed environment versus customers that might be still more on prem? What's kind of the mix there? >> Can I start and then I will let you build on. I want to add something to what Scott said earlier. Atlas is a very important integration point for us and in terms of the partnership that you mentioned the relation, I think one of the strengths of our partnership is at many different levels it's not just executive level, it's cross functional and also from very close field teams, marketing teams and engineering field teams working together And in terms of our customers, it's really organizations are trying to move toward modern data architecture. And as they are trying to build the modern data architecture there are the data in motion piece I will let Scott talk about, data in rest piece and as we have so much data coming from cloud, originating through mobile and web in the enterprise, especially the Fortune 500, that we talk, Fortune 100 we talked about, insurance, health care, Talco financial services and banking has a lot of legacy data stores. So our, really joint solution and the couple of first use cases, business use cases we targeted were around that. How do we enable these data stores and data in the modern data architecture? I will let Scott >> Yeah, I agree And so certainly we have a lot of customers already who are joint customers and so they can get the value of the partnership kind of cuz they've already made the right decision, right. I also think, though, there's a lot of green field opportunity for us because there are hundreds if not thousands of customers out there who have legacy data systems where their data is kind of locked away. And by the way, it's not to say the systems aren't functioning and doing a good job, they are. They're running business facing applications and all of that's really great, but that is a source of raw material that belongs also in the data lake, right, and can be, can certainly enhance the value of all the other data that's being built there. And so the value, frankly, of our partnership is really creating that easy bridge to kind of unlock that data from those legacy systems and get it in the data lake and then from there, the sky's the limit, right. Is it reference data that can then be used for consistency of response when you're joining it to social data and web data? Frankly, is it an online archive, and optimization of the overall data fabric and off loading some of the historical data that may not even be used in legacy systems and having a place to put it where it actually can be accessed. And so, there are a lot of great use cases. You're right, it's a very symbiotic relationship. I think there's only upside because we really do complement each other and there is a distinct value proposition not just for our existing customers but frankly for a large set of customers out there that have, kind of, the data locked away. >> So, how would you see do you see the data warehouse optimization sort of solution set continuing to expand its functional footprint? What are some things to keep pushing out the edge conditions, the realm of possibilities? >> Some of the areas that we are jointly focused on is we are liberating that data from the enterprise data warehouse or legacy architectures. Through the syncs or DMX-h we actually understand the path that data travel from, the meta data is something that we can now integrate into Atlas and publish into Atlas and have Atlas as the open data governance solution. So that's an area that definitely we see an opportunity to grow and also strengthen that joint solution. >> Sure, I mean extended provenance is kind of what you're describing and that's a big deal when you think about some of these legacy systems where frankly 90% of the costs of implementing them originally was actually building out those business rules and that meta data. And so being able to preserve that and bring it over into a common or an open platform is a really big deal. I'd say inside of the platform of course as we continue to create new performance advantages in, ya know, the latest releases of Hive as an example where we can get low latency query response times there's a whole new class of work loads that now is appropriate to move into this platform and you'll see us continue to move along those lines as we advance the technology from the open community. >> Well, congratulations on continuing this great, symbiotic as we said, partnership. It sounds like it's incredible strong on the technology side, on the strategic side, on the GTM side. I'd loved how you said liberating data so that companies can really unlock its transformational value. We want to thank both of you for Scott coming back on theCUBE >> Thank you. twice in one day. >> Twice in one day. Tendu, thank you as well >> Thank you. for coming back to theCUBE. >> Always a pleasure. For both of our CTO's that have joined us from Hortonworks and Syncsort and my co-host George Gilbert, I am Lisa Martin, you've been watching theCUBE live from day one of the DataWorks summit. Stick around, we've got great guests coming up (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)

from San Jose in the heart of Silicon Valley it's the kue covering big data sv 2016 now your host John furrier and George Gilbert okay welcome back on we are here live in Silicon Valley for the cubes looking angles flagship program we go out to the events and extract the signal from the noise i'm john furrier mykos george gilbert big data analyst at Wikibon calm our next guest is 10 do yoga coo to yogurt coo coo I you see your last name yo Joe okay I gots clothes GM with big David sinks or welcome back to the cube sink starts been a long time guess one of those companies we love to cover because your value publishes is right in the center of all the action around mainframes and you know Dave and I always love to talk about mainframe not mean frame guys we know that we remember those days and still powering a lot of the big enterprises so I got to ask you you know what's your take on the show here one of the themes that came up last night on crowd chatters why is enterprise data warehousing failing so you know got some conversation but you're seeing a transformation what do you guys see thank you for having me it's great to be here yes we are seeing the transformation of the next generation data warehouse and evolution of the data warehouse architecture and as part of that mainframes are a big part of this data warehouse architecture because still seventy percent of data is on the mainframes world's data seventy percent of world's data this is a large amount of data so when we talk about big data architecture and making big data and enterprise data useful for the business and having advanced analytics not just gaining operational efficiencies with the new architecture and also having new products new services available to the customers of those organizations this data is intact and making that part of this next-generation data warehouse architecture is a big part of the initiatives and we play a very strong core role in this bridging the gap between mainframes and the big data platforms because we have product offerings spanning across platforms and we are very focused on accessing and integrating data accessing and integrating in a secure way from mainframes to the big data plan one is one of the things that's the mainframe highlights kind of a dynamic in the marketplace and wrong hall customers whether they have many firms are not your customers who have mainframes they already have a ton of data their data full as we say in the cube they have a ton of data do it but they spend a lot of times you mentioned cleaning the data how do you guys specifically solve that because that's a big hurdle that they want to just put behind they want to clean fast and get on to other things yes we see a few different trends and challenges first of all from the