>> Live from Bahrain. It's theCUBE. Covering AWS Summit, Bahrain. Brought to you by, Amazon Web Services. >> Hi everyone, welcome back to our live coverage here Bahrain. It's theCUBE's exclusive coverage, our first time here in the Middle East. This is exclusive coverage of Amazon Web Services Summit. I'm John Furrier your co-host of theCUBE. Our next guest is Dalal Buhejji, who is the Chair Person for the Women in FinTech Initiative, a big growing organization here and the role of technology is about to change in a big way as Amazon Web Services Region comes online in 2019. It should spawn massive amounts of innovation, education, connections, networks. Thanks for joining us, thanks for coming on. >> Thank you, thank you for inviting me. It's a pleasure. >> So, what a great opportunity you have. FinTech's is a strategic initiative for this country. It's one of their core competencies. They see FinTech. >> Absolutely. I mean, FinTech is definitely growing in Bahrain. There's definitely a shift towards Financial Services moving towards innovation and digitalization. And, you know, we've seen women participate quite heavily when it comes to FinTech in Bahrain. And that's not something that's new to Bahrain. We've seen women that are a part of the Financial Services Sector for many decades. >> Yeah. >> So naturally, if the Financial Services Sector will innovate. >> Yeah. >> Then women will be part of this move. >> And the women movement is phenomenal. We had a breakfast we attended with Teresa Carlson. >> Yeah. >> Yesterday morning. >> Yes. >> It was absolutely a packed house. I even got kicked out of my table because, >> I wanted to participate >> (laughing) but I'm happy to give my chair up. There's so many women there. >> Yeah. >> And some Coders mostly new to tech. But this professionalization of digital >> (agrees) >> is going to impact FinTech more than ever. Take a minute to explain about the group. >> Yeah. >> The mission, what you guys are trying to do. How many people are in? What's your aspiration? >> Sure, it's a very recent and new initiative. It's a Women in FinTech Bahrain. It's a network of Professional women that are in the Financial Services Sector or that have impacted or worked in the FinTech Ecosystem. So, it's anywhere from a woman that work in the Central Bank of Bahrain, Founders of FinTech companies, people such as me, who attract inward investment and try to attract companies to set up in Bahrain. But also accelerators and incubators. >> Yeah. >> And we gather together. >> Yeah. >> And we're like, you know what? There's a lot of women in this FinTech Ecosystem and I'm sure a lot of them want to also be part of it. So, why don't we build awareness, empower more women, invite more women to come and be part of the growth of the FinTech Ecosystem in Bahrain. >> And Teresa Carlson was sharing some of her personal experience yesterday. >> Yeah. >> Unfortunately we didn't have a camera. I got audio, I'm going to put it as a podcast cause I thought it was so compelling. She said you know there's different ways to connect in and the most important thing is to be networked. >> Yes. >> And have each other's back. >> Absolutely. >> And amplify and support each other. This is normal. This is what people do. >> Absolutely. >> This is important as the growth comes. >> Yes. >> There's going to be a massive amount of opportunity coming into Bahrain. >> Absolutely. >> Big time. >> Yes, I mean, you nailed it. Like the network is actually how it's started. So we used to attend a number of events. Like the AWS Summit that we are here in today. And you know it's a bunch of women that just, you know, just happen to be part of the FinTech Ecosystem. >> Yeah. And we're like hang on, we might, there be might more women that are part of this that we're not aware of. >> Yeah. >> So why don't we make it formal in a way and call it a network where we attend more events. >> Yeah. >> Promote it more. >> Try to invite more people to attend and be part of it. So you know, not to shy away as being a women to be part of the FinTech revolution in Bahrain. >> You now one of the thing is wanted to ask you because this is a big trend that we're seeing in the United States. >> Yeah. >> And around the world. And OpenSource Software set the trend for this, is that co-creation, makes you feel part of a shared experience. >> Absolutely. >> And content and creation together makes people feel part of something. >> Yes. >> But is also creates a network effect. >> Yes. >> Network effects are some of the most important dynamics of bringing people together. >> Yes. >> But also extracting value and creating value. >> Yes. I mean, you know, once we initiated this Women in FinTech. It was two months ago, and what we did, we did this amazing picture. We took all this picture of us together and we got so much publicity out of it. So one of my colleagues visited Innovate Finance in UK, which is headed by a woman, and she also have women in FinTech a group. And we're exploring what can we do to collaborate. >> Yeah. >> I got a phone call a week ago from a VC, a Venture Capitalist who happens to be run by a woman and it's all about women entrepreneurs that seed money into FinTech companies or Startup companies that are funded by women. So it's all about the network as you said. >> Yeah and what's beautiful is that when you have this collaboration. >> Yeah. >> The people who create the value are going to shepard the extraction and sharing of that value in a community way. >> Absolutely. >> It's a community. The community is critical. Not over playing the hand or over driving. You know what I'm saying? It's like. >> Yes. >> It's a balance between community formation. >> Which is very crucial for a sector that's in it's initial stages and growing quite significantly. So you know, Bahrain has always been a Financial Services Hub. What we've seen that the sector has been digitalizing and innovating. >> Yeah. >> The last two years. So it's quite new. And you know, you need to gain as much knowledge as you want and the only way to do it is to create a network. >> Well I'm so impressed with what you're doing. >> Thank you. >> I really think Dalal of course our door is always open in Silicon Valley, Palo Alto, we'll amplify your message. >> Thank you. >> It's super important. So what is your goals and objectives? Share to the folks out there who're watching who might want to get involved. What are your goals? What are some of your objectives that you're trying to achieve. >> Right now it's initial stages. We are there just to create more awareness of women involvement in Bahrain, when it comes to FinTech, and to invite more women to participate in the growth of the FinTech Ecosystem in Bahrain. >> But you are also not limiting it to Bahrain, it's also externally. >> Of course not. >> You mentioned UK. >> Correct. Correct. Ultimately we will grow even further and have Bahrain as a FinTech hub for the region so we welcome any collaboration across borders. >> You know we were just talking about John Wood, who's a Senior Entrepreneur. Been around the block, very successful. Partnering with Teresa and Amazon at a high level. And he said, you know, what you read in the paper in the news and the media and then you come to Bahrain it's different. The ground truth here, when you look at, when you're on the ground. >> Yeah. >> Meeting people, and seeing all of this action, it's a different truth. >> Yes. >> What would you say to the folks watching here, who might watch it now live or on demand, what is the ground truth here in Bahrain? What's happening? How are women becoming the power engine? What's in general and the over all ecosystem at large? What's the story? How would you explain the phenomenon that is Bahrain? >> If you look at it beyond FinTech and look at it beyond Financial services. The involvement of women in the Private Sector is quite phenomenal in Bahrain. >> Yeah. And it goes way beyond just a few years ago. It's decades of the women involvement. We have women at Cabinet. We have women in, leading good you know, financial institutions or corporates. So the involvement of women has always been prominent in Bahrain. And we welcome and want to see more of women go towards digitalization now. >> Yeah. >> And give them the right means to be able to achieve that. >> And one of the things that's interesting is that when you're not here on the ground, you might have some old history of what the role of women here. >> Yeah. >> My 21 year old daughter Jacqueline asked me and my 17 year old daughter Caroline said. They were curios, they just don't know. They're like: "What's it like to be a woman there?" What's it like to be a woman in Bahrain? It's a very comfortable and liberal place to live in. You know, education was introduced first for women in the GCC. You see a lot of woman participate in any cross-sectors within the Private Sector. It's a very friendly place to live in. You know, and it's a great to see how knowledgeable those women are. They are hungry for knowledge. >> Yeah. >> And very impressive characters, I must say. So, you know, it's something to be, definitely to monitor and to watch very closely. Tala Fahkro came on earlier. >> Yes. >> She's the Executive Director of the EDB, Economic Development Board here in Bahrain. >> Correct. >> She said it's a learning culture. >> It is, it is. >> And it's always been a learning culture. >> Absolutely. >> And always been open and liberal, if you will. But open minded. >> Exactly. I mean, see Bahrain has always been in a very unique position, that it was a trade-hub for many years. So it's welcomed people of different cultures. >> Yeah. >> Which opened that learning culture. We're very much receptive to understanding what people from India or China or the US have to do. So it's a very as, I totally agree with Tala Fahkro and I think it is definitely a learning culture. >> Well certainly our door's open in Silicon Valley. >> Thank you. >> Thanks for spending the time. >> Thank you. >> And good luck with your opportunity. And how do people engage with you? Just give the plug, talk about. Is there a site? Is there a Facebook page? >> Is there? What are you? How do you guys engage? How should someone engage and join? >> So your main. >> The Women in FinTech network? >> So yeah, we're working on a, you know, a page for people to reach out to us. But in the meantime you can reach out to me on my LinkedIn page or Twitter page. And you know, please watch this space cause it's growing. And I look forward to, you know, showcasing more of the events during. Well co-create, share content with us, we're open to seeing what you do. Thanks for coming on tehCUBE. >> Thank you. >> We are here in Bahrain. This is theCUBE coverage. I'm John Furrier. You can reach me at on Twitter @Furrier, LinkedIn, Facebook, Telegram. I'm all over the place. Easy to find. theCUBE of course here for the first time here in Bahrain in the Middle East. Exploring the creativity, the entrepreneurship and the impact of the Amazon Web Services new Region. Coming online in early 2019. Should be a tsunami of innovation activity. New networks. New people. We're meeting new people. Stay with us for more after this short break. (electronic music)

(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)

(uplifting music) >> Hello, welcome back to theCUBE's coverage of CloudNativeSecurityCon, the inaugural event, in Seattle. I'm John Furrier, host of theCUBE, here in the Palo Alto Studios. We're calling it theCUBE Center. It's kind of like our Sports Center for tech. It's kind of remote coverage. We've been doing this now for a few years. We're going to amp it up this year as more events are remote, and happening all around the world. So, we're going to continue the coverage with this segment focusing on the data stack, entrepreneurial opportunities around all things security, and as, obviously, data's involved. And our next guest is a friend of theCUBE, and CUBE alumni from 2013, entrepreneur himself, turned, now, venture capitalist angel investor, with his own firm, Opher Kahane, Managing Director, Sonoma Ventures. Formerly the founder of Origami, sold to Intuit a few years back. Focusing now on having a lot of fun, angel investing on boards, focusing on data-driven applications, and stacks around that, and all the stuff going on in, really, in the wheelhouse for what's going on around security data. Opher, great to see you. Thanks for coming on. >> My pleasure. Great to be back. It's been a while. >> So you're kind of on Easy Street now. You did the entrepreneurial venture, you've worked hard. We were on together in 2013 when theCUBE just started. XCEL Partners had an event in Stanford, XCEL, and they had all the features there. We interviewed Satya Nadella, who was just a manager at Microsoft at that time, he was there. He's now the CEO of Microsoft. >> Yeah, he was. >> A lot's changed in nine years. But congratulations on your venture you sold, and you got an exit there, and now you're doing a lot of investments. I'd love to get your take, because this is really the biggest change I've seen in the past 12 years, around an inflection point around a lot of converging forces. Data, which, big data, 10 years ago, was a big part of your career, but now it's accelerated, with cloud scale. You're seeing people building scale on top of other clouds, and becoming their own cloud. You're seeing data being a big part of it. Cybersecurity kind of has not really changed much, but it's the most important thing everyone's talking about. So, developers are involved, data's involved, a lot of entrepreneurial opportunities. So I'd love to get your take on how you see the current situation, as it relates to what's gone on in the past five years or so. What's the big story? >> So, a lot of big stories, but I think a lot of it has to do with a promise of making value from data, whether it's for cybersecurity, for Fintech, for DevOps, for RevTech startups and companies. There's a lot of challenges in actually driving and monetizing the value from data with velocity. Historically, the challenge has been more around, "How do I store data at massive scale?" And then you had the big data infrastructure company, like Cloudera, and MapR, and others, deal with it from a scale perspective, from a storage perspective. Then you had a whole layer of companies that evolved to deal with, "How do I index massive scales of data, for quick querying, and federated access, et cetera?" But now that a lot of those underlying problems, if you will, have been solved, to a certain extent, although they're always being stretched, given the scale of data, and its utility is becoming more and more massive, in particular with AI use cases being very prominent right now, the next level is how to actually make value from the data. How do I manage the full lifecycle of data in complex environments, with complex organizations, complex use cases? And having seen this from the inside, with Origami Logic, as we dealt with a lot of large corporations, and post-acquisition by Intuit, and a lot of the startups I'm involved with, it's clear that we're now onto that next step. And you have fundamental new paradigms, such as data mesh, that attempt to address that complexity, and responsibly scaling access, and democratizing access in the value monetization from data, across large organizations. You have a slew of startups that are evolving to help the entire lifecycle of data, from the data engineering side of it, to the data analytics side of it, to the AI use cases side of it. And it feels like the early days, to a certain extent, of the revolution that we've seen in transition from traditional databases, to data warehouses, to cloud-based data processing, and big data. It feels like we're at the genesis of that next wave. And it's super, super exciting, for me at least, as someone who's sitting more in the coach seat, rather than being on the pitch, and building startups, helping folks as they go through those motions. >> So that's awesome. I want to get into some of these data infrastructure dynamics you mentioned, but before that, talk to the audience around what you're working on now. You've been a successful entrepreneur, you're focused on angel investing, so, super-early seed stage. What kind of deals are you looking at? What's interesting to you? What is Sonoma Ventures looking for, and what are some of the entrepreneurial dynamics that you're seeing right now, from a startup standpoint? >> Cool, so, at a macro level, this is a little bit of background of my history, because it shapes very heavily what it is that I'm looking at. So, I've been very fortunate with entrepreneurial career. I founded three startups. All three of them are successful. Final two were sold, the first one merged and went public. And my third career has been about data, moving data, passing data, processing data, generating insights from it. And, at this phase, I wanted to really evolve from just going and building startup number four, from going through the same motions again. A 10 year adventure, I'm a little bit too old for that, I guess. But the next best thing is to sit from a point whereby I can be more elevated in where I'm dealing with, and broaden the variety of startups I'm focused on, rather than just do your own thing, and just go very, very deep into it. Now, what specifically am I focused on at Sonoma Ventures? So, basically, looking at what I refer to as a data-driven application stack. Anything from the low-level data infrastructure and cloud infrastructure, that helps any persona in the data universe maximize value for data, from their particular point of view, for their particular role, whether it's data analysts, data scientists, data engineers, cloud engineers, DevOps folks, et cetera. All the way up to the application layer, in applications that are very data-heavy. And what are very typical data-heavy applications? FinTech, cyber, Web3, revenue technologies, and product and DevOps. So these are the areas we're focused on. I have almost 23 or 24 startups in the portfolio that span all these different areas. And this is in terms of the aperture. Now, typically, focus on pre-seed, seed. Sometimes a little bit later stage, but this is the primary focus. And it's really about partnering with entrepreneurs, and helping them make, if you will, original mistakes, avoid the mistakes I made. >> Yeah. >> And take it to the next level, whatever the milestone they're driving with. So I'm very, very hands-on with many of those startups. Now, what is it that's happening right now, initially, and why is it so exciting? So, on one hand, you have this scaling of data and its complexity, yet lagging value creation from it, across those different personas we've touched on. So that's one fundamental opportunity which is secular. The other one, which is more a cyclic situation, is the fact that we're going through a down cycle in tech, as is very evident in the public markets, and everything we're hearing about funding going slower and lower, terms shifting more into the hands of typical VCs versus entrepreneur-friendly market, and so on and so forth. And a very significant amount of layoffs. Now, when you combine these two trends together, you're observing a very interesting thing, that a lot of folks, really bright folks, who have sold a startup to a company, or have been in the guts of the large startup, or a large corporation, have, hands-on, experienced all those challenges we've spoken about earlier, in turf, maximizing value from data, irrespective of their role, in a specific angle, or vantage point they have on those challenges. So, for many of them, it's an opportunity to, "Now, let me now start a startup. I've been laid off, maybe, or my company's stock isn't doing as well as it used to, as a large corporation. Now I have an opportunity to actually go and take my entrepreneurial passion, and apply it to a product and experience as part of this larger company." >> Yeah. >> And you see a slew of folks who are emerging with these great ideas. So it's a very, very exciting period of time to innovate. >> It's interesting, a lot of people look at, I mean, I look at Snowflake as an example of a company that refactored data warehouses. They just basically took data warehouse, and put it on the cloud, and called it a data cloud. That, to me, was compelling. They didn't pay any CapEx. They rode Amazon's wave there. So, a similar thing going on with data. You mentioned this, and I see it as an enabling opportunity. So whether it's cybersecurity, FinTech, whatever vertical, you have an enablement. Now, you mentioned data infrastructure. It's a super exciting area, as there's so many stacks emerging. We got an analytics stack, there's real-time stacks, there's data lakes, AI stack, foundational models. So, you're seeing an explosion of stacks, different tools probably will emerge. So, how do you look at that, as a seasoned entrepreneur, now investor? Is that a good thing? Is that just more of the market? 'Cause it just seems like more and more kind of decomposed stacks targeted at use cases seems to be a trend. >> Yeah. >> And how do you vet that, is it? >> So it's a great observation, and if you take a step back and look at the evolution of technology over the last 30 years, maybe longer, you always see these cycles of expansion, fragmentation, contraction, expansion, contraction. Go decentralize, go centralize, go decentralize, go centralize, as manifested in different types of technology paradigms. From client server, to storage, to microservices, to et cetera, et cetera. So I think we're going through another big bang, to a certain extent, whereby end up with more specialized data stacks for specific use cases, as you need performance, the data models, the tooling to best adapt to the particular task at hand, and the particular personas at hand. As the needs of the data analysts are quite different from the needs of an NL engineer, it's quite different from the needs of the data engineer. And what happens is, when you end up with these siloed stacks, you end up with new fragmentation, and new gaps that need to be filled with a new layer of innovation. And I suspect that, in part, that's what we're seeing right now, in terms of the next wave of data innovation. Whether it's in a service of FinTech use cases, or cyber use cases, or other, is a set of tools that end up having to try and stitch together those elements and bridge between them. So I see that as a fantastic gap to innovate around. I see, also, a fundamental need in creating a common data language, and common data management processes and governance across those different personas, because ultimately, the same underlying data these folks need, albeit in different mediums, different access models, different velocities, et cetera, the subject matter, if you will, the underlying raw data, and some of the taxonomies right on top of it, do need to be consistent. So, once again, a great opportunity to innovate, whether it's about semantic layers, whether it's about data mesh, whether it's about CICD tools for data engineers, and so on and so forth. >> I got to ask you, first of all, I see you have a friend you brought into the interview. You have a dog in the background who made a little cameo appearance. And that's awesome. Sitting right next to you, making sure everything's going well. On the AI thing, 'cause I think that's the hot trend here. >> Yeah. >> You're starting to see, that ChatGPT's got everyone excited, because it's kind of that first time you see kind of next-gen functionality, large-language models, where you can bring data in, and it integrates well. So, to me, I think, connecting the dots, this kind of speaks to the beginning of what will be a trend of really blending of data stacks together, or blending of models. And so, as more data modeling emerges, you start to have this AI stack kind of situation, where you have things out there that you can compose. It's almost very developer-friendly, conceptually. This is kind of new, but kind of the same concept's been working on with Google and others. How do you see this emerging, as an investor? What are some of the things that you're excited about, around the ChatGPT kind of things that's happening? 'Cause it brings it mainstream. Again, a million downloads, fastest applications get a million downloads, even among all the successes. So it's obviously hit a nerve. People are talking about it. What's your take on that? >> Yeah, so, I think that's a great point, and clearly, it feels like an iPhone moment, right, to the industry, in this case, AI, and lots of applications. And I think there's, at a high level, probably three different layers of innovation. One is on top of those platforms. What use cases can one bring to the table that would drive on top of a ChatGPT-like service? Whereby, the startup, the company, can bring some unique datasets to infuse and add value on top of it, by custom-focusing it and purpose-building it for a particular use case or particular vertical. Whether it's applying it to customer service, in a particular vertical, applying it to, I don't know, marketing content creation, and so on and so forth. That's one category. And I do know that, as one of my startups is in Y Combinator, this season, winter '23, they're saying that a very large chunk of the YC companies in this cycle are about GPT use cases. So we'll see a flurry of that. The next layer, the one below that, is those who actually provide those platforms, whether it's ChatGPT, whatever will emerge from the partnership with Microsoft, and any competitive players that emerge from other startups, or from the big cloud providers, whether it's Facebook, if they ever get into this, and Google, which clearly will, as they need to, to survive around search. The third layer is the enabling layer. As you're going to have more and more of those different large-language models and use case running on top of it, the underlying layers, all the way down to cloud infrastructure, the data infrastructure, and the entire set of tools and systems, that take raw data, and massage it into useful, labeled, contextualized features and data to feed the models, the AI models, whether it's during training, or during inference stages, in production. Personally, my focus is more on the infrastructure than on the application use cases. And I believe that there's going to be a massive amount of innovation opportunity around that, to reach cost-effective, quality, fair models that are deployed easily and maintained easily, or at least with as little pain as possible, at scale. So there are startups that are dealing with it, in various areas. Some are about focusing on labeling automation, some about fairness, about, speaking about cyber, protecting models from threats through data and other issues with it, and so on and so forth. And I believe that this will be, too, a big driver for massive innovation, the infrastructure layer. >> Awesome, and I love how you mentioned the iPhone moment. I call it the browser moment, 'cause it felt that way for me, personally. >> Yep. >> But I think, from a business model standpoint, there is that iPhone shift. It's not the BlackBerry. It's a whole 'nother thing. And I like that. But I do have to ask you, because this is interesting. You mentioned iPhone. iPhone's mostly proprietary. So, in these machine learning foundational models, >> Yeah. >> you're starting to see proprietary hardware, bolt-on, acceleration, bundled together, for faster uptake. And now you got open source emerging, as two things. It's almost iPhone-Android situation happening. >> Yeah. >> So what's your view on that? Because there's pros and cons for either one. You're seeing a lot of these machine learning laws are very proprietary, but they work, and do you care, right? >> Yeah. >> And then you got open source, which is like, "Okay, let's get some upsource code, and let people verify it, and then build with that." Is it a balance? >> Yes, I think- >> Is it mutually exclusive? What's your view? >> I think it's going to be, markets will drive the proportion of both, and I think, for a certain use case, you'll end up with more proprietary offerings. With certain use cases, I guess the fundamental infrastructure for ChatGPT-like, let's say, large-language models and all the use cases running on top of it, that's likely going to be more platform-oriented and open source, and will allow innovation. Think of it as the equivalent of iPhone apps or Android apps running on top of those platforms, as in AI apps. So we'll have a lot of that. Now, when you start going a little bit more into the guts, the lower layers, then it's clear that, for performance reasons, in particular, for certain use cases, we'll end up with more proprietary offerings, whether it's advanced silicon, such as some of the silicon that emerged from entrepreneurs who have left Google, around TensorFlow, and all the silicon that powers that. You'll see a lot of innovation in that area as well. It hopefully intends to improve the cost efficiency of running large AI-oriented workloads, both in inference and in learning stages. >> I got to ask you, because this has come up a lot around Azure and Microsoft. Microsoft, pretty good move getting into the ChatGPT >> Yep. >> and the open AI, because I was talking to someone who's a hardcore Amazon developer, and they said, they swore they would never use Azure, right? One of those types. And they're spinning up Azure servers to get access to the API. So, the developers are flocking, as you mentioned. The YC class is all doing large data things, because you can now program with data, which is amazing, which is amazing. So, what's your take on, I know you got to be kind of neutral 'cause you're an investor, but you got, Amazon has to respond, Google, essentially, did all the work, so they have to have a solution. So, I'm expecting Google to have something very compelling, but Microsoft, right now, is going to just, might run the table on developers, this new wave of data developers. What's your take on the cloud responses to this? What's Amazon, what do you think AWS is going to do? What should Google be doing? What's your take? >> So, each of them is coming from a slightly different angle, of course. I'll say, Google, I think, has massive assets in the AI space, and their underlying cloud platform, I think, has been designed to support such complicated workloads, but they have yet to go as far as opening it up the same way ChatGPT is now in that Microsoft partnership, and Azure. Good question regarding Amazon. AWS has had a significant investment in AI-related infrastructure. Seeing it through my startups, through other lens as well. How will they respond to that higher layer, above and beyond the low level, if you will, AI-enabling apparatuses? How do they elevate to at least one or two layers above, and get to the same ChatGPT layer, good question. Is there an acquisition that will make sense for them to accelerate it, maybe. Is there an in-house development that they can reapply from a different domain towards that, possibly. But I do suspect we'll end up with acquisitions as the arms race around the next level of cloud wars emerges, and it's going to be no longer just about the basic tooling for basic cloud-based applications, and the infrastructure, and the cost management, but rather, faster time to deliver AI in data-heavy applications. Once again, each one of those cloud suppliers, their vendor is coming with different assets, and different pros and cons. All of them will need to just elevate the level of the fight, if you will, in this case, to the AI layer. >> It's going to be very interesting, the different stacks on the data infrastructure, like I mentioned, analytics, data lake, AI, all happening. It's going to be interesting to see how this turns into this AI cloud, like data clouds, data operating systems. So, super fascinating area. Opher, thank you for coming on and sharing your expertise with us. Great to see you, and congratulations on the work. I'll give you the final word here. Give a plugin for what you're looking for for startup seats, pre-seeds. What's the kind of profile that gets your attention, from a seed, pre-seed candidate or entrepreneur? >> Cool, first of all, it's my pleasure. Enjoy our chats, as always. Hopefully the next one's not going to be in nine years. As to what I'm looking for, ideally, smart data entrepreneurs, who have come from a particular domain problem, or problem domain, that they understand, they felt it in their own 10 fingers, or millions of neurons in their brains, and they figured out a way to solve it. Whether it's a data infrastructure play, a cloud infrastructure play, or a very, very smart application that takes advantage of data at scale. These are the things I'm looking for. >> One final, final question I have to ask you, because you're a seasoned entrepreneur, and now coach. What's different about the current entrepreneurial environment right now, vis-a-vis, the past decade? What's new? Is it different, highly accelerated? What advice do you give entrepreneurs out there who are putting together their plan? Obviously, a global resource pool now of engineering. It might not be yesterday's formula for success to putting a venture together to get to that product-market fit. What's new and different, and what's your advice to the folks out there about what's different about the current environment for being an entrepreneur? >> Fantastic, so I think it's a great question. So I think there's a few axes of difference, compared to, let's say, five years ago, 10 years ago, 15 years ago. First and foremost, given the amount of infrastructure out there, the amount of open-source technologies, amount of developer toolkits and frameworks, trying to develop an application, at least at the application layer, is much faster than ever. So, it's faster and cheaper, to the most part, unless you're building very fundamental, core, deep tech, where you still have a big technology challenge to deal with. And absent that, the challenge shifts more to how do you manage my resources, to product-market fit, how are you integrating the GTM lens, the go-to-market lens, as early as possible in the product-market fit cycle, such that you reach from pre-seed to seed, from seed to A, from A to B, with an optimal amount of velocity, and a minimal amount of resources. One big difference, specifically as of, let's say, beginning of this year, late last year, is that money is no longer free for entrepreneurs, which means that you need to operate and build startup in an environment with a lot more constraints. And in my mind, some of the best startups that have ever been built, and some of the big market-changing, generational-changing, if you will, technology startups, in their respective industry verticals, have actually emerged from these times. And these tend to be the smartest, best startups that emerge because they operate with a lot less money. Money is not as available for them, which means that they need to make tough decisions, and make verticals every day. What you don't need to do, you can kick the cow down the road. When you have plenty of money, and it cushions for a lot of mistakes, you don't have that cushion. And hopefully we'll end up with companies with a more agile, more, if you will, resilience, and better cultures in making those tough decisions that startups need to make every day. Which is why I'm super, super excited to see the next batch of amazing unicorns, true unicorns, not just valuation, market rising with the water type unicorns that emerged from this particular era, which we're in the beginning of. And very much enjoy working with entrepreneurs during this difficult time, the times we're in. >> The next 24 months will be the next wave, like you said, best time to do a company. Remember, Airbnb's pitch was, "We'll rent cots in apartments, and sell cereal." Boy, a lot of people passed on that deal, in that last down market, that turned out to be a game-changer. So the crazy ideas might not be that bad. So it's all about the entrepreneurs, and >> 100%. >> this is a big wave, and it's certainly happening. Opher, thank you for sharing. Obviously, data is going to change all the markets. Refactoring, security, FinTech, user experience, applications are going to be changed by data, data operating system. Thanks for coming on, and thanks for sharing. Appreciate it. >> My pleasure. Have a good one. >> Okay, more coverage for the CloudNativeSecurityCon inaugural event. Data will be the key for cybersecurity. theCUBE's coverage continues after this break. (uplifting music)

