(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)

(bright upbeat music) >> Hello and welcome to theCUBE's coverage of International Women's Day. I'm your host, John Furrier here in Palo Alto, California Studio and remoting is a great guest CUBE alumni, co-founder, technical co-founder and she's also the VP of Product at Platform9 Systems. It's a company pioneering Kubernetes infrastructure, been doing it for a long, long time. Madhura Maskasky, thanks for coming on theCUBE. Appreciate you. Thanks for coming on. >> Thank you for having me. Always exciting. >> So I always... I love interviewing you for many reasons. One, you're super smart, but also you're a co-founder, a technical co-founder, so entrepreneur, VP of product. It's hard to do startups. (John laughs) Okay, so everyone who started a company knows how hard it is. It really is and the rewarding too when you're successful. So I want to get your thoughts on what's it like being an entrepreneur, women in tech, some things you've done along the way. Let's get started. How did you get into your career in tech and what made you want to start a company? >> Yeah, so , you know, I got into tech long, long before I decided to start a company. And back when I got in tech it was very clear to me as a direction for my career that I'm never going to start a business. I was very explicit about that because my father was an entrepreneur and I'd seen how rough the journey can be. And then my brother was also and is an entrepreneur. And I think with both of them I'd seen the ups and downs and I had decided to myself and shared with my family that I really want a very well-structured sort of job at a large company type of path for my career. I think the tech path, tech was interesting to me, not because I was interested in programming, et cetera at that time, to be honest. When I picked computer science as a major for myself, it was because most of what you would consider, I guess most of the cool students were picking that as a major, let's just say that. And it sounded very interesting and cool. A lot of people were doing it and that was sort of the top, top choice for people and I decided to follow along. But I did discover after I picked computer science as my major, I remember when I started learning C++ the first time when I got exposure to it, it was just like a light bulb clicking in my head. I just absolutely loved the language, the lower level nature, the power of it, and what you can do with it, the algorithms. So I think it ended up being a really good fit for me. >> Yeah, so it clicked for you. You tried it, it was all the cool kids were doing it. I mean, I can relate, I did the same thing. Next big thing is computer science, you got to be in there, got to be smart. And then you get hooked on it. >> Yeah, exactly. >> What was the next level? Did you find any blockers in your way? Obviously male dominated, it must have been a lot of... How many females were in your class? What was the ratio at that time? >> Yeah, so the ratio was was pretty, pretty, I would say bleak when it comes to women to men. I think computer science at that time was still probably better compared to some of the other majors like mechanical engineering where I remember I had one friend, she was the single girl in an entire class of about at least 120, 130 students or so. So ratio was better for us. I think there were maybe 20, 25 girls in our class. It was a large class and maybe the number of men were maybe three X or four X number of women. So relatively better. Yeah. >> How about the job when you got into the structured big company? How did that go? >> Yeah, so, you know, I think that was a pretty smooth path I would say after, you know, you graduated from undergrad to grad school and then when I got into Oracle first and VMware, I think both companies had the ratios were still, you know, pretty off. And I think they still are to a very large extent in this industry, but I think this industry in my experience does a fantastic job of, you know, bringing everybody and kind of embracing them and treating them at the same level. That was definitely my experience. And so that makes it very easy for self-confidence, for setting up a path for yourself to thrive. So that was it. >> Okay, so you got an undergraduate degree, okay, in computer science and a master's from Stanford in databases and distributed systems. >> That's right. >> So two degrees. Was that part of your pathway or you just decided, "I want to go right into school?" Did it go right after each other? How did that work out? >> Yeah, so when I went into school, undergrad there was no special major and I didn't quite know if I liked a particular subject or set of subjects or not. Even through grad school, first year it wasn't clear to me, but I think in second year I did start realizing that in general I was a fan of backend systems. I was never a front-end person. The backend distributed systems really were of interest to me because there's a lot of complex problems to solve, and especially databases and large scale distributed systems design in the context of database systems, you know, really started becoming a topic of interest for me. And I think luckily enough at Stanford there were just fantastic professors like Mendel Rosenblum who offered operating system class there, then started VMware and later on I was able to join the company and I took his class while at school and it was one of the most fantastic classes I've ever taken. So they really had and probably I think still do a fantastic curriculum when it comes to distributor systems. And I think that probably helped stoke that interest. >> How do you talk to the younger girls out there in elementary school and through? What's the advice as they start to get into computer science, which is changing and still evolving? There's backend, there's front-end, there's AI, there's data science, there's no code, low code, there's cloud. What's your advice when they say what's the playbook? >> Yeah, so I think two things I always say, and I share this with anybody who's looking to get into computer science or engineering for that matter, right? I think one is that it's, you know, it's important to not worry about what that end specialization's going to be, whether it's AI or databases or backend or front-end. It does naturally evolve and you lend yourself to a path where you will understand, you know, which systems, which aspect you like better. But it's very critical to start with getting the fundamentals well, right? Meaning all of the key coursework around algorithm, systems design, architecture, networking, operating system. I think it is just so crucial to understand those well, even though at times you make question is this ever going to be relevant and useful to me later on in my career? It really does end up helping in ways beyond, you know, you can describe. It makes you a much better engineer. So I think that is the most important aspect of, you know, I would think any engineering stream, but definitely true for computer science. Because there's also been a trend more recently, I think, which I'm not a big fan of, of sort of limited scoped learning, which is you decide early on that you're going to be, let's say a front-end engineer, which is fine, you know. Understanding that is great, but if you... I don't think is ideal to let that limit the scope of your learning when you are an undergrad phrase or grad school. Because later on it comes back to sort of bite you in terms of you not being able to completely understand how the systems work. >> It's a systems kind of thinking. You got to have that mindset of, especially now with cloud, you got distributed systems paradigm going to the edge. You got 5G, Mobile World Congress recently happened, you got now all kinds of IOT devices out there, IP of devices at the edge. Distributed computing is only getting more distributed. >> That's right. Yeah, that's exactly right. But the other thing is also happens... That happens in computer science is that the abstraction layers keep raising things up and up and up. Where even if you're operating at a language like Java, which you know, during some of my times of programming there was a period when it was popular, it already abstracts you so far away from the underlying system. So it can become very easier if you're doing, you know, Java script or UI programming that you really have no understanding of what's happening behind the scenes. And I think that can be pretty difficult. >> Yeah. It's easy to lean in and rely too heavily on the abstractions. I want to get your thoughts on blockers. In your career, have you had situations where it's like, "Oh, you're a woman, okay seat at the table, sit on the side." Or maybe people misunderstood your role. How did you deal with that? Did you have any of that? >> Yeah. So, you know, I think... So there's something really kind of personal to me, which I like to share a few times, which I think I believe in pretty strongly. And which is for me, sort of my personal growth began at a very early phase because my dad and he passed away in 2012, but throughout the time when I was growing up, I was his special little girl. And every little thing that I did could be a simple test. You know, not very meaningful but the genuine pride and pleasure that he felt out of me getting great scores in those tests sort of et cetera, and that I could see that in him, and then I wanted to please him. And through him, I think I build that confidence in myself that I am good at things and I can do good. And I think that just set the building blocks for me for the rest of my life, right? So, I believe very strongly that, you know, yes, there are occasions of unfair treatment and et cetera, but for the most part, it comes from within. And if you are able to be a confident person who is kind of leveled and understands and believes in your capabilities, then for the most part, the right things happen around you. So, I believe very strongly in that kind of grounding and in finding a source to get that for yourself. And I think that many women suffer from the biggest challenge, which is not having enough self-confidence. And I've even, you know, with everything that I said, I've myself felt that, experienced that a few times. And then there's a methodical way to get around it. There's processes to, you know, explain to yourself that that's actually not true. That's a fake feeling. So, you know, I think that is the most important aspect for women. >> I love that. Get the confidence. Find the source for the confidence. We've also been hearing about curiosity and building, you mentioned engineering earlier, love that term. Engineering something, like building something. Curiosity, engineering, confidence. This brings me to my next question for you. What do you think the key skills and qualities are needed to succeed in a technical role? And how do you develop to maintain those skills over time? >> Yeah, so I think that it is so critical that you love that technology that you are part of. It is just so important. I mean, I remember as an example, at one point with one of my buddies before we started Platform9, one of my buddies, he's also a fantastic computer scientists from VMware and he loves video games. And so he said, "Hey, why don't we try to, you know, hack up a video game and see if we can take it somewhere?" And so, it sounded cool to me. And then so we started doing things, but you know, something I realized very quickly is that I as a person, I absolutely hate video games. I've never liked them. I don't think that's ever going to change. And so I was miserable. You know, I was trying to understand what's going on, how to build these systems, but I was not enjoying it. So, I'm glad that I decided to not pursue that. So it is just so important that you enjoy whatever aspect of technology that you decide to associate yourself with. I think that takes away 80, 90% of the work. And then I think it's important to inculcate a level of discipline that you are not going to get sort of... You're not going to get jaded or, you know, continue with happy path when doing the same things over and over again, but you're not necessarily challenging yourself, or pushing yourself, or putting yourself in uncomfortable situation. I think a combination of those typically I think works pretty well in any technical career. >> That's a great advice there. I think trying things when you're younger, or even just for play to understand whether you abandon that path is just as important as finding a good path because at least you know that skews the value in favor of the choices. Kind of like math probability. So, great call out there. So I have to ask you the next question, which is, how do you keep up to date given all the changes? You're in the middle of a world where you've seen personal change in the past 10 years from OpenStack to now. Remember those days when I first interviewed you at OpenStack, I think it was 2012 or something like that. Maybe 10 years ago. So much changed. How do you keep up with technologies in your field and resources that you rely on for personal development? >> Yeah, so I think when it comes to, you know, the field and what we are doing for example, I think one of the most important aspect and you know I am product manager and this is something I insist that all the other product managers in our team also do, is that you have to spend 50% of your time talking to prospects, customers, leads, and through those conversations they do a huge favor to you in that they make you aware of the other things that they're keeping an eye on as long as you're doing the right job of asking the right questions and not just, you know, listening in. So I think that to me ends up being one of the biggest sources where you get tidbits of information, new things, et cetera, and then you pursue. To me, that has worked to be a very effective source. And then the second is, you know, reading and keeping up with all of the publications. You guys, you know, create a lot of great material, you interview a lot of people, making sure you are watching those for us you know, and see there's a ton of activities, new projects keeps coming along every few months. So keeping up with that, listening to podcasts around those topics, all of that helps. But I think the first one I think goes in a big way in terms of being aware of what matters to your customers. >> Awesome. Let me ask you a question. What's the most rewarding aspect of your job right now? >> So, I think there are many. So I think I love... I've come to realize that I love, you know, the high that you get out of being an entrepreneur independent of, you know, there's... In terms of success and failure, there's always ups and downs as an entrepreneur, right? But there is this... There's something really alluring about being able to, you know, define, you know, path of your products and in a way that can potentially impact, you know, a number of companies that'll consume your products, employees that work with you. So that is, I think to me, always been the most satisfying path, is what kept me going. I think that is probably first and foremost. And then the projects. You know, there's always new exciting things that we are working on. Even just today, there are certain projects we are working on that I'm super excited about. So I think it's those two things. >> So now we didn't get into how you started. You said you didn't want to do a startup and you got the big company. Your dad, your brother were entrepreneurs. How did you get into it? >> Yeah, so, you know, it was kind of surprising to me as well, but I think I reached a point of VMware after spending about eight years or so where I definitely packed hold and I could have pushed myself by switching to a completely different company or a different organization within VMware. And I was trying all of those paths, interviewed at different companies, et cetera, but nothing felt different enough. And then I think I was very, very fortunate in that my co-founders, Sirish Raghuram, Roopak Parikh, you know, Bich, you've met them, they were kind of all at the same journey in their careers independently at the same time. And so we would all eat lunch together at VMware 'cause we were on the same team and then we just started brainstorming on different ideas during lunchtime. And that's kind of how... And we did that almost for a year. So by the time that the year long period went by, at the end it felt like the most logical, natural next step to leave our job and to, you know, to start off something together. But I think I wouldn't have done that had it not been for my co-founders. >> So you had comfort with the team as you knew each other at VMware, but you were kind of a little early, (laughing) you had a vision. It's kind of playing out now. How do you feel right now as the wave is hitting? Distributed computing, microservices, Kubernetes, I mean, stuff you guys did and were doing. I mean, it didn't play out exactly, but directionally you were right on the line there. How do you feel? >> Yeah. You know, I think that's kind of the challenge and the fun part with the startup journey, right? Which is you can never predict how things are going to go. When we kicked off we thought that OpenStack is going to really take over infrastructure management space and things kind of went differently, but things are going that way now with Kubernetes and distributed infrastructure. And so I think it's been interesting and in every path that you take that does end up not being successful teaches you so much more, right? So I think it's been a very interesting journey. >> Yeah, and I think the cloud, certainly AWS hit that growth right at 2013 through '17, kind of sucked all the oxygen out. But now as it reverts back to this abstraction layer essentially makes things look like private clouds, but they're just essentially DevOps. It's cloud operations, kind of the same thing. >> Yeah, absolutely. And then with the edge things are becoming way more distributed where having a single large cloud provider is becoming even less relevant in that space and having kind of the central SaaS based management model, which is what we pioneered, like you said, we were ahead of the game at that time, is becoming sort of the most obvious choice now. >> Now you look back at your role at Stanford, distributed systems, again, they have world class program there, neural networks, you name it. It's really, really awesome. As well as Cal Berkeley, there was in debates with each other, who's better? But that's a separate interview. Now you got the edge, what are some of the distributed computing challenges right now with now the distributed edge coming online, industrial 5G, data? What do you see as some of the key areas to solve from a problem statement standpoint with edge and as cloud goes on-premises to essentially data center at the edge, apps coming over the top AI enabled. What's your take on that? >> Yeah, so I think... And there's different flavors of edge and the one that we focus on is, you know, what we call thick edge, which is you have this problem of managing thousands of as we call it micro data centers, rather than managing maybe few tens or hundreds of large data centers where the problem just completely shifts on its head, right? And I think it is still an unsolved problem today where whether you are a retailer or a telecommunications vendor, et cetera, managing your footprints of tens of thousands of stores as a retailer is solved in a very archaic way today because the tool set, the traditional management tooling that's designed to manage, let's say your data centers is not quite, you know, it gets retrofitted to manage these environments and it's kind of (indistinct), you know, round hole kind of situation. So I think the top most challenges are being able to manage this large footprint of micro data centers in the most effective way, right? Where you have latency solved, you have the issue of a small footprint of resources at thousands of locations, and how do you fit in your containerized or virtualized or other workloads in the most effective way? To have that solved, you know, you need to have the security aspects around these environments. So there's a number of challenges that kind of go hand-in-hand, like what is the most effective storage which, you know, can still be deployed in that compact environment? And then cost becomes a related point. >> Costs are huge 'cause if you move data, you're going to have cost. If you move compute, it's not as much. If you have an operating system concept, is the data and state or stateless? These are huge problems. This is an operating system, don't you think? >> Yeah, yeah, absolutely. It's a distributed operating system where it's multiple layers, you know, of ways of solving that problem just in the context of data like you said having an intermediate caching layer so that you know, you still do just in time processing at those edge locations and then send some data back and that's where you can incorporate some AI or other technologies, et cetera. So, you know, just data itself is a multi-layer problem there. >> Well, it's great to have you on this program. Advice final question for you, for the folks watching technical degrees, most people are finding out in elementary school, in middle school, a lot more robotics programs, a lot more tech exposure, you know, not just in Silicon Valley, but all around, you're starting to see that. What's your advice for young girls and people who are getting either coming into the workforce re-skilled as they get enter, it's easy to enter now as they stay in and how do they stay in? What's your advice? >> Yeah, so, you know, I think it's the same goal. I have two little daughters and it's the same principle I try to follow with them, which is I want to give them as much exposure as possible without me having any predefined ideas about what you know, they should pursue. But it's I think that exposure that you need to find for yourself one way or the other, because you really never know. Like, you know, my husband landed into computer science through a very, very meandering path, and then he discovered later in his career that it's the absolute calling for him. It's something he's very good at, right? But so... You know, it's... You know, the reason why he thinks he didn't pick that path early is because he didn't quite have that exposure. So it's that exposure to various things, even things you think that you may not be interested in is the most important aspect. And then things just naturally lend themselves. >> Find your calling, superpower, strengths. Know what you don't want to do. (John chuckles) >> Yeah, exactly. >> Great advice. Thank you so much for coming on and contributing to our program for International Women's Day. Great to see you in this context. We'll see you on theCUBE. We'll talk more about Platform9 when we go KubeCon or some other time. But thank you for sharing your personal perspective and experiences for our audience. Thank you. >> Fantastic. Thanks for having me, John. Always great. >> This is theCUBE's coverage of International Women's Day, I'm John Furrier. We're talking to the leaders in the industry, from developers to the boardroom and everything in between and getting the stories out there making an impact. Thanks for watching. (bright upbeat music)

(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

>> Hello and welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. Got a variety of interviews across the gamut from topics, women in tech, mentoring, pipelining, developers, open source, executives. Stanford's having International Women's Day celebration with the women in data science, which we're streaming that live as well. Variety of programs. In this segment, Meagen Eisenberg, friend of theCUBE, she's the CMO of Laceworks, is an amazing executive, got a great journey story as a CMO but she's also actively advising startups, companies and really pays it forward. I want to say Meagen, thank you for coming on the program and thanks for sharing. >> Yeah, thank you for having me. I'm happy to be here. >> Well, we're going to get into some of the journey celebrations that you've gone through and best practice what you've learned is pay that forward. But I got to say, one of the things that really impresses me about you as an executive is you get stuff done. You're a great CMO but also you're advised a lot of companies, you have a lot of irons in the fires and you're advising companies and sometimes they're really small startups to bigger companies, and you're paying it forward, which I love. That's kind of the spirit of this day. >> Yeah, I mean, I agree with you. When I think about my career, a lot of it was looking to mentors women out in the field. This morning I was at a breakfast by Eileen and we had the CEO of General Motors on, and she was talking about her journey nine years as a CEO. And you know, and she's paying it forward with us. But I think about, you know, when you're advising startups, you know, I've gathered knowledge and pattern recognition and to be able to share that is, you know, I enjoy it. >> Yeah. And the startups are also fun too, but it's not always easy and it can get kind of messy as you know. Some startups don't make it some succeed and it's always like the origination story is kind of rewritten and then that's that messy middle. And then it's like that arrows that don't look like a straight line but everyone thinks it's great and you know, it's not for the faint of heart. And Teresa Carlson, who I've interviewed many times, former Amazon, now she's the president of Flexport, she always says, sometimes startups on certain industries aren't for the faint of heart so you got to have a little bit of metal, right? You got to be tough. And some cases that you don't need that, but startups, it's not always easy. What have you learned? >> Yeah, I mean, certainly in the startup world, grit, creativity. You know, when I was at TripActions travel company, pandemic hits, nobody's traveling. You cut budget, you cut heads, but you focus on the core, right? You focus on what you need to survive. And creativity, I think, wins. And, you know, as a CMO when you're marketing, how do you get through that noise? Even the security space, Lacework, it's a fragmented market. You've got to be differentiated and position yourself and you know, be talking to the right target audience and customers. >> Talk about your journey over the years. What have you learned? What's some observations? Can you share any stories and best practices that someone watching could learn from? I know there's a lot of people coming into the tech space with the generative AI things going on in Cloud computing, scaling to the edge, there's a lot more aperture for technical jobs as well as just new roles and new roles that haven't, you really don't go to college for anymore. You got cybersecurity you're in. What are some of the things that you've done over your career if you can share and some best practices? >> Yeah, I think number one, continual learning. When I look through my career, I was constantly reading, networking. Part of the journey is who you're meeting along the way. As you become more senior, your ability to hire and bring in talent matters a lot. I'm always trying to meet with new people. Yeah, if I look at my Amazon feed of books I've bought, right, it kind of chronicle of my history of things I was learning about. Right now I'm reading a lot about cybersecurity, how the, you know, how how they tell me the world ends is the one I'm reading most recently. But you've got to come up to speed and then know the product, get in there and talk to customers. Certainly on the marketing front, anytime I can talk with the customer and find out how they're using us, why they love us, that, you know, helps me better position and differentiate our company. >> By the way, that book is amazing. I saw Nicole speak on Tuesday night with John Markoff and Palo Alto here. What a great story she told there. I recommend that book to everyone. It goes in and she did eight years of research into that book around zero day marketplaces to all the actors involved in