(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)

(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)

>>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.

(upbeat music) >> Man: We're good. >> Hey everyone. Welcome back to theCube's live coverage of AWS Summit NYC. We're in New York City, been here all day. Lisa Martin, John Furrier, talking with AWS partners ecosystem folks, customers, AWS folks, you name it. Next up, one of our alumni, rejoins us. Please welcome Anshu Sharma the co-founder and CEO of Skyflow. Anshu great to have you back on theCube. >> Likewise, I'm excited to be back. >> So I love how you guys founded this company. Your inspiration was the zero trust data privacy vault pioneered by two of our favorites, Apple and Netflix. You started with a simple question. What if privacy had an API? So you built a data privacy vault delivered as an API. Talk to us, and it's only in the last three and a half years. Talk to us about a data privacy vault and what's so unique about it. >> Sure. I think if you think about all the key challenges we are seeing in our personal lives when we are dealing with technology companies a lot of anxiety is around what happens to my data, right? If you want to go to a pharmacy they want to know not just your health ID number but they want to know your social security number your credit card number, your phone number and all of that information is actually useful because they need to be able to engage with you. And it's true for hospitals, health systems. It's true for your bank. It's true for pretty much anybody you do business with even an event like this. But then question that keeps coming up is where does this data go? And how is it protected? And the state of the art here has always been to keep kind of, keep it protected when it's in storage but almost all the breaches, all the hacks happen not because you've steal somebody's disc, but because someone enters through an API or a portal. So the question we asked was we've been building different shapes of containers for different types of data. You don't store your logs in a data warehouse. You don't store your analytical data in a regular RDBMS. Similarly, you don't store your passwords and usernames you store them in identity systems. So if PI is so special why isn't it a container that's used for storing PII? So that's how the idea of Pii.World came up. >> So you guys just got a recent funding, a series B financing which means for the folks out there that don't know the inside baseball, must people do, means you're doing well. It's hard to get that round of funding means you're up and growing to the right. What's the differentiator? Why are you guys so successful? Why the investment growth, what's the momentum driver? >> So I think in some ways we took one of the most complex problems, data privacy, like half the people can't even describe like, does data privacy mean like I have to be GDPR compliant or does it actually mean I'm protecting the data? So you have multiple stakeholders in any company. If you're a pharma company, you may have a chief privacy officer, a data officer, this officer, that officer, and all of these people were talking and the answer was buy more tools. So if you look around behind our back, there's probably dozens of companies out there. One protecting data in an API call another protecting data in a database, another one data warehouse. But as a CEO, CTO, I want to know what happens to my social security number from a customer end to end. So we said, if you can radically simplify the whole thing and the key insight was you can simplify it by actually isolating and protecting this data. And this architecture evolved on its own at companies like Apple and other places, but it takes dozens of engineers for those companies to build it out. So we like, well, the pattern will makes sense. It logically kind is just common sense. So instead of selling dozens of tools, we can just give you a very simple product, which is like one API call, you know, protect this data... >> So like Stripe is for a plugin for a financial transaction you plug it into the app, similar dynamic here, right? >> Exactly. So it's Stripe for payments, Twilio for Telephony. We have API for everything, but if you have social security numbers or pan numbers you still are like relying on DIY. So I think what differentiated us and attracted the investors was, if this works, >> It's huge. every company needs it. >> Well, that's the integration has become the key thing. I got to ask you because you mentioned GDPR and all the complexities around the laws and the different regulations. That could be a real blocker in a wet blanket for innovation. >> Anshu: Yes. >> And with the market we're seeing here at, at your Summit New York, small event. 10,000 people, more people here than were at Snowflake Summit as an example. And they're the hottest company in data. So this small little New York event is proven that that world is growing. So why should this wet blanket, these rules slow it down? How do you balance it? 'Cause that's a concern. If you checking all the boxes you're never actually building anything. >> So, you know, we just ran into a couple of customers who still are struggling with moving from the data center to AWS Cloud. Now the fact that here means they want to but something is holding them back. I also met the AI team of Amazon. They're doing some amazing work and they're like, the biggest hindrance for them is making customers feel safe when they do the machine learning. Because now you're opening up the data sets to more people. And in all of those cases your innovation basically stops because CSO is like, look you can't put PII in the cloud unprotected. And with the vault architecture we call it privacy by architecture. So there's a term called privacy by design. I'm like what the, is privacy by design, right? >> John: It's an architecture. (John laughing) >> But if you are an architecture and a developer like me I was like, I know what architecture is. I don't know what privacy by design is. >> So you guys are basically have that architecture by design which means foundational based services. So you're providing that as a service. So other people don't have to build the complex. >> Anshu: Exactly. >> You know that you will be Apple's backend team to build that privacy with you you get all that benefit. >> Exactly. And traditionally, people have had to make compromises. If you encrypt the data and secure it, then you can't use it. Using a proprietary polymorphic encryption technology you can actually have your cake and eat it to. So what that means for customers is, if you want to protect data in Snowflake or REDshare, use Skyflow with it. We have integrations to databases, to data lakes, all the common workflow tools. >> Can you give us a customer example that you think really articulates the value of what Skyflow is delivering? >> Well, I'll give you two examples. One in the FinTech space, one in the health space. So in the FinTech space this is a company called Nomi Health. They're a large payments processor for the health insurance market. And funnily enough, their CTO actually came from Goldman Sachs. He actually built apple card. (John laughing) Right? That if we all have in our phones. And he saw our product and he's like, for my new company, I'm going to just use you guys because I don't want to go hire 20 engineers. So for them, we had a HIPAA compliant environment a PCI compliant environment, SOC 2 compliant environment. And he can sleep better at night because he doesn't have to worry what is my engineer in Poland or Ukraine doing right now? I have a vault. I have rules set up. I can audit it. Everything is logged. Similarly for Science 37, they run clinical trials globally. They wanted to solve data residency. So for them the problem was, how do I run one common global instance? When the rules say you have to break everything up and that's very expensive. >> And so I love this. I'm a customer. For them a customer. I love it. You had me at hello, API integration. I love it. How much does it cost? What's it going to cost me? How do I need to think about my operationalizing? 'Cause I know with an API, I can do that. Am I paying by the usage, by the drink? How do I figure out? >> So we have programs for startups where it's really really inexpensive. We get them credits. And then for enterprises, we basically have a platform fee. And then based on the amount of data PII, we charge them. We don't nickel and dime the customers. We don't like the usage based model because, you don't know how many times you're going to hit an API. So we usually just based on the number of customer records that you have and you can hit them as many time as you want. There's no API limits. >> So unlimited record based. >> Exactly. that's your variable. >> Exactly. We think about you buying odd zero, for example, for authentication you pay them by the number of active users you have. So something similar. >> So you run on AWS, but you just announced a couple of new GTM partners, MuleSoft and plan. Can you talk to us about, start with MuleSoft? What are you doing and why? And the same with VLA? >> Sure. I mean, MuleSoft was very interesting customers who were adopting our products at, you know, we are buying this product for our new applications but what about our legacy code? We can't go in there and add APIs there. So the simplest way to do integration in the legacy world is to use an integration broker. So that's where MuleSoft integration came out and we announced that. It's a logical place for you to swap out real social security numbers with, you know, fake ones. And then we also announced a partnership with SnowFlake, same thing. I think every workload as it's moving to the cloud needs some kind of data protection with it. So I think going forward we are going to be announcing even more partnerships. So you can imagine all the places you're storing PII today whether it's in a call center solution or analytics solution, there's a PII story there. >> Talk about the integration aspect because I love the momentum. I get everything makes secure the customers all these environments, integrations are super important to plug into. And then how do I essentially operate you on my side? Do I import the records? How do you connect to my environment in my databases? >> So it's really, really easy when you encrypt the data and use Skyflow wall, we create what is called a format preserving token, which is essentially replacing a social security number with something that looks like an SSN but it's not. So that there's no schema changes involved. You just have to do that one time swap over and then in terms of integrations, most of these integrations are prebuilt. So Snowflake integration is prebuilt. MuleSoft integration is prebuilt. We're going to announce some new ones. So the goal is for off the table in platforms like Snowflake and MuleSoft, we prebuilt all the integrations. You can build your own. It takes about like a day. And then in terms of data import basically it's the same standard process that you would use with any other data store. >> Got to ask you about data breaches. Obviously the numbers in 2021 were huge. We're seeing so much change in the cyber security landscape ransomware becoming a household word, a matter of when but not if... How does Skyflow help organizations protect themselves or reduce the number of breaches so that they are not the next headline? >> You know, the funny thing about breaches is again and again, we see people doing the same mistakes, right? So Equifax had a breach four years ago where a customer portal, you know, no customer support rep should have access to a 100 million people's data. Like is that customer agent really accessing 100 million? But because we've been using legacy security tools they either give you access or don't give you access. And that's not how it's going to work. Because if I'm going to engage with the pharmacy and airline they need to be able to use my data in multiple different places. So you need to have fine grain controls around it. So I think the reason we keep getting breaches is cybersecurity industry is selling, 10s of billions of dollars worth of tools in the name of security but they cannot be applied at a fine grain level enough. I can't say things like for my call center agent that's living in Phoenix, Arizona they can only verify last four digits, but the same call center worker in Philippines can't even see that. So how do you get all that granular control in place? Is really why we keep seeing data breaches. So the Equifax breach, the Shopify breach the Twitter breaches, they're all the same. Like again and again, it's either an inside person or an external person who's gotten in. And once you're in and this is the whole idea of zero trust as you know. Once you're in, you can access all the data. Zero trust means that you don't assume that you actually isolate PII separately. >> A lot of the cybersecurity issues as you were talking about, are people based. Somebody clicking on something or gaining access. And I always talk to security experts about how do you control for the people aspect besides training, awareness, education. Is Skyflow a facilitator of that in a way that we haven't seen before? >> Yeah. So I think what ends up happening is, people even after they have breaches, they will lock down the system that had the breach, but then they have the same data sitting in a partner database, maybe a customer database maybe a billing system. So by centralizing and isolating PII in one system you can then post roles based access control rules. You can put limitations around it. But if you try to do that across hundreds of DS bases, you're just not going to be able to do it because it's basically just literally impossible, so... >> My final question for you is on, for me is you're here at AWS Summits, 10,000 people like I said. More people here than some big events and we're just in New York city. Okay. You actually work with AWS. What's next for you guys as you got the fresh funding, you guys looking for more talent, what's your next mountain you're going to climb? Tell us what's next for the company. Share your vision, put a plug in for the company. >> Well, it's actually very simple. Today we actually announced that we have a new chief revenue officer who's joining us. Tammy, she's joined us from LaunchDarkly which is it grew from like, you know, single digits to like over nine digits in revenue. And the reason she's joining Skyflow is because she sees the same inflection point hitting us. And for us that means more marketing, more sales, more growth in more geographies and more partnerships. And we think there's never been a better time to solve privacy. Literally everything that we deal with even things like rove evade issues eventually ties back into a issue around privacy. >> Lisa: Yes. >> AWS gets the model API, you know, come on, right? That's their model. >> Exactly. So I think if you look at the largest best companies that have been built in the last 20 years they took something that should have been simple but was not. There used to be Avayas of the world, selling Telephony intel, Twilio came and said, look an API. And we are trying to do the same to the entire security compliance and privacy industry is to narrow the problem down and solve it once. >> (indistinct) have it. We're going to get theCube API. (Lisa laughing) That's what we're going to do. All right. >> Thank you so much. >> Awesome. Anshu, thank you for joining us, talking to us about what's new at Skyflow. It sounds like you got that big funding investment. Probably lots of strategic innovation about to happen. So you'll have to come back in a few months and maybe at next reinvent in six months and tell us what's new, what's going on. >> Last theCube interview was very well received. People really like the kind of questions you guys asked. So I love this show and I think... >> It's great when you're a star like you, you got good market, great team, smart. I mean, look at this. I mean, what slow down are we talking about here? >> Yeah. I don't see... >> There is no slow down on the enterprise. >> Privacy's hot and it's incredibly important and we're only going to be seeing more and more of it. >> You can talk to any CIO, CSO, CTO or the board and they will tell you there is no limit to the budget they have for solving the core privacy issues. We love that. >> John: So you want to move on to building? >> Lisa: Obviously that must make you smile. >> John: You solved a big problem. >> Thank you. >> Awesome. Anshu, thank you again. Congrats on the momentum and we'll see you next time and hear more on the evolution of Skyflow. Thank you for your time. >> Thank you. >> For John furrier, I'm Lisa Martin. You're watching theCube live from New York City at AWS Summit NYC 22. We'll be right back with our next guest. So stick around. (upbeat music)

>> Hey everyone, welcome to theCUBE's coverage of the AWS startup showcase. This is season two, episode three of our ongoing series featuring AWS and its big ecosystem of partners. This particular season is focused on MarTech, emerging cloud scale customer experiences. I'm your host, Lisa Martin, and I'm pleased to be joined by James Fang, the VP of product marketing at mparticle. James, welcome to the program. Great to have you on. >> Thanks for having me. >> Tell us a little bit about mparticle, what is it that you guys do? >> Sure, so we're mparticle, we were founded in 2013, and essentially we are a customer data platform. What we do is we help brands collect and organize their data. And their data could be coming from web apps, mobile apps, existing data sources like data warehouses, data lakes, et cetera. And we help them help them organize it in a way where they're able to activate that data, whether it's to analyze it further, to gather insights or to target them with relevant messaging, relevant offers. >> What were some of the gaps in the market back then as you mentioned 2013, or even now, that mparticle is really resolving so that customers can really maximize the value of their customer's data. >> Yeah. So the idea of data has actually been around for a while, and you may have heard the buzzword 360 degree view of the customer. The problem is no one has really been actually been able to, to achieve it. And it's actually, some of the leading analysts have called it a myth. Like it's a forever ending kind of cycle. But where we've kind of gone is, first of all customer expectations have really just inflated over the years, right? And part of that was accelerated due to COVID, and the transformation we saw in the last two years, right. Everyone used to, you know, have maybe a digital footprint, as complimentary perhaps to their physical footprint. Nowadays brands are thinking digital first, for obvious reasons. And the data landscape has gotten a lot more complex, right? Brands have multiple experiences, on different screens, right? And, but from the consumer perspective, they want a complete end to end experience, no matter how you're engaging with the brand. And in order to, for a brand to deliver that experience they have to know, how the customers interacted before in each of those channels, and be able to respond in as real time as possible, to those experiences. >> So I can start an interaction on my iPad, maybe carry it through or continue it on my laptop, go to my phone. And you're right, as a, as a consumer, I want the experience across all of those different media to be seamless, to be the same, to be relevant. You talk about the customer 360, as a marketer I know that term well. It's something that so many companies use, interesting that you point out that it's really been, largely until companies like mparticle, a myth. It's one of those things though, that everybody wants to achieve. Whether we're talking about healthcare organization, a retailer, to be able to know everything about a customer so that they can deliver what's increasingly demanded that personalized, relevant experience. How does mparticle fill some of the gaps that have been there in customer 360? And do you say, Hey, we actually deliver a customer 360. >> Yeah, absolutely. So, so the reason it's been a myth is for the most part, data has been- exists either in silos, or it's kind of locked behind this black box that the central data engineering team or sometimes traditionally referred to as IT, has control over, right? So brands are collecting all sorts of data. They have really smart people working on and analyzing it. You know, being able to run data science models, predictive models on it, but the, the marketers and the people who want to draw insights on it are asking how do I get it in, in my hands? So I can use that data for relevant targeting messaging. And that's exactly what mparticle does. We democratize access to that data, by making it accessible in the very tools that the actual business users are are working in. And we do that in real time, you don't have to wait for days to get access to data. And the marketers can even self-service, they're able to for example, build audiences or build computed insights, such as, you know, average order value of a customer within the tool themselves. The other main, the other main thing that mparticle does, is we ensure the quality of that data. We know that activation is only as as good, when you can trust that data, right? When there's no mismatching, you know, first name last names, identities that are duplicated. And so we put a lot of effort, not only in the identity resolution component of our product but also being able to ensure that the consistency of that data when it's being collected meets the standard that you need. >> So give us a, a picture, kind of a topology of a, of a customer data platform. And what are some of the key components that it contains, then I kind of want to get into some of the use cases. >> Yeah. So at, at a core, a lot of customer data platforms look similar. They're responsible first of all for the collection of data, right? And again, that could be from web mobile sources, as well as existing data sources, as well as third party apps, right? For example, you may have e-commerce data in a Shopify, right. Or you may have, you know, a computer model from a, from a warehouse. And then the next thing is to kind of organize it somehow, right? And the most common way to do that is to unify it, using identity resolution into this idea of customer profiles, right. So I can look up everything that Lisa or James has done, their whole historical record. And then the third thing is to be able to kind of be able to draw some insights from that, whether to be able to build an audience membership on top of that, build a predictive model, such as the churn risk model or lifetime value of that customer. And finally is being able to activate that data, so you'll be able to push that data again, to those relevant downstream systems where the business users are actually using that data to, to do their targeting, or to do more interesting things with it. >> So for example, if I go to the next Warrior's game, which I predict they're going to win, and I have like a mobile app of the stadium or the team, how, and I and I'm a season ticket holder, how can a customer data platform give me that personalized experience and help to, yeah, I'd love to kind of get it in that perspective. >> Yeah. So first of all, again, in this modern day and age consumers are engaging with brands from multiple devices, and their attention span, frankly, isn't that long. So I may start off my day, you know, downloading the official warriors app, right. And I may be, you know browsing from my mobile phone, but I could get distracted. I've got to go join a meeting at work, drop off my kids or whatever, right? But later in the day I had in my mind, I may be interested in purchasing tickets or buying that warriors Jersey. So I may return to the website, or even the physical store, right. If, if I happen to be in the area and what the customer data platform is doing in the background, is associating and connecting all those online and offline touchpoints, to that user profile. And then now, I have a mar- so let's say I'm a marker for the golden state warriors. And I see that, you know, this particular user has looked at my website even added to their cart, you know, warriors Jersey. I'm now able to say, Hey, here's a $5 promotional coupon. Also, here's a special, limited edition. We just won, you know, the, the Western conference finals. And you can pre-book, you know, the, you know the warriors championships Jersey, cross your fingers, and target that particular user with that promotion. And it's much more likely because we have that contextual data that that user's going to convert, than just blasting them on a Facebook or something like that. >> Right. Which all of us these days are getting less and less patient with, Is those, those broad blasts through social media and things like that. That was, I love that example. That was a great example. You talked about timing. One of the things I think that we've learned that's in very short supply, in the last couple of years is people's patience and tolerance. We now want things in nanoseconds. So, the ability to glean insights from data