 Live from Las Vegas, it's theCUBE, covering AWS re-invent 2017, presented by AWS, Intel, and our ecosystem of partners. Welcome back to Las Vegas. We're live here on theCUBE. We continue our coverage of re-invent, the AWS, the Big Tent. As we were just talking about with our guests, Justin Warren, John Walls here, your host here on theCUBE. And we're joined by Kiran Bagashpur, who's the CEO of Ignia Systems. And Kiran, thanks for being with us here on theCUBE. Great to be here. Now we were talking about, you know, this is the Big Tent. Now, it didn't used to be that way, right? Nope, nope. It wasn't that long ago. This was, I wouldn't say a specialty show, but you say this is certainly taking on a very different vibe, a very different feel. I mean, explain that a little bit before we get into Ignias and what you're doing here. Absolutely. You know, I was first here in 2012, I believe it was the first year that they had AWS re-invent. And it was a very different feel, much smaller, maybe about 6,000 or so people, mostly engineers, hardcore engineers who were discovering this new cool set of toys, if you will, or tools that was quite revolutionary and itchy at that time. Fast forward now, it's much more of a mainstream show. It's much more corporate IT, lots and lots of large enterprises are present out here. There is still a lot of developers, but it's more the DevOps, more people who are operationalizing this rather than building on it for the very first time. So big change from early stage to very mainstream right now. And Justin, you made a comment, I mean, to the extent of a jacket, I've got a suit and tie, a jacket. You've all been the shows where maybe the wardrobe is maybe a little different. But this is maybe illustrative of, again, of the maturation of the marketplace and the expansion of the marketplace. Yeah, you go to some of the developer conferences and you'll see a lot more people with spiked purple hair and utility kilts. I've yet to see a single utility kilts here at the show. So it does feel, unlike at previous years where there's been a lot more, a lot of engineers and people are still here in hoodies and casual clothes, but there are a lot more suits. There's clearly a lot more money here and it's become a little bit more corporate. It'd be interesting to see how it transitions over the next couple of years, whether Amazon or AWS is able to maintain that kind of developer vibe as all of these other companies come in and start to see, actually, this is a pretty robust and mature ecosystem now. Yeah, and obviously the expansion reflects that. I mean, you're here exhibiting for the first time. Yes, we are. You're both back at K37 if you're here at the show. Go see Ignis. Let's talk a little bit about what you do and why are you here? Who are you trying to talk to this week and why does this week matter? That's great. So what we do, Ignis is an early-stage company. We have launched our company a year ago. We have a bunch of customers right now. We are sort of growing very nicely at this stage. And what we do is enable businesses, enterprises, with lots and lots of file data on-premises, as well as in the public cloud, to better manage and have a handle on this. So our customers tend to be businesses with sort of literally billions of files, hundreds of petabytes or dozens of petabytes, spread across a lot of systems, traditional legacy network-attached storage systems on-premises. And what they are seeing in their growth is, they're going from one data center to multiple data centers within their own infrastructure, and now to multiple clouds. And as this core asset data continues to grow, they look to folks like us to help manage that better. So the very first thing we do is we enable them to back up and protect all this data on-premises into public clouds like AWS. So we literally have scalable solutions which go into their data center, talk to all of their filers as they're called, interrogate all of that data, and create a copy of that into AWS S3 or Glacier. Yeah, there's a lot of companies who are struggling with the idea, well, two things really is, one is being able to manage data everywhere because data has gravity, as people like to say, but also this multi-cloud idea and being able to manage my data in multiple physical locations. Some of it will be on my own site, some of it will be in Colo, some of it will be in one or, as you say, multiple clouds. That real hybrid IT way of things. What are you seeing as the driver behind that need to have this data in multiple locations? Yeah, that's a great question, you know, the further, the things that we see is, look, things have remained on-premises, it's not gone away, and things continue to grow on-premises, and Amazon recognizes that, that's why you've seen starting last year into this year, a lot more push into hybrid clouds, if you will. You saw that with the big partnership with VMware and so on. So that's continuing to grow, but in the same time, they're having new applications being born in the cloud or leveraging the cloud. So one thing which is very common for a lot of our customers is, they have infrastructure on-premises, which is already paid for and continues to grow, but they want to leverage the public clouds AWS for its elasticity and its agility to be able to burst into it and use it as they see fit. Now to do that, you require agility of applications and data between on-premises and the public clouds, and say AWS. So that's kind of where we come in to go help them in that. And the other thing we're also seeing is, customers are not in a single cloud, even if they started in one place, they're starting to exist in multiple different locations. Good example will be, most of our customers tell us that say a Google Cloud has the advantage for things like AI and machine learning, whereas Amazon has the most mature infrastructure. So they might quite have a lot of infrastructure and data on-premises as well as in Amazon, but they might be running a bunch of new applications which are leveraging the machine learning APIs in Google Cloud. But then how do you get the data from on-premises, Amazon Cloud into Google Cloud, use it, but not leave it around and triple pay for it all around? So that's really the management challenge. Yeah, so you mentioned a particular use case there that happens to use Google Cloud, so AI and machine learning is something, and I'm hearing that and talking to customers myself, is that they like to use different clouds for different reasons. So what are some of the workloads that you're seeing from customers who are needing to put their data, not just on-site, but they say, you know what, I want to burst into the cloud, I want to use some of that elasticity that cloud is so great at. What are some of the workloads that you're seeing them use your product for? Yeah, I'll give you a great example. Let's take the example, the world of the movie world, right? So lots of, it's all digital right now, the data is created and you're going to go create heavily CGI or computer generated effects using lots and lots of compute cores. When