security. And it was very interesting. >> Yeah, I mean, it definitely wakes you up, makes you think about what's going on in the world. Very relevant. >> It's like, yeah, it was happening all the time, wasn't it. All the hacking. But this brings me, this brings up an interesting point though, because you're in a cybersecurity area, which by the way, it's changing very fast. It's becoming a bigger industry. It's not just male dominated, although it is now, it's still male dominated, but it's becoming much more and then just tech. >> Yeah, I mean it's a constantly evolving threat landscape and we're learning, and I think more than ever you need to be able to use the data that companies have and, you know, learn from it. That's one of the ways we position ourselves. We're not just about writing rules that won't help you with those zero day attacks. You've got to be able to understand your particular environment and at any moment if it changes. And that's how we help you detect a threat. >> How is, how are things going with you? Is there any new things you guys got going on? Initiatives or programs for women in tech and increasing the range of diversity inclusion in the industry? Because again, this industry's getting much wider too. It's not just specialized, it's also growing. >> Yes, actually I'm excited. We're launching secured by women, securedbywomen.com and it's very much focused on women in the industry, which some studies are showing it's about 25% of security professionals are women. And we're going to be taking nominations and sponsoring women to go to upcoming security events. And so excited to launch that this month and really celebrate women in security and help them, you know, part of that continual learning that I talked about, making sure they're there learning, having the conversations at the conferences, being able to network. >> I have to ask you, what inspired you to pursue the career in tech? What was the motivation? >> You know, if I think way back, originally I wanted to be on the art side and my dad said, "You can do anything as long as it's in the sciences." And so in undergrad I did computer science and MIS. Graduated with MIS and computer science minor. And when I came out I was a IT engineer at Cisco and you know, that kind of started my journey and decided to go back and get my MBA. And during that process I fell in love with marketing and I thought, okay, I understand the buyer, I can come out and market technology to the IT world and developers. And then from there went to several tech companies. >> I mean my father was an engineer. He had the same kind of thing. You got to be an engineer, it's a steady, stable job. But that time, computer science, I mean we've seen the evolution of computer science now it's the most popular degree at Berkeley we've heard and around the world and the education formats are changing. You're seeing a lot of people's self-training on YouTube. The field has really changed. What are some of the challenges you see for folks trying to get into the industry and how would you advise today if you were talking to your young self, what would you, what would be the narrative? >> Yeah, I mean my drawback then was HTML pages were coming out and I thought it would be fun to design, you know, webpages. So you find something you're passionate about in the space today, whether it's gaming or it's cybersecurity. Go and be excited about it and apply and don't give up, right? Do whatever you can to read and learn. And you're right, there are a ton of online self-help. I always try to hire women and people who are continual learners and are teaching themselves something. And I try to find that in an interview to know that they, because when you come to a business, you're there to solve problems and challenges. And the folks that can do that and be innovative and learn, those are the ones I want on my team. >> It's interesting, you know, technology is now impacting society and we need everyone involved to participate and give requirements. And that kind of leads my next question for you is, like, in your opinion, or let me just step back, let me rephrase. What are some of the things that you see technology being used for, for society right now that will impact people's lives? Because this is not a gender thing. We need everybody involved 'cause society is now digital. Technology's pervasive. The AI trends now we're seeing is clearly unmasking to the mainstream that there's some cool stuff happening. >> Yeah, I mean, I think ChatGPT, think about that. All the different ways we're using it we're writing content and marketing with it. We're, you know, I just read an article yesterday, folks are using it to write children's stories and then selling those stories on Amazon, right? And the amount that they can produce with it. But if you think about it, there's unlimited uses with that technology and you've got all the major players getting involved on it. That one major launch and piece of technology is going to transform us in the next six months to a year. And it's the ability to process so much data and then turn that into just assets that we use and the creativity that's building on top of it. Even TripActions has incorporated ChatGPT into your ability to figure out where you want when you're traveling, what's happening in that city. So it's just, you're going to see that incorporated everywhere. >> I mean we've done an interview before TripAction, your other company you were at. Interesting point you don't have to type in a box to say, I'm traveling, I want a hotel. You can just say, I'm going to Barcelona for Mobile World Congress, I want to have a good time. I want some tapas and a nice dinner out. >> Yes. Yeah. That easy. We're making it easy. >> It's efficiency. >> And actually I was going to say for women specifically, I think the reason why we can do so much today is all the technology and apps that we have. I think about DoorDash, I think about Waze you know, when I was younger you had to print out instructions. Now I get in the car real quick, I need to go to soccer practice, I enter it, I need to pick them up at someone's house. I enter it. It's everything's real time. And so it takes away all the things that I don't add value to and allows me to focus on what I want in business. And so there's a bunch of, you know, apps out there that have allowed me to be so much more efficient and productive that my mother didn't have for sure when I was growing up. >> That is an amazing, I think that actually illustrates, in my opinion, the best example of ChatGPT because the maps and GPS integration were two techs, technologies merged together that replace driving and looking at the map. You know, like how do you do that? Like now it's automatically. This is what's going to happen to creative, to writing, to ideation. I even heard Nicole from her book read said that they're using ChatGPT to write zero day exploits. So you seeing it... >> That's scary stuff. You're right. >> You're seeing it everywhere. Super exciting. Well, I got to ask you before you get into some of the Lacework things that you're involved with, cause I think you're doing great work over there is, what was the most exciting projects you've worked on in your career? You came in Cisco, very technical company, so got the technical chops, CSMIS which stands for Management of Information Science for all the young people out there, that was the state of the art back then. What are some of the exciting things you've done? >> Yeah, I mean, I think about, I think about MongoDB and learning to market to developers. Taking the company public in 2017. Launching Atlas database as a service. Now there's so much more of that, you know, the PLG motion, going to TripActions, you know, surviving a pandemic, still being able to come out of that and all the learnings that went with it. You know, they recently, I guess rebranded, so they're Navan now. And then now back in the security space, you know, 14 years ago I was at ArcSite and we were bought by HP. And so getting back into the security world is exciting and it's transformed a ton as you know, it's way more complicated than it was. And so just understanding the pain of our customers and how we protect them as is fun. And I like, you know, being there from a marketing standpoint. >> Well we really appreciate you coming on and sharing that. I got to ask you, for folks watching they might be interested in some advice that you might have for them and their career in tech. I know a lot of young people love the tech. It's becoming pervasive in our lives, as we mentioned. What advice would you give for folks watching that want to start a career in tech? >> Yeah, so work hard, right? Study, network, your first job, be the best at it because every job after that you get pulled into a network. And every time I move, I'm hiring people from the last job, two jobs before, three jobs before. And I'm looking for people that are working hard, care, you know, are continual learners and you know, add value. What can you do to solve problems at your work and add value? >> What's your secret networking hack or growth hack or tip that you can share? Because you're a great networker by the way. You're amazing and you do add a lot of value. I've seen you in action. >> Well, I try never to eat alone. I've got breakfast, I've got lunch, I've got coffee breaks and dinner. And so when I'm at work, I try and always sit and eat with a team member, new group. If I'm out on the road, I'm, you know, meeting people for lunch, going for dinner, just, you know, don't sit at your desk by yourself and don't sit in the hotel room. Get out and meet with people. >> What do you think about now that we're out of the pandemic or somewhat out of the pandemic so to speak, events are back. >> Yes. >> RSA is coming up. It's a big event. The bigger events are getting bigger and then the other events are kind of smaller being distributed. What's your vision of how events are evolving? >> Yeah, I mean, you've got to be in person. Those are the relationships. Right now more than ever people care about renewals and you are building that rapport. And if you're not meeting with your customers, your competitors are. So what I would say is get out there Lacework, we're going to be at RSA, we're going to be at re:Inforce, we're going to be at all of these events, building relationships, you know, coffee, lunch, and yeah, I think the future of events are here to stay and those that don't embrace in person are going to give up business. They're going to lose market share to us. >> And networking is obviously very key on events as well. >> Yes. >> A good opportunity as always get out to the events. What's the event networking trick or advice do you give folks that are going to get out to the networking world? >> Yeah, schedule ahead of time. Don't go to an event and expect people just to come by for great swag. You should be partnering with your sales team and scheduling ahead of time, getting on people's calendars. Don't go there without having 100 or 200 meetings already booked. >> Got it. All right. Let's talk about you, your career. You're currently at Lacework. It's a very hot company in a hot field, security, very male dominated, you're a leader there. What's it like? What's the strategies? How does a woman get in there and be successful? What are some tricks, observations, any data you can share? What's the best practice? What's the secret sauce from Meagen Eisenberg? >> Yes. Yeah, for Meagen Eisenberg. For Lacework, you know, we're focused on our customers. There's nothing better than getting, being close to them, solving their pain, showcasing them. So if you want to go into security, focus on their, the issues and their problems and make sure they're aware of what you're delivering. I mean, we're focused on cloud security and we go from build time to run time. And that's the draw for me here is we had a lot of, you know, happy, excited customers by what we were doing. And what we're doing is very different from legacy security providers. And it is tapping into the trend of really understanding how much data you have and what's happening in the data to detect the anomalies and the threats that are there. >> You know, one of the conversations that I was just having with a senior leader, she was amazing and I asked her what she thought of the current landscape, the job market, the how to get promoted through the careers, all those things. And the response was interesting. I want to get your reaction. She said interdisciplinary skills are critical. And now more than ever, the having that, having a set of skills, technical and social and emotional are super valuable. Do you agree? What's your reaction to that and what would, how would you reframe that? >> Yeah, I mean, I completely agree. You can't be a leader without balance. You've got to know your craft because you're developing and training your team, but you also need to know the, you know, how to build relationships. You're not going to be successful as a C-level exec if you're not partnering across the functions. As a CMO I need to partner with product, I need to partner with the head of sales, I need to partner with finance. So those relationships matter a ton. I also need to attract the right talent. I want to have solid people on the team. And what I will say in the security, cybersecurity space, there's a talent shortage and you cannot hire enough people to protect your company in that space. And that's kind of our part of it is we reduce the number of alerts that you're getting. So you don't need hundreds of people to detect an issue. You're using technology to show, you know, to highlight the issue and then your team can focus on those alerts that matter. >> Yeah, there's a lot of emerging markets where leveling up and you don't need pedigree. You can just level up skill-wise pretty quickly. Which brings me to the next question for you is how do you keep up with all the tech day-to-day and how should someone watching stay on top of it? Because I mean, you got to be on top of this stuff and you got to ride the wave. It's pretty turbulent, but it's still growing and changing. >> Yeah, it's true. I mean, there's a lot of reading. I'm watching the news. Anytime something comes out, you know, ChatGPT I'm playing with it. I've got a great network and sharing. I'm on, you know, LinkedIn reading articles all the time. I have a team, right? Every time I hire someone, they bring new information and knowledge in and I'm you know, Cal Poly had this learn by doing that was the philosophy at San Luis Obispo. So do it. Try it, don't be afraid of it. I think that's the advice. >> Well, I love some of the points you mentioned community and network. You mentioned networking. That brings up the community question, how could people get involved? What communities are out there? How should they approach communities? 'Cause communities are also networks, but also they're welcoming people in that form networks. So it's a network of networks. So what's your take on how to engage and work with communities? How do you find your tribe? If someone's getting into the business, they want support, they might want technology learnings, what's your approach? >> Yeah, so a few, a few different places. One, I'm part of the operator collective, which is a strong female investment group that's open and works a lot with operators and they're in on the newest technologies 'cause they're investing in it. Chief I think is a great organization as well. You've got a lot of, if you're in marketing, there's a ton of CMO networking events that you can go to. I would say any field, even for us at Lacework, we've got some strong CISO networks and we do dinners around you know, we have one coming up in the Bay area, in Boston, New York, and you can come and meet other CISOs and security leaders. So when I get an invite and you know we all do, I will go to it. I'll carve out the time and meet with others. So I think, you know, part of the community is get out there and, you know, join some of these different groups. >> Meagen, thank you so much for spending the time. Final question for you. How do you see the future of tech evolving and how do you see your role in it? >> Yeah, I mean, marketing's changing wildly. There's so many different channels. You think about all the social media channels that have changed over the last five years. So when I think about the future of tech, I'm looking at apps on my phone. I have three daughters, 13, 11, and 8. I'm telling you, they come to me with new apps and new technology all the time, and I'm paying attention what they're, you know, what they're participating in and what they want to be a part of. And certainly it's going to be a lot more around the data and AI. I think we're only at the beginning of that. So we will continue to, you know, learn from it and wield it and deal with the mass amount of data that's out there. >> Well, you saw TikTok just got banned by the European Commission today around their staff. Interesting times. >> It is. >> Meagen, thank you so much as always. You're a great tech athlete. Been following your career for a while, a long time. You're an amazing leader. Thank you for sharing your story here on theCUBE, celebration of International Women's Day. Every day is IWD and thanks for coming on. >> Thank you for having me. >> Okay. I'm John Furrier here in theCUBE Studios in Palo Alto. Thank you for watching, more to come stay with us. (bright music)

(upbeat electronic music) >> Okay, welcome back to Supercloud 2, live here in Palo Alto. I'm John Furrier, with Dave Vellante. Again, all day wall-to-wall coverage, just had a great interview with Walmart, we've got a Next interview coming up, you're going to hear from Bob Muglia and Tristan Handy, two experts, both experienced entrepreneurs, executives in technology. We're here to break down what just happened with Walmart, and what's coming up with George Gilbert, former colleague, Wikibon analyst, Gartner Analyst, and now independent investor and expert. George, great to see you, I know you're following this space. Like you read about it, remember the first days when Dataverse came out, we were talking about them coming out of Berkeley? >> Dave: Snowflake. >> John: Snowflake. >> Dave: Snowflake In the early days. >> We, collectively, have been chronicling the data movement since 2010, you were part of our team, now you've got your nose to the grindstone, you're seeing the next wave. What's this all about? Walmart building their own super cloud, we got Bob Muglia talking about how these next wave of apps are coming. What are the super apps? What's the super cloud to you? >> Well, this key's off Dave's really interesting questions to Walmart, which was like, how are they building their supercloud? 'Cause it makes a concrete example. But what was most interesting about his description of the Walmart WCMP, I forgot what it stood for. >> Dave: Walmart Cloud Native Platform. >> Walmart, okay. He was describing where the logic could run in these stateless containers, and maybe eventually serverless functions. But that's just it, and that's the paradigm of microservices, where the logic is in this stateless thing, where you can shoot it, or it fails, and you can spin up another one, and you've lost nothing. >> That was their triplet model. >> Yeah, in fact, and that was what they were trying to move to, where these things move fluidly between data centers. >> But there's a but, right? Which is they're all stateless apps in the cloud. >> George: Yeah. >> And all their stateful apps are on-prem and VMs. >> Or the stateful part of the apps are in VMs. >> Okay. >> And so if they really want to lift their super cloud layer off of this different provider's infrastructure, they're going to need a much more advanced software platform that manages data. And that goes to the -- >> Muglia and Handy, that you and I did, that's coming up next. So the big takeaway there, George, was, I'll set it up and you can chime in, a new breed of data apps is emerging, and this highly decentralized infrastructure. And Tristan Handy of DBT Labs has a sort of a solution to begin the journey today, Muglia is working on something that's way out there, describe what you learned from it. >> Okay. So to talk about what the new data apps are, and then the platform to run them, I go back to the using what will probably be seen as one of the first data app examples, was Uber, where you're describing entities in the real world, riders, drivers, routes, city, like a city plan, these are all defined by data. And the data is described in a structure called a knowledge graph, for lack of a, no one's come up with a better term. But that means the tough, the stuff that Jack built, which was all stateless and sits above cloud vendors' infrastructure, it needs an entirely different type of software that's much, much harder to build. And the way Bob described it is, you're going to need an entirely new data management infrastructure to handle this. But where, you know, we had this really colorful interview where it was like Rock 'Em Sock 'Em, but they weren't really that much in opposition to each other, because Tristan is going to define this layer, starting with like business intelligence metrics, where you're defining things like bookings, billings, and revenue, in business terms, not in SQL terms -- >> Well, business terms, if I can interrupt, he said the one thing we haven't figured out how to APIify is KPIs that sit inside of a data warehouse, and that's essentially what he's doing. >> George: That's what he's doing, yes. >> Right. And so then you can now expose those APIs, those KPIs, that sit inside of a data warehouse, or a data lake, a data store, whatever, through APIs. >> George: And the difference -- >> So what does that do for you? >> Okay, so all of a sudden, instead of working at technical data terms, where you're dealing with tables and columns and rows, you're dealing instead with business entities, using the Uber example of drivers, riders, routes, you know, ETA prices. But you can define, DBT will be able to define those progressively in richer terms, today they're just doing things like bookings, billings, and revenue. But Bob's point was, today, the data warehouse that actually runs that stuff, whereas DBT defines it, the data warehouse that runs it, you can't do it with relational technology >> Dave: Relational totality, cashing architecture. >> SQL, you can't -- >> SQL caching architectures in memory, you can't do it, you've got to rethink down to the way the data lake is laid out on the disk or cache. Which by the way, Thomas Hazel, who's speaking later, he's the chief scientist and founder at Chaos Search, he says, "I've actually done this," basically leave it in an S3 bucket, and I'm going to query it, you know, with no caching. >> All right, so what I hear you saying then, tell me if I got this right, there are some some things that are inadequate in today's world, that's not compatible with the Supercloud wave. >> Yeah. >> Specifically how you're using storage, and data, and stateful. >> Yes. >> And then the software that makes it run, is that what you're saying? >> George: Yeah. >> There's one other thing you mentioned to me, it's like, when you're using a CRM system, a human is inputting data. >> George: Nothing happens till the human does something. >> Right, nothing happens until that data entry occurs. What you're talking about is a world that self forms, polling data from the transaction system, or the ERP system, and then builds a plan without human intervention. >> Yeah. Something in the real world happens, where the user says, "I want a ride." And then the software goes out and says, "Okay, we got to match a driver to the rider, we got to calculate how long it takes to get there, how long to deliver 'em." That's not driven by a form, other than the first person hitting a button and saying, "I want a ride." All the other stuff happens autonomously, driven by data and analytics. >> But my question was different, Dave, so I want to get specific, because this is where the startups are going to come in, this is the disruption. Snowflake is a data warehouse that's in the cloud, they call it a data cloud, they refactored it, they did it differently, the success, we all know it looks like. These areas where it's inadequate for the future are areas that'll probably be either disrupted, or refactored. What is that? >> That's what Muglia's contention is, that the DBT can start adding that layer where you define these business entities, they're like mini digital twins, you can define them, but the data warehouse isn't strong enough to actually manage and run them. And Muglia is behind a company that is rethinking the database, really in a fundamental way that hasn't been done in 40 or 50 years. It's the first, in his contention, the first real rethink of database technology in a fundamental way since the rise of the relational database 50 years ago. >> And I think you admit it's a real Hail Mary, I mean it's quite a long shot right? >> George: Yes. >> Huge potential. >> But they're pretty far along. >> Well, we've been talking on theCUBE for 12 years, and what, 10 years going to AWS Reinvent, Dave, that no one database will rule the world, Amazon kind of showed that with them. What's different, is it databases are changing, or you can have multiple databases, or? >> It's a good question. And the reason we've had multiple different types of databases, each one specialized for a different type of workload, but actually what Muglia is behind is a new engine that would essentially, you'll never get rid of the data warehouse, or the equivalent engine in like a Databricks datalake house, but it's a new engine that manages the thing that describes all the data and holds it together, and that's the new application platform. >> George, we have one minute left, I want to get real quick thought, you're an investor, and we know your history, and the folks watching, George's got a deep pedigree in investment data, and we can testify against that. If you're going to invest in a company right now, if you're a customer, I got to make a bet, what does success look like for me, what do I want walking through my door, and what do I want to send out? What companies do I want to look at? What's the kind of of vendor do I want to evaluate? Which ones do I want to send home? >> Well, the first thing a customer really has to do when they're thinking about next gen applications, all the people have told you guys, "we got to get our data in order," getting that data in order means building an integrated view of all your data landscape, which is data coming out of all your applications. It starts with the data model, so, today, you basically extract data from all your operational systems, put it in this one giant, central place, like a warehouse or lake house, but eventually you want this, whether you call it a fabric or a mesh, it's all the data that describes how everything hangs together as in one big knowledge graph. There's different ways to implement that. And that's the most critical thing, 'cause that describes your Uber landscape, your Uber platform. >> That's going to power the digital transformation, which will power the business transformation, which powers the business model, which allows the builders to build -- >> Yes. >> Coders to code. That's Supercloud application. >> Yeah. >> George, great stuff. Next interview you're going to see right here is Bob Muglia and Tristan Handy, they're going to unpack this new wave. Great segment, really worth unpacking and reading between the lines with George, and Dave Vellante, and those two great guests. And then we'll come back here for the studio for more of the live coverage of Supercloud 2. Thanks for watching. (upbeat electronic music)