Big Data initiatives everybody is really trying to either gain operational efficiency business agility and make use of some of the data they weren't able to make use of before and enrich this data with some of the new data sources they might be actually adding to the data pipeline or they are trying to provide new products and services to their customers so when we talk about the mainframe data it's a it's really a how you access this mainframe data in a secure way and how you make that data preparation very easy for the data scientists the data scientists are still spending close to eighty percent of their time in data preparation and if you can't think of it when we talk about the compute frameworks like spark MapReduce flink versus the technology stack technologies these should not be relevant to the data scientist they should be just worried about how do i create my data pipeline what are the new insights that I'm trying to get from this data the simplification we bring in that data cleansing and data preparation is one well we are bringing simple way to access and integrate all of the enterprise data not just the legacy mainframe and the relational data sources and also the emerging data sources with streaming data sources the messaging frameworks new data sources we also make this in a cross-platform secure way and some of the new features for example we announced where we were simply the best in terms of accessing all of the mainframe data and having this available on Hadoop and spark we now also makes park and Hadoop understand this data in its original format you do not have to change the original record format which is very important for highly regulated industries like financial services banking and insurance and health care because you want to be able to do the data sanitization and data cleansing and yet bring that mainframe data in its original format for audit and compliance reasons okay so this is this is the product i think where you were telling us earlier that you can move the processing you can move the data from the mainframe do processing at scale and at cost that's not possible or even ii is is easy on the mainframe do it on a distributed platform like a dupe it preserves its original sort of way of being encoded send it back but then there's also this new way of creating a data fabric that we were talking about earlier where it used to be sort of point-to-point from the transactional systems to the data warehouse and now we've basically got this richer fabric and your tools sitting on some technologies perhaps like spark and Kafka tell us what that world looks like and how it was different from we see a greater interest in terms of the concept of a database because some organizations call it data as a service some organizations call it a Hadoop is a service but ultimately an easy way of publishing data and making data available for both the internal clients of the organization's and external clients of the organization's so Kafka is in the center of this and we see a lot of other partners of us including hot dog vendors like Cloudera map r & Horton works as well as data bricks and confluent are really focused on creating that data bus and servicing so we play a very strong there because phase one project for these organizations how do I create this enterprise data lake or enterprise data hub that is usually the phase one project because for advanced analytics or predictive analytics or when you make an engine your mortgage application you want to be able to see that change on your mobile phone under five minutes likewise when you make a change in your healthcare coverage or telecom services you want to be able to see that under five minutes on your phone these things really require easy access to that enterprise data hub what we have we have a tool called data funnel this basically simplifies in a one click and reduces the time for creating the enterprise data hub significantly and our customers are using this to migrate and make I would not say my great access data from the database tables like db2 for example thousands of tables populating an automatically mapping metadata whether that metadata is higher tables or parquet files or whatever the format is going to be in the distributed platform so this really simplifies the time to create the enterprise data hub it sounds actually really interesting when I'm hearing what you're saying the first sort of step was create this this data lake lets you know put data in there and start getting our feet wet and learning new analysis patterns but what if I'm hearing you correctly you're saying now radiating out of that is a new sort of data backbone that's much lower latency that gets data out of the analytic systems perhaps back into the operational systems or into new systems at a speed that we didn't do before so that we can now make decisions or or do an analysis and make decisions very quickly yes that's true basically operational intelligence and mathematics are converging okay and in that convergence what we are basically seeing is that I'm analyzing security data I'm analyzing telemetry data that's a streamed and I want to be able to react as fast as possible and some of the interest in the emerging computer platforms is really driven by this they eat the use case right many of our customers are basic saying that today operating under five minutes is enough for me however I want to be prepared I want to future-proof my applications because in a year it might be that I have to respond under a minute even in sub seconds when they talk about being future proofed and you mentioned to time you know time sort of brackets on either end our customers saying they're looking at a speed that current technologies don't support in other words are they evaluating some things that are you know essentially research projects right now you know very experimental or do they see a set of technologies that they can pick and choose from to serve those different latency needs we published a Hadoop survey earlier this year in january according to the results from that Hadoop survey seventy percent of the respondents were actually evaluating spark and this is very confused consistent with our customer base as well and the promise of spark is driven by multiple use cases and multiple workload including predictive analytics and streaming analytics and bat analytics all of these use cases being able to run on the same platform and all of the Hadoop vendors are also supporting this so we see as our customer base are heavy enterprise customers they are in production already in Hadoop so running spark on top of their Hadoop cluster is one way they are looking for future proofing their applications and this is where we also bring value because we really abstract that insulate the user while we are liberating all of the data from the enterprise whether it's on the relational legis data warehouse or it's on the mainframe side or it's coming from new web clients we are also helping them insulate their applications because they don't really need to worry about what's the next compute framework that's going to be the fastest most reliable and low latency they need to focus on the application layer they need to focus on creating that data pipeline today I want to ask you about the state of syncsort you guys have been great success with the mainframe this concept of data funneling or you can bring stuff in very fast new management new ownership what's the update on the market dynamics because now ingestion zev rethink data sources how do you guys view what's the plan for syncsort going forward share with the folks out there sure our new investors clearlake capital is very supportive of both organic and inorganic growth so acquisitions are one of the areas for us we plan to actually make one or two acquisitions this year and companies with the products in the near adjacent markets are real value add