>> Welcome back everyone to the closing remarks here before we kick off our ecosystem portion of the program. We're live in Palo Alto for theCUBE special presentation of Supercloud 2. It's the second edition, the first one was in August. I'm John Furrier with Dave Vellante. Here to wrap up with our special guest analyst George Gilbert, investor and industry legend former colleague of ours, analyst at Wikibon. George great to see you. Dave, you know, wrapping up this day what in a phenomenal program. We had a contribution from industry vendors, industry experts, practitioners and customers building and redefining their company's business model. Rolling out technology for Supercloud and multicloud and ultimately changing how they do data. And data was the theme today. So very, very great program. Before we jump into our favorite parts let's give a shout out to the folks who make this possible. Free contents our mission. We'll always stay true to that mission. We want to thank VMware, alkira, ChaosSearch, prosimo for being sponsors of this great program. We will have Supercloud 3 coming up in a month or so, or two months. We'll see. Or sooner, we don't know. But it'll be more about security, but a lot more momentum. Okay, so that's... >> And don't forget too that this program not going to end now. We've got a whole ecosystem speaks track so stay tuned for that. >> John: Yeah, we got another 20 interviews. Feels like it. >> Well, you're going to hear from Saks, Veronika Durgin. You're going to hear from Western Union, Harveer Singh. You're going to hear from Ionis Pharmaceuticals, Nick Taylor. Brian Gracely chimes in on Supecloud. So he's the man behind the cloud cast. >> Yeah, and you know, the practitioners again, pay attention to also to the cloud networking interviews. Lot of change going on there that's going to be disruptive and actually change the landscape as well. Again, as Supercloud progresses to be the next big thing. If you're not on this next wave, you'll drift what, as Pat Gelsinger says. >> Yep. >> To kick off the closing segments, George, Dave, this is a wave that's been identified. Again, people debate the word all you want Supercloud. It is a gateway to multicloud eventually it is the standard for new applications, new ways to do data. There's new computer science being generated and customer requirements being addressed. So it's the confluence of, you know, tectonic plates shifting in the industry, new computer science seeing things like AI and machine learning and data at the center of it and new infrastructure all kind of coming together. So, to me, that's my takeaway so far. That is the big story and it's going to change society and ultimately the business models of these companies. >> Well, we've had 10, you know, you think about it we came out of the financial crisis. We've had 10, 12 years despite the Covid of tech success, right? And just now CIOs are starting to hit the brakes. And so my point is you've had all this innovation building up for a decade and you've got this massive ecosystem that is running on the cloud and the ecosystem is saying, hey, we can have even more value by tapping best of of breed across clouds. And you've got customers saying, hey, we need help. We want to do more and we want to point our business and our intellectual property, our software tooling at our customers and monetize our data. So you have all these forces coming together and it's sort of entering a new era. >> George, I want to go to you for a second because you are big contributor to this event. Your interview with Bob Moglia with Dave was I thought a watershed moment for me to hear that the data apps, how databases are being rethought because we've been seeing a diversity of databases with Amazon Web services, you know, promoting no one database rules of the world. Now it's not one database kind of architecture that's puling these new apps. What's your takeaway from this event? >> So if you keep your eye on this North Star where instead of building apps that are based on code you're building apps that are defined by data coming off of things that are linked to the real world like people, places, things and activities. Then the idea is, and the example we use is, you know, Uber but it could be, you know, amazon.com is defined by stuff coming off data in the Amazon ecosystem or marketplace. And then the question is, and everyone was talking at different angles on this, which was, where's the data live? How much do you hide from the developer? You know, and when can you offer that? You know, and you started with Walmart which was describing apps, traditional apps that are just code. And frankly that's easier to make that cross cloud and you know, essentially location independent. As soon as you have data you need data management technology that a customer does not have the sophistication to build. And then the argument was like, so how much can you hide from the developer who's building data apps? Tristan's version was you take the modern data stack and you start adding these APIs that define business concepts like bookings, billings and revenue, you know, or in the Uber example like drivers and riders, you know, and ETA's and prices. But those things execute still on the data warehouse or data lakehouse. Then Bob Muglia was saying you're not really hiding enough from the developer because you still got to say how to do all that. And his vision is not only do you hide where the data is but you hide how to sort of get at all that code by just saying what you want. You define how a car and how a driver and how a rider works. And then those things automatically figure out underneath the cover. >> So huge challenges, right? There's governance, there's security, they could be big blockers to, you know, the Supercloud but the industry's going to be attacking that problem. >> Well, what's your take? What's your favorite segment? Zhamak Dehghani came on, she's starting in that company, exclusive news. That was big notable moment for theCUBE. She launched her company. She pioneered the data mesh concept. And I think what George is saying and what data mesh points to is something that we've been saying for a long time. That data is now going to flip the script on how apps behave. And the Uber example I think is illustrated 'cause people can relate to Uber. But imagine that for every business whether it's a manufacturing business or retail or oil and gas or FinTech, they can look at their business like a game almost gamify it with data, riders, cars you know, moving data around the value of data. This is something that Adam Selipsky teased out at AWS, Dave. So what's your takeaway from this Supercloud? Where are we in your mind? Well big thing is data products and decentralizing your data architecture, but putting data in the hands of domain experts who can actually monetize the data. And I think that's, to me that's really exciting. Because look, data products financial industry has always been doing building data products. Mortgage backed securities is a data product. But why should the financial industry have all the fun? I mean virtually every organization can tap its ecosystem build data products, take its internal IP and processes and software and point it to the world and actually begin to make money out of it. >> Okay, so let's go around the horn. I'll start, I'll get you guys some time to think. Next question, what did you learn today? I learned that I think it's an infrastructure game and talking to Kit Colbert at VMware, I think it's all about infrastructure refactoring and I think the data's going to be an ingredient that's going to be operating system like. I think you're going to see the infrastructure influencing operations that will enable Superclouds to be real. And developers won't even know what a Supercloud is because they'll be using it. It's the operations focus is going to be very critical. Just like DevOps movements started Cloud native I think you're going to see a data native movement and I think infrastructure is critical as people go to the next level. That's my big takeaway today. And I'll say the data conversation is at the center. I think security, data are going to be always active horizontally scalable concepts, but every company's going to reset their infrastructure, how it looks and if it's not set up for data and or things that there need to be agile on, it's going to be a non-starter. So I think that's the cloud NextGen, distributed computing. >> I mean, what came into focus for me was I think the hyperscaler is going to continue to do their thing, you know, and be very, very successful and they're each coming at it from different approaches. We talk about this all the time in theCUBE. Amazon the best infrastructure, you know, Google's got its you know, data and AI thing and it's playing catch up and Microsoft's got this massive estate. Okay, cool. Check. The next wave of innovation which is coming from data, I've always said follow the data. That's where the where the money's going to be is going to come from other places. People want to be able to, organizations want to be able to share data across clouds across their organization, outside of their ecosystem and make money with that data sharing. They don't want to FTP it anymore. I got it. You take it. They want to work with live data in real time and I think the edge, we didn't talk much about the edge today is going to even take that to a new level real time inferencing at the edge, AI and and being able to do new things with data that we haven't even seen. But playing around with ChatGPT, it's blowing our mind. And I think you're right, it's like when we first saw the browser, holy crap, this is going to change the world. >> Yeah. And the ChatGPT by the way is going to create a wave of machine learning and data refactoring for sure. But also Howie Liu had an interesting comment, he was asked by a VC how much to replicate that and he said it's in the hundreds of millions, not billions. Now if you asked that same question how much does it cost to replicate AWS? The CapEx alone is unstoppable, they're already done. So, you know, the hyperscalers are going to continue to boom. I think they're going to drive the infrastructure. I think Amazon's going to be really strong at silicon and physics and squeeze every ounce atom out of every physical thing and then get latency as your bottleneck and the rest is all going to be... >> That never blew me away, a hundred million to create kind of an open AI, you know, competitor. Look at companies like Lacework. >> John: Some people have that much cash on the balance sheet. >> These are security companies that have raised a billion dollars, right? To compete. You know, so... >> If you're not shifting left what do you do with data, shift up? >> But, you know. >> What did you learn, George? >> I'm listening to you and I think you're helping me crystallize something which is the software infrastructure to enable the data apps is wide open. The way Zhamak described it is like if you want a data product like a sales and operation plan, that is built on other data products, like a sales plan which has a forecast in it, it has a production plan, it has a procurement plan and then a sales and operation plan is actually a composition of all those and they call each other. Now in her current platform, you need to expose to the developer a certain amount of mechanics on how to move all that data, when to move it. Like what happens if something fails. Now Muglia is saying I can hide that completely. So all you have to say is what you want and the underlying machinery takes care of everything. The problem is Muglia stuff is still a few years off. And Tristan is saying, I can give you much of that today but it's got to run in the data warehouse. So this trade offs all different ways. But again, I agree with you that the Cloud platform vendors or the ecosystem participants who can run across Cloud platforms and private infrastructure will be the next platform. And then the cloud platform is sort of where you run the big honking centralized stuff where someone else manages the operations. >> Sounds like middleware to me, Dave >> And key is, I'll just end with this. The key is being able to get to the data, whether it's in a data warehouse or a data lake or a S3 bucket or an object store, Oracle database, whatever. It's got to be inclusive that is critical to execute on the vision that you just talked about 'cause that data's in different systems and you're not going to put it all into some new system. >> So creating middleware in the cloud that sounds what it sounds like to me. >> It's like, you discovered PaaS >> It's a super PaaS. >> But it's platform services 'cause PaaS connotes like a tightly integrated platform. >> Well this is the real thing that's going on. We're going to see how this evolves. George, great to have you on, Dave. Thanks for the summary. I enjoyed this segment a lot today. This ends our stage performance live here in Palo Alto. As you know, we're live stage performance and syndicate out virtually. Our afternoon program's going to kick in now you're going to hear some great interviews. We got ChaosSearch. Defining the network Supercloud from prosimo. Future of Cloud Network, alkira. We got Saks, a retail company here, Veronika Durgin. We got Dave with Western Union. So a lot of customers, a pharmaceutical company Warner Brothers, Discovery, media company. And then you know, what is really needed for Supercloud, good panels. So stay with us for the afternoon program. That's part two of Supercloud 2. This is a wrap up for our stage live performance. I'm John Furrier with Dave Vellante and George Gilbert here wrapping up. Thanks for watching and enjoy the program. (bright music)

(bright upbeat music) >> Now welcome back to theCUBE as we continue our coverage here at AWS re:Invent 22, or of course in the Venetian here in Las Vegas. It's Wednesday day, I guess two and a half of the show. Things are going really well here and we're going to move our attention now to banking and tech like so many other verticals that financial industry making huge moves with their digital plays. And we're going to talk about that with a couple of guys that know what that is all about. Ken Meyer, chief Information and Experience Officer at Truist. Ken, good to see you, sir. >> Good to have me. Thank you very much. >> It is. And Gys Hyman, who is a principal at Deloitte the lead though for a converge prosperity we'll explain that in just a second. Hi, it's good to see you as well. >> Nice, yeah. Awesome having me. >> Right, so jump in on that for a converge prosperity so we understand what the product is and or what the opportunity is for what Deloitte brought to market for what folks like Truist are putting into practice. >> Yeah, so converge prosperity is really our focus where we building solutions production reduced solutions for the financial services industry. So if you think about the demand on the bank side right now is they want to launch new products, new services those innovative products and services that they want to take to market. And they also want to modernize, a lot of their sort of legacy infrastructure and modernize some of the components within their architecture. So we, together with AWS actually, are co-investing sort of in a multi-year strategy where we're saying, let's build these solutions that we can take to market that can sort of help these banks be more agile, launch products faster to market and also help address the modernization journey for the banks. And that's really sitting within the converged prosperity business unit within Deloitte. >> And Ken, for you, what was the attraction or what is the attraction in terms of that kind of an offering? >> Well, I think when you think about the banking journey to the Cloud, a lot of folks look on the channel experiences and they've leveraged Cloud to create differentiated experiences that are just... They couldn't build before with the speed of the scale and everything else. But, the challenges that many banks have is once you get below that layer there's a lot of legacy type technology that lives in the product offerings that we all offer. So, the idea of these folks or others that are trying to make that a little bit easier to kind of connect that front end to that backend all with the true modern stack is something that's differentiated. >> Yeah, if I hear pulling this big old weight along with me right? >> That's right. >> I've got all this old stuff but I still have to use some of the old stuff. >> Well, and some of the old stuff works, right? It runs. So, why would you want to mess up anything that's running? And so even that if these folks and others can find ways to start breaking it into more modular pieces so we can consume things differently than we've done before versus take that big old elephant on every time, it's a differentiator. >> So what's the trick then in making sure that what is new is working with what is old? Because what is new these days, obviously you know, faster, sleek, or, I mean, we go on and on as opposed to what you were working with in Legacy. So what's the trick there, Gys in making sure that you're doing the right match up? >> It's part of the approach. I mean, you can either do a big bang approach which is sort of lengthy and high risk for the bank which is obviously we don't see a lot of appetite in the banks to take the big bang approach or the large transformational approach. And then the approach that we sort of take to this is to say, and that we're seeing that success in the market is around more a phased approach which we call on the edge, is really to say, how can we launch something on the side, and take that to market really quick to show the benefit to the business and demonstrate success. And at the same time have a really sort of modular architecture that you can say, you don't have to have this monolith solution that you need to plug or replace your existing one. That you can really sort of componentize that and say which are the components that we want to start replacing in a phased approach, with these new next generation technologies, which, yeah. >> The part that he mentioned there at the end on module architecture is a 100% the key, right? I mean, architecture matters probably now more than ever. When you're trying to stitch all these things to together and you can find ways to make it a little bit more seamless versus some of the monoliths that we've dealt with in the past, it's extremely helpful. >> So, give us some examples here for what your experience has been then. >> Yeah... >> 'I mean, you are as I see you're the experience officer, so, (all laughing) >> We'll leave that one alone, (all laughing loudly) but now, I mean I think some of the on the edge stuff that Gys has been talking about, we're a large bank, we have subsidiaries we have a subsidiary, which is our national lending platform and LightStream as an example. And we decided to say, Hey let's really learn from what we could do with a more modern core banking platform. And we ultimately stood that up in production and we're close to going generally available but we've got production accounts on modern core platforms that we're learning every day from. And it's not just the learning on the tech side, that tech side might be the easy part, to be honest. The change in this technology and the different technologies that are available it really is impacting how we run our operations. So moving from batch processing which has always been how banks operate to this concept of real time processing, that's a big step. And not only does it change in the operations and how we service our clients but now you got to think about compliance, right? And legal and all of the the risk elements, >> 'security changing. >> Security, all that. It's all a part of that change. So you could say that the tech is really hard and it's difficult and we got to look at the architecture but at the end of the day, it's about bringing the entire organization back to the table to say how do we do this different? And how do we create a better experience and create new value for our clients through the technology. >> All right, can you gimme an example though? I mean, about some... What's going to be the biggest change you think then? >> I think operationally is the biggest change, in my opinion. I mean, when you start thinking about the way in which we've worked for years around this concept of batch processing, so, you know, yes, you make a deposit at your branch and that's really nice and we might credit you and you do the memo post but it doesn't clear until the night runs and you finish your batch process, at 3:00 AM and then all your downstream systems run and all of that. And even the concept of collecting checks, right? And thinking about all the item processing aspects of that when you start talking about real time and it immediately you make your deposit and immediately balances your general ledger and it clears and it's all right there at that point in time, all of those processes go away, right? The batch processes change the way in which all the downstream impacts for reporting and analytics and all of those things, it all changes. And so that can be really scary, but it also can be really exciting when you think about creating new products and new services that are truly real time and changing the way in which we operate with our clients. >> So scary and exciting, right? A couple of moods or situations that maybe some folks in banking don't want to be in, right? I don't like scary. I want here... >> Well change is scary for a lot of people right? But there's an evolution in this space. >> Gys, what are you seeing with your experiences in terms of... >> 'i think that it also creates opportunity to sort of (clears throat) to lay the foundation of how do you coexist between the old world and the new world? And these modern technologies really allow you to sort of, put a event driven platform in place to say, understand that the banking world is fairly batch driven right now as Ken's comment and also the broader banking ecosystem is still batch driven but it allows us to put a platform in place to say how do we coexist with that batch environment and the real time environment? And the other thing that the banks are trying to do is they're trying to work with a number of sort of the fintechs out there in the market, these leading fintechs that are offering new products and services that they can embed into their offering and then offer as a service to their customers at the end of the day. But doing that with existing banking technology is difficult because it's not as modular, it's not as open. These next generation technologies and certainly the solution that we building with AWS is really sort of that power strip or that fabric layer where we are allowing fintechs to plug in easier into that ecosystem and into the bank's ecosystem as well. >> Yeah, I think with Goldman Sachs is that what I read is that one that comes to mind about repurposing and making it available to their client base. >> They certainly building a platform model where they're bringing other ecosystem partners into their platform and then they're offering out as business services to their customers. >> So, Ken how do you get buy in on this? I mean, because, it sure looks good on paper, right? But when it comes to time actually execute and implement, you need, you know, buy in for more than just your slice of the business, right? >> Yeah, I mean, I think there's a lot of different elements that come into getting that buy in and kind of making that leap and starting to experiment. One is our clients, right? So our clients are demanding new products, new services they don't expect things like maintenance windows and other, like they want what they want when they want it. So if you're listening to your clients and they want more product innovation and they want everything available when they need it that's clear. Cloud and security, we've... Everything that we've ever done when it comes to moving workloads or building experiences with the Cloud has been by continuing to increase our security posture. So we can create a more secure environment and a more available environment because we've have deployments that are spanning different regions and they're continuously available, and the automation and the speed in which we can go to market. I mean, when you can create a new product and launch a new product in weeks versus months or years in the past with all of the complexity and create simplicity while also using modern capabilities to create intelligent experiences, that's a game changer. And it's hard to argue with it but I think the other part of it that's a reality is that we're facing a really interesting time where there's not a whole lot of COBAL programmers laying around these days. And so at some point there's going to be a workforce issue. >> Skills gap. >> Absolutely. >> Yeah. So looking at it with through your Deloitte lens, then I mean, that's a very real threat, right? Is it not to all of a sudden, not a regression but certainly a delay in progress, (chuckles) how do we overcome that as far as training and skills and whatever? >> We believe that this on the edge approach also, the other benefit to the bank is it allows the benefit to sort of test and learn a little bit with these new technologies. These new Cloud native, Cloud based technologies are very different, you know, different skill set that you need in the design side and the build side and also in the maintain and operate side. But it allows the bank to sort of take more of a phased approach and sort of get the training wheels around the skills, get comfortable around how these different platforms work, and how do you slowly sort of phase that into the organization, which makes a big difference. >> So what's the training part of that then? I mean, what does that look like? >> It's training at the engineering level to say there are a new set of tools. You know if you think about the Cloud the infrastructure layer, those technologies are very different from, servicing the on-prem technologies that the banks are used to servicing. So that's certainly at the engineering level there's a difference in training, but there's also a different training required at how do you configure and work with these new next generation, core platforms, which opens up a whole set of opportunities of what are the types of financial services products that the banks can take to market, but they work very differently than your sort of your traditional more monolithic technologies. And then I think the bigger area, as Ken mentioned is on the servicing side, the bank has now got a say we are now introducing a new solution together with an old solution, and how do we coexist and how do we create a servicing layer for the customer to have that sort of consistent experience across all the new but also your middle office and your back office and your front office, people have got to work on this platform and how do you not give them a broken experience in the... >> 'well, and your clients don't care about what your black end platforms look like. >> All right. That's right. >> So you want to be able to, it's kind of it's do it on the device, right? It doesn't matter what if it's a hundred years old or if it's 10 days old, it doesn't matter. >> Right, right. You talked about the modular configurations, right? Are some more critical than others? I mean, not knowing what that looks like or have you been able to give feedback on the converged prosperity side, say, you know, fine tune... >> 'i give a lot of feedback. >> Okay, (all laughing loudly). >> We won't do his review right now. >> All right, all right, good. All right, so let's not do... >> 'no, I think that we've done a lot of learning together throughout all of these processes because before they had this really fancy converge prosperity thing we were just working together, right? And we've been able to learn along the way and there's some learnings that are great for us and there's other things that they can tailor for a broader set of clients, which is great. I mean that's what the partnerships about is continuing to learn together and Gys and his team have been phenomenal partners. As we think about being very, very intentional about how we launch products on these new platforms we give a lot of feedback on, Hey these are areas that might be really important for you to think about as you look to build out your side of the platform. And some of those things we might consume some of those things we might not, and that's okay. But for us it's about truly partnering and doing that test and learn and trying to learn about how does this impact all of the downstream stuff because it's not just about technology, although we like to think it is every once in a while this is about clients. And so you got to continue to put them in the front and then similarly, our teammates that we have because they're servicing those clients on the front lines every day. >> Yeah, they'll tell you if it's working. >> 'they will tell you. >> 'you'll know right away. >> Absolutely. >> See what kind of use we have going on here and... >> 'a 100%. >> And if something's broken or not. Just real quick about the relationship going forward then, like you've launched converged prosperity, right? It's been out in the marketplace less than a year, but you got it up and running, things are going well. When you hear feedback like this from Ken and others what kind of fine tunings going on in here with you? And then from your side of the equation too what do you want more of? What do you want to see more of here in 2023? >> Yeah, I mean we work with sort of the non-traditional banks out there. So we work with FinTech that want to launch banking offerings. So there are a lot of lessons that we learn from them in terms of what are the features and capabilities that they're looking for. We work with some of the larger banks that are saying, we want to be more modular in terms of how we consume the banking suite solution. We don't want to take sort of an end-to-end proposition. We really want to take selective components of that banking suite solution and embed it into our existing or new infrastructure. And I think the lessons for us is really just around what are the new customer capabilities that their customers are looking for that we should be building for in our platform. I think the other thing that you need to look at is these next generation core banking platforms they are like any new software business they are growing, they are learning and they are maturing. So you are also looking for customers that have the appetite to grow with some of those and whose product roadmap aligns with those vendors out there. So, I guess for us, it's important to work with partners that are willing to work with us and walk that journey. But we also feel that these technologies and solutions are really.... Banks are moving past design, past thinking, they are really now thinking about how do we start implementing and making it real and how do we take that initial use case sort of to market out there. >> Yeah, I would say, if I simplify what Gys and team have done they've taken modernization of commodity services, right? So, banks don't want to just go out and build commodities all the time, right? That's not how we're going to differentiate. So we need to be thinking about what are the different ways that we can create a competitive advantage against everybody else who has, a lot of different and similar products and service offerings. So, if we can continue to look and help influence roadmaps and also consume some of those types of services that are truly commoditized, and we can go focus on the modernization of the areas that are the biggest possible competitive advantages for us then there's a lot of value in that type of value problem. >> I know we didn't have time for this, but do you have a pick by the way, in the national championship in college football? >> I'm a dog. >> I know you are, (laughing loudly). >> I'm a dog. >> How about them dogs? >> How about them dogs? >> All right, thanks for the time guys. >> Thanks much. >> Really do appreciate it much. Really great session. Talking about banking and what's going on at the bottom really is AWS, driving things and making it happen. All right, you've been watching the Cube, of course. The leader in high tech coverage. (bright upbeat music)