and act on it in real time is no longer really a nice to have that's really table stakes for any type of organization. Talk to us about how mparticle facilitates that real time data, from an insights perspective and from an activation standpoint. >> Yeah. You bring up a good point. And this is actually one of the core differentiators of mparticle compared to the other CDPs is that, our architecture from the ground up is built for real time. And the way we do that is, we use essentially a real time streaming architecture backend. Essentially all the data points that we collect and send to those downstream destinations, that happens in milliseconds, right? So the moment that that user, again, like clicks a button or adds something to their shopping cart, or even abandons that shopping cart, that downstream tool, whether it's a marketer, whether it's a business analyst looking at that data for intelligence, they get that data within milliseconds. And our audience computations also happens within seconds. So again, if you're, if you have a targeted list for a targeted campaign, those updates happen in real time. >> You gave an- you ran with the Warrior's example that I threw at you, which I love, absolutely. Talk to me. You must have though, a favorite cu- real world customer example of mparticle's that you think really articulates the value to organizations, whether it's to marketers operators and has some nice, tangible business outcomes. Share with me if you will, a favorite customer story. >> Yeah, definitely one of mine and probably one of the- our most well known's is we were actually behind the scenes of the Whopper jr campaign. So a couple of years ago, Burger King ran this really creative ad where the, effectively their goal was to get their mobile app out, as well as to train, you know, all of us back before COVID days, how to order on our mobile devices and to do things like curbside checkout. None of us really knew how to do that, right. And there was a challenge of course that, no one wants to download another app, right? And most apps get downloaded and get deleted right out away. So they ran this really creative promotion where, if you drove towards a McDonald's, they would actually fire off a text message saying, Hey, how about a Whopper for 99 cents instead? And you would, you would, you would receive a text message personalized just for you. And you'd be able to redeem that at any burger king location. So we were kind of the core infrastructure plumbing the geofencing location data, to partner of ours called radar, which handles you geofencing, and then send it back to a marketing orchestration vendor to be able to fire that targeted message. >> Very cool. I, I, now I'm hungry. You, but there's a fine line there between knowing that, okay, Lisa's driving towards McDonald's let's, you know, target her with an ad for a whopper, in privacy. How do you guys help organizations in any industry balance that? Cause we're seeing more and more privacy regulations popping up all over the world, trying to give consumers the ability to protect either the right to forget about me or don't use my data. >> Yeah. Great question. So the first way I want to respond to that is, mparticle's really at the core of helping brands build their own first party data foundation. And what we mean by that is traditionally, the way that brands have approached marketing is reliant very heavily on second and third party data, right? And most that second-third party data is from the large walled gardens, such as like a Facebook or a TikTok or a Snapchat, right? They're they're literally just saying, Hey find someone that is going to, you know fit our target profile. And that data is from people, all their activity on those apps. But with the first party data strategy, because the brand owns that data, we- we can guarantee that or the brands can guarantee to their customers it's ethically sourced, meaning it's from their consent. And we also help brands have governance policies. So for example, if the user has said, Hey you're allowed to collect my data, because obviously you want to run your business better, but I don't want any my information sold, right? That's something that California recently passed, with CPRA. Then brands can use mparticle data privacy controls to say, Hey, you can pass this data on to their warehouses and analytics platforms, but don't pass it to a platform like Facebook, which potentially could resell that data. >> Got it, Okay. So you really help put sort of the, the reigns on and allow those customers to make those decisions, which I know the mass community appreciates. I do want to talk about data quality. You talked about that a little bit, you know, and and data is the lifeblood of an organization, if it can really extract value from it and act on it. But how do you help organizations maintain the quality of data so that what they can do, is actually deliver what the end user customer, whether it's a somebody buying something on a, on a eCommerce site or or, a patient at a hospital, get what they need. >> Yeah. So on the data quality front, first of all I want to highlight kind of our strengths and differentiation in identity resolution. So we, we run a completely deterministic algorithm, but it's actually fully customizable by the customer depending on their needs. So for a lot of other customer data providers, platform providers out there, they do offer identity resolution, but it's almost like a black box. You don't know what happens. And they could be doing a lot of fuzzy matching, right. Which is, you know, probabilistic or predictive. And the problem with that is, let's say, you know, Lisa your email changed over the years and CDP platform may match you with someone that's completely not you. And now all of a sudden you're getting ads that completely don't fit you, or worse yet that brand is violating privacy laws, because your personal data is is being used to target another user, which which obviously should not, should not happen, right? So because we're giving our customers complete control, it's not a black box, it's transparent. And they have the ability to customize it, such as they can specify what identifiers matter more to them, whether they want to match on email address first. They might've drawn on a more high confidence identifier like a, a hash credit card number or even a customer ID. They have that choice. The second part about ensuring data quality is we act actually built in schema management. So as those events are being collected you could say that, for example, when when it's a add to cart event, I require the item color. I require the size. Let's say it's a fashion apparel. I require the size of it and the type of apparel, right? And if, if data comes in with missing fields, or perhaps with fields that don't match the expectation, let's say you're expecting small, medium, large and you get a Q, you know Q is meaningless data, right? We can then enforce that and flag that as a data quality violation and brands can complete correct that mistake to make sure again, all the data that's flowing through is, is of value to them. >> That's the most important part is, is to make sure that the data has value to the organization, and of course value to whoever it is on the other side, the, the end user side. Where should customers start, in terms of working with you guys, do you recommend customers buy an all in one marketing suite? The best, you know, build a tech stack of best of breed? What are some of those things that you recommend for folks who are going, all right, We, maybe we have a CDP it's been under delivering. We can't really deliver that customer 360, mparticle, help us out. >> Yeah, absolutely. Well, the best part about mparticle is you can kind of deploy it in phases, right. So if you're coming from a world where you've deployed a, all in one marketing suite, like a sales force in Adobe, but you're looking to maybe modernize pieces of a platform mparticle can absolutely help with that initial step. So let again, let's say all you want to do is modernize your event collection. Well, we can absolutely, as a first step, for example, you can instrument us. You can collect all your data from your web and mobile apps in real time, and we can pipe to your existing, you know Adobe campaign manager, Salesforce, marketing cloud. And later down the line, let's say, you say I want to, you know, modernize my analytics platform. I'm tired of using Adobe analytics. You can swap that out, right again with an mparticle place, a marketer can or essentially any business user can flip the switch. And within the mparticle interface, simply disconnect their existing tool and connect a new tool with a couple of button clicks and bam, the data's now flowing into the new tool. So it mparticle really, because we kind of sit in the middle of all these tools and we have over 300 productized prebuilt integrations allows you to move away from kind of a locked in, you know a strategy where you're committed to a vendor a hundred percent to more of a best of breed, agile strategy. >> And where can customers that are interested, go what's your good and market strategy? How does that involve AWS? Where can folks go and actually get and test out this technology? >> Yeah. So first of all, we are we are AWS, a preferred partner. and we have a couple of productized integrations with AWS. The most obvious one is for example, being able to just export data to AWS, whether it's Redshift or an S3 or a kinesis stream, but we also have productized integrations with AWS, personalized. For example, you can take events, feed em to personalize and personalize will come up with the next best kind of content recommendation or the next best offer available for the customer. And mparticle can ingest that data back and you can use that for personalized targeting. In fact, Amazon personalize is what amazon.com themselves use to populate the recommended for use section on their page. So brands could essentially do the same. They could have a recommended for you carousel using Amazon technology but using mparticle to move the data back and forth to, to populate that. And then on top of that very, very soon we'll be also launching a marketplace kind of entry. So if you are a AWS customer and you have credits left over or you just want to transact through AWS, then you'll have that option available as well. >> Coming soon to the AWS marketplace. James, thank you so much for joining me talking about mparticle, how you guys are really revolutionizing the customer data platform and allowing organizations and many industries to really extract value from customer data and use it wisely. We appreciate your insights and your time. >> Thank you very much, Lisa >> For James Fang, I'm Lisa Martin. You're watching theCube's coverage of the AWS startup showcase season three, season two episode three, leave it right here for more great coverage on theCube, the leader in live tech coverage.

>> Narrator: The cube presents, the Kubecon and Cloudnativecon Europe, 2022 brought to you by Red Hat, the cloud native computing foundation and its ecosystem partners. >> Welcome to Valencia, Spain in Kubecon and Cloudnativecon Europe, 2022. I'm your host Keith Townsend. It's been a amazing day, three days of coverage 7,500 people, 170 sponsors, a good mix of end user organizations, vendors, just people with open source at large. I've loved the conversations. We're not going to stop that coverage just because this is the last session of the conference. Colin Murphy, senior software engineer, Adobe, >> Adobe. >> Oh, wow. This is going to be fun. And then Liam Randall, the chair of CNCF Cloud Native WebAssembly Day. >> That's correct. >> And CNCF & CEO of Cosmonic. >> That's right. >> All right. First off, let's talk about the show. How has this been different than other, if at all of other Kubecons? >> Well, first I think we all have to do a tremendous round of applause, not only for the vendors, but the CNC staff and all the attendees for coming out. And you have to say, Kubecon is back. The online experiences have been awesome but this was the first one, where Hallwaycon was in full effect. And you had the opportunity to sit down and meet with so many intelligent and inspiring peers and really have a chance to learn about all the exciting innovations that have happened over the last year. >> Colin. >> Yeah, it's been my most enjoyable Kubecon I've ever been to. And I've been to a bunch of them over the last few years. Just the quality of people. The problems that we're solving right now, everywhere from this newer stuff that we're talking about today with WebAssembly but then all these big enterprises trying to getting involved in Kubernetes >> Colin, to your point about the problems that we're solving, in many ways the pandemic has dramatically accelerated the pace of innovation, especially inside the CNCF, which is by far the most critical repository of open source projects that enterprises, governments and individuals rely on around the world, in order to deliver new experiences and to have coped and scaled out within the pandemic over the last few years. >> Yeah, I'm getting this feel, this vibe of the overall show that feels like we're on the cuff for something. There's other shows throughout the year, that's more vendor focused that talk about cloud native. But I think this is going to be the industry conference where we're just getting together and talking about it and it's going to probably be, in the next couple of years, the biggest conference of the year, that's just my personal opinion. >> I actually really strongly agree with you. And I think that the reason for that is the diversity that we get from the open source focus of Kubecon Kubecon has started where the industry really started which was in shared community projects. And I was the executive at Capital One that led the donation of cloud custodian into the CNCF. And I've started and put many projects here. And one of the reasons that you do that is so that you can build real scalable communities, Vendors that oftentimes even have competing interest but it gives us a place where we can truly collaborate where we can set aside our personal agendas and our company's agendas. And we can focus on the problems at hand. And how do we really raise the bar for technology for everybody. >> Now you two are representing a project that, you know as we look at kind of, how the web has evolved the past few decades, there's standards, there's things that we know that work, there's things that we know that don't work and we're beyond cloud native, we're kind of resistant to change. Funny enough. >> That's right. >> So WebAssembly, talk to me about what problem is WebAssembly solving that need solving? >> I think it's fitting that here on the last day of Kubecon, we're starting with the newest standard for the web and for background, there's only four languages that make up what we think of as the modern web. There's JavaScript, there's HTML, there's CSS, and now there's a new idea that's WebAssembly. And it's maybe not a new idea but it's certainly a new standard, that's got massive adoption and acceleration. WebAssembly is best thought of as almost like a portable little virtual machine. And like a lot of great ideas like JavaScript, it was originally designed to bring new experiences to browsers everywhere. And as organizations looked at the portability and security value props that come from this tiny little virtual machine, it's made a wonderful addition to backend servers and as a platform for portability to bring solutions all the way out to the edge. >> So what are some of the business cases for WebAssembly? Like what problem, what business problem are we solving? >> So it, you know, we would not have been able to bring Photoshop to the web without WASM. >> Wow. >> And just to be clear, I had nothing to do with that effort. So I want to make sure everybody understands, but if you have a lot of C++ or C code and you want to bring that experience to the web browser which is a great cost savings, cause it's running on the client's machines, really low latency, high performance experiences in the browser, WASM, really the only way to go. >> So I'm getting hints of fruit berry, Java. >> Liam: Yeah, absolutely. >> Colin: Definitely. >> You know, the look, WebAssembly sounds similar to promises you've heard before, right ones, run anywhere. The difference is, is that WebAssembly is not driven by any one particular vendor. So there's no one vendor that's trying to bring a plug in to every single device. WebAssembly was a recognition, much like Kubecon, the point that we started with around the diversity of thought ideas and representation of shared interest, of how do we have a platform that's polyglot? Many people can bring languages to it, and solutions that we can share and then build from there. And it is unlocking some of the most amazing and innovative experiences, both on the web backend servers and all the way to the edge. Because WebAssembly is a tiny little virtual machine that runs everywhere. Adobe's leadership is absolutely incredible with the things that they're doing with WebAssembly. They did this awesome blog post with the Google Chrome team that talked about other performance improvements that were brought into Chrome and other browsers, in order to enable that kind of experience. >> So I get the general concept of WebAssembly and it's one of those things that I have to ask the question, and I appreciate that Adobe uses it but without the community, I mean, I've dedicated some of my team's resources over the years to some really cool projects and products that just died on the buying cause there was no community around. >> Yeah. >> Who else uses WebAssembly? >> Yeah, I think so. We actually, inside the CNCF now, have an entire day devoted just to WebAssembly and as the co-chair of the CNCF Cloud Native WebAssembly Day, we really focus on bringing those case studies to the forefront. So some of the more interesting talks that we had here and at some of the precursor weekend conferences were from BMW, for example, they talked about how they were excited about not only WebAssembly, but a framework that they use on WebAssembly called WASM cloud, that lets them a flexibly scale machine learning models from their own edge, in their own vehicles through to their developer's workstations and even take that data onto their regular cloud Kubernetes and scale analysis and analytics. They invested and they just released a machine learning framework for one of the many great WebAssembly projects called WASM cloud, which is a CNCF project, a member project here in the CNCF. >> So how does that fit in overall landscape? >> So think of WebAssembly, like you think of HTML. It's a technology that gives you a lot of concept and to accelerate your journey on those technologies, people create frameworks. For example, if you were going to write a UI, you would not very likely start with an empty document you'd start with a react or view. And in a similar vein, if you were going to start a new microservice or backend application, project for WebAssembly, you might use WASM cloud or you might use ATMO or you might use a Spin. Those are three different types of projects. They all have their own different value props and their own different opinions that they bring to them. But the point is is that this is a quickly evolving space and it's going to dramatically change the type of experiences that we bring, not only to web browsers but to servers and edges everywhere. >> So Colin, you mentioned C+ >> Colin: Yeah. >> And other coding. Well , talk to me about the ramp up. >> Oh, well, so, yeah, so, C++ there was a lot of work done in scripting, at Adobe. Taking our C++ code and bringing it into the browser. A lot of new instructions, Cimdi, that were brought to make a really powerful experience, but what's new now is the server side aspect of things. So, just what kind of, what Liam was talking about. Now we can run this stuff in the data center. It's not just for people's browsers anymore. And then we can also bring it out to the edge too, which is a new space that we can take advantage of really almost only through WebAssembly and some JavaScript. >> So wait, let me get this kind of under hook. Before, if I wanted a rich experience, I have to run a heavy VDI instance on the back end so that I'm basically getting remote desktop calls from a light thin client back to my backend server, that's heavy. >> That is heavy. >> WebAssembly is alternative to that? >> Yes, absolutely. Think of WebAssembly as a tiny little CPU that is a shim, that we can take the places that don't even traditionally have a concept of a processor. So inside the browser, for example, traditionally cloud native development on the backend has been dominated by things like Docker and Docker is a wonderful technology and Container is a wonderful technology that really drove the last 10 years of cloud native with the great lift and shift, if you will. Take our existing applications, package them up in this virtual desktop and then deliver them. But to deliver the next 10 years of experiences, we need solutions that let us have portability first and a security model that's portable across the entire landscape. So this isn't just browsers and servers on the back end, WebAssembly creates an a layer of equality from truly edge to edge. It's can transcend different CPUs, different operating systems. So where containers have this lower bound off you need to be running Linux and you need to be in a place where you're going to bring Kubernetes. WebAssembly is so small and portable, it transcends that lower bound. It can go to places like iOS. It can go to places like web browsers. It can even go to teeny tiny CPUs that don't even traditionally have a full on operating systems inside them. >> Colin: Right, places where you can't run Docker. >> So as I think about that, and I'm a developer and I'm running my back end and I'm running whatever web stack that I want, how does this work? Like, how do I get started with it? >> Well, there's some great stuff Liam already mentioned with WASM cloud and Frmion Spin. Microsoft is heavily involved now on providing cloud products that can take advantage of WebAssembly. So we've got a lot of languages, new languages coming in.net and Ruby, Rust is a big one, TinyGo, really just a lot of places to get involved. A lot of places to get started. >> At the highest level Finton Ryan, when he was at Gartner, he's a really well known analyst. He wrote something profound a few years ago. He said, WebAssembly is the one technology, You don't need a strategy to adopt. >> Mm. >> Because frankly you're already using it because there's so many wonderful experiences and products that are out there, like what Adobe's doing. This virtual CPU is not just a platform to run on cloud native and to build applications towards the edge. You can embed this virtual CPU inside of applications. So cases where you would want to allow your users to customize an application or to extend functionality. Give you an example, Shopify is a big believer in WebAssembly because while their platform covers, two standard deviations or 80% of the use cases, they have a wonderful marketplace of extensions that folks can use in order to customize the checkout process or apply specialized discounts or integrate into a partner ecosystem. So when you think about the requirements for those scenarios, they line up to the same requirements that we have in browsers and servers. I want real security. I want portability. I want reuseability. And ultimately I want to save money and go faster. So organizations everywhere should take a few minutes and do a heads up and think about one, where WebAssembly is already in their environment, inside of places like Envoy and Istio, some of the most popular projects in the cloud native ecosystem, outside of Kubernetes. And they should perhaps consider studying, how WebAssembly can help them to transform the experiences that they're delivering for their customers. This may be the last day of Kubecon, but this is certainly not the last time we're going to be talking about WebAssembly, I'll tell you that. >> So, last question, we've talked a lot about how to get started. How about day two, when I'm thinking about performance troubleshooting and ensuring clients have a great experience what's day two operation like? >> That's a really good question. So there's, I know that each language kind of brings their own tool chain and their, and you know we saw some great stuff on, on WASM day. You can look it up around the .net experience for debugging, They really tried to make it as seamless and the same as it was for native code. So, yeah, I think that's a great question. I mean, right now it's still trying to figure out server side, It's still, as Liam said, a shifting landscape. But we've got some great stuff out here already >> You know, I'd make an even bigger call than that. When I think about the last 20 years as computing has evolved, we've continued to move through these epics of tech that were dominated by a key abstraction. Think about the rise of virtualization with VMware and the transition to the cloud. The rise of containerization, we virtualized to OS. The rise of Kubernetes and CNCF itself, where we virtualize cloud APIs. I firmly believe that WebAssembly represents the next epic of tech. So I think that day two WebAssembly continues to become one of the dominant themes, not only across cloud native but across the entire technical computing landscape. And it represents a fundamentally gigantic opportunity for organizations such as Adobe, that are always market leading and at the cutting edge of tech, to bring new experiences to their customers and for vendors to bring new platforms and tools to companies that want to execute on that opportunity. >> Colin Murphy, Liam Randall, I want to thank you for joining the Cube at Kubecon Cloudnativecon 2022. I'm now having a JavaScript based app that I want to re-look at, and maybe re-platforming that to WebAssembly. It's some lot of good stuff there. We want to thank you for tuning in to our coverage of Kubecon Cloudnativecon. And we want to thank the organization for hosting us, here from Valencia, Spain. I'm Keith Townsend, and you're watching the Cube, the leader in high tech coverage. (bright music)

