>> Narrator: Live from San Jose in the heart of Silicon Valley, it's the CUBE covering DataWorks Summit 2017 brought to you by Hortonworks. >> Hey, welcome back to the CUBE live from the DataWorks Summit day 2. We've been here for a day and a half talking with fantastic leaders and innovators, learning a lot about what's happening in the world of big data, the convergence with Internet of Things Machine Learning, artificial intelligence, I could go on and on. I'm Lisa Martin, my co-host is George Gilbert and we are joined by a couple of guys, one is a Cube alumni, Itamar Ankorion, CMO of Attunity, Welcome back to the Cube. >> Thank you very much, good to be here, thank you Lisa and George. >> Lisa: Great to have you. >> And Arvind Rajagopalan, the Director of Technology Services for Verizon, welcome to the Cube. >> Thank you. >> So we were chatting before we went on, and Verizon, you're actually going to be presenting tomorrow, at the DataWorks summit, tell us about building... the journey that Verizon has been on building a Data Lake. >> Oh, Verizon is over the last 20 years, has been a large corporation, made up of a lot of different acquisitions and mergers, and that's how it was formed in 20 years back, and as we've gone through the journey of the mergers and the acquisitions over the years, we had data from different companies come together and form a lot of different data silos. So the reason we kind of started looking at this, is when our CFO started asking questions around... Being able to answer One Verizon questions, it's as simple as having Days Payable, or Working Capital Analysis across all the lines of businesses. And since we have a three-major-ERP footprint, it is extremely hard to get that data out, and there was a lot of manual data prep activities that was going into bringing together those One Verizon views. So that's really what was the catalyst to get the journey started for us. >> And it was driven by your CFO, you said? >> Arvind: That's right. >> Ah, very interesting, okay. So what are some of the things that people are going to hear tomorrow from your breakout session? >> Arvind: I'm sorry, say that again? >> Sorry, what are some of the things that the people, the attendees from your breakout session, are going to learn about the steps and the journey? >> So I'm going to primarily be talking about the challenges that we ran into, and share some around that, and also talk about some of the factors, such as the catalysts and what drew us to sort of moving in that direction, as well as getting to some architectural components, from high-level standpoint, talk about certain partners that we work with, the choices we made from an architecture perspective and the tools, as well as to kind of close the loop on, user adoption and what users are seeing in terms of business value, as we start centralizing all of the data at Verizon from a backoff as Finance and Supply Chains standpoint. So that's kind of what I'm looking at talking tomorrow. >> Arvind, it's interesting to hear you talk about sort of collecting data from essentially backoff as operational systems in a Data Lake. Were there... I assume that the state is sort of more refined and easily structured than the typical stories we hear about Data Lakes. Were there challenges in making it available for exploration and visualization, or were all the early-use cases really just Production Reporting? >> So standard reporting across the ERP systems is very mature and those capabilities are there, but then you look at across-ERP systems and we have three major ERP systems for each of the lines of businesses, when you want to look at combining all of the data, it's very hard, and to add to that, you pointed on self-service discovery, and visualization across all three datas, that's even more challenging, because it takes a lot of heavy lift, to normalize all of the data and bring it into one centralized platform, and we started off the journey with Oracle, and then we had SAP HANA, we were trying to bring all the data together, but then we were looking at systems in our non-SAP ERP systems and bringing that data into a SAP-kind of footprint, one, the cost was tremendously high, also there was a lot of heavy lift and challenges in terms of manually having to normalize the data and bring it into the same kind of data models. And even after all of that was done, it was not very self-service oriented for our users and Finance and Supply Chain. >> Let me drill into two of those things. So it sounds like the ETL process of converting it into a consumable format was very complex, and then it sounds like also, the discoverability, like where a tool, perhaps like Elation, might help, which is very, very immature right now, or maybe not immature, it's still young. Is that what was missing, or why was the ETL process so much more heavyweight than with a traditional data warehouse? >> The ETL processes, there's a lot of heavy lifting there involved, because of the proprietary data structures of the ERP systems, especially SAP is... The data structures and how the data is used across clustered and pool tables, is very proprietary. And on top of that, bringing the data formats and structures from a PeopleSoft ERP system which are supporting different lines of businesses, so there are a lot of customization that's gone into place, there are specific things that we use in the ERPs, in terms of the modules and how the processes are modeled in each of the lines of businesses, complicates things a lot. And then you try and bring all these three different ERPs, and the nuances that they have over the years, try and bring them together, it actually makes it very complex. >> So tell us then, help us understand, how