(calm tech music) >> Hello everyone. Welcome back to the AWS Cube coverage of Reinvent 2022. I'm John Fur, host of the Cube. We got a great interview segment here co-creating innovation with E.design. We got Shigeo Kuwabara who is with the President and the Chief Executive Officer E.design Insurance, and Akiko Hora Senior Managing Director Financial Services in Japan Inclusion and Diversity Lead at Accenture Japan. Thank you for joining me today. Thanks for coming on the cube. >> You're welcome, You're welcome, Thank you. >> I love this topic. E.design Create co-creating innovation automobile insurance with a product called "&e" It's cloud-based advanced automobile insurance system you guys built and called Safe Driving Together an initiative that uses data to reduce accidents. So great stuff. So let's get into it. Tell us about eDesign Insurance and your vision behind transforming to insurance tech company. Combining the technology, new type of automobile insurance for a digital age. >> Okay. With the pandemic of Covid 19 dissertation is accelerating at rapid pace everywhere. First, insurance were required to define the kind of easy to use, meaningful service they wanted to offer their customers. eDesign in collaboration with Accenture, sought to redefine the company's mission, vision and values by embracing the customer experience in a new way. While a customer's traditional view of automobile insurance is "just in case" Accenture and eDesign form the view that what customers really want is accident prevention. With a redefined objective of co-creating with customers not only peace of mind in the event of an accident, but also a world without accidents. ANDI developed a service that uses cutting edge digital technologies to create a safer and more secure car experience. >> Akiko talk about from insurance perspective and Accenture you know, we know about FinTech, you got InsureTech this is a segment that's growing rapidly, lot of data lot of new capabilities with the cloud. Can you share your thoughts on this new opportunity? >> This is a new innovation for many insurance client especially who owns, the traditional policyholder and the new generations. So they that give the new experience for customers, it makes a big change for the customer experience, and that eDesign is leading this experience in the world I think. >> Awesome. What are the key features of the advanced cloud-based automobile insurance system you guys call ANDI, and how does it work? >> The most advanced full crowd insurance system in the world and it embraces digital convenience to the fullest with a concept of creating safety with data; ANDI enables that initiative Safe Driving Together. It designs new initiative, aims to use available data to reduce the risk and causes of an accident, and to make society as a whole, as a whole safer and more secure. >> Why did you choose Accenture and AWS for this innovation? What unique value do they bring? >> Good question about Accenture. Accenture supported us in a wide range of areas including business, design, and IT. In addition to the industry knowledge embodiment of vision, and definition requirements. The PMO eliminated communication loss between the business and IT sites, and as a result the development was completed in a short period of time. In addition, Accenture studies in cutting edge digital technologies such as AI and data analysis is necessary to become an insured insurance company. And I appreciate Accenture's ability to provide such capabilities as well. >> Akiko talk about the IOT implementation here. A lot of data, a lot of design work. >> Yeah >> Take us through the experience. >> Okay. >> And how does Amazon and Accenture come together. >> ANDI and to support safe driving with eDesign insurance for the compact IOT car sensor with this size to put free charge for all of the policyholders to use a language mobile app. The system captures capture and monitors the drivers driving data, diagnosed and driving mood, and driving behavior which is safe or not and supports safe driving. In the event of the accident the system automatically detect the impact and can summarize the accident situation which is very difficult for the driver to recognize by themselves, and the location, location data. And many others and driver can then report the accident with single tap on their smartphone, very easy. And request assistance or repair shop on the spot. It's very safe and also very smooth for the giving the good experience for customers. >> I know Accenture has great expertise, that's one. But you have been in both involved in this smart market rollout. Can you explain that? The smart market rollout? >> Yeah, it's, it was very interesting that we we had the very smooth importation with eDesign and especially AWS allow us to give the open and crowd system to strong collaboration with many other ecosystem partners and many AI sensors and many IOT sensors opportunity. That gives us a lot of experience and give more opportunity for an eScape company like eDesign sample, so that can be more smooth and open implementation for the future. >> That's great rollout. You know we love this example of AWS Accenture eDesign co-creation. It reminds me of the big super cloud trend where industries can be refactored and and and scaled up. So how was ANDI built and what were the requirements driving the technical solution? >> We, we, we, we brought, we planned the architecture how that works for the future and especially Kuwabarason and the great leadership. He doesn't like something which already in the market and also which can be more fit for the future, the solution which fit for the future and maybe that can allow market customers to have big experience. That's why we, we choose open crowd, new trend, new digital trend and IOT or whatever. That gives our architecture definition, which can, lead by Kuwabarason with AWS with this crowd solution as well as with very packaged basis and also open connection with many other AI in the new technology. So that's why it can be more, this solution going to be grow more in the future and we will have more surprises in the future. Kuwabarason if you have some add add comment please >> Go Ahead. >> (laughing) >> Go ahead. What's your thought? Share? >> Thank, thank you Horason very good comment (laugh). So in collaboration with Accenture, I could develop our team's capability. Because we are working together like one team. That is a key success factor I think. >> Talk about the customer experience, and the results. What feedback have you received from your customers and what does the data say? >> Okay. One interesting feedback we receive is "I was always concerned about my wife's love of driving, but by showing her the ANDI driving score, I was able to point it out to her objectively, which was very helpful." That was a good feedback. In this way there are many positive feedback about the ability of visualize the safety, and danger of ones own driving. When I hear customers say that they can now drive more safely because they can objectively identify their bad driving through ANDI's safe driving program I feel very happy that we created ANDI >> Kiko your thoughts? >> Yeah, it's, it's very obvious that the customers likes how, customers likes the sensor saying how they are driving and they, they they sense my driving behavior is safe they are going to be confident. If not, they going to be very careful in the future that's happening. And maybe that can be aligned with insurance which eDesign is giving is more they feel more confident to drive in in many areas. And also that can give more opportunity that they can have more new type of insurance and new experience with the car. That's, that's kind of the interesting make up of power of the driving including the sensor would be happening. That can be good news for us and we can be more creative to think about new experience for customers. >> Congratulations for receiving the highest IT grand prize from the IT award sponsored by the Japan Institute of Information Technology. What's next for eDesign? Congratulations. What's next? How do you take it further, to change to transform the insurance business? >> Okay. I believe ANDI's strength lies in its data. By sharing data with our customers in a timely manner we contribute to their safe driving. We hope to work with customers to create a safe driving experience that is based on parts and that can be enjoyed like a game. Furthermore, we would like to create a society and community where accidents are less likely to occur. Based on the accumulated data in cooperation with local governments and other organizations. We'd like to contribute to the realization of such a safe and secure society by acquiring and analyzing solid data through ANDI On what kind of accidents occur and under what circumstances. >> Akiko Big awards. What's next? AWS, Accenture, eDesign take us through the vision. >> Yeah, it's, it's, I'm, I'm looking forward to do to do the next things and actually eDesign have not only auto insurance, they cover more home and also many others. So that can be giving the more safer opportunity for customers. They can leave their home very smoothly and even some disaster happening, they can escape very safely. Whatever happening in the family like childcare or maybe even their pet have some challenges we can take care of them and that's kind of many experience which which can align with eDesign's insurance. Most of the things we can give lot of safe and with data and also some IOT things and also insurance that's giving the more opportunity and something can truly resolve the social issue. That can be many opportunities. So that's why we have some plan. But we like to we like to keep a secret for the next future. >> Safe driving together, unlock benefits by gamifying and creating cloud-based advanced data, IOT sensors, encouraging drivers to work together to be safe. This is very, very an important story and thank you so much for sharing. eDesign, thank you for coming on. Congratulations on your awards, and transforming insurance tech. It should be fun. Not a hassle. Thank you for sharing. >> Thank you very much. >> Very much. >> Okay. eDesign co-creating innovation. This is the story of Cloud Next Generation. I'm John Fur the Cube, part of the AWS Reinvent 2022 Cube coverage here with Accenture. Thanks for watching. (calm tech music)

>>Good evening, brilliant humans. My name is Savannah Peterson and very delighted to be streaming to you. Live from the Cube Studios here in Motor City, Michigan. I've got John Furrier on my left. John, this is our last interview of the day. Energy just seems to keep oozing. How >>You doing? Take two, Three days of coverage, the queue love segments. This one's great cuz we have a practitioner who's implementing all the hard core talks to be awesome. Can't wait to get into it. >>Yeah, I'm very excited for this one. If it's not very clear, we are a community focused community is a huge theme here at the show at Cape Con. And our next guests are actually a provider and a customer. Turning it over to you. Lisa and Jake, welcome to the show. >>Thank you so much for having us. >>It's great to be here. It is our pleasure. Lisa, you're with Cockroach. Just in case the audience isn't familiar, give us a quick little sound bite. >>We're a distributed sequel database. Highly scalable, reliable. The database you can't kill, right? We will survive the apocalypse. So very resilient. Our customers, mostly retail, FinTech game meet online gambling. They, they, they need that resiliency, they need that scalability. So the indestructible database is the elevator pitch >>And the success has been very well documented. Valuation obviously is a scorp guard, but huge customers. We were at the Escape 19. Just for the record, the first ever multi-cloud conference hasn't come back baby. Love it. It'll come back soon. >>Yeah, well we did a similar version of it just a month ago and I was, that was before Cockroach. I was a different company there talking a lot about multi-cloud. So, but I'm, I've been a car a couple of years now and I run community, I run developer relations. I'm still also a CNCF ambassador, so I lead community as well. I still run a really large user group in the San Francisco Bay area. So we've just >>Been in >>Community, take through the use case. Jake's story set us up. >>Well I would like Jake to take him through the use case and Cockroach is a part of it, but what they've built is amazing. And also Jake's history is amazing. So you can start Jake, >>Wherever you take >>Your Yeah, sure. I'm Jake, I'm CEO and co-founder of Offset. Oted is the commercial entity behind Spice Dvy and Spice Dvy is a permission service. Cool. So a permission service is something that lets developers and let's platform teams really unlock the full potential of their applications. So a lot of people get stuck on My R back isn't flexible enough. How do I do these fine grain things? How do I do these complex sharing workflows that my product manager thinks is so important? And so our service enables those platform teams and developers to do those kinds of things. >>What's your, what's your infrastructure? What's your setup look like? What, how are you guys looking like on the back end? >>Sure. Yeah. So we're obviously built on top of Kubernetes as well. One of the reasons that we're here. So we use Kubernetes, we use Kubernetes operators to orchestrate everything. And then we use, use Cockroach TV as our production data store, our production backend data store. >>So I'm curious, cause I love when these little matchmakers come together. You said you've now been presenting on a little bit of a road show, which is very exciting. Lisa, how are you and the team surfacing stories like Jakes, >>Well, I mean any, any place we can obviously all the social medias, all the blogs, How >>Are you finding it though? >>How, how did you Oh, like from our customers? Yeah, we have an open source version so people start to use us a long time before we even sometimes know about them. And then they'll come to us and they'll be like, I love Cockroach, and like, tell me about it. Like, tell me what you build and if it's interesting, you know, we'll we'll try to give it some light. And it's always interesting to me what people do with it because it's an interesting technology. I like what they've done with it. I mean the, the fact that it's globally distributed, right? That was like a really important thing to you. Totally. >>Yeah. We're also long term fans of Cockroach, so we actually all work together out of Workbench, which was a co-working space and investor in New York City. So yeah, we go way back. We knew the founders. I, I'm constantly saying like if I could have invested early in cockroach, that would've been the easiest check I could have ever signed. >>Yeah, that's awesome. And then we've been following that too and you guys are now using them, but folks that are out there looking to have the, the same challenges, what are the big challenges on selecting the database? I mean, as you know, the history of Cockroach and you're originating the story, folks out there might not know and they're also gonna choose a database. What's the, what's the big challenge that they can solve that that kind of comes together? What, what would you describe that? >>Sure. So we're, as I said, we're a permission service and per the data that you store in a permission service is incredibly sensitive. You need it to be around, right? You need it to be available. If the permission service goes down, almost everything else goes down because it's all calling into the permission service. Is this user allowed to do this? Are they allowed to do that? And if we can't answer those questions, then our customer is down, right? So when we're looking at a database, we're looking for reliability, we're looking for durability, disaster recovery, and then permission services are one of the only services that you usually don't shard geographically. So if you look at like AWS's iam, that's a global service, even though the individual things that they run are actually sharded by region. So we also needed a globally distributed database with all of those other properties. So that's what led us >>To, this is a huge topic. So man, we've been talking about all week the cloud is essentially distributed database at this point and it's distributed system. So distributed database is a hot topic, totally not really well reported. A lot of people talking about it, but how would you describe this distributed trend that's going on? What are the key reasons that they're driving it? What's making this more important than ever in your mind, in your opinion? >>I mean, for our use case, it was just a hard requirement, right? We had to be able to have this global service. But I think just for general use cases, a distributed database, distributed database has that like shared nothing architecture that allows you to kind of keep it running and horizontally scale it. And as your requirements and as your applications needs change, you can just keep adding on capacity and keep adding on reliability and availability. >>I'd love to get both of your opinion. You've been talking about the, the, the, the phases of customers, the advanced got Kubernetes going crazy distributed, super alpha geek. Then you got the, the people who are building now, then you got the lagers who are coming online. Where do you guys see the market now in terms of, I know the Alphas are all building all the great stuff and you guys had great success with all the top logos and they're all doing hardcore stuff. As the mainstream enterprise comes in, where's their psychology, what's on their mind? What's, you share any insight into your perspective on that? Because we're seeing a lot more of it folks becoming like real cloud players. >>Yeah, I feel like in mainstream enterprise hasn't been lagging as much as people think. You know, certainly there's been pockets in big enterprises that have been looking at this and as distributed sequel, it gives you that scalability that it's absolutely essential for big enterprises. But also it gives you the, the multi-region, you know, the, you have to be globally distributed. And for us, for enterprises, you know, you need your data near where the users are. I know this is hugely important to you as well. So you have to be able to have a multi-region functionality and that's one thing that distributed SQL lets you build and that what we built into our product. And I know that's one of the things you like too. >>Yeah, well we're a brand new product. I mean we only founded the company two years ago, but we're actually getting inbound interest from big enterprises because we solve the kinds of challenges that they have and whether, I mean, most of them already do have a cockroach footprint, but whether they did or didn't, once they need to bring in our product, they're going to be adopting cockroach transitively anyway. >>So, So you're built on top of Cockroach, right? And Spice dv, is that open source or? >>It >>Is, yep. Okay. And explain the role of open source and your business model. Can you take a minute to talk about the relevance of that? >>Yeah, open source is key. My background is, before this I was at Red Hat. Before that we were at CoreOS, so CoreOS acquisition and before that, >>One of the best acquisitions that ever happened for the value. That was a great, great team. Yeah, >>We, we, we had fun and before that we built Qua. So my co-founders and I, we built Quay, which is a, a first private docker registry. So CoreOS and, and all of those things are all open source or deeply open source. So it's just in our dna. We also see it as part of our go-to market motion. So if you are a database, a lot of people won't even consider what you're doing without being open source. Cuz they say, I don't want to take a, I don't want to, I don't want to end up in an Oracle situation >>Again. Yeah, Oracle meaning they go, you get you locked in, get you in a headlock, Increase prices. >>Yeah. Oh yeah, >>Can, can >>I got triggered. >>You need to talk about your PTSD there >>Or what. >>I mean we have 20,000 stars on GitHub because we've been open and transparent from the beginning. >>Yeah. And it >>Well, and both of your projects were started based on Google Papers, >>Right? >>That is true. Yep. And that's actually, so we're based off of the Google Zans of our paper. And as you know, Cockroach is based off of the Google Span paper and in the the Zanzibar paper, they have this globally distributed database that they're built on top of. And so when I said we're gonna go and we're gonna make a company around the Zabar paper, people would go, Well, what are you gonna do for Span? And I was like, Easy cockroach, they've got us covered. >>Yeah, I know the guys and my friends. Yeah. So the question is why didn't you get into the first round of Cockroach? She said don't answer that. >>The question he did answer though was one of those age old arguments in our community about pronunciation. We used to argue about Quay, I always called it Key of course. And the co-founder obviously knows how it's pronounced, you know, it's the et cd argument, it's the co cuddl versus the control versus coo, CTL Quay from the co-founder. That is end of argument. You heard it here first >>And we're keeping it going with Osted. So awesome. A lot of people will say Zeed or, you know, so we, we just like to have a little ambiguity >>In the, you gotta have some semantic arguments, arm wrestling here. I mean, it keeps, it keeps everyone entertained, especially on the over the weekend. What's, what's next? You got obviously Kubernetes in there. Can you explain the relationship between Kubernetes, how you're handling Spice dv? What, what does the Kubernetes piece fit in and where, where is that going to be going? >>Yeah, great question. Our flagship product right now is a dedicated, and in a dedicated, what we're doing is we're spinning up a single tenant Kubernetes cluster. We're installing all of our operator suite, and then we're installing the application and running it in a single tenant fashion for our customers in the same region, in the same data center where they're running their applications to minimize latency. Because of this, as an authorization service, latency gets passed on directly to the end user. So everybody's trying to squeeze the latency down as far as they can. And our strategy is to just run these single tenant stacks for people with the minimal latency that we can and give them a VPC dedicated link very similar to what Cockroach does in their dedicated >>Product. And the distributed architecture makes that possible because it's lighter way, it's not as heavy. Is that one of the reasons? >>Yep. And Kubernetes really gives us sort of like a, a level playing field where we can say, we're going going to take the provider, the cloud providers Kubernetes offering, normalize it, lay down our operators, and then use that as the base for delivering >>Our application. You know, Jake, you made me think of something I wanted to bring up with other guests, but now since you're here, you're an expert, I wanna bring that up, but talk about Super Cloud. We, we coined that term, but it's kind of multi-cloud, is that having workloads on multiple clouds is hard. I mean there are, they are, there are workloads on, on clouds, but the complexity of one clouds, let's take aws, they got availability zones, they got regions, you got now data issues in each one being global, not that easy on one cloud, nevermind all clouds. Can you share your thoughts on how you see that progression? Because when you start getting, as its distributed database, a lot of good things might come up that could fit into solving the complexity of global workloads. Could you share your thoughts on or scoping that problem space of, of geography? Yeah, because you mentioned latency, like that's huge. What are some of the other challenges that other people have with mobile? >>Yeah, absolutely. When you have a service like ours where the data is small, but very critical, you can get a vendor like Cockroach to step in and to fill that gap and to give you that globally distributed database that you can call into and retrieve the data. I think the trickier issues come up when you have larger data, you have huge binary blobs. So back when we were doing Quay, we wanted to be a global service as well, but we had, you know, terabytes, petabytes of data that we were like, how do we get this replicated everywhere and not go broke? Yeah. So I think those are kind of the interesting issues moving forward is what do you do with like those huge data lakes, the huge amount of data, but for the, the smaller bits, like the things that we can keep in a relational database. Yeah, we're, we're happy that that's quickly becoming a solved >>Problem. And by the way, that that data problem also is compounded when the architecture goes to the edge. >>Totally. >>I mean this is a big issue. >>Exactly. Yeah. Edge is something that we're thinking a lot about too. Yeah, we're lucky that right now the applications that are consuming us are in a data center already. But as they start to move to the edge, we're going to have to move to the edge with them. And it's a story that we're gonna have to figure out. >>All right, so you're a customer cockroach, what's the testimonial if I put you on the spot, say, hey, what's it like working with these guys? You know, what, what's the, what's the, you know, the founders, so you know, you give a good description, little biased, but we'll, we'll we'll hold you on it. >>Yeah. Working with Cockroach has been great. We've had a couple things that we've run into along the way and we've gotten great support from our account managers. They've brought in the right technical expertise when we need it. Cuz what we're doing with Cockroach is not you, you couldn't do it on Postgres, right? So it's not just a simple rip and replace for us, we're using all of the features of Cockroach, right? We're doing as of system time queries, we're doing global replication. We're, you know, we're, we're consuming it all. And so we do need help from them sometimes and they've been great. Yeah. >>And that's natural as they grow their service. I mean the world's changing. >>Well I think one of the important points that you mentioned with multi-cloud, we want you to have the choice. You know, you can run it in in clouds, you can run it hybrid, you can run it OnPrem, you can do whatever you want and it's just, it's one application that you can run in these different data centers. And so really it's up to you how do you want to build your infrastructure? >>And one of the things we've been talking about, the super cloud concept that we've been issue getting a lot of contrary, but, but people are leaning into it is that it's the refactoring and taking advantage of the services. Like what you mentioned about cockroach. People are doing that now on cloud going the lift and shift market kind of had it time now it's like hey, I can start taking advantage of these higher level services or capability of someone else's stack and refactoring it. So I think that's a dynamic that I'm seeing a lot more of. And it sounds like it's working out great in this situation. >>I just came from a talk and I asked them, you know, what don't you wanna put in the cloud and what don't you wanna run in Kubernetes or on containers and good Yeah. And the customers that I was on stage with, one of the guys made a joke and he said I would put my dog in a container room. I could, he was like in the category, which is his right, which he is in the category of like, I'll put everything in containers and these are, you know, including like mis critical apps, heritage apps, since they don't wanna see legacy anymore. Heritage apps, these are huge enterprises and they wanna put everything in the cloud. Everything >>You so want your dog that gets stuck on the airplane when it's on the tarmac. >>Oh >>God, that's, she was the, don't take that analogy. Literally don't think about that. Well that's, >>That's let's not containerize. >>There's always supply chain concern. >>It. So I mean going macro and especially given where we are cncf, it's all about open source. Do y'all think that open source builds a better future? >>Yeah and a better past. I mean this is, so much of this software is founded on open source. I, we wouldn't be here really. I've been in open source community for many, many years so I wouldn't say I'm biased. I would say this is how we build software. I came from like in a high school we're all like, oh let's build a really cool application. Oh you know what? I built this cuz I needed it, but maybe somebody else needs it too. And you put it out there and that is the ethos of Silicon Valley, right? That's where we grew up. So I've always had that mindset, you know, and social coding and why I have three people, right? Working on the same thing when one person you could share it's so inefficient. All of that. Yeah. So I think it's great that people work on what they're really good at. You know, we all, now you need some standardization, you need some kind of control around this whole thing. Sometimes some foundations to, you know, herd the cats. Yeah. But it's, it's great. Which is why I'm a c CF ambassador and I spend a lot of time, you know, in my free time talking about open source. Yeah, yeah. >>It's clear how passionate you are about it. Jake, >>This is my second company that we founded now and I don't think either of them could have existed without the base of open source, right? Like when you look at I have this cool idea for an app or a company and I want to go try it out, the last thing I want to do is go and negotiate with a vendor to get like the core data component. Yeah. To even be able to get to the >>Prototypes. NK too, by the way. Yeah. >>Hey >>Nk >>Or hire, you know, a bunch of PhDs to go and build that core component for me. So yeah, I mean nobody can argue that >>It truly is, I gotta say a best time if you're a developer right now, it's awesome to be a developer right now. It's only gonna get better. As we were riff from the last session about productivity, we believe that if you follow the digital transformation to its conclusion, developers and it aren't a department serving the business, they are the business. And that means they're running the show, which means that now their entire workflow is gonna change. It's gonna be have to be leveraging services partnering. So yeah, open source just fills that. So the more code coming up, it's just no doubt in our mind that that's go, that's happening and will accelerate. So yeah, >>You know, no one company is gonna be able to compete with a community. 50,000 users contributing versus you riding it yourself in your garage with >>Your dogs. Well it's people driven too. It's humans not container. It's humans working together. And here you'll see, I won't say horse training, that's a bad term, but like as projects start to get traction, hey, why don't we come together as, as the world starts to settle and the projects have traction, you start to see visibility into use cases, functionality. Some projects might not be, they have to kind of see more kind >>Of, not every feature is gonna be development. Oh. So I mean, you know, this is why you connect with truly brilliant people who can architect and distribute sequel database. Like who thought of that? It's amazing. It's as, as our friend >>You say, Well let me ask you a question before we wrap up, both by time, what is the secret of Kubernetes success? What made Kubernetes specifically successful? Was it timing? Was it the, the unambitious nature of it, the unification of it? Was it, what was the reason why is Kubernetes successful, right? And why nothing else? >>Well, you know what I'm gonna say? So I'm gonna let Dave >>First don't Jake, you go first. >>Oh boy. If we look at what was happening when Kubernetes first came out, it was, Mesosphere was kind of like the, the big player in the space. I think Kubernetes really, it had the backing from the right companies. It had the, you know, it had the credibility, it was sort of loosely based on Borg, but with the story of like, we've fixed everything that was broken in Borg. Yeah. And it's better now. Yeah. So I think it was just kind and, and obviously people were looking for a solution to this problem as they were going through their containerization journey. And I, yeah, I think it was just right >>Place, the timing consensus of hey, if we just let this happen, something good might come together for everybody. That's the way I felt. I >>Think it was right place, right time, right solution. And then it just kind of exploded when we were at Cores. Alex Povi, our ceo, he heard about Kubernetes and he was like, you know, we, we had a thing called Fleet D or we had a tool called Fleet. And he's like, Nope, we're all in on Kubernetes now. And that was an amazing Yeah, >>I remember that interview. >>I, amazing decision. >>Yeah, >>It's clear we can feel the shift. It's something that's come up a lot this week is is the commitment. Everybody's all in. People are ready for their transformation and Kubernetes is definitely gonna be the orchestrator that we're >>Leveraging. Yeah. And it's an amazing community. But it was, we got lucky that the, the foundational technology, I mean, you know, coming out of Google based on Go conferences, based on Go, it's no to coincidence that this sort of nature of, you know, pods horizontally, scalable, it's all fits together. I does make sense. Yeah. I mean, no offense to Python and some of the other technologies that were built in other languages, but Go is an awesome language. It's so, so innovative. Innovative things you could do with it. >>Awesome. Oh definitely. Jake, I'm very curious since we learned on the way and you are a Detroit native? >>I am. Yep. I grew up in the in Warren, which is just a suburb right outside of Detroit. >>So what does it mean to you as a Michigan born bloke to be here, see your entire community invade? >>It is, I grew up coming to the Detroit Auto Show in this very room >>That brought me to Detroit the first time. Love n a I a s. Been there with our friends at Ford just behind us. >>And it's just so interesting to me to see the accumulation, the accumulation of tech coming to Detroit cuz it's really not something that historically has been a huge presence. And I just love it. I love to see the activity out on the streets. I love to see all the restaurants and coffee shops full of people. Just, I might tear up. >>Well, I was wondering if it would give you a little bit of that hometown pride and also the joy of bringing your community together. I mean, this is merging your two probably most core communities. Yeah, >>Yeah. Your >>Youth and your, and your career. It doesn't get more personal than that really. Right. >>It's just been, it's been really exciting to see the energy. >>Well thanks for going on the queue. Thanks for sharing. Appreciate it. Thanks >>For having us. Yeah, thank you both so much. Lisa, you were a joy of ball of energy right when you walked up. Jake, what a compelling story. Really appreciate you sharing it with us. John, thanks for the banter and the fabulous questions. I'm >>Glad I could help out. >>Yeah, you do. A lot more than help out sweetheart. And to all of you watching the Cube today, thank you so much for joining us live from Detroit, the Cube Studios. My name is Savannah Peterson and we'll see you for our event wrap up next.

