(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante. >> If it weren't for Pat Gelsinger, Intel's future would be a disaster. Even with his clear vision, fantastic leadership, deep technical and business acumen, and amazing positivity, the company's future is in serious jeopardy. It's the same story we've been telling for years. Volume is king in the semiconductor industry, and Intel no longer is the volume leader. Despite Intel's efforts to change that dynamic With several recent moves, including making another go at its Foundry business, the company is years away from reversing its lagging position relative to today's leading foundries and design shops. Intel's best chance to survive as a leader in our view, will come from a combination of a massive market, continued supply constraints, government money, and luck, perhaps in the form of a deal with apple in the midterm. Hello, and welcome to this week's "Wikibon CUBE Insights, Powered by ETR." In this "Breaking Analysis," we'll update you on our latest assessment of Intel's competitive position and unpack nuggets from the company's February investor conference. Let's go back in history a bit and review what we said in the early 2010s. If you've followed this program, you know that our David Floyer sounded the alarm for Intel as far back as 2012, the year after PC volumes peaked. Yes, they've ticked up a bit in the past couple of years but they pale in comparison to the volumes that the ARM ecosystem is producing. The world has changed from people entering data into machines, and now it's machines that are driving all the data. Data volumes in Web 1.0 were largely driven by keystrokes and clicks. Web 3.0 is going to be driven by machines entering data into sensors, cameras. Other edge devices are going to drive enormous data volumes and processing power to boot. Every windmill, every factory device, every consumer device, every car, will require processing at the edge to run AI, facial recognition, inference, and data intensive workloads. And the volume of this space compared to PCs and even the iPhone itself is about to be dwarfed with an explosion of devices. Intel is not well positioned for this new world in our view. Intel has to catch up on the process, Intel has to catch up on architecture, Intel has to play catch up on security, Intel has to play catch up on volume. The ARM ecosystem has cumulatively shipped 200 billion chips to date, and is shipping 10x Intel's wafer volume. Intel has to have an architecture that accommodates much more diversity. And while it's working on that, it's years behind. All that said, Pat Gelsinger is doing everything he can and more to close the gap. Here's a partial list of the moves that Pat is making. A year ago, he announced IDM 2.0, a new integrated device manufacturing strategy that opened up its world to partners for manufacturing and other innovation. Intel has restructured, reorganized, and many executives have boomeranged back in, many previous Intel execs. They understand the business and have a deep passion to help the company regain its prominence. As part of the IDM 2.0 announcement, Intel created, recreated if you will, a Foundry division and recently acquired Tower Semiconductor an Israeli firm, that is going to help it in that mission. It's opening up partnerships with alternative processor manufacturers and designers. And the company has announced major investments in CAPEX to build out Foundry capacity. Intel is going to spin out Mobileye, a company it had acquired for 15 billion in 2017. Or does it try and get a $50 billion valuation? Mobileye is about $1.4 billion in revenue, and is likely going to be worth more around 25 to 30 billion, we'll see. But Intel is going to maybe get $10 billion in cash from that, that spin out that IPO and it can use that to fund more FABS and more equipment. Intel is leveraging its 19,000 software engineers to move up the stack and sell more subscriptions and high margin software. He got to sell what he got. And finally Pat is playing politics beautifully. Announcing for example, FAB investments in Ohio, which he dubbed Silicon Heartland. Brilliant! Again, there's no doubt that Pat is moving fast and doing the right things. Here's Pat at his investor event in a T-shirt that says, "torrid, bringing back the torrid pace and discipline that Intel is used to." And on the right is Pat at the State of the Union address, looking sharp in shirt and tie and suit. And he has said, "a bet on Intel is a hedge against geopolitical instability in the world." That's just so good. To that statement, he showed this chart at his investor meeting. Basically it shows that whereas semiconductor manufacturing capacity has gone from 80% of the world's volume to 20%, he wants to get it back to 50% by 2030, and reset supply chains in a market that has become important as oil. Again, just brilliant positioning and pushing all the right hot buttons. And here's a slide underscoring that commitment, showing manufacturing facilities around the world with new capacity coming online in the next few years in Ohio and the EU. Mentioning the CHIPS Act in his presentation in The US and Europe as part of a public private partnership, no doubt, he's going to need all the help he can get. Now, we couldn't resist the chart on the left here shows wafer starts and transistor capacity growth. For Intel, overtime speaks to its volume aspirations. But we couldn't help notice that the shape of the curve is somewhat misleading because it shows a two-year (mumbles) and then widens the aperture to three years to make the curve look steeper. Fun with numbers. Okay, maybe a little nitpick, but these are some of the telling nuggets we pulled from the investor day, and they're important. Another nitpick is in our view, wafers would be a better measure of volume than transistors. It's like a company saying we shipped 20% more exabytes or MIPS this year than last year. Of course you did, and your revenue shrank. Anyway, Pat went through a detailed analysis of the various Intel businesses and promised mid to high double digit growth by 2026, half of which will come from Intel's traditional PC they center in network edge businesses and the rest from advanced graphics HPC, Mobileye and Foundry. Okay, that sounds pretty good. But it has to be taken into context that the balance of the semiconductor industry, yeah, this would be a pretty competitive growth rate, in our view, especially for a 70 plus billion dollar company. So kudos to Pat for sticking his neck out on this one. But again, the promise is several years away, at least four years away. Now we want to focus on Foundry because that's the only way Intel is going to get back into the volume game and the volume necessary for the company to compete. Pat built this slide showing the baby blue for today's Foundry business just under a billion dollars and adding in another $1.5 billion for Tower Semiconductor, the Israeli firm that it just acquired. So a few billion dollars in the near term future for the Foundry business. And then by 2026, this really fuzzy blue bar. Now remember, TSM is the new volume leader, and is a $50 billion company growing. So there's definitely a market there that it can go after. And adding in ARM processors to the mix, and, you know, opening up and partnering with the ecosystems out there can only help volume if Intel can win that business, which you know, it should be able to, given the likelihood of long term supply constraints. But we remain skeptical. This is another chart Pat showed, which makes the case that Foundry and IDM 2.0 will allow expensive assets to have a longer useful life. Okay, that's cool. It will also solve the cumulative output problem highlighted in the bottom right. We've talked at length about Wright's Law. That is, for every cumulative doubling of units manufactured, cost will fall by a constant percentage. You know, let's say around 15% in semiconductor world, which is vitally important to accommodate next generation chips, which are always more expensive at the start of the cycle. So you need that 15% cost buffer to jump curves and make any money. So let's unpack this a bit. You know, does this chart at the bottom right address our Wright's Law concerns, i.e. that Intel can't take advantage of Wright's Law because it can't double cumulative output fast enough? Now note the decline in wafer starts and then the slight uptick, and then the flattening. It's hard to tell what years we're talking about here. Intel is not going to share the sausage making because it's probably not pretty, But you can see on the bottom left, the flattening of the cumulative output curve in IDM 1.0 otherwise known as the death spiral. Okay, back to the power of Wright's Law. Now, assume for a second that wafer density doesn't grow. It does, but just work with us for a second. Let's say you produce 50 million units per year, just making a number up. That gets you cumulative output to $100 million in, sorry, 100 million units in the second year to take you two years to get to that 100 million. So in other words, it takes two years to lower your manufacturing cost by, let's say, roughly 15%. Now, assuming you can get wafer volumes to be flat, which that chart showed, with good yields, you're at 150 now in year three, 200 in year four, 250 in year five, 300 in year six, now, that's four years before you can take advantage of Wright's Law. You keep going at that flat wafer start, and that simplifying assumption we made at the start and 50 million units a year, and well, you get to the point. You get the point, it's now eight years before you can get the Wright's Law to kick in, and you know, by then you're cooked. But now you can grow the density of transistors on a chip, right? Yes, of course. So let's come back to Moore's Law. The graphic on the left says that all the growth is in the new stuff. Totally agree with that. Huge term that Pat presented. Now he also said that until we exhaust the periodic table of elements, Moore's Law is alive and well, and Intel is the steward of Moore's Law. Okay, that's cool. The chart on the right shows Intel going from 100 billion transistors today to a trillion by 2030. Hold that thought. So Intel is assuming that we'll keep up with Moore's Law, meaning a doubling of transistors every let's say two years, and I believe it. So bring that back to Wright's Law, in the previous chart, it means with IDM 2.0, Intel can get back to enjoying the benefits of Wright's Law every two years, let's say, versus IDM 1.0 where they were failing to keep up. Okay, so Intel is saved, yeah? Well, let's bring into this discussion one of our favorite examples, Apple's M1 ARM-based chip. The M1 Ultra is a new architecture. And you can see the stats here, 114 billion transistors on a five nanometer process and all the other stats. The M1 Ultra has two chips. They're bonded together. And Apple put an interposer between the two chips. An interposer is a pathway that allows electrical signals to pass through it onto another chip. It's a super fast connection. You can see 2.5 terabytes per second. But the brilliance is the two chips act as a single chip. So you don't have to change the software at all. The way Intel's architecture works is it takes two different chips on a substrate, and then each has its own memory. The memory is not shared. Apple shares the memory for the CPU, the NPU, the GPU. All of it is shared, meaning it needs no change in software unlike Intel. Now Intel is working on a new architecture, but Apple and others are way ahead. Now let's make this really straightforward. The original Apple M1 had 16 billion transistors per chip. And you could see in that diagram, the recently launched M1 Ultra has $114 billion per chip. Now if you take into account the size of the chips, which are increasing, and the increase in the number of transistors per chip, that transistor density, that's a factor of around 6x growth in transistor density per chip in 18 months. Remember Intel, assuming the results in the two previous charts that we showed, assuming they were achievable, is running at 2x every two years, versus 6x for the competition. And AMD and Nvidia are close to that as well because they can take advantage of TSM's learning curve. So in the previous chart with Moore's Law, alive and well, Intel gets to a trillion transistors by 2030. The Apple ARM and Nvidia ecosystems will arrive at that point years ahead of Intel. That means lower costs and significantly better competitive advantage. Okay, so where does that leave Intel? The story is really not resonating with investors and hasn't for a while. On February 18th, the day after its investor meeting, the stock was off. It's rebound a little bit but investors are, you know, they're probably prudent to wait unless they have really a long term view. And you can see Intel's performance relative to some of the major competitors. You know, Pat talked about five nodes in for years. He made a big deal out of that, and he shared proof points with Alder Lake and Meteor Lake and other nodes, but Intel just delayed granite rapids last month that pushed it out from 2023 to 2024. And it told investors that we're going to have to boost spending to turn this ship around, which is absolutely the case. And that delay in chips I feel like the first disappointment won't be the last. But as we've said many times, it's very difficult, actually, it's impossible to quickly catch up in semiconductors, and Intel will never catch up without volume. So we'll leave you by iterating our scenario that could save Intel, and that's if its Foundry business can eventually win back Apple to supercharge its volume story. It's going to be tough to wrestle that business away from TSM especially as TSM is setting up shop in Arizona, with US manufacturing that's going to placate The US government. But look, maybe the government cuts a deal with Apple, says, hey, maybe we'll back off with the DOJ and FTC and as part of the CHIPS Act, you'll have to throw some business at Intel. Would that be enough when combined with other Foundry opportunities Intel could theoretically produce? Maybe. But from this vantage point, it's very unlikely Intel will gain back its true number one leadership position. If it were really paranoid back when David Floyer sounded the alarm 10 years ago, yeah, that might have made a pretty big difference. But honestly, the best we can hope for is Intel's strategy and execution allows it to get competitive volumes by the end of the decade, and this national treasure survives to fight for its leadership position in the 2030s. Because it would take a miracle for that to happen in the 2020s. Okay, that's it for today. Thanks to David Floyer for his contributions to this research. Always a pleasure working with David. Stephanie Chan helps me do much of the background research for "Breaking Analysis," and works with our CUBE editorial team. Kristen Martin and Cheryl Knight to get the word out. And thanks to SiliconANGLE's editor in chief Rob Hof, who comes up with a lot of the great titles that we have for "Breaking Analysis" and gets the word out to the SiliconANGLE audience. Thanks, guys. Great teamwork. Remember, these episodes are all available as podcast wherever you listen. Just search "Breaking Analysis Podcast." You'll want to check out ETR's website @etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You could always get in touch with me on email, david.vellante@siliconangle.com or DM me @dvellante, and comment on my LinkedIn posts. This is Dave Vellante for "theCUBE Insights, Powered by ETR." Have a great week. Stay safe, be well, and we'll see you next time. (upbeat music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> Techniques to protect sensitive data have evolved over thousands of years, literally. The pace of modern data protection is rapidly accelerating and presents both opportunities and threats for organizations. In particular, the amount of data stored in the cloud combined with hybrid work models, the clear and present threat of cyber crime, regulatory edicts, and the ever expanding edge and associated use cases should put CXOs on notice that the time is now to rethink your data protection strategies. Hello, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we're going to explore the evolving world of data protection and share some information on how we see the market changing in the competitive landscape for some of the top players. Steve Kenniston, AKA the Storage Alchemist, shared a story with me, and it was pretty clever. Way back in 4000 BC, the Sumerians invented the first system of writing. Now, they used clay tokens to represent transactions at that time. Now, to prevent messing with these tokens, they sealed them in clay jars to ensure that the tokens, i.e the data, would remain secure with an accurate record that was, let's call it quasi, immutable, and lived in a clay vault. And since that time, we've seen quite an evolution of data protection. Tape, of course, was the main means of protecting data and backing data up during most of the mainframe era. And that carried into client server computing, which really accentuated and underscored the issues around backup windows and challenges with RTO, recovery time objective and RPO recovery point objective. And just overall recovery nightmares. Then in the 2000's data reduction made disk-based backup more popular and pushed tape into an archive last resort media. Data Domain, then EMC, now Dell still sell many purpose-built backup appliances as do others as a primary backup target disc-based. The rise of virtualization brought more changes in backup and recovery strategies, as a reduction in physical resources squeezed the one application that wasn't under utilizing compute, i.e, backup. And we saw the rise of Veem, the cleverly-named company that became synonymous with data protection for virtual machines. Now, the cloud has created new challenges related to data sovereignty, governance, latency, copy creep, expense, et cetera. But more recently, cyber threats have elevated data protection to become a critical adjacency to information security. Cyber resilience to specifically protect against attacks is the new trend being pushed by the vendor community as organizations are urgently looking for help with this insidious threat. Okay, so there are two major disruptors that we're going to talk about today, the cloud and cyber crime, especially around ransoming your data. Every customer is using the cloud in some way, shape, or form. Around 76% are using multiple clouds, that's according to a recent study by Hashi Corp. We've talked extensively about skill shortages on theCUBE, and data protection and security concerns are really key challenges to address, given that skill shortage is a real talent gap in terms of being able to throw people at solving this problem. So what customers are doing, they're either building out or they're buying really mostly building abstraction layers to hide the underlying cloud complexity. So what this does... The good news is it's simplifies provisioning and management, but it creates problems around opacity. In other words, you can't see sometimes what's going on with the data. These challenges fundamentally become data problems, in our view. Things like fast, accurate, and complete backup recovery, compliance, data sovereignty, data sharing. I mentioned copy creep, cyber resiliency, privacy protections. These are all challenges brought to fore by the cloud, the advantages, the pros, and the cons. Now, remote workers are especially vulnerable. And as clouds span rapidly, data protection technologies are struggling to keep pace. So let's talk briefly about the rapidly-expanding public cloud. This chart shows worldwide revenue for the big four hyperscalers. As you can see, we projected that they're going to surpass $115 billion in revenue in 2021. That's up from 86 billion last year. So it's a huge market, it's growing in the 35% range. The interesting thing is last year, 80-plus billion dollars in revenue, but 100 billion dollars was spent last year by these firms in cap ex. So they're building out infrastructure for the industry. This is a gift to the balance of the industry. Now to date, legacy vendors and the surrounding community have been pretty defensive around the cloud. Oh, not everything's going to move to the cloud. It's not a zero sum game we hear. And while that's all true, the narrative was really kind of a defensive posture, and that's starting to change as large tech companies like Dell, IBM, Cisco, HPE, and others see opportunities to build on top of this infrastructure. You certainly see that with Arvind Krishna comments at IBM, Cisco obviously leaning in from a networking and security perspective, HPE using language that is very much cloud-like with its GreenLake strategy. And of course, Dell is all over this. Let's listen to how Michael Dell is thinking about this opportunity when he was questioned on the queue by John Furrier about the cloud. Play the clip. So in my view, Michael nailed it. The cloud is everywhere. You have to make it easy. And you have to admire the scope of his comments. We know this guy, he thinks big. He said, "Enables everything." He's basically saying is that technology is at the point where it has the potential to touch virtually every industry, every person, every problem, everything. So let's talk about how this informs the changing world of data protection. Now, we all know, we've seen with the pandemic, there's an acceleration in toward digital, and that has caused an escalation, if you will, in the data protection mandate. So essentially what we're talking about here is the application of Michael Dell's cloud everywhere comments. You've got on-prem, private clouds, hybrid clouds. You've got public clouds across AWS, Azure, Google, Alibaba. Really those are the big four hyperscalers. You got many clouds that are popping up all their place. But multi-cloud, to that Hashi Corp data point, 75, 70 6%. And then you now see the cloud expanding out to the edge, programmable infrastructure heading out to the edge. So the opportunity here to build the data protection cloud is to have the same experiences across all these estates with automation and orchestration in that cloud, that data protection cloud, if you will. So think of it as an abstraction layer that hides that underlying complexity, you log into that data protection cloud, it's the same experience. So you've got backup, you've got recovery, you can handle bare metal. You can do virtualized backups and recoveries, any cloud, any OS, out to the edge, Kubernetes and container use cases, which is an emerging data protection requirement. And you've got analytics, perhaps you've got PII, personally identifiable information protection in there. So the attributes of this data protection cloud, again, abstracts the underlying cloud primitives, takes care of that. It also explodes cloud native technologies. In other words, it takes advantage of whether it's machine learning, which all the big cloud players have expertise in, new processor models, things like graviton, and other services that are in the cloud natively. It doesn't just wrap it's on-prem stack in a container and shove it into the cloud, no. It actually re architects or architects around those cloud native services. And it's got distributed metadata to track files and volumes and any organizational data irrespective of location. And it enables sets of services to intelligently govern in a federated governance manner while ensuring data integrity. And all this is automated and an orchestrated to help with the skills gap. Now, as it relates to cyber recovery, air-gap solutions must be part of the portfolio, but managed outside of that data protection cloud that we just briefly described. The orchestration and the management must also be gaped, if you will. Otherwise, (laughs) you don't have an air gap. So all of this is really a cohort to cyber security or your cybersecurity strategy and posture, but you have to be careful here because your data protection strategy could get lost in this mess. So you want to think about the data protection cloud as again, an adjacency or maybe an overlay to your cybersecurity approach. Not a bolt on, it's got to be fundamentally architectured from the bottom up. And yes, this is going to maybe create some overheads and some integration challenges, but this is the way in which we think you should think about it. So you'll likely need a partner to do this. Again, we come back to the skill skills gap if we're seeing the rise of MSPs, managed service providers and specialist service providers. Not public cloud providers. People are concerned about lock-in, and that's really not their role. They're not high-touch services company. Probably not your technology arms dealer, (clear throat) excuse me, they're selling technology to these MSPs. So the MSPs, they have intimate relationships with their customers. They understand their business and specialize in architecting solutions to handle these difficult challenges. So let's take a look at some of the risk factors here, dig a little bit into the cyber threat that organizations face. This is a slide that, again, the Storage Alchemists, Steve Kenniston, shared with me. It's based on a study that IBM funds with the Panmore Institute, which is a firm that studies these things like cost of breaches and has for many, many, many years. The slide shows the total cost of a typical breach within each dot and on the Y axis and the frequency in percentage terms on the horizontal axis. Now, it's interesting. The top two compromise credentials and phishing, which once again proves that bad user behavior trumps good security every time. But the point here is that the adversary's attack vectors are many. And specific companies often specialize in solving these problems often with point products, which is why the slide that we showed from Optiv earlier, that messy slide, looks so cluttered. So there's a huge challenge for companies. And that's why we've seen the emergence of cyber recovery solutions from virtually all the major players. Ransomware and the solar winds hack have made trust the number one issue for CIOs and CISOs and boards of directors. Shifting CISO spending patterns are clear. They're shifting largely because they're catalyzed by the work from home. But outside of the moat to endpoint security, identity and access management, cloud security, the horizontal network security. So security priorities and spending are changing. And that's why you see the emergence of disruptors like we've covered extensively, Okta, CrowdStrike, Zscaler. And cyber resilience is top of mind, and robust solutions are required. And that's why companies are building cyber recovery solutions that are most often focused on the backup corpus because that's a target for the bad guys. So there is an opportunity, however, to expand from just the backup corpus to all data and protect this kind of 3, 2, 1, or maybe it's 3, 2, 1, 1, three copies, two backups, a backup in the cloud and one that's air gaped. So this can be extended to primary storage, copies, snaps, containers, data in motion, et cetera, to have a comprehensive data protection strategy. And customers, as I said earlier, are increasingly looking to manage service providers and specialists because of that skills gap. And that's a big reason why automation is so important in orchestration. And automation and orchestration, I'll emphasize, on the air gap solutions should be separated physically and logically. All right, now let's take a look at some of the ETR data and some of the players. This is a chart that we like to show often. It's a X-Y axis. And the Y axis is net score, which is a measure of spending momentum. And the horizontal axis is market share. Now, market share is an indicator of pervasiveness in the survey. It's not spending market share, it's not market share of the overall market, it's a term that ETR uses. It's essentially market share of the responses within the survey set. Think of it as mind share. Okay, you've got the pure plays here on this slide, in the storage category. There is no data protection or backup category. So what we've done is we've isolated the pure plays or close to pure plays in