for us so that's one area in addition to organic growth in terms of the organic growth our investments are really we have been very successful with a lot of organizations insurance financial services banking and healthcare many many of the verticals very successful with helping our customers create the enterprise data hub integrate access all of the data integrated and now carrying them to the next generating generation frameworks those are the areas that we have been partnering with them the next is for us is really having streaming data sources as well as batch data sources through the single data pipeline and this includes bringing telemetry data and security data to the advanced analytics as well okay so it sounds like you're providing a platform that can handle the today's needs which were mostly batch but the emerging ones which are streaming and so you've got that sort of future proofing that customers are looking for once they've got that those types of data coming together including stuff from the mainframe that they want might want to enrich from public sources what new things do you see them doing predictive analytics and machine learning is a big part of this because ultimately once there are different phases right operational efficiency phase was the low-hanging fruit for many organizations I want to understand what I can do faster and serve my clients faster and create that operational efficiency in a cost-effective scalable way second was what our new for go to market opportunities with transformative applications what can I do by recognizing how my telco customers are interacting with the SAS services to help and how like under a couple of minutes I react to their responses or cell service is the second one and then the next phase is that how do I use this historical data in addition to the streaming of data rapidly I'm collecting to actually predict and prevent some of the things and this is already happening with a guy with banking for example it's really with the fraud detection a lot of predictive analysis happens so advanced analytics using AI advanced analytics using machine learning will be a very critical component of this moving forward this is really interesting because now you're honing in on a specific industry use case and something that you know every vendor is trying to sort of solve the fraud detection fraud prevention how repeatable is it across your customers is this something they have to build from scratch because there's no templates that get them fifty percent of the way there seventy percent of the way there actually there's an opportunity here because if you look at the health care or telco or financial services or insurance verticals there are repeating patterns and that one is fraud for fraud or some of the new use cases in terms of customer churn analytics or cosmetics estate so these patterns and the compliance requirements in these verticals creates an opportunity actually to come up with application applications for new companies start for new startups okay then do final question share with the folks out there to view the show right now this is ten years of Hadoop seven years of this event Big Data NYC we had a great event there New York City Silicon Valley what's the vibe here in Silicon Valley here this is one of the best events I really enjoy strata San Jose and I'm looking forward two days of keynotes and hearing from colleagues and networking with colleagues this is really the heartbeat happens because with the hadoop world and strata combined actually we started seeing more business use cases and more discussions around how to enable the business users which means the technology stack is maturing and the focus is really on the business and creating more insights and value for the businesses ten do you go to welcome to the cube thanks for coming by really appreciate it go check out our Dublin event on fourteenth of April hadoop summit will be in europe for that event of course go to SiliconANGLE TV check out our women in check every week we feature women in tech on wednesday thanks for joining us thanks for sharing the inside would sink so i really appreciate it thanks for coming by this turkey will be right back with more coverage live and Silicon Valley into the short break you

(upbeat music) >> Hey everyone. Welcome to theCUBE's special program series "Women of the Cloud", brought to you by AWS. I'm your host for the program, Lisa Martin. Very pleased to welcome back one of our alumni to this special series, Dr. Tendu Yogurtcu joins us, the CTO of Precisely. >> Lisa: Tendu, it's great to see you, it's been a while, but I'm glad that you're doing so well. >> Geez, it's so great seeing you too, and thank you for having me. >> My pleasure. I want the audience to understand a little bit about you. Talk to me a little bit about you, about your role and what are some of the great things that you're doing at Precisely. >> Of course. As CTO, my current role is driving technology vision and innovation, and also coming up with expansion strategies for Precisely's future growth. Precisely is the leader in data integrity. We deliver data with trust, with maximum accuracy, consistency, and also with context. And as a CTO, keeping an eye on what's coming in the business space, what's coming up with the emerging challenges is really key for me. Prior to becoming CTO, I was General Manager for the Syncsort big data business. And previously I had several engineering and R&D leadership roles. I also have a bit of academia experience. I served as a part-time faculty in computer science department in a university. And I am a person who is very tuned to giving back to my community. So I'm currently serving as a advisory board member in the same university. And I'm also serving as a advisory board member for a venture capital firm. And I take pride in being a dedicated advocate for STEM education and STEM education for women in particular, and girls in the underserved areas. >> You have such a great background. The breadth of your background, the experience that you have in the industry as well in academia is so impressive. I've known you a long time. I'd love the audience to get some recommendations from you. For those of the audience looking to grow and expand their careers in technology, what are some of the things that you that you've experienced that you would recommend people do? >> First, stay current. What is emerging today is going to be current very quickly. Especially now we are seeing more change and change at the increased speed than ever. So keeping an eye on on what's happening in the market if you want to be marketable. Now, some of the things that I will say, we have shortage of skills with data science, data engineering with security cyber security with cloud, right? We are here talking about cloud in particular. So there is a shortage of skills in the emerging technologies, AI, ML, there's a shortage of skills also in the retiring technologies. So we are in this like spectrum of skills shortage. So stay tuned to what's coming up. That's one. And on the second piece is that the quicker you tie what you are doing to the goals of the business, whether that's revenue growth whether that's customer retention or cost optimization you are more likely to grow in your career. You have to be able to articulate what you are doing and how that brings value to business to your boss, to your customers. So that becomes an important one. And then third one is giving back. Do something for the women in technology while being a woman in technology. Give back to your community whether that's community is gender based or whether it's your alumni, whether it's your community social community in your neighborhood or in your country or ethnicity. Give back to your community. I think that's becoming really important. >> I think so too. I think that paying it forward is so critical. I'm sure that you have a