 Live, from Midtown Manhattan, it's theCUBE. Covering Big Data, New York City, 2017. Brought to you by SiliconANGLE Media and it's ecosystem sponsors. Welcome back everyone. Live here in New York, day three coverage winding down for three days of wall-to-wall coverage. CUBE covering Big Data NYC in conjunction with Strata Data, formerly Strata Hadoop and Hadoop World, all part of the Big Data ecosystem. Our next guest is Nishad Bardo-Liwala, co-founder and Chief Product Officer of Pexata. Hot startup in the space, you're getting a lot of kudos, congratulations. Of course, they launched theCUBE in 2013, three years ago when we started theCUBE as a separate event for Morioli. So great to see that success. And Stephanie McReynel's been on multiple times, VP of Marketing, and Elation. Welcome back, good to see you guys. Thank you. So we're winding down, so great kind of wrap-up segment here in addition to the partnership that you guys have. So let's first talk about, before we get to the wrap-up of the show and kind of bring together the week here and kind of summarize everything. So what your partnership you guys have. Pexata, you guys have been doing extremely well. Congratulations, Prakash was talking on theCUBE. Great success, you guys work hard for it. I'm happy for you. But partnering is everything. Ecosystem is everything. Elation, their collaboration with Data, that's their ethos, they're very user-centric. Yes. From the founders. Seemed like a good fit, what's their deal? It's a very natural fit between the two companies. When we started down the path of building new information management capabilities, it became very clear that the market had a strong need for both finding data, right? What do I actually have? I need an inventory, especially if my data's an Amazon S3, my data's an Azure Blob Storage, my data's on-premise and HDFS, my data's in databases, it's all over the place. And I need to be able to find it. And then once I find it, I want to be able to prepare it. And so one of the things that really drove this partnership was the very common interests that both companies have in number one, pushing user experience. I love the Elation product. It's very easy to use, it's very intuitive. Really, it's a delightful thing to work with. And at the same time, they also share our interest in working in these hybrid, multi-cloud environments. So what we've done and what we announced here at Strata is actually this bi-directional integration between the products. You can start in Elation and find a data set that you want to work with, see what collaboration or notes or business metadata people have created, and then say, I want to go see this in Paxata. And in a single click, you can actually then open it up in Paxata and profile that data. Vice versa, you can also be in Paxata and prepare data. And then with a single click, push it back. And then everybody who works with Elation actually now has knowledge of where that data is. So it's a really nice synergy. So you push the user data back to Elation because that's what they care a lot about, the cataloging and making the user-centric view work. Does it make it there? So you provide kind of, it's almost a flow back and forth. It's a handshake, if you will, with the data. Is that right? Yeah, I mean, the idea is to keep the analyst or the user of that data, data scientist, even in some cases a business user, keep them in the flow of their work as much as possible. But give them the advantage of understanding what others in the organization have done with that data prior, and then allow them to transform it and then share that knowledge back with the rest of the community that might be working with that data. So give me an example, because I got to just, I like your Excel spreadsheet concept because that's obvious, right? People know what Excel spreadsheet is, so it's Excel-like, that's an easy tam to go after all Microsoft users, I get to ensure a thing. With this one, just take me through a use case. I've got a good example. Okay, take me through. It's very common in a data lake for your data to be compressed. And when data is compressed to an end user, it looks like a black box. So if the data is compressed in Avro or Parquet or it's even like JSON format, a business user has no idea what's in that file. So what we do is we find the file for them. It may have some comments on that file of how that data's been used in past projects that we infer from looking at how others have used that data in Elation. So you put metadata around it. We put a whole bunch of metadata around it. It might be comments that people have made. It might be actual observations, annotations. And the great thing that we can do with Paxata is open that Avro file or Parquet file, open it up so that you can actually see the data elements themselves. So all of a sudden a business user has access without having to use a command line utility or understand anything about compression and how you open that file up. So as Paxata is spitting out their nuggets of value back to you, you're kind of understanding it, translating it to the user. And they get to do their thing. You get to do your thing. Is it making an Avro or a Parquet file? It's easy to use as Excel, basically. Which is great, right? Now you've enabled a whole new class of people to use that. Well, when people just get turned off when it's anything like jargon or like, oh, what is that? I'm afraid it's a phishing. It's like click on the, you know. The scary thing is that in a data lake environment, in a lot of