backup and data protection. Now notice that red line, that red is kind of our subjective view of anything that's over that 40% line is elevated. And you can see only Rubrik, and the July survey is over that 40% line. I'll show you the ends in a moment. Smaller ends, but still, Rubrik is the only one. Now, look at Cohesity and Rubrik in the January 2020. So last year, pre-pandemic, Cohesity and Rubrik, they've come well off their peak for net score. Look at Veeam. Veeam, having studied this data for the last say 24 hours months, Veeam has been steady Eddy. It is really always in the mid to high 30s, always shows a large shared end, so it's coming up in the survey. Customers are mentioning Veeam. And it's got a very solid net score. It's not above that 40% line, but it's hovering just below consistently. That's very impressive. Commvault has steadily been moving up. Sanjay Mirchandani has made some acquisitions. He did the Hedvig acquisition. They launched Metallic, that's driving cloud affinity within Commvault's large customer base. So it's good example of a legacy player pivoting and evolving and transforming itself. Veritas, it continues to under perform in the ETR surveys relative to the other players. Now, for context, let's add IBM and Dell to the chart. Now just note, this is IBM and Dell's full storage portfolio. The category in the taxonomy at ETR is all storage. Just previous slide, I isolated on the pure plays. But this now adds in IBM and Dell. It probably representative of where they would be. Probably Dell larger on the horizontal axis than IBM, of course. And you could see the spending momentum accordingly. So you can see that in the data chart that we've inserted. So some smaller ends for Rubrik and Cohesity. But still enough to pay attention, it's not like one or two. When you're 20-plus, 15-plus 25-plus, you can start to pay attention to trends. Veeam, again, is very impressive. It's net score is solid, it's got a consistent presence in the dataset, it's clear leader here. SimpliVity is small, but it's improving relative to last several surveys. And we talked about Convolt. Now, I want to emphasize something that we've been hitting on for quite some time now. And that's the Renaissance that's coming in compute. Now, we all know about Moore's Law, the doubling of transistor density every two years, 18 to 24 months. And that leads to a doubling of performance in that timeframe. X86, that x86 curve is in the blue. And if you do the math, this is expressed in trillions of operations per second. The orange line is representative of Apples A series, culminating in the A15, most recently. The A series is what Apple is now... Well, it's the technology basis for what's inside M1, the new Apple laptops, which is replacing Intel. That's that that orange line there, we'll come back to that. So go back to the blue line for a minute. If you do the math on doubling performance every 24 months, it comes out to roughly 40% annual improvement in processing power per year. That's now moderated. So Moore's Law is waning in one sense, so we wrote a piece Moore's Law is not dead. So I'm sort of contradicting myself there. But the traditional Moore's Law curve on x86 is waning. It's probably now down to around 30%, low 30s. But look at the orange line. Again, using the A series as an indicator, if you combine then the CPU, the NPU, which neuro processing unit, XPU, pick whatever PU you want, the accelerators, the DSPs, that line is growing at 100% plus per year. It's probably more accurately around 110% a year. So there's a new industry curve occurring, and it's being led by the Arm ecosystem. The other key factor there, and you're seeing this in a lot of use cases, a lot of consumer use cases, Apple is an example, but you're also seeing it in things like Tesla, Amazon with AWS graviton, the Annapurna acquisition, building out graviton and nitro, that's based on Arm. You can get from design to tape out in less than two years. Whereas the Intel cycles, we know, they've been running it four to five years now. Maybe Pat Gelsinger is compressing those. But Intel is behind. So organizations that are on that orange curve are going to see faster acceleration, lower cost, lower power, et cetera. All right, so what's the tie to data protection. I'm going to leave you with this chart. Arm has introduced it's confidential, compute architecture and is ushering in a new era of security and data protection. Zero trust is the new mandate. And what Arm has it's done with what they call realms is create physical separation of the vulnerable components by creating essentially physical buckets to put code in and to put data in, separate from the OS. Remember, the OS is the most valuable entry point for hackers or one of them because it contains privileged access, and it's a weak link because of things like memory leakages and vulnerabilities. And malicious code can be placed by bad guys within data in the OS and appear benign, even though it's anything but. So in this, all the OS does is create API calls to the realm controller. That's the only interaction. So it makes it much harder for bad actors to get access to the code and the data. And importantly, very importantly, it's an end-to-end architecture. So there's protection throughout. If you're pulling data from the edge and bringing it back to the on-prem or the cloud, you've got that end to end architecture and protection throughout. So the link to data protection is that backup software vendors need to be the most trusted of applications. Backup software needs to be the most trusted of applications because it's one of the most targeted areas in a cyber attack. Realms provide an end-to-end separation of data and code from the OS and it's a better architectural construct to support zero trust and confidential computing and critical use cases like data protection/backup and other digital business apps. So our call to action is backup software vendors, you can lead the charge. Arm is several years ahead at the moment, ahead of Intel, in our view. So you've got to pay attention to that, research that. We're not saying over rotate, but go investigate that. And use your relationships with Intel to accelerate its version of this architecture. Or ideally, the industry should agree on common standards and solve this problem together. Pat Gelsinger told us in theCUBE that if it's the last thing he's going to do in his industry life, he's going to solve this security problem. That's when he was at VMware. Well, Pat, you're even in a better place to do it now. You don't have to solve it yourself, you can't, and you know that. So while you're going about your business saving Intel, look to partner with Arm. I know it sounds crazy to use these published APIs and push to collaborate on an open source architecture that addresses the cyber problem. If anyone can do it, you can. Okay, that's it for today. Remember, these episodes are all available as podcasts. All you got to do is search Braking Analysis Podcast. I publish weekly on wikibond.com and siliconangle.com. Or you can reach me @dvellante on Twitter, email me at david.vellante@siliconangle.com. And don't forget to check out etr.plus for all the survey and data action. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching, everybody. Be well, and we'll see you next time. (gentle music)

>>Mhm. Yes. Hello. Welcome back to the cubes coverage of dr khan 2021 virtual. I'm john for your host of the cube. We're messing my fair principal technologist at AWS amazon Web services messman. Thank you for coming on the cube, appreciate it. Um >>Thank you. Thank you for having me. >>Great to see you love this amazon integration with doctor want to get into that in a second. Um Been great to see the amazon cloud native integration working well. E. C. S very popular. Every interview I've done at reinvent uh every year it gets better and better more adoption every year. Um Tell us what's going on with amazon E. C. S because you have Pcs anywhere and now that's being available. >>Yeah that's fine, that's correct, join and uh yeah so customers has been appreciating the value and the simplicity of VCS for many years now. I mean we we launched GCS back in 2014 and we have seen great adoption of the product and customers has always been appreciating. Uh the fact that it was easy to operate and easy to use. Uh This is a journey with the CS anywhere that started a few years ago actually. And we started this journey uh listening to customers that had particular requirements. Um I'd like to talk about, you know, the the law of the land and the law um uh of the physic where customers wanted to go all in into uh into the cloud, but they did have this exception that they need to uh deal with with the application that could not move to the cloud. So as I said, this journey started three years ago when we launched outpost. Um and outpost is our managed infrastructure that customers can deploy in their own data centers. And we supported Pcs on day one on outpost. Um having that said, there are lots of customers that came to us and said we love outputs but there are certain applications and certain requirements, uh such as compliance or the fact simply that we have like assets that we need to reuse in our data center uh that we want to use and before we move into into the cloud. So they were asking us, we love the simplicity of Vcs but we have to use gears that we have in our data center. That is when we started thinking about Pcs anywhere. So basically the idea of VCS anywhere is that you can use e c s E C as part of that, you know, and love um uh appreciated the simplicity of using Pcs but using your customer managed infrastructure as the data plane, basically what you could do is you can define your application within the Ec. S country plane and deploy those applications on customer own um infrastructure. What that means from a very practical perspective is that you can deploy this application on your managed infrastructure ranging from uh raspberry pis this is the demo that we show the invent when we pronounce um e c s anywhere all the way up to bare metal server, we don't really care about the infrastructure underneath. As long as it supported, the OS is supported. Um we're fine with that. >>Okay, so let's take this to the next level and actually the big theme at dr Connors developer experience, you know, that's kind of want to talk about that and obviously developer productivity and innovation have to go hand in hand. You don't want to stunt the innovation equation, which is cloud, native and scale. Right. So how does the developer experience improve with amazon ECs and anywhere now that I'm on, on premises or in the cloud? Can you take me through? What's the improvements around pcs and the developer? >>Yeah I would argue that the the what you see as anywhere solved is more for operational aspect and the requirements that more that are more akin to the operation team that that they need to meet. Uh We're working very hard to um to improve the developing experience on top of the CS beyond what we're doing with the CS anywhere. So um I'd like to step back a little bit and maybe tell a little bit of a story of why we're working on those things. So um the customer as I said before, continue to appreciate the simplicity and the easier views of E. C. S. However what we learn um over the years is that as we added more features to E. C. S, we ended up uh leveraging more easy. Um AWS services um example uh would be a load balancer integration or secret manager or Fc. Or um other things like service discovery that uses underneath other AWS products like um clubman for around 53. And what happened is that the end user experience, the developer experience became a little bit more complicated because now customers opportunity easy of use of these fully managed services. However they were responsible for time and watering all uh together in the application definition. So what we're working on to simplify this experience is we're working on tools that kind of abstract these um this verbal city that you get with pcs. Um uh An example is a confirmation template that a developer we need to use uh to deploy an application leveraging all of these features. Could then could end up being uh many hundreds of transformation lines um in the in the in the definition of the service. So we're working on new tools and new capabilities to make this experience better. Uh Some of them are C d k uh the copilot cli, dws, copilot cli those are all instruments and technologies and tools that we're building to abstract that um uh verbosity that I was alluding to and this is where actually also the doctor composed integration with the CS falls in. >>Yeah, I'm just gonna ask you that the doctor piece because actually it's dr khan all the developers love containers, they love what they do. Um This is a native, you know, mindset of shifting left with security. How is the relationship with the Docker container ecosystem going with you guys? Can you take him in to explain for the folks here watching this event and participating in the community, explain the relationship with Docker container specifically. >>Yeah, absolutely. Uh so basically we started working with dR many, many years ago, um uh Pcs was based on on DR technology when we launch it. Uh and it's still using uh DR technology and last year we started to collaborate with dR more closely um when DR releases the doctor composed specification um as an open source projects. So basically doctor is trying to use the doctor composed specification to create uh infrastructure product gnostic, uh way to deploy Docker application um uh using those specification in multiple infrastructure as part of these journey, we work with dr to support pcs as a back end um for um for the specification, basically what this means from a very practical perspective, is that you can take a doctor composed an existing doctor composed file. Um and doctor says that there are 650,000 doctor composed files spread across the top and all um uh lose control uh system um over the world. And basically you can take those doctor composed file and uh composed up and deploy transparently um into E. C. S Target on AWS. So basically if we go back to what I was alluding to before, the fact that the developer would need to author many 100 line of confirmation template to be able to take their application and deploy it into the cloud. What they need to do now is um offering a new file, a um a file uh with a very clear and easy to use dr composed syntax composed up and deploy automatically on AWS. Um and using Pcs Fargate um and many other AWS services in the back end. >>And what's the expectation in your mind as you guys look at the container service to anywhere model the on premise and without post, what does he what's the vision? Because that's again, another question mark for me, it's like, okay, I get it totally makes sense. Um, but containers are showing the mainstream enterprises, not the hyper skills. You guys always been kind of the forward thinkers, but you know, main street enterprise, I call it. They're picking up adoption of containers in a massive way. They're looking at cloud native specifically as the place for modern application development period. That's happening. What's the story? Say it again? Because I want to make sure I get this right e C s anywhere if I want to get on premises hybrid, What's it mean for me? >>Uh, this goes back to what I was saying at the beginning. So there are there are there when we have been discussing here are mostly to or token of things. Right. So the fact that we enable these big enterprises to meet their requirements and meet their um their um checkboxes sometimes to be able to deploy outside of AWS when there is a need to do that. This could be for edge use cases or for um using years that exist in the data center. So this is where e c s anywhere is basically trying, this is what uh pcs anywhere is trying to address. There is another orthogonal discussion which is developer experience, uh and that development experience is being addressed by these additional tools. Um what I like to say is that uh the confirmation is becoming a little bit like assembler in a sense, right? It's becoming very low level, super powerful, but very low level and we want to abstract and bring the experience to the next level and make it simple for developers to leverage the simplicity of some of these tools including Docker compose um and and and being able to deploy into the cloud um and getting all the benefits of the cloud scalability, electricity and security. >>I love the assembler analogy because you think about it. A lot of the innovation has been kind of like low level foundational and if you start to see all the open source activity and the customers, the tooling does matter. And I think that's where the ease of use comes in. So the simplicity totally makes sense. Um can you give an example of some simplicity piece? Because I think, you know, you guys, you know, look at looking at ec. S as the cornerstone for simplicity. I get that. Can you give an example to walk us through a day in the life of of an example >>uh in an example of simplicity? Yeah, supposedly in action. Yeah. Well, one of the examples that I usually do and there is this uh, notion of being served less and I think that there is a little bit of a, of an obsession around surveillance and trying to talk about surveillance for so many things. When I talk about the C. S, I like to use another moniker that is version less. So to me, simplicity also means that I do not have to um update my service. Right? So the way E C. S works is that engineering in the service team keeps producing and keeps delivering new features for PCS overnight for customers to wake up in the morning and consuming those features without having to deal with upgrades and updates. I think that this is a very key, um, very key example of simplicity when it comes to e C s that is very hard to find um in other, um, solutions whether there are on prime or in the cloud. >>That's a great example in one of the big complaints I hear just anecdotally around the industry is, you know, the speed of the minds of business, want the apps to move faster and the iteration with some craft obviously with security and making sure things buttoned up, but things get pulled back. It's almost slowed down because the speed of the innovation is happening faster than the compliance of some sort of old governance model or code reviews. I want to approve everything. So there's a balance between making sure what's approved, whether security or some pipeline procedures and what not. >>So that I could have. I cannot agree more with you. Yeah, no, it's absolutely true because I think that we see these very interesting um, uh, economy, I would say between startups moving super fast and enterprises try to move fast but forced to move at their own speed. So when we when we deliver services based on, for example, open source software uh, that customers need to um, look after in terms of upgrade to latest release. What we usually see is start up asking us can you move faster? There is a new version of that software, can you enable us to deploy that version? And then on the other hand of the spectrum, there are these big enterprises trying to move faster but not so much that are asking us can use lower. Can you slow down a little bit? Right, because I cannot keep that pigs. So it's a very it's a very interesting um, um, a very interesting time to be alive. >>You know, one of the, one of the things that pop up into these conversations when you talk, when I talk to VP of engineering of companies and then enterprises that the operational efficiency, you got developer productivity and you've got innovation right, you've got the three kind of things going on there knobs and they all have to turn up. People want more efficiency of the operations, they want more developed productivity and more innovation. What's interesting is you start seeing, okay, it's not that easy. There's also a team formation and I know Andy Jassy kinda referred to this in his keynote at Reinvent last year around thinking differently around your organizational but you know, that could be applied to technologists too. So I'd love to get your thoughts while you're here. I know you blog about this and you tweet about this but this is kind of like okay if these things are all going to be knobs, we turned up innovation efficiency, operationally and develop productivity. What's the makeup of the team? Because some are saying, you have an SRE embedded, you've got the platform engineering, you've got version lists, you got survival is all these things are going on all goodness. But does that mean that the teams have to change? What's your thoughts on that you want to get your perspective? >>Yeah, no, absolutely. I think that there was a joke going around that um as soon as you see a job like VP of devoPS, I mean that is not going to work, right? Because these things are needs to be like embedded into each team, right? There shouldn't be a DEVOPS team or anything, it would be just a way of working. And I totally agree with you that these knobs needs to go insane, right? And you cannot just push too hard on innovation which are not having um other folks um to uh to be able to, you know, keep that pace um with you. And we're trying to health customers with multiple uh tools and services to try to um have not only developers and making developer experience uh better but also helping people that are building these underneath platforms. Like for example, prod on AWS protein is a good example of this, where we're focusing on helping these um teams that are trying to build platforms because they are not looking themselves as being a giant or very fast. But they're they're they're measured on being secure, being compliant and being, you know, within a guardrail uh that an enterprise um regulated enterprise needs to have. So we need to have all of these people um both organizationally as well as with providing tools and technologies that have them in their specific areas um to succeed. >>Yeah. And what's interesting about all this is that you know I think we're also having conversations and and again you're starting to see things more clearly here at dr khan we saw some things that coop con which the joke there was not joke but the observation was it's less about kubernetes which is now becoming boring, lee reliable to more about cloud native applications under the covers with program ability. So as all this is going on there truly is a flip of the script. You can actually re engineer and re factor everything, not just re platform your applications in I. T. At once. Right now there's a window whether it's security or whatever. Now that the containers and and the doctor ecosystem and the container ecosystem and the The kubernetes, you've got KS and you got six far gay and all the stuff of goodness. Companies can actually do this right now. They can actually change everything. This is a unique time. This window might close are certainly changed if you're not on it now, it's the same argument of the folks who got caught in the pandemic and weren't in the cloud got flat footed. So you're seeing that example of if you weren't in the cloud up during the pandemic before the pandemic, you were probably losing during the pandemic, the ones that one where the already guys are in the cloud. Now the same thing is true with cloud native. You're not getting into it now, you're probably gonna be on the wrong side of history. What's your reaction to that? >>Yeah, No, I I I agree totally. I I like to think about this. I usually uh talk about this if I can stay back step back a little bit and I think that in this industry and I have gray areas and I have seen lots of things, I think that there has been too big Democratisation event in 90 that happened and occurred in the last 30 years. So the first one was from, you know from when um the PC technology has been introduced, distributed computing from the mainframe area and that was the first Democratisation step. Right? So everyone had access to um uh computers so they could do things if you if you fast forward to these days. Um uh what happened is that on top of that computer, whatever that became a server or whatever, there is a state a very complex stack of technologies uh that allow you to deployment and develop and deploy your application. Right. But that stack of technology and the complexity of that stack of technology is daunting in some way. Right? So it is in a bit access and democratic access to technology. So to me this is what cloud enabled, Right? So the next step of democratisation was the introduction of services that allow you to bypass that stack, which we call undifferentiated heavy lifting because you know, um you don't get paid for managing, I don't know any M. R. Server or whatever, you get paid for extracting values through application logic from that big stack. So I totally agree with you that we're in a unique position to enable everyone um with what we're building uh to innovate a lot faster and in a more secure way. >>Yeah. And what comes out, I totally agree. And I think that's a great historical view and I think let's bring this down to the present today and then bring this as the as the bridge to the future. If you're a developer you could. And by the way, no matter whether you're programming infrastructure or just writing software or even just calling a PS and rolling your own, composing your services, it's programmable and it's just all accessible. So I think that that's going to change the again back to the three knobs, developer productivity or just people productivity, operational efficiency, which is scale and then innovation, which is the business logic where I think machine learning starts to come in, right? So if you can get the container thing going, you start tapping into that control plane. It's not so much just the data control plane. It's like a software control plane. >>Yeah, no, absolutely. The fact that you can, I mean as I said, I have great hair. So I've seen a lot of things and back in the days, I mean the, I mean the whole notion of being able to call an api and get 10 servers for example or today, 10 containers. It would be like, you know, almost a joke, right? So we spent a lot of time racking and um, and doing so much manual stuff that was so ever prone because we usually talk about velocity and agility, but we, we rarely talk about, you know, the difficulties and the problems that doing things manually introduced in the process, the way that you can get wrong. >>You know, you know, it reminds me of this industry and I was like finally get off my lawn in the old days. I walk to school with no shoes on in the snow. We had to build our own colonel and our own graphics libraries and then now they have all these tools. It's like, you're just an old, you know, coder, but joking aside, you know that experience, you're bringing up appointments for the younger generation who have never loaded a Linux operating system before or had done anything like that level. It's not so much old versus young, it's more of a systems thinking, he said distributed computing. If you look at all the action, it's essentially distributed computing with new software paradigm and it's a system architecture. It's not so much software engineering, software developer, you know, this that it's just basically all engineering at this point, all software. >>It is, it is very much indeed. It's uh, it's whole software, there is no other um, there is no other way to call it. It's um, I mean we go back to talk about, you know, infrastructure as code and everything is now uh corridor software in in in a way. It's, yeah. >>This is great to have you on. Congratulations. A CS anywhere being available. It's great stuff. Um, and great to see you and, and great to have this conversation. Um, amazon web services obviously, uh, the world has has gone super cloud. Uh, now you have distributed computing with edge iot exploding beautifully, which means a lot of new opportunities. So thanks for coming on. >>Thank you very much for having me. It was a pleasure. Okay, cube >>Coverage of Dr Khan 2021 virtual. This is the Cube. I'm John for your host. Thanks for watching.

