>> Narrator: Live, from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, David Vellante and George Gilbert. >> Back to Boston, everybody, where the city is bracing for a big snowstorm. Still euphoric over the Patriots' big win. Aaron Colcord is here, he's the director of engineering at FIS Global, and he's joined by Dave Favela, who's the director of BI at FIS Global. Gentlemen, welcome to theCUBE. It's good to see you. >> Yeah, thank you. >> Thank you very much. >> Thanks so much for coming on. So Dave, set it up. FIS Global, the company that does a ton of work in financial services that nobody's ever heard of. >> Yeah, absolutely, absolutely. Yeah, we serve and touch virtually every credit union or bank in the United States, and have services that extend globally, and that ranges anywhere from back office services to technology services that we provide by way of mobile banking or online banking. And so, we're a Fortune 500 company with a reach, like I said, throughout the nation and globally. >> So, you're a services company that provides, sort of, end-to-end capabilities for somebody who wants to start a bank, or upgrade their infrastructure? >> Absolutely, yeah. So, whether you're starting a bank or whether you're an existing bank looking to offer some type of technology, whether it's back-end processing services, mobile banking, bill pay, peer-to-peer payments, so, we are considered a FinTech company, and one of the largest FinTech companies there is. >> And Aaron, your role as the director of engineering, maybe talk about that a little bit. >> My role is primarily about the mobile data analytics, about creating a product that's able to not only be able to give the basic behavior of our mobile application, but be able to actually dig deeper and create interesting analytics, insights into the data, to give our customers understanding about not only the mobile application, but be able to even, as we're building right now, a use case for being able to take action on that data. >> So, I mean, mobile obviously is sweeping the banking industry by storm, I mean, banks have always been, basically, IT companies, when you think about it, a huge component of IT, but now mobile comes in and, maybe talk a little bit about, sort of the big drivers in the business, and how, you know, mobile is fitting in. >> Absolutely. So, first of all, you see a shift that's happening with the end user: you, David, as a user of mobile banking, right? You probably have gone to the branch maybe once in the last 90 days, but have logged into mobile banking 10 times. So, we've seen anywhere from an eight to nine time shift in usage and engagement on the digital channel, and what that means is, more interactions and more touch points that the bank is getting off of the consumer behavior. And so, what we're trying to do here is turn that into getting to know the customer profile better, so that they could better serve in this digital channel, where there's a lot more interactions occurring. >> Yeah, I mean, you look at the demographic, too. I mean, my kids don't even use cheques. Right, I mean, it's all, everything's done on mobile, Venmo, or whatever, the capabilities they have. So, what's the infrastructure behind that that enables it? I mean, it can't be what it used to be. I mean, probably back-end still is, but what else do you have to create to enable that? >> Well, it's been a tremendous amount of transformation on the back-ends over the last ten years, and particularly when we talk about how that interaction has changed, from becoming a more formal experience to becoming a more intimate experience through the mobile client. But, more specifically to the back-end, we have actually implemented Apache Spark as one of our platforms, to actually help transform and move the data faster. Mobile actually creates a tremendous amount of back-end activity, sometimes even more than what we were able to see in other channels. >> Yeah, and if you think about it, if you just kind of step back a little bit, this is about core banking, right, and as you speak to IT systems, and so, if you think about all the transactions that happen on the daily, whether you're in branch, at ATM, on a mobile device, it's processed through a core banking system, and so one of the challenges that, I think, this industry and FinTech is up against is that, you've got all these legacy old systems that have been built that can't compute all this data at a fast enough rate, and so for us, bringing in Aaron, this is about, how do you actually leverage new technology, and take the technical data of the old systems, data schemas and models, and marry the two to provide data, key data that's been generated. >> Dave: Without shutting down the business. >> Without shutting down the business. >> Because that's the hard part. >> Can you elaborate on that, because that's non-trivial. It used to be when banks merged, it could take years for the back-off of systems to come together. So now, let's say a bank comes to you, they have their, I don't want to say legacy systems, it's the systems they've built up over time, but they want the more modern capabilities. How do you marry the two? >> Would you take a first stab? >> Well, it is actually a very complicated process, because you always have to try to understand data itself, and