>> Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Welcome back here at AWS re:Invent. Day three of our coverage here on theCUBE. We have been here since Tuesday, bringing you all kind of sights and insights from the show floor here. Some 40 guests that we've had on this set alone. Have a person that's actually four sets around here. There's a lot of content to capture. A lot of excitement in the air. And I'm John, that's Rebecca. I don't have to tell you that, you know that. We're joined by Ankit Khandelwal, who's the Senior Director of Engineering to Kyvos Insights. Good to see you, Ankit. >> Thank you, good to be here. >> And Ajay Anand, who's the Vice President of Products and Marketing at Kyvos as well. Thank you for joining us gentlemen. We appreciate the time. >> It's good to be back with you. >> All right, so share a little bit, just for folks at home who are watching and may not be familiar with Kyvos. I doubt there are many. (Rebecca laughing) But just in case, share with us a little bit, and with them, your core mission. >> Yeah, so what Kyvos does is we deliver the capability of doing instant business intelligence on data at a massive scale, either on-premises or in the cloud. So, one of the big problems people have is when they're trying to connect from their BI tools to huge amounts of data, it takes a long time for the data to come back into the tool. As they are dragging and dropping, they don't get that interactive response. So we solve that by building a BI consumption layer on top of the big data. And what that enables you to do is, you know, once we've pre-processed that data and built multi-dimensional cubes, then you can get that interactive response time, right. So the core technology is OLAP, which has been around for a long time. But what we do is we make OLAP scale to huge amounts of data and really take advantage of the capabilities of the cloud, or big clusters, and on-premises environments, and really scale out with the cloud. >> Can you give us some examples of who your customers are and the kind of specific problems you're solving for them? >> Sure, some of our customers have spoken publicly about us, so I can share what they said. Walgreens spoke about us at the Tableau Conference just a couple of weeks ago. And they're solving problems that they had never imagined they'd be able to solve before. Dealing with hundreds of billions of rows of data and getting instant responses. And these customers are building multi-dimensional cubes at a scale that's never been done before. 100 terabyte cubes. Walgreens is an example of that. Verizon has spoken about us at other conferences as well. >> Ankit, I'd like to know what your take is on, as we were just talking about, the volume that you're dealing with here. Like never before. How do you help your customers figure out what matters? What's important and what's not, because most, or I shouldn't say, much of what they generate really doesn't matter, and yet there are some valuable nuggets in there that they are still trying to extract and then analyze appropriately. So how do you help them with that job? >> Yeah, so you know what happens is organizations and enterprises keep getting more and more data. They take it to a data lake. Now, the data on the ground wasn't enough, and now you have other services which helps you get the data from even space. Andy announced that you can get data from satellite. So all this data. Now once that data reaches the data lake, the next challenge that comes to, or in front of a business user is, how do you really get the ROI out of it? Now when I say ROI, basically know I am talking about ROI of data. And the ROI of data actually improves, comes only when, the data goes in the hands of the business user. So that's the times Kyvos comes into the picture because you want your data and you want your business users to analyze it. It has to be super fast and that's what Kyvos does, number one, and number two, the business users want their data to see in a way that they want. So basically, Kyvos helps you to actually define a semantic layer, put a business view on top of your data. So that a business user actually sees the data the way they want. So those are the things that Kyvos provides and helps the business user to actually get the insights out of the data. >> So this week at AWS, you launched Version 5. Tell our viewers a little bit more about what Version 5 entails, some of the capacities. >> Right, so one big thing is the capability to do Elastic OLAP in the cloud. So the OLAP capability being able to really leverage the infrastructure cost-effectively, scale out to deal with big loads and scale it down as you're building these multi-dimensional cubes. So really being able to deal with the infrastructure cost-effectively and deal with massive amounts of data as you're building these cubes. So you can decide, I want to build a 100 terabyte cube and just spin up the right amount of infrastructure that you need to build that cube and then shrink it down. So that elastic capability both for cube building as well as querying. At Walgreens, they talk about dealing with hundreds and thousands of users both internal and external all connecting to this data using Tableau or some other BI tool, and being able to deliver that instant response to them. So having that elastic capability is the new capability we're offering. >> I think the point is, as Andy was talking about in his yesterday's keynote, if you can do it fast then why not do it fast? I think that's where cloud comes into the picture. That with our Kyvos 5 release, once you set up your Kyvos on the cloud, it could actually use that scalability or the elasticity of the cloud for its benefit and for the benefit of the customer. As the load increases, is that the complexity increases. We could actually scale out and deliver the performance that we promised to deliver. And then once the load actually reduces then we could again reduces the resources that we're consuming and that's how we actually reduce the cost that is borne by the customer. So essentially, that is again, you're now giving them better ROI on the hardware that they're investing on. >> So how do you pump the breaks a little