 from San Jose, it's theCUBE, presenting Big Data Silicon Valley brought to you by SiliconANGLE Media and its ecosystem partners. Welcome back to theCUBE. We are live on our first day of coverage at our event, Big Data SV. I'm Lisa Martin with my co-host, George Gilbert. We are at this really cool venue in downtown San Jose. We invite you to come by today, tonight, for our cocktail party. It's called Forger Tasting Room and Eatery. Tasty stuff, really, really good. We are down the street from the Strada Data Conference and we're excited to welcome to theCUBE a first time guest, Kunal Agarwal, the CEO of Unravel Data. Kunal, welcome to theCUBE. Thank you so much for having me. So, I'm a marketing girl. I love the name Unravel Data. Thank you. Two year old company. Tell us a bit about what you guys do and why that name. What's the implication there with respect to Big Data? Yeah, we are a application performance management company and Big Data applications are just very complex and the name Unravel is all about unraveling the mysteries of Big Data and understanding why things are not performing well and not really needing a PhD to do so. We're simplifying application performance management for the Big Data stack. Excellent. So, you know, one of the things that a lot of people were talking about with Hadoop, originally it was this, you know, cauldron of innovation. Yeah. You know, the let a thousand flowers bloom in terms of all the Apache projects. Right. But then once we tried to get it into operation, we discovered there's a, you know. There's an overhead. There's an overhead. There's a downside to it. Maybe tell us why you both need to know, you need to know how people have done this many, many times. Yeah. How you need to learn from experience and then how you can apply that even in an environment where someone hasn't been doing it for that long. Right. So, if I back up a little bit, Big Data is powerful, right? It's giving companies an advantage that they never had and data is an asset to all of these different companies. Now they're running everything from BI, machine learning, artificial intelligence, IoT, streaming applications on top of it for various reasons. Maybe it is to create a new product, to understand the customers better, et cetera. But as you rightly pointed out, when you start to implement all of these different applications and jobs, it's very, very hard. It's because Big Data is very complex. With that great power comes a lot of complexity. And what we started to see is a lot of companies, while they want to create these applications and provide that differentiation to their company, they just don't have enough expertise as well in-house to go in, write good applications, maintain these applications, and even manage the underlying infrastructure and cluster that all these applications are running on. So we took it upon ourselves where we thought, hey, if we simplify application performance management and if we simplify ongoing management challenges, then these companies would run more Big Data applications. They would be able to expand their use cases and not really be fearful of, hey, we don't know how to go and solve these problems. Do we actually rely on our system that is so complex and new? And that's the gap that Unravel fills, which is we monitor and manage not only one component of the Big Data ecosystem, but like you pointed out, it's a full zoo of all of these systems. You have Hadoop and you have Spark and you have Kafka for data ingestion. You may have some NoSQL systems and newer MPP platforms as well. So the vision of Unravel is really to be that one place where you can come in and understand what's happening with your applications in your system, overall, and be able to resolve those problems in an automatic, simple way. So all right, let's start at the concrete level of what a developer might get out of something that's wrapped in Unravel and then tell us what the administrator experiences. Absolutely, so if you are a Big Data developer, you've got in a business requirement that, hey, go and make this application that understands our customers better, right? They may choose a tool of their liking, maybe Hive, maybe Spark, maybe Kafka for data ingestion. And what they'll do is they'll write an app first in dev in their dev environment or the QA environment, and then they'll see, hey, maybe this application is failing or maybe this application is not performing as fast as I wanted to, or even worse, that this application is starting to hog a lot of resources which may slow down my other applications. Now, to understand what's causing these kind of problems, today developers really need a PhD to go and decipher them. They have to look at tons of law logs, law logs, metrics, configuration settings, and then try to stitch the story up in their head, try to figure out what is the effect, what is the cause, maybe it's this problem, maybe some other problem, and then do trial and error to try, you know, resolving that particular issue. Now, what we've seen is big data developers come in a variety of flavors. You have the hardcore developers who truly understand Spark and Hadoop and everything, but then 80% of the people submitting these applications are data scientists or business analysts who may understand SQL, who may know Python, but don't necessarily know what distributed computing and parallel processing and all of these things really are and where can inefficiencies and problems really lie. So we give them this one view, which will connect all of these different data sources and then tell them in plain English, this is the problem, this is why this problem happened, and this is how you can go and resolve it, thereby getting them unstuck and making it very simple for them to go in and get the performance that they're getting. So these, they're the developers upfront and you're giving them