(electronic music) >> Announcer: Live from San Jose, California, it's The Cube, covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. We are live in Silicon Valley for Big Data Silicon Valley. Our companion showed at Big Data NYC in conjunction with Strata Hadoop, Big Data Week. Our next guest is Murthy Mathiprakasam, with the director of product marketing Informatica. Did I get it right? >> Murthy: Absolutely (laughing)! >> Okay (laughing), welcome back. Good to see you again. >> Good to see you! >> Informatica, you guys had a AMIT on earlier yesterday, kicking off our event. It is a data lake world out there, and the show theme has been, obviously beside a ton of machine learning-- >> Murthy: Yep. >> Which has been fantastic. We love that because that's a real trend. And IOT has been a subtext to the conversation and almost a forcing function. Every year the big data world is getting more and more pokes and levers off of Hadoop to a variety of different data sources, so a lot of people are taking a step back, and a protracted view of their landscape inside their own companies and, saying, Okay, where are we? So kind of a checkpoint in the industry. You guys do a lot of work with customers, your history with Informatica, and certainly over the past few years, the change in focus, certainly on the product side, has been kind of interesting. You guys have what looks like to be a solid approach, a abstraction layer for data and metadata, to be the keys to the kingdom, but yet not locking it down, making it freely available, yet provide the governance and all that stuff. >> Murthy: Exactly. >> And my interview with AMIT laid it all out there. But the question is what are the customers doing? I'd like to dig in, if you could share just some of the best practices. What are you seeing? What are the trends? Are they taking a step back? How is IOT affecting it? What's generally happening? >> Yeah, I know, great question. So it has been really, really exciting. It's been kind of a whirlwind over the last couple years, so many new technologies, and we do get the benefit of working with a lot of very, very, innovative organizations. IOT is really interesting because up until now, IOT's always been sort of theoretical, you're like, what's the thing? >> John: Yeah. (laughing) What's this Internet of things? >> But-- >> And IT was always poo-pooing someone else's department (laughing). >> Yeah, exactly. But we have actually have customers doing this now, so we've been working with automative manufacturers on connected vehicle initiatives, pulling sensor data, been working with oil and gas companies, connected meters and connected energy, manufacturing, logistics companies, looking at putting meters on trucks, so they can actually track where all the trucks are going. Huge cost savings and service delivery kind of benefits from all this stuff, so you're absolutely right IOT, I think is finally becoming real. And we have a streaming solution that kind of works on top of all the open source streaming platforms, so we try to simplify everything, just like we have always done. We did that MapReduce, with Spark, now with all the streaming technologies. You gave a graphical approach where you can go in and say, Well, here's what the kind of processing we want. You'd lay it out visually and it executes in the Hadoop cluster. >> I know you guys have done a great job with the product, it's been very complimentary you guys, and it's almost as if there's been an transformation within Informatica. And I know you went private and everything, but a lot of good product shops there. You guys got a lot good product guys, so I got to ask you the question, I don't see IOT sometimes as an operational technology component, usually running their own stacks, not even plugged into IT, so that's the whole another story. I'll get to that in a second. But the trend here is you have the batch world, companies that have been in this ecosystem here that are on the show floor, at O'Reilly Media, or talking to us on The Cube. Some have been just pure play batch-related! Then the fashionable steaming technologies have come out, but what's happened with Spark, you're starting to see the collision between batch and realtime-- >> Umm-hmm. >> Called streaming or what not. And at the center of that's the deep learning, it's the IOT, and it's the AI, that's going to be at the intersection of these two colliding forces, so you can't have a one-trick pony here and there. You got to kind of have a blended, more of a holistic, horizontal, scalable approach. >> Murthy: Yes. >> So I want to get your reaction to that. And two, what product gaps and organizational gaps and process gaps emerge from this trend? And what do you guys do? So, three-part question. >> Murthy: Yeah (laughing). >> Go ahead. Go ahead. >> I'll try to cover all three. >> So, first, the collision and your reaction to that trend. >> Murthy: Yeah, yeah. >> And then the gaps. >> Absolutely. So basically, you know Informatica, we've supported every type of kind of variation of these type of environments, and so we're not really a believer in it's this or that. It's not on premise or cloud, it's not realtime or batch. We want to make it