you come towards the end of the movie, there's a crunch time when they need way more compute than they have available within their data centers. In fact, in the past, there used to be a vibrant side business where little boutique companies would rent you servers and they would literally carve that into your data center for six weeks and take it away again. So now that's gone and you'd rather use the public cloud, you'd use Amazon and EC2 instances for that workload. That's a good example which everybody can relate to, hey, it's crunch time movies coming up for release, I have a lot more work to do. But that pattern exists in pretty much every industry whether it's drug discovery or it's electronic design. Everywhere there is a need to go burst beyond what you have available and that kind of drives the adoption for workflows which already exist on premise to also adopt the cloud. Yeah, tell me about manageability. I mean, it's about multi-cloud. And obviously as you parcel out your assets and decide what data is going to reside in what environment, managing all that and managing the cost of all that. I mean, how do you kind of keep a corral on that and also help your clients get a handle on where their data is going? Yeah, I mean, it's all a sudden, I don't know, right? That's kind of what we exist to do which is help customers manage this data asset that they have across multiple locations no matter where it lives. The first thing we do in our journey with our customers is just back that stuff up which is on premises into the cloud. So it gets a copy of the data into the public cloud. Now that enables workflows like being able to use the cloud for disaster recovery or use the cloud for burst computing very well. But it's beyond just that. It's also, how do you get the data where it lives which could be on premise on a tier one filer to where it needs to be? Perhaps the public cloud for a backup or a DR or a burst use case or perhaps into a separate cloud for using machine learning. And when you do this, how do you ensure you have one copy, one protected copy of the data, not three or four in every place? In fact, if you look at the world today on premises, already customers will tell us and if they have hundreds of systems that it's not infrequent that hey, they have infrastructure in Santa Clara as well as in Israel. And it's the same copy which exists in both places because they have no way of globally looking at this in one single way. That's kind of what we do is hey, what is your data assets? Where do they live? How do we ensure you have one copy of it or end copies as you desire but not a proliferation of that data sets? Three, how do we get the data from where it lives to where it's needed in a programmatic systematic way that your end users can sort of help themselves to rather than requiring an IT trouble ticket and somebody going through a manual process. So those are sort of good sets of early things we are helping customers out with. The other thing which goes into here and this is where the cloud comes in again is we are targeted customers who are looking at literally billions, tens of billions of files, hundreds of terabytes, tens of petabytes to hundreds of petabytes of data spread across many locations and many hundreds of systems. How do you get your hand, your head around that? It's beyond human scale. And that's only possible with software and sort of machine learning if you want to use the buzzword. And that's the sort of next place where you come in and you provide a human comprehensible structure for this sort of data which continues to grow. And it's important because this is core assets for businesses today. Yeah, we were discussing this only earlier on both of the sets. Actually, is that idea of automation because humans don't scale. So when you have this, you know, billions of files as you're talking about that's just not tractable for humans to deal with. So what are some of the automation and autonomous systems capabilities that IgneS has? So the first thing we do is, you know, go ahead and ensure you're automatically and at scale being able to discover all of that data, right? So think of, you know, if you look in the consumer world, really what the web is is goes and crawls every website and indexes all of the data. Well, we do that, except within the enterprise for their unstructured file data which happens to live on a NetApp filer or a Dell cluster or maybe it's living in AWS inside S3. Where we go crawl all of that, index all of that and give you a view into that. That's the first level simple way of doing that. But then the next level beyond that is you can give a level of structure on that because it's not useful to just find it. You want to know what you have, where you have it, how it's changing, who is accessing it, what applications are accessing what data, what applications are modifying what data. Today that is an extremely manual process within businesses. Yeah, yeah, so in order to make sense of that, again, you're trying to appeal to developers. So what APIs and sort of that programmatic aspect do you have for that rather than having to employ 1,900 humans who all have to sit there and drive around with GUI interfaces? Since our customers tend to be sort of more on the business side of IT today who are trying to go understand about this data, the interfaces we provide them is clearly the higher level abstraction of what that data looks like or how they want to go interact with that. But everything you do in the modern world is API enabled and the vision is clearly to go expose all of this through API such that customers, developers within their organizations can go consume it. Yeah. So before we let you go, I want to talk about your presence here. The decision to exhibit, it's not a light one, I know that. At the end of the day, when you walk out of here on Thursday, what do you want to accomplish and I guess from in terms of the kinds of audience that you're hoping to be exposed to, who would that be? So the customers, the prospects we talk to are typically businesses, enterprises with lots and lots of unstructured data. So people in the media world, in the design world, any form of design is electronic and automated today. You know, geospatial imaging, all of these folks and they are all present here at this show. This is the show to be. You know, in the past it used to be Microsoft PDC. It was VM world, it was Oracle world. Today it is AWS re-invent and they are all here and for us, its success, if we walk out of this, being exposed to a whole bunch of people, we as a smaller organization could not have had immediate access to without coming to this show and that's what I think we get out of here. Well, good luck on the next three days. It sounds like you're off to a great start in the right place at the right time. Yes, indeed. And we wish you all the best down the road. Thank you. Kiran, thank you for being here. Thank you very much. Live on theCUBE, you're watching us here at re-invent, we're at AWS's big show here in Las Vegas, we'll be back with more live coverage in just a moment.