the Data Lake made that easier. Was it because you didn't have to do all the refinement before it got there. And tell us how Attunity helped make that possible. >> Oh absolutely, so I think that's one of the big things, why we picked the Hortonworks as one of our key partners in terms of buidling out the Data Lake, it just came on greed, you aren't necessarily worried about doing a whole lot of ETL before you bring the data in, and it also provides with the tools and the technologies from a lot other partners. We have a lot of maturity now, better provided self-service discovery capabilities for ad hoc analysis and reporting. So this is helpful to the users because now they don't have to wait for prolonged IT development cycles to model the data, do the ETL and build reports for the to consume, which sometimes could take weeks and months. Now in a matter of days, they're able to see the data they're looking for and they're able to start the analysis, and once they start the analysis and the data is accessible, it's a matter of minutes and seconds looking at the different tools, how they want to look at it, how they want to model it, so it's actually being a huge value from the perspective of the users and what they're looking to do. >> Speaking of value, one of the things that was kind of thematic yesterday, we see enterprises are now embracing big data, they're embracing Hadoop, it's got to coexist within our ecosystem, and it's got to inter-operate, but just putting data in a Data Lake or Hadoop, that's not the value there, it's being able to analyze that data in motion, at rest, structured, unstructured, and start being able to glean or take actionable insights. From your CFO's perspective, where are you know of answering some of the questions that he or she had, from an insights perspective, with the Data Lake that you have in place? >> Yeah, before I address that, I wanted to quickly touch upon and wrap up George's question, if you don't mind. Because one of the key challenges, and I do talk about how Attunity helped. I was just about to answer the question before we moved on, so I just want to close the loop on that a little bit. So in terms of bringing the data in, the data acquisition or ingestion is key aspect of it, and again, looking at the proprietary data structures from the ERP systems is very complex, and involves a multi-step process to bring the data into a strange environment, and be able to put it in the swamp bring it into the Lake. And what Attunity has been able to help us with is, it has the intelligence to look at and understand the proprietary data structures of the ERPs, and it is able to bring all the data from the ERP source systems directly into Hadoop, without any stops, or staging data bases along the way. So it's been a huge value from that standpoint, I'll get into more details around that. And to answer your question, around how it's helping from a CFO standpoint, and the users in Finance, as I said, now all the data is available in one place, so it's very easy for them to consume the data, and be able to do ad hoc analysis. So if somebody's looking to, like I said earlier, want to look at and calculate base table, as an example, or they want to look at working capital, we are actually moving data using Attunity, CDC replicate product, we're getting data in real-time, into the Data Lake. So now they're able to turn things around, and do that kind of analysis in a matter of hours, versus overnight or in a matter of days, which was the previous environment. >> And that was kind of one of the things this morning, is it's really about speed, right? It's how fast can you move and it sounds like together with Attunity, Verizon is really not only making things simpler, as you talked about in this kind of model that you have, with different ERP systems, but you're also really able to get information into the right hands much, much faster. >> Absolutely, that's the beauty of the near real-time, and the CDC architecture, we're able to get data in, very easily and quickly, and Attunity also provides a lot of visibility as the data is in flight, we're able to see what's happening in the source system, how many packets are flowing through, and to a point, my developers are so excited to work with a product, because they don't have to worry about the changes happening in the source systems in terms of DDL and those changes are automatically understood by the product and pushed to the destination of Hadoop. So it's been a game-changer, because we have not had any downtime, because when there are things changing on the source system side, historically we had to take downtime, to change those configurations and the scripts, and publish it across environments, so that's been huge from that standpoint as well. >> Absolutely. >> Itamar, maybe, help us understand where Attunity can... It sounds like there's greatly reduced latency in the pipeline between the operational systems and the analytic system, but it also sounds like you still need to essentially reformat the data, so that it's consumable. So it sounds like there's an ETL pipeline that's just much, much faster, but at the same time, when it's like, replicate, it sounds like that goes without transformations. So help us sort of understand that nuance. >> Yeah, that's a great question, George. And indeed in the past few years, customers have been focused predominantly on getting the data to the Lake. I actually think it's one of the changes in the fame, we're hearing here in the show and the last few months is, how do we move to start using the data, the great applications on the data. So we're kind of moving to the next step, in the last few years we focused a lot on innovating and