>>mhm Yes. >>Welcome back to the Cube coverage of Red Hat summit 21 2021. I'm john for host of the Cubans virtual this year as we start preparing to come out of Covid a lot of great conversations here happening around technology. This is the emerging technology with Red hat segment. We've got three great guests steve watt manager, distinguished engineer at Red Hat hurl saying senior software engineer Red Hat and luke Hines, who's the senior software engineer as well. We got the engineering team steve, you're the the team leader, emerging tech within red hat. Always something to talk about. You guys have great tech chops that's well known in the industry and I'll see now part of IBM you've got a deep bench um what's your, how do you view emerging tech um how do you apply it? How do you prioritize, give us a quick overview of the emerging tech scene at Redhead? >>Yeah, sure. It's quite a conflated term. The way we define emerging technologies is that it's a technology that's typically 18 months plus out from commercialization and this can sometimes go six months either way. Another thing about it is it's typically not something on any of our product roadmaps within the portfolio. So in some sense, it's often a bit of a surprise that we have to react to. >>So no real agenda. And I mean you have some business unit kind of probably uh but you have to have first principles within red hat, but for this you're looking at kind of the moon shot, so to speak, the big game changing shifts. Quantum, you know, you got now supply chain from everything from new economics, new technology because that kind of getting it right. >>Yeah, I think we we definitely use a couple of different techniques to prioritize and filter what we're doing. And the first is something will pop up and it will be like, is it in our addressable market? So our addressable market is that we're a platform software company that builds enterprise software and so, you know, it's got to be sort of fit into that is a great example if somebody came up came to us with an idea for like a drone command center, which is a military application, it is an emerging technology, but it's something that we would pass on. >>Yeah, I mean I didn't make sense, but he also, what's interesting is that you guys have an open source D N A. So it's you have also a huge commercial impact and again, open sources of one of the 4th, 5th generation of awesomeness. So, you know, the good news is open source is well proven. But as you start getting into this more disruption, you've got the confluence of, you know, core cloud, cloud Native, industrial and IOT edge and data. All this is interesting, right. This is where the action is. How do you guys bring that open source community participation? You got more stakeholders emerging there before the break down, how that you guys manage all that complexity? >>Yeah, sure. So I think that the way I would start is that, you know, we like to act on good ideas, but I don't think good ideas come from any one place. And so we typically organize our teams around sort of horizontal technology sectors. So you've got, you know, luke who's heading up security, but I have an edge team, cloud networking team, a cloud storage team. Cloud application platforms team. So we've got these sort of different areas that we sort of attack work and opportunities, but you know, the good ideas can come from a variety of different places. So we try and leverage co creation with our customers and our partners. So as a good example of something we had to react to a few years ago, it was K Native right? So the sort of a new way of doing service um and eventing on top of kubernetes that was originated from google. Whereas if you look at Quantum right, ibms, the actual driver on quantum science and uh that originated from IBM were parole. We'll talk about exactly how we chose to respond to that. Some things are originated organically within the team. So uh luke talking about six law is a great example of that, but we do have a we sort of use the addressable market as a way to sort of focus what we're doing and then we try and land it within our different emerging technologies teams to go tackle it. Now. You asked about open source communities, which are quite interesting. Um so typically when you look at an open source project, it's it's there to tackle a particular problem or opportunity. Sometimes what you actually need commercial vendors to do is when there's a problem or opportunity that's not tackled by anyone open source project, we have to put them together to create a solution to go tackle that thing. That's also what we do. And so we sort of create this bridge between red hat and our customers and multiple different open source projects. And this is something we have to do because sometimes just that one open source project doesn't really care that much about that particular problem. They're motivated elsewhere. And so we sort of create that bridge. >>We got two great uh cohorts here and colleagues parole on the on the Quantum side and you got luke on the security side. Pro I'll start with you. Quantum is also a huge mentioned IBM great leadership there. Um Quantum on open shift. I mean come on. Just that's not coming together for me in my mind, it's not the first thing I think of. But it really that sounds compelling. Take us through, you know, um how this changes the computing landscape because heterogeneous systems is what we want and that's the world we live in. But now with distributed systems and all kinds of new computing modules out there, how does this makes sense? Take us through this? >>Um yeah john's but before I think I want to explain something which is called Quantum supremacy because it plays very important role in the road map that's been working on. So uh content computers, they are evolving and they have been around. But right now you see that they are going to be the next thing. And we define quantum supremacy as let's say you have any program that you run or any problems that you solve on a classical computer. Quantum computer would be giving you the results faster. So that is uh, that is how we define content supremacy when the same workload are doing better on content computer than they do in a classical computer. So the whole the whole drive is all the applications are all the companies, they're trying to find avenues where Quantum supremacy are going to change how they solve problems or how they run their applications. And even though quantum computers they are there. But uh, it is not as easily accessible for everyone to consume because it's it's a very new area that's being formed. So what, what we were thinking, how we can provide a mechanism that you can you don't connect this deal was you have a classical world, you have a country world and that's where a lot of thought process been. And we said okay, so with open shift we have the best of the classical components. You can take open shift, you can develop, deploy around your application in a country raised platform. What about you provide a mechanism that the world clothes that are running on open shift. They are also consuming quantum resources or they are able to run the competition and content computers take the results and integrate them in their normal classical work clothes. So that is the whole uh that was the whole inception that we have and that's what brought us here. So we took an operator based approach and what we are trying to do is establish the best practices that you can have these heterogeneous applications that can have classical components. Talking to our interacting the results are exchanging data with the quantum components. >>So I gotta ask with the rise of containers now, kubernetes at the center of the cloud native value proposition, what work clothes do you see benefiting from the quantum systems the most? Is there uh you guys have any visibility on some of those workloads? >>Uh So again, it's it's a very new, it's very it's really very early in the time and uh we talk with our customers and every customers, they are trying to identify themselves first where uh these contacts supremacy will be playing the role. What we are trying to do is when they reach their we should have a solution that they that they could uh use the existing in front that they have on open shift and use it to consume the content computers that may or may not be uh, inside their own uh, cloud. >>Well I want to come back and ask you some of the impact on the landscape. I want to get the look real quick because you know, I think security quantum break security, potentially some people have been saying, but you guys are also looking at a bunch of projects around supply chain, which is a huge issue when it comes to the landscape, whether its components on a machine in space to actually handling, you know, data on a corporate database. You guys have sig store. What's this about? >>Sure. Yes. So sick store a good way to frame six store is to think of let's encrypt and what let's encrypt did for website encryption is what we plan to do for software signing and transparency. So six Door itself is an umbrella organization that contains various different open source projects that are developed by the Six door community. Now, six door will be brought forth as a public good nonprofit service. So again, we're very much basing this on the successful model of let's Encrypt Six door will will enable developers to sign software artifacts, building materials, containers, binaries, all of these different artifacts that are part of the software supply chain. These can be signed with six door and then these signing events are recorded into a technology that we call a transparency log, which means that anybody can monitor signing events and a transparency log has this nature of being read only and immutable. It's very similar to a Blockchain allows you to have cryptographic proof auditing of our software supply chain and we've made six stores so that it's easy to adopt because traditional cryptographic signing tools are a challenge for a lot of developers to implement in their open source projects. They have to think about how to store the private keys. Do they need specialist hardware? If they were to lose a key then cleaning up afterwards the blast radius. So the key compromise can be incredibly difficult. So six doors role and purpose essentially is to make signing easy easy to adopt my projects. And then they have the protections around there being a public transparency law that could be monitored. >>See this is all about open. Being more open. Makes it more secure. Is the >>thief? Very much yes. Yes. It's that security principle of the more eyes on the code the better. >>So let me just back up, is this an open, you said it's gonna be a nonprofit? >>That's correct. Yes. Yes. So >>all of the code is developed by the community. It's all open source. anybody can look at this code. And then we plan alongside the Linux Foundation to launch a public good service. So this will make it available for anybody to use if your nonprofit free to use service. >>So luke maybe steve if you can way into on this. I mean, this goes back. If you look back at some of the early cloud days, people were really trashing cloud as there's no security. And cloud turns out it's a more security now with cloud uh, given the complexity and scale of it, does that apply the same here? Because I feel this is a similar kind of concept where it's open, but yet the more open it is, the more secure it is. And then and then might have to be a better fit for saying I. T. Security solution because right now everyone is scrambling on the I. T. Side. Um whether it's zero Trust or Endpoint Protection, everyone's kind of trying everything in sight. This is kind of changing the paradigm a little bit on software security. Could you comment on how you see this playing out in traditional enterprises? Because if this plays out like the cloud, open winds, >>so luke, why don't you take that? And then I'll follow up with another lens on it which is the operate first piece. >>Sure. Yes. So I think in a lot of ways this has to be open this technology because this way we have we have transparency. The code can be audited openly. Okay. Our operational procedures can be audit openly and the community can help to develop not only are code but our operational mechanisms so we look to use technology such as cuba netease, open ship operators and so forth. Uh Six store itself runs completely in a cloud. It is it is cloud native. Okay, so it's very much in the paradigm of cloud and yeah, essentially security, always it operates better when it's open, you know, I found that from looking at all aspects of security over the years that I've worked in this realm. >>Okay, so just just to add to that some some other context around Six Law, that's interesting, which is, you know, software secure supply chain, Sixth floor is a solution to help build more secure software secure supply chains, more secure software supply chain. And um so um there's there's a growing community around that and there's an ecosystem of sort of cloud native kubernetes centric approaches for building more secure software. I think we all caught the solar winds attack. It's sort of enterprise software industry is responding sort of as a whole to go and close out as many of those gaps as possible, reduce the attack surface. So that's one aspect about why 6th was so interesting. Another thing is how we're going about it. So we talked about um you mentioned some of the things that people like about open source, which is one is transparency, so sunlight is the best disinfectant, right? Everybody can see the code, we can kind of make it more secure. Um and then the other is agency where basically if you're waiting on a vendor to go do something, um if it's proprietary software, you you really don't have much agency to get that vendor to go do that thing. Where is the open source? If you don't, if you're tired of waiting around, you can just submit the patch. So, um what we've seen with package software is with open source, we've had all this transparency and agency, but we've lost it with software as a service, right? Where vendors or cloud service providers are taking package software and then they're making it available as a service but that operationalize ng that software that is proprietary and it doesn't get contributed back. And so what Lukes building here as long along with our partners down, Lawrence from google, very active contributor in it. Um, the, is the operational piece to actually run sixth or as a public service is part of the open source project so people can then go and take sixth or maybe run it as a smaller internal service. Maybe they discover a bug, they can fix that bug contributed back to the operational izing piece as well as the traditional package software to basically make it a much more robust and open service. So you bring that transparency and the agency back to the SAS model as well. >>Look if you don't mind before, before uh and this segment proportion of it. The importance of immune ability is huge in the world of data. Can you share more on that? Because you're seeing that as a key part of the Blockchain for instance, having this ability to have immune ability. Because you know, people worry about, you know, how things progress in this distributed world. You know, whether from a hacking standpoint or tracking changes, Mutability becomes super important and how it's going to be preserved in this uh new six doorway. >>Oh yeah, so um mutability essentially means cannot be changed. So the structure of something is set. If it is anyway tampered or changed, then it breaks the cryptographic structure that we have of our public transparency service. So this way anybody can effectively recreate the cryptographic structure that we have of this public transparency service. So this mutability provides trust that there is non repudiation of the data that you're getting. This data is data that you can trust because it's built upon a cryptographic foundation. So it has very much similar parallels to Blockchain. You can trust Blockchain because of the immutable nature of it. And there is some consensus as well. Anybody can effectively download the Blockchain and run it themselves and compute that the integrity of that system can be trusted because of this immutable nature. So that's why we made this an inherent part of Six door is so that anybody can publicly audit these events and data sets to establish that there tamper free. >>That is a huge point. I think one of the things beyond just the security aspect of being hacked and protecting assets um trust is a huge part of our society now, not just on data but everything, anything that's reputable, whether it's videos like this being deep faked or you know, or news or any information, all this ties to security again, fundamentally and amazing concepts. Um I really want to keep an eye on this great work. Um Pearl, I gotta get back to you on Quantum because again, you can't, I mean people love Quantum. It's just it feels like so sci fi and it's like almost right here, right, so close and it's happening. Um And then people get always, what does that mean for security? We go back to look and ask them well quantum, you know, crypto But before we get started I wanted, I'm curious about how that's gonna play out from the project because is it going to be more part of like a C. N. C. F. How do you bring the open source vibe to Quantum? >>Uh so that's a very good question because that was a plan, the whole work that we are going to do related to operators to enable Quantum is managed by the open source community and that project lies in the casket. So casket has their own open source community and all the modification by the way, I should first tell you what excuse did so cute skin is the dedicate that you use to develop circuits that are run on IBM or Honeywell back in. So there are certain Quantum computers back and that support uh, circuits that are created using uh Houston S ticket, which is an open source as well. So there is already a community around this which is the casket. Open source community and we have pushed the code and all the maintenance is taken care of by that community. Do answer your question about if we are going to integrate it with C and C. F. That is not in the picture right now. We are, it has a place in its own community and it is also very niche to people who are working on the Quantum. So right now you have like uh the contributors who who are from IBM as well as other uh communities that are specific specifically working on content. So right now I don't think so, we have the map to integrated the C. N. C. F. But open source is the way to go and we are on that tragic Torri >>you know, we joke here the cube that a cubit is coming around the corner can can help but we've that in you know different with a C. But um look, I want to ask you one of the things that while you're here your security guru. I wanted to ask you about Quantum because a lot of people are scared that Quantum is gonna crack all the keys on on encryption with his power and more hacking. You're just comment on that. What's your what's your reaction to >>that? Yes that's an incredibly good question. This will occur. Okay. And I think it's really about preparation more than anything now. One of the things that we there's a principle that we have within the security world when it comes to coding and designing of software and this aspect of future Cryptography being broken. As we've seen with the likes of MD five and Sha one and so forth. So we call this algorithm agility. So this means that when you write your code and you design your systems you make them conducive to being able to easily swap and pivot the algorithms that use. So the encryption algorithms that you have within your code, you do not become too fixed to those. So that if as computing gets more powerful and the current sets of algorithms are shown to have inherent security weaknesses, you can easily migrate and pivot to a stronger algorithms. So that's imperative. Lee is that when you build code, you practice this principle of algorithm agility so that when shot 256 or shot 5 12 becomes the shar one. You can swap out your systems. You can change the code in a very least disruptive way to allow you to address that floor within your within your code in your software projects. >>You know, luke. This is mind bender right there. Because you start thinking about what this means is when you think about algorithmic agility, you start thinking okay software countermeasures automation. You start thinking about these kinds of new trends where you need to have that kind of signature capability. You mentioned with this this project you're mentioning. So the ability to actually who signs off on these, this comes back down to the paradigm that you guys are talking about here. >>Yes, very much so. There's another analogy from the security world, they call it turtles all the way down, which is effectively you always have to get to the point that a human or a computer establishes that first point of trust to sign something off. And so so it is it's a it's a world that is ever increasing in complexity. So the best that you can do is to be prepared to be as open as you can to make that pivot as and when you need to. >>Pretty impressive, great insight steve. We can talk for hours on this panel, emerging tech with red hat. Just give us a quick summary of what's going on. Obviously you've got a serious brain trust going on over there. Real world impact. You talk about the future of trust, future of software, future of computing, all kind of going on real time right now. This is not so much R and D as it is the front range of tech. Give us a quick overview of >>Yeah, sure, yeah, sure. The first thing I would tell everyone is go check out next that red hat dot com, that's got all of our different projects, who to contact if you're interested in learning more about different areas that we're working on. And it also lists out the different areas that we're working on, but just as an overview. So we're working on software defined storage, cloud storage. Sage. Well, the creator of Cf is the person that leads that group. We've got a team focused on edge computing. They're doing some really cool projects around um very lightweight operating systems that and kubernetes, you know, open shift based deployments that can run on, you know, devices that you screw into the sheet rock, you know, for that's that's really interesting. Um We have a cloud networking team that's looking at over yin and just intersection of E B P F and networking and kubernetes. Um and then uh you know, we've got an application platforms team that's looking at Quantum, but also sort of how to advance kubernetes itself. So that's that's the team where you got the persistent volume framework from in kubernetes and that added block storage and object storage to kubernetes. So there's a lot of really exciting things going on. Our charter is to inform red hats long term technology strategy. We work the way my personal philosophy about how we do that is that Red hat has product engineering focuses on their product roadmap, which is by nature, you know, the 6 to 9 months. And then the longer term strategy is set by both of us. And it's just that they're not focused on it. We're focused on it and we spend a lot of time doing disambiguate nation of the future and that's kind of what we do. We love doing it. I get to work with all these really super smart people. It's a fun job. >>Well, great insights is super exciting, emerging tack within red hat. I'll see the industry. You guys are agile, your open source and now more than ever open sources, uh, product Ization of open source is happening at such an accelerated rate steve. Thanks for coming on parole. Thanks for coming on luke. Great insight all around. Thanks for sharing. Uh, the content here. Thank you. >>Our pleasure. >>Thank you. >>Okay. We were more, more redhead coverage after this. This video. Obviously, emerging tech is huge. Watch some of the game changing action here at Redhead Summit. I'm john ferrier. Thanks for watching. Yeah.

