 from the Sunnyvale, California in the heart of Silicon Valley. It's the queue. Covering Accelerator Journey to AI. Brought to you by NetApp. Hi, I'm Peter Burris. Welcome to another conversation here from the Data Visionary Center at NetApp's headquarters in beautiful Sunnyvale, California. Being joined today by Santosh Rao. Santosh is a Senior Technical Director at NetApp and specifically Santosh, we're going to talk about some of the challenges and opportunities associated with AI and how NetApp is making that possible. Welcome to the queue. Thank you Peter, I'm excited to be here. Thank you for that. So Santosh, what is your role at NetApp? Why don't we start there? Wonderful, glad to be here. My name is Santosh Rao. I'm a Senior Technical Director at NetApp, part of the Product Operations Group and I've been here 10 years. My role is to drive up new lines of opportunity for NetApp, build up new product businesses and the most recent one has been AI. So I've been focused on bootstrapping and incubating the AI effort at NetApp for the last nine months now. Been excited to be part of this effort right now. So nine months of talking both internally but spending time with customers too. What are customers telling you that are NetApp's opportunities and what NetApp has to do to respond to those opportunities? Yeah, that's a great question. We're seeing a lot of focus around expanding the digital transformation to really get value out of the data and start looking at AI and deep learning in particular as a way to prove the ROI on the opportunities that they've had and AI and deep learning requires a tremendous amount of data. So we're actually fascinated to see the amount of data sets that customers are starting to look at. A petabyte of data is sort of the minimum size of data set and so when you think about petabyte scale data lakes the first thing you want to think about is how do you optimize the TCO for the solution? NetApp is seen as a leader in that just because of our rich heritage of storage efficiency. A lot of these are video image and audio files and so you're seeing a lot of unstructured data in general and we are a leader in NFS as well. So a lot of that starts to come together from a NetApp perspective and that's where customers see us as the leader in NFS, leader in files and leader in storage efficiency all coming together. And you want to join that together with some leadership especially in GPUs so that leads to Nvidia. So you've announced an interesting partnership between NetApp and Nvidia. How did that factor into your products and where do you think that goes? Yeah, it's kind of interesting how that came about because when you look at the industry, it's a small place and some of the folks driving the NVIDIA leadership have been working with us in the past when we've bootstrapped converged infrastructures with other vendors. And so we are known to have been a 10 year mature vendor in the converged infrastructure space and so the way this came about was NVIDIA is clearly a leader in the GPU and AI acceleration from a compute perspective but they're also seen as a long history of GPU virtualization and GPU graphics acceleration. So when they look at NetApp, what NetApp brings to NVIDIA is just the converged infrastructure, the maturity of that solution, the depth that we have in the enterprise and the rich partner ecosystem. All of that starts to come together and some of the players in this particular case have had a run at it in the past working on virtualization based converged infrastructures in the past. So it's an exciting time. We are really looking forward to working closely with NVIDIA. So NVIDIA brings these lightning facts machines optimized for some of the new data types, data forms, data structures associated with AI but they got to be fed. You got to get the data to them. So what is NetApp doing from a standpoint of the underlying hardware to improve the overall performance and ensure that these solutions really scream for customers? Yeah, it's kind of interesting because when you look at how customers are designing this, they're thinking about digital transformation as what is the flow of that data? What am I doing to create new sensors and endpoints that create data? How do I flow that data in? How do I forecast how much data I'm going to create quarter over quarter, year over year? How many endpoints? What is the resolution of the data? And then as that starts to come into the data center they got to think about where are the bottlenecks? So you start looking at a wide range of bottlenecks. You look at the edge data aggregation. Then you start looking at network bandwidth to push data into the core data centers. You got to think smart about some of these things. For example, no matter how much network bandwidth you throw at it, you want to reduce the amount of data you're moving. So smart data movement technologies like SnapBitter, which NetApp brings to the table are some things that we uniquely enable compared to others. The fact of the matter is when you take a common operating system like ONTAP and you can layer it across the edge core and cloud, that gives us some unnatural advantages. We can do things that you can't do in a silo where you've got a commodity server trying to push data and having to do raw full copies of data into the data center. So we think smart data movement is a huge opportunity. When you look at the core, obviously it's a workhorse. You've got the random sampling of data into this hardware and we think the A800 is a workhorse built for AI. It is a beast of a system in terms of performance. It does about 25 gigabytes per second just on a dual controller pair. And you'll recall we've spent a several number of years building out the foundation of Clustered ONTAP to allow us to scale to the gigantic sizes. And so 24 node or 12 controller pair A800 gets us to about 300 gigabytes per second and over 11 million IOPS, if you think about that. That's over about four to six times greater than anybody else in the industry. So when you think about NVIDIA's investment in DGX and their performance investment they've made there, we think only NEDAP can keep up with that in terms of performance. So 11 million IOPS, phenomenal performance