(melodic music) >> Good afternoon. Welcome back to Chicago. Lisa Martin here with John Furrier. Day one of our coverage of Ansible Fest 2022. John, it's great to be back in person. People are excited to be here. >> Yeah. We've had some great conversations with folks from Ansible and the community and the partner side. >> Yeah. One of the things I always love talking about John, is talking with organizations that have been around for a long time that maybe history, maybe around nearly a hundred years, how are they embracing technology to modernize? Yeah, we got a great segment here with the financial services leader, end user of Ansible. So it's be great segment. >> Absolutely. Please welcome Noor Shadid to the program, the senior SVP, excuse me, senior technology manager at Wells Fargo. Noor it's great to have you on theCUBE. Thank you for joining us. >> Of course. Happy to be here. >> Thanks. >> Talk a little bit about technology at Wells Fargo. I was mentioning to you I've been a longtime customer and I've seen the bank evolve incredibly so in the years I've been with it. But... >> Yeah. >> ...talk about Wells Fargo was a technology-driven company. >> Yeah. So I like to consider Wells, right? Being in a financial institution company. So I consider us a technology company that does banking as a customer, right? Like we were talking about. There's so much that we've been able to release over the couple of years, right? I mean, decades worth of automation and technology has been coming out, but lately, right? The way we provide for our customers, how fast at scale, what we're doing for our customers, it's been, it's been significant, right? And I think our goal is always how can we enhance the process for our customers and how can we provide them the next best thing? And I think technology has really allowed us to evolve with our customers. >> The customers. We are so demanding these days. Right? I think one of the things that short supplied in the last two years was patience and tolerance. >> Yes. >> People. And I don't think that's going to rubber band back? >> Yeah. No, I don't think so. >> So how, talk to us about how Wells is using automation to really drive innovation and, surprise and delight those customers on a minute by minute basis. >> Yeah. And so, you know, if you think about banking, we've been able, with automation, we've been able to bring banking into the 21st century. You do not have to go to a branch to manage your money anymore. You do not have to go, you know, go to deposit your check inside of a branch. You can do it through your mobile app, right? That's driven by automation and innovation, right? And, you know, we have all of these back ends tools working for us to help get us to this next generation of, of banking. We can instantly send money to each other. We don't have to worry about, I need to go and figure out how I'm going to get money to this person and I need to wait, you know, X amount of days. You, you have the ability and you have, you feel safe being able to manage your money at the organization. And so automation has really allowed us to get to this place where we can constantly enhance and provide features and reliability to our customers. >> It's interesting you mentioned that you guys are a technology can have it do banking reminds me of the old iPhone analogy. It's a computer that happens to make phone calls. >> Yeah. >> So like, this is the similar mindset. How do you guys keep up? >> Yeah. >> With the technology? >> So it's tough, right? Because there's so much that comes out. And I think the only thing that's constant in technology is change, right? Because it's constantly evolving. But what we do is we, integrate very well with these new tools. We do proof of concepts where we try to, you know, what's on the market, what's hot, how can we involve, like, how can we involve these new tools in our processes? How can we provide a better end result for our customers by bringing in these new tools? So we have a lot of different teams that bring, you know, their jobs are to like, do these proof of concepts and help us build and evolve our own strategies, right? So it keeps us, it keeps us on our toes and I think it keeps, you know, all these new things that are coming out in the market. We're a part of it. We want to evolve with those, what the latest and greatest is. And it's, it's been working right as customers of financial services and us managing our money through, you know, through banks. It's been great. >> So the business is the application. >> Yes. >> And how do you guys make that happen when it comes down to getting the teams aligned? What's the culture like? Explain. >> Yeah. So at Wells we have evolved so much over the, over the last few years. The culture right now is we want to make changes. You know, we are making changes. We want to drive through innovation. We want to be able to provide our, you know, it's a developer centric approach right now, right? We want to push to the next and the greatest. And so everybody is excited and everybody's adapting to all of what's happening in the environment right now. So it's been great because we are able to use all of these new features and tools and things that we were just talking about by allowing our developers to do that work and allowing people to learn these new skills and be able to apply them in their jobs, which is now creating this, you know, a better result for our customers because we're releasing at such a faster pace. And at scale. >> Talk about how, you talked about multiple groups in the organization really investing in innovative technology. How do you get buy-in? What's that sort of pyramid like up to the top level? >> Yeah. >> Because to your point, you're making changes very quickly and consumers demand it. >> Yep. >> You can do everything from home these days. >> Yep. >> You don't have to go into a branch. >> Yeah, yeah. >> Which has changed dramatically in the last it's. >> Powerful few years. Yeah. >> But how, what's that buy-in conversation like from our leadership? >> Yeah. If you don't have leadership buy-in, it's very difficult to make those changes happen. But we at Wells have such a strong support from our leadership to be a part of the change and be, you know, constantly evolve and get better. So the way we work, cause we're such a large organization, you know, we bring in our business, you know, our business teams and we talk to them about what is it that's best going to better our customers. How do we also not just support external but internal, right? How do we provide these automated tools or processes for people to want to do this next work and, and do these, you know, these new releases for our customers. And so we bring in our business partners and, and we bring in our leadership and, our stakeholders and we kind of present to them, you know, this is what we're trying to do. This is the return that you'll get. This is what our customers will also receive. And this is, you know, this is how we keep evolving with that. >> How has the automation culture changed? Because big discussion here is reuse, teamwork, I call it multiplayer kind of organizations where people are working together. 'Cause that's a big theme of automation. >> Yeah. >> Reuse, leverage. >> Yep. >> Can you explain how you guys look at that? >> Yeah. It's changed the way that we do banking because we're eliminating a lot of the repetitive tasks in the toil because we have partners that are developing these, you know, services. So specifically with Ansible, we have these playbooks, rather than having every customer write the same playbook but with their own little, you know, flavor to it, we're able to create these generic patterns that customers can just consume simply by just going into a tool, filling out you know, filling out that playbook template, credentials, or whatever it is that they need and executing it. They don't have to worry about developing something from scratch. And it also allows our customers to feel safe because they don't have to have those skills out the box to be able to use these automation tools, right? They can use what's already been written and executed. >> So that make things go faster with the benefits or what? Speed? >> Faster stability, right? We're now speed, stability, scalability, because we're now able to use this at scale. It's not just individual teams trying to do this within small spaces. We're able to reliable, right? Automation allows us to be reliable internally and for our customers. Because you're not asking, there's no human intervention when you're automating, right? You have these opportunities now for people to just, it's one click, you know, one click solution or you're, you're end to end. You got self-healing involved. It's really driving the way that we do our work today. >> So automation sounds like it's really fueling the internal employee experience at Wells... >> Yes. >> ...as well as the customer experience. And those two things are like this to me. They're inextricably linked. >> A hundred percent because if you need it, they need to be together, right? You want your internal to also be happy because they want to be able to develop these solutions and provide these automation opportunities for our teams, right? And so with the customers, they're constantly seeing these great features come out, right? We can, you know, with AIML today, we're now able to detect fraud significantly. What we would've, what we could've done a couple years ago. And, and developers are excited to be able to do that, right? To be able to learn all these new tools and new technologies. >> What's interesting Wells is you guys are like an edge application. Obviously everyone's got banking in their hand. FinTech obviously money's involved. So there's people interested in getting that money. >> Yeah. >> Security hackers or whatnot. So when you got speed and you got the consistency, I get that. As you look at securing the app, that becomes a big part of what, what's the conversations like there? >> Yeah. >> 'Cause that's the number one concern. And it's an Edge app. I got my mobile, I got my desktop. >> Yeah. >> Everything's in the cloud on premise. >> Yeah. And, and I think for us, security is number one. You know, we want to make sure that we are providing the best for our customers and that they feel safe. Banking, whatever financial service you're working with, you want to feel like you can trust that your money with those services. Right? So what we do is we make sure that our security partners are with us from day one. They're a part of the process. They're automating their pieces as well. We don't want to rely on humans to do a lot of the manual work and do the checking and the logging. You want it to be through automation and new tools, right? You want it to be done through trusted services. You don't, you know, security is right there with us. They're part of our technology organization. They are in the technology org. So they're the ones that are helping us get to that next generation to provide, you know, more secure processes and services for customers. >> And that's key for trust. >> Yes. >> And trust is critical to reduce churn and to, you know, increase the customer lifetime value. But, but people, I mean, especially with the amount of generations that are alive today in banking, you need to be able to deliver that trust intrinsically to any customer. >> Yes, a hundred percent. And you want to be able to not only trust the service but yourself that you can do it. You know, when you go into your app and you make a payment, or when you go in and you want to send, you know, you want to send money to a different, you know, a different bank account, you want to be able to know that what you just did is secure and is where you plan to send it. And so being able to create that environment and provide those services is, is everything right for our customers. >> What are some of the state-of-the-art kind of techniques or trade craft around building apps? 'Cause I mean, basically you're digitally transformed. I mean, you guys are technology first. >> Yeah. >> The app is the company. >> Yeah. >> That's, that's the bank. How do you stay current? What's some of the state of the art things that you guys do that wasn't around just a few years ago? >> Yeah, I mean, right now just using, we're using tools like Terraform and Ansible. We're making sure that those two are hand in hand working well together. So when we work on provisioning, when we, during provisioning where it's all, you know, it's automated, fully end to end, you know, AI ops, right? Being able to detect reoccurring issues that are happening. So if you have a incident we want to learn from that incident and we want to be able to create, you know, incident tickets without having to rely on a human to find that, you know, that problem that was occurring and self-healing, right? All of this is starting to evolve and bringing in the, the proper alerting tools, bringing in the pro, you know, the right automation tools to allow that self-healing to work. That's, you know, these are things that we didn't have, you know, year, decade ago. This is all coming out now as we're starting to progress and, and really take innovation and, you know, automation itself.... >> What's the North star internally when you guys say, hey, you know, down five years down the road, bridge to the future, we're transforming, we've continued to innovate. Scale is a big deal. Data, data sovereignty, all these things are coming up. And what's the internal conversation like when you talk about a future state? >> Yeah, I think right now we're on our cloud transformation journey, right? We're moving right now. We have workloads into our two CSPs or public cloud. Also providing a better service for infrastructure and being able to provide services internally at a faster space, right? So moving into the public cloud, making sure everything's virtualized, moving away from hard, you know, physical hardware or physical servers. That's kind of the journey that we're on right now. Right? Also, machine learning. We want to be able to rely on these, you know, bots. We want to be able to rely on, on things learning from what we're doing so that we don't make the same mistakes again. >> Where would you say the most value or the highest ROI that you've gotten from automation today? Where is that in the organization? >> There's so much, but what I mean because of all of the work that we're doing, there's a lot that I could list, but what I will say is that the ability to allow self-healing in our environments without causing issues is a very big return. Automating failovers, right? I think a lot of our financial institutions have made that a priority where they want to make sure that their applications are active, active and also that when things do go wrong, there is something in place to make sure that that incident actually doesn't, you know, take down any problems. I think it's just also investing in people. Right now, the market is hot and we want to make sure that people feel like they're being able to contribute, they're using the latest and greatest tools. They're able to upskill within our own environments at the firm. And I think our organization does an amazing job of prioritizing people. And so we see the return because we're prioritizing people. And I think, you know, a lot of institutions are trying, you know, people first, people first. But I can say that at Wells, because we are actually driving this, we're allowing, you know, we're enforcing that. We want our engineers to get the certifications. We're providing, you know, vouchers so that people can get those clouds certifications. It's when you do that and you put people first, everything kind of comes together. And I think, you know, a lot of what we see in our industry, it's not really the technology that's the problem, it's process because you're so, you know, we're working at large scales. Our environments are massive. So, you know, my three years at Wells have seen a significant amount of change that has really driven us to be.... >> On that point better. How about changing of the roles? IT, I mean, back in the day, IT serves the business, you know, IT is the business now, right? As, as you've been pointing out. What does the roles change of as automation scales in, is it the operator? I mean, we know what's going on with dev's devs are doing more IT in the CICD pipe lining. >> Yep. >> So we see that velocity check, good cloud native development. What's the op scene look like? It seems to be a multi-tool role. >> Yeah. >> Where the versatility of the skill set... >> Yep. >> ...is the quick learner. >> Yep, able to adapt. >> And yeah, what's your view on this new persona that's emerging from this new opportunity? >> Yeah, and I think it's a great question because if you think about where we're going, and even the term DevOps, right? It means so many things to different people. But literally when you think about what DevOps is allowing our developers and our operations to work together on one team, it's allowing, you know, our operation engineers aren't, you know, years ago, ops engineers were not doing the development work. They were relying on somebody to do the development work and they were just supporting making sure our systems were always available, right? Our engineers are ops are now doing the development work. They're able to contribute and to get, they're writing their own playbooks. They're able to take them into production and ensure that they're, being used correctly. We are change driven execution organization. Everything is driven through change and allowing our ops engineers or production score engineers to write their own playbooks, right? And they know what's happening in the environment. It's powerful. >> Yeah. You're seeing DevOps become a job title. >> Yeah (laughs). >> Used to be like a function of philosophy... >> Yeah, yeah. >> ... and then SRE's... >> SRE's. >> SRE are like how many servers do you have? I don't know, a cloud, what's next? (all laugh) >> What's next? Yeah, I think with SREs it's, you know, it's important that if you have site reliability engineers, you're working towards, you know, those non-functional requirements... >> Yeah. >> ...making sure that you're handling those key components that are required to ensure that our systems, our applications and our integrations, you know, are up there and they're meeting the standards that we set for those other faults. >> And, and I think Red Hat Ansible nailed it here because infrastructure is code. We get that infrastructure has configuration as code, but OPS says code really is that SRE outcome. SRE also came from the Google background, but that means infrastructure's just doing, it's thing. >> Yes. >> The ops is automated. >> Yes. >> That's an interesting concept. >> Yeah, because it's not, you know, it's still new, right? A lot of organizations used to see, and they probably still see operations as being the, you know, their role is just to make sure that the lights are on and they have specific access so they, you know, they're not touching code, but the people that are doing the work and know the environment should really be the ones under creating the content for it. So yeah, I mean it's crazy what's happening now. >> So I got an analogy that's going to be banking analogy, but for tech, you know, back in the automation, Oh, going to put my job out of business, ATMs are going to put the teller out of business as more tellers now than there are before the ATMs. So that metaphor applies into tech where people are like, "What am I auto? What's automating away? Is it my job?" And so actually people know it's not. >> Yeah. >> But what does that free up? So if you assume, if you believe that's good, you say, okay, all the grunt work and the low level on differentiated heavy lifting gets automated away. >> Yeah. >> Great. What does that free up the talent to do? >> Yeah, so when you, and that's great that you bring it up because I think people fear, you know, of automation, especially people that weren't doing automation in the past and now their roles are now they're able to automate those roles out. They're fearful that they don't have a space, a role anymore. But that's not the case at all. What we prioritize is now that those new engineers have this new skill set, apply them. Start using it to be a part of this transformation, right? We're moving from, we went from physical to virtual to now, you know, we're moving into the public, moving into the cloud, right? And that, that transformation, you need people who are ramping up their skill sets, you know, being a part of one of the tools that I own is terraform at Wells that, you know, right now our priority is we're trying to ramp up the organization to learn terraform, right? We want people to learn, you know, this new syntax, this new, you know, HCL and it's, you know, people have been automating some of the stuff that they're doing in their day to day and now trying to learn something new so that they can contribute to this new transformation. >> So new functionality, higher value services? >> Yes, yeah. >> It brings tremendous opportunity for those folks involved in automation. >> Yes. >> or on so many levels. >> Yep. >> Last question, Noor for you is what, you know, as we are rounding out calendar year 2022, entering into 2023, that patience is, that we talked about is still not coming back. What's next for Wells as a technology company that does banking? >> I mean, you name it, we're working on it, because we want to be able to deliver the best for our customers. And I think right now, you know, our digital transformation strategy and, and moving into the public cloud and getting our applications re-architected so that we are moving into microservice driven apps, right? We're moving these workloads into the public cloud in a seamless way. We're not lifting and shifting so that we're not causing more problems into the environment. Right. And I think our, our, our goal is right, Like I was saying earlier, people and evolving with the technology that's coming out. We're not, you know, we are a part of the change and we are happy to be a part of that change and making those changes happen. >> People first. >> Awesome, awesome stuff. >> Automation first sounds outstanding and I will never look at Wells Fargo as a bank again. >> Yeah. (laughter) >> Perfect. Perfect. >> Yeah, that's awesome. >> It's been such a pleasure having you on the program, talking about how transformative Wells has been and continues to be. >> Yeah. >> We appreciate your insights and your time. >> Thank you. >> Thank you so much. It was lovely being her. Pleasure here. Thank you guys. >> For our guest and John Furrier, I'm Lisa Martin. You've been watching theCUBE all day, I'm sure, live from Chicago at Ansible Fest 2022. We hope you have a wonderful rest of your day and John and I will see you tomorrow morning.

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>Welcome back to The Cube's, continuous coverage with day two of UI Path forward. Five. My name is Dave Ante. I'm here with my co-host Dave Nicholson. And you are watching The Cube. It's all about the robots, the automations, the transformations and beyond. Audi Gana is here. She's group executive at Vodacom and Niva is back. She's senior director of telecommunications industry for UiPath. Ladies, welcome to the Cube. >>I thank you very much. >>So Vodacom a leading telco in in Africa across the continent. Tell us more about the company. >>The company is a traditionally telecommunications company, but our vision 2025 is first to transition from being a telco to a technology company. So you'll find that a lot of the use cases that we've actually started embarking on, combined the combination of telco and FinTech. And we've got a lot of RPA bot also supporting the FinTech platform, which is quite a major step in our strategy. >>So, you know, it's interesting Mark Andreessen's famous comment, Every company's a software company. I like to think every company's a technology company, technology driven. So what does that actually mean for you? Is it like a split brain between FinTech? Cuz it's pretty clear that FinTech is always a highly, you know, technology oriented and telecom. Are they sort of together driving a, a technology business? How does that >>Work? It's, it's a, it's a converge use of the technology to add value to the customer. So what we wanna do is to get to the point where we have converged services where the Telecommunicate, cuz at the end of the day in the African market, you'll find that there's a lot of markets that are unbanked. So you find that a cell phone is a means of communication and a a mobile platform for the users. So it's a natural progression for our company to actually play in both spaces. And I think one of the things I find quite interesting is the levels of trust that a lot of citizens have in our financial platform. In that even some of the governments are paying social grants using the platform. And so it almost becomes, without the phones a lot of people cannot function type of scenario. >>Nevas is your role a global role? >>Yes, it is a global role. >>Okay. So it's interesting cause you're I think based down under, right? I am. Is that true? Okay. Obviously spent some time in, in, in the African continent. How are you, what are you seeing in terms of the, the trends in, in telecommunications that, and are you noticing there's gotta be differences across different regions? You know, a lot of times you hear, oh no, there's really kind of a global world out and I know it is, but telco seems to be one of the industries that has some uniqueness within the different breaches. What are you seeing? >>Look, we are privileged to work with more than 200 telecoms around the world. But clearly from a technology perspective, there are some regions that have embraced technology sooner than the others, particularly when it comes to automation. Now we do have use cases with all of them that we are, you know, the 200 we are working with. But the extent to which they become strategic partners, Varie is, you know, what I find is in, in the US we are doing a lot in the customer experience space with the telecoms in aj it's more back of house. And with telecoms like Vodacom, it's really strategic. You know, automation is being applied practically in every facet of work. And you know, sometimes that could be because the demand is just so great for connectivity, you know, at times there's a skills gap, but it does vary. But what's reassuring is that there is a journey and you know, at this event what I have seen is telecoms wanting to learn from other telecoms. And I must say Artie has been in huge demand. We did about 22 meetings yesterday with others wanting to know, which again is that strategic trend. >>Artie, my understanding is you've been at this for a while, this automation journey for quite some time and p i pass. Interesting. I mean it's a company that's founded in 2005 and kind of did sort of its own thing for a long time and then realized it had lightning in a bottle Yeah. For a mid last decade. But my understanding is you've seen it all. You've seen the, the legacy platforms. And so tell me about your personal journey with automation and then the companies. >>Okay, so there's the automation pre rpa, which was strangely enough, I come from banking, got a finance degree, did automated ations in one of the bigger banks. And somehow I transitioned. And I mean from a history perspective, the one of the previous platforms, which was the biggest one at the time, that's where I got to learn about rpa. And then there's another vendor that we then use in another company. So this is almost my third vendor that I'm experiencing in the RPA space. Having joined RPA space in 2015, apparently I'm kind of a veteran, >>So, So what are you seeing is what's the difference between, I mean let's call UI path, that was sort of a modern focus on simple to deploy. That's really how they get started when I first found them. How do you compare what UiPath has? And there are others, there are other modern platforms to sort of the legacy platforms. What's the >>Difference? I think it's the diversity and the applicability of the technology across multiple industries is something that still amazes me up until today. Because the kind of customers I've been meeting today, I, I would not, I met a guy who owns an ice cream company and I'm like, where would automation come in here? But he's actually quite a big customer of, of UiPath, you know. So I think that's one thing I appreciate. I think the ease of use, it has actually allowed for a lot of people to be part of the digital transformation. I think in the his, in the past technology has been seen as something that was a bit elite and that you needed to have X amount of skills and level of education. Whereas the RPA industry has almost bridged that gap in actually bringing along as many people in the journey in terms of digital transformation. And the fourth industrial revolution is now starting to become more inclusive >>Horizontal across industries. >>Yeah. >>So Vodacom headquartered in South Africa. Okay. But presence throughout the continent. Yes. I imagine that various geographies have various twists and quirks to them and different needs. But as a general premise, the African continent has led the rest of the world in terms of embracing these little mobile devices for the most mission. Critical from a personal perspective things, right? Yeah. So if you, if you're already trusting all of your finances and even interaction with your government from a financial perspective. Yeah. When you say technology platform technology is moving forward, what's more critical from that? Or how do you, how do you, how do you branch off from that? What are some of the other things that you can share with us that you're looking towards in the future that may, that that may trickle over here eventually? >>So I think what one of the things we started playing around with quite well is actually the convergence of machine learning, AI and rpa. You would find that a lot of research will tell you that this is the future of the automation and for us, we are actually living the future in that we have civil use cases that are actually extracting a lot of business value. Where we've realized that RPA in of itself, and this is obviously oversimplifying the technology is almost the unlimited hands on keyboards that you could ever have, right? And then machine learning and AI almost the becomes the unlimited brains. So when you then combine the tool, you almost have this strong technology that can revolutionize how we operate and service our customers. >>Well how do, how does that translate? Can you translate that into a user experience at this point? So I mean, we're talking about people who they, they have a motor license, they don't have a desktop computer at home. Yeah. This is their portal into the world. >>So you find that if you're speaking pure telco, and I'm obviously over simplifying there some nowhere an engineer, right? But I think at, at a very simplified level, there's a lot of legacy technology that is used in the telco space and you'll find that because of that, there's a lot of lack of integration. And you'll find that the reasons why a lot of customers call corners is because there's poor integration in a lot of instances. And it's, it's, it's, it's ad hoc. So it's not as if the system is failed completely. So what we've now done is to try and see how do we use machine learning to pick up on those anomalies on the network, right? And because each time something breaks, right, it's almost a fixed way to fix it and therefore the machine learning picking up there normally almost the hands over to the RPA bot to fix the problem within the network element. But that means is that from a customer experience perspective, instead of you actually realizing there's a problem, we've fixed it before you even know that there's a problem. And therefore, and as you can imagine, it means that you then call the course into less because now you don't have the reason to complain because we've proactively identified the problem and we proactively use RPA then to fix it. So we almost have the almost like a self-healing element in within the, the, the RPA AI space. >>You know, I think of, we don't talk about the data, we haven't talked about the data much this week. I think in many respects this industry is, is data industry. Our automation is all about what you can do with the data. You said unlimited hands, unlimited brains. Cuz to me you have unlimited data and a lot of times you just can't handle it. Yeah. So what's the data angle on all this? >>So firstly, I know a lot of people will say data is the new oil. No, >>Right? So I would never >>Say that. I always though, I think I always ask people if I give you a bucket of brain crude oil, right? What are you gonna do with it? Right? Right. And similar to data, right? So I want to almost equate data to that crude oil element, but if you don't know how to refine it, process it, get it to be reliable, it's very useless in of its natural sense. So I think one of the things we've realized is that leveraging of the analogy of the, the machine learning in the brain, if you are in the sales space, you forever trying to push new sales, right? And then chances are when a customer leaves you, you are almost in a reactor state. So, and I imagine a world where you could proactively identify a customer with the propensity to leave your company because a lot of customers don't just, they are situations where they'll be walking down the street competitor calls them, they leave, not because they were unhappy, but a lot of customers actually had several engagements with us that were not pleasant, whatever the definition is. >>So we then saw there was almost five types of attributes that resulted in customers leaving us. So what then that said was imagine if you are an account manager, right? And you got told UiPath P two I limited has experienced 1, 2, 3, 4. Right? Actually, please go engage with them because something is happening. It means that as an account manager, you are then equipped to have a meaningful engagement with the customer because you're saying, hi UiPath, I see you've had X amount of job calls and you've had x amount of complaints in our call center. What is happening is it could be, could be your network, maybe the tower where you are, do you, And then the conversation becomes so meaningful. And I think even during covid what we found is lot of customers started using less of our data, not because they were unhappy, but it became an affordability thing, right? >>Because this is a thousands and thousands of, of data elements and pieces around Yeah. About customer transactions. There's no way one human would be able to go through all of the data and make me meaningful decisions out of it. So we then found that some customers were complaining about affordability. So we then built another model that says if an account manager is talking to a customer and they're struggling from an affordability perspective, what's the next best offer you can make to your customer while you're engaging? And then if in, if, if now your UiPath takes up that offer, then you'd find that the bot does the post engagement provisioning on the system. Because now if you then said, I've only, I can only afford 10 lines, but only pay 10 gigs, but not 10 lines and 20 gigs, that is at least better than us losing the customer. >>Yeah. Right? And we offering them almost a downward migration type of situation. Then the bot does that on the system. So you would find that we almost playing in the space of a human, human centered, intelligent automation where machine learning becomes the brain, the person is amplified in how they operate at the customer. And then the RPA bot becomes the hands that executes on that. And as the account manager you focusing on engagement and convincing, which is really what people are great and selling as opposed to going through all of the pro cause VOCA is a lot of products. So as opposed to having a person going through the products and trying to find the best product for you, you know, so we, we are using machine learning to assist the >>Humans. I I mean in every, every interaction is consistent in that case. I know I sometimes have to call three or four times to find a professional that knows enough that can help me. Yes. Such a frustrating thing as a consumer. So you are, are you, you're attacking churn with automation. So we haven't even talked about how you guys are working together, your journey and all that stuff, but, but how are you guys working here? What are you, what are you doing? You know, in addition to what you just described with with ui. iPad? >>So I think my portfolio's quite wide. So I am, my team is in every single vertical in the organization from customer care to the consumer enterprise business units to finance technology, network compliance. And we do all of this in about six countries, right? So one of the things we've actually realized is that if we are looking at customer service, we wanted to understand why do customers call us? And I think I came from a point of ignorance because I'm not from telco, so I actually realized that if we're talking billing and finance revenue assurance, customers call us because we build them arly. But technically speaking it's our systems that there's something that resulted in the customer calling us. So why do we not know about our own systems? Why are we waiting for the customers to call us? And literally those are the questions I was asking cuz I felt like why are we, why are we waiting for the customers to call us? >>So we then then found a way to try and see within the billing systems where do the breakages happen, right? So that we fix them before the customer has to call us again. So then again from a billing perspective, it means that cuz it the billing element can come in two ways where we are giving you a service and not charging you for it. We then have revenue leakage or we, you are consuming something and we are overcharging you. Then you call us and say, Whatcom is stealing my data. Yeah, you're right out there. I promise you nobody wakes up in the morning and wants to take one gig of your data. So it almost becomes a day integrity initiative that results in good customer service but then result in eradication of course. As opposed to us waiting for customers to tell us what the problem is and trying to help them fast. Cuz that's generally always been what I've picked up the energy around customer service. How do we help you fast? I'm saying why must you call us when our systems had fail that? So we almost trying to see how do we use the technology internally to give customers a better experience and then also have the financial benefits that we are now starting to see happening in the, >>What's the scope of, of your like how many automations, how many bots? Can you give us a sense >>Of this? So right now I think we over on with all of the four, five countries that we are in, we over 400 bots. Wow. Okay. So we started in 2004 years ago, this is my fourth year in Voca. We, and we are not using just one product with UiPath. It became a platform because as we became across more kinds of problems, I think what I've appreciated about part is how we've actually created a partnership. Instead of them trying to sell me products for the sake of consuming products, it became a, this is my problem, right? And then somehow they would whip out the product that solves my problem type of thing. So it became a ecosystem of solutions that >>You must love hanging out with Artie. >>I absolutely do and love, you know, I've spent a career in telecommunications myself and you know, the best days were when you could deliver an outstanding customer experience. And as you can see from what Artie has achieved when you were more proactive and predictive, you can serve your customers so much more effectively and that just lift the morale of the team because we all, you know, have this purpose in doing our jobs. But this is automation and AI built into every part of that customer journey. So end to end, you know, the customer's much happier. You know there's a problem before the customer knows you can solve the problem in most cases before they even know. And that's just what we are all in business to do to make things better. >>Great story. Thank you so much for sharing. Appreciate coming back >>In the queue. Thank you very much. Thank >>You. Thank you for watching. Keep it right there. Don't forget, go to silicon angle.com, all the news, go to the cube.net. You'll see me all these videos are available on demand as well as the other events that we do. Dave VTE for Dave Nicholson. Keep it right there. Right back at forward five UI.