(upbeat music) >> Hi everybody, this is Dave Volante. And with me is Sandeep Singh, he is the vice president of Storage Marketing at Hewlett Packard Enterprise. And we're going to riff on some of the trends in the industry, what we're seeing. And we got a little treat for you. Sandeep, great to see you man. >> Dave, it's a pleasure to be here. >> You and I've known each other for a long time. We've had some great discussions, some debates, some intriguing mind benders. What are you seeing out there in Storage? So much has changed. What are the key trends you're seeing and let's get into it. >> Yeah, across the board, as you said, so much has changed. When you reflect back at the underlying transformation that's taken place with data, cloud and AI across the board. First of all, for our customers they're seeing this massive data explosion that literally now spans edge to core to cloud. They're also seeing a diversity of the application workloads across the board. And the emphasis that it's placing is on the complexity that underlies overall infrastructure and data management. Across the board, we're hearing a lot from customers about just the underlying infrastructure complexity and the infrastructure sprawl. And then the second element of that is really extending into the complexity of data management. >> So it's interesting you're talking about data management. You remember you and I, we were in Andover. It was probably like five years ago and all we were talking about was media. Flash this and flash that, and at the time that was kind of the hot storage topic. Well, flash came in addressing some of the mics that we historically talked about it. Now the problem statement is really kind of quote unquote metaphorically moving up the stack if you will, you mentioned management but let's dig into that a little bit. I mean, what is management? I mean, a lot of people that means different things to different people. You talk to a database person or a backup person. How do you look at management? What does that mean to you? >> Yeah, Dave, you mentioned that the flash came in and it actually accelerated the overall speed and latency that storage was delivering to the application workloads. But fundamentally when you look back at storage over a couple of decades the underlying way of how you're managing storage hasn't fundamentally changed. There's still an incredible amount of complexity for IT. It's still a manual admin driven experience for customers. And what that's translating to is more often than not IT is in the world of firefighting and it's leaves them unable to help with them more strategic projects to innovate for the business. And basically IT has that pressure point of moving beyond that and helping bring greater levels of agility that line of business owners are asking for and to be able to deliver on more of the strategic projects. So that's one element of it. The second element that we're hearing from customers about is as more and more data just continues to explode from edge to core to cloud. And as basically the infrastructure has grown from just being on-Prem to being at the Edge to being in the cloud. Now that complexity is expanding from just being on-Prem to across multiple different clouds. So when you look across the date data life cycle how do you store it? How do you secure it? How do you basically protect it and archive it and analyze that data. That end to end life cycle management of data today resides on just a fragmented set of overall infrastructure and tools and processes and administrative boundaries. That's creating a massive challenge for customers. And the impact of that ultimately is essentially comes at a cost to agility, to innovation and ultimately business risk. >> Yeah, so we've seen obviously the cloud has addressed a lot of these problems but the problem is the cloud is in the cloud and much of my stuff, most of my stuff, isn't in the cloud. So I have all these other workloads that are either on-Prem and now you've got this emerging Edge. And so I wonder if we could just talk a little vision here for a minute. I mean what I've been envisioning is this abstraction layer that cuts across all weather. It doesn't really matter where it is. If it's on-Prem, if it's across cloud, if it's in the cloud, on the edge, we could talk about what that all means. But if customers that I talked to they're sort of done with the complexity of that underlying infrastructure. They want technology to take care of that. They want automation they want AI brought in to that equation. And it seems like we're from the cusp of the decade where that might happen. What's your take? >> Well, yeah, certainly I mentioned that data cloud and AI are really the disruptive forces, better propelling. The digital transformation for customers. Cloud has set the standard for agility and AI driven insights and intelligence are really helping to make the underlying infrastructure invisible and customers are looking for this notion of being able to get that cloud operational agility pretty much everywhere because they're discovering that that's a game changer. And yet a lot of their application workloads and data is on-Prem and is increasingly growing at the edge. So they want same experience to be able to truly bring that agility to wherever their data in absolute. And that's one of the things that we're continuing to hear from customers. >> And this problem is just going to get worse. I mean for decades we marched to the cadence of Moore's Law and everybody's going to forgets about Moore's Law. And say, "Ah, it's dying or whatever." But actually when you look at the processing power that's coming out now, it's more than doubling every two years, quadrupling every two years. So now you've got this capability in your hands and application design minors, storage companies, networking companies. They're going to have all this power to now bring in AI and do things that we've never even imagined before. So it's not about the box and the speeds and feeds of the box. It's really more about this abstraction layer that I was talking about. The management if you will that you were discussing and what we can do in terms of being able to power new workloads in machine intelligence, it's this kind of ubiquitous, call it the cloud but it's expanding pretty much everywhere in every part of our lives even to the edge you think about autonomous vehicles, you think about factories it's actually quite mind boggling where we're headed. >> It is and you touched upon AI. And certainly when you look at infrastructure, for example there's been a ton of complexity in infrastructure management. One of the studies that was done actually by IDC indicated that over 90% of the challenges that arise, for example ultimately down at the storage infrastructure layer that's powering the apps ultimately arises from way above the stack all the way from the server layer on down where even the virtual machine layer. And there, for example, AIOps for infrastructure has become a game changer for customers to be able to bring the power of AI and machine learning and multi-variate analysis to be able to predict and prevent issues. Dave, you also touched upon Edge and across the board. What we're seeing is the Enterprise Edge is becoming that frontier for customer experiences and the opportunity to reimagine customer experiences as well as just the frontier for commerce that's happening. When you look at retail and manufacturing and or financial services. So across the board with the data growth that's happening and this Edge becoming the strategic frontier for delivering the customer experiences how you power your application workloads there and how you deliver that data and protect that data and be able to seamlessly manage that overall infrastructure. As you mentioned abstracted away at a higher level becomes incredibly important for customers. >> So interesting to hear how the conversations changed. I'd like to say, I go back to whatever it was five years ago, we're talking about flash storage class memory, NVMe and those things are still there but your emphasis now you're talking about machine learning, AI, math around deep learning. It's really software is really what you're focusing on these days. >> Very much so. Certainly this notion of software and services that are delivering and unlocking a whole new experience for customers that's really the game changer going forward for customers. And that's what we're focused on. >> Well, I said we had a little surprise for you. So you guys are having an event on May 4th. It's called Unleash The Power of Data. What's that event all about Sandeep? >> Yeah. We are very much excited about our May 4th event. As you mentioned, it's called Unleash The Power of Data. And as most organizations today are data driven and data is at the heart of what they're doing. We're excited to invite everyone to join this event. And through this event we're unveiling a new vision for data that accelerates the data driven transformation from Edge to cloud. This event promises to be a pivotal event and one that IT admins, cloud architects, virtual machine admins, vice presidents, directors of IT and CIO really won't want to mess. Across the board this event is just bringing a new way of articulating the overall problem statement and in market in focused the articulation of the trends that we were just discussing. It's an event that's going to be hosted by a Business and Technology Journalist, Shabani Joshi. It will feature a market in panel with a focus on the crucial role that data is playing in customers digital transformation. It will also include and feature Antonio Neary, CEO of HPE and Tom black, senior vice president and general manager of HPE Storage Business and industry experts including Julia Palmer, research vice president at Gartner. We will unveil game changing HPE innovations that will make it possible for organizations across Edge to cloud to unleash the power of data. >> Sounds like great event. I presume I can go to hpe.com and get information, is it a registered event? How does that all work? Yeah, we invite everyone to visit hpe.com and by visiting there you can click and save the date of May 4th at 8:00 AM Pacific. We invite everyone to join us. We couldn't be more excited to get to this event and be able to share the vision and game-changing HPE innovations. >> Awesome. So I don't have to register, right? I don't have to give up my three children's name and my social security number to attend your event. Is that right? >> No registration required, come by, click on hpe.com. Save the date on your calendar. And we very much look forward to having everyone join us for this event. >> I love it, it's pure content event. I'm not going to get a phone call afterwards saying, "Hey, buy some stuff from me." That could come other channels but so that's good. Thank you for that. Thanks for providing that service to the industry. I'm excited to see what you guys are going to be announcing that day and look Sandeep. I mean, like I said, we've known each other a while. We've seen a lot of trends but the next 10 years it ain't going to look like the last 10 is it? >> It's going to be very different and we couldn't be more excited. >> Well, Sandeep, thanks so much for coming to theCube and riffing with me on the industry and giving us a preview for your event. Good luck with that. And always great to see you. >> Thanks a lot, Dave. Always great to see you as well. >> All right. And thank you everybody. This is Dave Volante for theCube and we'll see you next time. 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>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> Moore's Law is dead, right? Think again. Massive improvements in processing power combined with data and AI will completely change the way we think about designing hardware, writing software and applying technology to businesses. Every industry will be disrupted. You hear that all the time. Well, it's absolutely true and we're going to explain why and what it all means. Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we're going to unveil some new data that suggests we're entering a new era of innovation that will be powered by cheap processing capabilities that AI will exploit. We'll also tell you where the new bottlenecks will emerge and what this means for system architectures and industry transformations in the coming decade. Moore's Law is dead, you say? We must have heard that hundreds, if not, thousands of times in the past decade. EE Times has written about it, MIT Technology Review, CNET, and even industry associations that have lived by Moore's Law. But our friend Patrick Moorhead got it right when he said, "Moore's Law, by the strictest definition of doubling chip densities every two years, isn't happening anymore." And you know what, that's true. He's absolutely correct. And he couched that statement by saying by the strict definition. And he did that for a reason, because he's smart enough to know that the chip industry are masters at doing work arounds. Here's proof that the death of Moore's Law by its strictest definition is largely irrelevant. My colleague, David Foyer and I were hard at work this week and here's the result. The fact is that the historical outcome of Moore's Law is actually accelerating and in quite dramatically. This graphic digs into the progression of Apple's SoC, system on chip developments from the A9 and culminating with the A14, 15 nanometer bionic system on a chip. The vertical axis shows operations per second and the horizontal axis shows time for three processor types. The CPU which we measure here in terahertz, that's the blue line which you can't even hardly see, the GPU which is the orange that's measured in trillions of floating point operations per second and then the NPU, the neural processing unit and that's measured in trillions of operations per second which is that exploding gray area. Now, historically, we always rushed out to buy the latest and greatest PC, because the newer models had faster cycles or more gigahertz. Moore's Law would double that performance every 24 months. Now that equates to about 40% annually. CPU performance is now moderated. That growth is now down to roughly 30% annual improvements. So technically speaking, Moore's Law as we know it was dead. But combined, if you look at the improvements in Apple's SoC since 2015, they've been on a pace that's higher than 118% annually. And it's even higher than that, because the actual figure for these three processor types we're not even counting the impact of DSPs and accelerator components of Apple system on a chip. It would push this even higher. Apple's A14 which is shown in the right hand side here is quite amazing. It's got a 64 bit architecture, it's got many, many cores. It's got a number of alternative processor types. But the important thing is what you can do with all this processing power. In an iPhone, the types of AI that we show here that continue to evolve, facial recognition, speech, natural language processing, rendering videos, helping the hearing impaired and eventually bringing augmented reality to the palm of your hand. It's quite incredible. So what does this mean for other parts of the IT stack? Well, we recently reported Satya Nadella's epic quote that "We've now reached peak centralization." So this graphic paints a picture that was quite telling. We just shared the processing powers exploding. The costs consequently are dropping like a rock. Apple's A14 cost the company approximately 50 bucks per chip. Arm at its v9 announcement said that it will have chips that can go into refrigerators. These chips are going to optimize energy usage and save 10% annually on your power consumption. They said, this chip will cost a buck, a dollar to shave 10% of your refrigerator electricity bill. It's just astounding. But look at where the expensive bottlenecks are, it's networks and it's storage. So what does this mean? Well, it means the processing is going to get pushed to the edge, i.e., wherever the data is born. Storage and networking are going to become increasingly distributed and decentralized. Now with custom silicon and all that processing power placed throughout the system, an AI is going to be embedded into software, into hardware and it's going to optimize a workloads for latency, performance, bandwidth, and security. And remember, most of that data, 99% is going to stay at the edge. And we love to use Tesla as an example. The vast majority of data that a Tesla car creates is never going to go back to the cloud. Most of it doesn't even get persisted. I think Tesla saves like five minutes of data. But some data will connect occasionally back to the cloud to train AI models and we're going to come back to that. But this picture says if you're a hardware company, you'd better start thinking about how to take advantage of that blue line that's exploding, Cisco. Cisco is already designing its own chips. But Dell, HPE, who kind of does maybe used to do a lot of its own custom silicon, but Pure Storage, NetApp, I mean, the list goes on and on and on either you're going to get start designing custom silicon or you're going to get disrupted in our view. AWS, Google and Microsoft are all doing it for a reason as is IBM and to Sarbjeet Johal said recently this is not your grandfather's semiconductor business. And if you're a software engineer, you're going to be writing applications that take advantage of all the data being collected and bringing to bear this processing power that we're talking about to create new capabilities like we've never seen it before. So let's get into that a little bit and dig into AI. You can think of AI as the superset. Just as an aside, interestingly in his book, "Seeing Digital", author David Moschella says, there's nothing artificial about this. He uses the term machine intelligence, instead of artificial intelligence and says that there's nothing artificial about machine intelligence just like there's nothing artificial about the strength of a tractor. It's a nuance, but it's kind of interesting, nonetheless, words matter. We hear a lot about machine learning and deep learning and think of them as subsets of AI. Machine learning applies algorithms and code to data to get "smarter", make better models, for example, that can lead to augmented intelligence and help humans make better decisions. These models improve as they get more data and are iterated over time. Now deep learning is a more advanced type of machine learning. It uses more complex math. But the point that we want to make here is that today much of the activity in AI is around building and training models. And this is mostly happening in the cloud. But we think AI inference will bring the most exciting innovations in the coming years. Inference is the deployment of that model that we were just talking about, taking real time data from sensors, processing that data locally and then applying that training that has been developed in the cloud and making micro adjustments in real time. So let's take an example. Again, we love Tesla examples. Think about an algorithm that optimizes the performance and safety of a car on a turn, the model take data on friction, road condition, angles of the tires, the tire wear, the tire pressure, all this data, and it keeps testing and iterating, testing and iterating, testing iterating that model until it's ready to be deployed. And then the intelligence, all this intelligence goes into an inference engine which is a chip that goes into a car and gets data from sensors and makes these micro adjustments in real time on steering and braking and the like. Now, as you said before, Tesla persist the data for very short time, because there's so much of it. It just can't push it back to the cloud. But it can now ever selectively store certain data if it needs to, and then send back that data to the cloud to further train them all. Let's say for instance, an animal runs into the road during slick conditions, Tesla wants to grab that data, because they notice that there's a lot of accidents in New England in certain months. And maybe Tesla takes that snapshot and sends it back to the cloud and combines it with other data and maybe other parts of the country or other regions of New England and it perfects that model further to improve safety. This is just one example of thousands and thousands that are going to further develop in the coming decade. I want to talk about how we see this evolving over time. Inference is where we think the value is. That's where the rubber meets the road, so to speak, based on the previous example. Now this conceptual chart shows the percent of spend over time on modeling versus inference. And you can see some of the applications that get attention today and how these applications will mature over time as inference becomes more and more mainstream, the opportunities for AI inference at the edge and in IOT are enormous. And we think that over time, 95% of that spending is going to go to inference where it's probably only 5% today. Now today's modeling workloads are pretty prevalent and things like fraud, adtech, weather, pricing, recommendation engines, and those kinds of things, and now those will keep getting better and better and better over time. Now in the middle here, we show the industries which are all going to be transformed by these trends. Now, one of the point that Moschella had made in his book, he kind of explains why historically vertically industries are pretty stovepiped, they have their own stack, sales and marketing and engineering and supply chains, et cetera, and experts within those industries tend to stay within those industries and they're largely insulated from disruption from other industries, maybe unless they were part of a supply chain. But today, you see all kinds of cross industry activity. Amazon entering grocery, entering media. Apple in finance and potentially getting into EV. Tesla, eyeing insurance. There are many, many, many examples of tech giants who are crossing traditional industry boundaries. And the reason is because of data. They have the data. And they're applying machine intelligence to that data and improving. Auto manufacturers, for example, over time they're going to have better data than insurance companies. DeFi, decentralized finance platforms going to use the blockchain and they're continuing to improve. Blockchain today is not great performance, it's very overhead intensive all that encryption. But as they take advantage of this new processing power and better software and AI, it could very well disrupt traditional payment systems. And again, so many examples here. But what I want to do now is dig into enterprise AI a bit. And just a quick reminder, we showed this last week in our Armv9 post. This is data from ETR. The vertical axis is net score. That's a measure of spending momentum. The horizontal axis is market share or pervasiveness in the dataset. The red line at 40% is like a subjective anchor that we use. Anything above 40% we think is really good. Machine learning and AI is the number one area of spending velocity and has been for awhile. RPA is right there. Very frankly, it's an adjacency to AI and you could even argue. So it's cloud where all the ML action is taking place today. But that will change, we think, as we just described, because data's going to get pushed to the edge. And this chart will show you some of the vendors in that space. These are the companies that CIOs and IT buyers associate with their AI and machine learning spend. So it's the same XY graph, spending velocity by market share on the horizontal axis. Microsoft, AWS, Google, of course, the big cloud guys they dominate AI and machine learning. Facebook's not on here. Facebook's got great AI as well, but it's not enterprise tech spending. These cloud companies they have the tooling, they have the data, they have the scale and as we said, lots of modeling is going on today, but this is going to increasingly be pushed into remote AI inference engines that will have massive processing capabilities collectively. So we're moving away from that peak centralization as Satya Nadella described. You see Databricks on here. They're seen as an AI leader. SparkCognition, they're off the charts, literally, in the upper left. They have extremely high net score albeit with a small sample. They apply machine learning to massive data sets. DataRobot does automated AI. They're super high in the y-axis. Dataiku, they help create machine learning based apps. C3.ai, you're hearing a lot more about them. Tom Siebel's involved in that company. It's an enterprise AI firm, hear a lot of ads now doing AI and responsible way really kind of enterprise AI that's sort of always been IBM. IBM Watson's calling card. There's SAP with Leonardo. Salesforce with Einstein. Again, IBM Watson is right there just at the 40% line. You see Oracle is there as well. They're embedding automated and tele or machine intelligence with their self-driving database they call it that sort of machine intelligence in the database. You see Adobe there. So a lot of typical enterprise company names. And the point is that these software companies they're all embedding AI into their offerings. So if you're an incumbent company and you're trying not to get disrupted, the good news is you can buy AI from these software companies. You don't have to build it. You don't have to be an expert at AI. The hard part is going to be how and where to apply AI. And the simplest answer there is follow the data. There's so much more to the story, but we just have to leave it there for now and I want to summarize. We have been pounding the table that the post x86 era is here. It's a function of volume. Arm volumes