(upbeat music) >> Hi, everyone welcome back to theCUBE's main stage coverage of DockerCon 2022. We got a great guest from Intel here, Ajay Mungara Senior Director of Edge Software and AI at Intel talking about cloud native and AI workloads at The Edge and building a better developer ecosystem for The Edge which we all know those where the actions going cloud native, compute data, data as code. These are things we've been talking about, so Ajay, welcome to theCUBE. >> Thank you, John. I'm really happy to be here in DockerCon and everything we do Docker makes it better. >> Well, you guys have done a lot in your career and looking at your background, The Edge was manufacturing the old school IOT stuff. Now that's converged completely in with cloud native IP technologies. Everything's kind of happening now at The Edge. This is where the problems are now shifting in solving because of the goodness of the cloud and what that's done for cloud operations which essentially distributed computing is making The Edge the battleground for where the innovation's happening. Could you just share with us your view of why The Edge is so important and why it's different than what we've been seeing in pure cloud on and on premise data centers? >> Yeah, you know 75% of the data that is getting generated of late is happening at The Edge. Okay, so there's a lot of value, there's a lot of value that's getting generated at The Edge because most of the compute we want to move it where closest to the data because of latency issues, bandwidth issues, security issues all of those things is getting people to move compute storage data towards more at The Edge. There's also one big shift from a developer point of view where 51% of all of the developers in the world have deployed in somewhere the other cloud native Docker based solutions out there, okay. What we are seeing is the combination of cloud computing, networking, edge computing all of that coming together. And that is where it is pushing the envelope from The Edge perspective. And one of the big drivers is AI at The Edge as well, right. The Edge inference workloads that is really happening with camera as one of the sensors is really driving that compute. And your question about what's so different about it. The challenges at The Edge are compounded because it's bringing together the operational technology, the information technology processes and cloud computing environments along with networking all together. So when a developer wants to build a solution for The Edge they have to figure out what part of that workload sits in the cloud, how they're going to move that workload towards The Edge using some form of networking. How are they going to protect the data in transport as well as at rest, because Edge devices can get stolen, you know. So there is all of these challenges about like how do you like figure out the trade offs between price, performance, functionality, power, heat, size, weight everything matters when you talk about The Edge. So anyway, that is why we see those differences. >> It's interesting you know you do a little go back in history and distribute computing, the movies still the same. Remember back in the day when I was breaking into the business memory was the bottleneck and storage was the resource. And you had to swap out memory, and as a developer you had to deal with that. Then memory became abundant and storage was the problem. Now you got networking is the latency problem. So again, these are a challenges that developers have to weave through, I was going to ask the question of why is The Edge important for the and what's in it for the developer, why should they care about The Edge? And I think what you were saying is there's design decisions going on around how to code, can you elaborate on what's in it for the developer? Why should they care about The Edge? >> Developers have to really care about The Edge is because when you are really building a solution you cannot move the data and make all the decisions at the cloud because it's late, right, sometimes latency, your bandwidth costs, your solution costs are going to get increased. And because of security and privacy concerns sometimes you have to make those decisions at The Edge itself. You will have to figure out only take the data strategically to the cloud where it makes sense, okay. And that is the reason why developers have no choice but they have to focus on the combination of cloud networking and edge, and that's where we are seeing a large scale set of deployments that are happening today. >> Yeah, and I can see the business value too which is one of the big themes that DockerCon this year is tracks on that people talking about that. Are you seeing trends like headless retail, which is basically, it's not Shopify managed service, it's more of you build your own stack and you put the head on there which is the application and business model. >> Right. >> So again, that's an example. There's also the manufacturing, there's automotive all kinds of use cases where there's money making opportunities, right. So there's business value there, so the developer's going to be pulled to The Edge 'cause they're in the front lines now. So this is about making The Edge ready, and I want to hear your thoughts on what Intel's doing to make that developer environment ready for The Edge because we know the developer on the front lines today and that front line vanguard will be The Edge. What's it look like? >> Exactly, right, so what we have done is we have created this environment for developers which we call it as IntelDevCloud. And in this dev cloud which is Kubernetes based environment where we support all of the Docker workloads and it's based off of Red Hat OpenShift. And we thought about this a little differently. What we did is it's a cloud environment where you could use a browser to do all of your development build test and all of that. But we also took a whole range of these edge devices and we made it available in the cloud. So as a developer, you don't have to have an edge device sitting at your desk. You have an edge device or a plethora of edge devices sitting in the cloud. So you have one environment where you have cloud, you have network, and you have all these edge nodes. So you could start building your solution, you could start building your cloud native or edge native solutions, test it, benchmark it, and figure out how and what type of combination that you actually need for your final solution as you said in retail, in smart cities, in healthcare, any of these vertical markets and get your solution closer to being a deployment ready. >> Yeah, and I love your description by the way it's called a container playground. I mean, it's just comes across as fun. And I think this idea of having these nodes available you guys bring a lot of expertise at the table. That's almost like your local host for Edge devices, right? You can work with it in a safe environment, am I getting that right. >> You're getting that right, and in fact, during the pandemic when we are all working remote, right, nobody has access to these labs where you have all these Edge devices available to you, you could actually play with all these network simulators everything. Now with dev all these developers spread all over the world, you don't have access to as many of those edge devices. So now with browser, with this container playground, you could develop any of your Docker composed, Docker based container workloads and try it on all of these edge devices which may range from an Intel's point of view, CPUs, VPUs, GPUS, anything, right. >> We know there's a lot of compute at The Edge which always ever helps in Intel but your north star is about making it easier for the developers as you guys invest cloud network and The Edge and the cloud native world, that's the goal. How do you do that? And what should the developers optimize for it sounds like they're going to learn with this playground that you have the dev cloud. What are you seeing that they're going to learn to optimize for? Is it like I use the oldest school example of memory optimization, swapping memory out and that kind of thing but what's the new issues that need to be optimized for your developer. >> If you're a developer you got to optimize for your edge AI workloads, right, so that means AI inference workloads. You have to look at like saying that how can I take like a model that is developed in a some type of a cloud environment, like a TensorFlow model or a Pieto model, bring it down to The Edge. And then you have to do inference workloads. You need to understand to do this inference, what type of compute you need, what type of storage do you need? What type of memory do you need? And we give you those options where you could optimize those type of inference AI, inference workloads, you could actually do that. Then you also can decide like what type of decisions you want to make at The Edge what decisions you want to make at the cloud. We give you those options and flexibility for you to build those solutions. >> Great. >> One last point I'll make is there's a lot of legacy applications that have been developed which is traditional embedded applications. We are also want to teach developers how to take these applications and containerize them. How to take advantage of the cloud native DevOps type of paradigms that it would make your life easier when it comes to scaling your solution, deploying your solution worldwide. >> All right, Ajay, thanks so much for coming on theCUBE DevCloud, a container playground. Now back to you at the main stage at DockerCon. (upbeat music)

>> Announcer: TheCUBE presents Dell Technologies World brought to you by Dell. >> Welcome back to theCUBE's coverage, day one, Dell Technologies World live from Las Vegas at the Venetian. Lisa Martin here with Dave Vellante and John Furrier. Guys let's talk, first of all, first time back in person since Dell Tech World 2019. Lots going on, lots of news today. I'm going to start with you, Dave, since you're closest to me. What are some of the things that have impressed you at this first in-person event in three years? >> Well, the first thing I want to say is, so John and I, we started theCUBE in 2010, John, right? In Boston, EMC World. Now of course, Dell owns EMC, so wow. It's good to be back here. Dell's built this beautiful set. I'd say the number one thing that's surprised me was how many people were here. Airport was packed, cab lines, the line at the Palazzo, the hotel, to get in was, you know, probably an hour long. And there's, I thought there'd be maybe 5,000 people here. I would say it's closer to eight. So the hall was packed today and everybody was pumped. Michael Dell was so happy to be up on stage. He talked, I dunno if you guys saw his keynote. He basically talked, obviously how great it is to be back, but he talked about their mission, building technologies that enable that better human condition. There was a big, you know, chewy words, right? And then they got into, you know, all the cool stuff they're doing so we can get into it. But they had CVS up on stage, they had USAA on stage. A big theme was trust. Which of course, if you're Dell, you know, you want people to trust you. I guess the other thing is this is the first live event they've had since the VMware spin. >> Right. >> So in 2019 they owned VMware. VMware's no longer a part of the income statement. Dell had a ton of debt back then. Now Dell's balance sheet looks actually better than VMware's because they restructured everything. And so it's a world without VMware where now with VMware their gross margins were in the 30-plus percent range. Now they're down to 20%. So we're now asking what's next for Dell? And they stood up on stage, we can talk about it some more, but a lot of multi-cloud, a lot of cyber resilience, obviously big themes around APEX, you know, hybrid work, John. So, well let's get into that. >> What are some of the key things that you heard today? >> Well, first of all, the customers on stage are always great. Dell's Technologies, 10 years for theCUBE and their history. I saw something back here, 25 years with celebrating precision, the history of Michael Dell's journey and the current Dell Technologies with EMC folded in and a little bit of VMware DNA still in there even though they're separated out. Just has a loyal set of customers. And you roam the hallways here, you see a lot of people know Dell, love Dell. Michael Dell himself was proud to talk before the event about he's number one, Dave, in PC market share. That's been his goal to beat HP for years. (laughing) And so he's got that done. But they're transforming their business cause they have to, the data center is now cloud. Cloud is now the distributed computing. Dell has all the piece parts today. We've covered this three years ago. Now it's turned into multi-cloud, which is multi-vendor, as a service is how the consumers consume, innovate with data, that's kind of the raw material. Future of work, and obviously the partners that they have. So I think Dell is going to continue to maintain the news of being the great in the front lines as a data-center-slash-enterprise, now cloud, Edge player. So, you know, I'm impressed with their constant reinvention of the company and the news hits all the cards: Snowflake partnership, cutting edge company in the cloud, partnership with Snowflake, APEX, their product that's innovating at the Edge, this new kind of product that's going to bring it together. Unifying, all those themes, Dave, are all hitting the marks. >> Chuck Whitten up on stage, obviously he was the multicloud, you know, conversation. And I think the vision that they they're laying out and Jeff Clarke talked about it as well, is a term that John and I coined. We can't remember who coined it, John or me, "supercloud." >> Yeah. (laughing) >> And they're talking about building an abstraction layer, building on top of the clouds, connecting on-prem to the clouds, across clouds, out to the Edge, hiding the underlying complexity, Dell managing all that. That's their vision. It's aspirational today but that really is supercloud. And it's more than multi-cloud. >> You coined the term supercloud. >> Did I? >> We riffed together. I called it sub-cloud. >> Oh, that's right. And then I said, no, it's got to float over. Super! Superman flies. (John laughs) Right, that's right. >> Sub-cloud, not really a good name. Nobody wants to be sub of anything. >> I think my kid gave it to me, John, actually. (laughing) >> Well if we do know that Michael Dell watches theCUBE, he's been on theCUBE many times. He watches theCUBE, clearly he's paying attention! >> Yeah, well I hope so. I mean, we write a lot about this and we talk to a lot of customers and talk to a lot of people. But let's talk about the announcements if we can. So... The APEX cyber recovery service, you know, ransomware recovery. They're now also running that on AWS and Azure. So that's big. We heard Presidio, they was super thrilled about that. So they're... The thing I'd say about that is, you know, Dell used to be really defensive about cloud. Now I think they're leaning in. They're saying, "Hey we're not going to spend, you know, Charles Fitzgerald, the snarky guy, does some good work on CAPEX. I mean, you look at how much the cloud guys are spending on CAPEX a year, $30, $40 billion. >> They can't compete. >> On cloud CAPEX. Dell doesn't want compete. >> John: You can't compete. >> Build on top of that, so that's a gift. So that's cool. You mentioned the Snowflake announcement. I thought that was big. What that is... It's very interesting, so Frank Slootman has always said, "We're not doing a half-way house, we're in the cloud." Okay, so square that circle for me. Now Snowflake's coming on-prem. Well, yeah, what they're doing is allowing customers to keep data in a Dell object store, ECS or other object stores. But use Snowflake. So non-native Snowflake data on-prem. So that expands Snowflake cloud. What it also does is give Dell a little sizzle, a little better partner and there's a path to cloud migration if that's where the customers want to go. >> Well, I mean, I would say that that's a dangerous game because we've seen that movie before, VMware and AWS. >> Yeah but that we've talked about this. Don't you think that was the right move for VMware? >> At the time, but if you don't nurture the relationship AWS will take all those customers, ultimately, from VMware. >> But that product's still doing very well. We'll see with NetApp is another one. NetApp on AWS. I forget what they call it, but yeah, file and AWS. So that was, go ahead. >> I was just going to say, what's the impact of Snowflake? Why do you think Snowflake chose Dell? >> Because Dell's a $101 billion company and they have a huge distribution channel and a lot of common customers. >> They own storage on the premise. >> Yep. And so Snowflake's looking for, you know, storage options on which they can, you know, bring data into their cloud. Snowflake wants the data to go from on-prem into the cloud. There's no question about that. >> And I would add another thing, is that Snowflake can't do what Dell Technologies does on-premises with storage and Dell can't do what Snowflake's doing. So I think it's a mutual short-term and medium-term benefit to say, "Hey you want to run on Snowflake? You need some services there? Great, but come back and use Dell." So that to me, I think that's a win-win for Snowflake. Just the dangerous game is, whoever can develop the higher-level services in the cloud will ultimately be the winner. >> But I think the thing I would say there is, as I said, Snowflake would love for the migration to occur, but they realize it's not always going to happen. And so why not partner with a company like Dell, you know, start that pipeline. And for Dell, hey, you know, why fight fashion, as Jeremy Burton would say. The other thing was Project Alpine, which is file, block and object across cloud. That's again setting up this supercloud. And then APEX. I mean, APEX is the discussion. We had a one-on-one session, a bunch of analysts with Jeff Woodrow who runs ISG. We were supposed to be talking about ISG, all we talked about is APEX. Then we had another session with APEX and all we talked about, of course, is APEX. So, they're still figuring that out, I would say, at this point. They don't quite have product market fit and I think they'd admit that, but they're working hard on scaling engineering, trying to figure out the channel model, the compensation. You know, taking their time even, but moving fast if you know what I mean. >> I mean, Dave, I think the big trend that's jumping out of me here is that, something that we've been covering, the headless cloud, meaning if you can do as a service, which is one of Dell's major points today, that to me, everyone is a PaaS layer. I think everyone that's building digital transformation apps has to be their own SaaS. So they either do that with somebody, a man in service, which fits beautifully into that trend, or do it own. Now e-commerce has this nailed down. Shopify or build your own on top of the cloud. So headless retail's a hot trend. You're going to start to see that come into the enterprise where the enterprise can have their cake and eat it too and take advantage of managed services where they don't have expertise. So those two things right there I think is going to drive a lot of growth for Dell. >> So essentially Lisa, what Dell is doing is saying, "Okay, the timing's good with the VMware spin." They say, "Now we're going to build our own cloud as a service, APEX." And they're starting with infrastructure as a service, you know, storage as a service. Obviously cyber recovery is a service. So you're going to get compute and storage and data protection. Eventually they'll move into other areas. And it's really important for them to do that to have their own cloud, but they've got to build up the ecosystem. Snowflake is a small example. My view, they need hundreds and hundreds of Snowflakes to fill the gaps, you know, move up the stack in middleware and database and DevOps. I mean, they should be partnering with HashiCorp. They should be partnering with all these companies that do DevOps stuff. They should be... I'd like to see them, frankly, partner with competitors to their data protection group. Why, you know, sounds crazy, but if you're going to build a cloud, look at AWS. They partner with everybody, right? And so that's what a true cloud experience looks like. You've got this huge menu. And so I think Dell's going to have to try to differentiate from HP. HPE was first, right, and they're all in. Dell's saying we're going to let the customers tell us where to go. And so they, I think one differentiation is their ecosystem, their ability to build that ecosystem. Yeah, but HP's got a good distribution channel too. Just not as big as Dell's. >> They all got the assets in it, but they're transforming. So I think at the end of the day, as Dell and even HPE transforms, they got to solve the customer problems and reduce the complexity. So again, the managed services piece with APEX is huge. I think having the building blocks for multi hybrid cloud at the Edge, just, you can't go wrong with that. If the customers can deploy it and consume it. >> What were some of the messages that you heard from, you mentioned CVS on stage, USAA on stage. Dell's always been very, very customer-focused. They've got some great brands. What did you hear from that customer's voice that shows you they're going in the right direction? >> Well first of all, the customers are longstanding customers of Dell Technologies, so that's one recognition of the ongoing partnerships. But they're also messaged up with Dell's messaging, right? They're telling the Dell story. And what I heard from the Dell story was moving fast and reducing complexity is their number one goal. They see the cloud option has to be there. Cloud native, Edge came up a little bit and the role of data. So I think all the new application development today that's relevant has a data as code kind of concept. Data engineering is the hottest skillset on the planet right now. And data engineering is not data science. So you start to see top-level CSOs and CIOs saying the new modern applications have to have data embedded in. It's just too hard. It's too hard to find that engineering team. So I heard the customer saying, we love the direction, we love the managed services. And by the way, we want to have that supply chain and cyber risk reduced. So yeah, big endorsement for Dell. >> You know, the biggest transformation in Dell, the two biggest transformations. One was the financials. You know, the income statement is totaled at a $101 billion company, growing at 17% a year. That's actually quite remarkable. But the flip side of that, the other big transformation was the customer. And with the acquisition of EMC but specifically VMware, it changed the whole conversation for Dell with customers. I think pre-2015, you wouldn't have had that type of narrative up on stage with customers. Cause it was, you know, compellant and it was equal logic and it was small businesses. Now you're talking about really deep strategic relationships that were enabled by that transformation. So my point is, to answer your question, it's going to be really interesting to see what happens post-VMware because when VMware came together with Dell, the industry didn't like it. The VMware ecosystem was like (growls) Dell. Okay, but customers loved it, right? And that's one of the things I heard on stage today. They didn't say, oh, well we love the VMware. But he mentioned VMware, the CTO from USAA. So Dell configured this commercial agreement with VMware, Michael Dell's the chairman of both companies. So that was part of the incentive. The other incentive is Dell is the number one distribution channel for VMware. So I think they now have that muscle memory in place where they've earned that trust. And I think that will continue on past the spin. It was actually quite brilliant the way they've orchestrated that. >> Yeah, Lisa, one more thing I want to add to that is that what I heard also was, you got the classic "here's how you be a leader in the modern era." It's a big leadership