a long list of mentors and sponsors that have guided you along the way. Giving back to the community paying it forward I think is so important. For others who might be a few years behind us or even maybe have been in tech for the same amount of time that are looking to grow and expand their career having those mentors and sponsors of women who've been through the trenches is inspiring. It's so helpful. And it really is something that we need to do from a diversity perspective alone, right? >> Correct. Correct. And we have seen that, we have seen, for example Covid impact in women in particular. Diverse studies done by girls who quote on Accenture that showed that actually 50% of the women above age 35 were actually dropping out of the technology. And those numbers are scary. However, on the other side we have also seen incredible amount of technology innovation during that time with cloud adoption increasing with the ability to actually work remotely if you are even living in not so secure areas, for example that created more opportunities for women to come back to workforce as well. So we can turn the challenges to opportunities and watch out for those. I would say tipping points. >> I love that you bring up such a great point. There are so, so the, the data doesn't lie, right? The data shows that there's a significant amount of churn for women in technology. But to your point, there are so many opportunities. You mentioned a minute ago the skills gap. One of the things we talk about often on theCUBE and we're talking about cybersecurity which is obviously it's a global risk for companies in every industry, is that there's massive opportunity for people of, of any type to be able to grow their skills. So knowing that there's trend, but there's also so much opportunity for women in technology to climb the ladder is kind of exciting. I think. >> It is. It is exciting. >> Talk to me a little bit about, I would love for the audience to understand some of your hands-on examples where you've really been successful helping organizations navigate digital transformation and their entry and success with cloud computing. What are some of those success stories that you're really proud of? >> Let me think about, first of all what we are seeing is with the digital transformation in general, every single business every single vertical is becoming a technology company. Telecom companies are becoming a technology company. Financial services are becoming a technology company and manufacturing is becoming a technology company. So every business is becoming technology driven. And data is the key. Data is the enabler for every single business. So when we think about the challenges, one of the examples that I give a big challenge for our customers is I can't find the critical data, I can't access it. What are my critical data elements? Because I have so high volumes growing exponentially. What are the critical data elements that I should care and how do I access that? And we work at Precisely with 99 of Fortune 100. So we have two 12,000 customers in over a hundred countries which means we have customers whose businesses are purely built on cloud, clean slate. We also have businesses who have very complex set of data platforms. They have financial services, insurance, for example. They have critical transactional workloads still running on mainframes, IBM i servers, SAP systems. So one of the challenges that we have, and I work with key customers, is on how do we make data accessible for advanced analytics in the cloud? Cloud opens up a ton of open source tools, AI, ML stack lots of tools that actually the companies can leverage for that analytics in addition to elasticity in addition to easy to set up infrastructure. So how do we make sure the data can be actually available from these transactional systems, from mainframes at the speed that the business requires. So it's not just accessing data at the speed the business requires. One of our insurance customers they actually created this data marketplace on Amazon Cloud. And the, their challenge was to make sure they can bring the fresh data on a nightly basis initially and which became actually half an hour, every half an hour. So the speed of the business requirements have changed over time. We work with them very closely and also with the Amazon teams on enabling bringing data and workloads from the mainframes and executing in the cloud. So that's one example. Another big challenge that we see is, can I trust my data? And data integrity is more critical than ever. The quality of data, actually, according to HBR Harvard Business Review survey, 47% of every new record of data has at least one critical data error, 47%. So imagine, I was talking with the manufacturing organization couple of weeks ago and they were giving me an example. They have these three letter quotes for parts and different chemicals they use in the manufacturing. And the single letter error calls a shutdown of the whole manufacturing line. >> Wow. >> So that kind of challenge, how do I ensure that I can actually have completeness of data cleanness of data and consistency in that data? Moreover, govern that on a continuous basis becomes one of the use cases that we help customers. And in that particular case actually we help them put a data governance framework and data quality in their manufacturing line. It's becoming also a critical for, for example ESG, environment, social and governance, supply chain, monitoring the supply chain, and assessing ESG metrics. We see that again. And then the third one, last one. I will give an example because I think it's important. Hybrid cloud becoming critical. Because there's a purest view for new companies. However, facilitating flexible deployment models and facilitating cloud and hybrid cloud is also where we really we can help our customers. >> You brought up some amazingly critical points where it comes to data. You talked about, you know, a minute ago, every company in every industry has to become a technology company. You could also say every company across every industry has to become a data company. They have to become a software company. But to your point, and what it sounds like precisely is really helping organizations to do is access the data access data that has high integrity data that is free of errors. Obviously that's business critical. You talked about the high percentage of errors that caused manufacturing shutdown. Businesses can't, can't have that. That could potentially be life-ending for an organization. So it sounds like what you're talking about data accessibility, data integrity data governance and having that all in real time is table stakes for businesses. Whether it's your grocery store, your local coffee shop a manufacturing company, and e-commerce company. It's table stakes globally these days. >> It is, and you made a very good point actually, Lisa when you talked about the local coffee shop or the retail. One other interesting statistic is that almost 80% of every data has a location attribute. So when we talk about data integrity we no longer talk about just, and consistency of data. We also talk about context, right? When you are going, for example, to a new town you are probably getting some reminders about where your favorite coffee shop is or what telecom company has an office in that particular town. Or if you're an insurance company and a hurricane is hitting southern Florida. Then you want to know how the path of that hurricane is going to impact your customers and predict the claims before they happen. Also understand the propensity of the potential customers that you don't yet have. So location and context, those additional attributes of demographics, visitations are creating actually more confident business insights. >> Absolutely. And and as the consumer we're becoming more and more demanding. We want to be able to transact things so easily whether