are a way for volumes are 10X those of x86. Pat Gelsinger understands this. That's why he made that big announcement. He's trying to transform the company. The importance of volume in terms of lowering the cost of semiconductors it can't be understated. And today, we've quantified something that we haven't really seen much of and really haven't seen before. And that's that the actual performance improvements that we're seeing in processing today are far outstripping anything we've seen before, forget Moore's Law being dead that's irrelevant. The original finding is being blown away this decade and who knows with quantum computing what the future holds. This is a fundamental enabler of AI applications. And this is most often the case the innovation is coming from the consumer use cases first. Apple continues to lead the way. And Apple's integrated hardware and software model we think increasingly is going to move into the enterprise mindset. Clearly the cloud vendors are moving in this direction, building their own custom silicon and doing really that deep integration. You see this with Oracle who kind of really a good example of the iPhone for the enterprise, if you will. It just makes sense that optimizing hardware and software together is going to gain momentum, because there's so much opportunity for customization in chips as we discussed last week with Arm's announcement, especially with the diversity of edge use cases. And it's the direction that Pat Gelsinger is taking Intel trying to provide more flexibility. One aside, Pat Gelsinger he may face massive challenges that we laid out a couple of posts ago with our Intel breaking analysis, but he is right on in our view that semiconductor demand is increasing. There's no end in sight. We don't think we're going to see these ebbs and flows as we've seen in the past that these boom and bust cycles for semiconductor. We just think that prices are coming down. The market's elastic and the market is absolutely exploding with huge demand for fab capacity. Now, if you're an enterprise, you should not stress about and trying to invent AI, rather you should put your focus on understanding what data gives you competitive advantage and how to apply machine intelligence and AI to win. You're going to be buying, not building AI and you're going to be applying it. Now data as John Furrier has said in the past is becoming the new development kit. He said that 10 years ago and he seems right. Finally, if you're an enterprise hardware player, you're going to be designing your own chips and writing more software to exploit AI. You'll be embedding custom silicon in AI throughout your product portfolio and storage and networking and you'll be increasingly bringing compute to the data. And that data will mostly stay where it's created. Again, systems and storage and networking stacks they're all being completely re-imagined. If you're a software developer, you now have processing capabilities in the palm of your hand that are incredible. And you're going to rewriting new applications to take advantage of this and use AI to change the world, literally. You'll have to figure out how to get access to the most relevant data. You have to figure out how to secure your platforms and innovate. And if you're a services company, your opportunity is to help customers that are trying not to get disrupted are many. You have the deep industry expertise and horizontal technology chops to help customers survive and thrive. Privacy? AI for good? Yeah well, that's a whole another topic. I think for now, we have to get a better understanding of how far AI can go before we determine how far it should go. Look, protecting our personal data and privacy should definitely be something that we're concerned about and we should protect. But generally, I'd rather not stifle innovation at this point. I'd be interested in what you think about that. Okay. That's it for today. Thanks to David Foyer, who helped me with this segment again and did a lot of the charts and the data behind this. He's done some great work there. Remember these episodes are all available as podcasts wherever you listen, just search breaking it analysis podcast and please subscribe to the series. We'd appreciate that. Check out ETR's website at ETR.plus. We also publish a full report with more detail every week on Wikibon.com and siliconangle.com, so check that out. You can get in touch with me. I'm dave.vellante@siliconangle.com. You can DM me on Twitter @dvellante or comment on our LinkedIn posts. I always appreciate that. This is Dave Vellante for theCUBE Insights powered by ETR. Stay safe, be well. And we'll see you next time. (bright music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, >>we're now going to explore the vision of the future of cloud computing From the perspective of one of the leaders in the field, J G >>Share >>a pure off is the vice president of As Your Data ai and Edge at Microsoft G. Welcome to the Cuban cloud. Thanks so much for participating. >>Well, thank you, Dave, and it's a real pleasure to be here with you. And I just wanna welcome the audience as well. >>Well, jg judging from your title, we have a lot of ground to cover, and our audience is definitely interested in all the topics that are implied there. So let's get right into it. You know, we've said many times in the Cube that the new innovation cocktail comprises machine intelligence or a I applied to troves of data. With the scale of the cloud. It's it's no longer, you know, we're driven by Moore's law. It's really those three factors, and those ingredients are gonna power the next wave of value creation and the economy. So, first, do you buy into that premise? >>Yes, absolutely. we do buy into it. And I think, you know, one of the reasons why we put Data Analytics and Ai together is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from a I as being on that continues off, having started off with really things like analytics and proceeding toe. You know, machine learning and the use of data. Interesting breaks. Yes. >>I'd like to get some more thoughts around a data and how you see the future data and the role of cloud and maybe how >>Microsoft, you >>know, strategy fits in there. I mean, you, your portfolio, you got you got sequel server, Azure, Azure sequel. You got arc, which is kinda azure everywhere for people that aren't familiar with that. You've got synapse. Which course that's all the integration a data warehouse, and get things ready for B I and consumption by the business and and the whole data pipeline and a lot of other services as your data bricks you got You got cosmos in their, uh, Blockchain. You've got open source services like Post Dress and my sequel. So lots of choices there. And I'm wondering, you know, how do you think about the future of Of of Cloud data platforms? It looks like your strategies, right tool for the right job? Is that fair? >>It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today is that customer was the Sikh really a comprehensive proposition? And when I say a comprehensive proposition, it is sometimes not just about saying that. Hey, listen way No, you're a sequel server company. We absolutely trust that you have the best Azure sequel database in the cloud, but tell us more. We've got data that's sitting in her group systems. We've got data that's sitting in Post Press in things like mongo DB, right? So that open source proposition today and data and data management and database management has become front and center, so are really sort of push. There is when it comes to migration management, modernization of data to present the broadest possible choice to our customers so we can meet them where they are. However, when it comes to analytics. One of the things they asked for is give us a lot more convergence use. You know it, really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like synapse Fitzer, where in just land any kind of data in the leg and then use any compute engine on top of it to drive insights from it. So, fundamentally, you know, it is that flexibility that we really sort of focus on to meet our customers where they are and really not pushing our dogma and our beliefs on it. But to meet our customers according to the way they have deployed stuff like this. >>So that's great. I want to stick on this for a minute because, you know, I know when when I have guests on like yourself, do you never want to talk about you know, the competition? But that's all we ever talk about. That's all your customers ever talk about, because because the counter to that right tool for the right job and that I would say, is really kind of Amazon's approach is is that you got the single unified data platform, the mega database that does it all. And that's kind of Oracle's approach. It sounds like you wanna have your cake and eat it, too, so you you got the right tool for the right job approach. But you've got an integration layer that allows you to have that converge database. I wonder if you could add color to that and you confirm or deny what I just said. >>No, that's a That's a very fair observation, but I I say there's a nuance in what I sort of describe when it comes to data management. When it comes to APS, we have them customers with the broadest choice. Even in that, even in that perspective, we also offer convergence. So, case in point, when you think about Cosmos TV under that one sort of service, you get multiple engines, but with the same properties, right global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native AB toe, adopt cosmos Davey and adopted in a way that it's and choose an engine that is most flexible. Tow them, however you know when it comes to say, you know, writing a sequel server, for example from organizing it you know you want. Sometimes you just want to lift and shift it into things. Like I asked In other cases, you want to completely rewrite it, so you need to have the flexibility of choice there that is presented by a legacy off What's its on premises? When it moved into things like analytics, we absolutely believe in convergence, right? So we don't believe that look, you need to have a relation of data warehouse that is separate from a loop system that is separate from, say, a B I system. That is just, you know, it's a bolt on for us. We love the proposition off, really building things that are so integrated that once you land data, once you prep it inside the lake, you can use it for analytics. You can use it for being. You can use it for machine learning. So I think you know, are sort of differentiated. Approach speaks for itself there. Well, >>that's that's interesting, because essentially, again, you're not saying it's an either or, and you're seeing a lot of that in the marketplace. You got some companies say no, it's the Data Lake and others saying No, no put in the data warehouse and that causes confusion and complexity around the data pipeline and a lot of calls. And I'd love to get your thoughts on this. Ah, lot of customers struggled to get value out of data and and specifically data product builders of frustrated that it takes too long to go from. You know, this idea of Hey, I have an idea for a data service and it could drive monetization, but to get there, you gotta go through this complex data lifecycle on pipeline and beg people to add new data sources. And do you do you feel like we have to rethink the way that we approach data architectures? >>Look, I think we do in the cloud, and I think what's happening today and I think the place where I see the most amount of rethink the most amount of push from our customers to really rethink is the area of analytics in a I. It's almost as if what worked in the past will not work going forward. Right? So when you think about analytics on in the Enterprise today, you have relational systems, you have produced systems. You've got data marts. You've got data warehouses. You've got enterprise data warehouses. You know, those large honking databases that you use, uh, to close your books with right? But when you start to modernize it, what deep you are saying is that we don't want to simply take all of that complexity that we've built over say, you know, 34 decades and simply migrated on mass exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see shape or form inside the lake. Once you landed inside the lake, you can essentially use a synapse studio toe. Prep it in the way that you like, use any compute engine of your choice and and operate on this data in any way that you see fit. So, case in point, if you want to hydrate relation all data warehouse, you can do so if you want to do ad hoc analytics using something like spark. You can do so if you want to invoke power. Bi I on that data or b i on that data you can do so if you want to bring in a machine learning model on this breath data you can do so, so inherently. So when customers buy into this proposition, what it solves for them and what it gives them is complete simplicity, right? One way to land the data, multiple ways to use it. And it's all eso. >>Should we think of synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way toe? Think about it. >>Yeah, you can think of it that way. It abstracts away, Dave a couple of things. It takes away the type of data, you know, sort of the complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data and fundamentally puts it in a place where it can be now consumed by any sort of entity inside the actual proposition. And by that token, even data breaks. You know, you can, in fact, use data breaks in in sort off an integrated way with a synapse, Right, >>Well, so that leads me to this notion of and then wonder if you buy into it s Oh, my inference is that a data warehouse or a data lake >>could >>just be a node in inside of a global data >>mesh on. >>Then it's synapses sort of managing, uh, that technology on top. Do you buy into that that global data mesh concept >>we do. And we actually do see our customers using synapse and the value proposition that it brings together in that way. Now it's not where they start. Often times when a customer comes and says, Look, I've got an enterprise data warehouse, I want to migrate it or I have a group system. I want to migrate it. But from there, the evolution is absolutely interesting to see. I give you an example. You know, one of the customers that we're very proud off his FedEx And what FedEx is doing is it's completely reimagining its's logistics system that basically the system that delivers What is it? The three million packages a day on in doing so in this covert times, with the view of basically delivering our covert vaccines. One of the ways they're doing it is basically using synapse. Synapse is essentially that analytic hub where they can get complete view into their logistic processes. Way things are moving, understand things like delays and really put all that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, you know, is one of my favorite, uh, we see once customers buy into it, they essentially can do other things with it. So an example of this is, uh is really my favorite story is Peace Parks Initiative. It is the premier Air White Rhino Conservancy in the world. They essentially are using data that has landed in azure images in particular. So, basically, you know, use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue so that this part with about 200 rangers can scramble surgically versus having to read range across the last area that they cover. So What do you see here is you know, the importance is really getting your data in order. Landed consistently. Whatever the kind of data ideas build the right pipelines and then the possibilities of transformation are just endless. >>Yeah, that's very nice how you worked in some of the customer examples. I appreciate that. I wanna ask you, though, that that some people might say that putting in that layer while it clearly adds simplification and e think a great thing that they're begins over time to be be a gap, if you will, between the ability of that layer to integrate all the primitives and all the peace parts on that, that you lose some of that fine grain control and it slows you down. What would you say to that? >>Look, I think that's what we excel at, and that's what we completely sort of buy into on. It's our job to basically provide that level off integration that granularity in the way that so it's an art, absolutely admit it's an art. There are areas where people create simplicity and not a lot of you know, sort of knobs and dials and things like that. But there are areas where customers want flexibility, right? So I think just to give you an example of both of them in landing the data inconsistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. Just 100 to do it. When it comes to computing and reducing this data analyzing this data, they want flexibility. This is one of the reasons why we say, Hey, listen, you want to use data breaks? If you're you're buying into that proposition and you're absolutely happy with them, you can plug plug it into it. You want to use B I and no, essentially do a small data mart. You can use B I If you say that. Look, I've landed in the lake. I really only want to use em melt, bringing your animal models and party on. So that's where the flexibility comes in. So that's sort of really sort of think about it. Well, >>I like the strategy because, you know, my one of our guest, Jim Octagon, e E. I think one of the foremost thinkers on this notion of off the data mesh and her premises that that that data builders, data product and service builders air frustrated because the the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system, you could get products to market much, much, much faster. So but that seems to be your philosophy. But I'm gonna jump ahead to do my ecosystem question. You've mentioned data breaks a couple of times. There's another partner that you have, which is snowflake. They're kind of trying to build out their own, uh, data cloud, if you will, on global mesh in and the one hand, their partner. On the other hand, there are competitors. How do you sort of balance and square that circle? >>Look, when I see snowflake, I actually see a partner. You know that when we essentially you know, we are. When you think about as you know, this is where I sort of step back and look at Azure as a whole and in azure as a whole. Companies like snowflakes are vital in our ecosystem, right? I mean, there are places we compete, but you know, effectively by helping them build the best snowflake service on Asia. We essentially are able toe, you know, differentiate and offer a differentiated value proposition compared to, say, a Google or on AWS. In fact, that's being our approach with data breaks as well, where you know they are effectively on multiple club, and our opportunity with data breaks is toe essentially integrate them in a way where we offer the best experience. The best integrations on Azure Barna That's always been a focus. >>That's hard to argue with. Strategy. Our data with our data partner eat er, shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I wanna come back thio ai a little bit. It's obviously one of the fastest growing areas in our in our survey data. As I said, clearly, Microsoft is a leader in this space. What's your what's your vision of the future of machine intelligence and how Microsoft will will participate in that opportunity? >>Yeah, so fundamentally, you know, we've built on decades of research around, you know, around, you know, essentially, you know, vision, speech and language that's being the three core building blocks and for the for a really focused period of time we focused on essentially ensuring human parody. So if you ever wondered what the keys to the kingdom are it, czar, it's the most we built in ensuring that the research posture that we've taken there, what we then done is essentially a couple of things we focused on, essentially looking at the spectrum. That is a I both from saying that, Hollis and you know it's gotta work for data. Analysts were looking toe basically use machine learning techniques, toe developers who are essentially, you know, coding and building a machine learning models from scratch. So for that select proposition manifesto us, as you know, really a. I focused on all skill levels. The other court thing we've done is that we've also said, Look, it will only work as long as people trust their data and they can trust their AI models. So there's a tremendous body of work and research we do in things like responsibility. So if you ask me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum off a I, and you can sort of come together for any skill level, and we keep that responsibly. I proposition. Absolutely strong now against that canvas, Dave. I'll also tell you that you know, as edge devices get way more capable, right where they can input on the edge, see a camera or a mike or something like that, you will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality. But the core really is all skill levels and that responsible denia. >>Yeah, So that that brings me to this notion of wanna bring an edge and and hybrid cloud Understand how you're thinking about hybrid cloud multi cloud. Obviously one of your competitors, Amazon won't even say the word multi cloud you guys have, Ah, you know, different approach there. But what's the strategy with regard? Toe, toe hybrid. You know, Do you see the cloud you bringing azure to the edge? Maybe you could talk about that and talk about how you're different from the competition. >>Yeah, I think in the edge from Annette, you know, I live in I'll be the first one to say that the word nge itself is conflated. Okay, It's, uh but I will tell you, just focusing on hybrid. This is one of the places where you know I would say the 2020 if I would have looked back from a corporate perspective. In particular, it has Bean the most informative because we absolutely saw customers digitizing moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became really from a cloud computing perspective and an example of this is we understood it's not all or nothing. So sometimes customers want azure consistency in their data centers. This is where things like Azure stack comes in. Sometimes they basically come to us and say, We want the flexibility of adopting flexible pattern, you know, platforms like, say, containers orchestra, Cuban Pettis, so that we can essentially deployed wherever you want. And so when we design things like art, it was built for that flexibility in mind. So here is the beauty of what's something like our can do for you. If you have a kubernetes endpoint anywhere we can deploy and as your service onto it, that is the promise, which means if for some reason, the customer says that. Hey, I've got this kubernetes endpoint in AWS and I love as your sequel. You will be able to run as your sequel inside AWS. There's nothing that stops you from doing it so inherently you remember. Our first principle is always to meet our customers where they are. So from that perspective, multi cloud is here to stay. You know, we're never going to be the people that says, I'm sorry, we will never see a But it is a reality for our customers. >>So I wonder if we could close. Thank you for that by looking, looking back and then and then ahead. And I wanna e wanna put forth. Maybe it's, Ah criticism, but maybe not. Maybe it's an art of Microsoft, but But first you know, you get Microsoft an incredible job of transitioning. It's business as your nominee president Azzawi said. Our data shows that so two part question First, Microsoft got there by investing in the cloud, really changing its mind set, I think, in leveraging its huge software state and customer base to put Azure at the center of its strategy, and many have said me included that you got there by creating products that air Good enough. You know, we do a 1.0, it's not that great. And the two Dato, and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expanding market. >>How >>do you respond to that? Is that is that a fair comment? Ume or than good enough? I wonder if you could share your >>thoughts, gave you? You hurt my feelings with that question. I don't hate me, g getting >>it out there. >>So there was. First of all, thank you for asking me that. You know, I am absolutely the biggest cheerleader. You'll find a Microsoft. I absolutely believe you know that I represent the work off almost 9000 engineers and we wake up every day worrying about our customer and worrying about the customer condition and toe. Absolutely. Make sure we deliver the best in the first time that we do. So when you take the platter off products we've delivered in nausea, be it as your sequel, be it as your cosmos TV synapse as your data breaks, which we did in partnership with data breaks, a za machine learning and recently when we prevail, we sort off, you know, sort of offered the world's first comprehensive data government solution in azure purview. I would humbly submit to you that we're leading the way and we're essentially showing how the future off data ai and the actual work in the cloud. >>I'd be disappointed if you if you had If you didn't, if you capitulated in any way J g So so thank you for that. And the kind of last question is, is looking forward and how you're thinking about the future of cloud last decade. A lot about your cloud migration simplifying infrastructure management, deployment SAS if eyeing my enterprise, lot of simplification and cost savings. And, of course, the redeployment of resource is toward digital transformation. Other other other valuable activities. How >>do >>you think this coming decade will will be defined? Will it be sort of more of the same? Or is there Is there something else out there? >>I think I think that the coming decade will be one where customers start one law outside value out of this. You know what happened in the last decade when people leave the foundation and people essentially looked at the world and said, Look, we've got to make the move, you know, the largely hybrid, but we're going to start making steps to basically digitize and modernize our platforms. I would tell you that with the amount of data that people are moving to the cloud just as an example, you're going to see use of analytics ai for business outcomes explode. You're also going to see a huge sort of focus on things like governance. You know, people need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all other privacy and compliance regulations out there. Essentially, they're complying this posture. So I think the unlocking of outcomes versus simply Hey, I've saved money Second, really putting this comprehensive sort off, you know, governance, regime in place. And then, finally, security and trust. It's going to be more paramount than ever before. Yeah, >>nobody's gonna use the data if they don't trust it. I'm glad you brought up your security. It's It's a topic that hits number one on the CEO list. J G. Great conversation. Obviously the strategy is working, and thanks so much for participating in Cuba on cloud. >>Thank you. Thank you, David. I appreciate it and thank you to. Everybody was tuning in today. >>All right? And keep it right there. I'll be back with our next guest right after this short break.