message. But then when you heard some of the notes, software-defined, multi-cloud with an emphasis on operations, Dave. So, okay, if you're a good leader, stay with Dell in operations. So you see strategy and operations kind of coming together around cloud. But big software defined multi-cloud data operational story. And I think those customers are kind of on that. You know, you got to maintain your operations. DevOps is operations, DevSecOps is operations. So big, like, don't get too greedy on the modern, shiny new toy, you know, in the cloud. >> Yeah, it's a safe bet, right? For infrastructure. I mean, HPE is a good bet too, but I mean Dell's got a way broader portfolio, bigger supply chain. It's got the end-to-end with the desktop, laptop, you know, the client side business, you know, a bigger services organization. And now the big challenge in my mind for Dell is okay, what's next? And I think they got to get into data management, obviously build up as a service, build up their cloud. They need software in their portfolio. I mean, you know, 20% gross margin company, it just, Wall Street's not as interested. You know, if they want to build more value, which they do, they've got to get more into software and I think you're going to see that. Again, I think you're going to see more M&A. I'd love to see more organic R&D instead of stock buybacks but I get why they have to do that. >> Well one of the things I'm looking at, Dave, in terms of what I think the future impact's going to be is the generational shift with the gen-Z and millennials running IT in the modern era. Not your old school rack-and-stack data center mentality. And then ultimately the scoreboard will determine, in my mind, the winner in their race is, where are the workloads running? Right? The workloads, and then also what's the application development scene look like? What do the apps look like? What are they building on? What's scaling them, what's running them? And the Edge is going to be a big part of that. So to me, operations, Edge, workloads and the development and then the workforce shift. >> And I do think Edge, I'm glad you brought up Edge. Edge is, you know, so fragmented but I think there's going to be a massive opportunity in Edge. There's going to be so much compute at the Edge. Dell talked about it, so much data. It's unclear to me right now how they go after that other than in pockets, like we heard from Gill. I believe they're going to do really well in retail. No question there. >> Yeah. >> But there's so much other industrial aisle IT- >> The telco space of towers, Edge. >> And Dell's, you know, Dell's server business, eh okay, it's got Intel and AMD inside, okay great. Their high margins come from storage, not from compute. Not the case with AWS. AWS had 35% operating margins last quarter. Oracle and Microsoft, that's the level that they're at. And I'd love to see Dell figure out a way to get paid more for their compute expertise. And that's going to take some R&D. >> John: Yeah, yeah. >> Last question guys, as we wrap up our wrap of day one. Given everything that we've all been through the last couple of years, what is your overall summary of what Dell announced today? The vibe of the show? How well have they fared the last two years? >> Well, I mean, they had a remarkable last two years. In a large part thanks to the client business. I think today you're seeing, you know, them lift the veil on what's next. And I think their story is coherent. There's, again, financially, they're a much more sound company, much better balance sheet. Not the most attractive income statement from a margin standpoint and they got work to do there. But wow, as far as driving revenue, they know how to sell. >> Yeah, I mean to me, I think looking back to before the pandemic, when we were here on the stage last, we were talking end-to-end, Dell leadership. And I say the biggest thing is Dell's catching up fast, faster than I thought. And I think they got, they're skating to where the puck is going, Dave, and I'll tell you why. The end-to-end I thought wouldn't be a total flyer if the Edge got too dynamic, but the fact that the Edge is growing so fast, it's more complex, that's actually given Dell more time. So to me, what I see happening is Dell having that extra time to nail the Edge piece, cause if they get there, if they get there, then they'll have their core competency. And why do I say that? Cause hardware is back. Server god boxes are going to be back. You're going to see servers at the Edge. And look at the failure of Amazon's Outpost, okay? Amazon's Outpost was essentially hardware. That's Dell's business. So you talk about like compute as a cloud but they really didn't do well with deploying compute like Dell does with servers. EKS is kicking ass at the Edge. So serverless with hardware, I think, is going to be the killer solution at the Edge. A combination of cloud and Edge hardware. And the Edge looks more like a data center than the cloud looks like the data center, so- >> So you're saying hardware matters? >> HardwareMatters.com. >> I think that's what I heard. >> HardwareMatters.com, check out that site, coming soon. (all laughing) >> I think it matters more than ever, you know- >> Blockchain, silicon advances. >> I think reason hardware matters is cause it's barbelling. It's going from the box to the silicon and it's going, you know, upstream into software defined. >> Horizontally, scalability means good silicon at the Edge, under the cover, scaling all the stuff and machine learning and AI in the application. So we've said this on theCUBE now, what, five years now? >> Dave: Yeah, yep. >> Guys, we've got an action packed night tonight. Two days tomorrow and Wednesday. Michael Dell is on tomorrow. Chuck Whitten is on, Jeff Clarke, et cetera, et cetera. Caitlin Gordon is on Wednesday. >> All the heavy hitters are coming on. >> They're coming on, they're going to be... >> Dave: Allison Dew's coming on. >> Allison Dew's coming on. >> We're going to talk about the Matthew McConaughey interview, which was, I thought, fantastic. J.J. Davis is coming on. So we're going to have a great channel discussion, as well, with Cheryl Cook. >> That's right. >> A lot of the product people are coming on. We're going to be talking APEX, it's going to be good. With cyber recovery, the Storage Alchemist is coming on, John! (all laughing) >> Boy, I can't wait to see that one. >> Well stick around guys for our coverage all day tomorrow, Tuesday and Wednesday. Lisa Martin with Dave Vellante and John Furrier coming to you live from the Venetian in Las Vegas. This is Dell Technologies World 2022. We look forward to seeing you tomorrow and the next day. (bouncy, upbeat music)

(upbeat music) >> Welcome, everyone, to theCUBE's presentation of the AWS Startup Showcase. And this episode two of season two is called "Data as Code," the ongoing series covering exciting new startups in the AWS ecosystem. I'm John Furrier, your host of theCUBE. Today, we're excited to be joined by Jon Dahl, who is the co-founder and CEO of MUX, a hot new startup building cloud video for developers, video with data. John, great to see you. We did an interview on theCube Conversation. Went into big detail of the awesomeness of your company and the trend that you're on. Welcome back. >> Thank you, glad to be here. >> So, video is everywhere, and video for pivot to video, you hear all these kind of terms in the industry, but now more than ever, video is everywhere and people are building with it, and it's becoming part of the developer experience in applications. So people have to stand up video into their code fast, and data is code, video is data. So you guys are specializing this. Take us through that dynamic. >> Yeah, so video clearly is a growing part of how people are building applications. We see a lot of trends of categories that did not involve video in the past making a major move towards video. I think what Peloton did five years ago to the world of fitness, that was not really a big category. Now video fitness is a huge thing. Video in education, video in business settings, video in a lot of places. I think Marc Andreessen famously said, "Software is eating the world" as a pretty, pretty good indicator of what the internet is actually doing to the economy. I think there's a lot of ways in which video right now is eating software. So categories that we're not video first are becoming video first. And that's what we help with. >> It's not obvious to like most software developers when they think about video, video industries, it's industry shows around video, NAB, others. People know, the video folks know what's going on in video, but when you start to bring it mainstream, it becomes an expectation in the apps. And it's not that easy, it's almost a provision video is hard for a developer 'cause you got to know the full, I guess, stack of video. That's like low level and then kind of just basic high level, just play something. So, in between, this is a media stack kind of dynamic. Can you talk about how hard it is to build video for developers? How is it going to become easier? >> Yeah, I mean, I've lived this story for too long, maybe 13 years now, when I first build my first video stack. And, you know, I'll sometimes say, I think it's kind of a miracle every time a video plays on the internet because the internet is not a medium designed for video. It's been hijacked by video, video is 70% of internet traffic today in an unreliable, sort of untrusted network space, which is totally different than how television used to work or cable or things like that. So yeah, so video is hard because there's so many problems from top to bottom that need to be solved to make video work. So you have to worry about video compression encoding, which is a complicated topic in itself. You have to worry about delivering video around the world at scale, delivering it at low cost, at low latency, with good performance, you have to worry about devices and how every device, Android, iOS, web, TVs, every device handles video differently and so there's a lot of work there. And at the end of the day, these are kind of unofficial standards that everyone's using. So one of the miracles is like, if you want to watch a video, somehow you have to get like Apple and Google to agree on things, which is not always easy. And so there's just so many layers of complexity that are behind it. I think one way to think about it is, if you want to put an image online, you just put an image online. And if you want to put video online, you build complex software, and that's the exact problem that MUX was started to help solve. >> It's interesting you guys have almost creating a whole new category around video infrastructure. And as you look at, you mentioned stack, video stack. I'm looking at a market where the notion of a media stack is developing, and you're seeing these verticals having similar dynamics with cloud. And if you go back to the early days of cloud computing, what was the developer experience or entrepreneurial experience, you had to actually do a lot of stuff before you even do anything, provision a server. And this has all kind of been covered in great detail in the glory of Agile and whatnot. It was expensive, and you had that actually engineer before you could even stand up any code. Now you got video that same thing's happening. So the developers have two choices, go do a bunch of stuff complex, building their own infrastructure, which is like building a data center, or lean in on MUX and say, "Hey, thank you for doing all that years of experience building out the stacks to take that hard part away," but using APIs that they have. This is a developer focused problem that you guys are solving. >> Yeah, that's right. my last company was a company called Zencoder, that was an API to video encoding. So it was kind of an API to a small part of what MUX does today, just one of those problems. And I think the thing that we got right at Zencoder, that we're doing again here at MUX, was building four developers first. So our number one persona is a software developer. Not necessarily a video expert, just we think any developer should be able to build with video. It shouldn't be like, yeah, got to go be a specialist to use this technology, because it should become just of the internet. Video should just be something that any developer can work with. So yeah, so we build for developers first, which means we spend a lot of time thinking about API design, we spend a lot of time thinking about documentation, transparent pricing, the right features, great support and all those kind of things that tend to be characteristics of good developer companies. >> Tell me about the pipe lining of the products. I'm a developer, I work for a company, my boss is putting pressure on me. We need video, we have all this library, it's all stacking up. We hired some people, they left. Where's the video, we've stored it somewhere. I mean, it's a nightmare, right? So I'm like, okay, I'm cloud native, I got an API. I need to get my product to market fast, 'cause that is what Agile developers want. So how do you describe that acceleration for time to market? You mentioned you guys are API first, video first. How do these customers get their product into the market as fast as possible? >> Yeah, well, I mean the first thing we do is we put what we think is probably on average, three to four months of hard engineering work behind a single API call. So if you want to build a video platform, we tell our customers like, "Hey, you can do that." You probably need a team, you probably need video experts on your team so hire them or train them. And then it takes several months just to kind of to get video flowing. One API call at MUX gives you on-demand video or live video that works at scale, works around the world with good performance, good reliability, a rich feature set. So maybe just a couple specific examples, we worked with Robin Hood a few years ago to bring video into their newsfeed, which was hugely successful for them. And they went from talking to us for the first time to a big launch in, I think it was three months, but the actual code time there was like really short. I want to say they had like a proof of concept up and running in a couple days, and then the full launch in three months. Another customer of ours, Bandcamp, I think switched from a legacy provider to MUX in two weeks in band. So one of the big advantages of going a little bit higher in the abstraction layer than just building it yourself is that time to market. >> Talk about this notion of video pipeline 'cause I know I've heard people I talk about, "Hey, I just want to get my product out there. I don't want to get stuck in the weeds on video pipeline." What does that mean for folks that aren't understanding the nuances of video? >> Yeah, I mean, it's all the steps that it takes to publish video. So from ingesting the video, if it's live video from making sure that you have secure, reliable ingest of that live feed potentially around the world to the transcoding, which is we talked a little bit about, but it is a, you know, on its own is a massively complicated problem. And doing that, well, doing that well is hard. Part of the reason it's hard is you really have to know where you're publishing too. And you might want to transcode video differently for different devices, for different types of content. You know, the pipeline typically would also include all of the workflow items you want to do with the video. You want to thumbnail a video, you want clip, create clips of the video, maybe you want to restream the video to Facebook or Twitter or a social platform. You want to archive the video, you want it to be available for downloads after an event. If it's just a, if it's a VOD upload, if it's not live in the first place. You have all those things and you might want to do simulated live with the video. You might want to actually record something and then play it back as a live stream. So, the pipeline Ty typically refers to everything from the ingest of the video to the time that the bits are delivered to a device. >> You know, I hear a lot of people talking about video these days, whether it's events, training, just want peer to peer experience, video is powerful, but customers want to own their own platform, right? They want to have the infrastructure as a service. They kind of want platform as a service, this is cloud talk now, but they want to have their own capability to build it out. This allows them to get what they want. And so you see this, like, is it SaaS? Is it platform? People want customization? So kind of the general purpose video solution does it really exist or doesn't? I mean, 'cause this is the question. Can I just buy software and work or is it going to be customized always? How do you see that? Because this becomes a huge discussion point. Is it a SaaS product or someone's going to make a SaaS product? >> Yeah, so I think one of the most important elements of designing any software, but especially when you get into infrastructure is choosing an abstraction level. So if you think of computing, you can go all the way down to building a data center, you can go all the way down to getting a colo and racking a server like maybe some of us used to do, who are older than others. And that's one way to run a server. On the other extreme, you have just think of the early days of cloud competing, you had app engine, which was a really fantastic, really incredible product. It was one push deploy of, I think Python code, if I remember correctly, and everything just worked. But right in the middle of those, you had EC2, which was, EC2 is basically an API to a server. And it turns out that that abstraction level, not Colo, not the full app engine kind of platform, but the API to virtual server was the right abstraction level for maybe the last 15 years. Maybe now some of the higher level application platforms are doing really well, maybe the needs will shift. But I think that's a little bit of how we think about video. What developers want is an API to video. They don't want an API to the building blocks of video, an API to transcoding, to video storage, to edge caching. They want an API to video. On the other extreme, they don't want a big application that's a drop in white label video in a box like a Shopify kind of thing. Shopify is great, but developers don't want to build on top of Shopify. In the payments world developers want Stripe. And that abstraction level of the API to the actual thing you're getting tends to be the abstraction level that developers want to build on. And the reason for that is, it's the most productive layer to build on. You get maximum flexibility and also maximum velocity when you have that API directly to a function like video. So, we like to tell our customers like you, you own your video when you build on top of MUX, you have full control over everything, how it's stored, when it's stored, where it goes, how it's published, we handle all of the hard technology and we give our customers all of the flexibility in terms of designing their products. >> I want to get back some use case, but you brought that up I might as well just jump to my next point. I'd like you to come back and circle back on some references 'cause I know you have some. You said building on infrastructure that you own, this is a fundamental cloud concept. You mentioned API to a server for the nerds out there that know that that's cool, but the people who aren't super nerdy, that means you're basically got an interface into a server behind the scenes. You're doing the same for video. So, that is a big thing around building services. So what wide range of services can we expect beyond MUX? If I'm going to have an API to video, what could I do possibly? >> What sort of experience could you build? >> Yes, I got a team of developers saying I'm all in API to video, I don't want to do all that transit got straight there, I want to build experiences, video experiences on my app. >> Yeah, I mean, I think, one way to think about it is that, what's the range of key use cases that people do with video? We tend to think about six at MUX, one is kind of the places where the content is, the prop. So one of the things that use video is you can create great video. Think of online courses or fitness or entertainment or news or things like that. That's kind of the first thing everyone thinks of, when you think video, you think Netflix, and that's great. But we see a lot of really interesting uses of video in the world of social media. So customers of ours like Visco, which is an incredible photo sharing application, really for photographers who really care about the craft. And they were able to bring video in and bring that same kind of Visco experience to video using MUX. We think about B2B tools, videos. When you think about it, all video is, is a high bandwidth way of communicating. And so customers are as like HubSpot use video for the marketing platform, for business collaboration, you'll see a lot of growth of video in terms of helping businesses engage their customers or engage with their employees. We see live events obviously have been a massive category over the last few years. You know, we were all forced into a world where we had to do live events two years ago, but I think now we're reemerging into a world where the online part of a conference will be just as important as the in-person component of a conference. So that's another big use case we see. >> Well, full disclosure, if you're watching this live right now, it's being powered by MUX. So shout out, we use MUX on theCUBE platform that you're experiencing in this. Actually in real time, 'cause this is one application, there's many more. So video as code, is data as code is the theme, that's going to bring up the data ops. Video also is code because (laughs) it's just like you said, it's just communicating, but it gets converted to data. So data ops, video ops could be its own new category. What's your reaction to that? >> Yeah, I mean, I think, I have a couple thoughts on that. The first thought is, video is a way that, because the way that companies interact with customers or users, it's really important to have good monitoring and analytics of your video. And so the first product we ever built was actually a product called MUX video, sorry, MUX data, which is the best way to monitor a video platform at scale. So we work with a lot of the big broadcasters, we work with like CBS and Fox Sports and Discovery. We work with big tech companies like Reddit and Vimeo to help them monitor their video. And you just get a huge amount of insight when you look at robust analytics about video delivery that you can use to optimize performance, to make sure that streaming works well globally, especially in hard to reach places or on every device. That's we actually build a MUX data platform first because when we started MUX, we spent time with some of our friends at companies like YouTube and Netflix, and got to know how they use data to power their video platforms. And they do really sophisticated things with data to ensure that their streams well, and we wanted to build the product that would help everyone else do that. So, that's one use. I think the other obvious use is just really understanding what people are doing with their video, who's watching what, what's engaging, those kind of things. >> Yeah, data is definitely there. You guys mentioned some great brands that are working with you guys, and they're doing it because of the developer experience. And I'd like you to explain, if you don't mind, in your words, why is the MUX developer experience so good? What are some of the results you're seeing from your customers? What are they saying to you? Obviously when you win, you get good feedback. What are some of the things that they're saying and what specific develop experiences do they like the best? >> Yeah, I mean, I think that the most gratifying thing about being a startup founder is when your customers like what you're doing. And so we get a lot of this, but it's always, we always pay attention to what customers say. But yeah, people, the number one thing developers say when they think about MUX is that the developer experience is great. I think when they say that, what they mean is two things, first is it's easy to work with, which helps them move faster, software velocity is so important. Every company in the world is investing and wants to move quickly and to build quickly. And so if you can help a team speed up, that's massively valuable. The second thing I think when people like our developer experience is, you know, in a lot of ways that think that we get out of the way and we let them do what they want to do. So well, designed APIs are a key part of that, coming back to abstraction, making sure that you're not forcing customers into decisions that they actually want to make themselves. Like, if our video player only had one design, that that would not be, that would not work for most developers, 'cause developers want to bring their own design and style and workflow and feel to their video. And so, yeah, so I think the way we do that is just think comprehensively about how APIs are designed, think about the workflows that users are trying to accomplish with video, and make sure