>> Announcer: Live from San Jose in the heart of Silicon Valley, it's The Cube covering DataWorks Summit 2017. Brought to you by Horton works. >> Hi. Welcome back to The Cube. We are live at day one of 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, Joe Goldberg, Innovation Evangelist at BMC Software. Welcome back to The Cube. >> 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? 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've expected, but this let us really sort of make tangible what we have sort of always felt was, you know 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, BMC's known especially for handling workflows that operate more than a batch work >> Joe Goldberg: Yes So high certainty, very much predictability in terms of when things going to happen, how long's it 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 other customers that are wondering about this when you come back to the State of Automation that we want to move, everybody wants to move to interactive. >> Joe Goldberg: Yes. >> But often the jump to interactive takes place well in advance of predictability of how the data's actually being constructed and put together and aggregated in the back end. Talk a little bit about the priorities. How does one...? Cause 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 of 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 insides 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 realtime and streaming. But in order to really be able to be predictive, because you know there's no magic, we still don't know how to tell the future. The only 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 have actually 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 you know, a layer on top of your conventional applications and your conventional data, all of this new data formats and data does now arriving in real time in 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 you know, 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 code, it had to be written to handle complex jobs and it scared off a lot of folks into thinking about interactive. In the last 10 years, there's been some pretty significant advances in how we think about putting together batch workflows, become much more programmable. How does control (mumbles) and some of the other tool set that BNC 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 you think about delivering the outcomes of big data processing to other applications or to other segments? >> So, you know that's very, that's a great question. Its almost like, thanks for the set up. So, you can see. >> Well let's not ask it then. (laughs) >> You can see the shirt that I'm wearing and of course this is very intentional, but our history has been that we've come from 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 that's done upfront. And then the rigger for enterprise operations 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 application 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 under the code that you write and so it needs to be created right upfront and then moved together with all of the rest of your application components through that delivery pipeline in a CIDC fashion. And so that is what we have done. And again that what the concept is of Jawless. >> So, as you think about what the next step is, is batch going to, presumably batch will be sustained as mode of operation. How is it going to become even more comfortable to a lot of the development methodologys 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 Jawless 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 know, 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, you know sort of bugs in your code. And in order for jawless 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 Joe, I'm not going to shift us back last question here. Kind of looking at more of a business industry level, to do big data write, to bring Hadoop to an enterprise successfully, what are some of the mission critical elements that 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 test for how a big data application moves into their enterprise in terms of, not only how it's operated, but how is it made accessible to all of the constituents that need to use it. One of the key elements 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 in implementing any kind of technology and certainly big data. >> Excellent. Well, Joe Goldberg, thank you so much for coming back to the Cube and joining my cohost, Peter Burris and I for this great chat. And people can watch your keynote on Thursday. >> Yes. >> This week, on the 15th of June. So again for my cohost Peter Burris. I am Lisa Martin. Thanks so much for watching the Cube live, again at day one of the DataWorks Summit. Stick around. We'll be right back. (upbeat music)