>> John Furrier: Hello, welcome back to theCUBE's coverage of Cloud Native Security Con 2023 North America the inaugural event. I'm John Furrier, host of theCUBE, along with Dave Alonte and Lisa Martin covering from the studio. But we have on location Emmy Eide, who is with Red Hat, director of Supply Chain Security. Emmy, great to have you on from location. Thanks for joining us. >> Emmy Eide: Yeah, thank you. >> So everyone wants to know this event is new, it's an aural event, cloud native con, coup con. Very successful. Was this event successful? They all want to know what's going on there. What's the vibe? What's the tracks like? Is it different? Why this event? Was it successful? What's different? >> Yeah, I've really enjoyed being here. The food is wonderful. There's also quite a few vendors here that are just some really cool emerging technologies coming out and a lot from open source, which is really cool to see as well. The talks are very interesting. It's really, they're very diverse in subject but still all security related which is really cool to see. And there's also a lot of different perspectives of how to approach security problems and the people behind them, which I love to see. And it's very nice to hear the different innovative ideas that we can go about doing security. >> We heard from some startups as well that they're very happy with the, with the decision to have a dedicated event. Red Hat is no stranger to open source. Obviously coup con, you guys are very successful there in cloud native con, Now the security con. Why do you think they did this? What's the vibe? What's the rationale? What's your take on this? And what's different from a topic standpoint? >> For non-security specific like events? Is that what you mean? >> What's different from coup con, cloud native con, and here at the cloud native security con? Obviously security's the focus. Is it just deeper dives? Is it more under the hood? Is it root problems or is this beyond Kubernetes? What's the focus, I guess. People want to know, you know, why the new event? >> I mean, there's a lot of focus on supply chain security, right? Like that's the hot topic in security right now. So that's been a huge focus. I can't speak to the differences of those other conferences. I haven't been able to attend them. But I will say that having a security specific conference, it really focuses on the open community and how technology is evolving, and how do you apply security. It's not just talking about tools which I think other conferences tend to focus on just the tools and you can really, I think, get lost in that as someone trying to learn about security or trying to even implement security, but they talk about what it takes to implement those tools, What's behind the people behind implementing those tools? >> Let's get into some of the key topics that we've identified and get your reaction. One, supply chain security, which I know you'll give a lot of commentary on 'cause that's your focus. Also we heard, like, Liz Rice talking about the extended Berkeley packet filtering. Okay, that's big. You know, your root kernel management, that's big. Developer productivity was kind of implied around removing the blockers of security, making it, you know, more aligned with developer first mentality. So that seems to be our takeaway. What's your reaction to those things? You see the same thing? >> I don't have a specific reaction to those things. >> Do you see the same thing happening on the ground there? Are they covering supply? >> Oh, yeah. >> Those three things are they the big focus? >> Yeah. Yeah, I think it's all of those things kind of like wrapped into one, right? But yeah, there's... I'm not sure how to answer your question. >> Well, let's jump into supply chain for instance. 'Cause that has come up a lot. >> Sure. >> What's the focus there on the supply chain security? Is it SBOMs? Is it the container security? What's the key conversations and topics being discussed around supply chain security? >> Well, I think there's a lot of laughter around SBOM right now because no one can really define it, specifically, and everyone's talking about it. So there's, there's a lot more than just the SBOM conversation. We're talking about like full end-to-end development process and that whole software supply chain that goes with it. So there's everything from infrastructure, security, all the way through to like signing transparency logs. Really the full gambit of supply chain, which is is really neat to see because it is such a broad topic. I think a lot of folks now are involved in supply chain security in some way. And so just kind of bringing that to the surface of what are the different people that are involved in this space, thinking about, what's on the top of their mind when it comes to supply chain security. >> How would you scope the order of magnitude of the uptick in supply chain attacks? Is it pretty heavy right now or is it, you know, people with the hair on fire or is it... What's the, give us the taste of the temperature in the room on the supply chain attacks? >> I think most of the folks who are involved in the space understand just that it's increasing. I mean, like, what is it? A 742% increase average annual year, year over year in supply chain attacks. So the amount of attacks increasing is a little daunting, right, for most of us. But it is what it is. So I think most of us right now are just trying to come together to say, "What are you doing that works? This is what I'm doing that works." And in all the different facets of that. 'cause I think we try to throw, we try to throw tools at a lot of problems and this problem is so big and broad reaching that we really are needing to share best practices as a community and as a security community. So this has been, this conference has been really great for that. >> Yeah, I've heard that a lot. You know, too many tools, not enough platform thinking, not enough architecture, needs some structure. Are you seeing any best practice around frameworks and structure around how to start getting in and and building out more of a better approach or posture? I mean, what's that, what's the, what's the state of the union for supply chain, how to handle that? >> Well, I talked about that a little bit in my my keynote that I gave, actually, which was about... And I've heard other other leaders talk about it too. And obviously it keyed my ear just because I'm so passionate about it, about partnership. So you know, empathetic security where the security team that's enforcing the policies, creating the policies, guidelines is working with the teams that are actually doing the production and the development, hand-in-hand, right? Like I can sit there and tell you, "Hey, you have all these problems and here's your security checklist or framework you need to follow." But that's not going to do them any good and it's going to create a ton of holes, right? So actually partnering with them helping them to understand the risks that are associated with their very specific need and use case, because every product has a different kind of quirk to it, right? Like how it's being developed. It might use a different tool and if I sit there and say, "Hey, you need to log on to this, you need to like make your tool work this platform over here and it's not compatible." I'm going to have to completely reframe how I'm doing productization. I need to know that as a security practitioner because me disrupting productization is not something that I should be doing. And I've heard a couple a couple of folks kind of talking about that, the people aspect behind how we implement these tools, the frameworks and the platforms, and how do we draw out risk, right? Like how do we talk about risk with these teams and really make them understand so it's part of their core culture in their understanding. So when they go back to their, when they go back and having to make decisions without me in the room they know they can make those business decisions with the risk as part of that decision. >> I love that empathetic angle because that's really going to, what needs to happen. It's not just, "Hey, that's your department, see you later." Or not even having a knowledge of the information. This idea of team construction, team management is a huge cultural shift. I'm sure the reaction was very positive. How do you explain that to an organization that's out there? Like how do you... what's the first three steps you got to take? Is there anything that you can share for advice people watch you saying, "Yeah we need to we need to change how our teams operate and interact with each other." >> Yeah, I think the first step is to take a good hard look at yourself. And if you are standing there on an ivory tower with a clipboard, you're probably doing it wrong. Check the box security is never going to be any way that works long term. It's going to take you a long time to implement any changes. At Red Hat, we did not look ourselves. You know, we've been doing a lot of great things in supply chain security for a while, but really taking that look and saying, "How can we be more empathetic leaders in the security space?" So we looked at that, then you say, "Okay, what is my my rate of change going to happen?" So if I need to make so many security changes explaining to these organizations, you're actually going to go faster. We improved our efficiency by 2000% just by doing that, just by creating this more empathetic. So why it seems like it's more hands-on, so it's going to be harder, it's easy to send out an email and say, "Hey, meet the security standard, right?" That might seem like the easy way 'cause you don't have time to engage. It's so much faster if you actually engage and share that message and have a a common understanding between the teams that like, "I'm here to deliver a product, so is the security team. The security team's here to deliver that same product and I want to help you do it in a trusted way." Right? >> Yeah. Dave Alonte, my co-host, was just on a session. We were talking together about security teams jumping on every team and putting a C on their jersey to be like the captain of the intramural team, and being involved, and it goes beyond just like the checklist, like you said, "Oh, I got the SBOM list of materials and I got a code scanning thing." That's not enough, is what we're hearing. >> No. >> Is there a framework or a methodology to go beyond that? You got the empathetic, that's really kind of team issue. You got to go beyond some of the tactical things. What's next beyond, you got the empathy and what's that framework structure when you say where you say anything there? >> So what do you do after you have the empathy, right? >> Yeah. >> I would say Salsa is a good place to start, the software levels. Supply chain levels for software artifacts. It's a mouthful. That's a really good maturity framework to start with. No matter what size organization you have, they're just going to be coming out here soon with version one. They release 0.1 a few months back. That's a really good place to give yourself a gut check of where you are in maturity and where you can go, what are best practices. And then there's the SSDF, which is the Secure Software Development framework. I think NIST wrote that one. But that is also a really, a really good framework and they map really well to each other, actually, When you work through Salsa, you're actually working through the SSDF requirements. >> Awesome. Well, great to have you on and great to get that that knowledge. I have to ask you like coup con, I remember when it started in Seattle, their first coup con events, right? Kind of small, similar to this one, but there's a lot of end user activities. Certainly the CNCF kind of was coming together like right after that. What's the end user activity like there this week? That seems to always been the driver of these events. It's a little bit organic. You got some of the key experts coming together, focus. Have you observed any end user activity in terms of contributions, participation? What's the story on the end user piece there? Is it heavy? Is it light? What's the... >> Um, yeah... It seems moderate. I guess somewhere in the middle. I would say largely heavy, but there's definitely participation. There is a lot of communing and networking happening between different organizations to partner together, which is important. But I haven't really paid attention much to like the Twitter side of this. >> Yeah, you've been busy doing the keynotes. How's Red Hat doing all this? You guys have been great positioned with the cloud native movement. Been following the Red Hat's moves since OpenStack days. Really good, good line of product, good open source, Mojo, of course. Good product mix, right, and relevant. Where's the security focus here? Obviously, you guys are clearly focused on security. How's the Red Hat story going on over there? >> There was yesterday a really good talk that explains that super well. It was given by a Red Hatter, connecting all of the open source projects we've been a part of and kind of explaining them. And obviously again, I'm keying in 'cause it's a supply chain kind of conversation, but I'd recommend that anyone who's going to go back and watch these on YouTube to check that one out just to see kind of how we're approaching the security space as well as how we contribute back to the community in that way. >> Awesome. Great to have you on. Final word, I'll give you the final word. What's the big buzz on supply chain? How would you peg the progress there? Feeling good about where things are? What's the current progress on supply chain security? >> I think that it has opened up a lot of doors for communication between security organizations that have tended to be closed. I'm in product security. Product securities, information securities tend to not speak externally about what we're doing. So you don't want to, you know, look bad or you don't want to expose any risk that we have, right? But it is, I think, necessary to open those lines of communication, to be able to start tackling this. It's a big problem throughout all of our industries, and if one supply chain is attacked and those products are used in someone else's supply chain, that can continue, right? So I think it's good. We have a lot of work to do as an industry and the advancements in technology is going to make that a little bit more complicated. But I'm excited for it. >> You can just throw AI at it. That's the big, everyone's doing AI. Just throw AI at it, it'll solve it. Isn't that the new thing? >> I do secure AI though. >> Super important. I love what you're doing there. Supply chain, open source needs, supply chain security. Open source needs this big time. It has to be there. Thank you for the work that you do. Really appreciate you coming on. Thank you. >> Yeah, thanks for having me. >> Yeah, good stuff. Supply chain, critical to open source growth. Open source is going to be the key to success in the future with automation and AI right around the corner. And that's important. This theCUBE covers from cloud native con, security con in North America, 2023. I'm John Furrier. Thanks for watching.

(upbeat music) >> Hey everyone and welcome to theCUBE's coverage day one of CloudNativeSecurityCon '23. Lisa Martin here with John Furrier and Dave Vellante. Dave and John, great to have you guys on the program. This is interesting. This is the first inaugural CloudNativeSecurityCon. Formally part of KubeCon, now a separate event here happening in Seattle over the next couple of days. John, I wanted to get your take on, your thoughts on this being a standalone event, the community, the impact. >> Well, this inaugural event, which is great, we love it, we want to cover all inaugural events because you never know, there might not be one next year. So we were here if it happens, we're here at creation. But I think this is a good move for the CNCF and the Linux Foundation as security becomes so important and there's so many issues to resolve that will influence many other things. Developers, machine learning, data as code, supply chain codes. So I think KubeCon, Kubernetes conference and CloudNativeCon, is all about cloud native developers. And it's a huge event and there's so much there. There's containers, there's microservices, all that infrastructure's code, the DevSecOps on that side, there's enough there and it's a huge ecosystem. Pulling it as a separate event is a first move for them. And I think there's a toe in the water kind of vibe here. Testing the waters a little bit on, does this have legs? How is it organized? Looks like they took their time, thought it out extremely well about how to craft it. And so I think this is the beginning of what will probably be a seminal event for the open source community. So let's listen to the clip from Priyanka Sharma who's a CUBE alumni and executive director of the CNCF. This is kind of a teaser- >> We will tackle issues of security together here and further on. We'll share our experiences, successes, perhaps more importantly, failures, and help with the collecting of understanding. We'll create solutions. That's right. The practitioners are leading the way. Having conversations that you need to have. That's all of you. This conference today and tomorrow is packed with 72 sessions for all levels of technologists to reflect the bottoms up, developer first nature of the conference. The co-chairs have selected these sessions and they are true blue practitioners. >> And that's a great clip right there. If you read between the lines, what she's saying there, let's unpack this. Solutions, we're going to fail, we're going to get better. Linux, the culture of iterating. But practitioners, the mention of practitioners, that was very key. Global community, 72 sessions, co-chairs, Liz Rice and experts that are crafting this program. It seems like very similar to what AWS has done with re:Invent as their core show. And then they have re:Inforce which is their cloud native security, Amazon security show. There's enough there, so to me, practitioners, that speaks to the urgency of cloud native security. So to me, I think this is the first move, and again, testing the water. I like the vibe. I think the practitioner angle is relevant. It's very nerdy, so I think this is going to have some legs. >> Yeah, the other key phrase Priyanka mentioned is bottoms up. And John, at our predictions breaking analysis, I asked you to make a prediction about events. And I think you've nailed it. You said, "Look, we're going to have many more events, but they're going to be smaller." Most large events are going to get smaller. AWS is obviously the exception, but a lot of events like this, 500, 700, 1,000 people, that is really targeted. So instead of you take a big giant event and there's events within the event, this is going to be really targeted, really intimate and focused. And that's exactly what this is. I think your prediction nailed it. >> Well, Dave, we'll call to see the event operating system really cohesive events connected together, decoupled, and I think the Linux Foundation does an amazing job of stringing these events together to have community as the focus. And I think the key to these events in the future is having, again, targeted content to distinct user groups in these communities so they can be highly cohesive because they got to be productive. And again, if you try to have a broad, big event, no one's happy. Everyone's underserved. So I think there's an industry concept and then there's pieces tied together. And I think this is going to be a very focused event, but I think it's going to grow very fast. >> 72 sessions, that's a lot of content for this small event that the practitioners are going to have a lot of opportunity to learn from. Do you guys, John, start with you and then Dave, do you think it's about time? You mentioned John, they're dipping their toe in the water. We'll see how this goes. Do you think it's about time that we have this dedicated focus out of this community on cloud native security? >> Well, I think it's definitely time, and I'll tell you there's many reasons why. On the front lines of business, there's a business model for security hackers and breaches. The economics are in favor of the hackers. That's a real reality from ransomware to any kind of breach attacks. There's corporate governance issues that's structural challenges for companies. These are real issues operationally for companies in the enterprise. And at the same time, on the tech stack side, it's been very slow movement, like glaciers in terms of security. Things like DNS, Linux kernel, there are a lot of things in the weeds in the details of the bowels of the tech world, protocol levels that just need to be refactored. And I think you're seeing a lot of that here. It was mentioned from Brian from the Linux Foundation, mentioned Dan Kaminsky who recently passed away who found that vulnerability in BIND which is a DNS construct. That was a critical linchpin. They got to fix these things and Liz Rice is talking about the Linux kernel with the extended Berkeley Packet Filtering thing. And so this is where they're going. This is stuff that needs to be paid attention to because if they don't do it, the train of automation and machine learning is going to run wild with all kinds of automation that the infrastructure just won't be set up for. So I think there's going to be root level changes, and I think ultimately a new security stack will probably be very driven by data will be emerging. So to me, I think this is definitely worth being targeted. And I think you're seeing Amazon doing the same thing. I think this is a playbook out of AWS's event focus and I think that's right. >> Dave, what are you thoughts? >> There was a lot of talk in, again, I go back to the progression here in the last decade about what's the right regime for security? Should the CISO report to the CIO or the board, et cetera, et cetera? We're way beyond that now. I think DevSecOps is being asked to do a lot, particularly DevOps. So we hear a lot about shift left, we're hearing about protecting the runtime and the ops getting much more involved and helping them do their jobs because the cloud itself has brought a lot to the table. It's like the first line of defense, but then you've really got a lot to worry about from a software defined perspective. And it's a complicated situation. Yes, there's less hardware, yes, we can rely on the cloud, but culturally you've got a lot more people that have to work together, have to share data. And you want to remove the blockers, to use an Amazon term. And the way you do that is you really, if we talked about it many times on theCUBE. Do over, you got to really rethink the way in which you approach security and it starts with culture and team. >> Well the thing, I would call it the five C's of security. Culture, you mentioned that's a good C. You got cloud, tons of issues involved in cloud. You've got access issues, identity. you've got clusters, you got Kubernetes clusters. And then you've got containers, the fourth C. And then finally is the code itself, supply chain. So all areas of cloud native, if you take out culture, it's cloud, cluster, container, and code all have levels of security risks and new things in there that need to be addressed. So there's plenty of work to get done for sure. And again, this is developer first, bottoms up, but that's where the change comes in, Dave, from a security standpoint, you always point this out. Bottoms up and then middle out for change. But absolutely, the imperative is today the business impact is real and it's urgent and you got to pedal as fast as you can here, so I think this is going to have legs. We'll see how it goes. >> Really curious to understand the cultural impact that we see being made at this event with the focus on it. John, you mentioned the four C's, five with culture. I often think that culture is probably the leading factor. Without that, without getting those teams aligned, is the rest of it set up to be as successful as possible? I think that's a question that's- >> Well to me, Dave asked Pat Gelsinger in 2014, can security be a do-over at VMWorld when he was the CEO of VMware? He said, "Yes, it has to be." And I think you're seeing that now. And Nick from the co-founder of Palo Alto Networks was quoted on theCUBE by saying, "Zero Trust is some structure to give to security, but cloud allows for the ability to do it over and get some scale going on security." So I think the best people are going to come together in this security world and they're going to work on this. So you're going to start to see more focus around these security events and initiatives. >> So I think that when you go to the, you mentioned re:Inforce a couple times. When you go to re:Inforce, there's a lot of great stuff that Amazon puts forth there. Very positive, it's not that negative. Oh, the world is falling, the sky is falling. And so I like that. However, you don't walk away with an understanding of how they're making the CISOs and the DevOps lives easier once they get beyond the cloud. Of course, it's not Amazon's responsibility. And that's where I think the CNCF really comes in and open source, that's where they pick up. Obviously the cloud's involved, but there's a real opportunity to simplify the lives of the DevSecOps teams and that's what's critical in terms of being able to solve, or at least keep up with this never ending problem. >> Yeah, there's a lot of issues involved. I took some notes here from some of the keynote you heard. Security and education, training and team structure. Detection, incidents that are happening, and how do you respond to that architecture. Identity, isolation, supply chain, and governance and compliance. These are all real things. This is not like hand-waving issues. They're mainstream and they're urgent. Literally the houses are on fire here with the enterprise, so this is going to be very, very important. >> Lisa: That's a great point. >> Some of the other things Priyanka mentioned, exposed edges and nodes. So just when you think we're starting to solve the problem, you got IOT, security's not a one and done task. We've been talking about culture. No person is an island. It's $188 billion business. Cloud native is growing at 27% a year, which just underscores the challenges, and bottom line, practitioners are leading the way. >> Last question for you guys. What are you hoping those practitioners get out of this event, this inaugural event, John? >> Well first of all, I think this inaugural event's going to be for them, but also we at theCUBE are going to be doing a lot more security events. RSA's coming up, we're going to be at re:Inforce, we're obviously going to be covering this event. We've got Black Hat, a variety of other events. We'll probably have our own security events really focused on some key areas. So I think the thing that people are going to walk away from this event is that paying attention to these security events are going to be more than just an industry thing. I think you're going to start to see group gatherings or groups convening virtually and physically around core issues. And I think you're going to start to see a community accelerate around cloud native and open source specifically to help teams get faster and better at what they do. So I think the big walkaway for the customers and the practitioners here is that there's a call to arms happening and this is, again, another signal that it's worth breaking out from the core event, but being tied to it, I think that's a good call and I think it's a well good architecture from a CNCF standpoint and a worthy effort, so I give it a thumbs up. We still don't know what it's going to look like. We'll see what day two looks like, but it seems to be experts, practitioners, deep tech, enabling technologies. These are things that tend to be good things to hear when you're at an event. I'll say the business imperative is obvious. >> The purpose of an event like this, and it aligns with theCUBE's mission, is to educate and inspire business technology pros to action. We do it in theCUBE with free content. Obviously this event is a for-pay event, but they are delivering some real value to the community that they can take back to their organizations to make change. And that's what it's all about. >> Yep, that is what it's all about. I'm looking forward to seeing over as the months unfold, the impact that this event has on the community and the impact the community has on this event going forward, and really the adoption of cloud native security. Guys, great to have you during this keynote analysis. Looking forward to hearing the conversations that we have on theCUBE today. Thanks so much for joining. And for my guests, for my co-hosts, John Furrier and Dave Vellante. I'm Lisa Martin. You're watching theCUBE's day one coverage of CloudNativeSecurityCon '23. Stick around, we got great content on theCUBE coming up. (upbeat music)