that we have the right APIs, make sure they're the right information, we have the right webhooks, we have the right SDKs, all of those things in place so that they can build what they want. >> We were just having a conversation on theCUBE, Dave Vellante and I, and our team, and I'd love to get you a reaction to this. And it's more and more, a riff real quick. We're seeing a trend where video as code, data as code, media stack, where you're starting to see the emergence of the media developer, where the application of media looks a lot like kind of software developer, where the app, media as an app. It could be a chat, it could be a peer to peer video, it could be part of an event platform, but with all the recent advances, in UX designers, coders, the front end looks like an emergence of these creators that are essentially media developers for all intent and purpose, they're coding media. What's your reaction to that? How do you see that evolving? >> I think the. >> Or do you agree with it? >> It's okay. >> Yeah, yeah. >> Well, I think a couple things. I think one thing, I think this goes along through saying, but maybe it's disagreement, is that we don't think you should have to be an expert at video or at media to create and produce or create and publish good video, good audio, good images, those kind of things. And so, you know, I think if you look at software overall, I think of 10 years ago, the kind of DevOps movement, where there was kind of a movement away from specialization in software where the same software developer could build and deploy the same software developer maybe could do front end and back end. And we want to bring that to video as well. So you don't have to be a specialist to do it. On the other hand, I do think that investments and tooling, all the way from video creation, which is not our world, but there's a lot of amazing companies out there that are making it easier to produce video, to shoot video, to edit, a lot of interesting innovations there all the way to what we do, which is helping people stream and publish video and video experiences. You know, I think another way about it is, that tool set and companies doing that let anyone be a media developer, which I think is important. >> It's like DevOps turning into low-code, no-code, eventually it's just composability almost like just, you know, "Hey Siri, give me some video." That kind of thing. Final question for you why I got you here, at the end of the day, the decision between a lot of people's build versus buy, "I got to get a developer. Why not just roll my own?" You mentioned data center, "I want to build a data center." So why MUX versus do it yourself? >> Yeah, I mean, part of the reason we started this company is we have a pretty, pretty strong opinion on this. When you think about it, when we started MUX five years ago, six years ago, if you were a developer and you wanted to accept credit cards, if you wanted to bring payment processing into your application, you didn't go build a payment gateway. You just probably used Stripe. And if you wanted to send text messages, you didn't build your own SMS gateway, you probably used Twilio. But if you were a developer and you wanted to stream video, you built your own video gateway, you built your own video application, which was really complex. Like we talked about, you know, probably three, four months of work to get something basic up and running, probably not live video that's probably only on demand video at that point. And you get no benefit by doing it yourself. You're no better than anyone else because you rolled your own video stack. What you get is risk that you might not do a good job, maybe you do worse than your competitors, and you also get distraction where you've just taken, you take 10 engineers and 10 sprints and you apply it to a problem that doesn't actually really give you differentiated value to your users. So we started MUX so that people would not have to do that. It's fine if you want to build your own video platform, once you get to a certain scale, if you can afford a dozen engineers for a VOD platform and you have some really massively differentiated use case, you know, maybe, live is, I don't know, I don't have the rule of thumb, live videos maybe five times harder than on demand video to work with. But you know, in general, like there's such a shortage of software engineers today and software engineers have, frankly, are in such high demand. Like you see what happens in the marketplace and the hiring markets, how competitive it is. You need to use your software team where they're maximally effective, and where they're maximally effective is building differentiation into your products for your customers. And video is just not that, like very few companies actually differentiate on their video technology. So we want to be that team for everyone else. We're 200 people building the absolute best video infrastructure as APIs for developers and making that available to everyone else. >> John, great to have you on with the showcase, love the company, love what you guys do. Video as code, data as code, great stuff. Final plug for the company, for the developers out there and prospects watching for MUX, why should they go to MUX? What are you guys up to? What's the big benefit? >> I mean, first, just check us out. Try try our APIs, read our docs, talk to our support team. We put a lot of work into making our platform the best, you know, as you dig deeper, I think you'd be looking at the performance around, the global performance of what we do, looking at our analytics stack and the insight you get into video streaming. We have an emerging open source video player that's really exciting, and I think is going to be the direction that open source players go for the next decade. And then, you know, we're a quickly growing team. We're 60 people at the beginning of last year. You know, we're one 50 at the beginning of this year, and we're going to a add, we're going to grow really quickly again this year. And this whole team is dedicated to building the best video structure for developers. >> Great job, Jon. Thank you so much for spending the time sharing the story of MUX here on the show, Amazon Startup Showcase season two, episode two, thanks so much. >> Thank you, John. >> Okay, I'm John Furrier, your host of theCUBE. This is season two, episode two, the ongoing series cover the most exciting startups from the AWS Cloud Ecosystem. Talking data analytics here, video cloud, video as a service, video infrastructure, video APIs, hottest thing going on right now, and you're watching it live here on theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> Hey everyone. Welcome back to Las Vegas. The Cube is live. I can't say that enough. We are alive at AWS re:Invent 2021. Lisa Martin with Dave Nicholson. Hey Dave. >> Hey Lisa. >> Having a good day so far. >> So far, so good. >> We have an alumni back with us. We have about a hundred segments on the cube at AWS remit. We've got one of our original alumni back with us. Zaki Bajwa joins us the global head of partner solution engineers at Stripe. Zaki welcome back. >> Thank you, Lisa, thank you, Dave. Pleasure to be here. >> Lisa: Isn't it great to be back in person? >> Love it. Love it. Can't do a whiteboard virtually, you can, it's not the same. >> It's not the same and all those conversations I'm sure that you've had with partners and with customers the last couple of days that you just can't replicate that over zoom. >> Zaki: Exactly. >> So just for anyone who doesn't understand, AWS has a massive ecosystem of partners. So we'll get to talk about Stripe and AWS, but for anyone that doesn't know what Stripe is, give us the lowdown. You guys started 10 years ago. Talk to us about Stripe, the business strategy, what it's like today. >> Yeah, sure. So you guys know Stripe started 10 years ago by two brothers, John and Patrick Collison. And they've really focused on the developer and helping the developers accelerate digital commerce. Why? Cause the status quo at the time was one where a developer needed to, you know, build banking relationships with issuing banks, merchant banks, card networks, payment networks, tax liabilities, data compliance, and all of these manual processes that they had to deal with. So what Stripe aspires to do is build a complete commerce platform. Leveraging our integrated suite of products that is really allowing us to build what we call the global payments and treasury network. So if you think about the global payment and treasury network or what we call the G P T N it's meant to not only help abstract all of that complexity from a global payment infrastructure point of view, but also help move money in a simple and borderless and a programmable way just like we do in the internet. So that's the core essence of Stripe is to build this global payment treasury network to allow for money movement to happen in a simple and borderless manner. >> Simple and borderless two key things there. How has the business strategy evolved in the last 10 years and specifically in the last 20, 22 months? >> Yeah. Great question. So as you can imagine with COVID, you know, David you can order a cup of coffee or a brand new car, and that whole direct to consumer model has accelerated in COVID right. We've accelerated ourselves going to upwards of 6,000 employees. We've been able to answer or manage upwards of 170 billion API requests in the last 12 months alone. Right we deliver upwards of five nines from a availability performance point of view. That means 13 seconds of downtime or less a month. And we're doing this originally starting off for the developer David as you talked about allowing developers to deliver, you know, what I call process payments, accept payments and reconcile payments. But the evolution that you're talking about Lisa has really led to three key areas of focus that our users are requesting from us. And Stripe's first operating principle is really that user first mentality similar to the Amazons where we listen to our users and they're really asking for three key areas of focus. Number one is all around modernizing their digital commerce. So this is big enterprises coming to us and saying, whether I'm a uni lever or a Ford, how do you help me with a direct to consumer a e-commerce type platform? Number one. Secondly, is companies like Deliveroo and Lyft creating what we call marketplaces. Also think about Twitter and clubhouse, more solopreneurs entrepreneurs kind of marketplaces. Third is all around SaaS business models. So think about slack and Atlassian. That are customer vivers and accelerating the journey with us around digitizing digital commerce. So that's the first area of evolution. The second area is all around what we call embedded FinTech. So we know just like Amazon helped accelerate infrastructure as a service, platform as a service and function as a service. We're helping accelerate FinTech as a service. So we believe every company in every industry aspires to add more and more FinTech capabilities in their core services that they offer to their customers. So think about a Shopify or a Lyft they're adding more FinTech capabilities, leveraging Stripe APIs that they offer to their consumers. Likewise, when you think about a Monzo bank or a and 26, what we call Neo banks. They're creating more banking as a service component so a second area of evolution is all around FinTech as a service or embedded FinTech. And the third area of focus again, listen to our users is all around users are saying. Hey, Stripe, you have our financial data. How do you help us more with business operations and automating and optimizing our business operations? So this is revenue management, revenue reconciliation, financial reporting, all of the business processes, you and I know, code to cash, order to cash, pay to procure. Help us automate, optimize, and not just optimize, but help us create net new business models. So these are the three key areas of evolution that we've seen modernizing digital commerce, embedded FinTech, and then certainly last but not least business operations and automating that. >> And your target audience is the developers. Or are you having conversations now that are more, I mean, this is like transformative to industries and disruptive. Are you having conversations higher up in the chain? >> Great, great question. And this is the parallel with Amazon, just like Amazon started with developers, AWS. And then what up to the C-suite, if you will, we're seeing the same exact thing. Obviously our DNA is developer first making it intuitive, natural easy for developers to build on Stripe. But we're seeing more and more C-suite leaders come to us and saying, help us evolve our business model, help us modernize and digitize net new business models to get new revenue streams. So those parallel work streams are both developer mindset and C-suite led is certainly a big evolution for us. And we're looking to learn from our Amazon friends as to the success that they've had there. >> Do you have any examples of projects that developers have proposed that were at first glance, completely outlandish? Something that, you know, is there any sort of corner of the chart use case where Stripe didn't think of it, some developer came up with the idea, maybe it can't be done yet. If you have an example of that, that would be very interesting. >> Yeah, I'll give you two examples. So as I said, we're definitely a user first entity. That's our operating principle. We always think about the user. So let me go to developers and say, what are you struggling with? What are you thinking about? What are the next set of things you need from us? And a simple comment around tax started to come up and do you know in the U S there's 11,000 tax jurisdictions that you and you're selling something online have to abide to these different jurisdictions. So one of the things that we then evolved into is created a Stripe tax product, which initially users or developers were really struggling with and working on. So we created a Stripe tax product. We've done an acquisition called tax jar that helps us accelerate that journey for tax. The other one is this notion of low code that we see in the marketplace right now, where developers saying. Hey, give me more embeddables on top of the primitives that you've created on top of the APIs. So we went leveraging what our customers have already done, created things like a checkout capability, which is a simple redirect highly customized for conversion, which you can just integrate to one API. You have a full checkout capability. You can embed that into your platform, which didn't exist before and needed you to really integrate into different APIs. So all of these capabilities are what developers have really focused on and built that we've done leverage and Excel on. >> Yeah, I think between Lisa and myself, we've paid taxes in about 7,000 of those >> Lisa: Yeah, probably. >> Not 11,000 jurisdictions, but all the various sales taxes and everything else. So we're sort of familiar with it. >> I think so, so here we are, you know, on the floor at re-invent. Great, as we said to be back in person, the 10th annual, but with, as each year goes by AWS has a ecosystem of partners gets bigger and bigger. The flywheel gets, I don't know, I think faster and faster, the number of announcements that came out yesterday and today talk to us about some of the common traits that Stripe and AWS share. >> Yeah. So I've mentioned a few of them. One is certainly the user first mentality where we're listening to users. That tax example is a perfect one of how do we decide new features, new capability based on user first, Amazon does that better than anyone else. Second is that developer mindset focus on the developer. Those will be the core persona we target give you an example, Lyft, we all know Lyft. They wanted to create instant payouts for their drivers. So their developers came to us and say, our developers don't want to get paid. I'm sorry. Our drivers don't want to get paid in a week or two weeks. So we work with their developers who create a instant payout mechanism. Now in six months, over 40% of their drivers are using Stripe instant payout powered by Stripe. And that's a developer first mindset again, back to AWS. And then the third is really around the go to market. And the market opportunity is very similar. You talked about the developer persona and the C-suite very similar to Amazon. But also we're not just catering to enterprise and strategic big customers. We are just so much focused on startups, SMB, mid-market, digital native, just like Amazon is. And I would say the last parallel, which is probably the most important one is innovation. I come from enterprise software where we looked at monthly, quarterly, biannual, annual release cycles. Well, as Stripe, all of that goes out the door just like Amazon. We may have a hundred to a thousand APIs in motion at any time in alpha beta production. And just like Amazon we're iterating and releasing new innovations consistently. So I would say that's probably the most important one that we have with Amazon. >> So a lot of synergies there like deep integrated trusted partner synergies it sounds like. >> Agreed, definitely and then we're seeing this. I was going more as we are going more up market. We're seeing a demand for end to end solutions that require integrations with a CRM vendor for customer 360 with our accounting vendor for pivotal procure order to cash, billing accounting with a e-commerce company like Adobe Magento to do better econ. So more end to end solutions with these tech partners, we're working with our GSI to help deliver those end to end solutions. And certainly, but not least the dev agencies who are still sort of our core constituents that help us keep relevant with those developers. >> You mentioned this at the outset, but some things bear repeating. Can you go into a little more detail on the difference between me wanting to start up a business and take credit cards as payment 10 years ago? Let's say versus today, how much of the friction have you removed from that system? >> It is literally an hour to two hour process versus weeks and months before. >> But what are those steps? Like who would I, you mentioned this, again you mentioned this already, but the go through that, go through that again who would I have to reach out to, to make this happen? And we were talking, you know, relationships with banks, et cetera, et cetera. >> Yeah. So it starts at initiating and registering that company. So imagine you going and having to register a company today, you can do that with a Stripe Atlas product in a matter of hours, get your EIN number, get your tax jurisdictions on your registration as a Delaware entity within the U S you can be anywhere at globally and go do that within a matter of one hour. That's number one, you start there. From there, then it's a matter of embedding payment embeddables within your e-commerce platform, marketplace platform, et cetera. As you've heard us talk about seven lines of code to get payments going, you can quickly onboard accept payments, process payments, reconcile payments all within an hour. And that's just the start. But now you get into more complex use cases around marketplaces and multi-party connection. Multi-party payouts, different commission rates, different subscription models. Think about a flat tier model, a metered tier model, all of these different things that we've abstracted and allow you to just use one to three different integrations to help accelerate and use that in your digital commerce platform. So all of these different workflows have is what we've automated through our APIs. >> Dave: That's unbelievable. >> Yeah. >> It really is. >> It is unbelievable, the amount of automation and innovation that's gone on in such a short time period. What are some of the things as we kind of wrap up here that we can look forward to from stripe from a roadmap perspective, technology wise, partner wise? >> Yes. I mean, we have a slew of data you can imagine billions of billions of transactional data. And you guys know what we do with data is we're looking at fraud prevention. We're looking at, we have a product called radar that looks at fraud, we're doing acceptance, adaptive acceptance to do more AIML learned data and authorization. We're also looking at how do we feed a lot of this financial data into the right mechanisms to allow you to then create new business models on top of this, whether it's cross sell upsell to new user business capture. As well as you know, one of the things I did not talk about, which coming from a farming background is this notion of Stripe climate. Where we have upwards of 2000 companies across 37 countries that are leveraging our Stripe climate product to give back to tech advanced companies that are helping in carbon offset. And super exciting times there from an ESG environmental social governance point of view. So all of those combined is what excites us about the future at Stripe. >> Wow. The future seems unlimited. Lots going on. >> Super excited. Zaki, thank you so much for joining Dave and me talking about what's going on with Stripe. All the innovation that's going on. The synergies with AWS and what's coming down the pipe. We appreciate your insights and your time. >> Thank you, Lisa, thank you, David. Appreciated All right. For Dave Nicholson, I'm Lisa Martin. You're watching the Cube. The global leader in live tech coverage. (lighthearted piano music)

(soft electronic music) >> Welcome to this CUBE Conversation. I'm Lisa Martin. This conversation is part of our third AWS Startup Showcase for this year. I'm pleased to welcome Knox Anderson, the VP of Product Management at Sysdig. Knox, welcome to the program. >> Thanks for having me, Lisa. >> Talk to me a little bit about Sysdig, secure DevOps for containers, Kubernetes, and cloud. Give the audience an overview of what you guys do. >> So Sysdig is this secure DevOps platform that provides observability, security, and compliance functions for anyone that's adopting Kubernetes and Cloud. We really secure the entire lifecycle from source to production, so do things like scan your ISE for misconfiguration, monitor your runtime environments for threats and operational best practices. We provide a lot of capabilities around Prometheus Monitoring, as well, and then also let organizations perform incident response and compliance audits against these environments. >> So founded in 2013, talk to me about the gap in the market that you guys saw then and what some of the key challenges are that you saw for your customers. >> Yeah so we came to market around the same time as containers and Kubernetes and I'd say 2015 to 2018 we kept on saying it's the year of Kubernetes, it's the year of Kubernetes, it's the year of Kubernetes. And then really during the last year and a half in the COVID pandemic, Kubernetes has gone gangbusters. Every major cloud is seeing a huge adoption in their Kubernetes services so that's really our wedge into a lot of organizations. They're changing their platform to take advantages of containers and Kubernetes and you really have to rethink all of your security tooling, and that's when a company like Sysdig comes in. >> Talk to me about customers in terms of, especially in the last year and a half when things have been so dynamic, we've seen so much too, on the threat landscape front changing. Give me an example of a customer or two that you're really helped with solving some of their major challenges, here. >> Yeah, a great customer that we work with is SAP Concur and they kind of encompass a lot of the things that are nice about modern DevOps. So it's a DevOps team that's running a Kubernetes platform that thousands of developers are building their apps and deploying those onto. And they chose Sysdig because really it's not scalable to have every single data team ping that DevOps team and say what's the performance of my service, how is it responding, how can I get scanning integrated with that and so they use Sysdig as a platform that allows developers to easily onboard onto their Kubernetes clusters and then ensure that they're meeting compliance needs and FedRAMP needs for that platform that they deliver their core business apps on. >> Let's talk about the Sysdig's commitment to opensource on the Falco project. >> So Falco is a opensource project that we started at Sysdig, it's built on top of our core system core instrumentation. And so Falco meets a lot of your IDS or your file integrity monitoring requirements that you might have as you move to Kubernetes. And really, it's something we started at about 2016. In 2019, we donated that project to the CMCS which is the same governance body behind Kubernetes, Prometheus, and other kind of core building blocks of the climate of ecosystem. Since then, it's grown immensely. Companies like Shopify are using it to make sure that their PCI apps that they run Kubernetes are fully compliant. And so it's something that we are constantly contributing to the community also from even companies like AWS is a core contributor to the Falco project. And I'm really excited to see where it goes over the next year as Falco extends to also cover some cloud security use cases. >> What can you tell me about the relationship that Sysdig and AWS have? >> They've been a great partner. We internally run our SaaS on AWS so we're using AWS services to deliver our product to our customers. And then we've also really worked closely around how you can provide better security for services like Fargate. So we did working sessions with their engineering teams, learned what we could do to get the visibility that we need for tools like Falco and Sysdig to work seamlessly in Fargate environments. And last April we were able to kind of, AWS released that new functionality, Sysdig built on top of that, and we've already seen great adoption of customers using the Sysdig product on top of Fargate. >> Excellent. Well thank you very much, Knox, for stopping by theCUBE telling us about Sysdig, what you guys are doing ahead of the AWS Startup Showcase. We appreciate your time and your information. >> Thanks for having me. >> For Knox Anderson, I'm Lisa Martin. You're watching this CUBE Conversation. (soft electronic music)