(light music) >> Welcome back to Supercloud 22. I'm John Furrier, host of theCUBE. We're here Palo Alto for a big event. Supercloud 22, we've got a great ecosystem conversation here. Ramesh Prabagaran, who's the co-founder and CEO, Prosimo. Ramesh, great to see you. Thanks for coming on. >> Thanks for having me, John. >> So, I wanted to bring you in because we've had previous CUBE conversations around cloud networking, latency, you also have some, some pedigree, Viptela. The folks in the industry know that's been a deep tech company. >> Yep. >> You have been around the block. You've seen the movie before. You've seen the tech trends. You've seen the hype. You've seen the fluff. Where's the meat on the bone with Supercloud in your opinion? >> So it, it starts with what enterprises are struggling with, right? And if you take a very simple example, it's actually quite fresh in my mind because I was just having this conversation this morning. A large bank has an application sitting in AWS, right? And they have to provide the application access to the treasury, to their suppliers, to ticker feeds, to all their downstream partners, and so on and so forth. Guess what? They don't control, where all those things are. They're in very different regions and very different clouds. And so you, whether you like it or not, you have a problem here, right? And so it starts with, for the particular bank, what are the capabilities that they need, right? And so AWS provides a whole host of native capabilities, but they still need to build a few more things on top. So going by, essentially the definition of Supercloud, even within a single cloud you need to build a few more capabilities on top. That gets worsened by the fact that now you need to provide access to various other clouds, various other regions and, and so forth. So, whether we like it or not, this movie is here to stay. >> What's the difference between Supercloud and multi-cloud? Because multi-cloud, I've been saying, is not necessarily a market yet. >> Correct, yes. So, Supercloud is essentially the cloud native capabilities provided by the hyperscalers, get you probably 30, 40% of the way, right? But then, in order to deliver on a care about, right? In our case, from a cloud networking standpoint, that is experience, that's performance, reliability, zero trust access, and then so forth. You have to take that a little bit further, and so we have vendors, like us, that actually build capabilities on top of the hyperscalers, right? Now, even if you think of a single cloud, how you build that is different on AWS than it's on Azure, than on GCP. But do the customers care? No, they want to be able to consume it in exactly the same way across all of them. So, whether it's multi-cloud or a single cloud, you have a problem that is white space on top of the hyperscalers capabilities that you just need to build. >> And what problems is it solving today? Because again, I, again, multi-cloud, I've yet seen the problem. I kind of get what's happening. Multiple clouds do exist. Use cases matter, maybe best debri, but they're standalone. They're not really interoperating, so to speak. So people have been successful on, on public cloud. >> That's correct, yes. >> For use cases? >> Absolutely. So even if you take a single cloud, for example, right? You have multiple problems to, to address. So let's take the example of, I have users coming from various different regions, around the globe, and I have apps that are spread, maybe not across like all clouds, but single cloud, maybe multiple regions, right? Now, I have a reach problem, which is, I need to go from where the user is to where the application is sitting. I have an experience problem because if my spinning wheel shows up, I'm going to go crazy. I have a security problem because I want to make sure it's only me that have access to it, right? But does the cloud provider solve for this entirely? No, they give you the nuts, the bolts, or what we call ours essentially, what you need is a, is a latte. They give you really nice coffee beans, not just one flavor, 20 flavors of those. They give you raw sugar and a few other things. They give you five different flavors of milk, but you got to make your own latte. So, that's what we do. >> And this is where the infrastructure transformation's happening. >> Exactly. >> And the super paz layer, as Dave Vellante and I have talked about in cloud, is you have to integrate a native cloud. >> Correct. >> Which is beautiful. It's integrated, everything works together, there's a lot of lattes to be made or espressos. >> Exactly. >> I mean, tons of great things there. So, big check marks, double check, gold star for AWS. >> Correct. >> All good. Now, on premises, we've found that hybrid is a steady state. >> Exactly. >> Okay, that's cloud operations. Now, you got the edge. Where does the Supercloud strategy come in? For the folks watching there, it's like, "Hey, okay, I get that." "But I don't want to just buy into another vendor's hype." >> Absolutely. >> "I got to build my own cloud," to your point about the lattes. >> Correct. >> They have to make their own infrastructure an application environment to power the developer. >> Exactly. And, and hybrid is here to stay as, as you pointed out, right, John? So, I have my data center and let's say when most folks start out they start to like a single region of a single cloud, right? And what are you most concerned about there? Hey, can I migrate? Can I start to build applications in the public cloud, right? And all you care about is can you talk back into my data center? Like, as long as some basic hygiene is there that's all they care about, right? The problem happens when you go from, kind of, the first five EC2 instances to 50 to a hundred, then you have a few other things that you need to care about, right? That's really kind of where the, the Supercloud capabilities start to come in, right? Because you have the cloud native things you can make that work for the first few days, but then after that you need augmented capabilities. >> So Ramesh, some people will say, "Hey, John, Supercloud okay, it's funny, ha ha ha." But isn't it just SAS? >> No, SAS is a delivery mechanism, right? And so, so there is the capability and that is how do you want to consume, right? And so capabilities or cloud native capabilities or piece of software capabilities or (unintelligible) cluster form factor and so forth. How do you want to consume? Maybe it's a package form factor, it is a size, it could even be passive if it's sitting in the, in the element, and then so forth, right? And so you really want to distinguish those two. And, and, and that's how we see the, the industry evolve. >> Can Superclouds be specialty clouds? Like is Snowflake a Supercloud? Is Goldman Sachs financial cloud a Supercloud? >> Absolutely, right. So Supercloud is not like a, a conglomeration of multiple capabilities, right? It can be for a specific use case, it can be for a specific functionality. So we, we consider our capabilities by the definition as a Supercloud in, in networking, right? In cloud networking, in Prosimo. So, does that solve the entirety of what I want to do in the cloud? No, absolutely not. There's data, there's computers, a whole bunch of other things, but for a specialty you do have some Supercloud. >> Yeah, in fact, I had a note here. I was going to ask you will, when will there be specialty clouds, apps, identity, data, security, nteworking, we will see those? >> Absolutely, yeah. And, and those are slowly starting to brew, right? So you have, you have identity as one, you have networking as one, you have the zero trust piece as, as another one, you have data as a, as another one. So when all these things come together, absolutely. That's what, that's what enterprise customers care about. >> So I love infrastructure as code, that drove a lot of the evolution and revolution of DevOps. When are we going to see security as code and network as code? Or is it there? >> No networkers code, for sure. It's already, it's already there. It's probably in its early innings, I would say, but we are starting to see that already. The reason for that is really simple. Enough CIOs have yelled at their networking teams to say, "my app guys can get this done three," "four times a day, you get this done once a week." Right? And so, that has actually driven quite a bit of innovation, >> It's slow, >> It's slow, right? And so that's driven quite a bit of innovation. It all starts with, hey, can I build a Terraform provider and then just integrate into Terraform? But it doesn't, it doesn't stop there, right? There's a whole bunch of additional capabilities, a day in troubleshooting, a whole bunch of things that need to come together. But I would say networkers code has already started to, to, to take ship. >> Which, that's a great point about specialty clouds. What about vertical clouds too? 'Cause you got insurance, oil and gas, FinTech. Both sides of the stack can have specialty clouds. >> Absolutely, yeah. So, it, what's driving specialty clouds, right? Some of it is compliance, mainly because you just have to shard the data, and when you shard the data, the entirety gets, gets sharded, right? Some of it driven by use case, because some are a little more serverless, service mesh and intelligence focused, some are a little more infrastructure focused. So you do see that taking off. I would say we've seen a whole lot more, kind of, on the horizontal side, less on the vertical side, but that's really happening, right? >> Yeah, I think that, to me, indicates a Supercloud. The fact that the diversity of the application on the clouds themselves, someone could be spending, say, Liberty Mutual or Goldman Sachs. They were once spending that as CapEx. >> Exactly. >> Now it's OPEX, so they become a service provider. So, if you have scale with data and expertise, you become a Supercloud by default. And you don't have to pay for the CapEx, >> Yeah. You're already paying in. >> Exactly, yeah. >> And that's what snowflake basically did with data warehouse. >> That's right, yeah. >> I mean they're basically a data warehouse. Refactored on the cloud and then go, "whoa, let's go to Azure." >> Yeah. And, and where does that data decide do you ask that question? No, right? You just assume that, hey, retrospective of it's a single cloud, multiple regions, it's there. If it's stretched to multiple clouds, yes, it's just there, but you, you talk about like that. >> In our cloud already panel earlier, we talked about how companies are going fast on one native cloud, 'cause they don't want to have multiple development teams and different ops teams. They go all in say, hey, mostly AWS wins this, unless it's specially Azure productivity software or SQL database, go hard in on Amazon, get speed and velocity, get that flywheel, win, get scale, get value. Then go to Azure, provide that same value to that marketplace and other clouds. Then the next dot to connect is, can the customer have the same experience across the clouds? That's where it gets interesting. What's your thoughts on that? >> Actually, it gets interesting even when they go from a single cloud in a single region to multiple regions, right? And the, the more spread out the regions are, you have requirements around application performance, application experience and so forth. So, suddenly the networking conversation starts to become an experience and a performance conversation. The security conversation starts to become a zero trust conversation and so forth. And so you, you do see that, that interesting shift that's happening. >> Of course. >> Exactly. And then that gets worsened by the fact that now you have multiple clouds, multiple regions, and then... >> So you got regions, clouds, >> and then you have edge locations now. >> And edge. >> You mentioned edge. >> This, this is why I think multi-cloud is BS, because this is all coming so fast. You got to get your Supercloud first. >> Exactly. >> Then you extend into, what it looks like a multi-vendor or multifaceted environment that should be automated by that time. >> Exactly. >> So it's evolutionary, we're not there yet. >> Exactly. >> So you agree, no market yet? >> That's right, yes. So unless it's like the super large enterprises where we have seen a really good mix of multiple different clouds or super large enterprises where each business unit is free to choose the cloud of their choice for the application developers because they just like a certain cloud, right? >> Or negotiations. >> Or negotiations, right? Exactly, so there you find yourself in a healthy mix. It's not like you're 80, 10, 10. It's, it's a healthy mix of three different clouds, right? But vast majority of the enterprises, they have a concerted strategy, I have a primary cloud 'cause that's where two, two big CEOs shake hands and assign multi billion dollar deals, right? >> It's just a song with Howie Shute, who's now a Zscaler, former VMware. Probably know Howie, he's a legend in the community as well. We were talking about the old days of the data center and you remember that? We'll go back to our, into our, you know, historical views of experience. Back when the data center became popular this was the glass house. Mainframes to mini computers. It became a complex environment. You had to have pretty much a PhD or serious networking or some sort of technical background. And then IT was born, the local area networks, the mini computers, and the PCs change that dynamic. IT was born. Okay, and let's just say it, most IT guys aren't PhDs. >> Exactly. >> So what's happened there is democratization and the operations side of that wave. We're kind of going th&rough it now, don't ya think, with cloud? Like, you got to be super smart to wrangle the data. I mean, some of the data pipelining stuff is super complex, after Snowflake and data bricks. >> Absolutely. And largely depends on the maturity, right? Like, so once you pass a certain scale in the cloud the care abouts start to be very different. The care abouts are, how can I operate this at scale? Because I might have started off with a relatively inefficient infrastructure, right? But now if I start to operate that at scale with like thousands of VPCs and so forth, somebody is looking at an AWS bill there and going, "ah, no, no, no, we're not going to do that." >> We're getting to the good part now. So, so here's where I wanted to get to, 'Cause we're kind of getting there, The proof points of Supercloud is IT like operations, >> Correct. >> Easy. >> Yep. >> Not overstaffed and maybe an SRE model one too many. >> Yeah, exactly. >> What are the proof points do you see that would be evidence that Supercloud is working? >> So in a well functional model where we have seen enterprises take the applications that they care about and then move that into the public cloud or build it organically. If they have staffed their team, I think a good leading indicator is that they have staffed the team so that there are a bunch of guys who understand what it means for cloud native capabilities. There are a bunch of guys who then put it together and then you look at the care abouts, right? Ultimately at the end of the day, the goal, if you go higher up in the layers, is it about application experience? Is it about kind of reducing the blast radius of my security? Is it about my data cleanliness and, and hygiene? You don't care about kind of how the pipelining works underneath the covers or how do I put a transit gateway and this and that together? No, that's not what you care about. You care about kind of the outcomes and, and- >> Palmer (unintelligible) that VMware, when he was there. You just say the hardened top, no one talks about what's in an Intel processor. I mean it's just works. >> Exactly, yeah. And it's what applications you build on top of that Intel processor that actually makes it more powerful, right? And so the first evidence I would say is kind of how is the team structured? The second evidence would be kind of what, what are the care abouts for the guys that are building these applications, right? Because even the application developers more than the application, they care about kind of, is it helping? Is it delivering on the experience? Is it being used the way it's supposed to? >> Is it value? >> Exactly, right? And those are not areas that the cloud providers are solely focused on, right? Like you don't see an AWS or an Azure dashboard show that particular thing for the entirety of the application, they'll tell you for the ATR services that you, that you use, here's the SLA for each one of these services. >> And that's where the customer has to build it. >> Exactly right. Now, does that give you the full picture? No, it doesn't. Somebody has to pull this together. Somebody has to aggregate this together and then make sense as to whether this is working or not, right? So whether you call it Supercloud, or whether you call it kind of the care abouts on top of the cloud native stuff, they're all the same. I'm glad you guys came up with a, with a name for this. And I think it's going to be here to stay. >> Well, thank you for sharing your expertise. You got a great background in this area and you got, I think you guys are right on the front wave of this new change. I think a little bit early, but that's good, but don't be too early. >> Yeah, exactly. No, and, and, and that's really important, right, John? So, you don't want to be too early. You certainly don't want to be too late, but at the same time, the pace at which things are evolving are fast enough that you, you will see. I think when, if we have this conversation even three months from now, it might be a very different conversation. >> Yeah, people want to go fast and they don't want to get stuck with a vendor. They made a bad choice that slows 'em down 'cause they got problems to solve, things to build. >> Yeah, exactly. >> Ramesh, thanks for coming on, Supercloud 22, we're breaking it all down. We're exposing it out to everyone. We're discussing it. We're going to challenge it. But ultimately it is a thing. Supercloud 22. Thanks for watching. >> Wonderful, thanks John. (light music)

