 Talk of the show has been Hedapt, Hedapt. Won the startup contest last night. There were 10 finalists, Hedapt was chosen as the most innovative. And we're here with Ming Sheng Hong who is the chief data scientist. And Phillip Wickline who's the CTO of Hedapt. And one of the big things that Hedapt has talked about is bringing together the worlds of SQL and NoSQL and also enabling the integration of other tools so that people can actually access these capabilities and bring them to larger audiences. One of those tools being the visualization tools from companies like Tableau and other capabilities. And that's really what Hedapt's been focusing on. So first of all, welcome gentlemen. Oh, thank you. Appreciate you guys coming on. So we've got a demo. So I guess once you guys set up the demo and then we'll get right into it. Great. So while Ming Sheng gets that going, I'll just give you a little bit of an overview. So we've been spending a lot of time working on interactive speed queries, bringing that to the Hadoop world, something that's not really had previously. But also we're really focused on bringing this into the enterprise and connecting to enterprise BI tools and other things in that ecosystem that really matter and make it viable for people there. But what you'll also see during the demo, you'll see us using Tableau which is a great new tool that everyone's using these days. But you'll also see us bringing in other sorts of analyses into that world through our Adaptive Development Kit. And Ming Sheng will talk about that as we go through. So we're putting some advanced capabilities in that feed the analyses, the visualizations that we see. Are you ready to go? Sure. All right, take it away, Ming Sheng. Okay. So let me just describe the setting a little bit. So as Philip described earlier, this is showing some of the new capabilities that we're adding to Hadoop 2.0, released last week. And let me introduce the demo setting for you. So as you see, we have on the front end the Tableau BI setup. But you also see the full back panel at the bottom just to give you the feel that this is lifting the curtain of the demo and see what's behind the scene. So here we have a Hadoop log which is basically showing you all the querying and real-time loading activities we're doing on Hadoop. Then we have a data loading window which is showing you all the incremental data we're loading into Hadoop while doing the querying. So everything is happening at the same time. Then we have what we call Tableau JDBC Listener that really shows you all the SQL queries being generated by Tableau. So in a moment, when I show you activities with Tableau, you will see a lot of queries being generated and processed by Hadoop. And you will appreciate the interactivity there. So these are running native inside your platform, essentially. That's exactly right. So that's being integrated. And for the specific demo setting, this is my mapped laptop. But we're running a single node Linux virtual machine hosting the Hadaap backend and another Windows virtual machine showing Tableau. So definitely the main focus here is not scalability. Of course, based on Hadoop, we have inherited the scalability skills. We have customers running with dozens of terabytes. It's MPP type of scalability. But the point of the demo here is also it's agile. It's nimble. You can show everything within a laptop setting. It doesn't need millions of dollars of hardware infrastructure. At least no one has offered me a million dollars to buy that laptop. So you don't need big iron. No, maybe Larry will buy this for a million dollars. I don't know. So that's the setting. Finally, the last window is actually Mahaut log. So lots of you might know that Mahaut is one of the popular Hadoop-based machine learning engines that we have also integrated with Hadoop. We have integrated with Hadaap. So you don't need to be a big data scientist to leverage this tool. You can actually just click through the Tableau UI to invoke very sophisticated analytics, such as sentiment analysis that I will show you in a moment. So let's get started. So I, as a business analyst, can just refresh the dashboard, pay attention to the dashboard being rendered in real time, just a couple of seconds of time. So now if I want to draw into a specific area, let's say- I just want to jump in there and point out that it was actually a query going back into Hadoop, getting processed and turned around in real time. A lot of people are used to Hadoop as a batch processing system, but we really brought it forward to something that can handle this sort of interactive exploratory work. Yeah. If you were to connect a BI tool with a Hadoop backend based on the batch-oriented system, you have to wait for 10 minutes or even longer. That's not the interactive BI experience people know of. But if you click, draw into one of the market cities that you're analyzing, then we're really rendering the advanced dashboard here. So in this business scenario, I am the business analyst, want to understand the market trending of my energy drink product with my competitors. So I see New York City turning incredibly red a moment ago. So now I can draw into it and understand what's causing the change. So the first graph here showing you, okay, that's the revenue of my product in the recent past and the trend has been stable. So that's not the reason. But on the upper right corner, you see the social bus of my product compared to my competitors. And there's a huge spike with my competitors product. So that enables me to draw into it. So let's say I select this time window and it goes and grabs the real tweets mentioning my competitor's product within that time window. So this is the power of HADAP. You start with high level aggregation. You draw into raw data or the raw bits, the individual tweets, all within one platform. And social bus, what is that? Well, it's not really a standard function available in SQL or MapReduce. This is something that the data scientists or developers can build. And I haven't done the detailed research. My intuition tells me only 10,000 people in the world have the expertise to do it. The beauty of HADAP is once they do it once, right? They package it up as a standard SQL function which can then be exposed to BI tools such as Tableau so that now millions of BI users, the business analysts, can just invoke the sophisticated analysis like social bus, social sentiment by clicking buttons, dragging, dropping things. And this is a critical part of our strategy. We think it's really important to extend the basics of what you can do with SQL analytics with all of the advanced sorts of analytics that people are doing in a Hadoop-like platform. Enable that in a form that more business oriented and SQL oriented users can access. Can you guys talk about, just paint a picture of what it would be like if I did this sort of previous to your announcement, previous to HADAP. What would I have to go through to achieve what you just showed? Sure, so all of these capabilities might be available to you, but you'd probably be cobbling together a whole bunch of different systems. You'd be, to get this buzz analysis that Ming Cheng's talking about, you would have had to custom write that, probably as a MapReduce job, do that as a batch-oriented thing, enrich your data, probably export that data into some external database where you can serve up real-time querying like this. So you'd be pulling all of these systems together, you'd need specialized programming skills, MapReduce type, Java skills, and so forth. We're giving this to you in a package that really is targeted at the enterprise and at the sort of staff and capabilities that they have to make this something they can deploy, get these kinds of value out of rapidly, easily, with their existing tools, with their existing infrastructure. And when will this capability be available in the marketplace? This will be available in our version two, which is slated for Q1 of next year. Excellent. All right, good. Well, thanks very much for taking the time to show us that demo. Congratulations on your victory last night, and hopefully many more victories in the marketplace. All right, thank you very much. Pleasure. Okay, keep it right there. We'll be back with our next guest. We're live from the Strata, plus a Duke World Conference in New York City. This is theCUBE. I'm Dave Vellante. We'll be right back.