cases, people don't even label the files with extensions, right? They're just files. So what's started. It's like getting your pictures like DS, JPEG is like, what? Exactly. If you looked at your laptop and you didn't have JPEG or DOC or PPT, okay, I don't know what this file is. Well, what you have in the data lake environment is that you have thousands of these files that people don't really know what they are. And so with Elation, we have the ability to get all the value around the curation of the metadata and how people are using that data. But then somebody says, okay, but I understand that this file exists. What's in it, right? And then with click to profile, from Elation, you're immediately taken into Paxata and now you're actually looking at what's in that file. So you can very quickly go from, this looks interesting, to let me understand what's inside of it. And that's very powerful. Talk about Elation, because I had the CEO on, also their lead investor, Greg Sands, from Costin Noe Ventures. They're a pretty amazing team, but it's kind of out there, and no offense, it's kind of a compliment actually. They had a symbolic system, Stanford guy, right? He was like, the Predaprox, super smart. They're on something that's really unique, but it's almost too simple to be. Like, wait a minute, Google for the data, it's an awesome opportunity. How do you describe Elation to people who say, hey, what's this Elation thing? Yeah, so I think that the best way to describe it is it's the browser for all of the distributed data in the enterprise. So it's both, sorry, it's both the catalog and the browser that sits on top of it. So it sounds very simple, conceptually it's very simple, but they have a lot of richness in what they're able to do behind the scenes in terms of introspecting what type of work people are doing with data and then taking that knowledge and actually surfacing it to the end user. So for example, they have very powerful scenarios where they can watch what people are doing in different data sources and then based on that information, actually bubble up how queries are being used or the different patterns that people are doing to consume data with. So what we find really exciting is that this is something that is very complex under the covers, which Paxata is as well being built upon Spark, but they have put in the hard engineering work so that it looks simple to the end user and that's the exact same thing that we've tried to do. And that's the hard problem. Okay, Stephanie, back to, that was a great example, by the way. Can't wait to have our little analyst break down on the event, but back to Elation for you. So how do you talk about, you've been in the VP marketing of Elation, but you've been around the block, you know B2B tech big data. So you've seen a bunch of different, you were to trifecta, you were to other companies and you've seen a lot of waves of innovation come. What's different about Elation that people may not know about? How do you describe the differences? Because it sounds easy, oh, it's the browser, it's the catalog, but it's really hard. Is that the tech that's the secret? Is it the approach? How do you describe the value of Elation? I think an interesting about Elation is that we're solving a problem that since the dawn of the data warehouse has not been solved. And that is how to help end users really find and understand the data that they need to do their jobs. A lot of our customers talk about this. That's just, hold on, repeat that, because that's like a key thing. What problem hasn't been solved since the data warehouse? To be able to actually like find and fully understand, understand at the point of trust the data that you want to use for your analysis. And so, you know, in the world of, in the world of data warehousing. Why was it so hard? Well, because in the world of data warehousing, business people were told what data they should use. Someone in IT decided how to model the data, came up with a KPR calculation and told you as a business person, you as a CEO, this is how you're going to monitor your business. What business person wants to be told that by an IT guy? Well, it was bounded, it was bounded by IT. I mean, expression and discovery should be unbounded. Machine learning can take care of a lot of bounded stuff, I get that, but like, when you start to get into the discovery side of it, it should be- Well, no offense to the IT team, but they were doing their best to try to figure out how to make this technology work. Let me just look at the cost of good sold for storage. I mean, how many EMC drives was just expensive? IT was not cheap. They've been 10, 15, 20 years ago. So now when we have more self-service access to data and we can have more exploratory analysis, what data science really introduced and Hadoop introduced was this ability on demand to be able to create these structures. You have this more iterative world of how you can discover and explore data sets to come to an insight. The only challenges without simplifying that process, a business person is still lost, right? It's still lost in the data. So we simply call that a catalog, but a catalog is much more than- Indexed catalog, anthology, there's other words for it, right? Yeah, but I think it's interesting because a concept of a catalog as an inventory has been around forever in this space, but the concept of a catalog that learns from others' behavior without data, this concept of behavior I.O. that Aaron talked about earlier today, the fact that behavior of how people query data