it's in our personal life at the grocery store, at that cafe, or in our business life. So those demands from the customer are also really influencing the direction that companies need to go. And it's actually, I think it's quite exciting that the amount of personalization the location data that you talk about that comes in there and really helps companies in every industry deliver these the cloud can, these amazing, unique personalized experiences that really drive business forward. We could talk about that all day long. I have no problem. But I want to get in our final minutes here, Tendu. What do you see as in your crystal ball as next for the cloud? How do you see your role as CTO evolving? >> Sure. For what we are seeing in the cloud I think we will start seeing more and more focus on sustainability. Sustainable technologies and governance. Obviously cloud migrations cloud modernizations are helping with that. And we, we are seeing many of our customers they started actually assessing the ESG supply chain and reporting on metrics whether it's the percentage of face or energy consumption. Also on the social metrics on diversity age distribution and as well as compliance piece. So sustainability governance I think that will become one area. Second, security, we talked about IT security and data privacy. I think we will see more and more investments around those. Cybersecurity in particular. And ethical data access and ethics is becoming center to everything we are doing as we have those personalized experiences and have more opportunities in the cloud. And the third one is continued automation with AI, ML and more focus on automation because cloud enables that at scale. And the work that we need to do is too time-intensive and too manual with the amount of data. Data is powering every business. So automation is going to be an increased focus how my role evolves with that. So I have this unique combination. I have been open to non-linear career paths throughout my growth. So I have an understanding of how to innovate and build products that solve real business problems. I also have an understanding of how to sell them build partnerships that combined with the the scale of growth, the hyper growth that we have absorbed in precisely 10 times growth within the last 10 years through a combination of organic innovation and acquisitions really requires the speed of change. So change, implementing change at scale as well as at speed. So taking those and bringing them to the next challenge is the evolution of my role. How do I bring those and tackle keep an eye on what's coming as a challenge in the industry and how they apply those skills that I have developed throughout my career to that next challenge and evolve with it, bring the innovation to data to cloud and the next challenge that we are going to see. >> There's so much on the horizon. It's, there are certainly challenges, you know within technology, but there's so much opportunity. You've done such a great job highlighting your career path the, the big impact that you're helping organizations make leveraging cloud and the opportunity that's there for the rest of us to really get in there get our hands dirty and solve problems. Tendu, I always love our conversations. It's been such a pleasure having you back, back on theCUBE. Thank you for joining us on this special program series today. >> Thank you Lisa. And also thanks to AWS for the opportunity. >> Absolutely. This is brought, brought to us by AWS. For Dr.Tendu, you are good to go. I'm Lisa Martin. You're watching theCUBE special program series Women of the Cloud. We thank you so much for watching and we'll see you soon. (upbeat music)

>> From Tavern on the Green in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day. Brought to you by Veritas. (robotic music) >> We're back in the heart of Central Park. We're here at Tavern on the Green. Beautiful location for the Veritas Vision Day. You're watching theCUBE, my name is Dave Vellante. We go out to the events, we extract the signal from the noise, we got the CEO of Veritas here, Greg Hughes, newly minted, nine months in. Greg, thanks for coming on theCUBE. >> It's great to be here Dave, thank you. >> So let's talk about your nine. What was your agenda your first nine months? You know they talk about the 100 day plan. What was your nine month plan? >> Yeah, well look, I've been here for nine months, but I'm a boomerang. So I was here from 2003 to 2010. I ran all of global services, during that time and became the chief strategy officer after that. Was here during the merger by Semantic. And then ran the Enterprise Product Group. So I had all the products and all the engineering teams for all the Enterprise products. And really my starting point is the customer. I really like to hear directly from the customer. So I've spent probably 50% of my time out and about, meeting with customers. And at this point, I've met with a 100 different accounts all around the world. And what I'm hearing, makes me even more excited to be here. Digital transformation is real. These customers are investing a lot in digitizing their companies. And that's driving an explosion of data. That data all needs to be available and recoverable and that's where we step in. We're the best at that. >> Okay, so that was sort of alluring to you. You're right, everybody's trying to get digital transformation right. It changes the whole data protection equation. It kind of reminds me, in a much bigger scale, of virtualization. You remember, everybody had to rethink their backup strategies because you now have less physical resources. This is a whole different set of pressures, isn't it? It's like you can't go down, you have to always have access to data. Data is-- >> 24 by seven. >> Increasingly valuable. >> Yup. >> So talk a little bit more about the importance of data, the role of data, and where Veritas fits in. >> Well, our customers are using new, they're driving new applications throughout the enterprise. So machine learning, AI, big data, internet of things. And that's all driving the use of new data management technologies. Cassandra, Hadoop, Open Sequel, MongoDB. You've heard all of these, right? And then that's driving the use of new platforms. Hyper-converged, virtual machines, the cloud. So all this data is popping up in all these different areas. And without Veritas, it can exist, it'll just be in silos. And that becomes very hard to manage and protect it. All that data needs to be protected. We're there to protect everything. And that's really how we think about it. >> The big message we heard today was you got a lot of different clouds, you don't want to have a different data protection strategy for each cloud. So you've got to simplify that for people. Sounds easy, but from an R&D perspective, you've got a large install base, you've been around for a long, long time. So you've got to put investments to actually see that through. Talk about your R&D and investment strategy. >> Well, our investment strategy's very simple. We are the market share leader in data protection and software-defined storage. And that scale, gives us a tremendous advantage. We can use that scale to invest more aggressively than anybody else, in those areas. So we can cover all the workloads, we can cover wherever our customers are putting their data, and we can help them standardize on one provider of data protection, and that's us. So they don't have to have the complexity of point products in their infrastructure. >> So I wonder if we could talk, just a little veer here, and talk about the private equity play. You guys are the private equity exit. And you're seeing a lot of high profile PE companies. It used to be where companies would go to die, and now it's becoming a way for the PE guys to actually get step-ups, and make a lot of money by investing in companies, and building communities, investing in R&D. Some of the stuff