creating the solutions that facilitate and accelerate the process of getting data to the Lake, from a large scope of systems, including complex ones like SAP, and also making the process of doing that easier, providing real-time data that can both feed streaming architectures as well as batch ones. So once we got that covered, to your question, is what happens next, and one of the things we found, I think Verizon is also looking at it now and are being concomitant later. What we're seeing is, when you bring data in, and you want to adopt the streaming, or a continuous incremental type of data ingestion process, you're inherently building an architecture that takes what was originally a database, but you're kind of, in a sense, breaking it apart to partitions, as you're loading it over time. So when you land the data, and Arvind was referring to a swamp, or some customers refer to it as a landing zone, you bring the data into your Lake environment, but at the first stage that data is not structured, to your point, George, in a manner that's easily consumable. Alright, so the next step is, how do we facilitate the next step of the process, which today is still very manual-driven, has custom development and dealing with complex structures. So we actually are very excited, we've introduced, in the show here, we announced a new product by Attunity, Compose for Hive, which extends our Data Lake solutions, and what Compose of Hive is exactly designed to do, is address part of the problem you just described, where's when the data comes in and is partitioned, what Compose for Hive does, is it reassembles these partitions, and it then creates analytic-ready data sets, back in Hive, so it can create operational data stores, it can create historical data stores, so then the data becomes formatted, in a matter that's more easily accessible for users, who want to use analytic tools, VI-tools, Tableau, Qlik, any type of tool that can easily access a database. >> Would there be, as a next step, whether led by Verizon's requirements or Attunity's anticipation of broader customer requirements, something where, there's a, if not near real-time, but a very low latency landing and transformation, so that data that is time-sensitive can join the historical data. >> Absolutely, absolutely. So what we've done, is focus on real-time availability of data. So when we feed the data into the Data Lake, we fit it into ways, one is directly into Hive, but we also go through a streaming architecture, like Kafka, in the case of Hortonworks, can also fit also very well into HDF. So then the next step in the process, is producing those analytic data sets, or data source, out of it, which we enable, and what we do is design it together with our partners, with our inner customers. So again when we work on Replicate, then we worked on Compose, we worked very close with Fortune companies trying to deal with these challenges, so we can design a product. In the case of Compose for Hive for example, we have done a lot of collaboration, at a product engineering level, with Hortonworks, to leverage the latest and greatest in Hive 2.2, Hive LLAP, to be able to push down transformations, so those can be done faster, including real-time, so those datasets can be updated on a frequent basis. >> You talked about kind of customer requirements, either those specific or not, obviously talking to telecommunications company, are you seeing, Itamar, from Attunity's perspective, more of this need to... Alright, the data's in the Lake, or first it comes to the swamp, now it's in the Lake, to start partitioning it, are you seeing this need driven in specific industries, or is this really pretty horizontal? >> That's a good question and this is definitely a horizontal need, it's part of the infrastructure needs, so Verizon is a great customer, and we even worked similarly in telecommunications, we've been working with other customers in other industries, from manufacturing, to retail, to health care, to automotive and others, and in all of those cases it's on a foundation level, it's very similar architectural challenges. You need to ingest the data, you want to do it fast, you want to do it incrementally or continuously, even if you're loading directly into Hadoop. Naturally, when you're loading the data through a Kafka, or streaming architecture, it's a continuous fashon, and then you partition the data. So the partitioning of the data is kind of inherent to the architecture, and then you need to help deal with the data, for the next step in the process. And we're doing it both with Compose for Hive, but also for customers using streaming architectures like Kafka, we provide the mechanisms, from supporting or facilitating things like schema unpollution, and schema decoding, to be able to facilitate the downstream process of processing those partitions of data, so we can make the data available, that works both for analytics and streaming analytics, as well as for scenarios like microservices, where the way in which you partition the data or deliver the data, allows each microservice to pick up on the data it needs, from the relevant partition. >> Well guys, this has been a really informative conversation. Congratulations, Itamar, on the new announcement that you guys made today. >> Thank you very much. >> Lisa: Arvin, great to hear the use case and how Verizon really sounds quite pioneering in what you're doing, wish you continued success there, we look forward to hearing what's next for Verizon, we want to thank you for watching the CUBE, we are again live, day two, of the DataWorks summit, #DWS17, before me my co-host George Gilbert, I am Lisa Martin, stick around, we'll be right back. (relaxed techno music)