a whole new sort of tool chain or environment to solve the operational issues. So that if it's dev ops, it's really dev as much more sufficient. Yes, yes, I mean, all companies want to run fast. They don't want to be slowed down. If you have a problem, today they'll file a ticket, it'll go to the operations team, you wait a couple of days to get some more information back. That just means your business is slowed down. If things are simple enough where the application developers themselves can resolve a lot of these issues, that'll get the business unstuck and get them moving on further now. To the other point which you were asking, which is what about the operations and the app support people? So unravel is a great tool for them too because that helps them see what's happening holistically in the cluster. How are all the other applications behaving with each other? It's usually a multi-tenant, multi-application environment that these big data jobs are running on. So is my apps slowing down George's apps? Am I stealing resources from your applications? More so, not just about an individual application issue itself. So Unravel will give you visibility into each app as well as the overall cluster to help you understand cluster-wide problems. I'd love to get a maybe pill apart your target audience a little bit. You talked about DevOps, but also the business analyst, data scientist. When we talk about big data, data has such tremendous power to fuel a company. Like you said, use it to create and deliver new products. Are you talking with multiple audiences within a company? Do you start at DevOps and they bring in their peers? Or do you actually start maybe at the chief data officer level? What's that kind of entrance for Unravel? So the word I use to describe this is data ops instead of DevOps. So in the older world you had developers and you had operations people. Over here you have a data team and operations people. And that data team can comprise of the developers, the data scientists, the business analysts, et cetera as well. But you're right. Although we first target the operations role because they have to manage and monitor the system and make sure everything is running like a well-known machine, they are now spreading it out to the end users, meaning the developers themselves, saying don't come to me for every problem. Look at Unravel, try to solve it here. And if you cannot, then come to me. This is all again improving agility within the company, making sure that people have the necessary tools and insights to carry on with their day. Sounds like an enabler that operations would push down to the developers themselves. Even the managers and the CDOs for example, they want to see their ROI that they're getting from their big data investments. They want to see, I've put in these millions of dollars, I've got an infrastructure and these services set up. But how are we actually moving the needle forward? Are there any applications that we're actually putting in business? And is that driving any business value? So we will be able to give them a very nice dashboard, helping them understand what kind of throughput are you getting from your system? How many applications were you able to develop last week and onboard to your production environment? And what's the rate of innovation that's really happening inside your company on these big data ecosystems? It sort of brings up an interesting question on two prongs. One is the well-known sort of but inexact number about how many big data projects, I don't know whether they fail or didn't pay off. So there's going in and saying, hey, we can help you manage this because it was too complicated. But then there's also all the folks who decided, well, we really don't want to run it all on prem. We're not going to throw away everything we did there, but we're going to also put a lot of new investments in the cloud. Now, Wikibon has a term for that, which is true private cloud, which is when you have the operational processes that you use in the public cloud and you can apply them on prem. But there's not many products that help you do that. How can Unravel work? It's a very good question, George. We're seeing the world move more and more to a cloud environment, or I should say an on-demand environment, where you're not so bothered about the infrastructure and the services, but you want Spark as a dial tone. You want Kafka as a dial tone. You want a machine learning platform as a dial tone. You want to come in there, you want to put in your data and you want to just start running it. Unravel has been designed from the ground up to monitor and manage any of these environments. So Unravel can solve problems for your applications running on prem. And similarly, all the applications that are running on cloud. Now, on the cloud, there are other levels of problems as well. So of course you'll have applications that are slow, applications that are failing. You can solve those problems. But if you look at a cloud environment, a lot of these now provide you an auto-scaling capability. Meaning, hey, if this app doesn't run in the amount of time that we're hoping to run, let's add extra hardware and run this application. Well, if you just keep throwing machines at the problem, it's not going to solve your issue. Now, it doesn't decrease the time that it will take linearly with how many servers that you're actually throwing in there. So what we can help companies understand is, what is the resource requirement of a particular application? How should you be intelligently allocating resources to make sure that you're able to meet your time SLAs, your constraints of, hey, I need to finish this with X number of minutes, but at the same time, be intelligent about how much cost you're spending over there. Do you actually need 500 containers to go and run this app? Well, you may have needed 200. How do you