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a NetApp storage, right? So workloads are going to shift from the business applications, and these business applications, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SaaS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud then come compute, Kubernetes. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a ETAP storage, right? So workloads are going to shift from the business applications, and this business App again, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SAS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud and encompass compute whoever it is. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante snowflake's eye-popping ipo this week has the industry buzzing we have had dozens and dozens of inbound pr from firms trying to hook us offering perspectives on the snowflake ipo so they can pitch us on their latest and greatest product people are pumped and why not an event like this doesn't happen very often hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll give you our take on the snowflake ipo and address the many questions that we've been getting on the topic i'm also going to discuss at the end of this segment an angle for getting in on the ground floor and investments which is not for the faint of heart but it's something that i believe is worth talking about now let's first talk about the hottest ipo in software industry history first i want to say congratulations to the many people at snowflake you know the big hitters yeah they're all the news slootman mooglia spicer buffett benioff even scarpelli interestingly you know you don't hear much about the founders they're quite humble and we're going to talk about that in some future episodes but they created snowflake they had the vision and the smarts to bring in operators that could get the company to this point so awesome for them but you know i'm especially happy for the rank and file and the many snowflake people where an event like this it really can be life-changing versus the billionaires on the leaderboard so fantastic for you okay but let's get into the madness as you know by now snowflake ipod at a price of 120. now unless you knew a guy he paid around 245 at the open that's if you got in otherwise you bought at a higher price so you kind of just held your nose and made the trade i guess you know but snowflakes value it went from 33 billion to more than 80 billion in a matter of minutes now there's a lot of finger pointing going on this is this issue that people are claiming that it was underpriced and snowflake left four billion dollars on the table please stop that's just crazy to me snowflakes balance sheet is in great shape thanks to this offering and you know i'm not sure jamming later stage investors even more would have been the right thing to do this was a small float i think it was around 10 percent of the company so you would expect a sharp uptick on day one i had predicted a doubling to a 66 billion dollar valuation and it ended up around 70. now the big question that we now get is is this a fair valuation and can snowflake grow into its value we'll address this in more detail but the short answer is snowflake is overvalued in my opinion right now but it can grow into its valuation and of course as always they're going to be challenges now the other comment we get is yeah but the company is losing tons of money and i say no kidding that's why they're so valuable we've been saying for years that the street right now is rewarding growth because they understand that to compete in software you need to have massive scale so i'm not worried in the least about snowflakes bottom line not yet eventually i'm going to pay much closer attention to operating cash flow but right now i want to see growth i want to see them grow into their valuation now the other common question we get is should i buy when should i buy what are the risks and can snowflake compete with the biggest cloud vendors i'll say this before we get into it and i've said before look it's it's very rare that you're not going to get better buying opportunities than day one of an ipo and i think in this case you will i remember back in 2015 it was i think it was the first calendar for quarter and servicenow missed its earnings and the stock got hit and we had the opportunity to interview frank slootman then ceo of servicenow right after that and i think it's instructive to hear what he said let's listen roll the clip well yeah i think that a lot of the high-flying cloud companies and obviously we're one of them you know we're we're priced to perfection right um and that's that's not an easy place to be for uh for for anybody and you know we're not really focused on that it's it's this is a marathon you know every quarter is one mile marker you can't get too excited about you know one versus the other we're really pacing ourselves we're building you know an enterprise that's going to be here for for a long time you know and after that we saw the stock drop as low as 50 today servicenow is a 450 stock so my point is that snowflake like servicenow is going to be priced to perfection and there will be bumps in the road possibly macro factors or other and if you're a believer you'll have opportunities to get in so be patient now finally i'm going to make some comments later but i'll give you the bumper sticker right now i mean i calculated the weighted average price that the insiders paid on the the s1 that they paid for snowflake and it came out to around six dollars a share and i heard somebody say on tv it was five dollars but my weighted average math got me to six dollars regardless on day one of the ipo the insiders made a 50x return on their investment if you bought on day one you're probably losing some money or maybe about even and there are some ground floor opportunities that exist that are complicated and may be risky but if you're young and motivated or older and have some time to research i think you'll be interested in what i have to say later on all right let's compare snowflake to some other companies on a valuation basis this ought to be interesting so this chart shows some high flyers as compared to snowflake we show the company the trailing 12-month revenue the market cap at the close of the 16th which is the day that snowflake ipod and then we calculate and sort the data on the revenue multiple of the trailing 12 months and the last column is the year-on-year growth rate of the last quarter and i used trailing 12 months because it's simple and it's easy to understand and it makes the revenue multiple bigger so it's more dramatic and many prefer to use a forward revenue uh but that's why i put the growth rate there you can pick your own projected revenue growth and and do the math yourself so let's start with snowflake 400 million dollars in revenue and that's based on a newish pricing model of consumption not a sas subscription that locks you in for a year or two years or three years i love this model because it's true cloud and i've talked about it a while so for a while so i'm not going to dwell on it today but you can see the trailing 12-month revenue multiple is massive and the growth rate is 120 which is very very impressive for a company this size zoom we put zoom in the chart just because why not and and the growth grade is sick so so who knows how that correlates to the revenue multiple but as you can see snowflake actually tops the zoom frothiness on that metric now maybe zoom is undervalued i should take that back let's see i think crowdstrike is really interesting here and as a company that we've been following and talking about quite a bit in my last security breaking analysis they were at a 65 x trailing 12-month revenue multiple and you see how that's jumped since they reported and they beat expectations but they're similar in size to snowflake with a slower growth rate in a lower revenue multiple so there's some correlation between that growth rate and the revenue multiple sort of now snowflake pulled back on day two it was down early uh this morning as you would expect with both the market being off and maybe some profit taking you know if you got in an allocation at 120 why not take some profits and play with house money so snowflake's value is hovering today it actually bounced back is hovering today you're just under 70 billion and that that brings the revenue multiple down a bit but it's still very elevated now if you project 2x growth let's say 100 for next year and the stock stays in some kind of range which i think it likely will you could see snowflake coming down to crowdstrike revenue multiples in 12 months it'll depend of course on snowflakes earnings reports which i'm sure are going to beat estimates for the next several quarters and if if it's growing faster than these others at that time it should command a premium you know wherever the market prices market's going to go up it's going to go down but we'll look at all these companies i think on a relative basis snowflakes still should command a premium at higher growth rates so you can see also in this chart you've got shopify awesome mongodb twilio servicenow and their respective growth rates shopify incredibly impressive [ __ ] and twilio as well servicenow is like the old dog in this mix so that's kind of interesting now the other big question we get is can snowflake grow in to its valuation this is a chart we shared with you a bit ago and it talks to snowflake's total available market and its expansion opportunity there tam expansion this is something we saw slootman execute at servicenow when everybody underestimated that company's value and i'll briefly explain here look snowflake is disrupting the traditional data warehouse and data lake markets data lake spending is relatively small it's under 2 billion but data lakes they're inexpensive and that's what made them attractive the edw market however the enterprise data warehouse market is it's much much larger now traditional edws they're they're big they're slow they're cumbersome they're expensive and they're complicated but they've been operationalized and are critical for companies reporting and basic analytics but they've failed to live up to their promise of the 360 degree view of the customer and real-time analytics you know i had a customer tell me a while ago that my data warehouse it's like a snake swallowing a basketball he gave me example where a change in a regulation this was a financial company it would occur and it would force a change in the data model in their data warehouse and they'd have to ingest all this new data and the data warehouse choked and every time intel came out with a new processor they'd rush out they'd throw more compute at the problem he called this chasing the chips now what snowflake did was to envision a cloud native world where you could bring compute to massive data volumes on an elastic basis and only pay for what you use sounds so simple but technically snowflakes founders and those innovations of that innovation of separating compute from storage to leverage the flexibility of the cloud it really was profound and clearly based on this week's performance was the right call now i'll come back to this in a bit now where we think snowflake is going is to build a data cloud and and you can see this in the chart where your data can be ingested and accessed to perform near real-time analytics with machine learning and ai and snowflake's advantage as we've discussed in the past is that it runs on any cloud and it can ingest data from a variety of sources now there are some challenges here we're not saying that snowflake is going to participate in all these use cases that we show however with its resources now we expect snowflake to create new capabilities organically and then do tuck-in acquisitions that will allow it to attack many more more use cases in adjacent markets and so you look at this chart and the third layer if that's 60 billion it means snowflake needs to extend into the fourth layer because its valuation is already over 60 billion it's not going to get 100 market share so we call this next layer automated decision making this is where real time analytics and systems are making decisions for humans and acting in real time now clearly data is going to be a pretty critical part of this equation now at this point it's unclear that snowflake has the capability to go after this space as much of the data in this area is probably going to live at the edge but snowflake is betting on becoming a data data layer across clouds and presumably at the edge and as you can see this market is enormous so there's no lack of tam in our view for snowflakes that brings us to the other big question around competition everybody's talking about this look a lot of the investment thesis behind snowflakes snowflake is that slootman and his army including cfo mike scarpelli and what they did at servicenow will be repeated scarpelli is this operational guru he keeps the engine running you know with very very tight controls and you know what it's a pretty good bet snoopman and scarpelli and their team i'm not denying that but i will tell you that snowflake's competition is much more capable than what servicenow faced in its early days now here's a picture of some of the key competitors this is one of our favorites the xy graph and on the vertical axis is net score or spending momentum that is etr's version of velocity based on their quarterly surveys now i'm showing july survey october is in the works it's in the field as i speak on the horizontal axis is market share or pervasiveness in the data set so it's a proxy for market share it's it's based on mentions not dollars and and that's why microsoft is so far to the right because they're huge and they're everywhere and they get a lot of mentions the more relevant data to us is the position of snowflake it remains one of the highest net scores in the entire etr survey based not just the database sector aw aws is its biggest competitor because most of snowflake's business runs on aws but google bigquery you can see there is is technically the most capable relative to snowflake because it's a true cloud native database built from the ground up whereas aws took a database that was built for on-prem par excel and brilliantly really made it work in the cloud by re-architecting many of the pieces but it still has legacy parts to it now here's oracle oracle's huge it's slow growth overall but it's making investments in r d we've talked about that a lot and that's going to allow it to hold on to its customers huge base and you can see teradata and cloud era cloudera is a proxy for data lakes which are low cost as i said and cloudera which acquired hortonworks is credited with the commercialization of that whole big datum and hadoop movement and then teradata is in there as well which of course they've been around forever now there are a zillion other database players we've heard a lot of them from a lot of them this week is on that inbound pr that i talked about but these are the ones that we wanted to focus on today the bottom line is we expect snowflakes vertical axis spending momentum to remain elevated and we think it will continue to steadily move to the right now let's drill into this data a bit more here we break down the components of etr's net score and this is specifically for snowflake over time now remember lime green is new adoptions the forest green is spending more relative to last year than more five percent more uh than last year or or greater gray is flat spending the pink is less spending and the bright red is we're leaving the platform the line up top that's netscore which subtracts the red from the green is an indicator of spending velocity the yellow line at the bottom is market market share or pervasiveness in the survey based on mentions now note the the blue text there that's etr's number one takeaway on snowflake two h-20 spending intentions on snowflake continue to trend robustly mostly characterized by high customer acquisition and expansion rates new adoptions market share among all customers is simultaneously growing impressive let's now look at snowflake against the competition in fortune 500 customers now here we show net score or again spending momentum over time for some of the key competitors and you can see snowflakes net score has actually increased since the april survey again this is the july survey this was taken the april survey was taken at the height of the us lockdown so snowflake's net score is actually higher in the fortune 500 than it was overall which is a good proxy for spend because fortune 500 spends more google mongodb and microsoft also also show meaningful momentum growth since the april survey you know notably aws has come off its elevated levels from last october and april it's still strong but that's something that we're going to continue to watch finally let's look at snowflakes market share or pervasiveness within the big three cloud vendors again this is a cut on the fortune 500 and you can see there are 125 respondents within the big three cloud and the fortune 500 and 21 snowflake respondents within that base of 125 and you can see the steady and consistent growth of share not huge ends but enough to give some confidence in the data now again note the etr callout but this trend is occurring despite the fact that each of the big three cloud vendors has its own competitive offering okay but i want to stress this is not a layup for snowflake as i've said this is not servicenow part two it's a different situation so let's talk about that look the competition here is not bmc which was servicenow's target as much as i love the folks at bmc we're talking here about aws microsoft and google amazon with redshift is dialed into this i've said often that they have copycatted snowflake in many cases and last fall at re invent we heard andy jassy make a big deal about separating compute from storage and he took a kind of a swipe at snowflake without mentioning them by name but let's listen to what andy jassy had had to say and then we'll come back and talk about it play the clip then what we did is because we have nitro like i was talking about earlier we built unique instances that have very fast bandwidth so that if you actually need some of those data from s3 for a query it moves much faster than if you just had to leave it there with without that high speed bandwidth instance and so with ra3s you get to separate your storage from your compute if it turns out by the way on your local ssds that you're not using all the ssd on that local ssd you only pay for what you use so a pretty significant enhancement for customers using redshift at the same time if you think about the prevailing way that people are thinking about separating storage from compute letting people scale separately that way as well as how you're going to do this large-scale compute where you move the storage to the a bunch of awaiting compute nodes there are some issues with this that you got to think about the first is think about how much data you're going to have at the scale that we're at but then just fast forward a few years think about how much data you're going to actually have to move over the network to get to the compute and we so look first of all jassy is awesome he stands up at these events for like reinvent for two hours and it connects trends and business to technology he's got a very deep understanding of the tech he's amazing however what aws has done in separating compute and storage is good but it's not as elegant architecturally as snowflake aws essentially has tiered the storage off the cluster to lower the overall costs but you really you can't turn off the compute completely with snowflake they've truly separated compute and storage and the reason is that redshift is great but it's built on an on-prem architecture that was originally an on-prem architecture that they had to redo so when jassy talks about moving the data to compute what he's really saying is our architecture is such that we had to do this workaround which is actually quite clever but this whole narrative about the prevailing ways to separate compute from storage that's snowflake and moving the data's use the word data's plural to the compute it really doesn't apply to snowflake because they'll just move the compute to the data thank you hadoop for that profound concept now does this mean snowflake is going to cakewalk over redshift not at all aws is going to continue to innovate so snowflake had better keep moving fast multi-cloud new workloads adjacent markets tam expansion etc etc etc microsoft they're huge but as usual there's not a lot to say you know about them they're everywhere they put out 1.0 products they eventually get them right because with their heft they get mulligans that they turn into pars or birdies but i think snowflake is going to bring some innovations to azure and that they're going to get good traction there in my opinion now google bigquery is interesting by all accounts it gets very high technical marks google's playing the long game and i would expect that snowflake is going to have a harder time competing in google cloud than it does within aws and what i'm predicting for azure but we'll see the last point here is that many are talking about the convergence of analytic and operational and transaction databases and the thinking is this doesn't necessarily bode well for specialists like snowflake and i would say a couple of things here first is that while it's definitely true you're not seeing snowflake positioning today as responding at the point of transaction to say for instance influence and order in real time and this may have implications at the edge it's going to have a lot of real-time inferencing but we've learned there are a lot of ways to skin a cat and we see integration layers and innovative approaches emerging in the cloud that could address this gap and present opportunities for snowflake now the other thing i'd say is you know maybe that thinking misses something altogether with the idea of snowflake in that third data layer that we showed you in our tam chart that data as a service layer or data cloud which is maybe a giant opportunity that they are uniquely positioned to address because they're cloud agnostic they've got the vision and they've got the architecture to allow them to very simply ingest data and then serve it up to businesses nonetheless we're going to see this battle continue between what i've often talked about these integrated suites and converged databases in the case of oracle converged pipelines in the case of the cloud guys versus the best of breed players like snowflake we talk about this all the time and there really isn't one single answer it's really horses for courses and customer preferences okay well you know i know you've been waiting for for me to tell you about the angles on ground floor investing and you probably think this is going to be crazy but bear with me and i got to caution you this is a bit tongue-in-cheek and it's one big buyer beware but as i said the insiders on snowflake had a 50x return on day one you probably didn't so i want to talk about the confluence of software engineering crypto cryptography and game theory powered by the underlying value of blockchain and we're talking here about innovations around a new internet in a distributed web or d-web where many distributed computers come together to form one computer that guarantees trust between two or more users for a variety of use cases not just financial store like bitcoin but that too and the motivation behind this is the fact that a small number of companies say five or six today control the internet and have essentially co-opted the major protocols like tcp http smtp pop3 etc etc and these people that we're showing here on this chart they're working on these new innovations there are many of them but i just name a few here olaf carlson we he started poly chain capital to invest in core infrastructure around these new computing paradigms this gentleman mark nadal is someone who's working on new d apps tim berners-lee who invented the internet he's got a project called solid at mit and it emphasizes data ownership and privacy and of course satoshi got it all started when she invented bitcoin and created the notion of fractional shares and by the way the folks at andreessen horowitz are actively making bets in this space so you know maybe this is not so crazy but here's the premise if you're a little guy and you wanted to invest in snowflake you couldn't until late in the game if you wanted to invest in the lamp stack directly in the late 90s there was no way to do that you had to wait for red hat to go public or to get a piece of the linux action but in this world that we're talking about here there are opportunities that are not mainstream and often they're based yes on cryptocurrencies again it's dangerous there are scams and and losers but if you do your homework there are actually vehicles for you to get in on the ground floor and you know some of these innovations are going to take off you could get a 50x or 100 bagger but you have to do your research and there's no guarantee that these innovations are going to be able to take on the big internet giants but there are people really smart technologists and software engineers that are young they're mission driven and they're forming a collective voice against a dystopian future because they want to level the playing field on the internet and this may be the disruptive force that challenges today's giants and if your game i would take a look at the space and see if it's worth throwing a few dollars at okay a little tangent from snowflake but i wanted to put that out there snowflake wow closes its first trading week as a company worth 66 billion dollars roughly the same as goldman sachs worth more than vmware and the list goes on i mean what's what's more is there to say other than remember these episodes are all available as podcasts so please subscribe i publish weekly on wikibon.com and siliconangle.com so please check that out and please comment on my linkedin post or feel free to email me at david.velante at siliconangle.com this is dave vellante for the cube insights powered by etr thanks for watching everyone we'll see you next time you