(electronic music) >> Hello everybody, welcome back to Boston. This is Dave Vellante and you're watching theCUBE's continuous coverage of AWS re:Inforce 2022. We're here at the convention center in Boston where theCUBE got started in May of 2010. I'm really excited. Lena Smart is here, she's the chief information security officer at MongoDB rocket ship company We covered MongoDB World earlier this year, June, down in New York. Lena, thanks for coming to theCUBE. >> Thank you for having me. >> You're very welcome, I enjoyed your keynote yesterday. You had a big audience, I mean, this is a big deal. >> Yeah. >> This is the cloud security conference, AWS, putting its mark in the sand back in 2019. Of course, a couple of years of virtual, now back in Boston. You talked in your keynote about security, how it used to be an afterthought, used to be the responsibility of a small group of people. >> Yeah. >> You know, it used to be a bolt on. >> Yep. >> That's changed dramatically and that change has really accelerated through the pandemic. >> Yep. >> Just describe that change from your perspective. >> So when I started at MongoDB about three and a half years ago, we had a very strong security program, but it wasn't under one person. So I was their first CISO that they employed. And I brought together people who were already doing security and we employed people from outside the company as well. The person that I employed as my deputy is actually a third time returnee, I guess? So he's worked for, MongoDB be twice before, his name is Chris Sandalo, and having someone of that stature in the company is really helpful to build the security culture that I wanted. That's why I really wanted Chris to come back. He's technically brilliant, but he also knew all the people who'd been there for a while and having that person as a trusted second in command really, really helped me grow the team very quickly. I've already got a reputation as a strong female leader. He had a reputation as a strong technical leader. So us combined is like indestructible, we we're a great team. >> Is your scope of responsibility, obviously you're protecting Mongo, >> Yeah. >> How much of your role extends into the product? >> So we have a product security team that report into Sahir Azam, our chief product officer. I think you even spoke to him. >> Yeah, he's amazing. >> He's awesome, isn't he? He's just fabulous. And so his team, they've got security experts on our product side who are really kind of the customer facing. I'm also to a certain extent customer facing, but the product folks are the absolute experts. They will listen to what our customers need, what they want, and together we can then work out and translate that. I'm also responsible for governance risk and compliance. So there's a large portion of our customers that give us input via that program too. So there's a lot of avenues to allow us to facilitate change in the security field. And I think that's really important. We have to listen to what our customers want, but also internally. You know, what our internal groups need as well to help them grow. >> I remember last year, Re:invent 2021, I was watching a talk on security. It was the, I forget his name, but it was the individual who responsible for data center security. And one of the things he said was, you know, look it's not at the end of the day, the technology's important but it's not the technology. It's how you apply the tools and the practices and the culture- >> Right. That you build in the organization that will ultimately determine how successful you are at decreasing the ROI for the bad guys. >> Yes. >> Let's put it that way. So talk about the challenges of building that culture, how you go about that, and how you sustain that cultural aspect. >> So, I think having the security champion program, so that's just, it's like one of my babies, that and helping underrepresented groups in MongoDB kind of get on in the tech world are both really important to me. And so the security champion program is purely voluntary. We have over a hundred members. And these are people, there's no bar to join. You don't have to be technical. If you're an executive assistant who wants to learn more about security, like my assistant does, you're more than welcome. Up to, we actually people grade themselves, when they join us, we give them a little tick box. Like five is, I walk in security water. One is, I can spell security but I'd like to learn more. Mixing those groups together has been game changing for us. We now have over a hundred people who volunteer their time, with their supervisors permission, they help us with their phishing campaigns, testing AWS tool sets, testing things like queryable encryption. I mean, we have people who have such an in-depth knowledge in other areas of the business that I could never learn, no matter how much time I had. And so to have them- And we have people from product as security champions as well, and security, and legal, and HR, and every department is recognized. And I think almost every geographical location is also recognized. So just to have that scope and depth of people with long tenure in the company, technically brilliant, really want to understand how they can apply the cultural values that we live with each day to make our security program stronger. As I say, that's been a game changer for us. We use it as a feeder program. So we've had five people transfer from other departments into the security and GRC teams through this Champions program. >> Makes a lot of sense. You take somebody who walks on water in security, mix them with somebody who really doesn't know a lot about it but wants to learn and then can ask really basic questions, and then the experts can actually understand better how to communicate. >> Absolutely. >> To that you know that 101 level. >> It's absolutely true. Like my mom lives in her iPad. She worships her iPad. Unfortunately she thinks everything on it is true. And so for me to try and dumb it down, and she's not a dumb person, but for me to try and dumb down the message of most of it's rubbish, mom, Facebook is made up. It's just people telling stories. For me to try and get that over to- So she's a one, and I might be a five, that's hard. That's really hard. And so that's what we're doing in the office as well. It's like, if you can explain to my mother how not everything on the internet is true, we're golden. >> My mom, rest her soul, when she first got a- we got her a Macintosh, this was years and years and years ago, and we were trying to train her over the phone, and said, mom, just grab the mouse. And she's like, I don't like mice. (Lena laughs) There you go. I know, I know, Lena, what that's like. Years ago, it was early last decade, we started to think about, wow, security really has to become a board level item. >> Yeah. >> And it really wasn't- 2010, you know, for certain companies. But really, and so I had the pleasure of interviewing Dr. Robert Gates, who was the defense secretary. >> Yes. >> We had this conversation, and he sits on a number, or sat on a number of boards, probably still does, but he was adamant. Oh, absolutely. Here's how you know, here. This is the criticality. Now it's totally changed. >> Right. >> I mean, it's now a board level item. But how do you communicate to the C-Suite, the board? How often do you do that? What do you recommend is the right regime? And I know there's not any perfect- there's got to be situational, but how do you approach it? >> So I am extremely lucky. We have a very technical board. Our chairman of the board is Tom Killalea. You know, Amazon alum, I mean, just genius. And he, and the rest of the board, it's not like a normal board. Like I actually have the meeting on this coming Monday. So this weekend will be me reading as much stuff as I possibly can, trying to work out what questions they're going to ask me. And it's never a gotcha kind of thing. I've been at board meetings before where you almost feel personally attacked and that's not a good thing. Where, at MongoDB, you can see they genuinely want us to grow and mature. And so I actually meet with our board four times a year, just for security. So we set up our own security meeting just with board members who are specifically interested in security, which is all of them. And so this is actually off cadence. So I actually get their attention for at least an hour once a quarter, which is almost unheard of. And we actually use the AWS memo format. People have a chance to comment and read prior to the meeting. So they know what we're going to talk about and we know what their concerns are. And so you're not going in like, oh my gosh, what what's going to happen for this hour? We come prepared. We have statistics. We can show them where we're growing. We can show them where we need more growth and maturity. And I think having that level of just development of programs, but also the ear of the board has has helped me mature my role 10 times. And then also we have the chance to ask them, well what are your other CISOs doing? You know, they're members of other boards. So I can say to Dave, for example, you know, what's so-and-so doing at Datadog? Or Tom Killelea, what's the CISO of Capital One doing? And they help me make a lot of those connections as well. I mean, the CISO world is small and me being a female in the world with a Scottish accent, I'm probably more memorable than most. So it's like, oh yeah, that's the Irish girl. Yeah. She's Scottish, thank you. But they remember me and I can use that. And so just having all those mentors from the board level down, and obviously Dev is a huge, huge fan of security and GRC. It's no longer that box ticking exercise that I used to feel security was, you know, if you heated your SOC2 type two in FinTech, oh, you were good to go. You know, if you did a HERC set for the power industry. All right, right. You know, we can move on now. It's not that anymore. >> Right. It's every single day. >> Yeah. Of course. Dev is Dev at the Chario. Dev spelled D E V. I spell Dave differently. My Dave. But, Lena, it sounds like you present a combination of metrics, so, the board, you feel like that's appropriate to dig into the metrics. But also I'm presuming you're talking strategy, potentially, you know, gaps- >> Road roadmaps, the whole nine yards. Yep. >> What's the, you know, I look at the budget scenario. At the macro level, CIOs have told us, they came into the year saying, hey we're going to grow spending at the macro, around eight percent, eight and a half percent. That's dialed down a little bit post Ukraine and the whole recession and Fed tightening. So now they're down maybe around six percent. So not dramatically lower, but still. And they tell us security is still the number one priority. >> Yes. >> That's been the case for many, many quarters, and actually years, but you don't have an unlimited budget. >> Sure >> Right. It's not like, oh, here is an open checkbook. >> Right. >> Lena, so, how does Mongo balance that with the other priorities in the organization, obviously, you know, you got to spend money on product, you got to spend money and go to market. What's the climate like now, is it, you know continuing on in 2022 despite some of the macro concerns? Is it maybe tapping the brakes? What's the general sentiment? >> We would never tap the breaks. I mean, this is something that's- So my other half works in the finance industry still. So we have, you know, interesting discussions when it comes to geopolitics and financial politics and you know, Dev, the chairman of the board, all very technical people, get that security is going to be taken advantage of if we're seeing to be tapping the brakes. So it does kind of worry me when I hear other people are saying, oh, we're, you know, we're cutting back our budget. We are not. That being said, you also have to be fiscally responsible. I'm Scottish, we're cheap, really frugal with money. And so I always tell my team: treat this money as if it's your own. As if it's my money. And so when we're buying tool sets, I want to make sure that I'm talking to the CISO, or the CISO of the company that's supplying it, and saying are you giving me the really the best value? You know, how can we maybe even partner with you as a database platform? How could we partner with you, X company, to, you know, maybe we'll give you credits on our platform. If you look to moving to us and then we could have a partnership, and I mean, that's how some of this stuff builds, and so I've been pretty good at doing that. I enjoy doing that. But then also just in terms of being fiscally responsible, yeah, I get it. There's CISOs who have every tool that's out there because it's shiny and it's new and they know the board is never going to say no, but at some point, people will get wise to that and be like, I think we need a new CISO. So it's not like we're going to stop spending it. So we're going to get someone who actually knows how to budget and get us what the best value for money. And so that's always been my view is we're always going to be financed. We're always going to be financed well. But I need to keep showing that value for money. And we do that every board meeting, every Monday when I meet with my boss. I mean, I report to the CFO but I've got a dotted line to the CTO. So I'm, you know, I'm one of the few people at this level that's got my feet in both camps. You know budgets are talked at Dev's level. So, you know, it's really important that we get the spend right. >> And that value is essentially, as I was kind of alluding to before, it's decreasing the value equation for the hackers, for the adversary. >> Hopefully, yes. >> Right? Who's the- of course they're increasingly sophisticated. I want to ask you about your relationship with AWS in this context. It feels like, when I look around here, I think back to 2019, there was a lot of talk about the shared responsibility model. >> Yes. >> You know, AWS likes to educate people and back then it was like, okay, hey, by the way, you know you got to, you know, configure the S3 bucket properly. And then, oh, by the way, there's more than just, it's not just binary. >> Right, right. >> There's other factors involved. The application access and identity and things like that, et cetera, et cetera. So that was all kind of cool. But I feel like the cloud is becoming the first line of defense for the CISO but because of the shared responsibility model, CISO is now the second line of defense >> Yes. Does that change your role? Does it make it less complicated in a way? Maybe, you know, more complicated because you now got to get your DevSecOps team? The developers are now much more involved in security? How is that shifting, specifically in the context of your relationship with AWS? >> It's honestly not been that much of a shift. I mean, these guys are very proactive when it comes to where we are from the security standpoint. They listen to their customers as much as we do. So when we sit down with them, when I meet with Steve Schmidt or CJ or you know, our account manager, its not a conversation that's a surprise to me when I tell them this is what we need. They're like, yep, we're on that already. And so I think that relationship has been very proactive rather than reactive. And then in terms of MongoDB, as a tech company, security is always at the forefront. So it's not been a huge lift for me. It's really just been my time that I've taken to understand where DevSecOps is coming from. And you know, how far are we shifting left? Are we actually shifting right now? It's like, you know, get the balance, right? You can't be too much to one side. But I think in terms of where we're teaching the developers, you know, we are a company by developers for developers. So, we get it, we understand where they're coming from, and we try and be as proactive as AWS is. >> When you obviously the SolarWinds hack was a a major mile- I think in security, there's always something in the headlines- >> Yes. But when you think of things like, you know, Stuxnet, you know, Log4J, obviously Solarwinds and the whole supply chain infiltration and the bill of materials. As I said before, the adversary is extremely capable and sophisticated and you know, much more automated. It's always been automated attacks, but you know island hopping and infiltrating and self-forming malware and really sophisticated techniques. >> Yep. >> How are you thinking about that supply chain, bill of materials from inside Mongo and ultimately externally to your customers? >> So you've picked on my third favorite topic to talk about. So I came from the power industry before, so I've got a lot of experience with critical infrastructure. And that was really, I think, where a lot of the supply chain management rules and regulations came from. If you're building a turbine and the steel's coming from China, we would send people to China to make sure that the steel we were buying was the steel we were using. And so that became the H bomb. The hardware bill of materials, bad name. But, you know, we remember what it stood for. And then fast forward: President Biden's executive order. SBOs front and center, cloud first front and center. It's like, this is perfect. And so I was actually- I actually moderated a panel earlier this year at Homeland Security Week in DC, where we had a sneak CISA, So Dr. Allen Friedman from CISA, and also Patrick Weir from OWASP for the framework, CISA for the framework as well, and just the general guidance, and Snake for the front end. That was where my head was going. And MongoDB is the back-end database. And what we've done is we've taken our work with Snake and we now have a proof of concept for SBOs. And so I'm now trying to kind of package that, if you like, as a program and get the word out that SBOs shouldn't be something to be afraid of. If you want to do business with the government you're going to have to create one. We are offering a secure repository to store that data, the government could have access to that repository and see that data. So there's one source of truth. And so I think SBOs is going to be really interesting. I know that, you know, some of my peers are like, oh, it's just another box to tick. And I think it's more than that. I definitely- I've just, there's something percolating in the back of my mind that this is going to be big and we're going to be able to use it to hopefully not stop things like another Log4j, there's always going to be another Log4j, we know that. we don't know everything, the unknown unknown, but at least if we're prepared to go find stuff quicker than we were then before Log4j, I think having SBOs on hand, having that one source of truth, that one repository, I think is going to make it so much easier to find those things. >> Last question, what's the CISO's number one challenge? Either yours or the CISO, generally. >> Keeping up with the fire hose that is security. Like, what do you pick tomorrow? And if you pick the wrong thing, what's the impact? So that's why I'm always networking and talking to my peers. And, you know, we're sometimes like meerkats, you know. there's meerkats, you see like this, it's like, what do we talk about? But there's always something to talk about. And you just have to learn and keep learning. >> Last question, part B. As a hot technology company, that's, you know, rising star, you know not withstanding the tech lash and the stock market- >> Yeah. >> But Mongo's growing, you know, wonderfully. Do you find it easier to attract talent? Like many CISOs will say, you know, lack of talent is my biggest, biggest challenge. Do you find that that's not the challenge for you? >> Not at all. I think on two fronts, one, we have the champions program. So we've got a whole internal ecosystem who love working there. So the minute one of my jobs goes on the board, they get first dibs at it. So they'd already phoning their friends. So we've got, you know, there's ripple effects out from over a hundred people internally. You know, I think just having that, that's been a game changer. >> I was so looking forward to interviewing you, Lena, thanks so much for coming. >> Thank you, this was a pleasure. >> It was really great to have you. >> Thank you so much. Thank you. >> You're really welcome. All right, keep it right there. This is Dave Villante for theCUBE. We'll be right back at AWS Re:inforce22 right after this short break.

(digital music) >> And we're back live in New York. It's theCUBE. It's not SNL, it's better than SNL. Lisa Martin and John Furrier here with about 10,000 to 12,000 folks. (John chuckles) There is a ton of energy here. There's a ton of interest in what's going on. But one of the things that we know that AWS is really well-known for is its massive ecosystem. And one of its ecosystem partners is joining us. Please welcome Domenic Ravita, the VP of Product Marketing from SingleStore. Dominic, great to have you on the program. >> Well, thank you. Glad to be here. >> It's a nice opening, wasn't it? (Lisa and John laughing) >> I love SNL. Who doesn't? >> Right? I know. So some big news came out today. >> Yes. >> Funding. Good number. Talk to us a little bit about that before we dig in to SingleStore and what you guys are doing with AWS. >> Right, yeah. Thank you. We announced this morning our latest round, 116 million. We're really grateful to our customers and our investors and the partners and employees and making SingleStore a success to go on this journey of, really, to fulfill our mission to unify and simplify modern, real time data. >> So talk to us about SingleStore. Give us the value prop, the key differentiators, 'cause obviously customers have choice. Help us understand where you're nailing it. >> SingleStore is all about, what we like to say, the moments that matter. When you have an analytical question about what's happening in the moment, SingleStore is your best way to solve that cost-effectively. So that is for, in the case of Thorn, where they're helping to protect and save children from online trafficking or in the case of True Digital, which early in the pandemic, was a company in Southeast Asia that used anonymized phone pings to identify real time population density changes and movements across Thailand to have a proactive response. So really real time data in the moment can help to save lives quite literally. But also it does things that are just good commercially that gives you an advantage like what we do with Uber to help real time pricing and things like this. >> It's interesting this data intensity happening right now. We were talking earlier on theCUBE with another guest and we said, "Why is it happening now?" The big data has been around since the dupe days. That was hard to work with, then data lakes kicked in. But we seem to be, in the past year, everyone's now aware like, "Wow, I got a lot of data." Is it the pandemic? Now we're seeing customers understand the consequences. So how do you look at that? Because is it just timing, evolution? Are they now getting it or is the technology better? Is machine learning better? What's the forces driving the massive data growth acceleration in terms of implementing and getting stuff out, done? (chuckles) >> We think it's the confluence of a lot of those things you mentioned there. First of all, we just celebrate the 15-year anniversary of the iPhone, so that is like wallpaper now. It's just faded into our daily lives. We don't even think of that as a separate thing. So there's an expectation that we all have instant information and not just for the consumer interactions, for the business interactions. That permeates everything. I think COVID with the pandemic forced everyone, every business to try to move to digital first and so that put pressure on the digital service economy to mature even faster and to be digital first. That is what drives what we call data intensity. And more generally, the economic phenomenon is the data intensive era. It's a continuous competition and game for customers. In every moment in every location, in every dimension, the more data hat you have, the better value prop you can give. And so SingleStore is uniquely positioned to and focused on solving this problem of data intensity by bringing and unifying data together. >> What's the big customer success story? Can you share any examples that highlight that? What are some cool things that are happening that can illustrate this new, I won't say bit that's been flipped, that's been happening for a while, but can you share some cutting edge customer successes? >> It's happening across a lot of industries. So I would say first in financial services, FinTech. FinTech is always at the leading edge of these kind of technology adaptions for speeds and things like that. So we have a customer named IEX Cloud and they're focused on providing real time financial data as an API. So it's a data product, API-first. They're providing a lot of historical information on instruments and that sort of thing, as well as real time trending information. So they have customers like Seeking Alpha, for instance, who are providing real time updates on massive, massive data sets. They looked at lots of different ways to do this and there's the traditional, transactionals, LTP database and then maybe if you want to scale an API like theirs, you might have a separate end-memory cache and then yet another database for analytics. And so we bring all that together and simplify that and the benefit of simplification, but it's also this unification and lower latency. Another example is GE who basically uses us to bring together lots of financial information to provide quicker close to the end-of-month process across many different systems. >> So we think about special purpose databases, you mentioned one of the customers having those. We were in the keynote this morning where AWS is like, "We have the broadest set of special purpose databases," but you're saying the industry can't afford them anymore. Why and would it make SingleStore unique in terms of what you deliver? >> It goes back to this data intensity, in that the new business models that are coming out now are all about giving you this instant context and that's all data-driven and it's digital and it's also analytical. And so the reason that's you can't afford to do this, otherwise, is data's getting so big. Moving that data gets expensive, 'cause in the cloud you pay for every byte you store, every byte you process, every byte you move. So data movement is a cost in dollars and cents. It's a cost in time. It's also a cost in skill sets. So when you have many different specialized data sets or data-based technologies, you need skilled people to manage those. So that's why we think the industry needs to be simplified and then that's why you're seeing this unification trend across the database industry and other parts of the stack happening. With AWS, I mean, they've been a great partner of ours for years since we launched our first cloud database product and their perspective is a little bit different. They're offering choice of the specialty, 'cause many people build this way. But if you're going after real time data, you need to bring it. They also offer a SingleStore as a service on AWS. We offer it that way. It's in the AWS Marketplace. So it's easily consumable that way. >> Access to real time data is no longer a nice-to-have for any company, it's table stakes. We saw that especially in the last 20 months or so with companies that needed to pivot so quickly. What is it about SingleStore that delivers, that you talked about moments that matter? Talk about the access to real time data. How that's a differentiator as well? >> I think businesses need to be where their customers are and in the moments their customers are interacting. So that is the real time business-driver. As far as technology wise, it's not easy to do this. And you think about what makes a database fast? A major way of what makes it fast is how you store the data. And so since 2014, when we first released this, what Gartner called at the time, hybrid transaction/analytical processing or HTAP, where we brought transactional data and analytical data together. Fast forward five years to 2019, we released this innovation called Universal Storage, which does that in a single unified table type. Why that matters is because, I would say, basically cost efficiency and better speed. Again, because you pay for the storage and you pay for the movement. If you're not duplicating that data, moving it across different stores, you're going to have a better experience. >> One of the things you guys pioneered is unifying workloads. You mentioned some of the things you've done. Others are now doing it. Snowflake, Google and others. What does that mean for you guys? I mean, 'cause are they copying you? Are they trying to meet the functionality? >> I think. >> I mean, unification. I mean, people want to just store things and make it, get all the table stakes, check boxes, compliance, security and just keep coding and keep building. >> We think it's actually great 'cause they're validating what we've been seeing in the market for years. And obviously, they see that it's needed by customers. And so we welcome them to the party in terms of bringing these unified workloads together. >> Is it easy or hard? >> It's a difficult thing. We started this in 2014. And we've now have lots of production workloads on this. So we know where all the production edge cases are and that capability is also a building block towards a broader, expansive set of capabilities that we've moved onto that next phase and tomorrow actually we have an event called, The Real Time Data Revolution, excuse me, where we're announcing what's in that new product of ours. >> Is that a physical event or virtual? >> It's a virtual event. >> So we'll get the URL on the show notes, or if you know, just go to the new site. >> Absolutely. SingleStore Real Time Data Revolution, you'll find it. >> Can you tease us with the top three takeaways from Revolution tomorrow? >> So like I said, what makes a database fast? It's the storage and we completed that functionality three years ago with Universal Storage. What we're now doing for this next phase of the evolution is making enterprise features available and Workspaces is one of the foundational capabilities there. What SingleStore Workspaces does is it allows you to have this isolation of compute between your different workloads. So that's often a concern to new users to SingleStore. How can I combine transactions and analytics together? That seems like something that might be not a good thing. Well, there are multiple ways we've been doing that with resource governance, workload management. Workspaces offers another management capability and it's also flexible in that you can scale those workloads independently, or if you have a multi-tenant application, you can segment your application, your customer tenant workloads by each workspace. Another capability we're releasing is called Wasm, which is W-A-S-M, Web Assembly. This is something that's really growing in the open source community and SingleStore's contributing to that open source scene, CF project with WASI and Wasm. Where it's been mentioned mostly in the last few years has been in the browser as a more efficient way to run code in the browser. We're adapting that technology to allow you to run any language of your choice in the database and why that's important, again, it's for data movement. As data gets large in petabyte sizes, you can't move it in and out of Pandas in Python. >> Great innovation. That's real valuable. >> So we call this Code Engine with Wasm and- >> What do you call it? >> Code Engine Powered by Wasm. >> Wow. Wow. And that's open source? >> We contribute to the Wasm open source community. >> But you guys have a service that you- >> Yes. It's our implementation and our database. But Wasm allows you to have code that's portable, so any sort of runtime, which is... At release- >> You move the code, not the data. >> Exactly. >> With the compute. (chuckles) >> That's right, bring the compute to the data is what we say. >> You mentioned a whole bunch of great customer examples, GE, Uber, Thorn, you talked about IEX Cloud. When you're in customer conversations, are you dealing mostly with customers that are looking to you to help replace an existing database that was struggling from a performance perspective? Or are you working with startups who are looking to build a product on SingleStore? Is it both? >> It is a mix of both. I would say among SaaS scale up companies, their API, for instance, is their product or their SaaS application is their product. So quite literally, we're the data engine and the database powering their scale to be able to sign that next big customer or to at least sleep at night to know that it's not going to crash if they sign that next big costumer. So in those cases, we're mainly replacing a lot of databases like MySQL, Postgre, where they're typically starting, but more and more we're finding, it's free to start with SingleStore. You can run it in production for free. And in our developer community, we see a lot of customers running in that way. We have a really interesting community member who has a Minecraft server analytics that he's building based on that SingleStore free tier. In the enterprise, it's different, because there are many incumbent databases there. So it typically is a case where there is a, maybe a new product offering, they're maybe delivering a FinTech API or a new SaaS digital offering, again, to better participate in this digital service economy and they're looking for a better price performance for that real time experience in the app. That's typically the starting point, but there are replacements of traditional incumbent databases as well. >> How has the customer conversation evolved the last couple of years? As we talked about, one of the things we learned in the pandemic was access to real time data and those moments that matter isn't a nice-to-have anymore for businesses. There was that force march to digital. We saw the survivors, we're seeing the thrivers, but want to get your perspective on that. From the customers, how has the conversation evolved or elevated, escalated within an organization as every company has to be a data company? >> It really depends on their business strategy, how they are adapting or how they have adapted to this new digital first orientation and what does that mean for them in the direct interaction with their customers and partners. Often, what it means is they realize that they need to take advantage of using more data in the customer and partner interaction and when they come to those new ideas for new product introductions, they find that it's complicated and expensive to build in the old way. And if you're going to have these real time interactions, interactive applications, APIs, with all this context, you're going to have to find a better, more cost-effective approach to get that to market faster, but also not to have a big sprawling data-based technology infrastructure. We find that in those situations, we're replacing four or five different database technologies. A specialized database for key value, a specialized database for search- >> Because there's no unification before? Is that one of the reasons? >> I think it's an awareness thing. I think technology awareness takes a little bit of time, that there's a new way to do things. I think the old saying about, "Don't pave cow paths when the car..." You could build a straight road and pave it. You don't have to pave along the cow path. I think that's the natural course of technology adaption and so as more- >> And the- pandemic, too, highlighted a lot of the things, like, "Do we really need that?" (chuckles) "Who's going to service that?" >> That's right. >> So it's an awakening moment there where it's like, "Hey, let's look at what's working." >> That's right. >> Double down on it. >> Absolutely. >> What are you excited about new round of funding? We talked about, obviously, probably investments in key growth areas, but what excites you about being part of SingleStore and being a partner of AWS? >> SingleStore is super exciting. I've been in this industry a long time as an engineer and an engineering leader. At the time, we were MemSQL, came into SingleStore. And just that unification and simplification, the systems that I had built as a system engineer and helped architect did the job. They could get the speed and scale you needed to do track and trace kinds of use cases in real time, but it was a big trade off you had to make in terms of the complexity, the skill sets you needed and the cost and just hard to maintain. What excites me most about SingleStore is that it really feels like the iPhone moment for databases because it's not something you asked for, but once your friend has it and shows it to you, why would you have three different devices in your pocket with a flip phone, a calculator? (Lisa and Domenic chuckles) Remember these days? >> Yes. >> And a Blackberry pager. (all chuckling) You just suddenly- >> Or a computer. That's in there. >> That's right. So you just suddenly started using iPhone and that is sort of the moment. It feels like we're at it in the database market where there's a growing awareness and those announcements you mentioned show that others are seeing the same. >> And your point earlier about the iPhone throwing off a lot of data. So now you have data explosions at levels that unprecedented, we've never seen before and the fact that you want to have that iPhone moment, too, as a database. >> Absolutely. >> Great stuff. >> The other part of your question, what excites us about AWS. AWS has been a great partner since the beginning. I mean, when we first released our database, it was the cloud database. It was on AWS by customer demand. That's where our customers were. That's where they were building other applications. And now we have integrations with other native services like AWS Glue and we're in the Marketplace. We've expanded, that said we are a multi-cloud system. We are available in any cloud of your choice and on premise and in hybrid. So we're multi-cloud, hybrid and SaaS distribution. >> Got it. All right. >> Got it. So the event is tomorrow, Revolution. Where can folks go to register? What time does it start? >> 1:00 PM Eastern and- >> 1:00 PM. Eastern. >> Just Google SingleStore Real Time Data Revolution and you'll find it. Love for everyone to join us. >> All right. We look forward to it. Domenic, thank you so much for joining us, talking about SingleStore, the value prop, the differentiators, the validation that's happening in the market and what you guys are doing with AWS. We appreciate it. >> Thanks so much for having me. >> Our pleasure. For Domenic Ravita and John Furrier, I'm Lisa Martin. You're watching theCUBE, live from New York at AWS Summit 22. John and I are going to be back after a short break, so come back. (digital pulsing music)