(upbeat music) >> Hello, everyone, from Palo Alto, Lisa Martin here. This is The Cube's coverage of CloudNativeSecurityCon, the inaugural event. I'm here with John Furrier in studio. In Boston, Dave Vellante joins us, and our guest, Liz Rice, one of our alumni, is joining us from Seattle. Great to have everyone here. Liz is the Chief Open Source officer at Isovalent. She's also the Emeritus Chair Technical Oversight Committee at CNCF, and a co-chair of this new event. Everyone, welcome Liz. Great to have you back on theCUBE. Thanks so much for joining us today. >> Thanks so much for having me, pleasure. >> So CloudNativeSecurityCon. This is the inaugural event, Liz, this used to be part of KubeCon, it's now its own event in its first year. Talk to us about the importance of having it as its own event from a security perspective, what's going on? Give us your opinions there. >> Yeah, I think security was becoming so- at such an important part of the conversation at KubeCon, CloudNativeCon, and the TAG security, who were organizing the co-located Cloud Native Security Day which then turned into a two day event. They were doing this amazing job, and there was so much content and so much activity and so much interest that it made sense to say "Actually this could stand alone as a dedicated event and really dedicate, you know, all the time and resources of running a full conference, just thinking about cloud native security." And I think that's proven to be true. There's plenty of really interesting talks that we're going to see. Things like a capture the flag. There's all sorts of really good things going on this week. >> Liz, great to see you, and Dave, great to see you in Boston Lisa, great intro. Liz, you've been a CUBE alumni. You've been a great contributor to our program, and being part of our team, kind of extracting that signal from the CNCF cloud native world KubeCon. This event really kind of to me is a watershed moment, because it highlights not only security as a standalone discussion event, but it's also synergistic with KubeCon. And, as co-chair, take us through the thought process on the sessions, the experts, it's got a practitioner vibe there. So we heard from Priyanka early on, bottoms up, developer first. You know KubeCon's shift left was big momentum. This seems to be a breakout of very focused security. Can you share the rationale and the thoughts behind how this is emerging, and how you see this developing? I know it's kind of a small event, kind of testing the waters it seems, but this is really a directional shift. Can you share your thoughts? >> Yeah I'm just, there's just so many different angles that you can consider security. You know, we are seeing a lot of conversations about supply chain security, but there's also runtime security. I'm really excited about eBPF tooling. There's also this opportunity to talk about how do we educate people about security, and how do security practitioners get involved in cloud native, and how do cloud native folks learn about the security concepts that they need to keep their deployments secure. So there's lots of different groups of people who I think maybe at a KubeCon, KubeCon is so wide, it's such a diverse range of topics. If you really just want to focus in, drill down on what do I need to do to run Kubernetes and cloud native applications securely, let's have a really focused event, and just drill down into all the different aspects of that. And I think that's great. It brings the right people together, the practitioners, the experts, the vendors to, you know, everyone can be here, and we can find each other at a smaller event. We are not spread out amongst the thousands of people that would attend a KubeCon. >> It's interesting, Dave, you know, when we were talking, you know, we're going to bring you in real quick, because AWS, which I think is the bellweather for, you know, cloud computing, has now two main shows, AWS re:Invent and re:Inforce. Security, again, broken out there. you see the classic security events, RSA, Black Hat, you know, those are the, kind of, the industry kind of mainstream security, very wide. But you're starting to see the cloud native developer first with both security and cloud native, kind of, really growing so fast. This is a major trend for a lot of the ecosystem >> You know, and you hear, when you mention those other conferences, John you hear a lot about, you know, shift left. There's a little bit of lip service there, and you, we heard today way more than lip service. I mean deep practitioner level conversations, and of course the runtime as well. Liz, you spent a lot of time obviously in your keynote on eBPF, and I wonder if you could share with the audience, you know, why you're so excited about that. What makes it a more effective tool compared to other traditional methods? I mean, it sounds like it simplifies things. You talked about instrumenting nodes versus workloads. Can you explain that a little bit more detail? >> Yeah, so with eBPF programs, we can load programs dynamically into the kernel, and we can attach them to all kinds of different events that could be happening anywhere on that virtual machine. And if you have the right knowledge about where to hook into, you can observe network events, you can observe file access events, you can observe pretty much anything that's interesting from a security perspective. And because eBPF programs are living in the kernel, there's only one kernel shared amongst all of the applications that are running on that particular machine. So you don't- you no longer have to instrument each individual application, or each individual pod. There's no more need to inject sidecars. We can apply eBPF based tooling on a per node basis, which just makes things operationally more straightforward, but it's also extremely performant. We can hook these programs into events that typically very lightweight, small programs, kind of, emitting an event, making a decision about whether to drop a packet, making a decision about whether to allow file access, things of that nature. There's super fast, there's no need to transition between kernel space and user space, which is usually quite a costly operation from performance perspective. So eBPF makes it really, you know, it's taking the security tooling, and other forms of tooling, networking and observability. We can take these tools into the kernel, and it's really efficient there. >> So Liz- >> So, if I may, one, just one quick follow up. You gave kind of a space age example (laughs) in your keynote. When, do you think a year from now we'll be able to see, sort of, real world examples in in action? How far away are we? >> Well, some of that is already pretty widely deployed. I mean, in my keynote I was talking about Cilium. Cilium is adopted by hundreds of really big scale deployments. You know, the users file is full of household names who've been using cilium. And as part of that they will be using network policies. And I showed some visualizations this morning of network policy, but again, network policy has been around, pretty much since the early days of Kubernetes. It can be quite fiddly to get it right, but there are plenty of people who are using it at scale today. And then we were also looking at some runtime security detections, seeing things like, in my example, exfiltrating the plans to the Death Star, you know, looking for suspicious executables. And again, that's a little bit, it's a bit newer, but we do have people running that in production today, proving that it really does work, and that eBPF is a scalable technology. It's, I've been fascinated by eBPF for years, and it's really amazing to see it being used in the real world now. >> So Liz, you're a maintainer on the Cilium project. Talk about the use of eBPF in the Cilium project. How is it contributing to cloud native security, and really helping to change the dials on that from an efficiency, from a performance perspective, as well as a, what's in it for me as a business perspective? >> So Cilium is probably best known as a networking plugin for Kubernetes. It, when you are running Kubernetes, you have to make a decision about some networking plugin that you're going to use. And Cilium is, it's an incubating project in the CNCF. It's the most mature of the different CNIs that's in the CNCF at the moment. As I say, very widely deployed. And right from day one, it was based on eBPF. And in fact some of the people who contribute to the eBPF platform within the kernel, are also working on the Cilium project. They've been kind of developed hand in hand for the last six, seven years. So really being able to bring some of that networking capability, it required changes in the kernel that have been put in place several years ago, so that now we can build these amazing tools for Kubernetes operators. So we are using eBPF to make the networking stack for Kubernetes and cloud native really efficient. We can bypass some of the parts of the network stack that aren't necessarily required in a cloud native deployment. We can use it to make these incredibly fast decisions about network policy. And we also have a sub-project called Tetragon, which is a newer part of the Cilium family which uses eBPF to observe these runtime events. The things like people opening a file, or changing the permissions on a file, or making a socket connection. All of these things that as a security engineer you are interested in. Who is running executables who is making network connections, who's accessing files, all of these operations are things that we can observe with Cilium Tetragon. >> I mean it's exciting. We've chatted in the past about that eBPF extended Berkeley Packet Filter, which is about the Linux kernel. And I bring that up Liz, because I think this is the trend I'm trying to understand with this event. It's, I hear bottoms up developer, developer first. It feels like it's an under the hood, infrastructure, security geek fest for practitioners, because Brian, in his keynote, mentioned BIND in reference the late Dan Kaminsky, who was, obviously found that error in BIND at the, in DNS. He mentioned DNS. There's a lot of things that's evolving at the silicone, kernel, kind of root levels of our infrastructure. This seems to be a major shift in focus and rightfully so. Is that something that you guys talk about, or is that coincidence, or am I just overthinking this point in terms of how nerdy it's getting in terms of the importance of, you know, getting down to the low level aspects of protecting everything. And as we heard also the quote was no software secure. (Liz chuckles) So that's up and down the stack of the, kind of the old model. What's your thoughts and reaction to that? >> Yeah, I mean I think a lot of folks who get into security really are interested in these kind of details. You know, you see write-ups of exploits and they, you know, they're quite often really involved, and really require understanding these very deep detailed technical levels. So a lot of us can really geek out about the details of that. The flip side of that is that as an application developer, you know, as- if you are working for a bank, working for a media company, you're writing applications, you shouldn't have to be worried about what's happening at the kernel level. This might be kind of geeky interesting stuff, but really, operationally, it should be taken care of for you. You've got your work cut out building business value in applications. So I think there's this interesting, kind of dual track going on almost, if you like, of the people who really want to get involved in those nitty gritty details, and understand how the underlying, you know, kernel level exploits maybe working. But then how do we make that really easy for people who are running clusters to, I mean like you said, nothing is ever secure, but trying to make things as secure as they can be easily, and make things visual, make things accessible, make things, make it easy to check whether or not you are compliant with whatever regulations you need to be compliant with. That kind of focus on making things usable for the platform team, for the application developers who deliver apps on the platform, that's the important (indistinct)- >> I noticed that the word expert was mentioned, I mentioned earlier with Priyanka. Was there a rationale on the 72 sessions, was there thinking around it or was it kind of like, these are urgent areas, they're obvious low hanging fruit. Was there, take us through the selection process of, or was it just, let's get 72 sessions going to get this (Liz laughs) thing moving? >> No, we did think quite carefully about how we wanted to, what the different focus areas we wanted to include. So we wanted to make sure that we were including things like governance and compliance, and that we talk about not just supply chain, which is clearly a very hot topic at the moment, but also to talk about, you know, threat detection, runtime security. And also really importantly, we wanted to have space to talk about education, to talk about how people can get involved. Because maybe when we talk about all these details, and we get really technical, maybe that's, you know, a bit scary for people who are new into the cloud native security space. We want to make sure that there are tracks and content that are accessible for newcomers to get involved. 'Cause, you know, given time they'll be just as excited about diving into those kind of kernel level details. But everybody needs a place to start, and we wanted to make sure there were conversations about how to get started in security, how to educate other members of your team in your organization about security. So hopefully there's something for everyone. >> That education piece- >> Liz, what's the- >> Oh sorry, Dave. >> What the buzz on on AI? We heard Dan talk about, you know, chatGPT, using it to automate spear phishing. There's always been this tension between security and speed to market, but CISOs are saying, "Hey we're going to a zero trust architecture and that's helping us move faster." Will, in your, is the talk on the floor, AI is going to slow us down a little bit until we figure it out? Or is it actually going to be used as an offensive defensive tool if I can use that angle? >> Yeah, I think all of the above. I actually had an interesting chat this morning. I was talking with Andy Martin from Control Plane, and we were talking about the risk of AI generated code that attempts to replicate what open source libraries already do. So rather than using an existing open source package, an organization might think, "Well, I'll just have my own version, and I'll have an AI write it for me." And I don't, you know, I'm not a lawyer so I dunno what the intellectual property implications of this will be, but imagine companies are just going, "Well you know, write me an SSL library." And that seems terrifying from a security perspective, 'cause there could be all sorts of very slightly different AI generated libraries that pick up the same vulnerabilities that exist in open source code. So, I think we're going to go through a pretty interesting period of vulnerabilities being found in AI generated code that look familiar, and we'll be thinking "Haven't we seen these vulnerabilities before? Yeah, we did, but they were previously in handcrafted code and now we'll see the same things being generated by AI." I mean, in the same way that if you look at an AI generated picture and it's got I don't know, extra fingers, or, you know, extra ears or something that, (Dave laughs) AI does make mistakes. >> So Liz, you talked about the education, the enablement, the 72 sessions, the importance of CloudNativeSecurityCon being its own event this year. What are your hopes and dreams for the practitioners to be able to learn from this event? How do you see the event as really supporting the growth, the development of the cloud native security community as a whole? >> Yeah, I think it's really important that we think of it as a Cloud Native Security community. You know, there are lots of interesting sort of hacker community security related community. Cloud native has been very community focused for a long time, and we really saw, particularly through the tag, the security tag, that there was this growing group of people who were, really wanted to work at that intersection between security and cloud native. And yeah, I think things are going really well this week so far, So I hope this is, you know, the first of many additions of this conference. I think it will also be interesting to see how the balance between a smaller, more focused event, compared to the giant KubeCon and cloud native cons. I, you know, I think there's space for both things, but whether or not there will be other smaller focus areas that want to stand alone and justify being able to stand alone as their own separate conferences, it speaks to the growth of cloud native in general that this is worthwhile doing. >> Yeah. >> It is, and what also speaks to, it reminds me of our tagline here at theCUBE, being able to extract the signal from the noise. Having this event as a standalone, being able to extract the value in it from a security perspective, that those practitioners and the community at large is going to be able to glean from these conversations is something that will be important, that we'll be keeping our eyes on. >> Absolutely. Makes sense for me, yes. >> Yeah, and I think, you know, one of the things, Lisa, that I want to get in, and if you don't mind asking Dave his thoughts, because he just did a breaking analysis on the security landscape. And Dave, you know, as Liz talking about some of these root level things, we talk about silicon advances, powering machine learning, we've been covering a lot of that. You've been covering the general security industry. We got RSA coming up reinforced with AWS, and as you see the cloud native developer first, really driving the standards of the super cloud, the multicloud, you're starting to see a lot more application focus around latency and kind of controlling that, These abstraction layer's starting to see a lot more growth. What's your take, Dave, on what Liz and- is talking about because, you know, you're analyzing the horses on the track, and there's sometimes the old guard security folks, and you got open source continuing to kick butt. And even on the ML side, we've been covering some of these foundation models, you're seeing a real technical growth in open source at all levels and, you know, you still got some proprietary machine learning stuff going on, but security's integrating all that. What's your take and your- what's your breaking analysis on the security piece here? >> I mean, to me the two biggest problems in cyber are just the lack of talent. I mean, it's just really hard to find super, you know, deep expertise and get it quickly. And I think the second is it's just, it's so many tools to deal with. And so the architecture of security is just this mosaic and a mess. That's why I'm excited about initiatives like eBPF because it does simplify things, and developers are being asked to do a lot. And I think one of the other things that's emerging is when you- when we talk about Industry 4.0, and IIoT, you- I'm seeing a lot of tools that are dedicated just to that, you know, slice of the world. And I don't think that's the right approach. I think that there needs to be a more comprehensive view. We're seeing, you know, zero trust architectures come together, and it's going to take some time, but I think that you're going to definitely see, you know, some rethinking of how to architect security. It's a game of whack-a-mole, but I think the industry is just- the technology industry is doing a really really good job of, you know, working hard to solve these problems. And I think the answer is not just another bespoke tool, it's a broader thinking around architectures and consolidating some of those tools, you know, with an end game of really addressing the problem in a more comprehensive fashion. >> Liz, in the last minute or so we have your thoughts on how automation and scale are driving some of these forcing functions around, you know, taking away the toil and the muck around developers, who just want stuff to be code, right? So infrastructure as code. Is that the dynamic here? Is this kind of like new, or is it kind of the same game, different kind of thing? (chuckles) 'Cause you're seeing a lot more machine learning, a lot more automation going on. What's, is that having an impact? What's your thoughts? >> Automation is one of the kind of fundamental underpinnings of cloud native. You know, we're expecting infrastructure to be written as code, We're expecting the platform to be defined in yaml essentially. You know, we are expecting the Kubernetes and surrounding tools to self-heal and to automatically scale and to do things like automated security. If we think about supply chain, you know, automated dependency scanning, think about runtime. Network policy is automated firewalling, if you like, for a cloud native era. So, I think it's all about making that platform predictable. Automation gives us some level of predictability, even if the underlying hardware changes or the scale changes, so that the application developers have something consistent and standardized that they can write to. And you know, at the end of the day, it's all about the business applications that run on top of this infrastructure >> Business applications and the business outcomes. Liz, we so appreciate your time talking to us about this inaugural event, CloudNativeSecurityCon 23. The value in it for those practitioners, all of the content that's going to be discussed and learned, and the growth of the community. Thank you so much, Liz, for sharing your insights with us today. >> Thanks for having me. >> For Liz Rice, John Furrier and Dave Vellante, I'm Lisa Martin. You're watching the Cube's coverage of CloudNativeSecurityCon 23. (electronic music)

Hello and welcome back to Supercloud 2 where we examine the intersection of cloud and data in the 2020s. My name is Dave Vellante. Our Supercloud panel, our power panel is back. Maribel Lopez is the founder and principal analyst at Lopez Research. Sanjeev Mohan is former Gartner analyst and principal at Sanjeev Mohan. And Keith Townsend is the CTO advisor. Folks, welcome back and thanks for your participation today. Good to see you. >> Okay, great. >> Great to see you. >> Thanks. Let me start, Maribel, with you. Bob Muglia, we had a conversation as part of Supercloud the other day. And he said, "Dave, I like the work, you got to simplify this a little bit." So he said, quote, "A Supercloud is a platform." He said, "Think of it as a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." And then Nelu Mihai said, "Well, wait a minute. This is just going to create more stove pipes. We need more standards in an architecture," which is kind of what Berkeley Sky Computing initiative is all about. So there's a sort of a debate going on. Is supercloud an architecture, a platform? Or maybe it's just another buzzword. Maribel, do you have a thought on this? >> Well, the easy answer would be to say it's just a buzzword. And then we could just kill the conversation and be done with it. But I think the term, it's more than that, right? The term actually isn't new. You can go back to at least 2016 and find references to supercloud in Cornell University or assist in other documents. So, having said this, I think we've been talking about Supercloud for a while, so I assume it's more than just a fancy buzzword. But I think it really speaks to that undeniable trend of moving towards an abstraction layer to deal with the chaos of what we consider managing multiple public and private clouds today, right? So one definition of the technology platform speaks to a set of services that allows companies to build and run that technology smoothly without worrying about the underlying infrastructure, which really gets back to something that Bob said. And some of the question is where that lives. And you could call that an abstraction layer. You could call it cross-cloud services, hybrid cloud management. So I see momentum there, like legitimate momentum with enterprise IT buyers that are trying to deal with the fact that they have multiple clouds now. So where I think we're moving is trying to define what are the specific attributes and frameworks of that that would make it so that it could be consistent across clouds. What is that layer? And maybe that's what the supercloud is. But one of the things I struggle with with supercloud is. What are we really trying to do here? Are we trying to create differentiated services in the supercloud layer? Is a supercloud just another variant of what AWS, GCP, or others do? You spoken to Walmart about its cloud native platform, and that's an example of somebody deciding to do it themselves because they need to deal with this today and not wait for some big standards thing to happen. So whatever it is, I do think it's something. I think we're trying to maybe create an architecture out of it would be a better way of saying it so that it does get to those set of principles, but it also needs to be edge aware. I think whenever we talk about supercloud, we're always talking about like the big centralized cloud. And I think we need to think about all the distributed clouds that we're looking at in edge as well. So that might be one of the ways that supercloud evolves. >> So thank you, Maribel. Keith, Brian Gracely, Gracely's law, things kind of repeat themselves. We've seen it all before. And so what Muglia brought to the forefront is this idea of a platform where the platform provider is really responsible for the architecture. Of course, the drawback is then you get a a bunch of stove pipes architectures. But practically speaking, that's kind of the way the industry has always evolved, right? >> So if we look at this from the practitioner's perspective and we talk about platforms, traditionally vendors have provided the platforms for us, whether it's distribution of lineage managed by or provided by Red Hat, Windows, servers, .NET, databases, Oracle. We think of those as platforms, things that are fundamental we can build on top. Supercloud isn't today that. It is a framework or idea, kind of a visionary goal to get to a point that we can have a platform or a framework. But what we're seeing repeated throughout the industry in customers, whether it's the Walmarts that's kind of supersized the idea of supercloud, or if it's regular end user organizations that are coming out with platform groups, groups who normalize cloud native infrastructure, AWS multi-cloud, VMware resources to look like one thing internally to their developers. We're seeing this trend that there's a desire for a platform that provides the capabilities of a supercloud. >> Thank you for that. Sanjeev, we often use Snowflake as a supercloud example, and now would presumably would be a platform with an architecture that's determined by the vendor. Maybe Databricks is pushing for a more open architecture, maybe more of that nirvana that we were talking about before to solve for supercloud. But regardless, the practitioner discussions show. At least currently, there's not a lot of cross-cloud data sharing. I think it could be a killer use case, egress charges or a barrier. But how do you see it? Will that change? Will we hide that underlying complexity and start sharing data across cloud? Is that something that you think Snowflake or others will be able to achieve? >> So I think we are already starting to see some of that happen. Snowflake is definitely one example that gets cited a lot. But even we don't talk about MongoDB in this like, but you could have a MongoDB cluster, for instance, with nodes sitting in different cloud providers. So there are companies that are