know that? So Unravel will also help you get efficient with your run, not just faster, but also can it be a good multi-tenant citizen? Can I use limited resources to actually run this applications as well? So, Cannell, some of the things I'm hearing from a customer standpoint that are potential positive business outcomes are internal performance boost. It also sounds like sort of productivity improvements internally. And then also the opportunity to have the insight to deliver new products, but even I'm thinking of helping one make a retailer, for example, be able to do more targeted marketing. So the business outcomes and the impact that Unravel can make really seem to have pretty strong internal and external benefits. Is there a favorite customer story? I don't have to mention names that you think really speaks to your capabilities. So 100%. Improving performance is a very big factor of what Unravel can do, decreasing costs by improving productivity, by limiting the amount of resources that you're using is a very, very big factor. Now, amongst all of these companies that we work with, one key factor is improving reliability, which means, hey, it's fine that you can speed up this application, but sometimes I know the latency that I expect from an app. Maybe it's a second, maybe it's a minute, depending on the type of application. But what businesses cannot tolerate is this app taking five X amount of more time today. If it's going to finish in a minute, tell me and finish in a minute and make sure it finishes in a minute. And this is a big use case for all of the big data vendors because a lot of the customers are moving from terror data or from Vertica or from other relational databases onto Hortonworks or Cloudera or Amazon EMR. Why? Because it's one-tenth the amount of cost for running these workloads. But all the customers get frustrated and they say, I don't mind paying 10 X more money, but because over there it used to work. Over here there are just so many complications and I don't have reliability with these applications. So that's a big, big factor of how we actually help these customers get value out of the unraveled product. Okay, so a question on sort of, why aren't there so many other unravels? Yeah. From what I understood from past conversations, you can only really build the models that are at the heart of your capabilities based on sort of tons and tons of telemetry that cloud providers or sort of internet scale service providers have accumulated and that because they also have sort of a well-known set of configurations and a well-known kind of typology, in other words they're not a million degrees of freedom on any particular side. That you have a well-scoped problem and you have tons of data, so it's easier to build the models. So who else could do this? Yeah, so the difference between Unravel and other monitoring products is Unravel is not a monitoring product, it's an intelligent performance management suite. What that means is we don't just give you graphs and metrics and say, here's all the raw information, you go figure it out. Instead, we have to take it a step further where we're actually giving people answers. Now to develop something like that, you need full stack information, that's number one, meaning information from applications all the way down to infrastructure and everything in between. Why? Because problems can lie anywhere and if you don't have that full stack info, you're blind siding yourself or limiting the scope of the problems that you can actually search for. Secondly is, like you were rightly pointing out, how do I create answers from all this raw data? So you have to think like how an expert with big data would think, which is if there is a problem, what are the kinds of checks, balances, places that that person would look into and how would that person establish that this is indeed the root cause of the problem today and then how would that person actually resolve this particular problem? So we have a big team of scientists, researchers, in fact, my co-founder is a professor of computer science at Duke University, who's been researching database optimization techniques for the last decade. We have about 80 plus publications in this area, Starfish being one of them. We have a bunch of other publications which talk about how do you automate problem discovery, root cause analysis, as well as resolution to get best performance out of these different databases. And you're right, a lot of work has gone on the research side, but a lot of work has gone in understanding the needs of the customers. So we worked with some of the biggest companies out there which have some of the biggest big data clusters to learn from them what are some everyday ongoing management challenges that you face and then taking that problem to our data sets and figuring out how can we automate problem discovery? How can we proactively spot a lot of these errors? I joke around and I tell people that we're big data for big data, right? All these companies that we serve, they're gathering all of this data and they're trying to find patterns and they're trying to find some sort of an insight with our data. Our data is system-generated data, performance data, application data, and we're doing the exact same thing, which is figuring out inefficiencies, problems, cause and effect of things to be able to solve it in a more intelligent smart way. Well, Kunal, thank you so much for stopping by theCUBE and sharing how unravel data is helping to unravel the complexity of big data. Thank you so much. I appreciate it. You're a CUBE alumni. Absolutely. Thank you so much for having me. Kunal, thank you. Yeah, and we want to thank you for watching theCUBE. I'm Lisa Martin with George Gilbert. We're live at our own event, Big Data SV in downtown San Jose, California. Stick around, George and I will be right back with our next guest.