bit on the speed? I mean, in terms of making sure that you're in control? Because speed's one thing, right, very important to have, but we need reliability, you need accuracy, latency is not as much of an issue, but how do you, pump the brakes might not be the right description, but how do you ensure that speed is not an inhibitor and it's actually a facilitator? >> There's a whole bunch of enterprise capabilities that we have to provide. Dealing with the resilience so that it's always available to their business users. Dealing with concurrency as you really scale out with the large numbers of users. Dealing with security, right. So as I mentioned, at Walgreens they've got external users as well as internal users, all accessing the same cube, and they all need to see only what they're allowed to see, right. So we maintain that security, right from the user to the data, and we keep track of who's allowed to see what and expose only that. So all of those capabilities are built into the product. >> And as an engineer, I can actually say that again I would take the code from Warner this morning that, hey, you really architecture it well. So architected the product right from the beginning to not only deliver the performance but also to be scalable, deliver performance at a scale. To be secure and then in order to be reliable, fault ordering. So those things are inherently built into the product but then putting a patch on top of the product. >> We're hearing so much at this conference that many enterprises have really had the ah-ha moment. I need to go to the cloud. The security, the governance, those concerns are really falling by the wayside. So what's next? I mean, now that we have so many companies migrating, where do we go from here? >> I think, what we are seeing is a lot of companies are still in the process of migrating. So they've had on-premises infrastructures. Now they're moving to a hybrid cloud and then moving to potentially everything in the cloud. So delivering a seamless experience to the business user is extremely important. Business users shouldn't have to care whether the data is on premises or in a hybrid cloud or in the cloud itself. They should get that same interactive response, the same familiar user interface, and that's what our BI layer provides. By delivering that consumption layer that sits the same way on premises as it was in the cloud. It's a completely seamless experience for the user. >> And I think the performance or the skills still presents a problem. The thing is, how can you make it easy to use for the user? How can I make it smarter? So I think that's where we are going towards with our latest releases, with Kyvos 5. We're bring certain capabilities into the product so that the user doesn't have to bother about how do you really create that semantic layer. The product is smart enough to tell there what should be included in there and what to leave out of it. So smartness is one area which we are moving towards so that we can help the business user to get the performance at a scale with a lot of ease of use. >> I assume you guys have been here for a day or two, correct? >> Yes. >> Right, you met with a lot of customers. I again would assume, right? >> Right. >> So what is your take-away going to be from those direct conversations you've had here in terms of what you take back to Kyvos and maybe start putting into practice? What are you hearing about, this is my next roadblock, this is my next barrier, this is what I'm going to come to you to help me fix. >> We heard Andy's talk this morning or was it maybe Warner. >> Yesterday, Warner this morning, yeah. >> So Warner's talk where they talk about, 95% of what goes into AWS comes from feedback from their customers, and that's true with us to a large extent. We learn from our customers, as they deploy these cubes and their environments, but what's important to them. What are the critical areas that we need to overcome. Really understanding their business use cases and making sure that we build that smartness into the product so we can see what kind of intelligence are they looking to gather, what kind of analysis are they looking to do. And then we use that to build the smartness into the cube. So that the user doesn't need to figure this out themselves. So that's one of the new capabilities that we are providing and we're continuing to work on, is to build more and more smartness into the product. So it helps the user go where they want to go. >> And I think as we go to cloud, specifically AWS, how can we really use the services required by the cloud and then how can we really provide a layer of extraction on top of what is already there, so that then it becomes really easy for the user to use whatever we are providing. >> Right. >> Great. Yeah, just, and I don't want to convolute this with things that I don't need and time and effort. It's all about money at the end of the day, right? Save me money, save me time. >> Well, it's not just saving money but really the topline benefit, right. So expanding the business opportunity. So, we've got a bank that's doing risk analysis as they look for new investments. It used to take them days to do that risk analysis before they could make a decision. Now they can do it in seconds. So their ability to make a decision much faster and react to market conditions, really opens the door for them for much greater business opportunity and revenue. So it's not just cost savings that's driving this. It's taking advantage of the opportunity. >> You bet. >> Because if the queries don't really come fast. Let's say you as a person sitting here and you fire a query and then it takes a lot of time, and you go back and then have a cup of coffee and then come back. Your chain of thought's actually broken. So you cannot explore from the data otherwise you could integrate it'll actually come within seconds. >> Gentlemen, thank you for being here with us. I hope the show's gone well for you. It sure does sound like it's been a success, and we look forward to seeing you down the road. >> Great. >> Thank you. >> Good to be here. >> Thanks. >> From Kyvos. >> Back with more in just a bit here on theCUBE. You're watching AWS re:Invent. (bright music)