is an input and that input then informs a recommendation as an output is very powerful and that's where all the machine learning and AI comes to work. It's hidden underneath that concept of behavior I.O., but that's the real innovation that drives this rich catalog is how can we make active recommendations to a business person who doesn't have to understand the technology but they know how to apply that data to making a decision? Yeah, I mean that's key. Behavioral and contextual information has always been the two, you know, flywheels and analysis, whether you're talking search engine or you know data in general. Add recommendation. And I think what I like about the trends here at the Big Data NYC this week and we've been certainly seeing at the, you know, hundreds of CUBE events we've gone through over the past 12 months and more is that people are using data differently and that's a, and not only differently, this is baselining foundational things that I do, but the real innovators have a twist on it that give them an advantage, right? They see how they could use data and the trend is collective intelligence of the customer seems to be big. You guys are doing it, you're seeing patterns, you're automating the data. So it seems to be this flywheel of some data, get some collective data, what's your thoughts and reactions? Are people getting it? Just by people doing it by accident on purpose, kind of thing. People just fell on their head or they just, you see like, oh, I've just backed into this. I think that the companies that have emerged as the leaders in the last 15 or 20 years, Google being a great example, Amazon being a great example, these are companies whose entire business models were based on data. They've generated outsized returns, they are the leaders on the stock market. And I think that many companies have awoken to the fact that data as a monetizable asset to be turned into information either for analysis, to be turned into information for generating new products that can then be resold on the market. The leading edge companies have figured that out and are adopting technologies like Alehsha and like Paxata to get a competitive advantage in the business processes where they know they can make a difference inside of the enterprise. So I don't think it's a fluke at all. I think that most of these companies are being forced to go down that path because they have been shown the way in terms of the digital giants that are currently sort of ruling the enterprise tech world. I watched your thoughts on the week this week so far on the big trends. What are obvious, obviously AI, I don't need to talk about AI, but what were the big things that came out of it and what surprised you that didn't come out from a trend standpoint? Buzz, here it's just strata data and big data NYC. What were the big themes that you saw emerge and didn't emerge? What's the surprise? Any surprises? I mean, I think we're seeing in general the maturation of the market finally. People are finally realizing that, hey, it's not just about cool technology, it's not about what distribution or package it's about, can you actually drive return on investment? Can you actually drive insights and results from the stack? And so I think even the technologists that we were talking with today throughout the course of the show are starting to talk about it's that last mile of making the humans more intelligent about navigating this data where all the breakthroughs are gonna happen even in places like IoT, where you think about a lot of automation and you think about a lot of capability to use deep learning to maybe make some decisions. There's still a lot of human training that goes into that decision-making process and having agency at the edge. And so I think this acknowledgement that there should be balance between human input and what the technology can do is a nice breakthrough that's gonna help us get to the next level. What's missing? What do you see that people missed? That is super important that wasn't talked much about. Is there anything that jumps out at you? I'll let you think about it unless you have something to know. Yeah, I would say I completely agree with what Stephanie said, which is we are seeing the market mature and there is a compelling force to now justify business value for all the investments that people have made, right? The science experiment phase of the big data world is over. People now have to show a return on that investment. I think that being said though, this is my sort of way of being a little more provocative. I still think there's way too much emphasis on data science and not enough emphasis on the average business analyst who's doing work and the portion of the portion of the platform. It's kind of the same, it should be kind of the same thing. The data science should be just more an advanced analyst maybe. Right. But the idea that every person who works with data is suddenly going to understand different types of machine learning models and what's the right way to do hyperparameter tuning in other words that I could throw at you to show that I'm smart. Yes, I mean I think that's, you guys have a vision of the Excel and I can see how you can see that perspective because you see a future, I just think we're not there yet because I think the data science are still handcuffed and hamstrung by the fact that they're doing too much provisioning work. Right, I mean to your point about surfacing the insights, it's like the data science, oh you own it now, they become the CIS admin if you will