how to put those two things together. More specifically on the mobile client, because of the way that we are able to think about how data can be transformed and transported, we came up with a very flexible mechanism to allow data to actually be interpreted on the fly, and processed, so that when you talk about two different banks, by transforming it into this type of format, we're able to kind of reinterpret it and process it. >> Would this be, could you think of this as a very, very smart stream processor that, where ETL would be at the most basic layer, and then you're adding meaning to the data so that it shows up to the mobile client in a way that coheres to the user model that the user is experiencing on their device? >> I think that's a really good way of putting it, yeah. I mean, there's a, we like to think of it, I call it a semantic layer, of how you, one, treat ETL as one process, and then you have a semantic layer that you basically transform the bottom bits, so to speak, into components that you can then assemble semantically so that it starts making sense to the end user. >> And to that point, you know, to your integration question, it is very challenging, because you're trying to marry the old with the new, and we'll tease the section for tomorrow in which Aaron will talk about that, but for us, at enterprise grade, it has to be done very cautiously, right? And we're under heavy regulation and compliance and security, and so, it's not about abandoning the old, right? It's trying to figure out, how do we take that, what's been in place and been stable, and then couple it with the new technology that we're introducing. >> Which is interesting conversation, the old versus new, and I look at your title, Dave, and it's got 'BI' in it. I remember I interviewed Christian Chabot, who was then CEO of Tableau, and he's like, "Old, slow, BI", okay, now you guys are here talking about Spark. Spark's all about real-time and speed and memory, and everything else. Talk about the transformation in your role as this industry has transformed. >> Yeah, absolutely, so, when we think about business intelligence and creating that intelligence layer, we elected the mobile channel, right? Because we're seeing that most inner activities happen there. So for us, an intelligent BI solution is not just, you know, data management and analytics platform. There has to be the fulfillment. You talk a lot about actioning on your data. So for us, it's, if we could actually create, you know, intelligence layer to analytics level, how can we feed marketing solutions with this intelligence to have the full circle and insights back? I believe, the gentlemen, they were talking about the RISE Lab in this morning session. >> Dave: The follow-on to AMP, basically. >> Yeah, exactly. So, there it was all about that feedback loop, right? And so, for us, when we think about BI, the whole loop is from data management to end-to-end marketing solutions, and then back, so that we can serve the mobile customer. >> Well, so, you know, the original promise of the data warehouse was this 365, what you just described, right? And being able to effect business outcomes, and that is now the promise of so-called big data, even though people don't really like that term anymore, so, my question is, is it same line, new bottle, or is it really transformational? Are we going to live up to that challenge this time around? As practitioners, I'd really love your input on that. >> I think I'd love to expand on that. >> Absolutely. >> Yeah, I mean, I don't think it's, I think it's a whole new bottle and a whole new wine. David here is from wine country, and, there's definitely the, data warehouse introduced the important concepts, of which is a tremendous foundation for us to stand on. You know, you always like to stand on the shoulders of giants. It introduced a concept, but in the case of marrying the new with the old, there's a tremendous extra third dimension, okay? So, we have a velocity dimension when we start talking about Apache Spark. We can accelerate it, make it go quick, and we can get that data. There's another aspect there when we start talking about, for example, hey, different banks have different types of way that they like to talk to it, so now we're kind of talking about, there's variation in people's data, and Apache Spark, actually, is able to give that capability to process data that is different than each other, and then being able to marry it, down the pipe, together. And then the additional, what I think is actually making it into a new wine is, when we start talking about data, the traditional mechanism, data warehousing, that 360 view of the customer, they were thinking more of data as in, I like to think of it as, let's count beans, right? Let's just come up with what how many people were doing X, how many were doing this? >> Dave: Accurate reporting, yeah. >> Exactly, and if you think about it, it was driving the business through the rear-view mirror, because all you had to do was base it off of the historical information, and that's how we're going to drive the business. We're going to look in the rear-view mirror, we're going to look at what's been going on, and then we're going to see what's going on. And I think the transformation here is taking technologies and being able to say, how