(water running) (upbeat music) (electric buzzing) >> Fuel up! (upbeat music) (audience clapping) (upbeat music) >> Announcer: From around the globe. It's the queue with digital coverage of DockerCon live 2020, brought to you by Docker and its ecosystem partners. >> Hello everyone, welcome to DockerCon 2020. I'm John Furrier with theCUBE I'm in our Palo Alto studios with our quarantine crew. We have a great lineup here for DockerCon 2020. Virtual event, normally it was in person face to face. I'll be with you throughout the day from an amazing lineup of content, over 50 different sessions, cube tracks, keynotes, and we've got two great co-hosts here with Docker, Jenny Burcio and Bret Fisher. We'll be with you all day today, taking you through the program, helping you navigate the sessions. I'm so excited. Jenny, this is a virtual event. We talk about this. Can you believe it? Maybe the internet gods be with us today and hope everyone's having-- >> Yes. >> Easy time getting in. Jenny, Bret, thank you for-- >> Hello. >> Being here. >> Hey. >> Hi everyone, so great to see everyone chatting and telling us where they're from. Welcome to the Docker community. We have a great day planned for you. >> Guys great job getting this all together. I know how hard it is. These virtual events are hard to pull off. I'm blown away by the community at Docker. The amount of sessions that are coming in the sponsor support has been amazing. Just the overall excitement around the brand and the opportunities given this tough times where we're in. It's super exciting again, made the internet gods be with us throughout the day, but there's plenty of content. Bret's got an amazing all day marathon group of people coming in and chatting. Jenny, this has been an amazing journey and it's a great opportunity. Tell us about the virtual event. Why DockerCon virtual. Obviously everyone's canceling their events, but this is special to you guys. Talk about DockerCon virtual this year. >> The Docker community shows up at DockerCon every year, and even though we didn't have the opportunity to do an in person event this year, we didn't want to lose the time that we all come together at DockerCon. The conversations, the amazing content and learning opportunities. So we decided back in December to make DockerCon a virtual event. And of course when we did that, there was no quarantine we didn't expect, you know, I certainly didn't expect to be delivering it from my living room, but we were just, I mean we were completely blown away. There's nearly 70,000 people across the globe that have registered for DockerCon today. And when you look at DockerCon of past right live events, really and we're learning are just the tip of the iceberg and so thrilled to be able to deliver a more inclusive global event today. And we have so much planned I think. Bret, you want to tell us some of the things that you have planned? >> Well, I'm sure I'm going to forget something 'cause there's a lot going on. But, we've obviously got interviews all day today on this channel with John and the crew. Jenny has put together an amazing set of all these speakers, and then you have the captain's on deck, which is essentially the YouTube live hangout where we just basically talk shop. It's all engineers, all day long. Captains and special guests. And we're going to be in chat talking to you about answering your questions. Maybe we'll dig into some stuff based on the problems you're having or the questions you have. Maybe there'll be some random demos, but it's basically not scripted, it's an all day long unscripted event. So I'm sure it's going to be a lot of fun hanging out in there. >> Well guys, I want to just say it's been amazing how you structured this so everyone has a chance to ask questions, whether it's informal laid back in the captain's channel or in the sessions, where the speakers will be there with their presentations. But Jenny, I want to get your thoughts because we have a site out there that's structured a certain way for the folks watching. If you're on your desktop, there's a main stage hero. There's then tracks and Bret's running the captain's tracks. You can click on that link and jump into his session all day long. He's got an amazing set of line of sleet, leaning back, having a good time. And then each of the tracks, you can jump into those sessions. It's on a clock, it'll be available on demand. All that content is available if you're on your desktop. If you're on your mobile, it's the same thing. Look at the calendar, find the session that you want. If you're interested in it, you could watch it live and chat with the participants in real time or watch it on demand. So there's plenty of content to navigate through. We do have it on a clock and we'll be streaming sessions as they happen. So you're in the moment and that's a great time to chat in real time. But there's more, Jenny, getting more out of this event. You guys try to bring together the stimulation of community. How does the participants get more out of the the event besides just consuming some of the content all day today? >> Yes, so first set up your profile, put your picture next to your chat handle and then chat. John said we have various setups today to help you get the most out of your experience are breakout sessions. The content is prerecorded, so you get quality content and the speakers and chat so you can ask questions the whole time. If you're looking for the hallway track, then definitely check out the captain's on deck channel. And then we have some great interviews all day on the queue. So set up your profile, join the conversation and be kind, right? This is a community event. Code of conduct is linked on every page at the top, and just have a great day. >> And Bret, you guys have an amazing lineup on the captain, so you have a great YouTube channel that you have your stream on. So the folks who were familiar with that can get that either on YouTube or on the site. The chat is integrated in, So you're set up, what do you got going on? Give us the highlights. What are you excited about throughout your day? Take us through your program on the captains. That's going to be probably pretty dynamic in the chat too. >> Yeah, so I'm sure we're going to have lots of, stuff going on in chat. So no cLancaerns there about, having crickets in the chat. But we're going to be basically starting the day with two of my good Docker captain friends, (murmurs) and Laura Taco. And we're going to basically start you out and at the end of this keynote, at the end of this hour and we're going to get you going and then you can maybe jump out and go to take some sessions. Maybe there's some stuff you want to check out and other sessions that you want to chat and talk with the instructors, the speakers there, and then you're going to come back to us, right? Or go over, check out the interviews. So the idea is you're hopping back and forth and throughout the day we're basically changing out every hour. We're not just changing out the guests basically, but we're also changing out the topics that we can cover because different guests will have different expertise. We're going to have some special guests in from Microsoft, talk about some of the cool stuff going on there, and basically it's captains all day long. And if you've been on my YouTube live show you've watched that, you've seen a lot of the guests we have on there. I'm lucky to just hang out with all these really awesome people around the world, so it's going to be fun. >> Awesome and the content again has been preserved. You guys had a great session on call for paper sessions. Jenny, this is good stuff. What other things can people do to make it interesting? Obviously we're looking for suggestions. Feel free to chirp on Twitter about ideas that can be new. But you guys got some surprises. There's some selfies, what else? What's going on? Any secret, surprises throughout the day. >> There are secret surprises throughout the day. You'll need to pay attention to the keynotes. Bret will have giveaways. I know our wonderful sponsors have giveaways planned as well in their sessions. Hopefully right you feel conflicted about what you're going to attend. So do know that everything is recorded and will be available on demand afterwards so you can catch anything that you miss. Most of them will be available right after they stream the initial time. >> All right, great stuff, so they've got the Docker selfie. So the Docker selfies, the hashtag is just DockerCon hashtag DockerCon. If you feel like you want to add some of the hashtag no problem, check out the sessions. You can pop in and out of the captains is kind of the cool kids are going to be hanging out with Bret and then all they'll knowledge and learning. Don't miss the keynote, the keynote should be solid. We've got chain Governor from red monk delivering a keynote. I'll be interviewing him live after his keynote. So stay with us. And again, check out the interactive calendar. All you got to do is look at the calendar and click on the session you want. You'll jump right in. Hop around, give us feedback. We're doing our best. Bret, any final thoughts on what you want to share to the community around, what you got going on the virtual event, just random thoughts? >> Yeah, so sorry we can't all be together in the same physical place. But the coolest thing about as business online, is that we actually get to involve everyone, so as long as you have a computer and internet, you can actually attend DockerCon if you've never been to one before. So we're trying to recreate that experience online. Like Jenny said, the code of conduct is important. So, we're all in this together with the chat, so try to be nice in there. These are all real humans that, have feelings just like me. So let's try to keep it cool. And, over in the Catherine's channel we'll be taking your questions and maybe playing some music, playing some games, giving away some free stuff, while you're, in between sessions learning, oh yeah. >> And I got to say props to your rig. You've got an amazing setup there, Bret. I love what your show, you do. It's really bad ass and kick ass. So great stuff. Jenny sponsors ecosystem response to this event has been phenomenal. The attendance 67,000. We're seeing a surge of people hitting the site now. So if you're not getting in, just, Wade's going, we're going to crank through the queue, but the sponsors on the ecosystem really delivered on the content side and also the sport. You want to share a few shout outs on the sponsors who really kind of helped make this happen. >> Yeah, so definitely make sure you check out the sponsor pages and you go, each page is the actual content that they will be delivering. So they are delivering great content to you. So you can learn and a huge thank you to our platinum and gold authors. >> Awesome, well I got to say, I'm super impressed. I'm looking forward to the Microsoft Amazon sessions, which are going to be good. And there's a couple of great customer sessions there. I tweeted this out last night and let them get you guys' reaction to this because there's been a lot of talk around the COVID crisis that we're in, but there's also a positive upshot to this is Cambridge and explosion of developers that are going to be building new apps. And I said, you know, apps aren't going to just change the world, they're going to save the world. So a lot of the theme here is the impact that developers are having right now in the current situation. If we get the goodness of compose and all the things going on in Docker and the relationships, this real impact happening with the developer community. And it's pretty evident in the program and some of the talks and some of the examples. how containers and microservices are certainly changing the world and helping save the world, your thoughts. >> Like you said, a number of sessions and interviews in the program today that really dive into that. And even particularly around COVID, Clement Beyondo is sharing his company's experience, from being able to continue operations in Italy when they were completely shut down beginning of March. We have also in theCUBE channel several interviews about from the national Institute of health and precision cancer medicine at the end of the day. And you just can really see how containerization and developers are moving in industry and really humanity forward because of what they're able to build and create, with advances in technology. >> Yeah and the first responders and these days is developers. Bret compose is getting a lot of traction on Twitter. I can see some buzz already building up. There's huge traction with compose, just the ease of use and almost a call for arms for integrating into all the system language libraries, I mean, what's going on with compose? I mean, what's the captain say about this? I mean, it seems to be really tracking in terms of demand and interest. >> I think we're over 700,000 composed files on GitHub. So it's definitely beyond just the standard Docker run commands. It's definitely the next tool that people use to run containers. Just by having that we just buy, and that's not even counting. I mean that's just counting the files that are named Docker compose YAML. So I'm sure a lot of you out there have created a YAML file to manage your local containers or even on a server with Docker compose. And the nice thing is is Docker is doubling down on that. So we've gotten some news recently, from them about what they want to do with opening the spec up, getting more companies involved because compose is already gathered so much interest from the community. You know, AWS has importers, there's Kubernetes importers for it. So there's more stuff coming and we might just see something here in a few minutes. >> All right, well let's get into the keynote guys, jump into the keynote. If you missing anything, come back to the stream, check out the sessions, check out the calendar. Let's go, let's have a great time. Have some fun, thanks and enjoy the rest of the day we'll see you soon. (upbeat music) (upbeat music) >> Okay, what is the name of that Whale? >> Molly. >> And what is the name of this Whale? >> Mobby. >> That's right, dad's got to go, thanks bud. >> Bye. >> Bye. Hi, I'm Scott Johnson, CEO of Docker and welcome to DockerCon 2020. This year DockerCon is an all virtual event with more than 60,000 members of the Docker Community joining from around the world. And with the global shelter in place policies, we're excited to offer a unifying, inclusive virtual community event in which anyone and everyone can participate from their home. As a company, Docker has been through a lot of changes since our last DockerCon last year. The most important starting last November, is our refocusing 100% on developers and development teams. As part of that refocusing, one of the big challenges we've been working on, is how to help development teams quickly and efficiently get their app from code to cloud And wouldn't it be cool, if developers could quickly deploy to the cloud right from their local environment with the commands and workflow they already know. We're excited to give you a sneak preview of what we've been working on. And rather than slides, we thought we jumped right into the product. And joining me demonstrate some of these cool new features, is enclave your DACA. One of our engineers here at Docker working on Docker compose. Hello Lanca. >> Hello. >> We're going to show how an application development team collaborates using Docker desktop and Docker hub. And then deploys the app directly from the Docker command line to the clouds in just two commands. A development team would use this to quickly share functional changes of their app with the product management team, with beta testers or other development teams. Let's go ahead and take a look at our app. Now, this is a web app, that randomly pulls words from the database, and assembles them into sentences. You can see it's a pretty typical three tier application with each tier implemented in its own container. We have a front end web service, a middle tier, which implements the logic to randomly pull the words from the database and assemble them and a backend database. And here you can see the database uses the Postgres official image from Docker hub. Now let's first run the app locally using Docker command line and the Docker engine in Docker desktop. We'll do a Doc compose up and you can see that it's pulling the containers from our Docker organization account. Wordsmith, inc. Now that it's up. Let's go ahead and look at local host and we'll confirm that the application is functioning as desired. So there's one sentence, let's pull and now you and you can indeed see that we are pulling random words and assembling into sentences. Now you can also see though that the look and feel is a bit dated. And so Lanca is going to show us how easy it is to make changes and share them with the rest of the team. Lanca, over to you. >> Thank you, so I have, the source code of our application on my machine and I have updated it with the latest team from DockerCon 2020. So before committing the code, I'm going to build the application locally and run it, to verify that indeed the changes are good. So I'm going to build with Docker compose the image for the web service. Now that the image has been built, I'm going to deploy it locally. Wait to compose up. We can now check the dashboard in a Docker desktop that indeed our containers are up and running, and we can access, we can open in the web browser, the end point for the web service. So as we can see, we have the latest changes in for our application. So as you can see, the application has been updated successfully. So now, I'm going to push the image that I have just built to my organization's shared repository on Docker hub. So I can do this with Docker compose push web. Now that the image has been updated in the Docker hub repository, or my teammates can access it and check the changes. >> Excellent, well, thank you Lanca. Now of course, in these times, video conferencing is the new normal, and as great as it is, video conferencing does not allow users to actually test the application. And so, to allow us to have our app be accessible by others outside organizations such as beta testers or others, let's go ahead and deploy to the cloud. >> Sure we, can do this by employing a context. A Docker context, is a mechanism that we can use to target different platforms for deploying containers. The context we hold, information as the endpoint for the platform, and also how to authenticate to it. So I'm going to list the context that I have set locally. As you can see, I'm currently using the default context that is pointing to my local Docker engine. So all the commands that I have issued so far, we're targeting my local engine. Now, in order to deploy the application on a cloud. I have an account in the Azure Cloud, where I have no resource running currently, and I have created for this account, dedicated context that will hold the information on how to connect it to it. So now all I need to do, is to switch to this context, with Docker context use, and the name of my cloud context. So all the commands that I'm going to run, from now on, are going to target the cloud platform. So we can also check very, more simpler, in a simpler way we can check the running containers with Docker PS. So as we see no container is running in my cloud account. Now to deploy the application, all I need to do is to run a Docker compose up. And this will trigger the deployment of my application. >> Thanks Lanca. Now notice that Lanca did not have to move the composed file from Docker desktop to Azure. Notice you have to make any changes to the Docker compose file, and nor did she change any of the containers that she and I were using locally in our local environments. So the same composed file, same images, run locally and upon Azure without changes. While the app is deploying to Azure, let's highlight some of the features in Docker hub that helps teams with remote first collaboration. So first, here's our team's account where it (murmurs) and you can see the updated container sentences web that Lanca just pushed a couple of minutes ago. As far as collaboration, we can add members using their Docker ID or their email, and then we can organize them into different teams depending on their role in the application development process. So and then Lancae they're organized into different teams, we can assign them permissions, so that teams can work in parallel without stepping on each other's changes accidentally. For example, we'll give the engineering team full read, write access, whereas the product management team will go ahead and just give read only access. So this role based access controls, is just one of the many features in Docker hub that allows teams to collaboratively and quickly develop applications. Okay Lanca, how's our app doing? >> Our app has been successfully deployed to the cloud. So, we can easily check either the Azure portal to verify the containers running for it or simpler we can run a Docker PS again to get the list with the containers that have been deployed for it. In the output from the Docker PS, we can see an end point that we can use to access our application in the web browser. So we can see the application running in clouds. It's really up to date and now we can take this particular endpoint and share it within our organization such that anybody can have a look at it. >> That's cool Onka. We showed how we can deploy an app to the cloud in minutes and just two commands, and using commands that Docker users already know, thanks so much. In that sneak preview, you saw a team developing an app collaboratively, with a tool chain that includes Docker desktop and Docker hub. And simply by switching Docker context from their local environment to the cloud, deploy that app to the cloud, to Azure without leaving the command line using Docker commands they already know. And in doing so, really simplifying for development team, getting their app from code to cloud. And just as important, what you did not see, was a lot of complexity. You did not see cloud specific interfaces, user management or security. You did not see us having to provision and configure compute networking and storage resources in the cloud. And you did not see infrastructure specific application changes to either the composed file or the Docker images. And by simplifying a way that complexity, these new features help application DevOps teams, quickly iterate and get their ideas, their apps from code to cloud, and helping development teams, build share and run great applications, is what Docker is all about. A Docker is able to simplify for development teams getting their app from code to cloud quickly as a result of standards, products and ecosystem partners. It starts with open standards for applications and application artifacts, and active open source communities around those standards to ensure portability and choice. Then as you saw in the demo, the Docker experience delivered by Docker desktop and Docker hub, simplifies a team's collaborative development of applications, and together with ecosystem partners provides every stage of an application development tool chain. For example, deploying applications to the cloud