we've covered. We've followed Syncsort, BMC, Infor, a really interesting company, what's kind of an exit from PE, right? Dell, the biggest one of all. Riverbed, and of course Veritas. So, there's like a new private equity playbook. It's something you know well from your Silver Lake days. Describe what that dynamic is like, and how it's changed. >> Oh look, private equity's been involved in software for 10 or 15 years. It's been a very important area of investment in private equity. I've worked for private equity firms, worked for software companies, so I know it very well. And the basic idea is, continue the investment. Continue in the investment in the core products and the core customers, to make sure that there is continued enhancement and innovation, of the core products. With that, there'll be continuity in customer relationships, and those customer relationships are very valuable. That's really the secret, if you will, of the private equity playbook. >> Well and public markets are very fickle. I mean, they want growth now. They don't care about profits. I see you've got a very nice cash flow, you and some of the brethren that I mentioned. So that could be very attractive, particularly when, you know, public markets they ebb and flow. The key is value for customers, and that's going to drive value for shareholders. >> That's absolutely right. >> So talk about the TAM. Part of a CEOs job, is to continually find new ways, you're a strategy guy, so TAM expansion is part of the role. How do you look at the market? Where are the growth opportunities? >> We see our TAM, or our total addressable market, at being around $17 billion, cutting across all of our areas. Probably growing into high single digits, 8%. That's kind of a big picture view of it. When I like to think about it, I like to think about it from the themes I'm hearing from customers. What are our customers doing? They're trying to leverage the cloud. Most of our customers, which are large enterprises. We work with the blue-chip enterprises on the planet. They're going to move to a hybrid approach. They're going to on-premise infrastructure and multiple cloud providers. So that's really what they're doing. The second thing our customers are worried about is ransomware, and ransomware attacks. Spearfishing works, the bad guys are going to get in. They're going to put some bad malware in your environment. The key is to be resilient and to be able to restore at scale. That's another area of significant investment. The third, they're trying to automate. They're trying to make investments in automation, to take out manual labor, to reduce error rate. In this whole world, tape should go away. So one of the things our customers are doing, is trying to get rid of tape backup in their environment. Tape is a long-term retention strategy. And then finally, if you get rid of tape, and you have all your secondary data on disc or in the cloud, what becomes really cool, is you can analyze all that data. Out of bound, from the primary storage. That's one of the bigger changes I've seen since I've returned back to Veritas. >> So $17 billion, obviously, that transcends backup. Frankly, we go back to the early days of Veritas, I always thought of it as a data management company and sort of returned to those roots. >> Backup, software defined storage, compliance, all those areas are key to what we do. >> You mentioned automation. When you think about cloud and digital transformation, automation is fundamental, we had NBCUniversal on earlier, and the customer was talking about scripts and how scripts are fragile and they need to be maintained and it doesn't scale. So he wants to drive automation into his processes as much as possible, using a platform, a sort of API based, modern, microservices, containers. Kind of using all those terms. What does that mean for you guys in terms of your R&D roadmap, in terms of the investments that you're making in those types of software innovations? >> Well actually one of the things we're talking about today is our latest release of NetBackup 812, which had a significant investment in APIs and that allow our customers to use the product and automate processes, tie it together with their infrastructure, like ServiceNow, or whatever they have. And we're going to continue full throttle on APIs. Just having lunch with some customers just today, they want us to go even further in our APIs. So that's really core to what we're doing. >> So you guys are a little bit like the New England Patriots. You're the leader, and everybody wants to take you down. So you always start-- >> Nobody's confused me for Tom Brady. Although my wife looks... I'll stack her up against Giselle anytime, but I'm no Tom Brady. >> So okay, how do you maintain your leadership and your relevance for customers? A lot of VC money coming into the marketplace. Like I said, everybody wants to take the leader down. How do you maintain your leadership? >> We've been around for 25 years. We're very honored to have 95% of the Fortune 100, are our customers. If you go to any large country in the world it's very much like that. We work with the bluest of blue-chips, the biggest companies, the most complex, the most demanding (chuckling), the most highly regulated. Those are our customers. We steer the ship based on their input, and that's why we're relevant. We're listening to them. Our customer's extremely relevant. We're going to help them protect, classify, archive their data, wherever it is. >> So the first nine months was all about hearing from customers. So what's the next 12 to 18 months about for you? >> We're continuing to invest, delighted to talk about partnerships, and where those are going, as well. I think that's going to be a major emphasis of us to continue to drive our partnerships. We can't do this alone. Our customers use products from a variety of other players. Today we had Henry Axelrod, from Amazon Web Services, here talking about how we're working closely with Amazon. We announced a really cool partnership with Pure Storage. Our customers that use Pure Storage's all-flash arrays, they know their data's backed up and protected with Veritas and with NetBackup. It's continually make sure that across this ecosystem of partners, we are the one player that can help our large customers. >> Great, thank you for mentioning that ecosystem is a key part of it. The channel, that's how you continue to grow. You get a lot of leverage out of that. Well Greg, thanks very much for coming on theCUBE. Congratulations on your-- >> Dave, thank you. >> On the new role. We are super excited for you guys, and we'll be watching. >> I enjoyed it, thank you. >> All right. Keep it right there everybody we'll be back with our next guest. This is Dave Vellante, we're here in Central Park. Be right back, Veritas Vision, be right back. (robotic music)

