 Live from the MGM Grand Hotel in Las Vegas, extracting the signal from the noise. It's theCUBE covering Splunk.com 2015, brought to you by Splunk. Now, here are your hosts, John Furrier and Jeff Rick. Hello everyone, welcome back. We are live in Las Vegas for theCUBE at Splunk.com 2015. This is SiliconANGLE's flagship program. We go out to the events and extract the signal noise. I'm John Furrier with my co-host, Jeff Rick. Our next guest is Josh Rogers, president of Syncsort. Welcome back to theCUBE. Good to see you. Good to be here. Got the nice little thing going on there. We know it's all the ESPN guys who wear that, Jeff and I. We were talking about that last night. Good job, look good. Yeah, congratulations. Again, I got some news, more news, Syncsort. I mean, Guido Schroeder said she got a ship product. You guys always have news and announcements. Tell us some news. So the innovation continues to roll on. So today we announced version 1.3 of IronStream. IronStream is the leading mainframe forwarder for mainframe log data in the Splunk. When we released this product originally in Q4 of last year, we had, I think, six sources that we were shipping. So there's 240 some odd log types on the mainframe. And today's release actually takes that to 10x, that number, approaching 60 different sources that we ship. We expect that that covers about 80% of the logging that happens on the mainframe. We'll obviously continue to add to that in the future. One of the big investments that we put in in the 1.3 release is around security. So that's a big use case that we're seeing a lot of interest in, obviously. And not only have we beefed up the security records that SMF produces and some of the other repositories that exist on the mainframe, we've also incorporated technology from our acquisition of William Data Systems and there's in technology that's a network monitoring tool. We've taken all the network security information they collect and corporate that into the product. So now you've got a set of unique data that you can't get anywhere else that we can feed on a real-time basis securely into Splunk and it's just a great solution for customers and we're seeing terrific uptake. The show has been fantastic and we continue to kind of innovate with customers around use cases one of the other things that we're announcing is a set of apps that will be in Splunk base by the end of the month. So we'll have six apps that will be there and those reflect our customer use cases from production appointments. So we're really rapidly innovating this space and trying to allow customers to quickly take mainframe log data to complete that 360 degree view of their environment for application performance monitoring for IT operations and for security. The renaissance of the mainframe of the past few years really comes down to the result. We've talked about this in the past in theCUBE is that now the connectedness of the applications is integration is happening, not silos. So the trend is silo busting. Basically, get rid of silos. The mainframe used to be in that little corner doing their thing, chugging along. The gift that keeps on giving and great applications still written on that single thread or whatever it is. But now when you integrate it all in, when you got to get business data, business line managers, the data, you got to integrate across the silos. Do you agree with that? And two, how does Splunk fit in that for you guys? Is it on your radar? Does it come organically from your customer base thing? Hey, we're using Splunk. It's pretty gas and you guys should support it. How does that work? Yeah, so first of all, absolutely mainframe data is critical to making these big data repositories valuable. If I don't actually have the data from my system of record in my big data repository, my analytics are going to be not all that interesting. So we believe Sync sort of occupies a very unique position in the market in that we understand these big data, the big data landscape. We have the relationships with Splunk, with Cloudera, with Hortonworks to be able to understand how to integrate with their technologies. But more importantly and more uniquely, we have this expertise on the mainframe and this huge customer base where we're trusted. And so we'll be able to bring those two worlds together and take that system of record data and move it into these repositories for analytics. As it relates to Splunk, I mean the relationship actually came about out of our work we'd done in the Hadoop space. So we were at Hadoop World in 2013 and I attended a dinner. There happened to be somebody from Splunk. We struck up a conversation. They were having a challenge. They were getting requests from customers to be able to incorporate mainframe log data into the dashboards they were producing in Splunk. And they didn't have the expertise to be able to do that. They had talked to some other players in the market and a lot of the mainframe software companies have dedicated mainframe monitoring suites and so they didn't have the incentive to work with Splunk to kind of close this gap. We don't have that issue and we certainly have that expertise and so we quickly marshaled resources, quickly understood that this was a major opportunity and delivered the market leading product that you see today. And it's been a really fun ride and as we add customers and as we kind of see the pipeline build, we're constantly learning and we're constantly kind of innovating to incorporate those learnings into the product. It's just a great example of a win-win-win, right? There was an unmet need. The two of you got together, brought your best practices and delivered what the customers were really pining for for them to take as John said, the renaissance of the mainframe that continues to kind of be moved into this modern era with modern applications and the modern big data. So what a great way to see it and then execute on it. It's been remarkable and I think one of the things that's been interesting is the more we've learned about the opportunity, the more we realize how significant the opportunity is. Obviously Splunk wants to meet the requirements of our customers, but few people understand how much log data is generated by a mainframe. We have financial services customers that generate five to 10 terabytes of mainframe log data a day. This is a really significant source of information that represents terrific insights for all the applications we've talked about, but it also represents a meaningful market opportunity for us and for Splunk and so we're very focused on it. One of the things that's helped is the addition of Snehal to the executive team at Splunk. He comes from an environment where he really understood the mainframe and so he's been able to put a lot of focus on how we can work together to close this gap and so that's been a terrific relationship as well. And then you build the apps and you put them out in the Splunk store to kind of close it. So what are some of the top two or three of the six that you can share with us? Yeah, so one of the areas that people are focused on is obviously security and generally what people do in their mainframe