simple and no matter how you want to process the data, or where you want to process it. So customers who use our platform for their realtime or streaming solutions, are using the same interface, as if they were doing it batched. We just run it differently under the hood. And so, that simplifies and makes a lot of these initiatives more practical because you might start with a certain latency, and you think maybe it's okay to do it at one speed. Maybe you decide to change. It could be faster or slower, and you don't have to go through code rewrites and just starting completely from scratch. That's the benefit of the abstraction layer, like you were saying. And so, I think that's one way that organizations can shield themselves from the question because why even pose that question in the first... Why is it either this or that? Why not have a system that you can actually tune and maybe today you want to start batch, and tomorrow you evolve it to be more streaming and more realtime. Help me on the-- >> John: On the gaps-- >> Yes. >> Always product gaps because, again, you mentioned that you're solving it, and that might be an integration challenge for you guys. >> Yep. >> Or an integration solution for you guys, challenge, opportunity, whatever you guys want to call it. >> Absolutely! >> Organizational gaps maybe not set up for and then processed. >> Right. I think it was interesting that we actually went out to dinner with a couple of customers last night. And they were talking a lot about the organizational stuff because the technology they're using is Informatica, so that's part's easy. So, they're like, Okay, it's always the stuff around budgeting, it's around resourcing, skills gap, and we've been talking about this stuff for a long time, right. >> John: Yeah. >> But it's fascinating, even in 2017, it's still a persistent issue, and part of what their challenge was is that even the way IT projects have been funded in the past. You have this kind of waterfall-ish type of governance mechanism where you're supposed to say, Oh, what are you going to do over the next 12 months? We're going to allocate money for that. We'll allocate people for that. Like, what big data project takes 12 months? Twelve months you're going to have a completely (laughing) different stack that you're going to be working with. And so, their challenge is evolving into a more agile kind of model where they can go justify quick-hit projects that may have very unknown kind of business value, but it's just getting by in that... Hey, sometime might be discovered here? This is kind of an exploration-use case, discovery, a lot of this IOT stuff, too. People are bringing back the sensor data, you don't know what's going to coming out of that or (laughing)-- >> John: Yeah. >> What insights you're going to get. >> So there's-- >> Frequency, velocity, could be completely dynamic. >> Umm-hmm. Absolutely! >> So I think part of the best practice is being able to set outside of this kind of notion of innovation where you have funding available for... Get a small cross-functional team together, so this is part of the other aspect of your question, which is organizationally, this isn't just IT. You got to have the data architects from IT, you got to have the data engineers from IT. You got to have data stewards from the line of business. You got business analysts from the line of business. Whenever you get these guys together-- >> Yeah. >> Small core team, and people have been talking about this, right. >> John: Yeah. >> Agile development and all that. It totally applies to the data world. >> John: And the cloud's right there, too, so they have to go there. >> Murthy: That's right! Exactly. So you-- >> So is the 12-month project model, the waterfall model, however you want... maybe 24 months more like it. But the problem on the fail side there is that when they wake up and ship the world's changed, so there's kind of a diminishing return. Is that kind of what you're getting out there on that fail side? >> Exactly. It's all about failing fast forward and succeeding very quickly as well. And so, when you look at most of the successful organizations, they have radically faster project lifecycles, and this is all the more reason to be using something like Informatica, which abstracts all the technology away, so you're not mired in code rewrites and long development cycles. You just want to ship as quickly as possible, get the organization by in that, Hey, we can make this work! Here's some new insights that we never had before. That gets you the political capital-- >> John: Yeah. >> For the next project, the next project, and you just got to keep doing that over and over again. >> Yeah, yeah. I always call that agile more of a blank check in a safe harbor because, in case you fail forward, (laughing) I'm failing forward. (laughing) You keep your job, but there's some merit to that. But here's the trick question for you: Now let's talk about hybrid. >> Umm-hmm. >> On prem and cloud. Now, that's the real challenge. What are you guys doing there because now I don't want to have a job on prem. I don't want to have a job on the cloud. That's not redundancy, that's inefficient, that's duplicates. >> Yes. >> So