do we put not only predictive analytics inside play, but how do we actually allow the customer to take control and actually move forward? And then, as well, expand those use cases for variation, use that same technology to look for, between the data points, are there more data points that can be actually derived and moved forward on? >> George, I loved that description. You have, in one of your reports, I remember, George had this picture of this boat, and he said, "Oh, imagine trying to drive the boat", and it was looking at the wake (laughs), you know, right? Rather than looking in the rear-view mirror. >> But in addition to that, yeah, it's like driving the rear-view mirror, but you also said something interesting about, sort of, I guess the words I used to use were anticipating and influencing the customer. >> Aaron: Exactly. >> Can you talk about how much of that is done offline, like scoring profiles, and how much of that is done in real-time with the customer? >> Go ahead. >> Well, a lot of it still is still being done offline, mostly because, you know, as trying to serve a bank, you have to also be able to serve their immediate needs. So, really, we're evolving to actually build that use case around the real-time. We actually do have the technology already in place. We built the POCs, we built the technology inside, we're being able to move real-time, and we're ready to go there. >> So, what will be the difference? Me as a consumer, how will that change my experience? >> I think that would probably be best for you. >> Yeah, well, just got to step back a little bit, too, because, you know, what we're representing here is the digital channel mobile analytics, right? But, there's other areas within FIS Global that handles real-time payments with real-time analytics, such as a credit card division, right? So, both are happening sort of in parallel right now. For us, from our perspective on the mobile and digital front, the experience and how that's going to change is that, if you were a bank, and as a bank or a credit union you're receiving this behavioral data from our product, you want to be able to offer up better services that meet your consumer profile, right? And so, from our standpoint, we're working with other teams within FIS Global via Spark and Cloud, to essentially get that holistic profile to offer up those services that are more targeted, that are, I think, more meaningful to the consumer when they're in the mobile banking application. >> So, does FIS provide that sort of data service, that behavioral service, sort of as a turnkey service, or as a service, or is that something that you sort of teach the bank or the credit union how to fish? >> That's a really good question. We stated our mission statement as helping these institutions, creating a culture of being data-driven, right? So, give them the taste of data in a way that, you know, democratizing data, if you will, as we talked about this morning. >> Dave: Yeah, that's right. >> That concept's really important to us, because with that comes, give FIS more data, right? Send them more data, or have them teach us how to manage all this data, to have a data science experience, where we can go in and play with the data to create our own sub-targeting, because our belief is that, you know, our clients know their customers the best, so we're here to serve them with tools to do that. >> So, I want to come back to the role of Spark. I mean, Hadoop was profound, right, I mean, shipped five megabytes of code, a petabyte a day, no doubt about it. But at the same time, it was a heavy lift. It still is a heavy lift. So talk about the role of Spark in terms of catalyzing that vision that we've been talking about. >> Oh, definitely. So, Apache Spark, when we talk in terms of big data, big data got started with Hadoop, and MapReduce was definitely an interesting concept, but Apache Spark really lifted and accelerates the entire vision of big data. When you look at, for example, MapReduce, you need to go get a team of trained engineers, who are typically going to work in a lower level language like Java, and they no longer focus in on what the business objectives are. They're focusing on the programming objectives, the requirements. With Spark, because it takes a more high-level abstraction of how we process data, it means that you're more focusing on, what's the actual business case? How are we actually abstracting the data? How are we moving data? But then it also gives you that same capability to go inside the actual APIs, get a little bit lower, to modify it for what's your specific needs. So, I think the true transformation with Apache Spark is basically allowing us, now, like for example, in the presentation this morning, it was, there's a lot of people who are using Scala. We use Scala, ourselves. There's now a lot of people who are using Python, and everybody's using SQL. How does SQL, something that has survived so robustly for almost 30, 40 years, still keep on coming back like a boomerang on us? And it's because a language composed of four simple keywords is just so easy to use, and so descriptive and declarative, that allows us to actually just concentrate on the business, and I think that's actually the acceleration that Apache Spark actually brings to the business, is being able to just focus