environments is they source production data into test environments to create and they mask the PII information and they use that data for application testing. Well, one of the things that they want to make sure happens is that only people that have the rights to be able to use that data use that data. Today, there's no good system for a broad set of application testers to be able to understand who's accessing this test data. So one of our recent wins was just that. It was, you know, let's use Splunk and Iron Stream to deliver a comprehensive view across this test data assets, who's accessing that data and do they actually have the rights to do so? You know, and that's important, not just from a business perspective, but also from a compliance perspective. If you're a large global insurance provider, you know, you get fines if you're not complying, you know, with some of the regulations around security. So that's one, you know, in the IT operations space, you know, for years there's been arguments between the application owners and the operations teams around SLA's. You know, what is the SLA? What caused a miss in SLA? And historically, it's worked like this, you know? There's an SLA miss, the process didn't complete on time. The application owner says, hey, ops team, you know, you made a mistake, tell us what that was. Ops team says no, it was done, and an argument ensues. So one of our customers said, look, if we're going to, you know, incent the teams on meeting their SLA's, and we're going to bonus people appropriately to kind of derive performance in the organization, one of the things we need to do is to make this information available to all parties in a consumable fashion. And so they're going to use Iron Stream and Splunk to be able to distribute that information to all parties that haven't interested in it in a way that can be easily digested versus some of the legacy monitoring tools on the mainframe that take a subject matter expert to interpret the data. And it just doesn't lend itself to kind of a transparent and an objective view of information around what's happening with application performance. And that was a key theme in the keynote, really, the democratization of the data, to let more people have access to it because they can do different things whether they've got a different point of view. I mean, it's interesting, I was talking to a financial services customer, and this is just one instance of Splunk. They have 4,000 users. Most of the customers that we talk to that use traditional mainframe monitoring tools, they have a dozen users. Do you think it might be better and more valuable for the organization if you could open up that information to thousands of users that have an interest to understand that data versus having it sit with a dozen people that may or may not have a view of the overall kind of corporate objectives and the situation in the IT organization? Josh, talk about what's changed with SYNC sort of the past year or so. We've got big data, NYC next week, we have our event, we've got Strata, Hadoop, we're going on in conjunction with big data, NYC. A lot of stuff, you've been everywhere. We've just bump into each other all the time, so we see each other. So we know that the business is evolving. Cloud is certainly here. The role of data, no matter where it comes from, internet of things. So how has all this changed SYNC sort? And what are you guys doing? Have you guys shifted? You're staying on course. What's the, give us the update. Well, so I think what we've seen is the, if you look at the big data platforms in the marketplace, they're moving into a level of maturity where people can actually run production workloads and get real value on a sustained basis from. And so what that has done for us is it's made, those customers' ability to access their mainframe data assets and other assets as well, much more important. So in a Hadoop environment, people not only want to use the storage, they actually want to use the processing. And what we've been focused on is allowing customers to kind of ignite that data, to get it into that big data repository. More importantly, to also be able to kind of lower their overall cost structure around how they have historically done data processing. And so a lot of the solutions that we're coming to market with not only help people get new insights, but they also help them capture immediately cost savings. On the Hadoop side, we've done a lot of work around offload. So obviously taking ELT workloads out of data. Warehouses are moving that into Hadoop, but more recently, a lot of mainframe offload. So a lot of the COBOL batch programs that are effectively ETL workloads, helping those move into Hadoop, saving customers money immediately, because they reduce their CPU on the mainframe, which is how their software is charged. We go to market with Cognizant, with a solution called Big Frame, where they built a set of assets to help kind of automate that migration. And then obviously we provide a software solution that helps kind of that run in place. And then on the mainframe side, we continue to see more and more opportunities to help customers take advantage of their data like IronStream, but also to lower their costs. So we're doing a lot of work around moving existing mainframe workloads into Zip Engines, which is a non-chargeable CPU. So no strategy change really. I mean, same old, same old. Just trying to be the core, be the best mainframe. Digest the real opportunities that pop up. So we're continuing to see new opportunities. And they are, right? Absolutely. So those are the key things. That's the emerging market speed, right? Yes, absolutely. So you're busy. The other piece of this is that we are trying to ramp up and continue our work on the acquisition front. So we do have an active dialogue with a number of opportunities there. And we have very kind of prescriptive views on what adjacent markets look really attractive and can we kind of create synergy both in terms of our value proposition to customers as well as kind of acquisition of talent in marketplace. And certainly security now here is a big focus. Absolutely. Thoughts on that here? Well, that was one of the pieces behind the William Data Systems acquisition, right? Which is we can get that network security data in. It's not so much that we have a monitor we can sell to the market. It's that we now have unique data we can pump through IronStream into Splunk. And so we'll continue to look for anything that helps us tell a stronger security story for sure. SyncSort, your mainframe, modern mainframe, application, platform, systems operating system, whatever you call it, it means the mainframe. The big iron is out there. We call it big iron to big data. Big iron meets big data. It's a challenge that every enterprise has to kind of confront. And it's wrought with a lot of challenges but we have the expertise and the technology to solve those challenges. And certainly cloud's great for you too because more big iron in the cloud, virtual big iron. So big iron meets big data, SyncSort here on theCUBE. We'll have more after this short break on theCUBE. Be right back.