starting to do it. The advantage that these companies have, let's take Snowflake as an example, it's a centralized proprietary platform. And they are building the capabilities that are needed for supercloud. So they're building things like you can push down your data transformations. They have the entire security and privacy suite. Data ops, they're adding those capabilities. And if I'm not mistaken, it'll be very soon, we will see them offer data observability. So it's all works great as long as you are in one platform. And if you want resilience, then Snowflake, Supercloud, great example. But if your primary goal is to choose the most cost-effective service irrespective of which cloud it sits in, then things start falling sideways. For example, I may be a very big Snowflake user. And I like Snowflake's resilience. I can move from one cloud to another cloud. Snowflake does it for me. But what if I want to train a very large model? Maybe Databricks is a better platform for that. So how do I do move my workload from one platform to another platform? That tooling does not exist. So we need server hybrid, cross-cloud, data ops platform. Walmart has done a great job, but they built it by themselves. Not every company is Walmart. Like Maribel and Keith said, we need standards, we need reference architectures, we need some sort of a cost control. I was just reading recently, Accenture has been public about their AWS bill. Every time they get the bill is tens of millions of lines, tens of millions 'cause there are over thousand teams using AWS. If we have not been able to corral a usage of a single cloud, now we're talking about supercloud, we've got multiple clouds, and hybrid, on-prem, and edge. So till we've got some cross-platform tooling in place, I think this will still take quite some time for it to take shape. >> It's interesting. Maribel, Walmart would tell you that their on-prem infrastructure is cheaper to run than the stuff in the cloud. but at the same time, they want the flexibility and the resiliency of their three-legged stool model. So the point as Sanjeev was making about hybrid. It's an interesting balance, isn't it, between getting your lowest cost and at the same time having best of breed and scale? >> It's basically what you're trying to optimize for, as you said, right? And by the way, to the earlier point, not everybody is at Walmart's scale, so it's not actually cheaper for everybody to have the purchasing power to make the cloud cheaper to have it on-prem. But I think what you see almost every company, large or small, moving towards is this concept of like, where do I find the agility? And is the agility in building the infrastructure for me? And typically, the thing that gives you outside advantage as an organization is not how you constructed your cloud computing infrastructure. It might be how you structured your data analytics as an example, which cloud is related to that. But how do you marry those two things? And getting back to sort of Sanjeev's point. We're in a real struggle now where one hand we want to have best of breed services and on the other hand we want it to be really easy to manage, secure, do data governance. And those two things are really at odds with each other right now. So if you want all the knobs and switches of a service like geospatial analytics and big query, you're going to have to use Google tools, right? Whereas if you want visibility across all the clouds for your application of state and understand the security and governance of that, you're kind of looking for something that's more cross-cloud tooling at that point. But whenever you talk to somebody about cross-cloud tooling, they look at you like that's not really possible. So it's a very interesting time in the market. Now, we're kind of layering this concept of supercloud on it. And some people think supercloud's about basically multi-cloud tooling, and some people think it's about a whole new architectural stack. So we're just not there yet. But it's not all about cost. I mean, cloud has not been about cost for a very, very long time. Cloud has been about how do you really make the most of your data. And this gets back to cross-cloud services like Snowflake. Why did they even exist? They existed because we had data everywhere, but we need to treat data as a unified object so that we can analyze it and get insight from it. And so that's where some of the benefit of these cross-cloud services are moving today. Still a long way to go, though, Dave. >> Keith, I reached out to my friends at ETR given the macro headwinds, And you're right, Maribel, cloud hasn't really been about just about cost savings. But I reached out to the ETR, guys, what's your data show in terms of how customers are dealing with the economic headwinds? And they said, by far, their number one strategy to cut cost is consolidating redundant vendors. And a distant second, but still notable was optimizing cloud costs. Maybe using reserve instances, or using more volume buying. Nowhere in there. And I asked them to, "Could you go look and see if you can find it?" Do we see repatriation? And you hear this a lot. You hear people whispering as analysts, "You better look into that repatriation trend." It's pretty big. You can't find it. But some of the Walmarts in the world, maybe even not repatriating, but they maybe have better cost structure on-prem. Keith, what are you seeing from the practitioners that you talk to in terms of how they're dealing with these headwinds? >> Yeah, I just got into a conversation about this just this morning with (indistinct) who is an analyst over at GigaHome. He's reading the same headlines. Repatriation is happening at large scale. I think this is kind of, we have these quiet terms now. We have quiet quitting, we have quiet hiring. I think we have quiet repatriation. Most people haven't done away with their data centers. They're still there. Whether they're completely on-premises data centers, and they own assets, or they're partnerships with QTX, Equinix, et cetera, they have these private cloud resources. What I'm seeing practically is a rebalancing of workloads. Do I really need to pay AWS for this instance of SAP that's on 24 hours a day versus just having it on-prem, moving it back to my data center? I've talked to quite a few customers who were early on to moving their static SAP workloads onto the public cloud, and they simply moved them back. Surprising, I was at VMware Explore. And we can talk about this a little bit later on. But our customers, net new, not a lot that were born in the cloud. And they get to this point where their workloads are static. And they look at something like a Kubernetes, or a OpenShift, or VMware Tanzu. And they ask the question, "Do I need the scalability of cloud?" I might consider being a net new VMware customer to deliver this base capability. So are we seeing repatriation as the number one reason? No, I think internal IT operations are just naturally come to this realization. Hey, I have these resources on premises. The private cloud technologies have moved far along enough that I can just simply move this workload back. I'm not calling it repatriation, I'm calling it rightsizing for the operating model that I have. >> Makes sense. Yeah. >> Go ahead. >> If I missed something, Dave, why we are on this topic of repatriation. I'm actually surprised that we are talking about repatriation as a very big thing. I think repatriation is happening, no doubt, but it's such a small percentage of cloud migration that to me it's a rounding error in my opinion. I think there's a bigger problem. The problem is that people don't know where the cost is. If they knew where the cost was being wasted in the cloud, they could do something about it. But if you don't know, then the easy answer is cloud costs a lot and moving it back to on-premises. I mean, take like Capital One as an example. They got rid of all the data centers. Where are they going to repatriate to? They're all in the cloud at this point. So I think my point is that data observability is one of the places that has seen a lot of traction is because of cost. Data observability, when it first came into existence, it was all about data quality. Then it was all about data pipeline reliability. And now, the number one killer use case is FinOps. >> Maribel, you had a comment? >> Yeah, I'm kind of in violent agreement with both Sanjeev and Keith. So what are we seeing here? So the first thing that we see is that many people wildly overspent in the big public cloud. They had stranded cloud credits, so to speak. The second thing is, some of them still had infrastructure that was useful. So why not use it if you find the right workloads to what Keith was talking about, if they were more static workloads, if it was already there? So there is a balancing that's going on. And then I think fundamentally, from a trend standpoint, these things aren't binary. Everybody, for a while, everything was going to go to the public cloud and then people are like, "Oh, it's kind of expensive." Then they're like, "Oh no, they're going to bring it all on-prem 'cause it's really expensive." And it's like, "Well, that doesn't necessarily get me some of the new features and functionalities I might want for some of my new workloads." So I'm going to put the workloads that have a certain set of characteristics that require cloud in the cloud. And if I have enough capability on-prem and enough IT resources to manage certain things on site, then I'm going to do that there 'cause that's a more cost-effective thing for me to do. It's not binary. That's why we went to hybrid. And then we went to multi just to describe the fact that people added multiple public clouds. And now we're talking about super, right? So I don't look at it as a one-size-fits-all for any of this. >> A a number of practitioners leading up to Supercloud2 have told us that they're solving their cloud complexity by going in monocloud. So they're putting on the blinders. Even though across the organization, there's other groups using other clouds. You're like, "In my group, we use AWS, or my group, we use Azure. And those guys over there, they use Google. We just kind of keep it separate." Are you guys hearing this in your view? Is that risky? Are they missing out on some potential to tap best of breed? What do you guys think about that? >> Everybody thinks they're monocloud. Is anybody really monocloud? It's like a group is monocloud, right? >> Right. >> This genie is out of the bottle. We're not putting the genie back in the bottle. You might think your monocloud and you go like three doors down and figure out the guy or gal is on a fundamentally different cloud, running some analytics workload that you didn't know about. So, to Sanjeev's earlier point, they don't even know where their cloud spend is. So I think the concept of monocloud, how that's actually really realized by practitioners is primary and then secondary sources. So they have a primary cloud that they run most of their stuff on, and that they try to optimize. And we still have forked workloads. Somebody decides, "Okay, this SAP runs really well on this, or these analytics workloads run really well on that cloud." And maybe that's how they parse it. But if you really looked at it, there's very few companies, if you really peaked under the hood and did an analysis that you could find an actual monocloud structure. They just want to pull it back in and make it more manageable. And I respect that. You want to do what you can to try to streamline the complexity of that. >> Yeah, we're- >> Sorry, go ahead, Keith. >> Yeah, we're doing this thing where we review AWS service every day. Just in your inbox, learn about a new AWS service cursory. There's 238 AWS products just on the AWS cloud itself. Some of them are redundant, but you get the idea. So the concept of monocloud, I'm in filing agreement with Maribel on this that, yes, a group might say I want a primary cloud. And that primary cloud may be the AWS. But have you tried the licensed Oracle database on AWS? It is really tempting to license Oracle on Oracle Cloud, Microsoft on Microsoft. And I can't get RDS anywhere but Amazon. So while I'm driven to desire the simplicity, the reality is whether be it M&A, licensing, data sovereignty. I am forced into a multi-cloud management style. But I do agree most people kind of do this one, this primary cloud, secondary cloud. And I guarantee you're going to have a third cloud or a fourth cloud whether you want to or not via shadow IT, latency, technical reasons, et cetera. >> Thank you. Sanjeev, you had a comment? >> Yeah, so I just wanted to mention, as an organization, I'm complete agreement, no organization is monocloud, at least if it's a large organization. Large organizations use all kinds of combinations of cloud providers. But when you talk about a single workload, that's where the program arises. As Keith said, the 238 services in AWS. How in the world am I going to be an expert in AWS, but then say let me bring GCP or Azure into a single workload? And that's where I think we probably will still see monocloud as being predominant because the team has developed its expertise on a particular cloud provider, and they just don't have the time of the day to go learn yet another stack. However, there are some interesting things that are happening. For example, if you look at a multi-cloud example where Oracle and Microsoft Azure have that interconnect, so that's a beautiful thing that they've done because now in the newest iteration, it's literally a few clicks. And then behind the scene, your .NET application and your Oracle database in OCI will be configured, the identities in active directory are federated. And you can just start using a database in one cloud, which is OCI, and an application, your .NET in Azure. So till we see this kind of a solution coming out of the providers, I think it's is unrealistic to expect the end users to be able to figure out multiple clouds. >> Well, I have to share with you. I can't remember if he said this on camera or if it was off camera so I'll hold off. I won't tell you who it is, but this individual was sort of complaining a little bit saying, "With AWS, I can take their best AI tools like SageMaker and I can run them on my Snowflake." He said, "I can't do that in Google. Google forces me to go to BigQuery if I want their excellent AI tools." So he was sort of pushing, kind of tweaking a little bit. Some of the vendor talked that, "Oh yeah, we're so customer-focused." Not to pick on Google, but I mean everybody will say that. And then you say, "If you're so customer-focused, why wouldn't you do a ABC?" So it's going to be interesting to see who leads that integration and how broadly it's applied. But I digress. Keith, at our first supercloud event, that was on August 9th. And it was only a few months after Broadcom announced the VMware acquisition. A lot of people, myself included said, "All right, cuts are coming." Generally, Tanzu is probably going to be under the radar, but it's Supercloud 22 and presumably VMware Explore, the company really... Well, certainly the US touted its Tanzu capabilities. I wasn't at VMware Explore Europe, but I bet you heard similar things. Hawk Tan has been blogging and very vocal about cross-cloud services and multi-cloud, which doesn't happen without Tanzu. So what did you hear, Keith, in Europe? What's your latest thinking on VMware's prospects in cross-cloud services/supercloud? >> So I think our friend and Cube, along host still be even more offended at this statement than he was when I sat in the Cube. This was maybe five years ago. There's no company better suited to help industries or companies, cross-cloud chasm than VMware. That's not a compliment. That's a reality of the industry. This is a very difficult, almost intractable problem. What I heard that VMware Europe were customers serious about this problem, even more so than the US data sovereignty is a real problem in the EU. Try being a company in Switzerland and having the Swiss data solvency issues. And there's no local cloud presence there large enough to accommodate your data needs. They had very serious questions about this. I talked to open source project leaders. Open source project leaders were asking me, why should I use the public cloud to host Kubernetes-based workloads, my projects that are building around Kubernetes, and the CNCF infrastructure? Why should I use AWS, Google, or even Azure to host these projects when that's undifferentiated? I know how to run Kubernetes, so why not run it on-premises? I don't want to deal with the hardware problems. So again, really great questions. And then there was always the specter of the problem, I think, we all had with the acquisition of VMware by Broadcom potentially. 4.5 billion in increased profitability in three years is a unbelievable amount of money when you look at the size of the problem. So a lot of the conversation in Europe was about industry at large. How do we do what regulators are asking us to do in a practical way from a true technology sense? Is VMware cross-cloud great? >> Yeah. So, VMware, obviously, to your point. OpenStack is another way of it. Actually, OpenStack, uptake is still alive and well, especially in those regions where there may not be a public cloud, or there's public policy dictating that. Walmart's using OpenStack. As you know in IT, some things never die. Question for Sanjeev. And it relates to this new breed of data apps. And Bob Muglia and Tristan Handy from DBT Labs who are participating in this program really got us thinking about this. You got data that resides in different clouds, it maybe even on-prem. And the machine polls data from different systems. No humans involved, e-commerce, ERP, et cetera. It creates a plan, outcomes. No human involvement. Today, you're on a CRM system, you're inputting, you're doing forms, you're, you're automating processes. We're talking about a new breed of apps. What are your thoughts on this? Is it real? Is it just way off in the distance? How does machine intelligence fit in? And how does supercloud fit? >> So great point. In fact, the data apps that you're talking about, I call them data products. Data products first came into limelight in the last couple of years when Jamal Duggan started talking about data mesh. I am taking data products out of the data mesh concept because data mesh, whether data mesh happens or not is analogous to data products. Data products, basically, are taking a product management view of bringing data from different sources based on what the consumer needs. We were talking earlier today about maybe it's my vacation rentals, or it may be a retail data product, it may be an investment data product. So it's a pre-packaged extraction of data from different sources. But now I have a product that has a whole lifecycle. I can version it. I have new features that get added. And it's a very business data consumer centric. It uses machine learning. For instance, I may be able to tell whether this data product has stale data. Who is using that data? Based on the usage of the data, I may have a new data products that get allocated. I may even have the ability to take existing data products, mash them up into something that I need. So if I'm going to have that kind of power to create a data product, then having a common substrate underneath, it can be very useful. And that could be supercloud where I am making API calls. I don't care where the ERP, the CRM, the survey data, the pricing engine where they sit. For me, there's a logical abstraction. And then I'm building my data product on top of that. So I see a new breed of data products coming out. To answer your question, how early we are or is this even possible? My prediction is that in 2023, we will start seeing more of data products. And then it'll take maybe two to three years for data products to become mainstream. But it's starting this year. >> A subprime mortgages were a data product, definitely were humans involved. All right, let's talk about some of the supercloud, multi-cloud players and what their future looks like. You can kind of pick your favorites. VMware, Snowflake, Databricks, Red Hat, Cisco, Dell, HP, Hashi, IBM, CloudFlare. There's many others. cohesive rubric. Keith, I wanted to start with CloudFlare because they actually use the term supercloud. and just simplifying what they said. They look at it as taking serverless to the max. You write your code and then you can deploy it in seconds worldwide, of course, across the CloudFlare infrastructure. You don't have to spin up containers, you don't go to provision instances. CloudFlare worries about all that infrastructure. What are your thoughts on CloudFlare this approach and their chances to disrupt the current cloud landscape? >> As Larry Ellison said famously once before, the network is the computer, right? I thought that was Scott McNeley. >> It wasn't Scott McNeley. I knew it was on Oracle Align. >> Oracle owns that now, owns that line. >> By purpose or acquisition. >> They should have just called it cloud. >> Yeah, they should have just called it cloud. >> Easier. >> Get ahead. >> But if you think about the CloudFlare capability, CloudFlare in its own right is becoming a decent sized cloud provider. If you have compute out at the edge, when we talk about edge in the sense of CloudFlare and points of presence, literally across the globe, you have all of this excess computer, what do you do with it? First offering, let's disrupt data in the cloud. We can't start the conversation talking about data. When they say we're going to give you object-oriented or object storage in the cloud without egress charges, that's disruptive. That we can start to think about supercloud capability of having compute EC2 run in AWS, pushing and pulling data from CloudFlare. And now, I've disrupted this roach motel data structure, and that I'm freely giving away bandwidth, basically. Well, the next layer is not that much more difficult. And I think part of CloudFlare's serverless approach or supercloud approaches so that they don't have to commit to a certain type of compute. It is advantageous. It is a feature for me to be able to go to EC2 and pick a memory heavy model, or a compute heavy model, or a network heavy model, CloudFlare is taken away those knobs. and I'm just giving code and allowing that to run. CloudFlare has a massive network. If I can put the code closest using the CloudFlare workers, if I can put that code closest to where the data is at or residing, super compelling observation. The question is, does it scale? I don't get the 238 services. While Server List is great, I have to know what I'm going to build. I don't have a Cognito, or RDS, or all these other services that make AWS, GCP, and Azure appealing from a builder's perspective. So it is a very interesting nascent start. It's great because now they can hide compute. If they don't have the capacity, they can outsource that maybe at a cost to one of the other cloud providers, but kind of hiding the compute behind the surplus architecture is a really unique approach. >> Yeah. And they're dipping their toe in the water. And they've announced an object store and a database platform and more to come. We got to wrap. So I wonder, Sanjeev and Maribel, if you could maybe pick some of your favorites from a competitive standpoint. Sanjeev, I felt like just watching Snowflake, I said, okay, in my opinion, they had the right strategy, which was to run on all the clouds, and then try to create that abstraction layer and data sharing across clouds. Even though, let's face it, most of it might be happening across regions if it's happening, but certainly outside of an individual account. But I felt like just observing them that anybody who's traditional on-prem player moving into the clouds or anybody who's a cloud native, it just makes total sense to write to the various clouds. And to the extent that you can simplify that for users, it seems to be a logical strategy. Maybe as I said before, what multi-cloud should have been. But are there companies that you're watching that you think are ahead in the game , or ones that you think are a good model for the future? >> Yes, Snowflake, definitely. In fact, one of the things we have not touched upon very much, and Keith mentioned a little bit, was data sovereignty. Data residency rules can require that certain data should be written into certain region of a certain cloud. And if my cloud provider can abstract that or my database provider, then that's perfect for me. So right now, I see Snowflake is way ahead of this pack. I would not put MongoDB too far behind. They don't really talk about this thing. They are in a different space, but now they have a lakehouse, and they've got all of these other SQL access and new capabilities that they're announcing. So I think they would be quite good with that. Oracle is always a dark forest. Oracle seems to have revived its Cloud Mojo to some extent. And it's doing some interesting stuff. Databricks is the other one. I have not seen Databricks. They've been very focused on lakehouse, unity, data catalog, and some of those pieces. But they would be the obvious challenger. And if they come into this space of supercloud, then they may bring some open source technologies that others can rely on like Delta Lake as a table format. >> Yeah. One of these infrastructure players, Dell, HPE, Cisco, even IBM. I mean, I would be making my infrastructure as programmable and cloud friendly as possible. That seems like table stakes. But Maribel, any companies that stand out to you that we should be paying attention to? >> Well, we already mentioned a bunch of them, so maybe I'll go a slightly different route. I'm watching two companies pretty closely to see what kind of traction they get in their established companies. One we already talked about, which is VMware. And the thing that's interesting about VMware is they're everywhere. And they also have the benefit of having a foot in both camps. If you want to do it the old way, the way you've always done it with VMware, they got all that going on. If you want to try to do a more cross-cloud, multi-cloud native style thing, they're really trying to build tools for that. So I think they have really good access to buyers. And that's one of the reasons why I'm interested in them to see how they progress. The other thing, I think, could be a sleeping horse oddly enough is Google Cloud. They've spent a lot of work and time on Anthos. They really need to create a certain set of differentiators. Well, it's not necessarily in their best interest to be the best multi-cloud player. If they decide that they want to differentiate on a different layer of the stack, let's say they want to be like the person that is really transformative, they talk about transformation cloud with analytics workloads, then maybe they do spend a good deal of time trying to help people abstract all of the other underlying infrastructure and make sure that they get the sexiest, most meaningful workloads into their cloud. So those are two people that you might not have expected me to go with, but I think it's interesting to see not just on the things that might be considered, either startups or more established independent companies, but how some of the traditional providers are trying to reinvent themselves as well. >> I'm glad you brought that up because if you think about what Google's done with Kubernetes. I mean, would Google even be relevant in the cloud without Kubernetes? I could argue both sides of that. But it was quite a gift to the industry. And there's a motivation there to do something unique and different from maybe the other cloud providers. And I'd throw in Red Hat as well. They're obviously a key player and Kubernetes. And Hashi Corp seems to be becoming the standard for application deployment, and terraform, or cross-clouds, and there are many, many others. I know we're leaving lots out, but we're out of time. Folks, I got to thank you so much for your insights and your participation in Supercloud2. Really appreciate it. >> Thank you. >> Thank you. >> Thank you. >> This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more content from Supercloud2.