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. As I've said many times on the Cube for years, decades, even we've marched to the cadence of Moore's law, relying on the doubling of performance every 18 months or so. But no longer is this the mainspring of innovation for technology. Rather, it's the combination of data applying machine intelligence and the cloud supported by the relentless reduction of the cost of compute and storage and the build out of a massively distributed computer network. Very importantly, in the last several years, alternative processors have emerged to support offloading work and performing specific Test GP use of the most widely known example of this trend, with the ascendancy of in video for certain applications like gaming and crypto mining and, more recently, machine learning. But in the middle of last decade, we saw the early development focused on the DPU, the data processing unit, which is projected to make a huge impact on data centers in the coming years. As we move into the next era of Cloud. And with me is deep. Sindhu, who's this co founder and CEO of Fungible, a company specializing in the design and development of GPU deep Welcome to the Cube. Great to see you. >>Thank you, Dave. And thank you for having me. >>You're very welcome. So okay, my first question is, don't CPUs and GP use process data already? Why do we need a DPU? >>Um you know that that is a natural question to ask on. CPUs have been around in one form or another for almost, you know, 55 maybe 60 years. And, uh, you know, this is when general purpose computing was invented, and essentially all CPI use went to x 80 60 x 86 architecture. Uh, by and large arm, of course, is used very heavily in mobile computing, but x 86 primarily used in data center, which is our focus. Um, now, you can understand that that architectural off general purpose CPUs has been refined heavily by some of the smartest people on the planet. And for the longest time, uh, improvements you refer the Moore's Law, which is really the improvements off the price performance off silicon over time. Um, that, combined with architectural improvements, was the thing that was pushing us forward. Well, what has happened is that the architectural refinements are more or less done. Uh, you're not going to get very much. You're not going to squeeze more blood out of that storm from the general purpose computer architectures. What has also happened over the last decade is that Moore's law, which is essentially the doubling off the number of transistors, um, on a chip has slowed down considerably on and to the point where you're only getting maybe 10 20% improvements every generation in speed off the grandest er. If that. And what's happening also is that the spacing between successive generations of technology is actually increasing from 2, 2.5 years to now three, maybe even four years. And this is because we are reaching some physical limits in Seamus. Thes limits are well recognized, and we have to understand that these limits apply not just to general purpose if use, but they also apply to GP use now. General purpose, if used, do one kind of confrontation. They really general on bacon do lots and lots of different things. It is actually a very, very powerful engine, Um, and then the problem is it's not powerful enough to handle all computations. So this is why you ended up having a different kind of processor called the GPU, which specializes in executing vector floating point arithmetic operations much, much better than CPL. Maybe 2030 40 times better. Well, GPS have now been around for probably 15, 20 years, mostly addressing graphics computations. But recently, in the last decade or so, they have been used heavily for AI and analytics computations. So now the question is, why do you need another specialized engine called the DPU? Well, I started down this journey about almost eight years ago, and I recognize I was still at Juniper Networks, which is another company that I found it. I recognize that in the data center, um, as the workload changes due to addressing Mawr and Mawr, larger and larger corpus is of data number one. And as people use scale out as the standard technique for building applications, what happens is that the amount of East West traffic increases greatly. And what happens is that you now have a new type off workload which is coming, and today probably 30% off the workload in a data center is what we call data centric. I want to give you some examples of what is the data centric E? >>Well, I wonder if I could interrupt you for a second, because Because I want you to. I want those examples, and I want you to tie it into the cloud because that's kind of the topic that we're talking about today and how you see that evolving. It's a key question that we're trying to answer in this program. Of course, Early Cloud was about infrastructure, a little compute storage, networking. And now we have to get to your point all this data in the cloud and we're seeing, by the way, the definition of cloud expand into this distributed or I think the term you use is disaggregated network of computers. So you're a technology visionary, And I wonder, you know how you see that evolving and then please work in your examples of that critical workload that data centric workload >>absolutely happy to do that. So, you know, if you look at the architectural off cloud data centers, um, the single most important invention was scale out scale out off identical or near identical servers, all connected to a standard i p Internet network. That's that's the architectural. Now, the building blocks of this architecture er is, uh, Internet switches, which make up the network i p Internet switches. And then the servers all built using general purpose X 86 CPUs with D ram with SSD with hard drives all connected, uh, inside the CPU. Now, the fact that you scale these, uh, server nodes as they're called out, um, was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose computer? But this architectures, Dave, is it compute centric architectures and the reason it's a compute centric architectures. If you open this a server node, what you see is a connection to the network, typically with a simple network interface card. And then you have CP use, which are in the middle of the action. Not only are the CPUs processing the application workload, but they're processing all of the aisle workload, what we call data centric workload. And so when you connect SSD and hard drives and GPU that everything to the CPU, um, as well as to the network, you can now imagine that the CPUs is doing to functions it z running the applications, but it's also playing traffic cop for the I O. So every Io has to go to the CPU and you're executing instructions typically in the operating system, and you're interrupting the CPU many, many millions of times a second now. General Purpose CPUs and the architecture of the CPS was never designed to play traffic cop, because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's. It's critical that in this new architecture, where there's a lot of data, a lot of East West traffic, the percentage of work clothes, which is data centric, has gone from maybe 1 to 2% to 30 to 40%. I'll give you some numbers, which are absolutely stunning if you go back to, say, 1987 and which is, which is the year in which I bought my first personal computer. Um, the network was some 30 times slower. Then the CPI. The CPI was running at 50 megahertz. The network was running at three megabits per second. Well, today the network runs at 100 gigabits per second and the CPU clock speed off. A single core is about 3 to 2.3 gigahertz. So you've seen that there is a 600 x change in the ratio off I'll to compute just the raw clock speed. Now you can tell me that. Hey, um, typical CPUs have lots of lots, of course, but even when you factor that in, there's bean close toe two orders of magnitude change in the amount of ill to compute. There is no way toe address that without changing the architectures on this is where the DPU comes in on the DPU actually solves two fundamental problems in cloud data centers on these air. Fundamental. There's no escaping it, no amount off. Clever marketing is going to get around these problems. Problem number one is that in a compute centric cloud architectures the interactions between server notes are very inefficient. Okay, that's number one problem number one. Problem number two is that these data center computations and I'll give you those four examples the network stack, the storage stack, the virtualization stack and the security stack. Those four examples are executed very inefficiently by CBS. Needless to say that that if you try to execute these on GPS, you'll run into the same problem, probably even worse because GPS are not good at executing these data centric computations. So when U. S o What we were looking to do it fungible is to solve these two basic problems and you don't solve them by by just using taking older architectures off the shelf and applying them to these problems because this is what people have been doing for the for the last 40 years. So what we did was we created this new microprocessor that we call the DPU from ground doctor is a clean sheet design and it solve those two problems. Fundamental. >>So I want to get into that. But I just want to stop you for a second and just ask you a basic question, which is so if I understand it correctly, if I just took the traditional scale out, If I scale out compute and storage, you're saying I'm gonna hit a diminishing returns, It z Not only is it not going to scale linear linearly, I'm gonna get inefficiencies. And that's really the problem that you're solving. Is that correct? >>That is correct. And you know this problem uh, the workloads that we have today are very data heavy. You take a I, for example, you take analytics, for example. It's well known that for a I training, the larger the corpus of data relevant data that you're training on, the better the result. So you can imagine where this is going to go, especially when people have figured out a formula that, hey, the more data I collect, I can use those insights to make money. >>Yeah, this is why this is why I wanted to talk to you, because the last 10 years we've been collecting all this data. Now I want to bring in some other data that you actually shared with me beforehand. Some market trends that you guys cited in your research and the first thing people said is they want to improve their infrastructure on. They want to do that by moving to the cloud, and they also there was a security angle there as well. That's a whole nother topic. We could discuss the other staff that jumped out at me. There's 80% of the customers that you surveyed said they'll be augmenting their X 86 CPUs with alternative processing technology. So that's sort of, you know, I know it's self serving, but z right on the conversation we're having. So I >>want to >>understand the architecture. Er, aan den, how you've approached this, You've you've said you've clearly laid out the X 86 is not going to solve this problem. And even GP use are not going to solve this problem. So help us understand the architecture and how you do solve this problem. >>I'll be I'll be very happy to remember I use this term traffic cough. Andi, I use this term very specifically because, uh, first let me define what I mean by a data centric computation because that's the essence off the problem resolved. Remember, I said two problems. One is we execute data centric work clothes, at least in order of magnitude, more efficiently than CPUs or GPS, probably 30 times more efficiently on. The second thing is that we allow notes to interact with each other over the network much, much more efficiently. Okay, so let's keep those two things in mind. So first, let's look at the data centric piece, the data centric piece, um, for for workload to qualify as being data centric. Four things have to be true. First of all, it needs to come over the network in the form of packets. Well, this is all workloads, so I'm not saying anything new. Secondly, uh, this workload is heavily multiplex in that there are many, many, many computations that are happening concurrently. Thousands of them. Yeah, that's number two. So a lot of multiplexing number three is that this workload is state fel. In other words, you have to you can't process back. It's out of order. You have to do them in order because you're terminating network sessions on the last one Is that when you look at the actual computation, the ratio off I Oto arithmetic is medium to high. When you put all four of them together, you actually have a data centric workout, right? And this workload is terrible for general purpose, C p s not only the general purpose, C p is not executed properly. The application that is running on the CPU also suffers because data center workloads are interfering workloads. So unless you designed specifically to them, you're going to be in trouble. So what did we do? Well, what we did was our architecture consists off very, very heavily multi threaded, general purpose CPUs combined with very heavily threaded specific accelerators. I'll give you examples of some some of those accelerators, um, de Emma accelerators, then radio coding accelerators, compression accelerators, crypto accelerators, um, compression accelerators, thes air, just something. And then look up accelerators. These air functions that if you do not specialized, you're not going to execute them efficiently. But you cannot just put accelerators in there. These accelerators have to be multi threaded to handle. You know, we have something like 1000 different threads inside our DPU toe address. These many, many, many computations that are happening concurrently but handle them efficiently. Now, the thing that that is very important to understand is that given the paucity off transistors, I know that we have hundreds of billions of transistors on a chip. But the problem is that those transistors are used very inefficiently today. If the architecture, the architecture of the CPU or GPU, what we have done is we've improved the efficiency of those transistors by 30 times. Yeah, so you can use >>the real estate. You can use their real estate more effectively, >>much more effectively because we were not trying to solve a general purpose computing problem. Because if you do that, you know, we're gonna end up in the same bucket where General Focus CPS are today. We were trying to solve the specific problem off data centric computations on off improving the note to note efficiency. So let me go to Point number two, because that's equally important, because in a scale out architecture, the whole idea is that I have many, many notes and they're connected over a high performance network. It might be shocking for your listeners to hear that these networks today run at a utilization of no more than 20 to 25%. Question is why? Well, the reason is that if I tried to run them faster than that, you start to get back. It drops because there are some fundamental problems caused by congestion on the network, which are unsolved as we speak today. There only one solution, which is to use DCP well. DCP is a well known is part of the D. C. P I. P. Suite. DCP was never designed to handle the agencies and speeds inside data center. It's a wonderful protocol, but it was invented 42 year 43 years ago, now >>very reliable and tested and proven. It's got a good track record, but you're a >>very good track record, unfortunately, eats a lot off CPU cycles. So if you take the idea behind TCP and you say, Okay, what's the essence of TCP? How would you apply to the data center? That's what we've done with what we call F C P, which is a fabric control protocol which we intend toe open way. Intend to publish standards on make it open. And when you do that and you you embed F c p in hardware on top of his standard I P Internet network, you end up with the ability to run at very large scale networks where the utilization of the network is 90 to 95% not 20 to 25% on you end up with solving problems of congestion at the same time. Now, why is this important today that zall geek speak so far? But the reason this stuff is important is that it such a network allows you to disaggregate pool and then virtualized, the most important and expensive resource is in the data center. What are those? It's computer on one side, storage on the other side. And increasingly even things like the Ram wants to be disaggregated in food. Well, if I put everything inside a general purpose server, the problem is that those resource is get stranded because they're they're stuck behind the CPI. Well, once you disaggregate those resources and we're saying hyper disaggregate, the meaning, the hyper and the hyper disaggregate simply means that you can disaggregate almost all the resources >>and then you're gonna re aggregate them, right? I mean, that's >>obviously exactly and the network is the key helping. So the reason the company is called fungible is because we are able to disaggregate virtualized and then pull those resources and you can get, you know, four uh, eso scale out cos you know the large aws Google, etcetera. They have been doing this aggregation and pulling for some time, but because they've been using a compute centric architecture, er that this aggregation is not nearly as efficient as we could make on their off by about a factor of three. When you look at enterprise companies, they're off by any other factor of four. Because the utilization of enterprises typically around 8% off overall infrastructure, the utilization the cloud for A W S and G, C, P and Microsoft is closer to 35 to 40%. So there is a factor off almost, uh, 4 to 8, which you can gain by disaggregated and pulling. >>Okay, so I wanna interrupt again. So thes hyper scaler zehr smart. A lot of engineers and we've seen them. Yeah, you're right. They're using ah, lot of general purpose. But we've seen them, uh, move Make moves toward GP use and and embrace things like arm eso I know, I know you can't name names but you would think that this is with all the data that's in the cloud again Our topic today you would think the hyper scaler zehr all over this >>all the hyper scale is recognized it that the problems that we have articulated are important ones on they're trying to solve them. Uh, with the resource is that they have on all the clever people that they have. So these air recognized problems. However, please note that each of these hyper scale er's has their own legacy now they've been around for 10, 15 years, and so they're not in a position to all of a sudden turn on a dime. This is what happens to all companies at some >>point. Have technical debt. You mean they >>have? I'm not going to say they have technical debt, but they have a certain way of doing things on. They are in love with the compute centric way of doing things. And eventually it will be understood that you need a third element called the DPU to address these problems. Now, of course, you heard the term smart neck, and all your listeners must have heard that term. Well, a smart thing is not a deep you what a smart Nick is. It's simply taking general purpose arm cores put in the network interface on a PC interface and integrating them all in the same chip and separating them from the CPI. So this does solve the problem. It solves the problem off the data centric workload, interfering with the application work, work. Good job. But it does not address the architectural problem. How to execute data centric workloads efficiently. >>Yeah, it reminds me. It reminds me of you I I understand what you're saying. I was gonna ask you about smart. Next. It does. It's almost like a bridge or a Band Aid. It's always reminds me of >>funny >>of throwing, you know, a flash storage on Ah, a disc system that was designed for spinning disk gave you something, but it doesn't solve the fundamental problem. I don't know if it's a valid analogy, but we've seen this in computing for a long time. >>Yeah, this analogy is close because, you know. Okay, so let's let's take hyper scaler X. Okay, one name names. Um, you find that, you know, half my CPUs are twiddling their thumbs because they're executing this data centric workload. Well, what are you going to do? All your code is written in, uh, C c plus plus, um, on x 86. Well, the easiest thing to do is to separate the cores that run this workload. Put it on a different Let's say we use arm simply because you know x 86 licenses are not available to people to build their own CPUs. So arm was available, so they put a bunch of encores. Let's stick a PC. I express and network interface on you. Port that quote from X 86 Tow arm. Not difficult to do, but it does yield you results on, By the way, if, for example, um, this hyper scaler X shall we call them if they're able to remove 20% of the workload from general purpose CPUs? That's worth billions of dollars. So of course you're going to do that. It requires relatively little innovation other than toe for quote from one place to another place. >>That's what that's what. But that's what I'm saying. I mean, I would think again. The hyper scale is why Why can't they just, you know, do some work and do some engineering and and then give you a call and say, Okay, we're gonna We're gonna attack these workloads together. You know, that's similar to how they brought in GP use. And you're right. It's it's worth billions of dollars. You could see when when the hyper scale is Microsoft and and Azure, uh, and and AWS both announced, I think they depreciated servers now instead of four years. It's five years, and it dropped, like a billion dollars to their bottom line. But why not just work directly with you guys. I mean, Z the logical play. >>Some of them are working with us. So it's not to say that they're not working with us. So you know, all of the hyper scale is they recognize that the technology that we're building is a fundamental that we have something really special, and moreover, it's fully programmable. So you know, the whole trick is you can actually build a lump of hardware that is fixed function. But the difficulty is that in the place where the DPU would sit, which is on the boundary off a server, and the network is literally on that boundary, that place the functionality needs to be programmable. And so the whole trick is how do you come up with an architectural where the functionality is programmable? But it is also very high speed for this particular set of applications. So the analogy with GPS is nearly perfect because GP use, and particularly in video that's implemented or they invented coulda, which is a programming language for GPS on it made them easy to use mirror fully programmable without compromising performance. Well, this is what we're doing with DP use. We've invented a new architectures. We've made them very easy to program. And they're these workloads or not, Workload. The computation that I talked about, which is security virtualization storage and then network. Those four are quintessential examples off data centric, foreclosed on. They're not going away. In fact, they're becoming more and more and more important over time. >>I'm very excited for you guys, I think, and really appreciate deep we're gonna have you back because I really want to get into some of the secret sauce you talked about these accelerators, Erasure coding, crypto accelerators. I want to understand that. I know there's envy me in here. There's a lot of hardware and software and intellectual property, but we're seeing this notion of programmable infrastructure extending now, uh, into this domain, this build out of this I like this term dis aggregated, massive disaggregated network s so hyper disaggregated. Even better. And I would say this on way. I gotta go. But what got us here the last decade is not the same is what's gonna take us through the next decade. Pretty Thanks. Thanks so much for coming on the cube. It's a great company. >>You have it It's really a pleasure to speak with you and get the message of fungible out there. >>E promise. Well, I promise we'll have you back and keep it right there. Everybody, we got more great content coming your way on the Cube on Cloud, This is David. Won't stay right there.

