(upbeat music) >> Announcer: Live from San Franciso, it's theCUBE covering the Spark Summit 2017. Brought to you by Databricks. >> You are watching theCUBE at Spark Summit 2017 I'm David Goad here with my friend George Gilbert. How you doing, George? >> Good. >> All right, but the man of the hour is over to my left. I'd like to introduce a Databricks partner, and his name is Octavian Tanase, he's the SVP for Data ONTAP Software and Systems Group at NetApp. Octavian. >> Thank you for having us. >> All right well you have kind of an interesting background. We were chatting before, you started as an engineer, developer? >> Yeah, so I'm in an executive role right now but I have an interesting trajectory. Most people in a similar role come from a product management or sales background. I'm a former engineer and you know, somebody that has a passion for technology and now for customers and building interesting technologies. >> Okay, well if you have a passion for this technology then, I'd like to get your take on the market place a little bit. Tell us about the evolution of the mainstream and what you see changing. >> I think your data is the new currency of the 21st century. You have a desire and a thirst to get more out of your data. You have developers, you have analysts looking to build the next great application to mine your data for great business outcomes. NetApp as a data management company is very much interested in working with companies like Databricks and a bunch of hyperscalers to enable that type of solutions that either enable in place analytics or data lakes or you know, solutions that really enable developers and analysts to harness that part of the data. >> Mhmm. So ... Maybe walk us through what you've seen to date in terms of the mainstream use cases for big data and then tell us where you think they're going, but what walls need to be pushed back with the confection of technologies to get there. >> Originally what I've seen a lot of people investing in data lake technologies. Data lakes in a nutshell are massive containers that are simple to manage, scalable performant where you can aggregate a bunch of data sources and then you can run a map-produced type of workload to correlate that data, to harness that part of data, to draw conclusions. That was sort of the original track. Over time, I think there's a desire, given how dynamic and diverse that the data is, to build a lot of this analytics in-line, in real time. That's where companies like Databricks comes and that's where the cloud comes to enable both the agility as well as the type of real time behavior to getting those analytics. >> Now this is your first Spark Summit? >> Absolutely, happy to be here. >> Oh I know it's just the first day, but what have you learned so far? Any great questions from other participants? >> Well I think I see a lot of people innovating very fast. I see both established players paying attention, I see new companies looking to take advantage of this revolution that is happening, you know, around data and the data services and data analytics. >> Maybe tell us a little more what we were talking about before we started about how some customers who are very sensitive to their data want to keep it in their data centers or Equinix which still counts as pretty much theirs, but the compute is often the cloud somewhere. >> As you can imagine, we work with a lot of enterprise customers and one thing that I've learned in the last couple of years is that their thought process has evolved, you know, banks, large financial institutions. Two years ago, we're not even considering the cloud. And I see that now changing and I see them wanting to operate like a cloud provider, I see them want to take advantage of the flexibility and the agility of the cloud. I see them being more comfortable with the type of security capabilities that the cloud offers today. Security has been probably the most troublesome issue that folks have looked to overcome and then the gravity of the data. The reality is that the data, it's very distributed in dynamic, diverse in nature as I mentioned earlier. There's data created at the edge, data created in the data center, and people want to be able to process that data in real time regardless where data is without necessarily having to move it in some cases. Everybody's looking for data management solutions that enable mobility, you know, governments, management of that data and this enabling analytics, wherever that data is. >> You said some really interesting things in there which is, I mean I can see where the customer's data center extended to Equinix, where they want to bring the compute to the data because the data's heavier than the compute, but what about on the edge? Does it make sense to bring, is there enough data there to keep it there and bring compute down to the edge or do you co-locate compute persistently? And then how much of the compute is done at the edge? >> The reality is that you're probably going to see customers do both. There is more data created at the edge than in the history before. You'll see a lot of the data management companies invest in software-defined solutions that require a very small footprint, both from the storage point of view as well as compute. One of the advantages of technology like ONTAP is the investment that has been made to enable data reduction because your ability to store data at the edge is not really very good, so you want to have these capabilities to reduce the footprint by compression, by deduping, by compacting that data, and then making some smart decisions at the edge. Perhaps do some in-line, in-place analytics there and moving some of the data back into a central data center where more batch analytics can take place. >> But when you