 Live from Gillette Stadium in Foxboro, Massachusetts. It's theCUBE, covering VTUG's New England Winter Warmer 2017. Now your host, Stu Miniman. And we're back with SiliconANGLE Media's presentation of theCUBE, we're the worldwide leader in enterprise tech coverage. Happy to welcome back to the program, Steve Powell who's the CMO of Ignia Systems. Steve flew out from Seattle here to welcome to the home of the New England Patriots. Oh my gosh. Number 12, number 12. Yeah, you know, the 12th man representing here. That's right. You've got, I have to say, I almost canceled my season ticket when Pete Carroll was our coach, so luckily he's worked out better for you than he did for us. My wife's a Browns fan, she says the same thing about Bill Belichick. So, it's the coaching fraternity is kind of like the tech world. It's a small group, we all try to get to know each other and move around, so, you know, thanks for joining us. Yes, well thanks for having me. All right, so Steve, you know, we've been talking to you guys since you were coming out of stealth. Well, why don't you give our audience kind of, you know, what's the update on Ignias? Okay, well for those of you who don't know us, what Ignias really does is we offer an on-site private cloud storage service and that's our first offering. It's part of our greater mission of providing a true cloud for local data and what we basically offer today is an unstructured data store that's completely delivered as a service. We take our own equipment, we install it, we monitor it, we manage it, we even refresh it when necessary and all the customer has to do is really subscribe and that's it. It's all pay as you go and it's all zero touch for the customer. You know, we launched back in October as you recall and you know, one of the things that I think that's been really great since launching is that we've really started to see how customers that didn't know us are actually really evaluating, really I think the convergence of two trends. You know, one is there's this data growth, you know, trend that goes on and pretty much everybody we talk to is citing data growth rates, you know, on the order of doubling, you know, every three years where IT budgets are growing less than 5% a year. So there's this mismatch where basically everybody's hitting this juncture that what they used to do can't work because the data is growing fast in the budget. And at the same time, there's this data growth that's actually happening and the data growth is not from, you know, relational databases and structured data, but rather a lot of new applications that are logging sensor data that are supporting machine learning, AI. Really it's machine generated data being analyzed by machines with humans really just training, you know, the AI and the machine learning. Yes, Steve, I want to unpack that a little bit. Yeah. Let's talk because, you know, many of us that watch storage has been like, well, the storage industry, it needs to change. It's not about selling boxes. It's not about capacity. And even on unstructured data, it was kind of like, okay, well, what's creating data and what's actually valuable? How much is it just, do I stick it on a cheap tier? You know, what do I actually do with it? What's interesting you guys do, some of those use cases, you know, you throw the machine learning, machine data, things like sensors. Every time I hear that word, you know, that IOT buzzword kind of pumps into your head. But, you know, maybe you could talk to some of those, you know, what's bringing customers? What's that driving challenge that they have that you're helping solve that's different from the way storage has been done for many years? Yeah, I think the, that's a great question. And I think that there's just been a real transition. And I think the transition has been largely created by the kinds of data that we want to manage and that we want to curate. And as we're seeing these sort of large unstructured data sets, I mean, it starts with the data. So, you know, so as an example, you take equipment that used to exist in the past, like let's say in scientific computing. You used to have flow cytometers, which were just time series data. And then what's now happened is that associated with every flow cytometer is now a real time video feed. When you look at the old world of microscopy, what you used to do is you used to flash freeze a sample and basically take a picture of it. And now what you can do with lattice light shield microscopy is you can actually look at cells in vivo. You know, while they're alive and you can, you know, I've personally gotten to watch a T cell move through a collagen matrix. And that's all microscopy, but generating orders of magnitude more data. And as we're looking at these very, very different data sets, we're looking at very, very different kinds of computing. And what that requires is a very, very different kind of infrastructure. And so, you know, the infrastructure has just had to get, you know, a lot more intelligent and the architecture has had to get a lot different. And what we've noticed is that a lot of the patterns that are actually being built in the public cloud as they've taken kind of a fresh look at the computing models have really become appropriate for this new kind of computing. And we don't see that on the premises. And that's really what we set out to go do. Yeah, it's interesting, it's probably the wrong term, but it sounds like we're describing kind of like object storage 2.0, you know, 1.0. You know, I remember those healthcare use cases. Everybody, you know, when I was doing, you know, radiology, when you're doing certain, you know, healthcare and sciences, I need metadata, I need to understand that. But now there's just orders of magnitude more data and, you know, technologies are making, you know, it's denser, prices have come down. So the idea has been around for a little bit while, but it sounds like the technology's matured to allow kind of an explosion. Well, and it's just a computing model. It's like one of these things where we're really, because of the emergence of microservices, you know, one of the things that we've seen is applications want a restful interaction with the storage layer. And so it turns out that that tends to be very, very perfect for a cloud-like implementation where you can actually implement, you know, high-volume unstructured data really, really well via a restful API where in the old world of sort of POSIX semantics and that kind of transactional model, you just lost scalability. You know, you had a lot of proprietary hardware. You know, with NVRAM, you had proprietary interconnects, you know, with things like InfiniBand, you know, and nowadays, you know, being able to loosely couple distributed systems is really the name of the game. And that's ultimately what we aim to build at Igneous. And that's all the technology in terms of our commercial offering. The customer doesn't care what's behind it, but fundamentally what you're looking for is the scalability and resilience, you know, that the cloud offers, but doing that on premises. Yeah, so Steve, we had a really interesting crowd chat about a month or so ago talking about hybrid cloud. And the term, the thing I've been saying for the last probably year is as customers try to figure out, you know, what goes where in the cloud environment. You know, I've got SaaS, I've got public cloud, I've got, you know, my private cloud, it's, you know, follow the data and follow the applications. In the cloud, you know, things like mobile and