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>>Hello everyone. Welcome to the Super Cloud Event preview. I'm John Forry, host of the Cube, and with Dave Valante, host of the popular Super cloud events. This is Super Cloud two preview. I'm joined by industry leader and Cube alumni, Victoria Vigo, vice president of klos Cross Cloud Services at VMware. Vittorio. Great to see you. We're here for the preview of Super Cloud two on January 17th, virtual event, live stage performance, but streamed out to the audience virtually. We're gonna do a preview. Thanks for coming in. >>My pleasure. Always glad to be here. >>It's holiday time. We had the first super cloud on in August prior to VMware, explore North America prior to VMware, explore Europe prior to reinvent. We've been through that, but right now, super Cloud has got momentum. Super Cloud two has got some success. Before we dig into it, let's take a step back and set the table. What is Super Cloud and why is important? Why are people buzzing about it? Why is it a thing? >>Look, we have been in the cloud now for like 10, 15 years and the cloud is going strong and I, I would say that going cloud first was deliberate and strategic in most cases. In some cases the, the developer was going for the path of risk resistance, but in any sizable company, this caused the companies to end up in a multi-cloud world where 85% of the companies out there use two or multiple clouds. And with that comes what we call cloud chaos, because each cloud brings their own management tools, development tools, security. And so that increase the complexity and cost. And so we believe that it's time to usher a new era in cloud computing, which we, you call the super cloud. We call it cross cloud services, which allows our customers to have a single way to build, manage, secure, and access any application across any cloud. Lowering the cost and simplifying the environment. Since >>Dave Ante and I introduced and rift on the concept of Supercloud, as we talked about at reinvent last year, a lot has happened. Supercloud one, it was in August, but prior to that, great momentum in the industry. Great conversation. People are loving it, they're hating it, which means it's got some traction. Berkeley has come on board as with a position paper. They're kind of endorsing it. They call it something different. You call it cross cloud services, whatever it is. It's kind of the same theme we're seeing. And so the industry has recognized something is happening that's different than what Cloud one was or the first generation of cloud. Now we have something different. This Super Cloud two in January. This event has traction with practitioners, customers, big name brands, Sachs, fifth Avenue, Warner, media Financial, mercury Financial, other big names are here. They're leaning in. They're excited. Why the traction in the customer's industry converts over to, to the customer traction. Why is it happening? You, you get a lot of data. >>Well, in, in Super Cloud one, it was a vendor fest, right? But these vendors are smart people that get their vision from where, from the customers. This, this stuff doesn't happen in a vacuum. We all talk to customers and we tend to lean on the early adopters and the early adopters of the cloud are the ones that are telling us, we now are in a place where the complexity is too much. The cost is ballooning. We're going towards slow down potentially in the economy. We need to get better economics out of, of our cloud. And so every single customers I talked to today, or any sizable company as this problem, the developers have gone off, built all these applications, and now the business is coming to the operators and asking, where are my applications? Are they performing? What is the security posture? And how do we do compliance? And so now they're realizing we need to do something about this or it is gonna be unmanageable. >>I wanna go to a clip I pulled out from the, our video data lake and the cube. If we can go to that clip, it's Chuck Whitten Dell at a keynote. He was talking about what he calls multi-cloud by default, not by design. This is a state of the, of the industry. If we're gonna roll that clip, and I wanna get your reaction to that. >>Well, look, customers have woken up with multiple clouds, you know, multiple public clouds. On-premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was it can be, and it should be, it is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they wanna maintain an on-premise cloud. On-premise clouds are not going away. I mentioned edge Cloud, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud, by default we mean that's the state of the world. Today, our goal is to bring multi-cloud by design, as you heard. Yeah, I >>Mean, I, okay, Vittorio, that's, that's the head of Dell Technologies president. He obvious he runs it. Michael Dell's still around, but you know, he's the leader. This is a interesting observation. You know, he's not a customer. We have some customer equips we'll go to as well, but by default it kind of happened not by design. So we're now kind of in a zoom out issue where, okay, I got this environment just landed on me. What, what is the, what's your reaction to that clip of how multi-cloud has become present in, in everyone's on everyone's plate right now to deal with? Yeah, >>I it is, it is multi-cloud by default, I would call it by accident. We, we really got there by accident. I think now it's time to make it a strategic asset because look, we're using multiple cloud for a reason, because all these hyperscaler bring tremendous innovation that we want to leverage. But I strongly believe that in it, especially history repeat itself, right? And so if you look at the history of it, as was always when a new level of obstruction that simplify things, that we got the next level of innovation at the lower cost, you know, from going from c plus plus to Visual basic, going from integrating application at the bits of by layer to SOA and then web services. It's, it's only when we simplify the environment that we can go faster and lower cost. And the multi-cloud is ready for that level of obstruction today. >>You know, you've made some good points. You know, developers went crazy building great apps. Now they got, they gotta roll it out and operationalize it globally. A lot of compliance issues going on. The costs are going up. We got an economic challenge, but also agility with the cloud. So using cloud and or hybrid, you can get better agility. And also moving to the cloud, it's kind of still slow. Okay, so I get that at reinvent this year and at VMware explorer we were observing and we reported that you're seeing a transition to a new kind of ecosystem partner. Ones that aren't just ISVs anymore. You have ISVs, independent software vendors, but you got the emergence of bigger players that just, they got platforms, they have their own ecosystems. So you're seeing ecosystems on top of ecosystems where, you know, MongoDB CEO and the Databricks CEO both told me, we're not an isv, we're a platform built on a cloud. So this new kind of super cloudlike thing is going on. Why should someone pay attention to the super cloud movement? We're on two, we're gonna continue to do these out in the open. Anyone can participate. Why should people pay attention to this? Why should they come to the event? Why is this important? Is this truly an inflection point? And if they do pay attention, what should they pay attention to? >>I would pay attention to two things. If you are customers that are now starting to realize that you have a multi-cloud problem and the costs are getting outta control, look at what the leading vendors are saying, connect the dots with the early adopters and some of the customers that we are gonna have at Super Cloud two, and use those learning to not fall into the same trap. So I, I'll give you an example. I was talking to a Fortune 50 in Europe in my latest trip, and this is an a CIO that is telling me >>We build all these applications and now for compliance reason, the business is coming to me, I don't even know where they are, right? And so what I was telling him, so look, there are other customers that are already there. What did they do? They built a platform engineering team. What is the platform? Engineering team is a, is an operation team that understands how developers build modern applications and lays down the foundation across multiple clouds. So the developers can be developers and do their thing, which is writing code. But now you as a cio, as a, as a, as a governing body, as a security team can have the guardrail. So do you know that these applications are performing at a lower cost and are secure and compliant? >>Patura, you know, it's really encouraging and, and love to get your thoughts on this is one is the general consensus of industry leaders. I talked to like yourself in the round is the old way was soft complexity with more complexity. The cloud demand simplicity, you mentioned abstraction layer. This is our next inflection point. It's gotta be simpler and it's gotta be easy and it's gotta be performant. That's the table stakes of the cloud. What's your thoughts on this next wave of simplicity versus complexity? Because again, abstraction layers take away complexity, they should make it simpler. What's your thoughts? >>Yeah, so I'll give you few examples. One, on the development side and runtime. You, you one would think that Kubernetes will solve all the problems you have Kubernetes everywhere, just look at, but every cloud has a different distribution of Kubernetes, right? So for example, at VMware with tansu, we create a single place that allows you to deploy that any Kubernetes environment. But now you have one place to set your policies. We take care of the differences between this, this system. The second area is management, right? So once you have all everything deployed, how do you get a single object model that tells you where your stuff is and how it's performing, and then apply policies to it as well. So these are two areas and security and so on and so forth. So the idea is that figure out what you can abstract and make common across cloud. Make that simple and put it in one place while always allowing the developers to go underneath and use the differentiated features for innovation. >>Yeah, one of the areas I'm excited, I want to get your thoughts of too is, we haven't talked about this in the past, but it, I'll throw it out there. I think the, the new AI coming out chat, G P T and other things like lens, you see it and see new kinds of AI coming that's gonna be right in the heavy lifting opportunity to make things easier with AI and automation. I think AI will be a big factor in super cloud and, and cross cloud. What's your thoughts? >>Well, the one way to look at AI is, is one of the main, main services that you would want out of a multi-cloud, right? You want eventually, right now Google seems to have an edge, but you know, the competition creates, you know, innovation. So later on you wanna use something from Azure or from or from Oracle or something that, so you want at some point that is gonna be prone every single service in in the cloud is gonna be prone to obstruction and simplification. And I, I'm just excited about to see >>What book, I can't wait for the chat services to write code automatically for us. Well, >>They >>Do, they do. They're doing it now. They do. >>Oh, the other day, somebody, you know that I do this song par this for. So for fun sometimes. And somebody the other day said, ask the AI to write a parody song for multi-cloud. And so I have the lyrics stay >>Tuned. I should do that from my blog post. Hey, write a blog post on this January 17th, Victoria, thanks for coming in, sharing the preview bottom line. Why should people come? Why is it important? What's your final kind of takeaway? Billboard message >>History is repeat itself. It goes to three major inflection points, right? We had the inflection point with the cloud and the people that got left behind, they were not as competitive as the people that got on top o of this wave. The new wave is the super cloud, what we call cross cloud services. So if you are a customer that is experiencing this problem today, tune in to to hear from other customers in, in your same space. If you are behind, tune in to avoid the, the, the, the mistakes and the, the shortfalls of this new wave. And so that you can use multi-cloud to accelerate your business and kick butt in the future. >>All right. Kicking kick your names and kicking butt. Okay, we're here on J January 17th. Super Cloud two. Momentum continues. We'll be super cloud three. There'll be super cloud floor. More and more open conversations. Join the community, join the conversation. It's open. We want more voices. We want more, more industry. We want more customers. It's happening. A lot of momentum. Victoria, thank you for your time. Thank you. Okay. I'm John Farer, host of the Cube. Thanks for watching.

foreign welcome back to re invent 2022 we're wrapping up four days well one evening and three solid days wall-to-wall of cube coverage I'm Dave vellante John furrier's birthday is today he's on a plane to London to go see his nephew get married his his great Sister Janet awesome family the furriers uh spanning the globe and uh and John I know you wanted to be here you're watching in Newark or you were waiting to uh to get in the plane so all the best to you happy birthday one year the Amazon PR people brought a cake out to celebrate John's birthday because he's always here at AWS re invented his birthday so I'm really pleased to have two really special guests uh former Cube host Cube Alum great wikibon contributor Stu miniman now with red hat still good to see you again great to be here Dave yeah I was here for that cake uh the twitterverse uh was uh really helping to celebrate John's birthday today and uh you know always great to be here with you and then with this you know Awesome event this week and friend of the cube of many time Cube often Cube contributor as here's a cube analyst this week as his own consultancy sarbj johal great to see you thanks for coming on good to see you Dave uh great to see you stu I'm always happy to participate in these discussions and um I enjoy the discussion every time so this is kind of cool because you know usually the last day is a getaway day and this is a getaway day but this place is still packed I mean it's I mean yeah it's definitely lighter you can at least walk and not get slammed but I subjit I'm going to start with you I I wanted to have you as the the tail end here because cause you participated in the analyst sessions you've been watching this event from from the first moment and now you've got four days of the Kool-Aid injection but you're also talking to customers developers Partners the ecosystem where do you want to go what's your big takeaways I think big takeaways that Amazon sort of innovation machine is chugging along they are I was listening to some of the accessions and when I was back to my room at nine so they're filling the holes in some areas but in some areas they're moving forward there's a lot to fix still it doesn't seem like that it seems like we are done with the cloud or The Innovation is done now we are building at the millisecond level so where do you go next there's a lot of room to grow on the storage side on the network side uh the improvements we need and and also making sure that the software which is you know which fits the hardware like there's a specialized software um sorry specialized hardware for certain software you know so there was a lot of talk around that and I attended some of those sessions where I asked the questions around like we have a specialized database for each kind of workload specialized processes processors for each kind of workload yeah the graviton section and actually the the one interesting before I forget that the arbitration was I asked that like why there are so many so many databases and IRS for the egress costs and all that stuff can you are you guys thinking about reducing that you know um the answer was no egress cost is not a big big sort of uh um show stopper for many of the customers but but the from all that sort of little discussion with with the folks sitting who build these products over there was that the plethora of choice is given to the customers to to make them feel that there's no vendor lock-in so if you are using some open source you know um soft software it can be on the you know platform side or can be database side you have database site you have that option at AWS so this is a lot there because I always thought that that AWS is the mother of all lock-ins but it's got an ecosystem and we're going to talk about exactly we'll talk about Stu what's working within AWS when you talk to customers and where are the challenges yeah I I got a comment on open source Dave of course there because I mean look we criticized to Amazon for years about their lack of contribution they've gotten better they're doing more in open source but is Amazon the mother of all lock-ins many times absolutely there's certain people inside Amazon I'm saying you know many of us talk Cloud native they're like well let's do Amazon native which means you're like full stack is things from Amazon and do things the way that we want to do things and you know I talk to a lot of customers they use more than one Cloud Dave and therefore certain things absolutely I want to Leverage The Innovation that Amazon has brought I do think we're past building all the main building blocks in many ways we are like in day two yes Amazon is fanatically customer focused and will always stay that way but you know there wasn't anything that jumped out at me last year or this year that was like Wow new category whole new way of thinking about something we're in a vocals last year Dave said you know we have over 200 services and if we listen to you the customer we'd have over two thousand his session this week actually got some great buzz from my friends in the serverless ecosystem they love some of the things tying together we're using data the next flywheel that we're going to see for the next 10 years Amazon's at the center of the cloud ecosystem in the IT world so you know there's a lot of good things here and to your point Dave the ecosystem one of the things I always look at is you know was there a booth that they're all going to be crying in their beer after Amazon made an announcement there was not a tech vendor that I saw this week that was like oh gosh there was an announcement and all of a sudden our business is gone where I did hear some rumbling is Amazon might be the next GSI to really move forward and we've seen all the gsis pushing really deep into supporting Cloud bringing workloads to the cloud and there's a little bit of rumbling as to that balance between what Amazon will do and their uh their go to market so a couple things so I think I think we all agree that a lot of the the announcements here today were taping seams right I call it and as it relates to the mother of all lock-in the reason why I say that it's it's obviously very much a pejorative compare Oracle company you know really well with Amazon's lock-in for Amazon's lock-in is about bringing this ecosystem together so that you actually have Choice Within the the house so you don't have to leave you know there's a there's a lot to eat at the table yeah you look at oracle's ecosystem it's like yeah you know oracle is oracle's ecosystem so so that is how I think they do lock in customers by incenting them not to leave because there's so much Choice Dave I agree with you a thousand I mean I'm here I'm a I'm a good partner of AWS and all of the partners here want to be successful with Amazon and Amazon is open to that it's not our way or get out which Oracle tries how much do you extract from the overall I.T budget you know are you a YouTube where you give the people that help you create a large sum of the money YouTube hasn't been all that profitable Amazon I think is doing a good balance of the ecosystem makes money you know we used to talk Dave about you know how much dollars does VMware make versus there um I think you know Amazon is a much bigger you know VMware 2.0 we used to think talk about all the time that VMware for every dollar spent on VMware licenses 15 or or 12 or 20 were spent in the ecosystem I would think the ratio is even higher here sarbji and an Oracle I would say it's I don't know yeah actually 1 to 0.5 maybe I don't know but I want to pick on your discussion about the the ecosystem the the partner ecosystem is so it's it's robust strong because it's wider I was I was not saying that there's no lock-in with with Amazon right AWS there's lock-in there's lock-in with everything there's lock-in with open source as well but but the point is that they're they're the the circle is so big you don't feel like locked in but they're playing smart as well they're bringing in the software the the platforms from the open source they're picking up those packages and saying we'll bring it in and cater that to you through AWS make it better perform better and also throw in their custom chips on top of that hey this MySQL runs better here so like what do you do I said oh Oracle because it's oracle's product if you will right so they are I think think they're filing or not slenders from their go to market strategy from their engineering and they listen to they're listening to customers like very closely and that has sort of side effects as well listening to customers creates a sprawl of services they have so many services and I criticized them last year for calling everything a new service I said don't call it a new service it's a feature of a existing service sure a lot of features a lot of features this is egress our egress costs a real problem or is it just the the on-prem guys picking at the the scab I mean what do you hear from customers so I mean Dave you know I I look at what Corey Quinn talks about all the time and Amazon charges on that are more expensive than any other Cloud the cloud providers and partly because Amazon is you know probably not a word they'd use they are dominant when it comes to the infrastructure space and therefore they do want to make it a little bit harder to do that they can get away with it um because um yeah you know we've seen some of the cloud providers have special Partnerships where you can actually you know leave and you're not going to be charged and Amazon they've been a little bit more flexible but absolutely I've heard customers say that they wish some good tunning and tongue-in-cheek stuff what else you got we lay it on us so do our players okay this year I think the focus was on the upside it's shifting gradually this was more focused on offside there were less talk of of developers from the main stage from from all sort of quadrants if you will from all Keynotes right so even Werner this morning he had a little bit for he was talking about he he was talking he he's job is to Rally up the builders right yeah so he talks about the go build right AWS pipes I thought was kind of cool then I said like I'm making glue easier I thought that was good you know I know some folks don't use that I I couldn't attend the whole session but but I heard in between right so it is really adopt or die you know I am Cloud Pro for last you know 10 years and I think it's the best model for a technology consumption right um because of economies of scale but more importantly because of division of labor because of specialization because you can't afford to hire the best security people the best you know the arm chip designers uh you can't you know there's one actually I came up with a bumper sticker you guys talked about bumper sticker I came up with that like last couple of weeks The Innovation favorite scale they have scale they have Innovation so that's where the Innovation is and it's it's not there again they actually say the market sets the price Market you as a customer don't set the price the vendor doesn't set the price Market sets the price so if somebody's complaining about their margins or egress and all that I think that's BS um yeah I I have a few more notes on the the partner if you you concur yeah Dave you know with just coming back to some of this commentary about like can Amazon actually enable something we used to call like Community clouds uh your companies like you know Goldman and NASDAQ and the like where Industries will actually be able to share data uh and you know expand the usage and you know Amazon's going to help drive that API economy forward some so it's good to see those things because you know we all know you know all of us are smarter than just any uh single company together so again some of that's open source but some of that is you know I think Amazon is is you know allowing Innovation to thrive I think the word you're looking for is super cloud there well yeah I mean it it's uh Dave if you want to go there with the super cloud because you know there's a metaphor for exactly what you described NASDAQ Goldman Sachs we you know and and you know a number of other companies that are few weeks at the Berkeley Sky Computing paper yeah you know that's a former supercloud Dave Linthicum calls it metacloud I'm not really careful I mean you know I go back to the the challenge we've been you know working at for a decade is the distributed architecture you know if you talk about AI architectures you know what lives in the cloud what lives at the edge where do we train things where do we do inferences um locations should matter a lot less Amazon you know I I didn't hear a lot about it this show but when they came out with like local zones and oh my gosh out you know all the things that Amazon is building to push out to the edge and also enabling that technology and software and the partner ecosystem helps expand that and Pull It in it's no longer you know Dave it was Hotel California all of the data eventually is going to end up in the public cloud and lock it in it's like I don't think that's going to be the case we know that there will be so much data out at the edge Amazon absolutely is super important um there some of those examples we're giving it's not necessarily multi-cloud but there's collaboration happening like in the healthcare world you know universities and hospitals can all share what they're doing uh regardless of you know where they live well Stephen Armstrong in the analyst session did say that you know we're going to talk about multi-cloud we're not going to lead with it necessarily but we are going to actually talk about it and that's different to your points too than in the fullness of time all the data will be in the cloud that's a new narrative but go ahead yeah actually Amazon is a leader in the cloud so if they push the cloud even if they don't say AWS or Amazon with it they benefit from it right and and the narrative is that way there's the proof is there right so again Innovation favorite scale there are chips which are being made for high scale their software being tweaked for high scale you as a Bank of America or for the Chrysler as a typical Enterprise you cannot afford to do those things in-house what cloud providers can I'm not saying just AWS Google cloud is there Azure guys are there and few others who are behind them and and you guys are there as well so IBM has IBM by the way congratulations to your red hat I know but IBM won the award um right you know very good partner and yeah but yeah people are dragging their feet people usually do on the change and they are in denial denial they they drag their feet and they came in IBM director feed the cave Den Dell drag their feed the cave in yeah you mean by Dragon vs cloud deniers cloud deniers right so server Huggers I call them but they they actually are sitting in Amazon Cloud Marketplace everybody is buying stuff from there the marketplace is the new model OKAY Amazon created the marketplace for b2c they are leading the marketplace of B2B as well on the technology side and other people are copying it so there are multiple marketplaces now so now actually it's like if you're in in a mobile app development there are two main platforms Android and Apple you first write the application for Apple right then for Android hex same here as a technology provider as and I I and and I actually you put your stuff to AWS first then you go anywhere else yeah they are later yeah the Enterprise app store is what we've wanted for a long time the question is is Amazon alone the Enterprise app store or are they partner of a of a larger portfolio because there's a lot of SAS companies out there uh that that play into yeah what we need well and this is what you're talking about the future but I just want to make a point about the past you talking about dragging their feet because the Cube's been following this and Stu you remember this in 2013 IBM actually you know got in a big fight with with Amazon over the CIA deal you know and it all became public judge wheeler eviscerated you know IBM and it ended up IBM ended up buying you know soft layer and then we know what happened there and it Joe Tucci thought the cloud was Mosey right so it's just amazing to see we have booksellers you know VMware called them books I wasn't not all of them are like talking about how great Partnerships they are it's amazing like you said sub GC and IBM uh with