talk about that compaction, deduping, there was one more, but I think everyone gets the point. Are you talking about having a NetApp ONTAP device near the edge or on the edge? >> That device, it's actually software only. >> Ahh. >> You guys probably are aware of the fact that ONTAP now ships in three flavors, or three form factors. There is an engineered appliance, and we will likely do that for many years to come. But we also have ONTAP running in a virtual environment, either on KVM or Vmware as well as ONTAP running in the cloud. We've been running in the AWS cloud since 2014. We're also running in the Azure cloud. We are talking to other vendors to improve the ubiquity of software-defined ONTAP. >> Just to be really specific, we're told now that an edge gateway, not an edge device, but gateway, it's about two gigs in memory and two cores. Is that something a software-defined ONTAP would run on? >> Absolutely. You'll see us running on a variety of devices in the field with energy companies. You'll see ONTAP running in the tactical sphere, and we have projects that I can't really tell you about, but you'll find it broadly deployed on the edge. >> George: Okay. >> Yeah, let's talk a little bit about NetApp. What are some of the business outcomes you're looking for here? Do you have good executive sponsorship of these initiatives? >> We are very excited to be here. NetApp has been in the data management realm for a very, very long time. Yeah, analytics is a natural place, a great adjacency for us. We've been very fortunate to work with NoSQL type of companies. We've been very happy to collaborate with some of the leaders in analytics such as Databricks. We are entering the IOT space and enabling solutions that are really edge focused. So overall, this is a great fit for us and we're very excited to participate at the Summit. >> What do you think will be ... We've heard from Mata that sort of the state of the art in terms of, I hate to say the word, its fantasy, but like experimentation perhaps, is structured streaming, so continuous apps which are calling on deep learning models. Where would you play in that and what do you think ... What are the barriers there? What comes next? >> I think any complete analytics solution will need a bunch of services and some infrastructure that lends itself for that type of a workload, that type of a use case so you need, in some cases, very fast storage with super low latencies. In some cases you will need tremendous throughput. In some cases you will need that small footprint of an operating system running at the edge to enable some of that in-line processing. I think the market will evolve very fast. The solutions will evolve very fast and you will need the type of industry sponsorship by companies that really understand data management and that have made it their business for a very, very long time. I see that synergy that is being created between the innovation in analytics, the innovation that happens in the cloud, and the innovation that a company like NetApp does around a data fabric and around the type of services that are required to govern, to move, to secure, to protect that data in a very cost efficient way. >> This is kind of key, because people are struggling with having some sort of commonality in their architecture between the edge, on PRAM, and the cloud, but it could be at many different levels. What's your sweet spot for offering that? I mean, you talked about deduping and ... >> Compression and compaction. >> Compression and snapshots or whatever. Having that available in different form factors, what does that enable a customer to do, perhaps using different software on top? >> I'm glad that you asked. The reality is that we want to enable customers to consolidate both second and third platform applications on the ONTAP operating system. Customers will find not only flexibility, but consistency on the data management regardless of where data is. Whether it's in the cloud, near the cloud, or on the edge. We believe that we have the most flexible solution to enable data analytics, data management, that lends itself for all these use cases that enable next generation type of applications. >> Okay but if that predicated on having not just data ONTAP, but also a common application architecture on top? >> I think we wanted to enable a variety of solutions being based there. In some cases we're building glue. What do I mean by glue? It's for example, an NFS to HDFS connector that enable that translation from the native format for most of the data in a Hadoop or Spark type of EMR system. We're investing in enabling that flexibility and enabling that innovation that would happen by many of the companies that we see here on the floor today. >> George: Okay, that makes sense. >> We have just a minute to go here before the break. If you could talk to the entire Spark community, and you are right now on theCUBE, what's on your wish list? What do you wish people would do more of? Or if you could get help with something, what would it be? >> I think that my ask is continue to innovate. Push boundaries, and continue to be clever in partnering both with small vendors that are really innovating with tremendous space, as well as with established vendors that have really made the data management their business for many years and then are looking to participate in the ecosystem. >> Let's innovate together. >> All right, very good. >> Octavian, thank you so much for taking some time here out of your busy day to share with theCUBE, and we appreciate you being here >> Very good. >> Thank you so much. >> Pleasure >> Thanks, Octavian. >> That's right, you're watching theCUBE here at Spark Summit 2017. We'll see you in a few minutes with our next guest. (upbeat electronic music)