>> Announcer: Live from San Jose, California. It's The Cube, covering Big Data Silicon Valley 2017. (upbeat electronic music) >> California, Silicon Valley, at the heart of the big data world, this is The Cube's coverage of Big Data Silicon Valley in conjunction with Strata Hadoop, well of course we've been here for multiple years, covering Hadoop World for now our eighth year, now that's Strata Hadoop but we do our own event, Big Data SV in New York City and Silicon Valley, SV NYC. I'm John Furrier, my cohost George Gilbert, analyst at Wikibon. Our next guest is Tendü Yogurtçu with Syncsort, general manager of the big data, did I get that right? >> Yes, you got it right. It's always a pleasure to be at The Cube. >> (laughs) I love your name. That's so hard for me to get, but I think I was close enough there. Welcome back. >> Thank you. >> Great to see you. You know, one of the things I'm excited about with Syncsort is we've been following you guys, we talk to you guys every year, and it just seems to be that every year, more and more announcements happen. You guys are unstoppable. You're like what Amazon does, just more and more announcements, but the theme seems to be integration. Give us the latest update. You had an update, you bought Trillium, you got a hit deal with Hortonworks, you got integrated with Spark, you got big news here, what's the news here this year? >> Sure. Thank you for having me. Yes, it's very exciting times at Syncsort and I've probably say that every time I appear because every time it's more exciting than the previous, which is great. We bought Trillium Software and Trillium Software has been leading data quality over a decade in many of the enterprises. It's very complimentary to our data integration, data management portfolio because we are helping our customers to access all of their enterprise data, not just the new emerging sources in the connected devices and mobile and streaming. Also leveraging reference data, my main frame legacy systems and the legacy enterprise data warehouse. While we are doing that, accessing data, data lake is now actually, in some cases, turning into data swamp. That was a term Dave Vellante used a couple of years back in one of the crowd chats and it's becoming real. So, data-- >> Real being the data swamps, data lakes are turning into swamps because they're not being leveraged properly? >> Exactly, exactly. Because it's about also having access to write data, and data quality is very complimentary because dream has had trusted right data, so to enterprise customers in the traditional environments, so now we are looking forward to bring that enterprise trust of the data quality into data lake. In terms of the data integration, data integration has been always very critical to any organization. It's even more critical now that the data is shifting gravity and the amount of data organizations have. What we have been delivering in very large enterprise production environments for the last three years is we are hearing our competitors making announcements in those areas very recently, which is a validation because we are already running in very large production environments. We are offering value by saying "Create your applications for integrating your data," whether it's in the cloud or originating on the cloud or origination on the main frames, whether it's on the legacy data warehouse, you can deploy the same exact application without any recompilations, without any changes on your standalone Windows laptop or in Hadoop MapReduce, or Spark in the cloud. So this design once and deploy anywhere is becoming more and more critical with data, it's originating in many different places and cloud is definitely one of them. Our data warehouse optimization solution with Hortonworks and AtScale, it's a special package to accelerate this adoption. It's basically helping organizations to offload the workload from the existing Teradata or Netezza data warehouse and deploying in Hadoop. We provide a single button to automatically map the metadata, create the metadata in Hive or on Hadoop and also make the data accessible in the new environment and AtScale provides fast BI on top of that. >> Wow, that's amazing. I want to ask you a question, because this is a theme, so I just did a tweetup just now while you were talking saying "the theme this year is cleaning up the data lakes, or data swamps, AKA data lakes. The other theme is integration. Can you just lay out your premise on how enterprises should be looking at integration now because it's the multi-vendor world, it's the multi-cloud world, multi-data type and source with metadata world. How do you advise customers that have the plethora of action coming at them. IOT, you've got cloud, you've got big data, I've got Hadoop here, I got Spark over here, what's the integration formula? >> First thing is identify your business use cases. What's your business's challenge, what's your business goals, and the challenge, because that should be the real driver. We assist in some organizations, they start with the intention "we would like to create a data lake" without having that very clear understanding, what is it that I'm trying to solve with this data lake? Data as a service is really becoming a theme across multiple organizations, whether it's on the enterprise side or on some of the online retail organizations, for example. As part of that data as a service, organizations really need to adopt tools that are going to enable them to take advantage of the technology stack. The technology stack is evolving very rapidly. The skill sets are rare, and skill sets are rare because you need to be kind of making adjustments. Am I hiring Ph.D students who can program Scala in the most optimized way, or should I hire Java developers, or should I hire Python developers, the names of the tools in the stack, Spark one versus Spark two APIs, change. It's really evolving very rapidly. >> It's hard to find Scala developers, I mean, you go outside Silicon Valley. >> Exactly. So you need to be, as an organization, ours advises that you really need to find tools that are going to fit those business use cases and provide a single software environment, that data integration might be happening on premise now, with some of the legacy enterprise data warehouse, and it might happen in a hybrid, on premise and cloud environment in the near future and perhaps completely in the cloud. >> So standard tools, tools that have some standard software behind it, so you don't get stuck in the personnel hiring problem. Some unique domain expertise that's hard to hire. >> Yes, skill set is one problem, the second problem is the fact that the applications needs to be recompiled because the stack is evolving and the APIs are not compatible with the previous version, so that's the maintenance cost to keep up with things, to be able to catch up with the new versions of the stack, that's another area that the tools really help, because you want to be able to develop the application and deploy it anywhere in any complete platform. >> So Tendü, if I hear you properly, what you're saying is integration sounds great on paper, it's important, but there's some hidden costs there, and that is the skill set and then there's the stack recompiling, I'm making sure. Okay, that's awesome. >> The tools help with that. >> Take a step back and zoom out and talk about Syncsort's positioning, because you guys have been changing with the stacks as well, I mean you guys have been doing very well with the announcements, you've been just coming on the market all the time. What is the current value proposition for Syncsort today? >> The current value proposition is really we have organizations to create the next generation modern data architecture by accessing and liberating all enterprise data and delivering that data at the right time and the right quality data. It's liberate, integrate, with integrity. That's our value proposition. How do we do that? We provide that single software environment. You can have batch legacy data and streaming data sources integrated in the same exact environment and it enables you to adapt to Spark 2 or Flink or whichever complete framework is going to help them. That has been our value proposition and it is proven in many production deployments. >> What's interesting to is the way you guys have approached the market. You've locked down the legacy, so you have, we talk about the main frame and well beyond that now, you guys have and understand the legacy, so you kind of lock that down, protect it, make it secure, it's security-wise, but you do that too, but making sure it works because it's still data there, because legacy systems are really critical in the hybrid. >> Main frame expertise and heritage that we have is a critical part of our offering. We will continue to focus on innovation on the main frame side as well as on the distributed. One of the announcements that we made since our last conversation was we have partnership