even some video streaming, I think we understand how to do that. But why does on premises make sense for your customers, your workloads and your solution? Yeah, absolutely. And so, you know, first, a little bit on hybrid cloud. There are kind of two different definitions of hybrid cloud. One is kind of the AWS VMware scheme where what you're really looking to do is run your old stuff that you were running on-prem in the public cloud and you call that hybrid. But there's another way to look at it, which is to say, hey, let's take a look at the computing patterns that are being run in the public cloud. How do I bring that down to the premises? And the reason that you might want to do that is really two-fold. One is the gravity of the data. So it might just be that the data sets are too big to move back and forth, you know, over very thin internet pipes. And so you want to actually keep the data close to its source. The other is, you know, something that we've seen, which is really more of a preference, which is that while I think that cloud technologies are actually have a lot of capability for security, there are a lot more hoops for folks to run through to ensure that they're compliant with their own internal policies and where they've already set out a set of policies for how they run the stuff behind the firewall. Sometimes it's just simpler for them to actually keep all of the data on the premises and not actually have to worry about some of the issues and tracking and compliance issues associated with how you move the data around. Yeah, one of the things we've heard from users is when they use public cloud, one of the things they really like is, you know, sometimes the CFO is not fully on board, but, you know, buying things as a service. So they want to understand predictability, but they want to, you know, buy it as a service, understand how does your solution fit into that? Yeah, that's great. I think our solution fits into both trends really, really well, because what we're really offering, we talked a little bit about technology, but really fundamentally we're offering a service. And so when Ignis goes into a customer interaction is as a service, customers interact with our service via APIs and they get a bill for a subscription. And so it's an as a service model. You don't buy hardware, you don't install software, you don't have systems to manage. At the same time, there is a predictability that's a little bit of the downside of the public cloud because there's a fee, generally to access your data out of storage. And often when people don't actually understand their data access and their data movement patterns, the costs of running applications in the public cloud become quite unpredictable. And you actually don't run into that unpredictability with a solution like Ignis because our data is on your local area network and we don't charge you to access the data that's on your own network. So if I come to an event like this, if I'm thinking about my storage today, the conversation in the marketplace has been, well, the new choice is out there is there's, the HCI, the hyperconverged infrastructure, and there's flash, the AFA devices out there. And of course even the lines between those are blurring because I can have an all flash configuration of hyperconverged and some of the all flash array things are getting converged and put into more things. How do you help customers as the, what's the bullet point as to well, this is for this kind of application, this is for this solution and hey, there's this whole new category that you need to be thinking about. Yeah, I think that's perfect. And I think the real trick here is that there's a difference between your hot tier and your flash tier and your capacity tier. And fundamentally the flash tier is really good when time to first bite is very important. So that might be for your relational database applications and things of that sort where there tends to be a lot of searching through an index and so you've got a lot of low latency requirements. And then on the other hand what you have is a capacity tier. They may be your video surveillance, they may be your large unstructured documents, they may be your sensor data. And in those contexts, you don't necessarily need the time to first bite. What you really need is capacity throughput. And so the overhead of setting up for example, a restful connection is not significant when compared to the amount of data that actually needs to go through the system. And that's actually where restful semantics actually get superior to POSIX semantics when you have very, very large unstructured data sets. Hyperconverged is actually a little bit of a different world. And I think that while hyperconverged has worked out pretty well I think for virtualization workloads, we've really found that when it comes to these very, very large unstructured data sets, hyperconverged isn't necessarily always the way to go. You tend to find a utilization issue between your compute and your storage layers where you have to actually think about how you're balancing all this stuff. And so really the world that we've really seen emerge as new applications come forward is there's really a trend to write microservices that are stateless and to have them talk to a stateful layer. That's why in the public cloud there's a pattern of having things like elastic container services, talking to an S3 and we definitely see on premises that same type of thing that's going to emerge. There's going to be some time to get there. Admittedly, as I was mentioning kind of at the beginning, we've seen this really interesting set of interest patterns. One is from the folks who are developing these new applications that are utilizing on structured data. There's a lot of interest we're getting right now from IT folks that are just getting started with object storage to do secondary workflows, to do backups, to do archives. And it's been interesting that we've been getting a lot of interest in our service as a new way to approach some of these data protection workflows. All right, so Steve, last question I've got for you. Came out of stealth Q4 last year. What do we look for in 2017 for Mignus? Yeah, so I think that you'll see it on both of those fronts. I think that one thing that's going to be seen in 2017 is a lot more development on our side around building up the tool chain for folks to use for a data protection tier. And so, you know, we've got a new offering coming online we're calling Ignis Insights, which provides information about what's currently on your primary storage tiers. We've got a whole set of replication services. You know, they're coming up to do backup, archive, things like replication to the cloud. But what we're also really moving forward with is a lot of what's needed in the tool chain to really support hybrid and multi-clouds. So, you know, how you facilitate the data movement in and out of the cloud, as well how you do the auditing and management of the data, you know, no matter where it lives. All right, Steve Powell, really appreciate you catching up. And if you want to find out more about this category, check out cube365.net slash true private cloud. That's C-U-B-E, number 365.net slash true private cloud, which has resources from the whole industry, including from Ignis, including from Wikibon and theCUBE, as to what's happening kind of this true private cloud, hybrid cloud environment. We'll be back with lots more coverage here. Thanks for watching theCUBE. Since the dawn of the cloud, the cube has been there.