for today, but the future's going to demand every more. Where do you think these trends go? Yeah, well nobody really knows for sure. I mean we are the most exciting part of this journey is nobody knows where this is going. And this is where you need a future proof customers and you need to enable the technology to have sufficient legs in the architecture to have sufficient legs that no matter how it evolves and where customers go, the vendors working with customers can go there with them. And that's really when customers look at NEDAP and say, you guys are working with the cloud partners. You're now working with NVIDIA. And in the past you've worked with a variety of data source vendors. So we think we can work with NEDAP because you're not affiliated to any one of them and yet you're giving us that full range of solutions. So we think that performance is going to be key, acceleration of compute workloads is going to demand orders of magnitude performance improvement. We think data set efficiencies and storage efficiencies is absolutely key. And we think you got to really look at TCO because customers want to build these great solutions for the business, but they can't afford it unless vendors give them viable options. And so it's really up to partners like NVIDIA and NEDAP to work together to give customers the best of breed solutions that reduce the TCO, accelerate compute, accelerate the data pipeline and yet bring the cost of the overall solution down and make it simple to deploy and pre-integrated. These are the things customers are looking for and we think we have the best bet at getting there. So that leads to, great summary, but that leads to some interesting observations on what customers should be basing their decisions on. What would you say are the kind of two or three most crucial things that customers need to think about right now as they conceptualize where to go with their AI applications or their work loads, their AI projects and initiatives? So when customers are designing and building these solutions, they're thinking the entire data lifecycle. How am I getting this new type of data for digital transformation? What is the ingestion architecture? What are my data aggregation endpoints for ingestion? How am I going to build out my AI data sources? What are the types of data? Am I collecting sensor data? Is it a variety of images? Am I going to add an audio transcription? Is there video feeds that come in over time? So customers are having to think about the entire digital experience, the types of data, because that leads to the selection of data sources. For example, if you're going to be landing sensor data, you want to be looking at maybe graph databases. If you want to be landing log data, you're going to be looking at log analytics over time as well as AI. And you're going to look at video image and audio accordingly. So architecting these solutions requires an understanding of what is your digital experience? How does that evolve over time? What is the right and optimal data source to land the data so that you get the best experience from a search, from indexing, from a tiering, from analytics and AI? And then what is the flow of that data? And how do you architect it for a global experience? So how do you build out these data centers where you're not having to copy all data maybe into your global headquarters? Can you build out if you're a global company with presence across multiple geos, how do you architect for regional data centers to be self-contained? Because we're looking at exabyte scale opportunities in some of these. So I think that that's pretty much the two or three things that I'd say across the entire gamut of space here. Excellent. And turning that then into some simple observations about the fact that data still is physical. There's latency issues, there's cost of bandwidth issues, there's other types of issues. This notion of edge core cloud. How do you see the on tap operating system, the on tap product set facilitating being able to put data where it needs to be, while at the same time creating the options that a customer needs to use data as they need to use it. And the fact of the matter is these things cannot be achieved overnight. It takes a certain amount of foundational work that frankly takes several years. So the fact that on tap can run on small form factor hardware at the edge is a journey that we started several years ago. The fact that on tap can run on commodity white box hardware has been a journey that we have run over the last three, four years. Same thing in the cloud. We've virtualized on tap to the point that it can run on all hyperscalars. And now we are in the process of consuming on tap as a service where you don't even know that it is an infrastructure product or has been. And so the process of building an edge core and cloud data pipeline leverages the investments that we've made over time. When you think about the scale of compute data and performance needed, that is a five to six year journey in clustered on tap if you look at NetApp's past. So these are all elements that are coming together from a product and solution perspective. But the reality is they're leveraging years and years of investment that NetApp engineering has made in a way that the industry really did not invest in the same areas. So when we compare and contrast what NetApp has done versus the rest of the industry, at a time when people were building monolithic engineered systems, we were building software defined architectures. At a time when they were building tightly coupled systems for traditional enterprise, we were building flexible scale out systems that assumed that you would want to scale in modular increments. Now as the world has shifted from enterprise into third platform and web scale, we are finding all of those investments NetApp made over the years is really starting to pay off for us. And that's- Including some of the investments in how AI can be used to handle how on tap operates at each of those different levels of scale. Absolutely, yes. So on Tash Rao, technical director at NetApp, talking about AI, some of the new changes and the relationship between AI and storage. Thanks very much for being on theCUBE. Thank you, appreciate it.