with Compuware and we now bring in more data types about application failures, it's a Band-Aid data to Splunk for operational intelligence. We will continue to also support more delivery types, we have batch delivery, we have streaming delivery, and now replication into Hadoop has been a challenge so our focus is now replication from the B2 on mainframe and ISA on mainframe to Hadoop environments. That's what we will continue to focus on, mainframe, because we have heritage there and it's also part of big enterprise data lake. You cannot make sense of the customer data that you are getting from mobile if you don't reference the critical data sets that are on the mainframe. With the Trillium acquisition, it's very exciting because now we are at a kind of pivotal point in the market, we can bring that data validation, cleansing, and matching superior capabilities we have to the big data environments. One of the things-- >> So when you get in low latency, you guys do the whole low latency thing too? You bring it in fast? >> Yes, we bring it, that's our current value proposition and as we are accessing this data and integrating this part of the data lake, now we have capabilities with Trillium that we can profile that data, get statistics and start using machine learning to automate the data steward's job. Data stewards are still spending 75% of their time trying to clean the data. So if we can-- >> Lot of manual work labor there, and modeling too, by the way, the modeling and just the cleaning, cleaning and modeling kind of go hand in hand. >> Exactly. If we can automate any of these steps to drive the business rules automatically and provide right data on the data lake, that would be very valuable. This is what we are hearing from our customers as well. >> We've heard probably five years about the data lake as the center of gravity of big data, but we're hearing at least a bifurcation, maybe more, where now we want to take that data and apply it, operationalize it in making decisions with machine learning, predictive analytics, but at the same time we're trying to square this strange circle of data, the data lake where you didn't say up front what you wanted it to look like but now we want ever richer metadata to make sense out of it, a layer that you're putting on it, the data prep layer, and others are trying to put different metadata on top of it. What do you see that metadata layer looking like over the next three to five years? >> The governance is a very key topic and social organizations who are ahead of the game in the big data and who already established that data lake, data governance and even analytics governance becomes important. What we are delivering here with Trillium, we will have generally available by end of Q1. We are basically bringing business rules to the data. Instead of bringing data to business rules, we are taking the business rules and deploying them where the data exists. That will be key because of the data gravity you mentioned because the data might be in the Hadoop environment, there might be in a, like I said, enterprise data warehouse, and it might be originating in the cloud, and you don't want to move the data to the business rules. You want to move the business rules to where the data exists. Cloud is an area that we see more and more of our customers are moving forward. Two main use cases around our integration is one, because the data is originating in cloud, and the second one is archiving data to cloud, and we announced actually, tighter integration with cloud with our director earlier this week for this event, and that we have been in cloud deployments and we have actually an offering, an elastic MapReduce already and on AC too for couple of years now, and also on the Google cloud storage, but this announcement is primarily making deployments even easier by leveraging cloud director's elasticity for increasing and reducing the deployment. Now our customers will also take advantage of integration jobs from that elasticity. >> Tendü, it's great to have you on The Cube because you have an engineering mind but you're also now general manager of the business, and your business is changing. You're in the center of the action, so I want to get your expertise and insight into enterprise readiness concept and we saw last week at Google Cloud 2017, you know, Google going down the path of being enterprise ready, or taking steps, I don't think they're fully ready, but they're certainly serious about the cloud on the enterprise, and that's clear from Diane Green, who knows the enterprise. It sparked the conversation last week, around what does enterprise readiness mean for cloud players, because there's so many details in between the lines, if you will, of what products are, that integration, certification, SLAs. What's your take on the notion of cloud readiness? Vizaviz, Google and others that are bringing cloud compute, a lot of resources, with an IOT market that's now booming, big data evolving very, very fast, lot of realtime, lot of analytics, lot of innovation happening. What's the enterprise picture look like from a readiness standpoint? How do these guys get ready? >> From a big picture, for enterprise there are couple of things that these cannot be afterthought. Security, metadata lineage is part of data governance, and being able to have flexibility in the architecture, that they will not be kind of recreating the jobs that they might have all the way to deployed and on premise environments, right? To be able to have the same application running from on premise to cloud will be critical because it gives flexibility for adaptation in the enterprise. Enterprise may have some MapReduce jobs running on premise with the Spark jobs on cloud because they are really doing some predictive analytics, graph analytics on those, they want to be able to kind of have that flexible architecture where we hear this concept of a hybrid environment. You don't want to be deploying a completely different product in the cloud and redo your jobs. That flexibility of architecture, flexibility-- >> So having different code bases in the cloud versus on prem requires two jobs to do the same thing. >> Two jobs for maintaining, two jobs for standardizing, and two different skill sets of people potentially. So security, governance, and being able to access easily and have applications move in between environments will be very critical. >> So seamless integration between clouds and on prem first, and then potentially multi-cloud. That's table stakes in your mind. >> They are absolutely table stakes. A lot of vendors are trying to focus on that, definitely Hadoop vendors are also focusing on that. Also, one of the things, like when people talk about governance, the requirements are changing. We have been talking about single view and customer 360 for a while now, right? Do we have it right yet? The enrichment is becoming a key. With Trillium we made the recent announcement, the precise enriching, it's not just the address that you want to deliver and make sure that address should be correct, it's also the email address, and the phone number, is it mobile number, is it landline? It's enriched data sets that we have to be really dealing, and there's a lot of opportunity, and we are really excited because data quality, discovery and integration are coming together and we have a good-- >> Well Tendü, thank you for joining us, and congratulations as Syncsort broadens their scope to being a modern data platform solution provider for companies, congratulations. >> Thank you. >> Thanks for coming. >> Thank you for having me. >> This is The Cube here live in Silicon Valley and San Jose, I'm John Furrier, George Gilbert, you're watching our coverage of Big Data Silicon Valley in conjunction with Strata Hadoop. This is Silicon Angles, The Cube, we'll be right back with more live coverage. 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