for their department. And it's like, that's not their job. We need to get them out of data preparation. Yeah, get out of that. Right, you shouldn't be, you need to get data scientists and colleges to do that. You have two values, you have the user interface value which I love but you guys do the automation. So I think we're getting there. I see where you're coming from but still those data sciences have to set the tone for the generation, right? So it's kind of like, you got to get those guys productive. And it's not a, please go ahead. I mean it's somewhat interesting if you look at can the data scientists start to collaborate a little bit more with a common business person, you start to think about it as a little bit of like a scientific inquiry process, right? If you can have more innovators around the table and a common place to discuss what are the insights in this data and people bringing business perspective together with machine learning perspective or the knowledge of the higher end algorithms then maybe you can bring those next leaps forward. Great insight, my observation and I use crazy analogies, here's my crazy analogy. Years it's been about the engine of the Model T, the car and then we're going to have some horse and buggy. Now we got an engine in the car and it got wheels, it's got a chassis. And so it's been, it's about the apparatus of the car and then it evolves to the, hey this thing actually drives, it's a transportation, right? And you can actually go from A to B faster than the other guys and then people still think there's a horse and buggy market out there so that's, they got to go that. But now people are crashing. Now there's an art to driving the car, right? So whether you're a sports car or whatever, this is where the value piece I think hits home is that people are driving the data now, they're driving the value proposition. So I think that to me the big surprise here is how people aren't getting into the hype cycle. They love the hype in terms of like lead gen and AI but they're too busy for the hype. It's like I drive the value and this is not just BS either outcomes, it's like I'm busy, I got security, I got app development. And I think they're getting smarter about how they're valuing data. We're starting to see some economic models and some ways of putting actual numbers on what impact is this data having today. We do a lot of usage analysis with our customers and looking at, you know, they have a goal to distribute data across more of the organization and really get people using it in a self-service manner. And from that you're being able to calculate what actually is the impact. We're not just storing this for insurance policy reasons in this cheap. It's not some POC, don't do a POC. All right, so we're going to end the day in the segment on you guys in the last word. I want to just phrase it this way. Share an anecdotal story that you've heard from a customer or a prospective customer that looked at your product, not the joint product but your products each that blew you away and that would be a good thing to leave for people who doesn't buy it. But what was the coolest or nicest thing you heard someone say about Elation and Paxata? I mean, for me, the coolest thing they said was this is a social network for nerds. I finally feel like I found my home. Ha ha ha ha ha ha. Data nerds, okay? Data nerds. So if you're a data nerd, you know, you want a network. Elation is the place you want to be. So there is like profiles and like you guys have a profile for everybody who comes in and things like that. Yeah, so the interesting thing is part of our automation, when we go and we index the data sources, we also index the people that are accessing those sources. So you kind of have a leaderboard now of data users that can track one another in the system. And at eBay, the leader was this guy Caleb, who was their data scientist. And Caleb was famous because everyone in the organization would ask Caleb to prepare data for them. And Caleb was like well-known if you were around eBay for a while. Yeah, he was the master of the domain. Then when we turned on, you know, we were indexing tables on Teradata as well as their Hadoop implementation. And all of a sudden there are table structures that are Caleb underscore cust. Caleb underscore revenue. Caleb underscore, we're like wow. Caleb drove a lot of Teradata revenue. Awesome. Pexata, what was the coolest thing someone said about you in terms of being the nicest or coolest most relevant thing? So something that a prospect said earlier this week is that I've been hearing in our personal lives about self-driving cars, but seeing your product and where you're going with it, I see the path towards self-driving data, right? And that's really what we need to aspire towards. It's not about spending hours doing prep. It's not about spending hours doing manual inventories. It's about getting to the point that you can automate the usage to get to the outcomes that people are looking for. So I'm looking forward to self-driving information. Thank you so much, Stephanie, for the relation. Thanks so much. Congratulations on both of your success and great to see you guys partnering big, big community here. And this is the beginning. We see the big waves coming. So thanks for sharing perspective. Thank you very much. Your color commentary on our wrap up segment here for Big Data NYC. This is theCUBE live from New York, wrapping up great three days of coverage here in Manhattan. I'm John Furrier. Thanks for watching. See you next time.