in two commands. What you saw on the demo, well that's an extension of our strategic partnership with Microsoft, which we announced yesterday. And you can learn more about our partnership from Amanda Silver from Microsoft later today, right here at DockerCon. Another tool chain stage, the capability to scan applications for security and vulnerabilities, as a result of our partnership with Sneak, which we announced last week. You can learn more about that partnership from Peter McKay, CEO Sneak, again later today, right here at DockerCon. A third example, development team can automate the build of container images upon a simple get push, as a result of Docker hub integrations with GitHub and Alaska and Bitbucket. As a final example of Docker and the ecosystem helping teams quickly build applications, together with our ISV partners. We offer in Docker hub over 500 official and verified publisher images of ready to run Dockerized application components such as databases, load balancers, programming languages, and much more. Of course, none of this happens without people. And I would like to take a moment to thank four groups of people in particular. First, the Docker team, past and present. We've had a challenging 12 months including a restructuring and then a global pandemic, and yet their support for each other, and their passion for the product, this community and our customers has never been stronger. We think our community, Docker wouldn't be Docker without you, and whether you're one of the 50 Docker captains, they're almost 400 meetup organizers, the thousands of contributors and maintainers. Every day you show up, you give back, you teach new support. We thank our users, more than six and a half million developers who have built more than 7 million applications and are then sharing those applications through Docker hub at a rate of more than one and a half billion poles per week. Those apps are then run, are more than 44 million Docker engines. And finally, we thank our customers, the over 18,000 docker subscribers, both individual developers and development teams from startups to large organizations, 60% of which are outside the United States. And they spend every industry vertical, from media, to entertainment to manufacturing. healthcare and much more. Thank you. Now looking forward, given these unprecedented times, we would like to offer a challenge. While it would be easy to feel helpless and miss this global pandemic, the challenge is for us as individuals and as a community to instead see and grasp the tremendous opportunities before us to be forces for good. For starters, look no further than the pandemic itself, in the fight against this global disaster, applications and data are playing a critical role, and the Docker Community quickly recognize this and rose to the challenge. There are over 600 COVID-19 related publicly available projects on Docker hub today, from data processing to genome analytics to data visualization folding at home. The distributed computing project for simulating protein dynamics, is also available on Docker hub, and it uses spirit compute capacity to analyze COVID-19 proteins to aid in the design of new therapies. And right here at DockerCon, you can hear how Clemente Biondo and his company engineering in Gagne area Informatica are using Docker in the fight with COVID-19 in Italy every day. Now, in addition to fighting the pandemic directly, as a community, we also have an opportunity to bridge the disruption the pandemic is wreaking. It's impacting us at work and at home in every country around the world and every aspect of our lives. For example, many of you have a student at home, whose world is going to be very different when they returned to school. As employees, all of us have experienced the stresses from working from home as well as many of the benefits and in fact 75% of us say that going forward, we're going to continue to work from home at least occasionally. And of course one of the biggest disruptions has been job losses, over 35 million in the United States alone. And we know that's affected many of you. And yet your skills are in such demand and so important now more than ever. And that's why here at DockerCon, we want to try to do our part to help, and we're promoting this hashtag on Twitter, hashtag DockerCon jobs, where job seekers and those offering jobs can reach out to one another and connect. Now, pandemics disruption is accelerating the shift of more and more of our time, our priorities, our dollars from offline to online to hybrid, and even online only ways of living. We need to find new ways to collaborate, new approaches to engage customers, new modes for education and much more. And what is going to fill the needs created by this acceleration from offline, online? New applications. And it's this need, this demand for all these new applications that represents a great opportunity for the Docker community of developers. The world needs us, needs you developers now more than ever. So let's seize this moment. Let us in our teams, go build share and run great new applications. Thank you for joining today. And let's have a great DockerCon. >> Okay, welcome back to the DockerCon studio headquarters in your hosts, Jenny Burcio and myself John Furrier. u@farrier on Twitter. If you want to tweet me anything @DockerCon as well, share what you're thinking. Great keynote there from Scott CEO. Jenny, demo DockerCon jobs, some highlights there from Scott. Yeah, I love the intro. It's okay I'm about to do the keynote. The little green room comes on, makes it human. We're all trying to survive-- >> Let me answer the reality of what we are all doing with right now. I had to ask my kids to leave though or they would crash the whole stream but yes, we have a great community, a large community gather gathered here today, and we do want to take the opportunity for those that are looking for jobs, are hiring, to share with the hashtag DockerCon jobs. In addition, we want to support direct health care workers, and Bret Fisher and the captains will be running a all day charity stream on the captain's channel. Go there and you'll get the link to donate to directrelief.org which is a California based nonprofit, delivering and aid and supporting health care workers globally response to the COVID-19 crisis. >> Okay, if you jumping into the stream, I'm John Farrie with Jenny Webby, your hosts all day today throughout DockerCon. It's a packed house of great content. You have a main stream, theCUBE which is the mainstream that we'll be promoting a lot of cube interviews. But check out the 40 plus sessions underneath in the interactive calendar on dockercon.com site. Check it out, they're going to be live on a clock. So if you want to participate in real time in the chat, jump into your session on the track of your choice and participate with the folks in there chatting. If you miss it, it's going to go right on demand right after sort of all content will be immediately be available. So make sure you check it out. Docker selfie is a hashtag. Take a selfie, share it. Docker hashtag Docker jobs. If you're looking for a job or have openings, please share with the community and of course give us feedback on what you can do. We got James Governor, the keynote coming up next. He's with Red monk. Not afraid to share his opinion on open source on what companies should be doing, and also the evolution of this Cambrin explosion of apps that are going to be coming as we come out of this post pandemic world. A lot of people are thinking about this, the crisis and following through. So stay with us for more and more coverage. Jenny, favorite sessions on your mind for people to pay attention to that they should (murmurs)? >> I just want to address a few things that continue to come up in the chat sessions, especially breakout sessions after they play live and the speakers in chat with you, those go on demand, they are recorded, you will be able to access them. Also, if the screen is too small, there is the button to expand full screen, and different quality levels for the video that you can choose on your end. All the breakout sessions also have closed captioning, so please if you would like to read along, turn that on so you can, stay with the sessions. We have some great sessions, kicking off right at 10:00 a.m, getting started with Docker. We have a full track really in the how to enhance on that you should check out devs in action, hear what other people are doing and then of course our sponsors are delivering great content to you all day long. >> Tons of content. It's all available. They'll always be up always on at large scale. Thanks for watching. Now we got James Governor, the keynote. He's with Red Monk, the analyst firm and has been tracking open source for many generations. He's been doing amazing work. Watch his great keynote. I'm going to be interviewing him live right after. So stay with us and enjoy the rest of the day. We'll see you back shortly. (upbeat music) >> Hi, I'm James Governor, one of the co-founders of a company called RedMonk. We're an industry research firm focusing on developer led technology adoption. So that's I guess why Docker invited me to DockerCon 2020 to talk about some trends that we're seeing in the world of work and software development. So Monk Chips, that's who I am. I spent a lot of time on Twitter. It's a great research tool. It's a great way to find out what's going on with keep track of, as I say, there's people that we value so highly software developers, engineers and practitioners. So when I started talking to Docker about this event and it was pre Rhona, should we say, the idea of a crowd wasn't a scary thing, but today you see something like this, it makes you feel uncomfortable. This is not a place that I want to be. I'm pretty sure it's a place you don't want to be. And you know, to that end, I think it's interesting quote by Ellen Powell, she says, "Work from home is now just work" And we're going to see more and more of that. Organizations aren't feeling the same way they did about work before. Who all these people? Who is my cLancaern? So GitHub says has 50 million developers right on its network. Now, one of the things I think is most interesting, it's not that it has 50 million developers. Perhaps that's a proxy for number of developers worldwide. But quite frankly, a lot of those accounts, there's all kinds of people there. They're just Selena's. There are data engineers, there are data scientists, there are product managers, there were tech marketers. It's a big, big community and it goes way beyond just software developers itself. Frankly for me, I'd probably be saying there's more like 20 to 25 million developers worldwide, but GitHub knows a lot about the world of code. So what else do they know? One of the things they know is that world of code software and opensource, is becoming increasingly global. I get so excited about this stuff. The idea that there are these different software communities around the planet where we're seeing massive expansions in terms of things like open source. Great example is Nigeria. So Nigeria more than 200 million people, right? The energy there in terms of events, in terms of learning, in terms of teaching, in terms of the desire to code, the desire to launch businesses, desire to be part of a global software community is just so exciting. And you know, these, this sort of energy is not just in Nigeria, it's in other countries in Africa, it's happening in Egypt. It's happening around the world. This energy is something that's super interesting to me. We need to think about that. We've got global that we need to solve. And software is going to be a big part of that. At the moment, we can talk about other countries, but what about frankly the gender gap, the gender issue that, you know, from 1984 onwards, the number of women taking computer science degrees began to, not track but to create in comparison to what men were doing. The tech industry is way too male focused, there are men that are dominant, it's not welcoming, we haven't found ways to have those pathways and frankly to drive inclusion. And the women I know in tech, have to deal with the massively disproportionate amount of stress and things like online networks. But talking about online networks and talking about a better way of living, I was really excited by get up satellite recently, was a fantastic demo by Alison McMillan and she did a demo of a code spaces. So code spaces is Microsoft online ID, new platform that they've built. And online IDs, we're never quite sure, you know, plenty of people still out there just using the max. But, visual studio code has been a big success. And so this idea of moving to one online IDE, it's been around that for awhile. What they did was just make really tight integration. So you're in your GitHub repo and just be able to create a development environment with effectively one click, getting rid of all of the act shaving, making it super easy. And what I loved was it the demo, what Ali's like, yeah cause this is great. One of my kids are having a nap, I can just start (murmurs) and I don't have to sort out all the rest of it. And to me that was amazing. It was like productivity as inclusion. I'm here was a senior director at GitHub. They're doing this amazing work and then making this clear statement about being a parent. And I think that was fantastic. Because that's what, to me, importantly just working from home, which has been so challenging for so many of us, began to open up new possibilities, and frankly exciting possibilities. So Alley's also got a podcast parent-driven development, which I think is super important. Because this is about men and women rule in this together show parenting is a team sport, same as software development. And the idea that we should be thinking about, how to be more productive, is super important to me. So I want to talk a bit about developer culture and how it led to social media. Because you know, your social media, we're in this ad bomb stage now. It's TikTok, it's like exercise, people doing incredible back flips and stuff like that. Doing a bunch of dancing. We've had the world of sharing cat gifts, Facebook, we sort of see social media is I think a phenomenon in its own right. Whereas the me, I think it's interesting because it's its progenitors, where did it come from? So here's (murmurs) So 1971, one of the features in the emergency management information system, that he built, which it's topical, it was for medical tracking medical information as well, medical emergencies, included a bulletin board system. So that it could keep track of what people were doing on a team and make sure that they were collaborating effectively, boom! That was the start of something big, obviously. Another day I think is worth looking at 1983, Sorania Pullman, spanning tree protocol. So at DEC, they were very good at distributed systems. And the idea was that you can have a distributed system and so much of the internet working that we do today was based on radius work. And then it showed that basically, you could span out a huge network so that everyone could collaborate. That is incredibly exciting in terms of the trends, that I'm talking about. So then let's look at 1988, you've got IRC. IRC what developer has not used IRC, right. Well, I guess maybe some of the other ones might not have. But I don't know if we're post IRC yet, but (murmurs) at a finished university, really nailed it with IRC as a platform that people could communicate effectively with. And then we go into like 1991. So we've had IRC, we've had finished universities, doing a lot of really fantastic work about collaboration. And I don't think it was necessarily an accident that this is where the line is twofold, announced Linux. So Linux was a wonderfully packaged, idea in terms of we're going to take this Unix thing. And when I say package, what a package was the idea that we could collaborate on software. So, it may have just been the work of one person, but clearly what made it important, made it interesting, was finding a social networking pattern, for software development so that everybody could work on something at scale. That was really, I think, fundamental and foundational. Now I think it's important, We're going to talk about Linus, to talk about some things that are not good about software culture, not good about open source culture, not good about hacker culture. And that's where I'm going to talk about code of conduct. We have not been welcoming to new people. We got the acronyms, JFTI, We call people news, that's super unhelpful. We've got to find ways to be more welcoming and more self-sustaining in our communities, because otherwise communities will fail. And I'd like to thank everyone that has a code of conduct and has encouraged others to have codes of conduct. We need to have codes of conduct that are enforced to ensure that we have better diversity at our events. And that's what women, underrepresented minorities, all different kinds of people need to be well looked off to and be in safe and inclusive spaces. And that's the online events. But of course it's also for all of our activities offline. So Linus, as I say, I'm not the most charming of characters at all time, but he has done some amazing technology. So we got to like 2005 the creation of GIT. Not necessarily the distributed version control system that would win. But there was some interesting principles there, and they'd come out of the work that he had done in terms of trying to build and sustain the Linux code base. So it was very much based on experience. He had an itch that he needed to scratch and there was a community that was this building, this thing. So what was going to be the option, came up with Git foundational to another huge wave of social change, frankly get to logical awesome. April 20 April, 2008 GitHub, right? GiHub comes up, they've looked at Git, they've packaged it up, they found a way to make it consumable so the teams could use it and really begin to take advantage of the power of that distributed version control model. Now, ironically enough, of course they centralized the service in doing so. So we have a single point of failure on GitHub. But on the other hand, the notion of the poll request, the primitives that they established and made usable by people, that changed everything in terms of software development. I think another one that I'd really like to look at is Slack. So Slack is a huge success used by all different kinds of businesses. But it began specifically as a pivot from a company called Glitch. It was a game company and they still wanted, a tool internally that was better than IRC. So they built out something that later became Slack. So Slack 2014, is established as a company and basically it was this Slack fit software engineering. The focus on automation, the conversational aspects, the asynchronous aspects. It really pulled things together in a way that was interesting to software developers. And I think we've seen this pattern in the world, frankly, of the last few years. Software developers are influences. So Slack first used by the engineering teams, later used by everybody. And arguably you could say the same thing actually happened with Apple. Apple was mainstreamed by developers adopting that platform. Get to 2013, boom again, Solomon Hikes, Docker, right? So Docker was, I mean containers were not new, they were just super hard to use. People found it difficult technology, it was Easter Terek. It wasn't something that they could fully understand. Solomon did an incredible job of understanding how containers could fit into modern developer workflows. So if we think about immutable images, if we think about the ability to have everything required in the package where you are, it really tied into what people were trying to do with CICD, tied into microservices. And certainly the notion of sort of display usability Docker nailed that, and I guess from this conference, at least the rest is history. So I want to talk a little bit about, scratching the itch. And particularly what has become, I call it the developer authentic. So let's go into dark mode now. I've talked about developers laying out these foundations and frameworks that, the mainstream, frankly now my son, he's 14, he (murmurs) at me if I don't have dark mode on in an application. And it's this notion that developers, they have an aesthetic, it does get adopted I mean it's quite often jokey. One of the things we've seen in the really successful platforms like GitHub, Docker, NPM, let's look at GitHub. Let's look at over that Playfulness. I think was really interesting. And that changes the world of work, right? So we've got the world of work which can be buttoned up, which can be somewhat tight. I think both of those companies were really influential, in thinking that software development, which is a profession, it's also something that can and is fun. And I think about how can we make it more fun? How can we develop better applications together? Takes me to, if we think about Docker talking about build, share and run, for me the key word is share, because development has to be a team sport. It needs to be sharing. It needs to be kind and it needs to bring together people to do more effective work. Because that's what it's all about, doing effective work. If you think about zoom, it's a proxy for collaboration in terms of its value. So we've got all of these airlines and frankly, add up that their share that add up their total value. It's currently less than Zoom. So video conferencing has become so much of how we live now on a consumer basis. But certainly from a business to business perspective. I want to talk about how we live now. I want to think about like, what will come out all of this traumatic and it is incredibly traumatic time? I'd like to say I'm very privileged. I can work from home. So thank you to all the frontline workers that are out there that they're not in that position. But overall what I'm really thinking about, there's some things that will come out of this that will benefit us as a culture. Looking at cities like Paris, Milan, London, New York, putting a new cycling infrastructure, so that people can social distance and travel outside because they don't feel comfortable on public transport. I think sort of amazing widening pavements or we can't do that. All these cities have done it literally overnight. This sort of changes is exciting. And what does come off that like, oh there are some positive aspects of the current issues that we face. So I've got a conference or I've got a community that may and some of those, I've been working on. So Katie from HashiCorp and Carla from container solutions basically about, look, what will the world look like in developer relations? Can we have developer relations without the air miles? 'Cause developer advocates, they do too much travel ends up, you know, burning them out, develop relations. People don't like to say no. They may have bosses that say, you know, I was like, Oh that corporates went great. Now we're going to roll it out worldwide to 47 cities. That's stuff is terrible. It's terrible from a personal perspective, it's really terrible from an environmental perspective. We need to travel less. Virtual events are crushing it. Microsoft just at build, right? Normally that'd be just over 10,000 people, they had 245,000 plus registrations. 40,000 of them in the last day, right? Red Hat summit, 80,000 people, IBM think 90,000 people, GitHub Crushed it as well. Like this is a more inclusive way people can dip in. They can be from all around the world. I mentioned Nigeria and how fantastic it is. Very often Nigerian developers and advocates find it hard to get visas. Why should they be shut out of events? Events are going to start to become remote first because frankly, look at it, if you're turning in those kinds of numbers, and Microsoft was already doing great online events, but they absolutely nailed it. They're going to have to ask some serious questions about why everybody should get back on a plane again. So if you're going to do remote, you've got to be intentional about it. It's one thing I've learned some exciting about GitLab. GitLab's culture is amazing. Everything is documented, everything is public, everything is transparent. Think that really clear and if you look at their principles, everything, you can't have implicit collaboration models. Everything needs to be documented and explicit, so that anyone can work anywhere and they can still be part of the team. Remote first is where we're at now, Coinbase, Shopify, even Barkley says the not going to go back to having everybody in offices in the way they used to. This is a fundamental shift. And I think it's got significant implications for all industries, but definitely for software development. Here's the thing, the last 20 years were about distributed computing, microservices, the cloud, we've got pretty good at that. The next 20 years will be about distributed work. We can't have everybody living in San Francisco and London and Berlin. The talent is distributed, the talent is elsewhere. So how are we going to build tools? Who is going to scratch that itch to build tools to make them more effective? Who's building the next generation of apps, you are, thanks.