 Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017, brought to you by Hortonworks. Hi, welcome back to theCUBE. We are live at day one at the DataWorks Summit in San Jose in the heart of Silicon Valley, hosted by Hortonworks. We've had a great day so far, lots of innovation, lots of great announcements. We're very excited to be joined by one of this week's keynotes and CUBE alumni, Jill Goldberg, Innovation Evangelist at BMC Software. Welcome back to theCUBE. Thank you very much. Always a pleasure to be here. Exactly, and we're happy to have you back. So talk to us, what's happening with BMC and Hadoop? What are you guys doing there? What are people going to learn in your keynote on Thursday? So BMC has been really working with all of our customers to modernize not only our tool chain, but the way automation is used and deployed throughout the organization. We actually did a survey recently, the state of automation. We got pretty much the kind of results we would have expected, but this led us really sort of make tangible what we have sort of always felt was, the state of this kind of approach to how critical automation is in the enterprise. We had a response from leaders and CXOs that 93% thought that automation was key to helping them make that digital transformation that everyone is involved in today. So that's been one of the key elements that has really kind of driven everything that we've been doing with BMC today. Now, you, BMC's known, especially for handling workflows, that operate more than a batch. So high certainty, very much predictability in terms of when things are going to happen, how long they're going to take, what action's going to take place. Very, very complex types of processing takes place. I'm always fascinated, and I've talked to a lot of customers that are wondering about this. We've come back to this notion of automation, that we want to move, everybody wants to move to interactive, but often the jump to interactive takes place well in advance of the predictability of how the data is actually being constructed and put together and aggregated in the back end. Talk a little about the priorities. How does one, because it's really not a chicken and egg kind of a problem, how does one anticipate excellence in the other? So what we've been hearing, and actually I think in the previous Hortonworks or DataWorks Summit, we had one of our customers talk about their approach to what was a fundamental data architecture for them, which was the separation between the speed and batch layer. And I think you hear an awful lot of that kind of conversation. And they run in parallel. And from our perspective, managing the batch layer, really underpins the kind of real actionable insights that you can extract from the speed layer, which is focusing on capturing that very small percentage of what is really the signal in the data, but then being able to take that and enrich it with what you've been collecting and managing using the batch layer. I think that that's the kind of approach that we've seen from a lot of customers where certainly all of the cool stuff and the focus is on the interactive and the real time and streaming, but in order to really be able to be predictive because there's no magic. We still don't know how to tell the future. The only way to be able to do that is by making sure that you are basing yourself on history that is well sort of collected, curated, make sure that you actually have captured it, that you've enriched it from a variety of different sources, and that's where we come in. What we have been focusing on is providing a set of facilities for managing batch that is, I talk about hyper heterogeneity. I know that's a mouthful, but that's really what the new enterprise environment is like. So you add or layer on top of your conventional applications and your conventional data, all of this new data formats and data that is now arriving in real time at high volume. I think that taking that kind of an approach is really the only way that you can ensure that you are capturing all of your, ingesting all of the data that's coming in from all of your endpoints, including IOT applications, and really being able to combine it with all of the corporate sort of knowledge that you've accumulated through your traditional sources. So batches historically meant, again, a lot of precise codes that had to be written to handle complex jobs, and it scared off a lot of the folks that were thinking about interacting. But in the last 10 years, there've been some pretty significant advances in how we think about putting together batch workflows. They've got much more programmable. How does ControlM and some of the other tool set that BMC provides, how does it fit into, how does it look more like the types of application development tasks and methods that are becoming increasingly popular as we think about delivering the outcomes of big data processing to other applications or to other decision makers? So, you know, that's very, it's a great question. It's almost like thanks for the setup. So you can see- Well, let's not ask it. So you can see the shirt that I'm wearing, and of course this is very intentional. But, you know, our history has been that we've come from the sort of the data center operations focus. And the transition in the marketplace today has been that really the focus has shifted whether you talk about shift left or everything as code, where the new methods of building and delivering applications really look at everything manual that is done, coding to create an application is done upfront, and then the rigor for enterprise operation is built in through this automated delivery pipeline. And so obviously you have to invert this kind of approach that we've had in terms of layering management tools on at the very end, and instead you have to be able to inject them into your applications very early. So we feel that certainly it's true for all applications and it's I think doubly true in data applications that the automation and the operational instrumentation is an equal partner to your business logic and to the code that you write. And so it needs to be created right upfront and then move together with all of the rest of your application components through that delivery pipeline in a CICD fashion. And so that is what we have done. And again, that's what the concept is of jobs as code. So as you think about what the next step is, is Batch will be sustained as a mode of operation. How is it going to become even more comfortable to a lot of the development methodologies as we move forward? How do you think it's going to be evolved as a tool for increasing the amount of predictability in that back end? So I think that the key to continuing to evolve this jobs as code approach is to enable developers to be able to build and work with that operational plumbing in the same way they work with their business logic. Or any other resource. Exactly. So you think about what are the tools that developers have today when they build whether you're writing in Java or C or R or Scala. There are development environments. There are these tools that let you test, that let you step through your logic to be able to identify and find any flaws and sort of bugs in your code. And in order for jobs as code to really meet the test of being code, we are working on providing the same kind of capabilities to work with our objects that developers expect to have for programming languages. So Joman, I kind of shift us back. Last question here. Kind of looking at more of a business and industry level. To do big data right, to bring Hadoop to an enterprise successfully, what are some of the mission critical elements that the C suite really needs to embrace in order to be successful across big industries like healthcare, financial services, telco? So I think they have to be able to apply the same requirements and the tests for how a big data application moves into their enterprise in terms of not only how is it operated but how is it made accessible to all of the constituents that need to use it. One of the key elements that we hear frequently is that, and I think it's a danger that when technicians solely create what is the end deliverable tool, it frequently is very technical. And it has to be consumable by the people that actually need to use it. And so you have to strike this balance between providing sufficient technical sophistication and business usability. And I think that that's kind of a goal for being successful and implementing any kind of technology and certainly big data. Excellent. Well, Joe Goldberg, thank you so much for coming back to theCUBE and joining my co-host Peter Burris and I for this great chat. And people can watch your keynote on Thursday of this week on the 15th of June. So again, for my co-host Peter Burris, I'm Lisa Martin. Thanks so much for watching theCUBE live again at day one at the data work summit. Stick around, we'll be right back.