that's an issue. So how do you guys tee it up there for the customer? And what's the playbook for them, and people who are trying to scratching their heads saying, I want on prem. And Oracle got this right. Their earnings came out pretty good, same code on prem, off prem, same code base. So workloads can move depending upon the use cases. >> Yep. >> How do you guys compare? >> Actually that's the exact same approach that we're taking because, again, it's all about that customer shouldn't have to make the either or-- >> So for you guys, interfacing code same on prem and cloud. >> That's right. So you can run our big data solutions on Amazon, Microsoft, any kind of cloud Hadoop environment. We can connect to data sources that are in the cloud, so different SAAS apps. >> John: Umm-hmm. >> If you want to suck data out of there. We got all the out-of-the-box connectivity to all the major SAAS applications. And we can also actually leverage a lot of these new cloud processing engines, too. So we're trying to be the abstraction layer, so now it's not just about Spark and Spark streaming, there's all these new platforms that are coming out in the cloud. So we're integrating with that, so you can use our interface and then push down the processing to a cloud data processing system. So there's a lot of opportunity here to use cloud, but, again, we don't want to be... We want to make things more flexible. It's all about enabling flexibility for the organization. So if they want to go cloud, great. >> John: Yep. >> There's plenty of organizations that if they don't want to go cloud, that's fine, too. >> So if I get this right, standard interface on prem and cloud for the usability, under the hood it's integration points in clouds, so that data sources, whatever they are and through whatever could be Kinesis coming off Amazon-- >> Exactly! >> Into you guys, or Ah-jahs got some stuff-- >> Exactly! >> Over there, That all works under the hood. >> Exactly! >> Abstracts from the user. >> That's right! >> Okay, so the next question is, okay, to go that way, that means it's a multicloud world. You probably agree with that. Multicloud meaning, I'm a customer. I might have multiple workloads on multiple clouds. >> That's where it is today. I don't know if that's the endgame? And obviously all this is changing very, very quickly. >> Okay (laughing). >> So I mean, Informatica we're neutral across multiple vendors and everything. So-- >> You guys are Switzerland. >> We're the Switzerland (laughing), so we work with all the major cloud providers, and there's new one that we're constantly signing up also, but it's unclear how the market rule shipped out. >> Umm-hmm. >> There's just so much information out there. I think it's unlikely that you're going to see mass consolidation. We all know who the top players are, and I think that's where a lot of large enterprises are investing, but we'll see how things go in the future, too. >> Where should customers spend their focus because this you're seeing the clouds. I was just commenting about Google yesterday, with AMIT, AI, and others. That they're to be enterprise-ready. You guys are very savvy in the enterprising, there's a lot of table stakes, SLAs to integration points, and so, there's some clouds that aren't ready for prime time, like Google for the enterprise. Some are getting there fast like Amazon Ah-jahs super enterprise-friendly. They have their own problems and opportunities. But they are very strong on the enterprise. What do you guys advise customers? What are they looking at right now? Where should they be spending their time, writing more code, scripts, or tackling the data? How do you guys help them shift their focus? >> Yeah, yeah! >> And where-- >> And definitely not scripts (laughing). >> It's about the worst thing you can do because... And it's all for all the reasons we understand. >> Why is that? >> Well, again, we we're talking about being agile. There's nothing agile about manually sitting there, writing Java code. Think about all the developers that were writing MapReduce code three or four years ago (laughing). Those guys, well, they're probably looking for new jobs right now. And with the companies who built that code, they're rewriting all of it. So that approach of doing things at the lowest possible level doesn't make engineering sense. That's why the kind of abstraction layer approach makes so much better sense. So where should people be spending their time? It's really... The one thing technology cannot do is it can't substitute for context. So that's business context, understanding if you're in healthcare there's things about the healthcare industry that only that healthcare company could possibly know, and know about their data, and why certain data is structured the way it is. >> John: Yeah. >> Or financial services or retail. So business context is something that only that organization can possibly bring to the table, and organizational context, as you were alluding to before, roles and responsibilities, who should have access to data, who shouldn't have access to data, That's also something that can be prescribed from the outside. It's something that organizations have to figure