the the GSI you know Partnership of the year but what you guys were just talking about was the future and that's what I wanted to get to is because you know Amazon's been leading the way I I was listening to Werner this morning and that just reminded me of back in the days when we used to listen to IBM educate us give us a master class on system design and decoupled systems and and IO and everything else now Amazon is you know the master educator and it got me thinking how long will that last you know will they go the way of you know the other you know incumbents will they be disrupted or will they you know keep innovating maybe it's going to take 10 or 20 years I don't know yeah I mean Dave you actually you did some research I believe it was a year or so ago yeah but what will stop Amazon and the one thing that worries me a little bit um is the two Pizza teams when you have over 202 Pizza teams the amount of things that each one of those groups needs to take care of was more than any human could take care of people burn out they run out of people how many amazonians only last two or three years and then leave because it is tough I bumped into plenty of friends of mine that have been you know six ten years at Amazon and love it but it is a tough culture and they are driving werner's keynote I thought did look to from a product standpoint you could say tape over some of the seams some of those solutions to bring Beyond just a single product and bring them together and leverage data so there are some signs that they might be able to get past some of those limitations but I still worry structurally culturally there could be some challenges for Amazon to keep the momentum going especially with the global economic impact that we are likely to see in the next year bring us home I think the future side like we could talk about the vendors all day right to serve the community out there I think we should talk about how what's the future of technology consumption from the consumer side so from the supplier side just a quick note I think the only danger AWS has has that that you know Fred's going after them you know too big you know like we will break you up and that can cause some disruption there other than that I think they they have some more steam to go for a few more years at least before we start thinking about like oh this thing is falling apart or anything like that so they have a lot more they have momentum and it's continuing so okay from the I think game is on retail by the way is going to get disrupted before AWS yeah go ahead from the buyer's side I think um the the future of the sort of Technology consumption is based on the paper uh use and they actually are turning all their services to uh they are sort of becoming serverless behind the scenes right all analytics service they had one service left they they did that this year so every service is serverless so that means you pay exactly for the amount you use the compute the iops the the storage so all these three layers of course Network we talked about the egress stuff and that's a problem there because of the network design mainly because Google has a flatter design and they have lower cost so so they are actually squeezing the their their designing this their services in a way that you don't waste any resources as a buyer so for example very simple example when early earlier In This Cloud you will get a VM right in Cloud that's how we started so and you can get 20 use 20 percent of the VM 80 is getting wasted that's not happening now that that has been reduced to the most extent so now your VM grows as you grow the usage and if you go higher than the tier you picked they will charge you otherwise they will not charge you extra so that's why there's still a lot of instances like many different types you have to pick one I think the future is that those instances will go away the the instance will be formed for you on the fly so that is the future serverless all right give us bumper sticker Stu and then Serb G I'll give you my quick one and then we'll wrap yeah so just Dave to play off of sharp G and to wrap it up you actually wrote about it on your preview post for here uh serverless we're talking about how developers think about things um and you know Amazon in many ways you know is the new default server uh you know for the cloud um and containerization fits into the whole serverless Paradigm uh it's the space that I live in uh you know every day here and you know I was happy to see the last few years serverless and containers there's a blurring a line and you know subject we're still going to see VMS for a long time yeah yeah we will see that so give us give us your book Instagram my number six is innovation favorite scale that's my bumper sticker and and Amazon has that but also I I want everybody else to like the viewers to take a look at the the Google Cloud as well as well as IBM with others like maybe you have a better price to Performance there for certain workloads and by the way one vendor cannot do it alone we know that for sure the market is so big there's a lot of room for uh Red Hats of the world and and and Microsoft's the world to innovate so keep an eye on them they we need the competition actually and that's why competition Will Keep Us to a place where Market sets the price one vendor doesn't so the only only danger is if if AWS is a monopoly then I will be worried I think ecosystems are the Hallmark of a great Cloud company and Amazon's got the the biggest and baddest ecosystem and I think the other thing to watch for is Industries building on top of the cloud you mentioned the Goldman Sachs NASDAQ Capital One and Warner media these all these industries are building their own clouds and that's where the real money is going to be made in the latter half of the 2020s all right we're a wrap this is Dave Valente I want to first of all thank thanks to our great sponsors AWS for for having us here this is our 10th year at the cube AMD you know sponsoring as well the the the cube here Accenture sponsor to third set upstairs upstairs on the fifth floor all the ecosystem partners that came on the cube this week and supported our mission for free content our content is always free we try to give more to the community and we we take back so go to thecube.net and you'll see all these videos go to siliconangle com for all the news wikibon.com I publish weekly a breaking analysis series I want to thank our amazing crew here you guys we have probably 30 35 people unbelievable our awesome last session John Walls uh Paul Gillen Lisa Martin Savannah Peterson John Furrier who's on a plane we appreciate Andrew and Leonard in our ear and all of our our crew Palo Alto Boston and across the country thank you so much really appreciate it all right we are a wrap AWS re invent 2022 we'll see you in two weeks we'll see you two weeks at Palo Alto ignite back here in Vegas thanks for watching thecube the leader in Enterprise and emerging Tech coverage [Music]

>>Well, hello everybody. John Walls here and continuing our coverage here at AWS Reinvent 22 on the queue. We continue our segments here in the Global Startup program, which of course is sponsored by AWS Startup Showcase, and with us to talk about any scale as the co-founder and CEO of the company, Robert and n, you are Robert. Good to see you. Thanks for joining us. >>Yeah, great. And thank you. >>You bet. Yeah. Glad to have you aboard here. So let's talk about Annie Scale, first off, for those at home and might not be familiar with what you do. Yeah. Because you've only been around for a short period of time, you're telling me >>Company's about >>Three years now. Three >>Years old, >>Yeah. Yeah. So tell us all about it. Yeah, >>Absolutely. So one of the biggest things happening in computing right now is the proliferation of ai. AI is just spreading throughout every industry has the potential to transform every industry. But the thing about doing AI is that it's incredibly computationally intensive. So if you wanna do do ai, you're not, you're probably not just doing it on your laptop, you're doing it across many machines, many gpu, many compute resources, and that's incredibly hard to do. It requires a lot of software engineering expertise, a lot of infrastructure expertise, a lot of cloud computing expertise to build the software infrastructure and distributed systems to really scale AI across all of the, across the cloud. And to do it in a way where you're really getting value out of ai. And so that is the, the problem statement that AI has tremendous potential. It's incredibly hard to do because of the, the scale required. >>And what we are building at any scale is really trying to make that easy. So trying to get to the point where, as a developer, if you know how to program on your laptop, then if you know how to program saying Python on your laptop, then that's enough, right? Then you can do ai, you can get value out of it, you can scale it, you can build the kinds of, you know, incredibly powerful applica AI applications that companies like Google and, and Facebook and others can build. But you don't have to learn about all of the distributed systems and infrastructure. It just, you know, we'll handle that for you. So that's, if we're successful, you know, that's what we're trying to achieve here. >>Yeah. What, what makes AI so hard to work with? I mean, you talk about the complexity. Yeah. A lot of moving parts. I mean, literally moving parts, but, but what is it in, in your mind that, that gets people's eyes spinning a little bit when they, they look at great potential. Yeah. But also they look at the downside of maybe having to work your way through Pike mere of sorts. >>So, so the potential is definitely there, but it's important to remember that a lot of AI initiatives fail. Like a lot of initiative AI initiatives, something like 80 or 90% don't make it out of, you know, the research or prototyping phase and inter production. Hmm. So, some of the things that are hard about AI and the reasons that AI initiatives can fail, one is the scale required, you know, moving. It's one thing to develop something on your laptop, it's another thing to run it across thousands of machines. So that's scale, right? Another is the transition from development and prototyping to production. Those are very different, have very different requirements. Absolutely. A lot of times it's different teams within a company. They have different tech stacks, different software they're using. You know, we hear companies say that when they move from develop, you know, once they prototype and develop a model, it could take six to 12 weeks to get that model in production. >>And that often involves rewriting a lot of code and handing it off to another team. So the transition from development to production is, is a big challenge. So the scale, the development to production handoff. And then lastly, a big challenge is around flexibility. So AI's a fast moving field, you see new developments, new algorithms, new models coming out all the time. And a lot of teams we work with, you know, they've, they've built infrastructure. They're using products out there to do ai, but they've found that it's sort of locking them into rigid workflows or specific tools, and they don't have the flexibility to adopt new algorithms or new strategies or approaches as they're being developed as they come out. And so they, but their developers want the flexibility to use the latest tools, the latest strategies. And so those are some of the main problems we see. It's really like, how do you scale scalability? How do you move easily from development and production and back? And how do you remain flexible? How do you adapt and, and use the best tools that are coming out? And so those are, yeah, just those are and often reasons that people start to use Ray, which is our open source project in any scale, which is our, our product. So tell >>Me about Ray, right? Yeah. Opensource project. I think you said you worked on it >>At Berkeley. That's right. Yeah. So before this company, I did a PhD in machine learning at Berkeley. And one of the challenges that we were running into ourselves, we were trying to do machine learning. We actually weren't infrastructure or distributed systems people, but we found ourselves in order to do machine learning, we found ourselves building all sorts of tools, ad hoc tools and systems to scale the machine learning, to be able to run it in a reasonable amount of time and to be able to leverage the compute that we needed. And it wasn't just us people all across, you know, machine learning researchers, machine learning practitioners were building their own tooling and infrastructure. And that was one of the things that we felt was really holding back progress. And so that's how we slowly and kind of gradually got into saying, Hey, we could build better tools here. >>We could build, we could try to make this easier to do so that all of these people don't have to build their own infrastructure. They can focus on the actual machine learning applications that they're trying to build. And so we started, Ray started this open source project for basically scaling Python applications and scaling machine learning applications. And, well, initially we were running around Berkeley trying to get all of our friends to try it out and, and adopt it and, you know, and give us feedback. And if it didn't work, we would debug it right away. And that slow, you know, that gradually turned into more companies starting to adopt it, bigger teams starting to adopt it, external contributors starting to, to contribute back to the open source project and make it better. And, you know, before you know it, we were hosting meetups, giving to talks, running tutorials, and the project was just taking off. And so that's a big part of what we continue to develop today at any scale, is like really fostering this open source community, growing the open source user base, making sure Ray is just the best way to scale Python applications and, and machine learning applications. >>So, so this was a graduate school project That's right. You say on, on your way to getting your doctorate and now you commercializing now, right? Yeah. I mean, so you're being able to offer it, first off, what a journey that was, right? I mean, who would've thought Absolutely. I guess you probably did think that at some point, but >>No, you know, when we started, when we were working on Ray, we actually didn't anticipate becoming a company, or we at least just weren't looking that far ahead. We were really excited about solving this problem of making distributed computing easy, you know, getting to the point where developers just don't have to learn about infrastructure and distributed systems, but get all the benefits. And of course, it wasn't until, you know, later on as we were graduating from Berkeley and we wanted to continue really taking this project further and, and really solving this problem that it, we realized it made sense to start a company. >>So help me out, like, like what, what, and I might have missed this, so I apologize if I did, but in terms of, of Ray's that building block and essential for your, your ML or AI work down the road, you know, what, what is it doing for me or what, what will it allow me to do in either one of those realms that I, I can't do now? >>Yeah. And so, so like why use Ray versus not using Ray? Yeah, I think the, the answer is that you, you know, if you're doing ai, you need to scale. It's becoming, if you don't find that to be the case today, you probably will tomorrow, you know, or the day after that. And so it's really increasingly, it's a requirement. It's not an option. And so if you're scaling, if you're trying to build these scalable applications you are building, you're either going to use Ray or, or something like Ray or you're going to build the infrastructure yourself and building the infrastructure yourself, that's a long journey. >>So why take that on, right? >>And many of the companies we work with don't want to be in the business of building and managing infrastructure. No. Because, you know, if they, they want their their best engineers to build their product, right? To, to get their product to market faster. >>I want, I want you to do that for me. >>Right? Exactly. And so, you know, we can really accelerate what these teams can do and, you know, and if we can make the infrastructure something they just don't have to think about, that's, that's why you would choose to use Ray. >>Okay. You know, between a and I and ml are, are they different animals in terms of what you're trying to get done or what Ray can do? >>Yeah, and actually I should say like, it's not just, you know, teams that are new teams that are starting out, that are using Ray, many companies that have built, already built their own infrastructure will then switch to using Ray. And to give you a few examples, like Uber runs all their deep learning on Ray, okay. And, you know, open ai, which is really at the frontier of training large models and, and you know, pushing the boundaries of, of ai, they train their largest models using Ray. You know, companies like Shopify rebuilt their entire machine learning platform using Ray, >>But they started somewhere else. >>They had, this is all, you know, like, it's not like the v1, you know, of their, of their machine learning infrastructure. This is like, they did it a different way before, this is like the second version or the third iteration of of, of how they're doing it. And they realize often it's because, you know, I mean in the case of, of Uber, just to give you one example, they built a system called hova for scaling deep learning on a bunch of GPUs. Right Now, as you scale deep learning on GPUs for them, the bottleneck shifted away from, you know, as you scale GPU's training, the bottleneck shifted away from training and to the data ingest and pre-processing. And they wanted to scale data ingest and pre-processing on CPUs. So now Hova, it's a deep learning framework. It doesn't do the data ingest and pre-processing on CPUs, but you can, if you run Hova on top of Ray, you can scale training on GPUs. >>And then Ray has another library called Ray Data you can, that lets you scale the ingest and pre-processing on CPUs. You can pipeline them together. And that allowed them to train larger models on more data before, just to take one example, ETA prediction, if you get in an Uber, it tells you what time you're supposed to arrive. Sure. That uses a deep learning model called d eta. And before they were able to train on about two weeks worth of data. Now, you know, using Ray and for scaling the data, ingestive pre-processing and training, they can train on much more data. You know, you can get more accurate ETA predictions. So that's just one example of the kind of benefit they were able to get. Right. Also, because it's running on top of, of Ray and Ray has this ecosystem of libraries, you know, they can also use Ray's hyper parameter tuning library to do hyper parameter tuning for their deep learning models. >>They can also use it for inference and you know, because these are all built on top of Ray, they inherit the like, elasticity and fault tolerance of running on top of Ray. So really it simplifies things on the infrastructure side cuz there's just, if you have Ray as common infrastructure for your machine learning workloads, there's just one system to, to kind of manage and operate. And if you are, it simplifies things for the end users like the developers because from their perspective, they're just writing a Python application. They don't have to learn how to use three different distributed systems and stitch them together and all of this. >>So aws, before I let you go, how do they come into play here for you? I mean, are you part of the showcase, a startup showcase? So obviously a major partner and major figure in the offering that you're presenting >>People? Yeah, well you can run. So any scale is a managed ray service. Like any scale is just the best way to run Ray and deploy Ray. And we run on top of aws. So many of our customers are, you know, using Ray through any scale on aws. And so we work very closely together and, and you know, we have, we have joint customers and basically, and you know, a lot of the value that any scale is adding on top of Ray is around the production story. So basically, you know, things like high availability, things like failure handling, retry alerting, persistence, reproducibility, these are a lot of the value, the values of, you know, the value that our platform adds on top of the open source project. A lot of stuff as well around collaboration, you know, imagine you are, you, something goes wrong with your application, your production job, you want to debug it, you can just share the URL with your, your coworker. They can click a button, reproduce the exact same thing, look at the same logs, you know, and, and, and figure out what's going on. And also a lot around, one thing that's, that's important for a lot of our customers is efficiency around cost. And so we >>Support every customer. >>Exactly. A lot of people are spending a lot of money on, on aws. Yeah. Right? And so any scale supports running out of the box on cheaper like spot instances, these preempt instances, which, you know, just reduce costs by quite a bit. And so things like that. >>Well, the company is any scale and you're on the show floor, right? So if you're having a chance to watch this during reinvent, go down and check 'em out. Robert Ashihara joining us here, the co-founder and ceo and Robert, thanks for being with us. Yeah. Here on the cube. Really enjoyed it. Me too. Thanks so much. Boy, three years graduate program and boom, here you are, you know, with off to the enterprise you go. Very nicely done. All right, we're gonna continue our coverage here on the Cube with more here from Las Vegas. We're the Venetian, we're AWS Reinvent 22 and you're watching the Cube, the leader in high tech coverage.

(relaxing intro music) >> Welcome back to theCUBE's Coverage here live at reinvent 2022. We're here at the Executive Summit upstairs with the Accenture Set three sets broadcasting live four days with theCUBE. I'm John Furrier your host, with two great guests, cube alumnis, back Tanuja Randery, managing director Amazon web service for Europe middle East and Africa, known as EMEA. Welcome back to the Cube. >> Thank you. >> Great to see you. And Karthik Narain, who's the Accenture first cloud lead. Great to see you back again. >> Thank you. >> Thanks for coming back on. All right, so business transformation is all about digital transformation taken to its conclusion. When companies transform, they are now a digital business. Technologies powering value proposition, data security all in the keynotes higher level service at industry specific solutions. The dynamics of the industry are changing radically in front of our eyes for for the better. Karthik, what's your position on this as Accenture looks at this, we've covered all your successes during the pandemic with AWS. What, what do you guys see out there now as this next layer of power dynamics in the industry take place? >> I think cloud is getting interesting and I think there's a general trend towards specialization that's happening in the world of cloud. And cloud is also moving from a general purpose technology backbone to providing specific industry capabilities for every customer within various industries. But the industry cloud is not a new term. It has been used in the past and it's been used in the past in various degrees, whether that's building horizontal solutions, certain specialized SaaS software or providing capabilities that are horizontal for certain industries. But we see the evolution of industry cloud a little differently and a lot more dynamic, which is we see this as a marketplace where ecosystem of capabilities are going to come together to interact with a common data platform data backbone, data model with workflows that'll come together and integrate all of this stuff and help clients reinvent their industry with newer capabilities, but at the same time use the power of democratized innovation that's already there within that industry. So that's the kind of change we are seeing where customers in their strategy are going to implement industry cloud as one of the tenants as they go through their strategy. >> Yeah, and I see in my notes, fit for purposes is a buzzword people are talking about right size in the cloud and then just building on that. And what's interesting, Tanuja I want to get your thoughts because in the US we're one country, so yeah, integrating is kind of within services. You have purview over countries and these regions it's global impact. This is now a global environment. So it's not just the US North America, it's Latin America it's EMEA, this is another variable in the cross connecting of these fit for purpose. What's your view of the these industry specific solutions? >> Yeah, no and thanks Karthik 'cause I'm a hundred percent aligned. You know, I mean, you know this better than me, John, but 90% of workloads have not yet moved to the cloud. And the only way that we think that's going to happen is by bringing together business and IT. So what does that mean? It means starting with business use cases whether that's digital banking or smart connected factories or frankly if it's predictive maintenance or connected beds. But how do we take those use cases leverage them to really drive outcomes with the technology behind them? I think that's the key unlock that we have to get to. And very specifically, and Adam talked about this a lot today, but data, data is the single unifier for all of business and IT coming together to drive value, right? However, the issue is there's a ton of it, (John Furrier chuckling) right? In fact, fun fact if you put all the data that's going to be created over the next five years, which is more than the last 30 years, on a one terabyte little floppy, disk drive, remember those? Well that's going to be 15 round trips to the moon (John Furrier chuckling) and back. That's how much data it is. So our perspective is you got to unify, single data lake, you got to modernize with AI and ML, and then you're going to have to drive innovation on that. Now, I'll give you one tiny example if I may which I love Ryanair, big airline, 150 million passengers. They are also the largest supplier of ham and cheese sandwiches in the air. And catering at that scale is really difficult, right? If you have too much food wastage, sustainability issues, too little customers are really unhappy. So we work with them leveraging AWS cloud and AI ML to build a panini predictor. And in essence, it's taking the data they've got, data we've got, and actually giving them the opportunity to have just the right number of paninis. >> I love the lock and and the key is data to unlock the value. We heard that in the keynote. Karthik, you guys have been working together with AWS and a lot of successes. We've covered some of those on the cube. As you look at these industry solutions they're not the obvious big problems. They're like businesses, you know it could be the pizza shop it could be the dentist office, it could be any business any industry specific carries over. What is the key to unlock it? Is it the data? Is it the solution? What's that key? >> I think, you know the easier answer is all of the about, but like Tanuja said it all starts by bringing the data together and this is a funny thing. It's not creating new data. This data is there within enterprises. Our clients have these data the industries have the data, but for ages these data has been trapped in functional silos and organizations have been doing analytics within those functions. It's about bringing the data together whether that's a single data warehouse or a data mesh. Those are architectural considerations. But it's about bringing cross-functional data together as step one. Step two, is about utilizing the power of cloud for democratized innovation. It's no longer about one company trying to reinvent the wheel, or create a a new wheel within their enterprise. It's about looking around through the power of cloud marketplace to see if there's a solution that is already existing can we use that? Or if I've created something within my company can I use that as a service for others to use? So, the number one thing is using the power of democratized innovation. Second thing is how do you standardize and digitize functions that does not need to be reinvented every single time so that, you know, your organization can do it or you could use that or take that from elsewhere. And the third element is using the power of the platform economy or platforms to find new avenues of revenue opportunity, customer engagement and experiences. So these are all the things that differentiates organization, but all of this is underpinned by a unified data model that helps, you know, use all the (indistinct) there. >> Tanuja, you have mentioned earlier that not everyone has their journey of the cloud looks the same and certainly in the US and EMEA you have different countries and different areas. >> Yep. >> Their journeys are different. Some want speed and fees, some will roll their own. I mean data brick CEO, when I interviewed them that last week, they started database on a credit card swiped it and they didn't want any support. Amazon's