in on what you're actually trying to do, and focus in on your objectives, and it actually lowers the actual, that same team of engineers that you're using for MapReduce now become extremely more productive. I mean, when I look at the number of lines of codes that we had to do to figure out machine learning and Hadoop, to the amount of lines that you have to do in Apache Spark, it's tremendously, it's like, five lines in Apache Spark, 30 in MapReduce, and the system just responds and gives it to you a hundred times faster. >> Why Spark, too? I mean, Spark, when we saw it two years ago, to your point of this tidal wave of data, we saw more mobile phone adoption, we saw those people that were on mobile banking using it more, logging in more, and then we're seeing the proliferation of devices, right, in IoT, so for us, these are all these interaction and data points that is a tsunami that's coming our way, so that's when we strategically elected to go Spark, so we could handle the volume and compute storage- >> And Aaron, what you just described is, all the attention used to be on just making it work, and now it's putting to work, is really- >> Aaron: Right, exactly. >> You're seeing that in your businesses. >> Quick question. Do you see, now that you have this, sort of, lower and lower latency analytics and ability to access more of the, what previously were data silos, do you see services that are possible that banks couldn't have thought of before, beyond just making different products recommended at the appropriate moment, are there new things that banks can offer? >> It's interesting. On one hand, you free up their time from an analysis standpoint, to where they could actually start to get out of the weeds to think about new products and services, so, from that component, yes. From the standpoint of seeing pattern recognition in the data, and seeing what it can do aside from target marketing, our products are actually often used by our product owners internally to understand, what are the consumers doing on the device, so that they could actually come up with better services to ultimately serve them, aside from marketing solutions. >> Notwithstanding your political affiliations, we won't go there, but there's certainly a mood of, and a trend toward, deregulation, that's presumably good news for the financial services industry. Can you comment on that, or, what's the narrative going on in your customer base? Are they excited about fewer regulations, or is that just all political nonsense? Any thoughts? >> Yeah (laughs), you know, on one hand, why people come to FIS is because we do adhere to a compliance and regulation standpoint, right? >> Dave: Complexity is your friend, then (laughs). >> Absolutely, right, so they can trust us in that regard, right? And so, from our vantage point, will it go away entirely? No, absolutely not, right. I think Cloud introduces a whole new layer of complexity, because how do you handle Cloud computing and NPI, and PII data in the Cloud, and our customers look to us to make sure that, first and foremost, security for the end consumer is in place, and so, but I think it's an interesting question, and one that you are seeing end users click through without even viewing agreements or whatnot, they just want to get to product, right? So, you know, will it go away, or do we see it going away? No, but ... >> You guys don't read all that text, do you? (laughing) >> No comment? >> Required, required to. >> You know, no matter where it goes with the politics, I think there's a theme over the last 10 years, and the 10 years before. Things are transforming, things are evolving in ways, and sometimes going extremely, extremely fast in ways that we don't, surely can't anticipate. I think, if we were to think about just a mobile application, or the mobile bank experience 10 years ago, all we wanted was just to be able to see just the bank balance, and now we're able to take that same application and not only see our bank balance, but be able to deposit our cheque, or even replace the card in our pocket completely, with the mobile app, and I think we're going to see the exact same types of transformations over the industry over the next 10 years. Whether or not it's more regulation or different regulation, I think it's going to still speak to the same services, which FIS is there to help deliver. >> Yeah, and you're right, there are going to be new regulations, because they'll evolve, maybe out with the old, in with the new, you see, and global regulations are on run book, and you've got your Cloud, there's data locality, and you know, it's never-ending. That's great for your business. Fantastic. >> It comes down to trust, ultimately, right? I mean, they still, our customers still go to banks and credit unions because they trust them with their data, if you will, or their online currency, in some regards. So, you know, that's not going to change. >> Right, yeah. Well, Aaron, Dave, thanks very much for coming to theCUBE, it was great to have you. >> Thanks so much for talking with us. >> Absolutely, good luck with everything. >> Alright, keep it right there, buddy. We'll be back with our next guest. This is theCUBE. We're live from Boston, Spark Summit East, #SparkSummit. Be right back. >> I remember, when I had such a fantastic batting practice-