>> Hi, I'm Stu Miniman, and welcome back to theCUBE on Cloud talking about really important topics as to how developers, were changing how they build their applications, where they live, of course, long discussion we've had for a number of years. You know, how do things change in hybrid environments? We've been talking for years, public cloud and private cloud, and really excited for this session. We're going to talk about how edge environment and AI impact that. So happy to welcome back one of our CUBE alumni, Simon Crosby, is currently the Chief Technology Officer with Swim. He's got plenty of viewpoints on AI, the edge and knows the developer world well. Simon, welcome back. Thanks so much for joining us. >> Thank you, Stu, for having me. >> All right, so let's start for a second. Let's talk about developers. You know, it used to be, you know, for years we talked about, you know, what's the level of abstraction we get. Does it sit, you know, do I put it on bare metal? Do I virtualize it? Do I containerize it? Do I make it serverless? A lot of those things, you know that the app developer doesn't want to even think about but location matters a whole lot when we're talking about things like AI where do I have all my data that I could do my training? Where do I actually have to do the processing? And of course, edge just changes by orders of magnitude. Some of the things like latency and where data lives and everything like that. So with that as a setup, would love to get just your framework as to what you're hearing from developers and what we'll get into some of the solutions that you and your team are helping them to do their jobs. >> Well, you're absolutely right, Stu. The data onslaught is very real. Companies that I deal with are facing more and more real-time data from products from their infrastructure, from their partners whatever it happens to be and they need to make decisions rapidly. And the problem that they're facing is that traditional ways of processing that data are too slow. So perhaps the big data approach, which by now is a bit old, it's a bit long in the tooth, where you store data and then you analyze it later, is problematic. First of all, data streams are boundless. So you don't really know when to analyze, but second you can't store it all. And so the store then analyze approach has to change and Swim is trying to do something about this by adopting a process of analyze on the fly, so as data is generated, as you receive events you don't bother to store them. You analyze them, and then if you have to, you store the data, but you need to analyze as you receive data and react immediately to be able to generate reasonable insights or predictions that can drive commerce and decisions in the real world. >> Yeah absolutely. I remember back in the early days of big data, you know, real time got thrown around a little but it was usually I need to react fast enough to make sure we don't lose the customer, react to something, but it was, we gather all the data and let's move compute to the data. Today as you talk about, you know, real time streams are so important. We've been talking about observability for the last couple of years to just really understand the systems and the outputs more than looking back historically at where things were waiting for alerts. So could you give us some examples if you would, as to you know, those streams, you know, what is so important about being able to interact and leverage that data when you need it? And boy, it's great if we can use it then and not have to store it and think about it later, obviously there's some benefits there, because-- >> Well every product nowadays has a CPU, right? And so there's more and more data. And just let me give you an example, Swim processes real-time data from more than a hundred million mobile devices in real time, for a mobile operator. And what we're doing there is we're optimizing connection quality between devices and the network. Now that volume of data is more than four petabytes per day, okay. Now there is simply no way you can ever store that and analyze it later. The interesting thing about this is that if you adopt and analyze, and then if you really have to store architecture, you get to take advantage of Moore's Law. So you're running at CPU memory speeds instead of at disk speed. And so that gives you a million fold speed up, and it also means you don't have the latency problem of reaching out to, or about storage, database, or whatever. And so that reduces costs. So we can do it on about 10% of the infrastructure that they previously had for Hadoop style implementation. >> So, maybe it would help if we just explain. When we say edge people think of a lot of different things, is it, you know an IOT device sitting out at the edge? Are we talking about the Telecom edge? We've been watching AWS for years, you know, spider out their services and into various environments. So when you talk about the type of solutions you're doing and what your customers have, is it the Telecom edge? Is it the actual device edge, you know, where does processing happen and where do these you know, services that work on it live? >> So I think the right way to think about edge is where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have, but much data is encrypted between the original device, say, and the application. And so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze data in the clear. So edge computing is not so much a place in my view as the first opportunity you have to process data in the clear and to make sense of it. And then edge makes sense, in terms of latency, by locating, compute, as close as possible to the sources of data, to reduce latency and maximize your ability to get insights and return them to users, you know, quickly. So edge for me often is the cloud. >> Excellent, one of the other things I think about back from, you know, the big data days or even earlier, it was that how long it took to get from the raw data to processing that data, to be able to getting some insight, and then being able to take action. It sure sounds like we're trying to collapse that completely, is that, you know, how do we do that? You know, can we actually, you know, build the system so that we can, you know, in that real time, continuous model that you talk about, you know. Take care of it and move on. >> So one of the wonderful things, one of the wonderful things about cloud computing is that two major abstractions have really served us. And those are rest, which is static disk computing, and databases. And rest means any old server can do the job for me and then the database is just an API call away. The problem with that is that it's desperately slow. So when I say desperately slow, I mean, it's probably thrown away the last 10 years of Moore's law. Just think about it this way. Your CPU runs at gigahertz and the network runs at milliseconds. So by definition, every time you reach out to a data store you're going a million times slower than your CPU. That's terrible. It's absolutely tragic, okay. So a model which is much more effective is to have an in-memory computer architecture in which you engage in staple computation. So instead of having to reach out to a database every time to update the database and whatever, you know, store something, and then fetch it again a few moments later when the next event arrives, you keep state in memory and you compute on the fly as data arrives. And that way you get a million times speed up. You also end up with this tremendous cost reduction because you don't end up with as many instances having to compute, by comparison. So let me give you a quick example. If you go to a traffic.swim.ai you can see the real time state of the traffic infrastructure in Palo Alto. And each one of those intersections is predicting its own future. Now, the volume of data from just a few hundred lights in Palo Alto is about four terabytes a day. And sure you can deal with this in AWS Lambda. There are lots and lots of servers up there. But the problem is that the end to end per event latency is about 100 milliseconds. And, you know, if I'm dealing with 30,000 events a second, that's just too much. So solving that problem with a stateless architecture is extraordinarily expensive, more than $5,000 a month. Whereas the staple architecture which you could think of as an evolution of, you know, something reactive or the actor model, gets you, you know something like a 10th of the cost, okay. So cloud is fabulous for things that need to scale wide but a staple model is required for dealing with things which update you rapidly or regularly about their changes in state. >> Yeah, absolutely. You know, I think about if, I mentioned before AI training models, often, if you look at something like autonomous vehicles, the massive amounts of data that it needs to process, you know, has to happen in the public cloud. But then that gets pushed back down to the end device, in this case it's a car, because it needs to be able to react in real time and gets fed at a regular update, the new training algorithms that it has there. What are you seeing-- >> I have strong reason on this training approach and data science in general, and that is that there aren't enough data scientists or, you know, smart people to train these algorithms, deploy them to the edge and so on. And so there is an alternative worldview which is a much simpler one and that is that relatively simple algorithms deployed at scale to staple representatives, let's call them digital twins of things, can deliver enormous improvements in behavior as things learn for themselves. So the way I think the, at least this edge world, gets smarter is that relatively simple models of things will learn for themselves, create their own futures, based on what they can see and then react. And so this idea that we have lots and lots of data scientists dealing with vast amounts of information in the cloud is suitable for certain algorithms but it doesn't work for the vast majority of applications. >> So where are we with the state of what, what do developers need to think about? You mentioned that there's compute in most devices. That's true, but, you know, do they need some special Nvidia chip set out there? Are there certain programming languages that you are seeing more prevalent, interoperability, give us a little bit of, you know, some tips and tricks for those developing. >> Super, so number one, a staple architecture is fundamental and sure React is well known and there are ACA for example, and Spurling. Swim is another so I'm going to use some language and I would encourage you to look at swimos.org to go from play there. A staple architecture, which allows actors, small concurrent objects to stapely evolve their own state based on updates from the real world is fundamental. By the way, in Swim we use data to build these models. So these little agents, for things, we call them web agents because the object ID is a URI, they stapley evolve by processing their own real-world data, stapley representing it, And then they do this wonderful thing which is build a model on the fly. And they build a model by linking to things that they're related to. So a need section would link to all of its sensors but it would also link to all of its neighbors because the neighbors and linking is like a sub in Pub/Sub, and it allows that web agent then to continually analyze, learn, and predict on the fly. And so every one of these concurrent objects is doing this job of analyzing its own raw data and then predicting from that and streaming the result. So in Swim, you get streamed raw data in and what streams out is predictions, predictions about the future state of the infrastructure. And that's a very powerful staple approach which can run all their memory, no storage required. By the way, it's still persistent, so if you lose a node, you can just come back up and carry on but there's no need to store huge amounts of raw data if you don't need it. And let me just be clear. The volumes of raw data from the real world are staggering, right? So four terabytes a day from Palo Alto, but Las Vegas about 60 terabytes a day from the traffic lights. More than 100 million mobile devices is tens of petabytes per day, which is just too much to store. >> Well, Simon, you've mentioned that we have a shortage when it comes to data scientists and the people that can be involved in those things. How about from the developers side, do most enterprises that you're talking to do they have the skillset? Is the ecosystem mature enough for the company to get involved? What do we need to do looking forward to help companies be able to take advantage of this opportunity? >> Yeah, so there is this huge challenge in terms of, I guess, just cloud native skills. And this is exacerbated the more you get added to. I guess what you could think of is traditional kind of companies, all of whom have tons and tons of data sources. So we need to make it easy and Swim tries to do this by effectively using skills that people already have, Java or JavaScript, and giving them easy ways to develop, deploy, and then run applications without thinking about them. So instead of binding developers to notions of place and where databases are and all that sort of stuff if they can write simple object-oriented programs about things like intersections and push buttons, and pedestrian lights, and inroad loops and so on, and simply relate basic objects in the world to each other then we let data build the model by essentially creating these little concurrent objects for each thing, and they will then link to each other and solve the problem. We end up solving a huge problem for developers too, which is that they don't need to acquire complicated cloud-native skillsets to get to work. >> Well absolutely, Simon, it's something we've been trying to do for a long time is to truly simplify things. Want to let you have the final word. If you look out there, the opportunity, the challenge in the space, what final takeaways would you give to our audience? >> So very simple. If you adopt a staple competing architecture, like Swim, you get to go a million times faster. The applications always have an answer. They analyze, learn and predict on the fly and they go a million times faster. They use 10% less, no, sorry, 10% of the infrastructure of a store than analyze approach. And it's the way of the future. >> Simon Crosby, thanks so much for sharing. Great having you on the program. >> Thank you, Stu. >> And thank you for joining I'm Stu Miniman, thank you, as always, for watching theCUBE.

>> Narrator: From the Cube Studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hello and welcome back to this CUBE Conversation, I'm John Furrier here in theCUBE Studios, your hosts for our remote interviews as part of our coverage and continue to get the interviews during COVID-19. Great talk and session here about data warehouses, data lakes, data everything, hybrid cloud, and back on theCube for a return Cube alumni, virtual alumni, Jitesh Ghai senior vice president general manager of data management, Informatica. Great to see you come back. We had a great chat about privacy in the last session and data scale. Great to see you again. >> Likewise John, great seeing you is always a pleasure to join you and discuss some of the prevailing topics in the space of data. >> Well it's great that you're available on remote. And thanks for coming back again, because we want to dig into really the digital transformation aspect of the challenges that your customers have specifically around data warehouses and data lakes, because this has become a big topic. What are the biggest challenges that you guys see your customers facing with digital transformation? >> Yeah, great question. Really, it comes down to ensuring every digital transformation should be data-driven. There is a data work stream to help inform thoughtful insights that drive decisions to embark on and realize outcomes from the transformation. And for that you need a healthy, productive, modern, agile, flexible data and analytics stack. And so what we are enabling our customers realize is a modern cloud-native, cloud-first, data and analytics stack built on modern architectures of data lakes and data warehouses, all in the cloud. >> So you mentioned the data warehouse, modern cloud and the data lake. Tell us more about that. What's going on there. How does, how do customers approach that? Because it's not the old fashioned way, and data lakes been around for a while too, by the way, some people call it the data swamp, but they don't take care of it. Talk about those two things and how customers attack that strategic imperative to get it done right? >> Yeah, there's been a tremendous amount of disruption and innovation in the data and analytics stack. And what we're really seeing, I think you mentioned it is, 15 even 20 years ago, they were these things called data marts that the finance teams would report against, for financial reporting, regulatory compliance, et cetera. Then there was this, these things called data warehouses that were bringing together data from across the enterprise for comprehensive enterprise views to run the business as well as to perform reporting. And then with the advent of big data about five years ago, we had Hadoop-based data lakes, which as you mentioned, we're also in many cases, data swamps because of the lack of governance, lack of cataloging and insights into what is in the lake, who should, and shouldn't access the lake. And very quickly that itself got disrupted from Hadoop to Spark. And very quickly customers realize that, hey, you know what? Managing these 5,100, several hundred node, Hadoop lakes, Sparked lakes on-premise is extremely expensive and hardware extremely expensive and people extremely expensive and maintaining and patching and et cetera, et cetera. And so the demand very rapidly shifted to cloud-first, cloud-native data lakes. Equally, we're seeing customers realize the benefits of cloud-first cloud-native, the flexibility, the elasticity, the agility. And we're seeing them realize their data warehouses and reporting in the cloud as well for the same elastic benefits for performance as well as for economics. >> So what is the critical capabilities needed to be successful with kind of a modern data warehouse or a data lake that's a last to can scaling and providing value? What are those critical capabilities required to be successful? >> For sure, exactly. It's first and foremost cloud-first cloud-native, but, why are we Informatica, uniquely positioned and excited to enable, this modernization of the data and analytics stack in the cloud, as it comes down to foundational capabilities that we're recognized as a leader in, across the three magic quadrants of metadata management, data integration and data quality. Oftentimes, when folks are prototyping, they immediately start hand coding and, putting some data together through some ingestion, basic ingestion capability. And they think that they're building a data Lake or populating a data warehouse, but to truly build a system of record, you need comprehensive data management, integration and data quality capabilities. And that's really what we're offering to our customers as a cloud-first cloud-native. So that it's not just your data lakes and data warehouses that are cloud-first cloud-native. So is your data management stack so that you get the same flexibility, agility, resiliency, benefits. >> I don't think many people are really truly understand how important what you just said is the cloud-native capabilities. In addition to some of those things, it's really imperative to be built for the future. So with that, can you give me a couple of examples of customers that you can showcase to illustrate, the success of having the critical capabilities from Informatica. >> Yeah, what we've found is an enabler to be data-driven, requires organizations to bring data together to various applications and various sources of data on-premise in the cloud from SaaS apps, from a cloud PaaS databases, as well as from on-premise databases on-premise applications. And that's typically done in a data lake architecture. It's in that architecture that you have multiple zones of curation, you have a landing zone, a prep zone, and then it's certified datasets that you can democratize. And we spoke about some of this previously under the topic of data governance and privacy. What we are enabling with these capabilities of metadata management data integration, data quality is onboarding all of this data comprehensively processing it and getting it ready for analytics teams for data science teams. Kelly Services for example, is managing the recruitment of over a half a million candidates using greater data-driven insights within their data lake architecture, leveraging our integration quality metadata management capabilities to realize these outcomes. AXA XL is doing very similar things with their data lake and data warehousing architecture, to inform, the data science teams or more productive underwriting. So a tremendous amount of data-driven insights, being data-driven, being a data-driven organization really comes down to this foundational architecture of cloud data warehousing and data lakes, and the associated cloud-first cloud-native data management that we're enabling our customers, realize these, realize that becoming a data-driven organization. >> Okay, Jitesh, I got to put you on the spot on this one. I'm a customer pretend for a minute I'm a customer. I say, okay, I'm comfortable with my old fashion. My grandfather's data warehouse had it for years. It spits out the reports it needs to spit out, data lake I'm really not, I got it, I got a bunch of servers. Maybe we'll put our toe in the water there and try it out, but I'm good right now. I'm not sure I'm ready to go there. My boss is telling me, I'm telling them I'm good. I got a cloud strategy with Microsoft. I've got a cloud strategy with AWS on paper. We're going to go that way, but I'm not going to move. I need to just stay where I'm at. What do you say to that customer? First of all, I don't think anyone's that kind of that, well unless they're really in the legacy world, but may be they're locked in, but for the most part, they're saying, hey, I'm not ready to move. >> We see, we see both. We see the spectrum. We of course, to us data management, being cloud-first being cloud-native, necessitates that your capability support hybrid architectures. So there is a, there are a class of customers that for potentially regulatory compliance reasons, typically financial services, certainly comes to mind where they're decidedly, align state of their estate is on-premise. It's an old fashioned data centers. Well, those customers, we have market leading capabilities that we've had for many, many, many, many, many years. And that's fine. That works too. But we're naturally seeing organizations, even banks and financial services awakened to all the obvious benefits of a cloud-first strategy and are starting to modernize various pieces. First, it was just decommissioning data centers and moving their application and analytics and data estate to the cloud, as it's bring your own licenses as we refer to it. That very quickly, it has modernized to, I want to leverage the past data offerings within an AWS within an Azure, within a GCP. I want to leverage this modern data warehouse from Snowflake. And there, that's when customers are realizing this benefit and realizing the acceleration of value they can get by unshackling themselves from the burden of managing servers, managing the software, the operating system, as well as the associated applications, databases that need to be administered, upgraded, et cetera, abstracting away all of that so that they can really focus on the problem of data, collecting it, processing it, and enabling the larger lines of business to be data-driven, enabling those digital transformations that we were speaking about earlier. >> Well, I know you mentioned a Snowflake. I think they're actually hot company in Silicon Valley. They filed to go public. Everyone I've talked to loves working with them. They're easy to use and I think they're eating into Redshift a little bit from Amazon side. Certainly anyone's using old school data warehouses, Oh, they look at Snowflake is great. How does a customer who wants to get to that kind of experience set up for that? There's some that you guys do. We've had many conversations with some of the leaders at Informatica about this and your board members, and you've got to, you've got to set the foundation and you've got to get this done right. Take us through what it takes to do that. I mean, timetable, are we talking months, weeks, days, is that a migration for a year? It depends on how big it is, but if I do want to take that step to set my company up for these kinds of large cloud scale cloud-native benefits. >> Yeah, great question, great question John. Really, how customers approach it varies significantly. We have a segment of the market that really just picks up, our trial version free, but we have a freemium embedded within the Snowflake experience so that you can select us within as a Snowflake administrator and select us as the data management tooling that you want to use to start ingesting and onboarding and processing data within the Snowflake platform. We have customers that are building net new data warehouses for a line of business like marketing. Where they need, enterprise class, enterprise scale, data management as they service capabilities. And that's where we enable and support them. We also see customers recognizing that their on-premise data and analytics stack their cloud data Lake or their cloud data warehouse is too expensive, is not delivering on the latest and greatest features or the necessary insights. And therefore they are migrating that on-premise data warehouse to a cloud-native data warehouse, like Snowflake, like Redshift, BigQuery and so forth. And that's where we have technologies and capabilities that have helped them build this on-premise data warehouse, the business logic, all the ETL, the processing that was authored on-premise. We have a way of converting that and repurposing it within our cloud-first cloud-native metaphors, so that they get the benefit of continued value from their existing estate, but within a modern cloud-first cloud-native paradigm, that's elastic that serverless and so forth. >> Jitesh, always great to speak with you. You've got a great thought leadership, just an expertise, but also leading a big group within Informatica around data warehouses and data management in general, that you're the GM as well, you've got a PNL responsibility. Thanks for coming on. I do want to ask you while I got you here to react to some of the news, and how it means what it means for the enterprise. So I just did a panel session on Sunday. My new, "meet the analysts segment show" I'm putting together around the EU's recent decision to shoot down the privacy shield law in the UK, mainly because of the data sharing. GDPR is kicking in, California is doing something here. It kind of teases out the broader trend of data sharing, right? And responsibility. Well, I'm going to surveil you. You're going to say, it's not necessarily related to Informatica, so to speak, but it does kind of give a tell sign that, this idea of having your data to be managed so you can have kinds of the policies you need to be adaptive to. It turns out no one knows what's going on. I got data over here. I got data over there. So it's kind of data all over the place. And you know, one law says this, the other law contradicts it, tons of loopholes, but it points out what can happen when data gets out of control. >> Yeah, and then that's exactly right. And that's why, when I say metadata management is a critical foundational capability to build these modern data and analytics architectures it's because metadata management enables cataloging and understanding where all your data is, how it's proliferating and ensuring that it enables that it also enables governance as a result, because metadata management gives you technical metadata. It gives you business metadata. The combination on all of these different types of metadata enabled you to have an organized view of your data state, enable you to plan on how you want the process, manage work with the data and who you can and cannot share that data with. And that's that governing framework that enables organizations to be data-driven to democratize data, but within a governance framework. So extremely critical, but to democratize data, to be more data-driven you also need the govern data. And that's how metadata management with integration and quality really bring things together. >> And to have a user experience that's agile and modern contemporary, you got to have the compliance governance, but you've got to enable the application developers or the use cases to not be waiting. You got to be fast. >> That's exactly right. In this new modern world, digital transformation, faster pace, everybody wants to be data-driven. And that spans a spectrum of deeply technical data engineers, data analysts, data scientists, all the way to nontechnical business users that want to do some ad hoc analytics and want the data when they want it. And it's critical. We have built that on a foundation of intelligent metadata, or what we call a CLAIRE engine, and we have built the fit for use deliberate experiences. What are the appropriate personas, the deeply technical ones, wanting more technical experiences, all the way to nontechnical business users just want data in a simple data marketplace type of shopping paradigm. So critical to meet the UX requirements, the user experience requirements for there's a varied group of data consumers. >> Great to have you on I'll let you have the last word. Talk to the people who are watching this that may be a customer of yours, or may be in the need to be a customer of Informatica. What's your pitch? What would you say to that customer? Why Informatica? Give the pitch. >> Informatica is a laser focused singularly focused on the problem of data management. We are independent and neutral. So we work with your corporate standard, whether it's AWS, Azure, GCP, your best of breed selections, whether it's Snowflake or Databricks. And in many cases, we see the global 2000 select multiple cloud vendors. One division goes with AWS and other goes with Azure. And so the world of data analytics is decidedly multicloud. It's, while we recognize that data is proliferating everywhere, and there are multiple technologies and multiple PaaS offerings from various cloud vendors where data may reside including on-premise you want, and while all of that might be fragmented, you want a single data management capability within your organization that brings together metadata management, integration quality, and is increasingly automating the job of data management, leveraging AI and ML. So that in this data 4.0 world, Informatica is enabling AI power data management, so that you can get faster insights and be more data-driven and deliver more business outcomes. >> Jitesh Ghai, senior vice president, and general manager of data management at Informatica. You're watching our virtual coverage and remote interviews with all the Informatica thought leaders and experts and senior executives and customers here on theCUBE I'm John Furrier. Thanks for watching. 