 that they can adapt. And that's what Docker gives them. Portability is a huge topic for the enterprise because the technology. Live from Seattle, Washington, it's theCUBE. Covering DockerCon 2016, brought to you by Docker. Here's your host, John Furrier. Okay, welcome back everyone. We are here live in Seattle, Washington for DockerCon 2016 SiliconANGLE Media is theCUBE. This is our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier with my co-host, Brian Gracely, analysts at theCUBE, the Wikibon. Our next guest is Sandeepen Banerjee, who's the SVP of Engineering and Operations at Cluster HQ, welcome to theCUBE. Thank you. Love Sport in the red sweater, love that. Looking good with the logo, wearing the colors. All right, so I want to get the update. You guys are presenting here. What's going on with your tech? What are you guys presenting here at DockerCon? So we are Cluster HQ, the container data people. Containers started with making compute elastic and the way to do that two or three years ago was to make things as stateless as possible. And the mantra for making containers operational was to leave all state out of it. Now that doesn't work so well in real life, which is all about managing persistence. So we are the state management people for containers and at this show, you know, a lot of people are talking about state. We have big enterprise customers and technology providers like Hewlett Packard who are integrating with the container ecosystem, their storage systems. And it's an exciting time. The container world and the cloud world has realized that it is not only necessary to spin up state-less compute services, but all the compute has associated with it dependent services. You almost always need databases, monitoring, logging, health management. And the entire technology stack is growing up towards the real world, which brings in storage, persistence, databases and Cluster HQ. This is really hard problem. I want you to just explain to the folks out there the real danger and impact to managing state because it sounds, oh, it sounds, oh, stateless, stateless, I mean, and the dependencies. This is all like really a hard problem technically. This is a hard problem because- Share why it's important. Yeah, so normally when you elasticize a workload using containers, if a particular node dies, the container dies with it and can be brought up on a neighboring node. Unfortunately, if there was data involved, then that's not so easy. You cannot abandon a banking transaction halfway through and bring it up in some incomplete state on the next node. So you have to take care to make sure that the data is consistent. It is protected. It goes from one consistent state to another as the workload in a modern cloud data center hops around from machine to machine or hops across data center and follows the sun. So data at the end of the day are the crown jewels of any compute environment and to protect that requires a lot of operationalization, making it amenable to moving from machine to machine for sure, but also keeping it backed up, highly available, amenable to disaster recovery. So the entire life cycle of data is a long and complex one. And this is important because it would be fair to say that this is important now because data in flight now is table stakes and that this is really not, it's kind of a new problem that needs to be table stakes solved. The old days was, ah, get the data. It's not really real time. It's backed up somewhere. Get it. Is that reason why it's so? That's very well put. With containers, things are inherently move around. The compute jumps from machine to machine. The data has to follow. And if you do not take care when the data moves, you are in a world of hurt. People make copies, they leave them around in machines and they forget about them. So the centralized management of data in flight is absolutely stable stakes for our brave new world of cluster computing. Yeah, so a couple of years ago when cluster HQ announced they were going to do statefulness with containers, people thought you were a little crazy. You know, everybody was gung-ho on stateless. What have you learned over the last couple of years? Because this show, this ecosystem's gotten very crowded around storage and containers. What have you learned over the last couple of years to keep you out in front of this fast growing market and crowded market? So what we have learned that the initial place where we started was to make the developers life simple as they took production containers and moved them around in the typical docker and other container environments. We now see a lot of interest, not only from a DevOps person trying to operationalize data, but also from the CIO, the policy person, from all the people who are stewards and custodians of data. And that exposure has been recent and we believe we are seeing a lot of it from not only the DevOps stakeholders, but from the people from compliance side, from the CIO's office, from the legal team, all of who have a stake in keeping the data sanely managed. So in a nutshell, I think the constituency for interest in containerized data has grown even more broadly than where it was two years ago. So this is about much more than data storage, it's data policy, it's data retention, it's sort of life cycle. It's the life cycle of data. Yeah, yeah. So what's the biggest problem that you guys solve for customers when you break down the state, state list, persistence, all that stuff, assuming that's under the hood? What are you guys enabling for value? We provide a safe conveyor for your data to move within a data center and across data centers. So think of our technology as something that transports the data reliably, safely, with tracking numbers, you know where it is, if it goes missing, we find it for you. What's the kind of persona that you guys are selling to that are buying your product, that are using it and where you're winning? Who's that, is it typical buyer? Is it a new kind of persona? Who's the target customer for you guys, the ideal target? So we see a couple of clusters. The people who are building platforms as a service, typically the service providers, telcos of this work, they have a lot of appetite for promising data stewardship and backing it up with actual technology. So that has been a very important constituency for us. SwissCom is a customer who's using our technology in real production cases. We also see the large enterprises where stewardship of data is not a debate. It is a reality, which is gatekeeper for the adoption of the entire stack. And the very large B2C and B2B web-facing properties also have consumer data, which is even more sensitive in many respects. So we are seeing across the spectrum from service providers, from large enterprises and B2B B2C. Final question, the folks out there, young and old, the conversation always turns to computer science. This is probably one of the most exciting times to be in computer science or science or engineering. And certainly on the app development side, Docker is enabling a lot of growth. What's your opinion of the next 10, 20 years of science and engineering in this area? I mean, can you look back historically and say, is there a point in time? What kind of level of, I mean, I always say, it's like all three megatrends over the past 30 years combined into one. I mean, do you see that somewhere? We share your opinion in terms of solving problems, creating value. Yeah, yeah. So my own trajectory was prior to cluster HQ, I spent about 10 years at Google and that company is all about computer science at scale, making the intractable tractable. In the cloud ecosystem, that is very much a fundamental insight. For the container world, what we are looking increasingly at is for the stewardship and policy type of things that we talked about, machine learning plays an incredible role. It is going to be possible with cluster HQ's technology and other technologies very soon to be able to predict placement and in order to actually be able to place data optimally for you. Why do you have to decide where to bring up the node, where to put your storage cells? Why do you have to decide how to configure the system? With machine learning, we are going to be able to place your data in the right place at the right time so that your applications at the lowest latency is the best safety guarantees, the best reliability and so on. And not only to make the cloud a platform, but to make the cloud an intelligent platform, that's happening. That's happening. Intelligent platform, that's us with a cube. We're intelligent sharing the data right here for the most smart people in the show. We'll be right back with more live coverage from Dr. Cone after this short break. I'm John Furrier, Brian Grace Lee. This is The Cube.