>>Hello, everyone. Welcome to the cubes coverage here. New York city for AWS Amazon web services summit 2022. I'm John furrier, host of the cube with Dave ante. My co-host. We are breaking it down, getting an update on the ecosystem. As the GDP drops, inflations up gas prices up the enterprise continues to grow. We're seeing exceptional growth. We're here on the ground floor. Live at the Summit's packed house, 10,000 people. Eric Bradley's here. Chief STR at ETR, one of the premier enterprise research firms out there, partners with the cube and powers are breaking analysis that Dave does check that out as the hottest podcast in enterprise. Eric. Great to have you on the cube. Thanks for coming on. >>Thank you so much, John. I really appreciate the collaboration always. >>Yeah. Great stuff. Your data's amazing ETR folks watching check out ETR. They have a unique formula, very accurate. We love it. It's been moving the market. Congratulations. Let's talk about the market right now. This market is booming. Enterprise is the hottest thing, consumers kind of in the toilet. Okay. I said that all right, back out devices and, and, and consumer enterprise is still growing. And by the way, this first downturn, the history of the world where hyperscalers are on full pumping on all cylinders, which means they're still powering the revolution. >>Yeah, it's true. The hyperscalers were basically at this two sun system when Microsoft and an AWS first came around and everything was orbiting around it. And we're starting to see that sun cool off a little bit, but we're talking about a gradient here, right? When we say cool off, we're not talking to shutdown, it's still burning hot. That's for sure. And I can get it to some of the macro data in a minute, if that's all right. Or do you want me to go right? No, go go. Right. Yeah. So right now we just closed our most recent survey and that's macro and vendor specific. We had 1200 people talk to us on the macro side. And what we're seeing here is a cool down in spending. We originally had about 8.5% increase in budgets. That's cool down is 6.5 now, but I'll say with the doom and gloom and the headlines that we're seeing every day, 6.5% growth coming off of what we just did the last couple of years is still pretty fantastic as a backdrop. >>Okay. So you, you started to see John mentioned consumer. We saw that in Snowflake's earnings. For example, we, we certainly saw, you know, Walmart, other retailers, the FA Facebooks of the world where consumption was being dialed down, certain snowflake customers. Not necessarily, they didn't have mentioned any customers, but they were able to say, all right, we're gonna dial down, consumption this quarter, hold on until we saw some of that in snowflake results and other results. But at the same time, the rest of the industry is booming. But your data is showing softness within the fortune 500 for AWS, >>Not only AWS, but fortune 500 across the board. Okay. So going back to that larger macro data, the biggest drop in spending that we captured is fortune 500, which is surprising. But at the same time, these companies have a better purview into the economy. In general, they tend to see things further in advance. And we often remember they spend a lot of money, so they don't need to play catch up. They'll easily more easily be able to pump the brakes a little bit in the fortune 500. But to your point, when we get into the AWS data, the fortune 500 decrease seems to be hitting them a little bit more than it is Azure and GCP. I >>Mean, we're still talking about a huge business, right? >>I mean, they're catching up. I mean, Amazon has been transforming from owning the developer cloud startup cloud decade ago to really putting a dent on the enterprise as being number one cloud. And I still contest that they're number one by a long ways, but Azure kicking ass and catching up. Okay. You seeing people move to Azure, you got Charlie bell over there, Sean, by former Amazonians, Theresa Carlson, people are going over there, there there's lift over at Azure. >>There certainly is. >>Is there kinks in the arm or for AWS? There's >>A couple of kinks, but I think your point is really good. We need to take a second there. If you're talking about true pass or infrastructure is a service true cloud compute. I think AWS still is the powerhouse. And a lot of times the, the data gets a little muddied because Azure is really a hosted platform for applications. And you're not really sure where that line is drawn. And I think that's an important caveat to make, but based on the data, yes, we are seeing some kinks in the armor for AWS. Yes. Explain. So right now, a first of all caveat, 40% net score, which is our proprietary spending metric across the board. So we're not like raising any alarms here. It's still strong that said there are declines and there are declines pretty much across the board. The only spot we're not seeing a decline at all is in container, spend everything else is coming down specifically. We're seeing it come down in data analytics, data warehousing, and M I, which is a little bit of a concern because that, that rate of decline is not the same with Azure. >>Okay. So I gotta ask macro, I see the headwinds on the macro side, you pointed that out. Is there any insight into any underlying conditions that might be there on AWS or just a chronic kind of situational thing >>Right now? It seems situational. Other than that correlation between their big fortune 500, you know, audience and that being our biggest decline. The other aspect of the macro survey is we ask people, if you are planning to decline spend, how do you plan on doing it? And the number two answer is taking a look at our cloud spend and auditing it. So they're kind say, all right, you know, for the last 10 years it's been drunken, sail or spend, I >>Was gonna use that same line, you know, >>Cloud spend, just spend and we'll figure it out later, who cares? And then right now it's time to tighten the belts a little bit, >>But this is part of the allure of cloud at some point. Yeah. You, you could say, I'm gonna, I'm gonna dial it down. I'm gonna rein it in. So that's part of the reason why people go to the cloud. I want to, I wanna focus in on the data side of things and specifically the database. Let, just to give some context if, and correct me if I'm, I'm a little off here, but snowflake, which hot company, you know, on the planet, their net score was up around 80% consistently. It it's dropped down the last, you know, quarter, last survey to 60%. Yeah. So still highly, highly elevated, but that's relative to where Amazon is much larger, but you're saying they're coming down to the 40% level. Is that right? >>Yeah, they are. And I remember, you know, when I first started doing this 10 years ago, AWS at a 70%, you know, net score as well. So what's gonna happen over time is those adoptions are gonna get less and you're gonna see more flattening of spend, which ultimately is going to lower the score because we're looking for expansion rates. We wanna see adoption and increase. And when you see flattening a spend, it starts to contract a little bit. And you're right. Snowflake also was in the stratosphere that cooled off a little bit, but still, you know, very strong and AWS is coming down. I think the reason why it's so concerning is because a it's within the fortune 500 and their rate of decline is more than Azure right >>Now. Well, and, and one of the big trends you're seeing in database is this idea of converging function. In other words, bringing transaction and analytics right together at snowflake summit, they added the capability to handle transaction data, Mongo DB, which is largely mostly transactions added the capability in June to bring in analytic data. You see data bricks going from data engineering and data science now getting into snowflake space and analytics. So you're seeing that convergence Oracle is converging with my SQL heat wave and their core databases, couch base couch base is doing the same. Maria do virtually all these database companies are, are converging their platforms with the exception of AWS. AWS is still the right tool for the right job. So they've got Aurora, they've got RDS, they've got, you know, a dynamo DV, they've got red, they've got, you know, going on and on and on. And so the question everybody's asking is will that change? Will they start to sort of cross those swim lanes? We haven't seen it thus far. How is that affecting the data >>Performance? I mean, that's fantastic analysis. I think that's why we're seeing it because you have to be in the AWS ecosystem and they're really not playing nicely with others in the sandbox right now that now I will say, oh, Amazon's not playing nicely. Well, no, no. Simply to your point though, that there, the other ones are actually bringing in others at consolidating other different vendor types. And they're really not. You know, if you're in AWS, you need to stay within AWS. Now I will say their tools are fantastic. So if you do stay within AWS, they have a tool for every job they're advanced. And they're incredible. I think sometimes the complexity of their tools hurts them a little bit. Cause to your point earlier, AWS started as a developer-centric type of cloud. They have moved on to enterprise cloud and it's a little bit more business oriented, but their still roots are still DevOps friendly. And unless you're truly trained, AWS can be a little scary. >>So a common use case is I'm gonna be using Aurora for my transaction system and then I'm gonna ETL it into Redshift. Right. And, and I, now I have two data stores and I have two different sets of APIs and primitives two different teams of skills. And so that is probably causing some friction and complexity in the customer base that again, the question is, will they begin to expand some of those platforms to minimize some of that friction? >>Well, yeah, this is the question I wanted to ask on that point. So I've heard from people inside Amazon don't count out Redshift, we're making, we're catching up. I think that's my word, but they were kind of saying that right. Cuz Redshift is good, good database, but they're adding a lot more. So you got snowflake success. I think it's a little bit of a jealousy factor going on there within Redshift team, but then you got Azure synapse with the Synap product synapse. Yep. And then you got big query from Google big >>Query. Yep. >>What's the differentiation. What are you seeing for the data for the data warehouse or the data clouds that are out there for the customers? What's the data say, say to us? >>Yeah, unfortunately the data's showing that they're dropping a little bit whose day AWS is dropping a little bit now of their data products, Redshift and RDS are still the two highest of them, but they are starting to decline. Now I think one of the great data points that we have, we just closed the survey is we took a comparison of the legacy data. Now please forgive me for the word legacy. We're gonna anger a few people, but we Gotter data Oracle on-prem, we've got IBM. Some of those more legacy data warehouse type of names. When we look at our art survey takers that have them where their spend is going, that spends going to snowflake first, and then it's going to Google and then it's going to Microsoft Azure and, and AWS is actually declining in there. So when you talk about who's taking that legacy market share, it's not AWS right now. >>So legacy goes to legacy. So Microsoft, >>So, so let's work through in a little context because Redshift really was the first to take, you know, take the database to the cloud. And they did that by doing a one time license deal with par XL, which was an on-prem database. And then they re-engineered it, they did a fantastic job, but it was still engineered for on-prem. Then you along comes snowflake a couple years later and true cloud native, same thing with big query. Yep. True cloud native architecture. So they get a lot of props. Now what, what Amazon did, they took a page outta of the snowflake, for example, separating compute from storage. Now of course what's what, what Amazon did is actually not really completely separating like snowflake did they couldn't because of the architecture, they created a tearing system that you could dial down the compute. So little nuances like that. I understand. But at the end of the day, what we're seeing from snowflake is the gathering of an ecosystem in this true data cloud, bringing in different data types, they got to the public markets, data bricks was not able to get to the public markets. Yeah. And think is, is struggling >>And a 25 billion evaluation. >>Right. And so that's, that's gonna be dialed down, struggling somewhat from a go to market standpoint where snowflake has no troubles from a go to market. They are the masters at go to market. And so now they've got momentum. We talked to Frank sluman at the snowflake. He basically said, I'm not taking the foot off the gas, no way. Yeah. We, few of our large, you know, consumer customers dialed things down, but we're going balls to the >>Wall. Well, if you look at their show before you get in the numbers, you look at the two shows. Snowflake had their summit in person in Vegas. Data bricks has had their show in San Francisco. And if you compare the two shows, it's clear, who's winning snowflake is blew away from a, from a market standpoint. And we were at snowflake, but we weren't at data bricks, but there was really nothing online. I heard from sources that it was like less than 3000 people. So >>Snowflake was 1900 people in 2019, nearly 10,000. Yeah. In 2020, >>It's gonna be fun to sort of track that as a, as an odd caveat to say, okay, let's see what that growth is. Because in fairness, data, bricks, you know, a little bit younger, Snowflake's had a couple more years. So I'd be curious to see where they are. Their, their Lakehouse paradigm is interesting. >>Yeah. And I think it's >>And their product first company, yes. Their go to market might be a little bit weak from our analysis, but that, but they'll figure it out. >>CEO's pretty smart. But I think it's worth pointing out. It's like two different philosophies, right? It is. Snowflake is come into our data cloud. That's their proprietary environment. They're the, they think of the iPhone, right? End to end. We, we guarantee it's all gonna work. And we're in control. Snowflake is like, Hey, open source, no, bring in data bricks. I mean data bricks, open source, bring in this tool that too, now you are seeing snowflake capitulate a little bit. They announce, for instance, Apache iceberg support at their, at the snowflake summit. So they're tipping their cap to open source. But at the end of the day, they're gonna market and sell the fact that it's gonna run better in native snowflake. Whereas data bricks, they're coming at it from much more of an open source, a mantra. So that's gonna, you know, we'll see who look at, you had windows and you had apple, >>You got, they both want, you got Cal and you got Stanford. >>They both >>Consider, I don't think it's actually there yet. I, I find the more interesting dynamic right now is between AWS and snowflake. It's really a fun tit for tat, right? I mean, AWS has the S three and then, you know, snowflake comes right on top of it and announces R two, we're gonna do one letter, one number better than you. They just seem to have this really interesting dynamic. And I, and it is SLT and no one's betting against him. I mean, this guy's fantastic. So, and he hasn't used his war chest yet. He's still sitting on all that money that he raised to your point, that data bricks five, their timing just was a little off >>5 billion in >>Capital when Slootman hasn't used that money yet. So what's he gonna do? What can he do when he turns that on? He finds the right. >>They're making some acquisitions. They did the stream lit acquisitions stream. >>Fantastic >>Problem. With data bricks, their valuation is underwater. Yes. So they're recruiting and their MNAs. Yes. In the toilet, they cannot make the moves because they don't have the currency until they refactor the multiple, let the, this market settle. I I'm, I'm really nervous that they have to over factor the >>Valuation. Having said that to your point, Eric, the lake house architecture is definitely gaining traction. When you talk to practitioners, they're all saying, yeah, we're building data lakes, we're building lake houses. You know, it's a much, much smaller market than the enterprise data warehouse. But nonetheless, when you talk to practitioners that are actually doing things like self serve data, they're building data lakes and you know, snow. I mean, data bricks is right there. And as a clear leader in, in ML and AI and they're ahead of snowflake, right. >>And I was gonna say, that's the thing with data bricks. You know, you're getting that analytics at M I built into it. >>You know, what's ironic is I remember talking to Matt Carroll, who's CEO of auDA like four or five years ago. He came into the office in ma bro. And we were in temporary space and we were talking about how there's this new workload emerging, which combines AWS for cloud infrastructure, snowflake for the simple data warehouse and data bricks for the ML AI, and then all now all of a sudden you see data bricks yeah. And snowflake going at it. I think, you know, to your point about the competition between AWS and snowflake, here's what I think, I think the Redshift team is, you know, doesn't like snowflake, right. But I think the EC two team loves it. Loves it. Exactly. So, so I think snowflake is driving a lot of, >>Yeah. To John's point, there is plenty to go around. And I think I saw just the other day, I saw somebody say less than 40% of true global 2000 organizations believe that they're at real time data analytics right now. They're not really there yet. Yeah. Think about how much runway is left and how many tools you need to get to real time streaming use cases. It's complex. It's not easy. >>It's gonna be a product value market to me, snowflake in data bricks. They're not going away. Right. They're winning architectures. Yeah. In the cloud, what data bricks did would spark and took over the Haddo market. Yeah. To your point. Now that big data, market's got two players, in my opinion, snow flicking data, bricks converging. Well, Redshift is sitting there behind the curtain, their wild card. Yeah. They're wild card, Dave. >>Okay. I'm gonna give one more wild card, which is the edge. Sure. Okay. And that's something that when you talk about real time analytics and AI referencing at the edge, there aren't a lot of database companies in a position to do that. You know, Amazon trying to put outposts out there. I think it runs RDS. I don't think it runs any other database. Right. Snowflake really doesn't have a strong edge strategy when I'm talking the far edge, the tiny edge. >>I think, I think that's gonna be HPE or Dell's gonna own the outpost market. >>I think you're right. I'll come back to that. Couch base is an interesting company to watch with Capella Mongo. DB really doesn't have a far edge strategy at this point, but couch base does. And that's one to watch. They're doing some really interesting things there. And I think >>That, but they have to leapfrog bongo in my >>Opinion. Yeah. But there's a new architecture emerging at the edge and it's gonna take a number of years to develop, but it could eventually from an economic standpoint, seep back into the enterprise arm base, low end, take a look at what couch base is >>Doing. They hired an Amazon guard system. They have to leapfrog though. They need to, they can't incrementally who's they who >>Couch >>Base needs to needs to make a big move in >>Leap frog. Well, think they're trying to, that's what Capella is all about was not only, you know, their version of Atlas bringing to the cloud couch base, but it's also stretching it out to the edge and bringing converged database analytics >>Real quick on the numbers. Any data on CloudFlare, >>I was, I've been sitting here trying to get the word CloudFlare out my mouth the whole time you guys were talking, >>Is this another that's innovated in the ecosystem. So >>Platform, it was really simple for them early on, right? They're gonna get that edge network out there and they're gonna steal share from Akamai. Then they started doing exactly what Akamai did. We're gonna start rolling out some security. Their security is fantastic. Maybe some practitioners are saying a little bit too much, cuz they're not focused on one thing or another, but they are doing extremely well. And now they're out there in the cloud as well. You >>Got S3 compare. They got two, they got an S3 competitor. >>Exactly. So when I'm listening to you guys talk about, you know, a, a couch base I'm like, wow, those two would just be an absolute fantastic, you know, combination between the two of them. You mean >>CloudFlare >>Couch base. Yeah. >>I mean you got S3 alternative, right? You got a Mongo alternative basically in my >>Opinion. And you're going and you got the edge and you got the edge >>Network with security security, interesting dynamic. This brings up the super cloud date. I wanna talk about Supercloud because we're seeing a trend on we're reporting this since last year that basically people don't have to spend the CapEx to be cloud scale. And you're seeing Amazon enable that, but snowflake has become a super cloud. They're on AWS. Now they're on Azure. Why not tan expansion expand the market? Why not get that? And then it'll be on Google next, all these marketplaces. So the emergence of this super cloud, and then the ability to make that across a substrate across multiple clouds is a strategy we're seeing. What do you, what do you think? >>Well, honestly, I'm gonna be really Frank here. The, everything I know about the super cloud I know from this guy. So I've been following his lead on this and I'm looking forward to you guys doing that conference and that summit coming up from a data perspective. I think what you're saying is spot on though, cuz those are the areas we're seeing expansion in without a doubt. >>I think, you know, when you talk about things like super cloud and you talk about things like metaverse, there's, there's a, there, there look every 15 or 20 years or so this industry reinvents itself and a new disruption comes out and you've got the internet, you've got the cloud, you've got an AI and VR layer. You've got, you've got machine intelligence. You've got now gaming. There's a new matrix, emerging, super cloud. Metaverse there's something happening out there here. That's not just your, your father's SAS or is or pass. Well, >>No, it's also the spend too. Right? So if I'm a company like say capital one or Goldman Sachs, my it spend has traditionally been massive every year. Yes. It's basically like tons of CapEx comes the cloud. It's an operating expense. Wait a minute, Amazon has all the CapEx. So I'm not gonna dial down my budget. I want a competitive advantage. So next thing they know they have a super cloud by default because they just pivoted their, it spend into new capabilities that they then can sell to the market in FinTech makes total sense. >>Right? They're building out a digital platform >>That would, that was not possible. Pre-cloud >>No, it wasn't cause you weren't gonna go put all that money into CapEx expenditure to build that out. Not knowing whether or not the market was there, but the scalability, the ability to spend, reduce and be flexible with it really changes that paradigm entire. >>So we're looking at this market now thinking about, okay, it might be Greenfield in every vertical. It might have a power law where you have a head of the long tail. That's a player like a capital one, an insurance. It could be Liberty mutual or mass mutual that has so much it and capital that they're now gonna scale it into a super cloud >>And they have data >>And they have the data tools >>And the tools. And they're gonna bring that to their constituents. Yes, yes. And scale it using >>Cloud. So that means they can then service the entire vertical as a service provider. >>And the industry cloud is becoming bigger and bigger and bigger. I mean, that's really a way that people are delivering to market. So >>Remember in the early days of cloud, all the banks thought they could build their own cloud. Yeah. Yep. Well actually it's come full circle. They're like, we can actually build a cloud on top of the cloud. >>Right. And by the way, they can have a private cloud in their super cloud. Exactly. >>And you know, it's interesting cause we're talking about financial services insurance, all the people we know spend money in our macro survey. Do you know the, the sector that's spending the most right now? It's gonna shock you energy utilities. Oh yeah. I was gonna, the energy utilities industry right now is the one spending the most money I saw largely cuz they're playing ketchup. But also because they don't have these type of things for their consumers, they need the consumer app. They need to be able to do that delivery. They need to be able to do metrics. And they're the they're, they're the one spending right >>Now it's an arms race, but the, the vector shifts to value creation. So >>It's it just goes back to your post when it was a 2012, the trillion dollar baby. Yeah. It's a multi-trillion dollar baby that they, >>The world was going my chassis post on Forbes, headline trillion dollar baby 2012. You know, I should add it's happening. That's >>On the end. Yeah, exactly. >>Trillions of babies, Eric. Great to have you on the key. >>Thank you so much guys. >>Great to bring the data. Thanks for sharing. Check out ETR. If you're into the enterprise, want to know what's going on. They have a unique approach, very accurate in their survey data. They got a great market basket of, of, of, of, of data questions and people and community. Check it out. Thanks for coming on and sharing with. >>Thank you guys. Always enjoy. >>We'll be back with more coverage here in the cube in New York city live at summit 22. I'm John fur with Dave ante. We'll be right back.

(upbeat music) >> Okay, welcome back everyone to theCube's coverage of AWS re:Mars here in Las Vegas. I'm John Furrier, host of theCube. Got Alexey Surkov, Partner at Deloitte joining me today. We're going to talk about AI biased AI trust, trust in the AI for the, to save the planet to save us from the technology. Alexey thanks for coming on. >> Thank you for having me. >> So you had a line before you came on camera that describe the show, and I want you to say it if you don't mind because it was the best line that for me, at least from my generation. >> Alexey: Sure. >> That describes the show and then your role at Deloitte in it. >> Alexey: Sure. Listen, I mean, I, you know, it may sound a little corny, but to me, like I look at this entire show, at this whole building really, and like everybody here is trying to build a better Skynet, you know, better, faster, stronger, more potent, you know, and it's like, we are the only ones, like we're in this corner of like Deloitte trustworthy AI. We're trying to make sure that it doesn't take over the world. So that's, you know, that's the gist of it. How do you make sure that AI serves the good and not evil? How do you make sure that it doesn't have the risk? It doesn't, you know, it's well controlled that it does what we're, what we're asking it to do. >> And of course for all the young folks out there the Terminator is the movie and it's highly referenced in the nerd circles Skynet's evil and helps humanity goes away and lives underground and fights for justice and I think wins at the end. The Terminate three, I don't, I can't remember what happened there, but anyway. >> Alexey: I thought the good guys win, but, you know, that's. >> I think they do win at the end. >> Maybe. >> So that brings up the whole point because what we're seeing here is a lot of futuristic positive messages. I mean, three areas solve a lot of problems in the daily lives. You know, machine learning day to day hard problems. Then you have this new kind of economy emerging, you know, machine learning, driving new economic models, new industrial capabilities. And then you have this whole space save the world vibe, you know, like we discover the moon, new water sources maybe save climate change. So very positive future vibe here at re:Mars. >> Alexey: Absolutely. Yeah, and it was really exciting just watching, you know, watching the speakers talk about the future, and conquering space, and mining on the moon like it's happening already. It's really exciting and amazing. Yeah. >> Let's talk about what you guys are working at Deloitte because I think it's fascinating. You starting to see the digital transformation get to the edge. And when I say edge, I mean back office is done with cloud and you still have the old, you know, stuff that the old models that peoples will use, but now new innovative things are happening. Pushing software out there that's driving you with the FinTech, these verticals, and the trust is a huge factor. Not only do the consumers have a trust issues, who owns my data, there's also trust in the actual algorithms. >> Exactly. >> You guys are in the middle of this. What's your advice to clients, 'cause they want to push the envelope hard be cutting edge, >> Alexey: Right. >> But they don't want to pull back and get caught with their, you know, data out there that might been a misfire or hack. >> Absolutely. Well, I mean the simple truth is that, you know, with great power comes great responsibility, right? So AI brings a lot of promise, but there are a lot of risks, you know. You want to make sure that it's fair, that it's not biased. You want to make sure that it's explainable, that you can figure out and tell others what it's doing. You might want to make sure that it's well controlled, that it's responsible, that it's robust, that, you know, if somebody feeds it bad data, it doesn't produce results that don't make sense. If somebody's trying to provoke it, to do something wrong, that it's robust to those types of interactions. You want to make sure that it preserves privacy. You know, you want to make sure that it's secure, that nobody can hack into it. And so all of those risks are somewhat new. Not all of them are entirely new. As you said, the concept of model risk management has existed for many years. We want to make sure that each black box does what it's supposed to do. Just AI machine learning just raises it to the next level. And we're just trying to keep up with that and make sure that we develop processes, you know, controls that we look at technology that can orchestrate all this de-risking of transition to AI. >> Deloitte's a big firm. You guys saw you in the US open sponsorship was all over the TV. So that you're here at re:Mars show that's all about building up this next infrastructure in space and machine learning, what's the role you have with AWS and this re:Mars. And what's that in context of your overall relationship to the cloud players? >> Alexey: Well, we are, we're one of the largest strategic alliances for AWS, and AWS is one of the largest ones for Deloitte. We do a ton of work with AWS related to cloud, related to AI machine learning, a lot of these new areas. We did a presentation here just the other day on conversational AI, really cutting edge stuff. So we do all of that. So in some ways we participate in that part of the, the part of the room that I mentioned that is trying to kind of push the envelope and get the new technologies out there, but at the same time, Deloitte is a brand that carries a lot of, you know, history of trust, and responsibility, and controls, and compliance, and all of that comes, >> John: You get a lot of clients. I mean, you have big names. Get a lot of big name enterprises >> Right. >> That relied on you. >> Right, and so >> They rely on you now. >> Exactly, yeah. And so, it is natural for us to be in the marketplace, not only with the message of, you know, let's get to the better mouse trap in AI and machine learning, but also let's make sure that it's safe, and secure, and robust, and reliable, and trustworthy at the end of the day. And so, so this trustworthy message is intertwined with everything that we do in AI. We encourage companies to consider trustworthiness from the start. >> Yeah. >> It shouldn't be an afterthought, you know. Like I always say, you know, if you have deployed a bot and it's been deciding whether to issue loans to people, you don't want to find out that it was like, you know, biased against a certain type of (indistinct) >> I can just see in the boardroom, the bot went rogue. >> Right, yeah. >> Through all those loans you know. >> And you don't want to find out about it like six months later, right? That's too late, right? So you want to build in these controls from the beginning, right? You want to make sure that, you know, you are encouraging innovation, you're not stifling any development, and allowing your- >> There's a lot of security challenges too. I mean, it's like, this is the digital transformation sweet spot you're in right now. So I have to ask you, what's the use case, obviously call center's obvious, and bots, and having, you know, self-service capabilities. Where is the customers at right now on psychology and their appetite to push the envelope? And what do you guys see as areas that are most important for your customers to pay attention to? And then where do you guys ultimately deliver the value? >> Sure. Well, our clients are, I think, are aware of the risks of AI. They are not, that's not the first thing that they're thinking about for the most part. So when we come to them with this message they listen, they're very interested. And a lot of them have begun this journey of putting in kind of governance, compliance, controls, to make sure that as they are proceeding down this path of building out AI, that they're doing it responsibly. So it is in a nascent stage. >> John: What defines responsibility? >> Well, you want to, okay, so responsibility is really having governance. Like you have a, you build a robot dog, right? So, but you want to make sure that it has a leash, right? That it doesn't hurt anybody, right? That you have processes in place that at the end of the day, humans are in control, right? I don't want to go back to the Skynet analogy, right? >> John: Yeah. >> But humans should always be in control. There should always be somebody responsible for the functioning of the algorithm that can throw the switch at the right time, that can tweak it at the right time, that can make sure that you nudge it in the right direction that at no point should somebody be able to say, oh, well, it's not my fault. The algorithm did it, and that's why we're in the papers today, right? So that's the piece that's really complex, and what we try to do for our clients as Deloitte always does is kind of demystify that, right? >> John: Yeah. >> So what does it actually mean from a procedures, policies, >> John: Yeah, I mean, I think, >> Tools, technology, people. >> John: Yeah, I mean, this is like the classic operationalizing a new technology, managing it, making sure it doesn't get out of control if you will. >> Alexey: Exactly. >> Stay on the leash if you will. >> Alexey: Exactly. Yeah. And I guess one piece that I always like to mention is that, it's not to put breaks on these new technologies, right? It's not to try to kind of slow people down in developing new things. I actually think that making AI trustworthy is enabling the development of these technologies, right? The way to think about it is that, we have, you know, seat belts, and abs brakes, and, you know, airbags today. And those are all things that didn't exist like 100 years ago, but our cars go a lot faster, and we're a lot safer driving them. So, you know, when people say, oh, I hate seatbelts, you know, you're like, okay, yes, but first of all, there are some safety technologies that you don't even notice, which is how a lot of AI controls work. They blend into the background. And more importantly, the idea is for you to go faster, not slower. And that's what we're trying to enable our clients to do. >> Well, Alexey, great to have you on theCube. We love Deloitte come on to share their expertise. Final question for you is, where do you see this show going? Where do you guys, obviously you here, you're participating, you got a big booth here, where's this going? And what's next, where's the next dots that connect? Share your vision for this show, and kind of how it, or the ecosystem, and this ecosystem, and where you're going to intersect that? >> Wow. I mean, this show is already kind of pushing the boundaries. You know, we're talking about machine learning, artificial intelligence, you know, robotics, space. You know, I guess next thing I think, you know, we'll be probably spending a lot of time in the metaverse, right? So I can see like next time we come here, you know, half of us are wearing VR headsets and walking around and in meta worlds, but, you know, it's been an exciting adventure and, you know I'm really excited to partner and spend, you know spend time with AWS folks, and everybody here because they're really pushing the envelope on the future, and I look forward to next year >> The show is small, so it feels very intimate, which is actually a good feeling. And I think the other thing in metaverse I heard that too. I heard quantum. I said next, I heard, I've heard both those next year quantum and metaverse. >> Okay. >> Well, why not? >> Why not? Exactly, yeah. >> Thanks for coming on theCube. Appreciate it. >> Thank you. >> All right. It's theCube coverage here on the ground. Very casual Cube. Two days of live coverage. It's not as hot and and heavy as re:Invent, but it's a great show bringing all the best smart people together, really figure out the future, you know, solving problems day to day problems, and setting the new economy, the new industrial economy. And of course, a lot of the world problems are going to be helped and solved, very positive message space among other things here at re:Mars. I'm John furrier. Stay with us for more coverage after this short break. (upbeat music)