out. Everything else under the hood, there's no reason whatsoever to be mired in these long code cycles. >> John: Yeah. >> And then you got to rewrite it-- >> John: Yeah. >> And you got to maintain it. >> So automation is one level. >> Yep. >> Machine learning is a nice bridge between the taking advantage of either vertical data, or especially, data for that context. >> Yep. >> But then the human has to actually synthesize it. >> Right! >> And apply it. That's the interface. Did I get that right, that progression? >> Yeah, yeah. Absolutely! And the reason machine learning is so cool... And I'm glad you segway into that. Is that, so it's all about having the machine learning assist the human, right. So the humans don't go away. We still have to have people who understand-- >> John: Okay. >> The business context and the organizational context. But what machine learning can do is in the world of big data... Inherently, the whole idea of big data is that there's too much data for any human to mentally comprehend. >> John: Yeah. >> Well, you don't have to mentally comprehend it. Let the machine learning go through, so we've got this unique machine learning technology that will actually scan all the data inside of Hadoop and outside of Hadoop, and it'll identify what the data is-- >> John: Yeah. >> Because it's all just pattern matching and correlations. And most organizations have common patterns to their data. So we figured up all this stuff, and we can say, Oh, you got credit card information here. Maybe you should go look at that, if that's not supposed to be there (laughing). Maybe there's a potential violation there? So we can focus the manual effort onto the places where it matters, so now you're looking at issues, problems, instead of doing the day-to-day stuff. The day-to-day stuff is fully automated and that's not what organizations-- >> So the guys that are losing their jobs, those Java developers writing scripts, to do the queries, where should they be focusing? Where should they look for jobs? Because I would agree with you that their jobs would be because the the MapReduce guys and all the script guys and the Java guys... Java has always been the bulldozer of the programming language, very functional. >> Murthy: Yep. >> But where those guys go? What's your advice for... We have a lot of friends, I'm sure you do, too. I know a lot of friends who are Java developers who are awesome programmers. >> Yeah. >> Where should they go? >> Well, so first, I'm not saying that Java's going to go away, obviously (laughing). But I think Java-- >> Well, I mean, Java guys who are doing some of the payload stuff around some of the deep--- >> Exactly! >> In the bowels of big data. >> That's right! Well, there's always things that are unique to the organization-- >> Yeah. >> Custom applications, so all that stuff is fine. What we're talking about is like MapReduce coding-- >> Yeah, what should they do? What should those guys be focusing on? >> So it's just like every other industry you see. You go up the value stack, right. >> John: Right. >> So if you can become more of the data governor, the data stewards, look at policy, look at how you should be thinking about organizational context-- >> John: And governance is also a good area. >> And governance, right. Governance jobs are just going to explode here because somebody has to define it, and technology can't do this. Somebody has to tell the technology what data is good, what data is bad, when do you want to get flagged if something is going wrong, when is it okay to send data through. Whoever decides and builds those rules, that's going to be a place where I think there's a lot of opportunities. >> Murthy, final question. We got to break, we're getting the hook sign here, but we got Informatica World coming up soon in May. What's going to be on the agenda? What should we expect to hear? What's some of the themes that you could tease a little bit, get people excited. >> Yeah, yeah. Well, one thing we want to really provide a lot of content around the journey to the cloud. And we've been talking today, too, there's so many organizations who are exploring the cloud, but it's not easy, for all the reasons we just talked about. Some organizations want to just kind of break away, take out, rip out everything in IT, move all their data and their applications to the cloud. Some of them are taking more of a progressive journey. So we got customers who've been on the leading front of that, so we'll be having a lot of sessions around how they've done this, best practices that they've learned. So hopefully, it's a great opportunity for both our current audience who's always looked to us for interesting insights, but also all these kind of emerging folks-- >> Right. >> Who are really trying to figure out this new world of data. >> Murthy, thanks so much for coming on The Cube. Appreciate it. Informatica World coming up. You guys have a great solution, and again, making it easier (laughing) for people to get the data and put those new processes in place. This is The Cube breaking it down for Big Data SV here in conjunction with Strata Hadoop. I'm John Furrier. More live coverage after this short break. (electronic music)