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from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a Himalayan Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tomb of length thanks so much for coming in the queue thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to find the curve and we're grateful for being able to be protected in this way so all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get into it but here with us you know how you spend your time okay well I head up a date operations function and a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and liberate it as as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago of the Big Data move data became an asset you had a lot of shadow I people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that the banker in 1989 had to mainly just know debits credits and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all it's the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the face if data is not the main photo really um it's always been an asset I think organizations just never consciously knew that data was that okay so so what sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like self as challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data Ops what was that like what were some of the challenges that you faced and how'd you get through them okay first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like chatter IP but when you really acknowledge that data is an essay just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not abdicate its management to a separate function that's not cold to the business and oftentimes IT are seen as a support or an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability the business function it presides in business makes to product management makes to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing offense and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about they just kind of leave us to to it was was also a journey but that was kind of the first step into it in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could take us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Dave is that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our date of college and from that perspective it then brought the case for data quality management so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they then buy into into the concept so sometimes you need to just wait for a good Christ and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity all the other capabilities at the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that's when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think the the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a P&L any department worth its salt has a FDA or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities in managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that they data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely um in the sense that unless the whole business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific you say so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners will the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of of 2019 into the beginning of this year so what we did was they I'm worried about the sunlight I mean through the window you look creative to me like sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in mates 2019 coming in too early this year we we had long kind of achieved the the compliance and regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity um the one that comes to mind is we're a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to get go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a database fee from a reporting history and we were in a place where in some cases based on how our Ledger's roll up and the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements were reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can learn at any time I see the sales they've made which is very important right now at the time of covert nineteen from a form of productivity and executional competitiveness those are two great use cases of women lying so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data bills profanity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barret data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm going to ask you then so when gdpr hit were you able to very quickly leverage this capability and and apply and then maybe other of compliance edik as well well actually you know what we just now was post TDP our us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes they use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time it but we're able to do real-time reporting from a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because Dana monetization is actually the penis' t the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know it kind of happened with enterprise data warehouse but it was too slow it was complicated and it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you I mean like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're out who we the the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of ovid 19 well I mean because of of the quality of kind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government put us on lockdown relatively early in in the curve or in the cycle of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue or potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student and students with loans on our books a instant three month payment holiday assuming they were in good standing and we did that upfront though it was actually an opt-out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we had not been able to provide productivity dashboard and and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are now that people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various methods being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric but business lines care about you know into these dashboards you usually the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and in an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important stupid like I can go on for an hour talking about this topic but unfortunately we're we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to be applied in terms of the use cases that we can find the second one is really providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are really utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa to be like thanks again great having you thank you very much Dave appreciate the opportunity thank you for watching everybody that we go we are digging in the data ops we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music] you

live from Stanford University it's the hue covering Stanford women in data science 2020 brought to you by Silicon angle media hi and welcome to the cube I'm your host Sonia - Garrett and we're live at Stanford University covering wigs women in data science conference the fifth annual one and joining us today is Daphne Koller who is the co-founder who sari is the CEO and founder of in seat row that Daphne welcome to the cube nice to be here Sonia thank you for having me so tell us a little bit about in seat row how you how it you got it founded and more about your role so I've been working in the intersection of machine learning and biology and health for quite a while and it was always a bit of a an interesting journey in that the data sets were quite small and limited we're now in a different world where there's tools that are allowing us to create massive biological data sets that I think can help us solve really significant societal problems and one of those problems that I think is really important is drug discovery development where despite many important advancements the costs just keep going up and up and up and the question is can we use machine learning to solve that problem better and you talk about this more in your keynote so give us a few highlights of what you talked about so in the last you can think of drug discovery and development in the last 50 to 70 years as being a bit of a glass half-full glass half-empty the glass half-full is the fact that there's diseases that used to be a death sentence or of the sentence still a life long of pain and suffering that are now addressed by some of the modern-day medicines and I think that's absolutely amazing the other side of it is that the cost of developing new drugs has been growing exponentially in what's come to be known as Arun was law being the inverse of Moore's Law which is the one we're all familiar with because the number of drugs approved per billion u.s. dollars just keeps going down exponentially so the question is can we change that curve and you talked in your keynote about the interdisciplinary cold to tell us more about that I think in order to address some of the critical problems that were facing one needs to really build a culture of people who work together at from different disciplines each bringing their own insights and their own ideas into the mix so and in seat row we actually have a company that's half-life scientists many of whom are producing data for the purpose of driving machine learning models and the other half are machine learning people and data scientists who are working on those but it's not a handoff where one group produces the data and the other one consumes and interpreted but really they start from the very beginning to understand what are the problems that one could solve together how do you design the experiment how do you build the model and how do you derive insights from that that can help us make better medicines for people and I also wanted to ask you you co-founded Coursera so tell us a little bit more about that platform so I founded Coursera as a result of work that I'd been doing at Stanford working on how technology can make education better and more accessible this was a project that I did here a number of my colleagues as well and at some point in the fall of 2011 there was an experiment let's take some of the content that we've been we've been developing within it's within Stanford and put it out there for people to just benefit from and we didn't know what would happen would it be a few thousand people but within a matter of weeks with minimal advertising other than one New York Times article that went viral we had a hundred thousand people in each of those courses and that was a moment in time where you know we looked at this and said can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world and so I ended up taking a what was supposed to be a teary leave of absence from Stanford to go and co-found Coursera and I thought I'd go back after two years but the but at the end of that two-year period the there was just so much more to be done and so much more impact that we could bring to people all over the world people of both genders people of the different social economic status every single country around the world we I just felt like this was something that I couldn't not do and how did you why did you decide to go from an educational platform to then going into machine learning and biomedicine so I've been doing Coursera for about five years in 2016 and the company was on a great trajectory but it's primarily a Content company and around me machine learning was transforming the world and I wanted to come back and be part of that and when I looked around I saw machine learning being applied to ecommerce and the natural language and to self-driving cars but there really wasn't a lot of impact being made on the life science area and I wanted to be part of making that happen partly because I felt like coming back to our earlier comment that in order to really have that impact you need to have someone who speaks both languages and while there's a new generation of researchers who are bilingual in biology and in machine learning there's still a small group and there very few of those in kind of my age cohort and I thought that I would be able to have a real impact by building and company in the space so it sounds like your background is pretty varied what advice would you give to women who are just starting college now who may be interested in a similar field would you tell them they have to major in math or or do you think that maybe like there are some other majors that may be influential as well I think there's a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that found the is a really strong one there's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than a third computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection and how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies that are very strongly Rd focused like like in C tro and others having a technical co-founder is absolutely essential because it's fine to have an understanding of whatever the user needs and so on and come from the business side of it and a lot of companies have a business co-founder but not understanding what the technology can actually do is highly limiting because you end up hallucinating oh if we could only do this and yet that would be great but you can't and people end up oftentimes making ridiculous promises about what technology will or will not do because they just don't understand where the land mines sit and and where you're gonna hit real obstacles and in the path so I think it's really important to have a strong technical foundation in these companies and that being said where do you see an teacher in the future and and how do you see it solving say Nash that you talked about in your keynote so we hope that in seat row we'll be a fully integrated drug discovery and development company that is based on a slightly different foundation than a traditional pharma company where they grew up in the old approach of that is very much bespoke scientific analysis of the biology of different diseases and then going after targets or our ways of dealing with the disease that are driven by human intuition where I think we have the opportunity to go today is to build a very data-driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that the cord with people's preconceptions of what matters and what doesn't and so hopefully we'll be able to over time create enough data and apply machine learning to address key bottlenecks in the drug discovery development process so we can bring better drugs to people and we can do it faster and hopefully at much lower cost that's great and you also mentioned in your keynote that you think that 2020s is like a digital biology era so tell us more about that so I think if you look if you take a historical perspective on science and think back you realize that there's periods in history where one discipline has made a tremendous amount of progress in a relatively short amount of time because of a new technology or a new way of looking at things in the 1870s that discipline was chemistry was the understanding of the periodic table and that you actually couldn't turn lead into gold in the 1900s that was physics with understanding the connection between matter and energy and between space and time in the 1950s that was computing where silicon chips were suddenly able to perform calculations that up until that point only people have been able to do and then in 1990s there was an interesting bifurcation one was the era of data which is related to computing but also involves elements statistics and optimization of neuroscience and the other one was quantitative biology in which biology moved from a descriptive science of techsan amaizing phenomena to really probing and measuring biology in a very detailed and a high-throughput way using techniques like microarrays that measure the activity of 20,000 genes at once Oh the human genome sequencing of the human genome and many others but these two feels kind of evolved in parallel and what I think is coming now 30 years later is the convergence of those two fields into one field that I like to think of as digital biology where we are able using the tools that have and continue to be developed measure biology in entirely new levels of detail of fidelity of scale we can use the techniques of machine learning and data science to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do and that will have implications in biomaterials in energy in the environment in agriculture and I think also in human health and it's an incredibly exciting space to be in right now because just so much is happening and the opportunities to make a difference and make the world a better place are just so large that sounds awesome Daphne thank you for your insight and thank you for being on cute thank you I'm so neat agario thanks for watching stay tuned for more great

>>Live from Barcelona, Spain. It's the Cube covering Cisco Live 2020 right to you by Cisco and its ecosystem partners. >>Welcome back, everyone to the Cube's live coverage Day four of four days of wall to wall action here in Barcelona, Spain, for Cisco Live. 2020. I'm John Furrier with my co host Dave Volante, with a very special guest here to wrap up Cisco Live. The president of Europe, Middle East Africa and Russia. Francisco Wendy Mars Cube Alumni. Great to see you. Thanks for coming on to. I kind of put a book into the show here. Thanks for joining us. >>It's absolutely great to be here. Thank you. >>So what a transformation. As Cisco's business model of continues to evolve, we've been saying brick by brick, we still think big move coming. I think there's more action. I can sense the walls talking to us like Cisco live in the US and more technical announcement. In the next 24 months, you can see you can see where it's going. It's cloud, it's APS. It's policy based program ability. It's really a whole another business model shift for you and your customers. Technology shift in the business model shift. So I want to get your perspective this year. Opening. Keynote. Oh, you let it off Talking about the philosophy of the business model, but also the first presenter was not a networking guy. It was an application person. App dynamics. Yep, this is a shift. What's going on with Cisco? What's happening? What's the story? >>You know, if if you look for all of the work that we're doing is is really driven by what we see from requirements from our customers to change, that's happening in the market and it is all around. You know, if you think digital transformation is the driver organizations now are incredibly interested in, how do they capture that opportunity? How do they use technology to help them? But, you know, if you look at it, really, there's the three items that are so important it's the business model evolution. It's actually the business operations for for organizations. Plus, there people, they're people in the communities within that those three things working together. And if you look at it with, it's so exciting with application dynamics there because if you look for us within Cisco, that linkage off the application layer through into the infrastructure into the network. And bringing that linkage together is the most powerful thing because that's the insights and the value our customers are looking for. >>You know, we've been talking about the the innovation sandwich, you know, you got data in the middle and you've got technology and applications underneath. That's kind of what's going on here, but I'm glad you brought up the part about business model. This is operations and people in communities. During your keynote, you had a slide that laid out three kind of pillars. Yes, people in communities, business model and business operations. There was no 800 series in there. There was no product discussions. This is fundamentally the big shift that business models are changing. I tweeted provocatively, the killer wrap in digital business model. Because you think about it. The applications are the business. What's running under the covers is the technology, but it's all shifting and changing, so every single vertical every single business is impacted by. This is not like a certain secular thing in the industry. This is a real change. Can you describe how those three things are operating with that can >>sure. I think if you look from, you know, so thinking through those three areas. If you look at the actual business model itself, our business models is organizations are fundamentally changing and they're changing towards as consumers. We are all much more specific about what we want. We have incredible choice in the market. We are more informed than ever before. But also we are interested in the values of the organizations that we're getting the capability from us as well as the products and the services that naturally we're looking to gain. So if you look in that business model itself, this is about, you know, organizations making sure they stay ahead from a competitive standpoint about the innovation of portfolio that they're able to bring, but also that they have a strong, strong focus around the experience, that they're customer gains from an application, a touch standpoint that all comes through those different channels, which is at the end of the day, the application. Then if you look as to how do you deliver that capability through the systems, the tools, the processes? As we all evolve, our businesses have to change the dynamic within your organization to cope with that. And then, of course, in driving any transformation, the critical success factor is your people and your culture. You need your teams with you. The way teams operate now is incredibly different. It's no longer command and control. It's agile capability coming together. You need that to deliver on any transformation. Never, never mind. Let it be smooth, you know, in the execution they're all three together. >>But what I like about that model and I have to say, this is, you know, 10 years of doing the Cube, you see that marketing in the vendor community often leads what actually happens. Not surprising as we entered the last decade, there's a lot of talk about Cloud. Well, it kind of was a good predictor. We heard a lot about digital transformation. A lot of people roll their eyes and think it's a buzzword, but we really are. I feel like exiting this cloud era into the digital era. It feels, really, and there are companies that get it and are leaning in. There are others that maybe you're complacent. I'm wondering what you're seeing in Europe just in terms of everybody talks digital, every CEO wants to get it right. But there is complacency. Their financial services said Well, I'm doing pretty well, not on my watch. Others say, Hey, we want to be the disruptors and not get disrupted. What are you seeing in the region? In terms of that sentiment, >>I would say across the region, you know, there will always be verticals and industries that slightly more advanced than others. But I would say that the bulk of conversations that I'm engaged in independence of the industry or the country in which we're having that conversation in there is a acceptance off transfer. Digital transformation is here. It is affecting my business. I if I don't disrupt, I myself will be disrupted and we challenged Help me. So I You know, I'm not disputing the end state and the guidance and support soon drive the transition and risk mitigated manner, and they're looking for help in that there's actually pressure in the board room now around a what are we doing within within organizations within the enterprise service, right of the public sector, any type of style of company. There's that pressure point in the board room of Come on, we need to move it speed. >>Now the other thing about your model is technology plays a role and contribute. It's not the be all end. All that plays a role in each of those the business model of business operations developing and nurturing communities. Can you add more specifics? What role do you see technology in terms of advancing those three years? >>So I think, you know, if you look at it, technology is fundamental to all of those fears in regard. Teoh Theo innovation that differentiation technology could bring the key challenges. One being able to apply it in a manner where you can really see differentiation of value within the business. So and then the customer's organization. Otherwise, it's technology for the sake of technology. So we see very much a movement now to this conversation of talk about the use case, the use cases, the way by which that innovation could be used to deliver value to the organization on also different ways by which a company will work. Look at the collaboration Kate Capability that we announced earlier this week of helping to bring to life that agility. Look at the the APP D discussion of helping the link the layer of the application into the infrastructure of the network to get to root, cause identification quickly and to understand where you may have a problem before you actually arises and causes downtime many, many ways. >>I think the agility message has always been a technical conversation. Agile methodology, technology, softer development, No problem check. That's 10 years ago. But business agility is moving from a buzz word to reality. Exactly. That's what you're kind of getting. >>Their teams have. Teams operate, how they work and being able to be quick, efficient, stand up, stand down and operate in that way. >>You know, we were kind of thinking out loud on the Cube and just riffing with Fabio Gori on your team on Cisco's team about clarification with you, Gene Kim around kind of real time. What was interesting is we're like, Okay, it's been 13 years since the iPhone, and so 13 years of mobile in your territory in Europe, Middle East Africa mobility has been around before the iPhone, so more advanced data privacy much more advanced in your region. So you you you have a region that's pretty much I think, the tell signs for what's going on North American around the world. And so you think about that. You say Okay, how is value created? How the economics changing this is really the conversation about the business model is okay. If the value activities are shifting and being more agile and the economics are changing with SAS, if someone's not on this bandwagon is not an end state discussion, very. It's done Deal. >>Yeah, it's But I think also there were some other conversation which, which are very prevalent here, is in the region so around trust around privacy law, understanding compliance. If you look at data where data resides, portability of that data GDP our came from Europe has pushed out on those conversations will continue as we go over time. And if I also look at, you know, the dialogue that you saw, you know, within World Economic Forum around sustainability that is becoming a key discussion now within government here in Spain, you know, from a climate standpoint and many other areas >>as well. David, I've been riffing around this whole where the innovation is coming from. It's coming from your region, not so much the us US. We've got some great innovations. But look at Blockchain. Us is like, don't touch it pretty progressive outside United States. A little dangerous to, But that's where innovation is coming from, and this is really the key that we're focused on. I want to get your thoughts on. How do you see it going? Next level? The next level. Next. Gen Business model. What's your What's your vision? >>So I think there'll be lots of things if we look at things like it with the introduction. Introduction of artificial intelligence, Robotics capability five g of course, you know, on the horizon we have Mobile World Congress here in Barcelona a few weeks time. And if you talked about with the iPhone, the smartphone, of course, when four g was introduced, no one knew what the use case where that would be. It was the smartphone, which wasn't around at that time. So with five G and the capability there, that will bring again yet more change to the business model for different organizations and capability and what we can bring to market >>the way we think about AI privacy data ownership becomes more important. Some of the things you were talking about before. It's interesting what you're saying. John and Wendy, the GDP are set this standard and and you're seeing in the US they're stovepipes for that standard California is gonna do want every state is gonna have a difference, and that's going to slow things down. It's going to slow down progress. Do you see sort of an extension of GDP, our like framework of being adopted across the region, potentially accelerating some of these sticky issues and public policy issues that can actually move the market forward? >>I think I think that will because I think there'll be more and more if you look at this is terminology of data. Is the new oil What do you do with data? How do you actually get value from that data? Make intelligent business decisions around that? So, yeah, that's critical. But yet if you look for all of ours, we are extremely passionate about where's our data used again? Back to trust and privacy. You need compliance, you need regulation. And I think this is just the beginning off how we will see that >>evolving. You know, when you get your thoughts. David, I've been riffing for 10 years around the death of storage. Long live storage. But data needs to be stored somewhere. Networking is the same kind of conversation just doesn't go away. In fact, there's more pressure now to get the smartphone. That was 13 years ago, before that. Mobility, data and Video. Now super important driver. That's putting more pressure on you guys. And so hey, we did well, networking. So it's kind of like Moore's Law. More networking, more networking. So video and data are now big your thoughts on video and data video. >>But if you look out the Internet of the future, you know what? So if you look for all of us now, we are also demanding as individuals around capability and access of. That's an Internet of the future. The next phase. We want even more so they'll be more more requirement for speed availability, that reliability of service, the way by which we engage in we communicate. There's some fundamentals there, so continuing to grow, which is which is so, so exciting force. >>So you talk about digital transformation that's obviously in the mind of C level executives. I got to believe security is up. There is a topic one other. What's the conversation like in the corner office when you go visit your customers? >>So I think there's a There's a huge excitement around the opportunity, realizing the value of the of the opportunity on. You know, if you look at top of mind conversations around security around, making sure that you can make taint, maintain that fantastic customer experience because if you don't the customer go elsewhere, How do you do that? How do you enrich at all times and also looking at market? Jason sees, you know, as you go in a new tour at senior levels, within, within organizations independent of the industry in which they're in. They're a huge amount of commonalities that we see across those of consistent problems by which organizations are trying to solve. And actually, one of the big questions is what's the pace of change that I should operate us on? When is it too fast? And one is one of my too slow and trying to balance that is exciting but also a challenge for a company. >>So you feel like sentiment. There's still strong, even though we're 10 years into this, this bull market you get Brexit, China tensions with US US elections. But but generally you see sentiment still pretty strong demand. >>So I would say that the the the excitement around technology, the opportunity that is there around technology in its broader sense is greater than ever before. And I think it's on all of us to be able to help organizations to understand how they can consume and see value from us. But it's a fantastic times, >>gets economic indicators way. So >>I know you >>have to be careful, >>but really, the real I think I'm trying to get to is is the mindset of the CEO. The corner office right now is it is that we're gonna we're gonna grow short term by cutting or do we going to be aggressive and go after this incremental opportunity? And it's probably both. You see a lot of automation in cars >>both, and I think if you look fundamentally for organizations, it's it's the three things helped me to make money, how to save money, keep me out of trouble. So those are the pivots they all operate with on, you know, depending on where an organization is in its journey, whether they're start up there in the middle, the more mature and some of the different dynamics and the markets in which they operate in a well, there's all different variables, you know? So it's it's it's mixed. >>Wendy, thanks so much to spend the time to come on. The Cube really appreciate great keynote folks watching. If you haven't seen the keynote opening section, that's good. Second, the business model. I think it's really right on. I think that's gonna be a conversation will continue. So thanks for sharing that before we look. Before we leave, I want to just ask a question around, What? What's going on for you here in Barcelona? As the show winds down, you had all your activities. Take us in the day in the life of what you do. Customer meetings. What were some of those conversations? Take us inside inside. What? What goes on for you here? >>I tell you, it's been an amazing It's been amazing few days, So it's a combination of customer conversations around some of the themes We just talked about conversations with partners. There's investor companies that we invest in a Cisco that I've been spending some time with on also spending time with the teams as well. The definite zone, you know, is amazing. We have this afternoon the closing session where we got a fantastic, um, external guests who's coming in is going to be really exciting as well. And then, of course, the party tonight and will be announcing the next location, which I'm not going to reveal now. Later on today, >>we kind of figured it out because that's our job is to break news, but we're not gonna break it for you to have that. Hey, thank you so much for coming on. Really appreciate. When any market in Europe, Middle East Africa and Russia for Cisco she's got her hand on the pulse and the future is the business model. That's what's going on. Fundamentally radical change across the board in all areas. This is the Cube, bringing you all the action here in Barcelona. Thanks for watching. >>Yeah, yeah,