>>Okay, welcome back. Everyone. Live cube coverage here in Las Vegas for Amazon re Mars hot event, machine learning, automation, robotics, and space. Two days of live coverage. We're talking to all the hot technologists. We got all the action startups and segment on sustainability and F pan hero for vet global lead, Amazon sustainability data initiative. Thanks for coming on the cube. Can I get that right? Can >>You, you, you did. >>Absolutely. Okay, great. <laugh> thank >>You. >>Great to see you. We met at the analyst, um, mixer and, um, blown away by the story going on at Amazon around sustainability data initiative, because we were joking. Everything's a data problem now, cuz that's cliche. But in this case you're using data in your program and it's really kind of got a bigger picture. Take a minute to explain what your project is, scope of it on the sustainability. >>Yeah, absolutely. And thank you for the opportunity to be here. Yeah. Um, okay. So, um, I, I lead this program that we launched several years back in 2018 more specifically, and it's a tech for good program. And when I say the tech for good, what that means is that we're trying to bring our technology and our infrastructure and lend that to the world specifically to solve the problems related to sustainability. And as you said, sustainability, uh, inherently needs data. You need, we need data to understand the baseline of where we are and also to understand the progress that we are making towards our goals. Right? But one of the big challenges that the data that we need is spread everywhere. Some of it is too large for most people to be able to, um, access and analyze. And so, uh, what we're trying to tackle is really the data problem in the sustainability space. >>Um, what we do more specifically is focus on Democrat democratizing access to data. So we work with a broader community and we try to understand what are those foundational data sets that most people need to use in the space to solve problems like climate change or food security or think about sustainable development goals, right? Yeah. Yeah. Like all the broad space. Um, and, and we basically then work with the data providers, bring the data to the cloud, make it free and open to everybody in the world. Um, I don't know how deep you want me to go into it. There's many other layers into that. So >>The perspective is zooming out. You're, you're, you're looking at creating a system where the democratizing data means making it freely available so that practitioners or citizens, data, Wrangler, people interested in helping the world could get access to it and then maybe collaborate with people around the world. Is that right? >>Absolutely. So one of the advantages of using the cloud for this kind of, uh, effort is that, you know, cloud is virtually accessible from anywhere where you have, you know, internet or bandwidth, right? So, uh, when, when you put data in the cloud in a centralized place next to compute, it really, uh, removes the, the need for everybody to have their own copy. Right. And to bring it into that, the traditional way is that you bring the data next to your compute. And so we have this multiple copies of data. Some of them are on the petabyte scale. There's obviously the, the carbon footprint associated with the storage, but there's also the complexity that not everybody's able to actually analyze and have that kind of storage. So by putting it in the cloud, now anyone in the world independent of where of their computer capabilities can have access to the same type of data to solve >>The problems. You don't remember doing a report on this in 2018 or 2017. I forget what year it was, but it was around public sector where it was a movement with universities and academia, where they were doing some really deep compute where Amazon had big customers. And there was a movement towards a open commons of data, almost like a national data set like a national park kind of vibe that seems to be getting momentum. In fact, this kind of sounds like what you're doing some similar where it's open to everybody. It's kinda like open source meets data. >>Uh, exactly. And, and the truth is that these data, the majority of it's and we primarily work with what we call authoritative data providers. So think of like NASA Noah, you came me office organizations whose mission is to create the data. So they, their mandate is actually to make the data public. Right. But in practice, that's not really the case. Right. A lot of the data is stored like in servers or tapes or not accessible. Um, so yes, you bring the data to the cloud. And in this model that we use, Amazon never actually touches the data and that's very intentional so that we preserve the integrity of the data. The data provider owns the data in the cloud. We cover all the costs, but they commit to making it public in free to anybody. Um, and obviously the computer is next to it. So that's, uh, evaluated. >>Okay. Anna. So give me some examples of, um, some successes. You've had some of the challenges and opportunities you've overcome, take me through some of the activities because, um, this is really needed, right? And we gotta, sustainability is top line conversation, even here at the conference, re Mars, they're talking about saving climate change with space mm-hmm <affirmative>, which is legitimate. And they're talking about all these new things. So it's only gonna get bigger. Yeah. This data, what are some of the things you're working on right now that you can share? >>Yeah. So what, for me, honestly, the most exciting part of all of this is, is when I see the impact that's creating on customers and the community in general, uh, and those are the stories that really bring it home, the value of opening access to data. And, and I would just say, um, the program actually offers in addition to the data, um, access to free compute, which is very important as well. Right? You put the data in the cloud. It's great. But then if you wanna analyze that, there's the cost and we want to offset that. So we have a, basically an open call for proposals. Anybody can apply and we subsidize that. But so what we see by putting the data in the cloud, making it free and putting the compute accessible is that like we see a lot, for instance, startups, startups jump on it very easily because they're very nimble. They, we basically remove all the cost of investing in the acquisition and storage of the data. The data is connected directly to the source and they don't have to do anything. So they easily build their applications on top of it and workloads and turn it on and off if you know, >>So they don't have to pay for it. >>They have to pay, they basically just pay for the computes whenever they need it. Right. So all the data is covered. So that makes it very visible for, for a lot of startups. And then we see anything like from academia and nonprofits and governments working extensively on the data, what >>Are some of the coolest things you've seen come out of the woodwork in terms of, you know, things that built on top of the, the data, the builders out there are creative, all that heavy, lifting's gone, they're being creative. I'm sure there's been some surprises, um, or obvious verticals that jump healthcare jumps out at me. I'm not sure if FinTech has a lot of data in there, but it's healthcare. I can see, uh, a big air vertical, obviously, you know, um, oil and gas, probably concern. Um, >>So we see it all over the space, honestly. But for instance, one of the things that is very, uh, common for people to use this, uh, Noah data like weather data, because no, basically weather impacts almost anything we do, right? So you have this forecast of data coming into the cloud directly streamed from Noah. And, um, a lot of applications are built on top of that. Like, um, forecasting radiation, for instance, for the solar industry or helping with navigation. But I would say some of the stories I love to mention because are very impactful are when we take data to remote places that traditionally did not have access to any data. Yeah. And for instance, we collaborate with a, with a program, a nonprofit called digital earth Africa where they, this is a basically philanthropically supported program to bring earth observations to the African continents in making it available to communities and governments and things like illegal mining fighting, illegal mining are the forestation, you know, for mangroves to deep forest. Um, it's really amazing what they are doing. And, uh, they are managing >>The low cost nature of it makes it a great use case there >>Yes. Cloud. So it makes it feasible for them to actually do this work. >>Yeah. You mentioned the Noah data making me think of the sale drone. Mm-hmm <affirmative> my favorite, um, use case. Yes. Those sales drones go around many them twice on the queue at reinvent over the years. Yeah. Um, really good innovation. That vibe is here too at the show at Remar this week at the robotics showcases you have startups and growing companies in the ML AI areas. And you have that convergence of not obvious to many, but here, this culture is like, Hey, we have, it's all coming together. Mm-hmm <affirmative>, you know, physical, industrial space is a function of the new O T landscape. Mm-hmm <affirmative>. I mean, there's no edge in space as they say, right. So the it's unlimited edge. So this kind of points to the major trend. It's not stopping this innovation, but sustainability has limits on earth. We have issues. >>We do have issues. And, uh, and I, I think that's one of my hopes is that when we come to the table with the resources and the skills we have and others do as well, we try to remove some of these big barriers, um, that make it things harder for us to move forward as fast as we need to. Right. We don't have time to spend that. Uh, you know, I've been accounted that 80% of the effort to generate new knowledge is spent on finding the data you need and cleaning it. Uh, we, we don't have time for that. Right. So can we remove that UN differentiated, heavy lifting and allow people to start at a different place and generate knowledge and insights faster. >>So that's key, that's the key point having them innovate on top of it, right. What are some things that you wanna see happen over the next year or two, as you look out, um, hopes, dreams, KPIs, performance metrics, what are you, what are you driving to? What's your north star? What are some of those milestones? >>Yeah, so some, we are investing heavily in some areas. Uh, we support, um, you know, we support broadly sustainability, which as, you know, it's like, it's all over, <laugh> the space, but, uh, there's an area that is, uh, becoming more and more critical, which is climate risk. Um, climate risk, you know, for obvious reasons we are experienced, but also there's more regulatory pressures on, uh, business and companies in general to disclose their risks, not only the physical, but also to transition risks. And that's a very, uh, data heavy and compute heavy space. Right. And so we are very focusing in trying to bring the right data and the right services to support that kind of, of activity. >>What kind of break was you looking for? >>Um, so I think, again, it goes back to this concept that there's all that effort that needs to be done equally by so many people that we are all repeating the effort. So I'll put a plug here actually for a project we are supporting, which is called OS climates. Um, I don't know if you're familiar with it, but it's the Linux foundation effort to create an open source platform for climate risk. And so they, they bought the SMP global Airbus, you know, Alliance all these big companies together. And we are one of the funding partners to basically do that basic line work. What are the data that is needed? What are the basic tools let's put it there and do the pre-competitive work. So then you can do the build the, the, the competitive part on top of it. So >>It's kinda like a data clean room. >>It kind of is right. But we need to do those things, right. So >>Are they worried about comp competitive data or is it more anonymized out? How do you, >>It has both actually. So we are primarily contributing, contributing with the open data part, but there's a lot of proprietary data that needs to be behind the whole, the walls. So, yeah, >>You're on the cutting edge of data engineering because, you know, web and ad tech technologies used to be where all that data sharing was done. Mm-hmm <affirmative> for the commercial reasons, you know, the best minds in our industry quoted by a cube alumni are working on how to place ads better. Yeah. Jeff Acker, founder of Cloudera said that on the cube. Okay. And he was like embarrassed, but the best minds are working on how to make ads get more efficient. Right. But that tech is coming to problem solving and you're dealing with data exchange data analysis from different sources, third parties. This is a hard problem. >>Well, it is a hard problem. And I'll, I'll my perspective is that the hardest problem with sustainability is that it goes across all kinds of domains. Right. We traditionally been very comfortable working in our little, you know, swimming lanes yeah. Where we don't need to deal with interoperability and, uh, extracting knowledge. But sustainability, you, you know, you touch the economic side, it touches this social or the environmental, it's all connected. Right. And you cannot just work in the little space and then go sets the impact in the other one. So it's going to force us to work in a different way. Right. It's, uh, big data complex data yeah. From different domains. And we need to somehow make sense of all of it. And there's the potential of AI and ML and things like that that can really help us right. To go beyond the, the modeling approaches we've been done so >>Far. And trust is a huge factor in all this trust. >>Absolutely. And, and just going back to what I said before, that's one of the main reasons why, when we bring data to the cloud, we don't touch it. We wanna make sure that anybody can trust that the data is nowhere data or NASA data, but not Amazon data. >>Yes. Like we always say in the cube, you should own your data plane. Don't give it up. <laugh> well, that's cool. Great. Great. To hear the update. Is there any other projects that you're working on you think might be cool for people that are watching that you wanna plug or point out because this is an area people are, are leaning into yeah. And learning more young, younger talents coming in. Um, I, whether it's university students to people on side hustles want to play with data, >>So we have plenty of data. So we have, uh, we have over a hundred data sets, uh, petabytes and petabytes of data all free. You don't even need an AWS account to access the data and take it out if you want to. Uh, but I, I would say a few things that are exciting that are happening at Mars. One is that we are actually got integrated into ADX. So the AWS that exchange and what that means is that now you can find the open data, free data from a STI in the same searching capability and service as the paid data, right. License data. So hopefully we'll make it easier if I, if you wanna play with data, we have actually something great. We just announced a hackathon this week, uh, in partnership with UNESCO, uh, focus on sustainable development goals, uh, a hundred K in prices and, uh, so much data <laugh> you >>Too years, they get the world is your oyster to go check that out at URL at website, I'll see it's on Amazon. It use our website or a project that can join, or how do people get in touch with you? >>Yeah. So, uh, Amazon SDI, like for Amazon sustainability, that initiative, so Amazon sdi.com and you'll find, um, all the data, a lot of examples of customer stories that are using the data for impactful solutions, um, and much more >>So, and these are, there's a, there's a, a new kind of hustle going out there, seeing entrepreneurs do this. And very successfully, they pick a narrow domain and they, they own it. Something really obscure that could be off the big player's reservation. Mm-hmm <affirmative> and they just become fluent in the data. And it's a big white space for them, right. This market opportunities. And at the minimum you're playing with data. So this is becoming kind of like a long tail domain expertise, data opportunity. Yeah, absolutely. This really hot. So yes. Yeah. Go play around with the data, check it outs for good cause too. And it's free. >>It's all free. >>Almost free. It's not always free. Is it >>Always free? Well, if you, a friend of mine said is only free if your time is worth nothing. <laugh>. Yeah, >>Exactly. Well, Anna, great to have you on the cube. Thanks for sharing the stories. Sustainability is super important. Thanks for coming on. Thank you for the opportunity. Okay. Cube coverage here in Las Vegas. I'm Sean. Furier, we've be back with more day one. After this short break.

(upbeat music) >> Live on the floor in San Francisco for AWS Summit. I'm John Furrier, host of theCUBE. Here for the next two days getting all the action back in person. We're at AWS re:Invent, a few months ago. Now we're back, events are coming back and we're happy to be here with theCUBE. Bring all the action, also virtual, we have a hybrid cube. Check out theCUBE.net, siliconangle.com for all the coverage. After the event we've got a great guest ticking off here. Matthew Park, Director of Solutions Architecture with Innovation Solutions, the booth is right here. Matthew, welcome to theCUBE. >> Thank you very much, I'm glad to be here. >> So we're back in person. You're from Tennessee, we were chatting before you came on camera. It's great that be back to events. >> It's amazing, this is the first summit I've been to in what two, three years. >> It's awesome, we'll be at the AWS Summit in New York as well. A lot of developers and the big story this year is as developers look at cloud going, distributed computing you got on-premises, you got public cloud, you got the edge. Essentially the cloud operations is running everything, Dev sec Ops, everyone kind of sees that, you got containers, you got Kubernetes, you got cloud native. So the game is pretty much laid out, and the edge is with the action is. You guys are number one premier partner at SMB for edge. >> That's right. >> Tell us about what you guys doing at innovative and what you do. >> That's right, so I'm the director of solutions architecture. Me and my team are responsible for building out the solutions that are around especially the edge public cloud. For us edge is anything outside of an AWS availability zone. We are deploying that in countries that don't have AWS infrastructure in region. They don't have it-- >> Give an example. >> Example would be Panama. We have a customer there that needs to deploy some financial tech, data and compute is legally required to be in Panama but they love AWS, and they want to deploy AWS services in region. So they've taken EKS anywhere. We've put storage gateway and snowball in region, inside the country and they're running their FinTech on top of AWS services inside Panama. >> You know, what's interesting, Matthew is that we've been covering AWS since 2013 with theCUBE about their events, and we watched the progression. Andy Jassy was in charge and became the CEO. Now Adam Slepsky is in charge, but the edge has always been that thing they've been trying to avoid. I don't want to say trying to avoid. Of course Amazon listens to customers, they work backwards from the customers, we all know that. But the real issue is they're bread and butters, EC2 and S3. And then now they got tons of services, and the cloud is obviously successful, and we're seeing that. But the edge brings up a whole nother level. >> It does. >> Computing. >> It does. >> That's not set centralized in the public cloud. Now they got regions, so what is the issue with the edge? What's driving the behavior? Outpost came out as a reaction to competitive threats and also customer momentum around OT, operational technologies and IT merging. We see with the data at the edge, you got 5G, so it's pretty obvious, but there was a slow transition. What was the driver for the edge? What's the driver now for edge action for AWS? >> Data is the driver for the edge. Data has gravity, right? And it's pulling compute back to where the customer's generating that data and that's happening over and over again. You said it best Outpost was a reaction to a competitive situation. Whereas today we have over 15 AWS edge services and those are all reactions to things that customers need inside their data centers, on location or in the field like with media companies. >> Outpost is interesting, we always used to rip on theCUBE 'cause it's basically Amazon in a box pushed in the data center, running native, all this stuff. But now cloud native operations are becoming the standard. You're starting to see some standard, Deepak Singh's group is doing some amazing work with opensource, Raul's team on the AI side. Obviously you got Swam who's giving the keynote tomorrow. You got the big AI machine learning big part of that edge. Now you can say, okay, Outpost, is it relevant today? In other words, did Outpost do its job? 'Cause EKS anywhere seems to be getting a lot of momentum. You see local zones, the regions are kicking ass for Amazon. This edge piece is evolving. What's your take on EKS anywhere versus say Outpost? >> Yeah, I think Outpost did its job. It made customers that were looking at Outpost really consider, do I want to invest in this hardware? Do I want to have this Outpost in my data center? Do I want to manage this over the long term? A lot of those customers just transitioned to the public cloud. They went into AWS proper. Some of those customers stayed on prem because they did have use cases that were not a good fit for Outposts, they weren't a good fit in the customer's mind for the public AWS cloud inside an availability zone. Now what's happening is as AWS is pushing these services out and saying, we're going to meet you where you are with 5G. We're going to meet you where you are with wavelength. We're going to meet you where you are with EKS anywhere. I think it has really reduced the amount of times that we have conversations about Outposts and it's really increased, we can deploy fast. We don't have to spin up Outpost hardware. We can go deploy EKS anywhere in your VMware environment and it's increasing the speed of adoption for sure. >> All right so you guys are making a lot of good business decisions around managed cloud service. Innovative as that, you have the cloud advisory, the classic professional services for the specific edge piece and doing that outside of the availability zone and regions for AWS. Customers in these new areas that you're helping out are, they want cloud, they want to have modernization, modern applications. Obviously they got data machine learning and AI all part of that. What's the main product or gap that you're filling for AWS outside of their availability zones or their regions that you guys are delivering. What's the key? Is it they don't have a footprint? Is it that it's not big enough for them? What's the real gap, why are you so successful? >> So what customers want when they look towards the cloud is they want to focus on what's making them money as a business. They want to focus on their applications. They want to focus on their customers. So they look towards AWS cloud and say, AWS you take the infrastructure you take some of the higher layers and we'll focus on our revenue generating business but there's a gap there between infrastructure and revenue generating business that innovative slides into, we help manage the AWS environment. We help build out these things in local data centers for 32 plus year old company. We have traditional on-premises people that know about deploying hardware, that know about deploying VMware to host EKS anywhere. But we also have most of our company totally focused on the AWS cloud. So we're filling that gap in helping deploy these AWS services, manage them over the long term. So our customers can go to just primarily and totally focusing on their revenue generating business. >> So basically you guys are basically building AWS edges? >> Matthew: Correct. >> For companies. >> Matthew: Correct. >> Mainly because the needs are there, you got data, you got certain products, whether it's low latency type requirements, and then they still work with the regions, it's all tied together, is that how it works? >> And our customers, even the ones in the edge they also want us to build out the AWS environment inside the availability zone because we're always going to have a failback scenario. If we're going to deploy FinTech in the Caribbean we're going to talk about hurricanes. And we're going to talk about failing back into the AWS availability zones. So innovative is filling that gap across the board whether it be inside the AWS cloud or on the AWS edge. >> All right so I got to ask you on the, since you're at the edge in these areas, now, I won't say underserved but developing areas where you now have data and you have applications that are tapping into that requirement. It makes total sense, we're seeing that across the board. So it's not like it's an outlier, it's actually growing. >> Matthew: Yeah. >> There's also the crypto angle. You got the blockchain. Are you seeing any traction at the edge with blockchain? Because a lot of people are looking at the web three in these areas like Panama. And you mentioned FinTech in the islands, there are a lot of web three happening. What's your view on the web three world right now relative? >> We have some customers actually deploying crypto especially in the Caribbean. I keep bringing the Caribbean up, but it's top of my mind right now, we have customers that are deploying crypto. A lot of countries are choosing crypto to underlie parts of their central banks. So it's up and coming. I have some personal views that crypto is still searching for a use case. And I think it's searching a lot and we're there to help customers search for that use case. But crypto as a to technology lives really well on the AWS edge. And we're having more and more people talk to us about that. And ask for assistance in the infrastructure because they're developing new cryptocurrencies every day. It's not like they're deploying Ethereum or anything specific. They're actually developing new currencies and putting them out there on-- >> It's interesting. I mean, first of all we've been doing crypto for many, many years. We have our own little projects going on. But if you go talk to all the crypto people they say, look we do a smart concept. We use the blockchain. It's a lot of overhead. It's not really very technical already but it's a cultural shift but there's underserved use cases around use of money but they're all using the blockchain just for smart contracts, for instance, or certain transactions. And they go into Amazon for the database. They all, don't tell anyone we're using a centralized service. Well, what happened if decentralized? >> Yeah, and that's a conversation. >> It's a performance issue. >> Yeah and it's a cost issue and it's a development issue. So I think more and more as some of these currencies maybe come up, some of the smart contracts get into, they find their use cases. I think we'll start talking about how does that really live on AWS and what does it look like to build decentralized applications but with AWS hardware and services. >> All right so take me through a use case of a customer, Matthew, around the edge. So I'm a customer, pretend I'm a customer. Hey, we're in an underserved area. I want to modernize my business. And I got my developers that are totally peaked up on cloud but we've identified that it's just a lot of overhead latency issues. I need to have a local edge and serve my app. And I also want all the benefits of the cloud. So I want the modernization and I want to migrate to the cloud for all those cloud benefits and the goodness of the cloud. What's the answer? >> Yeah big thing is industrial manufacturing. That's one of the best use cases. Inside industrial manufacturing we can pull in many of the AWS edge services, we can bring in private 5G so that all the equipment inside that manufacturing plant can be hooked up. They don't have to pay huge overheads to deploy 5G. It's better than wifi for the industrial space. When we take computing down to that industrial area because we want to do pre-processing on the data. We want to gather some analytics. We deploy that with regular commercially available hardware, running VMware, and we deploy EKS anywhere on that. Inside of that manufacturing plant, we can do pre-processing on things coming out of the robotics depending on what we're manufacturing, right? And then we can take those refined analytics and for very low cost with maybe a little bit longer latency transmit those back to the AWS availability zone, the standard-- >> John: For data lake, or whatever. >> To the data lake, yeah data lake house, whatever it might be. And we can do additional data science on that once it gets to the AWS cloud. But a lot of that just in time business decisions, just in time manufacturing decisions can all take place on an AWS service or services inside that manufacturing plant. And that's one of the best use cases that we're seeing. >> And I think, I mean, we've been seeing this on theCUBE for many, many years, moving data around is very expensive. But also compute, going to the data that saves that cost on the data transfer but also on the benefits of the latency. So I have to ask you, by the way, that's standard best practice now for the folks watching, don't move the data unless you have to, but there's new things are developing. So I want to ask you what new are you seeing emerging once this new architecture's in place? Love that idea, localize everything, right at the edge, manufacturing, industrial, whatever the use case, retail, whatever it is. But now what does that change in the core cloud? There's a system element here, what's the new pattern? >> There's actually an organizational element as well. Because once you have to start making the decision do I put this compute at the point of use or do I put this compute in the cloud? Now you start thinking about where business decisions should be taking place. So not only are you changing your architecture you're actually changing your organization because you're thinking about a dichotomy you didn't have before. So now you say, okay, this can take place here. And maybe this decision can wait. And then how do I visualize that? >> By the way, it could be a bot too, doing the work for management. >> Yeah, exactly. >> You got observability going right. But you got to change the database architecture in the backs. There's new things developing. You've got more benefit. >> There are, there are. And we have more and more people that want to talk less about databases and want to talk more about data lakes because of this. They want to talk more about, customers are starting to talk about throwing away data. For the past maybe decade, it's been store everything. And one day we will have a data science team that we hire in our organization to do analytics on this decade of data. >> I mean, this is a great point. We don't have time to drill into, maybe we do another session on this but the one pattern we're seeing come of the past year is that throwing away data's bad. Even data lakes that so-called turn into data swamps. Actually is not the case. You look at Databrick, Snowflake and other successes out there. And even Time Series Data which may seem irrelevant efforts over actually matters when people start retraining their machine learning algorithms. >> Matthew: Yep. >> So as data becomes code, as we call it in our last showcase, we did, a whole event on this. The data's good in real time and in the lake. Because the iteration of the data feeds the machine learning training, things are getting better with the old data. So it's not throw it away. It's not just business benefits. There's all kinds of new scale. >> There are. And we have many customers that are running petabyte level. They're essentially data factories on premises, right? They're creating so much data and they're starting to say, okay we could analyze this in the cloud. We could transition it. We could move petabytes of data to the AWS cloud or we can run computational workloads on premises. We can really do some analytics on this data, transition those high level and sort of raw analytics back to AWS, run 'em through machine learning. And we don't have to transition 10, 12 petabytes of data into AWS. >> So I got to end the segment on a kind of a fun note. I was told to ask you about your personal background on premise architect, AWS cloud, and skydiving instructor. How does that all work together? What does this mean? You jumped out a plane and got a job. You got a customer to jump out? >> Kind of, so I was-- >> You jumped out? >> I was teaching skydiving before I started in the cloud space, this was 13, 14 years ago. I was a, I still am a skydiving instructor. I was teaching skydiving. And I heard out of the corner of my ear a guy that owned an MSP that was lamenting about storing data and how his customers are working. And he can't find enough people to operate all these workloads. So I walked over and said, hey, this is what I went to school for. I'd love to, I was living in a tent in the woods, teaching skydiving. I was like, I'd love to not live in a tent in the woods. So I started and the first day there we had a discussion, EC2 had just come out and-- >> This is amazing. >> Yeah and so we had this discussion, we should start moving customers here. And that totally revolutionized that business, that led to, that guy actually still owns skydiving airport. But through all of that and through being an on premises migrated me and myself, my career into the cloud. And now it feels like almost looking back and saying, now let's take what we learned in the cloud and apply those lessons in those services to on premises. >> It's such a great story, is going to, the whole growth mindset, pack your own parachute. >> Matthew: Exactly. >> The cloud in the early days was pretty much will the chute open? >> Matthew: Yeah. >> It was pretty much you had to roll your own cloud at that time. And so, you jump out a plane you got to make sure that parachute is going to open. >> And so was Kubernetes by the way, 2015 or so when that was coming out, it was, I mean, it was still, maybe it does still feel like that to some people. But it was the same kind of feeling that we had in the early days of AWS, the same feeling we have when-- >> It's pretty much now with you guys, it's more like a tandem jump. But a lot of this cutting edge stuff is like jumping out of an airplane. You got the right equipment. You got to do the right things. >> Exactly. >> John: Matthew, thanks for coming on theCUBE. Really appreciate it. Absolutely great conversation. >> Thanks for having me, thank you. >> Okay theCUBE's here live in San Francisco for AWS Summit. I'm John Furrier, host of theCUBE. We'll be at AWS Summit in New York coming up in the summer as well. Look up for that. Look at this calendar for all theCUBE action at theCUBE.net. We'll be right back with our next segment after this break. 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