knocking on their door saying, "you want support?" "No, we got it covered." Obviously they're from Berkeley and they're nerds, and they're cool. They can roll their own, but not everyone can. >> Yeah. >> And so you have a mix of customer profiles. How do you view that and what's your strategy? How do you get them over productive seeing that business value? What's that transformation look like? >> Yeah, John, you're absolutely right. So you've got those who are born in cloud, they're very savvy, they know exactly what they need. However, what I do find increasingly, even with these digital native customers, is they're also starting to talk business use cases. So they're talking about, "okay how do I take my platform and build a whole bunch of new services on top of that platform?" So, we still have to work with them on this business use case dimension for the next curve of growth that they want to drive. Currently with the global macroeconomic factors obviously they're also very concerned about profitability and costs. So that's one model. In the enterprise space, you have differences. >> Yeah. >> Right, You have the sort of very, very, very savvy enterprises, right? Who know exactly what they're looking for. But for them then it's about how do I lean into sustainability? In fact, we did a survey, and 77% of users that we surveyed said that they could accelerate their sustainably goals by using cloud. So in many cases they haven't cracked that and we can help them do that. So it's really about horses for courses there. And then, then with some other companies, they've done a lot of the basic infrastructure modernization. However, what they haven't been able to yet do is figure out how they're going to actually become a tech company. So I keep getting asked, can I become a tech company? How do I do that? Right? And then finally there are companies which don't have the skills. So if I go to the SMB segment, they don't always have the skills or the resources. And there using scalable market platforms like AWS marketplace, >> Yeah. >> Allows them to get access to solutions without having to have all the capabilities. So it really is- >> This is where partner network really kind of comes in. >> Absolutely. >> Huge value. Having that channel of solution providers I use that term specifically 'cause you're providing the solution for those folks. >> Yeah. Exact- >> And then the folks at the enterprise, we had a quote on the analyst segment earlier on our Cube, "spend more, save more." >> Yeah. >> That's the cloud equations, >> Yeah. because you're going to get it on sustainability you're going to save it on, you're going to save on cost recovery for revenue, time to revenue. So the cloud is the answer for a lot of enterprises out of the recession. >> Absolutely, and in fact, we need to lean in now you heard Adam say this, right? I mean the cost savings potential alone from on-prem to cloud is between 40 and 60 percent. Just that. But I don't think that's it John. >> The bell tightening he said is reigning some right size. Okay, but then also do more, he didn't say that, but analysts are generally saying, if you spend right on the cloud, you'll save more. That's a general thesis. >> Yeah. >> Do you agree with that? >> I absolutely think so. And by the way, usage is, people use it differently as they get smarter. We're constantly working with our customers by the way though, to continuously cost optimize. So you heard about our Graviton3 instances for example. We're using that to constantly optimize, but at the same time, what are the workloads that you haven't yet brought over to the cloud? (John Furrier chuckling) And so supply chain is a great idea. Our health cloud initiative. So we worked with Accenture on the Accenture Health Insights platform, which runs on AWS as an example or the Goldman Sachs one last year, if you remember. >> I do >> The financial cloud. So those, those are some of the things that I think make it easier for people to consume cloud and reimagine their businesses. >> It's funny, I was talking with Adam and we had a little debate about what an ISV is and I talked to the CEO of Mongo. They don't see themselves on the ISV. As they grew up on the cloud, they become platforms, they have their own ISVs and data bricks and Snowflake and others are developing that dynamic. But there's still ISVs out there. So there's a dynamic of growth going on and the need for partners and our belief is that the ecosystem is going to start doubling in size we believe, because of the demand for purpose built or so out of the box. I hate to use that word "out of the box", but you know turnkey solutions that you can buy another one if it breaks. But use the building blocks if you want to build the foundation. That is more durable, more customizable. Do that if you can. >> Well, >> but- >> we've got a phenomenal, >> shall we talk about this? >> Yeah, go get into- >> So, we've built a five year vision together, Accenture and us. which is called Velocity and you'll be much better in describing it, but I'll give you the simple version of Velocity which is taking AWS powered industry solutions and bringing it to market faster, more repeatable and at lower cost. And so think about vertical solutions sitting on a horizontal accelerator platform able to be deployed making transformation less complex. >> Yeah. >> Karthik, weight in on this, because I've talked to you about this before. We've said years ago the horizontal scalability of the cloud's a beautiful thing but verticals where the ML works great too. Now you got ML in all aspects of it. Horizontal verticals here now. >> Yeah, Yeah, absolutely. Again, the power of this kind of platform that we are launching, by the way we're launching tomorrow we are very excited about it, is, create a platform- >> What are you launching tomorrow? Hold on, I got news out there. What's launching? >> We are going to launch a giant platform, which will help clients accelerate their journey to industry cloud. So that's going to happen tomorrow. So what this platform would provide is that this is going to provide the horizontal capabilities that will help clients bootstrap their launch into cloud. And once they get into cloud, they would be able to build industry solutions on this. The way I imagine this is create the chassis that you need for your industry and then add the cartridges, industry cartridges, which are going to be solutions that are going to be built on top of it. And we are going to do this across various industries starting from, you know, healthcare, life sciences to energy to, you know, public services and so on and so forth >> You're going to create a channel machine. A channel creation machine, you're going to allow people to build their own solutions on top of that platform. And that's launching tomorrow. Make sure we get the news on that. >> Exactly. And- >> Ah, No, >> Sorry, and we genuinely believe the power of industry cloud, if you think about it in the past to create a solution one had to be an ISV to create a solution. What cloud is providing for industry today in the concept of industry clouds, this, industry companies are creating industry solution. The best example is, along with, you know, AWS and Accenture, Ecopetrol, which is a leader in the energy industry, has created a platform, you know called Water Intelligence and Management platform. And through this platform, they are attacking the audacious goal of water sustainability, which is going to be a huge problem for humanity that everybody needs to solve. As part of this platform, the goal is to reduce, you know, fresh water usage by 66% or zero, you know, you know, impact to, you know, groundwater is going to be the goal or ambition of Ecopetrol. So all of this is possible because industry players want to jump to the bandwagon because they have all the toolkit of of the cloud that's available with which they could build a software platform with which they can power their entire industry. >> And make money and have a good business. You guys are doing great. Final word, partnership. Where's it go next? You're doing great. Put a plugin for the Accenture AWS partnership. >> Well, I mean we have a phenomenal relationship and partnership, which is amazing. We really believe in the power of three which is the GSI, the ISV, and us together. And I have to go back to the thing I keep focused on 90% of workloads not in cloud. I think together we can enable those companies to come into the cloud. Very importantly, start to innovate launch new products and refuel the economy. So I think- >> We'll have to check on that >> Very, very optimistic. >> We'll have to check on that number. >> That seems a little- >> You got to check on that number. >> 90 seems a little bit amazing. >> 90% of workloads. >> That sounds, maybe, I'd be surprised. Maybe a little bit lower than that. Maybe. We'll see. >> We got to start turning it. >> It's still a lot. >> (laughs) It's still a lot. >> A lot more. Still first, still early days. Thanks so much for the conversation Karthik great to see you again Tanuja, thanks for your time. >> Thank you, John. >> Congratulations, on your success. Okay, this is theCube up here in the executive summit. You're watching theCube, the leader in high tech coverage, we'll be right back with more coverage here, and the Accenture set after the short break. (calm outro music)

(upbeat music) >> You got it, it's theCUBE. We are in Vegas. This is the Cube's live coverage day one of the full event coverage of AWS reInvent '22 from the Venetian Expo Center. Lisa Martin here with Dave Vellante. We love being in Vegas, Dave. >> Well, you know, this is where Super Cloud sort of was born. >> It is. >> Last year, just about a year ago. Steve Mullaney, CEO of of Aviatrix, you know, kind of helped us think it through. And we got some fun stories around. It's happening, but... >> It is happening. We're going to be talking about Super Cloud guys. >> I guess I just did the intro, Steve Mullaney >> You did my intro, don't do it again. >> Sorry I stole that from you, yeah. >> Steve Mullaney, joined just once again, one of our alumni. Steve, great to have you back on the program. >> Thanks for having me back. >> Dave: It's happening. >> It is happening. >> Dave: We talked about a year ago. Net Studio was right there. >> That was two years. Was that year ago, that was a year ago. >> Dave: It was last year. >> Yeah, I leaned over >> What's happening? >> so it's happening. It's happening. You know what, the thing I noticed what's happening now is the maturity of the cloud, right? So, if you think about this whole journey to cloud that has been, what, AWS 12 years. But really over the last few years is when enterprises have really kind of joined that journey. And three or four years ago, and this is why I came out of retirement and went to Aviatrix, was they all said, okay, now we're going to do cloud. You fast forward now three, four years from now, all of a sudden those five-year plans of evacuating the data center, they got one year left, two year left, and they're going, oh crap, we don't have five years anymore. We're, now the maturity's starting to say, we're starting to put more apps into the cloud. We're starting to put business critical apps like SAP into the cloud. This is not just like the low-hanging fruit anymore. So what's happening now is the business criticality, the scale, the maturity. And they're all now starting to hit a lot of limits that have been put into the CSPs that you never used to hit when you didn't have business critical and you didn't have that scale. They were always there. The rocks were always there. Just it was, you never hit 'em. People are starting to hit 'em now. So what's happening now is people are realizing, and I'm going to jump the gun, you asked me for my bumper sticker. The bumper sticker for Aviatrix is, "Good enough is no longer good enough." Now it's funny, it came in a keynote today, but what we see from our customers is it's time to upgrade the native constructs of networking and network security to be enterprise-grade now. It's no longer good enough to just use the native constructs because of a lack of visibility, the lack of controls, the lack of troubleshooting capabilities, all these things. "I now need enterprise grade networking." >> Let me ask you a question 'cause you got a good historical perspective on the industry. When you think about when Maritz was running VMWare. He was like any app, he said basically we're building a software mainframe. And they kind of did that, right? But then they, you know, hit the issue with scale, right? And they can't replicate the cloud. Are there things that we can draw from that experience and apply that to the cloud? What's the same, what's different? >> Oh yeah. So, 1992, do you remember what happened in 1992? I do this, weird German software company called SAP >> Yeah, R3. announced a release as R/3. Which was their first three-tier client-server application of SAP. Before that it ran on mainframes, TCP/IP. Remember that Protocol War? Guess what happened post-1992, everybody goes up like this. Infrastructure completely changes. Cisco, EMC, you name it, builds out these PCE client-server architectures. The WAN changes, MPLS, the campus, everything's home running back to that data center running SAP. That was the last 30 years ago. Great transformation of SAP. They've did it again. It's called S/4Hana. And now it's running and people are switching to S/4Hana and they're moving to the cloud. It's just starting. And that is going to alter how you build infrastructure. And so when you have that, being able to troubleshoot in hours versus minutes is a big deal. This is business critical, millions of dollars. This is not fun and games. So again, back to my, what was good enough for the last three or four years for enterprises no longer good enough, now I'm running business critical apps like SAP, and it's going to completely change infrastructure. That's happening in the cloud right now. And that's obviously a significant seismic shift, but what are some of the barriers that customers have been able to eliminate in order to get there? Or is it just good enough isn't good enough anymore? >> Barriers in terms of, well, I mean >> Lisa: The adoption. Yeah well, I mean, I think it's all the things that they go to cloud is, you know, the complexity, really, it's the agility, right? So the barrier that they have to get over is how do I keep the developer happy because the developer went to the cloud in the first place, why? Swipe the credit card because IT wasn't doing their job, 'cause every time I asked them for something, they said no. So I went around 'em. We need that. That's what they have to overcome in the move to the cloud. That is the obstacle is how do I deliver that visibility, that control, the enterprise, great functionality, but yet give the developer what they want. Because the minute I stop giving them that swipe the card operational model, what do you think they're going to do? They're going to go around me again and I can't, and the enterprise can't have that. >> That's a cultural shift. >> That's the main barrier they've got to overcome. >> Let me ask you another question. Is what we think of as mission critical, the definition changing? I mean, you mentioned SAP, obviously that's mission critical for operations, but you're also seeing new applications being developed in the cloud. >> I would say anything that's, I call business critical, same thing, but it's, business critical is internal to me, like SAP, but also anything customer-facing. That's business critical to me. If that app goes down or it has a problem, I'm not collecting revenue. So, you know, back 30 years ago, we didn't have a lot of customer-facing apps, right? It really was just SAP. I mean there wasn't a heck of a lot of cust- There were customer-facing things. But you didn't have all the digitalization that we have now, like the digital economy, where that's where the real explosion has come, is you think about all the customer-facing applications. And now every enterprise is what? A technology, digital company with a customer-facing and you're trying to get closer and closer to who? The consumer. >> Yeah, self-service. >> Self-service, B2C, everybody wants to do that. Get out of the middle man. And those are business critical applications for people. >> So what's needed under the covers to make all this happen? Give us a little double click on where you guys fit. >> You need consistent architecture. Obviously not just for one cloud, but for any cloud. But even within one cloud, forget multicloud, it gets worst with multicloud. You need a consistent architecture, right? That is automated, that is as code. I can't have the human involved. These are all, this is the API generation, you've got to be able to use automation, Terraform. And all the way from the application development platform you know, through Jenkins and all other software, through CICD pipeline and Terraform, when you, when that developer says, I want infrastructure, it has to go build that infrastructure in real time. And then when it says, I don't need it anymore it's got to take it away. And you cannot have a human involved in that process. That's what's completely changed. And that's what's giving the agility. And that's kind of a cloud model, right? Use software. >> Well, okay, so isn't that what serverless does, right? >> That's part of it. Absolutely. >> But I might still want control sometimes over the runtime if I'm running those mission critical applications. Everything in enterprise is a heterogeneous thing. It's like people, people say, well there's going to, the people going to repatriate back to on-prem, they are not repatriating back to on-prem. >> We were just talking about that, I'm like- >> Steve: It's not going to happen, right? >> It's a myth, it's a myth. >> And there's things that maybe shouldn't have ever gone into the cloud, I get that. Look, do people still have mainframes? Of course. There's certain things that you just, doesn't make sense to move to the new generation. There were things, certain applications that are very static, they weren't dynamic. You know what, keeping it on-prem it's, probably makes sense. So some of those things maybe will go back, but they never should have gone. But we are not repatriating ever, you know, that's not going to happen. >> No I agree. I mean, you know, there was an interesting paper by Andreessen, >> Yeah. >> But, I mean- >> Steve: Yeah it was a little self-serving for some company that need more funding, yeah. You look at the numbers. >> Steve: Yeah. >> It tells the story. It's just not happening. >> No. And the reason is, it's that agility, right? And so that's what people, I would say that what you need to do is, and in order to get that agility, you have to have that consistency. You have to have automation, you have to get these people out of the way. You have to use software, right? So it's that you have that swipe the card operational model for the developers. They don't want to hear the word no. >> Lisa: Right. >> What do you think is going to happen with AWS? Because we heard, I don't know if you heard Selipsky's keynote this morning, but you've probably heard the hallway talk. >> Steve: I did, yeah. >> Okay. You did. So, you know, connecting the dots, you know doubling down on all the primitives, that we expected. We kind of expected more of the higher level stuff, which really didn't see much of that, a little bit. >> Steve: Yeah. So, you know, there's a whole thing about, okay, does the cloud get commoditized? Does it not? I think the secret weapon's the ecosystem, right? Because they're able to sell through with guys like you. Make great margins on that. >> Steve: Yeah, well, yeah. >> What are your thoughts though on the future of AWS? >> IAS is going to get commoditized. So this is the fallacy that a lot of the CSPs have, is they thought that they were going to commoditize enterprise. It never happens that way. What's going to happen is infrastructure as a service, the lower level, which is why you see all the CSPs talking about what? Oracle Cloud, industry cloud. >> Well, sure, absolutely, yeah. >> We got to get to the apps, we got to get to SAP, we got to get to all that, because that's not going to get commoditized, right. But all the infrastructural service where AWS is king that is going to get commoditized, absolutely. >> Okay, so, but historically, you know Cisco's still got 60% plus gross margins. EMC always had good margin. How pure is the lone survivor in Flash? They got 70% gross margins. So infrastructure actually has always been a pretty good business. >> Yeah that's true. But it's a hell of a lot easier, particularly with people like Aviatrix and others that are building these common architectural things that create simplicity and abstract the way the complexities of underneath such that we allow your network to run an AWS, Azure, Google, Oracle, whatever, exactly the same. So it makes it a hell of a lot easier >> Dave: Super cloud. >> to go move. >> But I want to tap your brain because you have a good perspective of this because servers used to be a great margin business too on-prem and now it's not. It's a low margin business 'cause all the margin went to Intel. >> Yeah. But the cloud guys, you know, AWS in particular, makes a ton of dough on servers, so, or compute. So it's going to be interesting to see over time if that gets com- that's why they're going so hard after silicon. >> I think if they can, I think if you can capture the workload. So AWS and everyone else, as another example, this SAP, they call that a gravity workload. You know what gravity workload is? It's a black hole. It drags everything else with it. If you get SAP or Oracle or a mainframe app, it ain't going anywhere. And then what's going to happen is all your other apps are going to follow it. So that's what they're all going to fight for, is type of app. >> You said something earlier about, forget multicloud, for a moment, but, that idea of the super cloud, this abstraction layer, I mean, is that a real business value for customers other than, oh I got all these clouds, I need 'em to work together. You know, from your perspective from Aviatrix perspective, is it an opportunity for you to build on top of that? Or are you just looking at, look, I'm going to do really good work in AWS, in Azure? Now we're making the same experience. >> I hear this every single day from our customers is they look and they say, good enough isn't good enough. I've now hit the point, I'm hitting route limitations. I'm hitting, I'm doing things manually, and that's fine when I don't have that many applications or I don't have mission critical. The dogs are eating the dog food, we're going into the cloud and they're looking and then saying this is not an operational model for me. I've hit the point where I can't keep doing this, I can't throw bodies at this, I need software. And that's the opportunity for us, is they look and they say, I'm doing it in one cloud, but, and there's zero chance I'm going to be able to figure that out in the two or three other clouds. Every enterprise I talk to says multicloud is inevitable. Whether they're in it now, they all know they're going to go, because it's the business units that demand it. It's not the IT teams that demand it, it's the line of business that says, I like GCP for this reason. >> The driver's functionality that they're getting. >> It's the app teams that say, I have this service and GCP's better at it than AWS. >> Yeah, so it's not so much a cost game or the end all coffee mug, right? >> No, no. >> Google does this better than Microsoft, or better than- >> If you asked an IT person, they would rather not have multicloud. They actually tried to fight it. No, why would you want to support four clouds when you could support one right? That's insane. >> Dave and Lisa: Right. If they didn't have a choice and, and so it, the decision was made without them, and actually they weren't even notified until day before. They said, oh, good news, we're going to GCP tomorrow. Well, why wasn't I notified? Well, we're notifying you now. >> Yeah, you would've said, no. >> Steve: This is cloud bottle, let's go. >> Super cloud again. Did you see the Berkeley paper, sky computing I think they call it? Down at Berkeley, yep Dave Linthicum from Deloitte. He's talking about, I think he calls it meta cloud. It's happening. >> Yeah, yeah, yeah. >> It's happening. >> No, and because customers, customers want that. They... >> And talk about some customer example or two that you think really articulates the value of why it's happening and the outcomes that it's generating. >> I mean, I was just talking to Lamb Weston last night. So we had a reception, Lamb Weston, huge, frozen potatoes. They serve like, I dunno, some ungodly percentage of all the french fries to all the fast food. It's unbelievable what they do. Do you know, they have special chemicals they put on the french fries. So when you get your DoorDash, they stay crispy longer. They've invented that patented it. But anyway, it's all these businesses you've never heard of and they do all the, and again, they're moving to SAP or they're actually SAP in the cloud, they're one of the first ones. They did it through Accenture. They're pulling it back off from Accenture. They're not happy with the service they're getting. They're going to use us for their networking and network security because they're going to get that visibility and control back. And they're going to repatriate it back from a managed service and bring it back and run it in-house. And the SAP basis engineers want it to happen because they see the visibility and control that the infrastructure guy's going to get because of us, which leads to, all they care about is uptime and performance. That's it. And they're going to say the infrastructure team's going to lead to better uptime and better performance if it's running on Aviatrix. >> And business performance and uptime, business critical >> That is the business. That is the business. >> It is. So what are some of the things next coming down the pike from Aviatrix? Any secret sauce you can share? >> Lot of secrets. So, two secrets. One, the next thing people really want to do, embedded network security into the network. We've kind of talked about this. You're going to be seeing some things from us. Where does network security belong? In the network. Embedded in the fabric of the network, not as this dumb device called the next-gen firewall that you steer traffic to. It has to be into the fabric of what we do, what we call airspace. You're going to see us talk about that. And then the next thing, back to the maturity of the cloud, as they build out the core, guess what they're doing? It's this thing called edge, Dave, right? And guess what they're going to do? It's not about connecting the cloud to the edge to the cloud with dumb things like SD-WAN, right? Or SaaS. It's actually the other way around. Go into the cloud, turn around, look out at the edge and say, how do I extend the cloud out to the edge, and make it look like a VPC. That's what people are doing. Why, 'cause I want the operational model. I want all the things that I can do in the cloud out at the edge. And everyone knows it's been in networking. I've been in networking for 37 years. He who wins the core does what? Wins the edge, 'cause that's what happens. You do it first in the core and then you want one architecture, one common architecture, one consistent way of doing everything. And that's going to go out to the edge and it's going to look like a VPC from an operational model. >> And Amazon's going to support that, no doubt. >> Yeah, I mean every, you know, every, and then it's just how do you want to go do that? And us as the networking and network security provider, we're getting dragged to the edge by our customer. Because you're my networking provider. And that means, end to end. And they're trying to drag us into on-prem too, yeah. >> Lot's going on, you're going to have to come back- >> Because they want one networking vendor. >> But wait, and you say what? >> We will never do like switches and any of the keep Arista, the Cisco, and all that kind of stuff. But we will start sucking in net flow. We will start doing, from an operational perspective, we will integrate a lot of the things that are happening in on-prem into our- >> No halfway house. >> Copilot. >> No halfway house, no two architectures. But you'll take the data in. >> You want one architecture. >> Yeah. >> Yeah, totally. >> Right play. >> Amazing stuff. >> And he who wins the core, guess what's more strategic to them? What's more strategic on-prem or cloud? Cloud. >> It flipped three years ago. >> Dave: Yeah. >> So he who wins in the clouds going to win everywhere. >> Got it, We'll keep our eyes on that. >> Steve: Cause and effect. >> Thank you so much for joining us. We've got your bumper sticker already. It's been a great pleasure having you on the program. You got to come back, there's so, we've- >> You posting the bumper sticker somewhere? >> Lisa: It's going to be our Instagram. >> Oh really, okay. >> And an Instagram sto- This is new for you guys. Always coming up with new ideas. >> Raising the bar. >> It is, it is. >> Me advance, I mean, come on. >> I love it. >> All right, for our guest Steve Mullaney and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage.