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

(upbeat music) >> Welcome back to The Cubes coverage of WIDS 2023 the eighth Annual Women in Data Science Conference which is held at Stanford University. I'm your host, Lisa Martin. I'm really excited to be having some great co-hosts today. I've got Hannah Freytag with me, who is a data journalism master student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Hoang joins us, data scientist at Gilead. It's so great to have you, Kelly. >> Hi, thank you for having me today. I'm super excited to be here and share my journey with you guys. >> Let's talk about that journey. You recently got your PhD in information sciences, congratulations. >> Thank you. Yes, I just graduated, I completed my PhD in information sciences from University of Illinois Urbana-Champaign. And right now I moved to Bay Area and started my career as a data scientist at Gilead. >> And you're in better climate. Well, we do get snow here. >> Kelly: That's true. >> We proved that the last... And data science can show us all the climate change that's going on here. >> That's true. That's the topic of the data fund this year, right? To understand the changes in the climate. >> Yeah. Talk a little bit about your background. You were mentioning before we went live that you come from a whole family of STEM students. So you had that kind of in your DNA. >> Well, I consider myself maybe I was a lucky case. I did grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was at a very young age, I already see like datas, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. >> You aspired to be. >> Yes. >> I love that. >> So I consider myself in a lucky place in that way. But also, like during this journey to become a data scientist you need to navigate yourself too, right? Like you have this roots, like this foundation but then you still need to kind of like figure out yourself what is it? Is it really the career that you want to pursue? But I'm happy that I'm end up here today and where I am right now. >> Oh, we're happy to have you. >> Yeah. So you' re with Gilead now after you're completing your PhD. And were you always interested in the intersection of data science and health, or is that something you explored throughout your studies? >> Oh, that's an excellent question. So I did have background in computer science but I only really get into biomedical domain when I did my PhD at school. So my research during my PhD was natural language processing, NLP and machine learning and their applications in biomedical domains. And then when I graduated, I got my first job in Gilead Science. Is super, super close and super relevant to what my research at school. And at Gilead, I am working in the advanced analytics department, and our focus is to bring artificial intelligence and machine learning into supporting clinical decision making. And really the ultimate goal is how to use AI to accelerate the precision medicine. So yes, it's something very like... I'm very lucky to get the first job that which is very close to my research at school. >> That's outstanding. You know, when we talk about AI, we can't not talk about ethics, bias. >> Kelly: Right. >> We know there's (crosstalk) Yes. >> Kelly: In healthcare. >> Exactly. Exactly. Equities in healthcare, equities in so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead to really influence... I mean this, we're talking about something that's influencing life and death situations. >> Kelly: Right. >> How are you using AI in a way that is really maximizing the opportunities that AI can bring and maximizing the value in the data, but helping to dial down some of the challenges that come with AI? >> Yep. So as you may know already with the digitalization of medical records, this is nowaday, we have a tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines, means now the treatments for people can be really tailored to individual patients depending on their own like characteristic or demographic or whatever. And nature language processing and machine learning, and AI in general really play a key role in that innovation, right? Because like there's a vast amount of information of patients and patient journeys or patient treatment is conducted and recorded in text. So that's why our group was established. Actually our department, advanced analytic department in Gilead is pretty new. We established our department last year. >> Oh wow. >> But really our mission is to bring AI into this field because we see the opportunity now. We have a vast amount of data about patient about their treatments, how we can mine these data how we can understand and tailor the treatment to individuals. And give everyone better care. >> I love that you brought up precision medicine. You know, I always think, if I kind of abstract everything, technology, data, connectivity, we have this expectation in our consumer lives. We can get anything we want. Not only can we get anything we want but we expect whoever we're engaging with, whether it's Amazon or Uber or Netflix to know enough about me to get me that precise next step. I don't think about precision medicine but you bring up such a great point. We expect these tailored experiences in our personal lives. Why not expect that in medicine as well? And have a tailored treatment plan based on whatever you have, based on data, your genetics, and being able to use NLP, machine learning and AI to drive that is really exciting. >> Yeah. You recap it very well, but then you also bring up a good point about the challenges to bring AI into this field right? Definitely this is an emerging field, but also very challenging because we talk about human health. We are doing the work that have direct impact to human health. So everything need to be... Whatever model, machine learning model that you are building, developing you need to be precise. It need to be evaluated properly before like using as a product, apply into the real practice. So it's not like recommendation systems for shopping or anything like that. We're talking about our actual health. So yes, it's challenging that way. >> Yeah. With that, you already answered one of the next questions I had because like medical data and health data is very sensitive. And how you at Gilead, you know, try to protect this data to protect like the human beings, you know, who are the data in the end. >> The security aspect is critical. You bring up a great point about sensitive data. We think of healthcare as sensitive data. Or PII if you're doing a bank transaction. We have to be so careful with that. Where is security, data security, in your everyday work practices within data science? Is it... I imagine it's a fundamental piece. >> Yes, for sure. We at Gilead, for sure, in data science organization we have like intensive trainings for employees about data privacy and security, how you use the data. But then also at the same time, when we work directly with dataset, it's not that we have like direct information about patient at like very granular level. Everything is need to be kind of like anonymized at some points to protect patient privacy. So we do have rules, policies to follow to put that in place in our organization. >> Very much needed. So some of the conversations we heard, were you able to hear the keynote this morning? >> Yes. I did. I attended. Like I listened to all of them. >> Isn't it fantastic? >> Yes, yes. Especially hearing these women from different backgrounds, at different level of their professional life, sharing their journeys. It's really inspiring. >> And Hannah, and I've been talking about, a lot of those journeys look like this. >> I know >> You just kind of go... It's very... Yours is linear, but you're kind of the exception. >> Yeah, this is why I consider my case as I was lucky to grow up in STEM environment. But then again, back to my point at the beginning, sometimes you need to navigate yourself too. Like I did mention about, I did my pa... Sorry, my bachelor degree in Vietnam, in STEM and in computer science. And that time, there's only five girls in a class of 100 students. So I was not the smartest person in the room. And I kept my minority in that areas, right? So at some point I asked myself like, "Huh, I don't know. Is this really my careers." It seems that others, like male people or students, they did better than me. But then you kind of like, I always have this passion of datas. So you just like navigate yourself, keep pushing yourself over those journey. And like being where I am right now. >> And look what you've accomplished. >> Thank you. >> Yeah. That's very inspiring. And yeah, you mentioned how you were in the classroom and you were only one of the few women in the room. And what inspired or motivated you to keep going, even though sometimes you were at these points where you're like, "Okay, is this the right thing?" "Is this the right thing for me?" What motivated you to keep going? >> Well, I think personally for me, as a data scientist or for woman working in data science in general, I always try to find a good story from data. Like it's not, when you have a data set, well it's important for you to come up with methodologies, what are you going to do with the dataset? But I think it's even more important to kind of like getting the context of the dataset. Like think about it like what is the story behind this dataset? What is the thing that you can get out of it and what is the meaning behind? How can we use it to help use it in a useful way. To have in some certain use case. So I always have that like curiosity and encouragement in myself. Like every time someone handed me a data set, I always think about that. So it's helped me to like build up this kind of like passion for me. And then yeah. And then become a data scientist. >> So you had that internal drive. I think it's in your DNA as well. When you were one of five. You were 5% women in your computer science undergrad in Vietnam. Yet as Hannah was asking you, you found a lot of motivation from within. You embrace that, which is so key. When we look at some of the statistics, speaking of data, of women in technical roles. We've seen it hover around 25% the last few years, probably five to 10. I was reading some data from anitab.org over the weekend, and it shows that it's now, in 2022, the number of women in technical roles rose slightly, but it rose, 27.6%. So we're seeing the needle move slowly. But one of the challenges that still remains is attrition. Women who are leaving the role. You've got your PhD. You have a 10 month old, you've got more than one child. What would you advise to women who might be at that crossroads of not knowing should I continue my career in climbing the ladder, or do I just go be with my family or do something else? What's your advice to them in terms of staying the path? >> I think it's really down to that you need to follow your passion. Like in any kind of job, not only like in data science right? If you want to be a baker, or you want to be a chef, or you want to be a software engineer. It's really like you need to ask yourself is it something that you're really passionate about? Because if you really passionate about something, regardless how difficult it is, like regardless like you have so many kids to take care of, you have the whole family to take care of. You have this and that. You still can find your time to spend on it. So it's really like let yourself drive your own passion. Drive the way where you leading to. I guess that's my advice. >> Kind of like following your own North Star, right? Is what you're suggesting. >> Yeah. >> What role have mentors played in your career path, to where you are now? Have you had mentors on the way or people who inspired you? >> Well, I did. I certainly met quite a lot of women who inspired me during my journey. But right now, at this moment, one person, particular person that I just popped into my mind is my current manager. She's also data scientist. She's originally from Caribbean and then came to the US, did her PhDs too, and now led a group, all women. So believe it or not, I am in a group of all women working in data science. So she's really like someone inspire me a lot, like someone I look up to in this career. >> I love that. You went from being one of five females in a class of 100, to now having a PhD in information sciences, and being on an all female data science team. That's pretty cool. >> It's great. Yeah, it's great. And then you see how fascinating that, how things shift right? And now today we are here in a conference that all are women in data science. >> Yeah. >> It's extraordinary. >> So this year we're fortunate to have WIDS coincide this year with the actual International Women's Day, March 8th which is so exciting. Which is always around this time of year, but it's great to have it on the day. The theme of this International Women's Day this year is embrace equity. When you think of that theme, and your career path, and what you're doing now, and who inspires you, how can companies like Gilead benefit from embracing equity? What are your thoughts on that as a theme? >> So I feel like I'm very lucky to get my first job at Gilead. Not only because the work that we are doing here very close to my research at school, but also because of the working environment at Gilead. Inclusion actually is one of the five core values of Gilead. >> Nice. >> So by that, we means we try to create and creating a working environment that all of the differences are valued. Like regardless your background, your gender. So at Gilead, we have women at Gilead which is a global network of female employees, that help us to strengthen our inclusion culture, and also to influence our voices into the company cultural company policy and practice. So yeah, I'm very lucky to work in the environment nowadays. >> It's impressive to not only hear that you're on an all female data science team, but what Gilead is doing and the actions they're taking. It's one thing, we've talked about this Hannah, for companies, and regardless of industry, to say we're going to have 50% women in our workforce by 2030, 2035, 2040. It's a whole other ballgame for companies like Gilead to actually be putting pen to paper. To actually be creating a strategy that they're executing on. That's awesome. And it must feel good to be a part of a company who's really adapting its culture to be more inclusive, because there's so much value that comes from inclusivity, thought diversity, that ultimately will help Gilead produce better products and services. >> Yeah. Yes. Yeah. Actually this here is the first year Gilead is a sponsor of the WIDS Conference. And we are so excited to establish this relationship, and looking forward to like having more collaboration with WIDS in the future. >> Excellent. Kelly we've had such a pleasure having you on the program. Thank you for sharing your linear path. You are definitely a unicorn. We appreciate your insights and your advice to those who might be navigating similar situations. Thank you for being on theCUBE today. >> Thank you so much for having me. >> Oh, it was our pleasure. For our guests, and Hannah Freytag this is Lisa Martin from theCUBE. Coming to you from WIDS 2023, the eighth annual conference. Stick around. Our final guest joins us in just a minute.

(gentle music) >> Hello, and welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. I've got a great guest here, CUBE alumni and very impressive, inspiring, Rachel Mushahwar Skaff, who's a managing director and general manager at AWS. Rachel, great to see you. Thanks for coming on. >> Thank you so much. It's always a pleasure to be here. You all make such a tremendous impact with reporting out what's happening in the tech space, and frankly, investing in topics like this, so thank you. >> It's our pleasure. Your career has been really impressive. You worked at Intel for almost a decade, and that company is very tech, very focused on Moore's law, cadence of technology power in the industry. Now at AWS, powering next-generation cloud. What inspired you to get into tech? How did you get here and how have you approached your career journey, because it's quite a track record? >> Wow, how long do we have? (Rachel and John laugh) >> John: We can go as long as you want. (laughs) It's great. >> You know, all joking aside, I think at the end of the day, it's about this simple statement. If you don't get goosebumps every single morning that you're waking up to do your job, it's not good enough. And that's a bit about how I've made all of the different career transitions that I have. You know, everything from building out data centers around the world, to leading network and engineering teams, to leading applications teams, to going and working for, you know, the largest semiconductor in the world, and now at AWS, every single one of those opportunities gave me goosebumps. And I was really focused on how do I surround myself with humans that are better than I am, smarter than I am, companies that plan in decades, but live in moments, companies that invest in their employees and create like artists? And frankly, for me, being part of a company where people know that life is finite, but they want to make an infinite impact, that's a bit about my career journey in a nutshell. >> Yeah. What's interesting is that, you know, over the years, a lot's changed, and a theme that we're hearing from leaders now that are heading up large teams and running companies, they have, you know, they have 20-plus years of experience under their belt and they look back and they say, "Wow, "things have changed and it's changing faster now, "hopefully faster to get change." But they all talk about confidence and they talk about curiosity and building. When did you know that this was going to be something that you got the goosebumps? And were there blockers in your way and how did you handle that? (Rachel laughs) >> There's always blockers in our way, and I think a lot of people don't actually talk about the blockers. I think they make it sound like, hey, I had this plan from day one, and every decision I've made has been perfect. And for me, I'll tell you, right, there are moments in your life that mark a differentiation and those moments that you realize nothing will be the same. And time is kind of divided into two parts, right, before this moment and after this moment. And that's everything from, before I had kids, that's a pretty big moment in people's lives, to after I had kids, and how do you work through some of those opportunities? Before I got married, before I got divorced. Before I went to this company, after I left this company. And I think the key for all of those is just having an insatiable curiosity around how do you continue to do better, create better and make better? And I'll tell you, those blockers, they exist. Coming back from maternity leave, hard. Coming back from a medical leave, hard. Coming back from caring for a sick parent or a sick friend, hard. But all of those things start to help craft who you are as a human being, not as a leader, but as a human being, and allows you to have some empathy with the people that you surround yourself with, right? And for me, it's, (sighs) you can think about these blockers in one of two ways. You can think about it as, you know, every single time that you're tempted to react in the same way to a blocker, you can be a prisoner of your past, or you can change how you react and be a pioneer of the future. It's not a blocker when you think about it in those terms. >> Mindset matters, and that's really a great point. You brought up something that's interesting, I want to bring this up. Some of the challenges in different stages of our lives. You know, one thing that's come out of this set of interviews, this, of day and in conversations is, that I haven't heard before, is the result of COVID, working at home brought empathy about people's personal lives to the table. That came up in a couple interviews. What's your reaction to that? Because that highlights that we're human, to your point of view. >> It does. It does. And I'm so thankful that you don't ask about balance because that is a pet peeve of mine, because there is no such thing as balance. If you're in perfect balance, you are not moving and you're not changing. But when you think about, you know, the impact of COVID and how the world has changed since that, it has allowed all of us to really think about, you know, what do we want to do versus what do we have to do? And I think so many times, in both our professional lives and our personal lives, we get caught up in doing what we think we have to do to get ahead versus taking a step back and saying, "Hey, what do I want to do? "And how do I become a, you know, "a better human?" And many times, John, I'm asked, "Hey, "how do you define success or achievement?" And, you know, my answer is really, for me, the greatest results that I've achieved, both personally and professionally, is when I eliminate the word success and balance from my vocabulary, and replace them with two words: What's my contribution and what's my impact? Those things make a difference, regardless of gender. And I'll tell you, none of it is easy, ever. I think all of us have been broken, we've been stretched, we've been burnt out. But I also think what we have to talk about as leaders in the industry is how we've also found endurance and resilience. And when we felt unsteady, we've continued to go forward, right? When we can't decide, the best answer is do what's uncomfortable. And all of those things really stemmed from a part of what happened with COVID. >> Yeah, yeah, I love the uncomfortable and the balance highlight. You mentioned being off balance. That means you're growing, you're not standing still. I want to get your thoughts on this because one thing that has come out again this year, and last year as well, is having a team with you when you do it. So if you're off balance and you're going to stretch, if you have a good team with you, that's where people help each other. Not just pick them up, but like maybe get 'em back on track again. So, but if you're solo, you fall, (laughs) you fall harder. So what's your reaction to that? 'Cause this has come up, and this comes up in team building, workforce formation, goal setting, contribution. What's your reaction to that? >> So my reaction to that that is pretty simple. Nobody gets there on their own at all, right? Passion and ambition can only take you so far. You've got to have people and teams that are supporting you. And here's the funny thing about people, and frankly, about being a leader that I think is really important: People don't follow for you. People follow for who you help them become. Think about that for a second. And when you think about all the amazing things that companies and teams are able to do, it's because of those people. And it's because you have leaders that are out there, inspiring them to take what they believe is impossible and turn it into the possible. That's the power of teams. >> Can you give an example of your approach on how you do that? How do you build your teams? How do you grow them? How do you lead them effectively and also make 'em inclusive, diverse and equitable? >> Whew. I'll give you a great example of some work that we're doing at AWS. This year at re:Invent, for the first time in its history, we've launched an initiative with theCUBE called Women of the Cloud. And part of Women of the Cloud is highlighting the business impact that so many of our partners, our customers and our employees have had on the social, on the economic and on the financials of many companies. They just haven't had the opportunity to tell their story. And at Amazon, right, it is absolutely integral to us to highlight those examples and continue to extend that ethos to our partners and our customers. And I think one of the things that I shared with you at re:Invent was, you know, as U2's Bono put it, (John laughs) "We'll build it better than we did before "and we are the people "that we've been waiting for." So if we're not out there, advocating and highlighting all the amazing things that other women are doing in the ecosystem, who will? >> Well, I've got to say, I want to give you props for that program. Not only was it groundbreaking, it's still running strong. And I saw some things on LinkedIn that were really impressive in its network effect. And I met at least half a dozen new people I never would have met before through some of that content interaction and engagement. And this is like the power of the current world. I mean, getting the voices out there creates momentum. And it's good for Amazon. It's not just personal brand building for my next job or whatever, you know, reason. It's sharing and it's attracting others, and it's causing people to connect and meet each other in that world. So it's still going strong. (laughs) And this program we did last year was part of Rachel Thornton, who's now at MessageBird, and Mary Camarata. They were the sponsors for this International Women's Day. They're not there anymore, so we decided we're going to do it again because the impact is so significant. We had the Amazon Education group on. It's amazing and it's free, and we've got to get the word out. I mean, talk about leveling up fast. You get in and you get trained and get certified, and there's a zillion jobs out (laughs) there in cloud, right, and partners. So this kind of leadership is really important. What was the key learnings that you've taken away and how do you extend this opportunity to nurture the talent out there in the field? Because when you throw the content out there from great leaders and practitioners and developers, it attracts other people. >> It does. It does. So look, I think there's two types of people, people that are focused on being and people who are focused on doing. And let me give you an example, right? When we think about labels of, hey, Rachel's a female executive who launched Women of the Cloud, that label really limits me. I'd rather just be a great executive. Or, hey, there's a great entrepreneur. Let's not be a great entrepreneur. Just go build something and sell it. And that's part of this whole Women of the cloud, is I don't want people focused on what their label is. I want people sharing their stories about what they're doing, and that's where the lasting impact happens, right? I think about something that my grandmother used to tell me, and she used to tell me, "Rachel, how successful "you are, doesn't matter. "The lasting impact that you have "is your legacy in this very finite time "that you have on Earth. "Leave a legacy." And that's what Women of the Cloud is about. So that people can start to say, "Oh, geez, "I didn't know that that was possible. "I didn't think about my career in that way." And, you know, all of those different types of stories that you're hearing out there. >> And I want to highlight something you said. We had another Amazonian on the program for this day earlier and she coined a term, 'cause inside Amazon, you have common language. One of them is bar raising. Raise the bar, that's an Amazonian (Rachel laughs) term. It means contribute and improve and raise the bar of capability. She said, "Bar raising is gender neutral. "The bar is a bar." And I'm like, wow, that was amazing. Now, that means your contribution angle there highlights that. What's the biggest challenge to get that mindset set in culture, in these- >> Oh. >> 'Cause it's that simple, contribution is neutral. >> It absolutely is neutral, but it's like I said earlier, I think so many times, people are focused on success and being a great leader versus what's the contribution I'm making and how am I doing as a leader, you know? And when it comes to a lot of the leadership principles that Amazon has, including bar raising, which means insisting on the highest standards, and then those standards continue to raise every single time. And what that is all about is having all of our employees figure out, how do I get better every single day, right? That's what it's about. It's not about being better than the peer next to you. It's about how do I become a better leader, a better human being than I was yesterday? >> Awesome. >> You know, I read this really cute quote and I think it really resonates. "You meditate to upgrade your software "and you work out to upgrade your hardware." And while it's important that we're all ourselves at work, we can't deny that a lot of times, ourselves still need that meditation or that workout. >> Well, I hope I don't have any zero days in my software out there, so, but I'm going to definitely work on that. I love that quote. I'm going to use that. Thank you very much. That was awesome. I got to ask you, I know you're really passionate about, and we've talked about this, around, so you're a great leader but you're also focused on what's behind you in the generation, pipelining women leaders, okay? Seats at the table, mentoring and sponsorship. What can we do to build a strong pipeline of leaders in technology and business? And where do you see the biggest opportunity to nurture the talent in these fields? >> Hmm, you know, that's great, great question. And, you know, I just read a "Forbes" article by another Amazonian, Tanuja Randery, who talked about, you know, some really interesting stats. And one of the stats that she shared was, you know, by 2030, less than 25% of tech specialists will be female, less than 25%. That's only a 6% growth from where we are in 2023, so in seven years. That's alarming. So we've really got to figure out what are the kinds of things that we're going to go do from an Amazon perspective to impact that? And one of the obvious starting points is showcasing tech careers to girls and young women, and talking openly about what a technology career looks like. So specifically at Amazon, we've got an AWS Git IT program that helps schools and educators bring in tech role models to show them what potential careers look like in tech. I think that's one great way that we can help build the pipeline, but once we get the pipeline, we also have to figure out how we don't let that pipeline leak. Meaning how do we keep women and, you know, young women on their tech career? And I think big part of that, John, is really talking about how hard it is, but it's also greater than you can ever imagine. And letting them see executives that are very authentic and will talk about, geez, you know, the challenges of COVID were a time of crisis and accelerated change, and here's what it meant to me personally and here's what we were able to solve professionally. These younger generations are all about social impact, they're about economic impact and they're about financial impact. And if we're not talking about all three of those, both from how AWS is leading from the front, but how its executives are also taking that into their personal lives, they're not going to want to go into tech. >> Yeah, and I think one of the things you mentioned there about getting people that get IT, good call out there, but also, Amazon's going to train 30 million people, put hundreds of millions of dollars into education. And not only are they making it easier to get in to get trained, but once you're in, even savvy folks that are in there still have to accelerate. And there's more ways to level up, more things are happening, but there's a big trend around people changing careers either in their late 20s, early 30s, or even those moments you talk about, where it's before and after, even later in the careers, 40s, 50s. Leaders like, well, good experience, good training, who were in another discipline who re-skilled. So you have, you know, more certifications coming in. So there's still other pivot points in the pipeline. It's not just down here. And that, I find that interesting. Are you seeing that same leadership opportunities coming in where someone can come into tech older? >> Absolutely. You know, we've got some amazing programs, like Amazon Returnity, that really focuses on how do we get other, you know, how do we get women that have taken some time off of work to get back into the workforce? And here's the other thing about switching careers. If I look back on my career, I started out as a civil engineer, heavy highway construction. And now I lead a sales team at the largest cloud company in the world. And there were, you know, twists and turns around there. I've always focused on how do we change and how do we continue to evolve? So it's not just focused on, you know, young women in the pipeline. It's focused on all gender and all diverse types throughout their career, and making sure that we're providing an inclusive environment for them to bring in their unique skillsets. >> Yeah, a building has good steel. It's well structured. Roads have great foundations. You know, you got the builder in you there. >> Yes. >> So I have to ask you, what's on your mind as a tech athlete, as an executive at AWS? You know, you got your huge team, big goals, the economy's got a little bit of a headwind, but still, cloud's transforming, edge is exploding. What's your outlook as you look out in the tech landscape these days and how are you thinking about it? What your plans? Can you share a little bit about what's on your mind? >> Sure. So, geez, there's so many trends that are top of mind right now. Everything from zero trust to artificial intelligence to security. We have more access to data now than ever before. So the opportunities are limitless when we think about how we can apply technology to solve some really difficult customer problems, right? Innovation sometimes feels like it's happening at a rapid pace. And I also say, you know, there are years when nothing happens, and then there's years when centuries happen. And I feel like we're kind of in those years where centuries are happening. Cloud technologies are refining sports as we know them now. There's a surge of innovation in smart energy. Everyone's supply chain is looking to transform. Custom silicon is going mainstream. And frankly, AWS's customers and partners are expecting us to come to them with a point of view on trends and on opportunities. And that's what differentiates us. (John laughs) That's what gives me goosebumps- >> I was just going to ask you that. Does that give you goosebumps? How could you not love technology with that excitement? I mean, AI, throw in AI, too. I just talked to Swami, who heads up the AI and database, and we just talked about the past 24 months, the change. And that is a century moment happening. The large language models, computer vision, more compute. Compute's booming than ever before. Who thought that was going to happen, is still happening? Massive change. So, I mean, if you're in tech, how can you not love tech? >> I know, even if you're not in tech, I think you've got to start to love tech because it gives you access to things you've never had before. And frankly, right, change is the only constant. And if you don't like change, you're going to like being irrelevant even less than you like change. So we've got to be nimble, we've got to adapt. And here's the great thing, once we figure it out, it changes all over again. And it's not something that's easy for any of us to operate. It's hard, right? It's hard learning new technology, it's hard figuring out what do I do next? But here's the secret. I think it's hard because we're doing it right. It's not hard because we're doing it wrong. It's just hard to be human and it's hard to figure out how we apply all this different technology in a way that positively impacts us, you know, economically, financially, environmentally and socially. >> And everyone's different, too. So you got to live those (mumbles). I want to get one more question in before we, my last question, which is about you and your impact. When you talk to your team, your sales, you got a large sales team, North America. And Tanuja, who you mentioned, is in EMEA, we're going to speak with her as well. You guys lead the front lines, helping customers, but also delivering the revenue to the company, which has been fantastic, by the way. So what's your message to the troops and the team out there? When you say, "Take that hill," like what is the motivational pitch, in a few sentences? What's the main North Star message in today's marketplace when you're doing that big team meeting? >> I don't know if it's just limited to a team meeting. I think this is a universal message, and the universal message for me is find your edge, whatever that may be. Whether it is the edge of what you know about artificial intelligence and neural networks or it's the edge of how do we migrate our applications to the cloud more quickly. Or it's the edge of, oh, my gosh, how do I be a better parent and still be great at work, right? Find your edge, and then sharpen it. Go to the brink of what you think is possible, and then force yourself to jump. Get involved. The world is run by the people that show up, professionally and personally. (John laughs) So show up and get started. >> Yeah as Steve Jobs once said, "The future "that everyone looks at was created "by people no smarter than you." And I love that quote. That's really there. Final question for you. I know we're tight on time, but I want to get this in. When you think about your impact on your company, AWS, and the industry, what's something you want people to remember? >> Oh, geez. I think what I want people to remember the most is it's not about what you've said, and this is a Maya Angelou quote. "It's not about what you've said to people "or what you've done, "it's about how you've made them feel." And we can all think back on leaders or we can all think back on personal moments in our lives where we felt like we belonged, where we felt like we did something amazing, where we felt loved. And those are the moments that sit with us for the rest of our lives. I want people to remember how they felt when they were part of something bigger. I want people to belong. It shouldn't be uncommon to talk about feelings at work. So I want people to feel. >> Rachel, thank you for your time. I know you're really busy and we stretched you a little bit there. Thank you so much for contributing to this wonderful day of great leaders sharing their stories. And you're an inspiration. Thanks for everything you do. We appreciate you. >> Thank you. And let's go do some more Women of the Cloud videos. >> We (laughs) got more coming. Bring those stories on. Back up the story truck. We're ready to go. Thanks so much. >> That's good. >> Thank you. >> Okay, this is theCUBE's coverage of International Women's Day. It's not just going to be March 8th. That's the big celebration day. It's going to be every quarter, more stories coming. Stay tuned at siliconangle.com and thecube.net here, with bringing all the stories. I'm John Furrier, your host. Thanks for watching. (gentle music)

(intro music) >> "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> We're not going to- >> Hey everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante, I'm here with Dave Nicholson, day four of MWC23. I mean, it's Dave, it's, it's still really busy. And you walking the floors, you got to stop and start. >> It's surprising. >> People are cheering. They must be winding down, giving out the awards. Really excited. Pier, look at you and Elias here. He's the vice president of Engineering Technology for Edge Computing Offers Strategy and Execution at Dell Technologies, and he's joined by Dan Cummins, who's a fellow and vice president of, in the Edge Business Unit at Dell Technologies. Guys, welcome. >> Thank you. >> Thank you. >> I love when I see the term fellow. You know, you don't, they don't just give those away. What do you got to do to be a fellow at Dell? >> Well, you know, fellows are senior technical leaders within Dell. And they're usually tasked to help Dell solve you know, a very large business challenge to get to a fellow. There's only, I think, 17 of them inside of Dell. So it is a small crowd. You know, previously, really what got me to fellow, is my continued contribution to transform Dell's mid-range business, you know, VNX two, and then Unity, and then Power Store, you know, and then before, and then after that, you know, they asked me to come and, and help, you know, drive the technology vision for how Dell wins at the Edge. >> Nice. Congratulations. Now, Pierluca, I'm looking at this kind of cool chart here which is Edge, Edge platform by Dell Technologies, kind of this cube, like cubes course, you know. >> AK project from here. >> Yeah. So, so tell us about the Edge platform. What, what's your point of view on all that at Dell? >> Yeah, absolutely. So basically in a, when we create the Edge, and before even then was bringing aboard, to create this vision of the platform, and now building the platform when we announced project from here, was to create solution for the Edge. Dell has been at the edge for 30 years. We sold a lot of compute. But the reality was people want outcome. And so, and the Edge is a new market, very exciting, but very siloed. And so people at the Edge have different personas. So quickly realize that we need to bring in Dell, people with expertise, quickly realize as well that doing all these solution was not enough. There was a lot of problem to solve because the Edge is outside of the data center. So you are outside of the wall of the data center. And what is going to happen is obviously you are in the land of no one. And so you have million of device, thousand of million of device. All of us at home, we have all connected thing. And so we understand that the, the capability of Dell was to bring in technology to secure, manage, deploy, with zero touch, zero trust, the Edge. And all the edge the we're speaking about right now, we are focused on everything that is outside of a normal data center. So, how we married the computer that we have for many years, the new gateways that we create, so having the best portfolio, number one, having the best solution, but now, transforming the way that people deploy the Edge, and secure the Edge through a software platform that we create. >> You mentioned Project Frontier. I like that Dell started to do these sort of project, Project Alpine was sort of the multi-cloud storage. I call it "The Super Cloud." The Project Frontier. It's almost like you develop, it's like mission based. Like, "Okay, that's our North Star." People hear Project Frontier, they know, you know, internally what you're talking about. Maybe use it for external communications too, but what have you learned since launching Project Frontier? What's different about the Edge? I mean you're talking about harsh environments, you're talking about new models of connectivity. So, what have you learned from Project Frontier? What, I'd love to hear the fellow perspective as well, and what you guys are are learning so far. >> Yeah, I mean start and then I left to them, but we learn a lot. The first thing we learn that we are on the right path. So that's good, because every conversation we have, there is nobody say to us, you know, "You are crazy. "This is not needed." Any conversation we have this week, start with the telco thing. But after five minutes it goes to, okay, how I can solve the Edge, how I can bring the compute near where the data are created, and how I can do that secure at scale, and with the right price. And then can speak about how we're doing that. >> Yeah, yeah. But before that, we have to really back up and understand what Dell is doing with Project Frontier, which is an Edge operations platform, to simplify your Edge use cases. Now, Pierluca and his team have a number of verticalized applications. You want to be able to securely deploy those, you know, at the Edge. But you need a software platform that's going to simplify both the life cycle management, and the security at the Edge, with the ability to be able to construct and deploy distributed applications. Customers are looking to derive value near the point of generation of data. We see a massive explosion of data. But in particular, what's different about the Edge, is the different computing locations, and the constraints that are on those locations. You know, for example, you know, in a far Edge environment, the people that service that equipment are not trained in the IT, or train, trained in it. And they're also trained in the safety and security protocols of that environment. So you necessarily can't apply the same IT techniques when you're managing infrastructure and deploying applications, or servicing in those locations. So Frontier was designed to solve for those constraints. You know, often we see competitors that are doing similar things, that are starting from an IT mindset, and trying to shift down to cover Edge use cases. What we've done with Frontier, is actually first understood the constraints that they have at the Edge. Both the operational constraints and technology constraints, the service constraints, and then came up with a, an architecture and technology platform that allows them to start from the Edge, and bleed into the- >> So I'm laughing because you guys made the same mistake. And you, I think you learned from that mistake, right? You used to take X86 boxes and throw 'em over the fence. Now, you're building purpose-built systems, right? Project Frontier I think is an example of the learnings. You know, you guys an IT company, right? Come on. But you're learning fast, and that's what I'm impressed about. >> Well Glenn, of course we're here at MWC, so it's all telecom, telecom, telecom, but really, that's a subset of Edge. >> Yes. >> Fair to say? >> Yes. >> Can you give us an example of something that is, that is, orthogonal to, to telecom, you know, maybe off to the side, that maybe overlaps a little bit, but give us an, give us an example of Edge, that isn't specifically telecom focused. >> Well, you got the, the Edge verticals. and Pierluca could probably speak very well to this. You know, you got manufacturing, you got retail, you got automotive, you got oil and gas. Every single one of them are going to make different choices in the software that they're going to use, the hyperscaler investments that they're going to use, and then write some sort of automation, you know, to deploy that, right? And the Edge is highly fragmented across all of these. So we certainly could deploy a private wireless 5G solution, orchestrate that deployment through Frontier. We can also orchestrate other use cases like connected worker, or overall equipment effectiveness in manufacturing. But Pierluca you have a, you have a number. >> Well, but from your, so, but just to be clear, from your perspective, the whole idea of, for example, private 5g, it's a feature- >> Yes. >> That might be included. It happened, it's a network topology, a network function that might be a feature of an Edge environment. >> Yes. But it's not the center of the discussion. >> So, it enables the outcome. >> Yeah. >> Okay. >> So this, this week is a clear example where we confirm and establish this. The use case, as I said, right? They, you say correctly, we learned very fast, right? We brought people in that they came from industry that was not IT industry. We brought people in with the things, and we, we are Dell. So we have the luxury to be able to interview hundreds of customers, that just now they try to connect the OT with the IT together. And so what we learn, is really, at the Edge is different personas. They person that decide what to do at the Edge, is not the normal IT administrator, is not the normal telco. >> Who is it? Is it an engineer, or is it... >> It's, for example, the store manager. >> Yeah. >> It's, for example, the, the person that is responsible for the manufacturing process. Those people are not technology people by any means. But they have a business goal in mind. Their goal is, "I want to raise my productivity by 30%," hence, I need to have a preventive maintenance solution. How we prescribe this preventive maintenance solution? He doesn't prescribe the preventive maintenance solution. He goes out, he has to, a consult or himself, to deploy that solution, and he choose different fee. Now, the example that I was doing from the houses, all of us, we have connected device. The fact that in my house, I have a solar system that produce energy, the only things I care that I can read, how much energy I produce on my phone, and how much energy I send to get paid back. That's the only thing. The fact that inside there is a compute that is called Dell or other things is not important to me. Same persona. Now, if I can solve the security challenge that the SI, or the user need to implement this technology because it goes everywhere. And I can manage this in extensively, and I can put the supply chain of Dell on top of that. And I can go every part in the world, no matter if I have in Papua New Guinea, or I have an oil ring in Texas, that's the winning strategy. That's why people, they are very interested to the, including Telco, the B2B business in telco is looking very, very hard to how they recoup the investment in 5g. One of the way, is to reach out with solution. And if I can control and deploy things, more than just SD one or other things, or private mobility, that's the key. >> So, so you have, so you said manufacturing, retail, automotive, oil and gas, you have solutions for each of those, or you're building those, or... >> Right now we have solution for manufacturing, with for example, PTC. That is the biggest company. It's actually based in Boston. >> Yeah. Yeah, it is. There's a company that the market's just coming right to them. >> We have a, very interesting. Another solution with Litmus, that is a startup that, that also does manufacturing aggregation. We have retail with Deep North. So we can do detecting in the store, how many people they pass, how many people they doing, all of that. And all theses solution that will be, when we will have Frontier in the market, will be also in Frontier. We are also expanding to energy, and we going vertical by vertical. But what is they really learn, right? You said, you know you are an IT company. What, to me, the Edge is a pre virtualization area. It's like when we had, you know, I'm, I've been in the company for 24 years coming from EMC. The reality was before there was virtualization, everybody was starting his silo. Nobody thought about, "Okay, I can run this thing together "with security and everything, "but I need to do it." Because otherwise in a manufacturing, or in a shop, I can end up with thousand of devices, just because someone tell to me, I'm a, I'm a store manager, I don't know better. I take this video surveillance application, I take these things, I take a, you know, smart building solution, suddenly I have five, six, seven different infrastructure to run this thing because someone say so. So we are here to democratize the Edge, to secure the Edge, and to expand. That's the idea. >> So, the Frontier platform is really the horizontal platform. And you'll build specific solutions for verticals. On top of that, you'll, then I, then the beauty is ISV's come in. >> Yes. >> 'Cause it's open, and the developers. >> We have a self certification program already for our solution, as well, for the current solution, but also for Frontier. >> What does that involve? Self-certification. You go through you, you go through some- >> It's basically a, a ISV can come. We have a access to a lab, they can test the thing. If they pass the first screen, then they can become part of our ecosystem very easily. >> Ah. >> So they don't need to spend days or months with us to try to architect the thing. >> So they get the premature of being certified. >> They get the Dell brand associated with it. Maybe there's some go-to-market benefits- >> Yes. >> As well. Cool. What else do we need to know? >> So, one thing I, well one thing I just want to stress, you know, when we say horizontal platform, really, the Edge is really a, a distributed edge computing problem, right? And you need to almost create a mesh of different computing locations. So for example, even though Dell has Edge optimized infrastructure, that we're going to deploy and lifecycle manage, customers may also have compute solutions, existing compute solutions in their data center, or at a co-location facility that are compute destinations. Project Frontier will connect to those private cloud stacks. They'll also collect to, connect to multiple public cloud stacks. And then, what they can do, is the solutions that we talked about, they construct that using an open based, you know, protocol, template, that describes that distributed application that produces that outcome. And then through orchestration, we can then orchestrate across all of these locations to produce that outcome. That's what the platform's doing. >> So it's a compute mesh, is what you just described? >> Yeah, it's, it's a, it's a software orchestration mesh. >> Okay. >> Right. And allows customers to take advantage of their existing investments. Also allows them to, to construct solutions based on the ISV of their choice. We're offering solutions like Pierluca had talked about, you know, in manufacturing with Litmus and PTC, but they could put another use case that's together based on another ISV. >> Is there a data mesh analog here? >> The data mesh analog would run on top of that. We don't offer that as part of Frontier today, but we do have teams working inside of Dell that are working on this technology. But again, if there's other data mesh technology or packages, that they want to deploy as a solution, if you will, on top of Frontier, Frontier's extensible in that way as well. >> The open nature of Frontier is there's a, doesn't, doesn't care. It's just a note on the mesh. >> Yeah. >> Right. Now, of course you'd rather, you'd ideally want it to be Dell technology, and you'll make the business case as to why it should be. >> They get additional benefits if it's Dell. Pierluca talked a lot about, you know, deploying infrastructure outside the walls of an IT data center. You know, this stuff can be tampered with. Somebody can move it to another room, somebody can open up. In the supply chain with, you know, resellers that are adding additional people, can open these devices up. We're actually deploying using an Edge technology called Secure Device Onboarding. And it solves a number of things for us. We, as a manufacturer can initialize the roots of trust in the Dell hardware, such that we can validate, you know, tamper detection throughout the supply chain, and securely transfer ownership. And that's different. That is not an IT technique. That's an edge technique. And that's just one example. >> That's interesting. I've talked to other people in IT about how they're using that technique. So it's, it's trickling over to that side of the business. >> I'm almost curious about the friction that you, that you encounter because the, you know, you paint a picture of a, of a brave new world, a brave new future. Ideally, in a healthy organization, they have, there's a CTO, or at least maybe a CIO, with a CTO mindset. They're seeking to leverage technology in the service of whatever the mission of the organization is. But they've got responsibilities to keep the lights on, as well as innovate. In that mix, what are you seeing as the inhibitors? What's, what's the push back against Frontier that you're seeing in most cases? Is it, what, what is it? >> Inside of Dell? >> No, not, I'm saying out, I'm saying with- >> Market friction. >> Market, market, market friction. What is the push back? >> I think, you know, as I explained, do yourself is one of the things that probably is the most inhibitor, because some people, they think that they are better already. They invest a lot in this, and they have the content. But those are again, silo solutions. So, if you go into some of the huge things that they already established, thousand of store and stuff like that, there is an opportunity there, because also they want to have a refresh cycle. So when we speak about softer, softer, softer, when you are at the Edge, the software needs to run on something that is there. So the combination that we offer about controlling the security of the hardware, plus the operating system, and provide an end-to-end platform, allow them to solve a lot of problems that today they doing by themselves. Now, I met a lot of customers, some of them, one actually here in Spain, I will not make the name, but it's a large automotive. They have the same challenge. They try to build, but the problem is this is just for them. And they want to use something that is a backup and provide with the Dell service, Dell capability of supply chain in all the world, and the diversity of the portfolio we have. These guys right now, they need to go out and find different types of compute, or try to adjust thing, or they need to have 20 people there to just prepare the device. We will take out all of this. So I think the, the majority of the pushback is about people that they already established infrastructure, and they want to use that. But really, there is an opportunity here. Because the, as I said, the IT/OT came together now, it's a reality. Three years ago when we had our initiative, they've pointed out, sarcastically. We, we- >> Just trying to be honest. (laughing) >> I can't let you get away with that. >> And we, we failed because it was too early. And we were too focused on, on the fact to going. Push ourself to the boundary of the IOT. This platform is open. You want to run EdgeX, you run EdgeX, you want OpenVINO, you want Microsoft IOT, you run Microsoft IOT. We not prescribe the top. We are locking down the bottom. >> What you described is the inertia of, of sunk dollars, or sunk euro into an infrastructure, and now they're hanging onto that. >> Yeah. >> But, I mean, you know, I, when we say horizontal, we think scale, we think low cost, at volume. That will, that will win every time. >> There is a simplicity at scale, right? There is a, all the thing. >> And the, and the economics just overwhelm that siloed solution. >> And >> That's inevitable. >> You know, if you want to apply security across the entire thing, if you don't have a best practice, and a click that you can do that, or bring down an application that you need, you need to touch each one of these silos. So, they don't know yet, but we going to be there helping them. So there is no pushback. Actually, this particular example I did, this guy said you know, there are a lot of people that come here. Nobody really described the things we went through. So we are on the right track. >> Guys, great conversation. We really appreciate you coming on "theCUBE." >> Thank you. >> Pleasure to have you both. >> Okay. >> Thank you. >> All right. And thank you for watching Dave Vellante for Dave Nicholson. We're live at the Fira. We're winding up day four. Keep it right there. Go to siliconangle.com. John Furrier's got all the news on "theCUBE.net." We'll be right back right after this break. "theCUBE," at MWC 23. (outro 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)

(gentle music) >> Welcome back to our coverage here on theCUBE of AWS re:Invent 22. We are on day three, starting to wind down, but still a lot of exciting topics to cover here on the AWS Global Showcase, part of the startup program there at AWS. Joining us now, two representatives from LTI Mindtree. You say LTI Mindtree? I thought they were two different companies. Well, they're actually one and the same. Been together just a mere two weeks now. We'll hear more about that from Sid Bohra, who is the Chief Business Officer at LTI Mindtree and Ashish Varerkar, who is the Vice President of Cloud Success at LTI Mindtree. Gentlemen, thanks for being with us here on theCUBE. >> Pleasures all ours. >> Thank you. >> And congratulations. So two weeks in the making in its infancy, still in the honeymoon period, but how's the two weeks been? Everything all right? >> Well, two weeks have been very exciting. >> I'll bet. >> Well, I would say the period prior to that was just as exciting as you can imagine. >> John: Oh, sure. And we are super excited about what the future holds for this company because we truly believe that we have a remarkable opportunity to create value for our clients as one company. >> Well let's talk about LTI Mind tree then a little bit. Ashish, I'll let you carry the ball on this. Tell us about your services, about your core focus, and about those opportunities that Siddharth was just telling us about. >> So I think with the two companies coming together, we have a larger opportunity to like go to market with our end to end business transformation services and leveraging cloud platforms, right? So, and that's what we do. My responsibility particularly is to see to it that what customers are deploying on cloud is aligned to their business outcomes and then take it forward from there. >> Yeah, Vice President of Cloud Success, that gives you a lot of runway, right? Does it not? I mean, how do you define success in the cloud? Because there are a lot of different areas of complexity with which companies are dealing. >> So I think you would agree that in today's scenario, customers are not looking for a platform, right? But they're looking for a platform which can deliver business value. They're looking at business value and resiliency and then at the end, the cost, right? So if you're able to deliver these three things to the customer through the cloud implementation, I think that's success for us. >> Right. We've talked about transformation a lot this week and modernization, right, which is those are two pretty key buzzwords right now we're hearing a lot of. So when you see said, you know, companies come to you and they say, okay, it's time for us to make this commitment. Do they make it generally wholeheartedly? Is there still some trepidation of the unknown? Because there's a lot of, as we've said, complexity to this, it's multidimensional. We can go public, we can go hybrid, we can go multicloud. I mean, we got a lot of flavors. >> Yeah >> Absolutely. >> No, we see a spectrum. There are customers who are very early in the journey of getting onto cloud and are a little uncertain about what value they can get out of it. And on the other end of the spectrum, there are companies who are well into the journey who have understood what are the benefits of truly leveraging cloud who also understand what are the challenges they will face in getting onto the journey. So we get to meet a spectrum of customers, I would say. If you ask me where do bulk of them lie, I would say early in their journey. I would say there are only a handful who have that maturity where they can predict what's exactly going to happen on the cloud journey, what value they will accumulate through the process. So there's a lot of hand holding to be done, a lot of, you know, solving together to be done with our clients. >> You know, it is such a dynamic environment too, right? You have new opportunities that seem to be developed and released on a daily basis, almost, right? There's a large amount of flexibility, I would think, that has to be in place because where you think you're going to go today might not be where you wind up in six months. >> That's true. >> Is that fair? >> Absolutely fair. And I think from that perspective, if you look at the number of services that AWS provides, right? And what customers are looking for is how can they compose their business processes using this multiple services in a very seamless manner. And most of the announcements that we have seen during the re:Invent as well, they're talking about seamless connectivity between their services. They're talking about security, they're talking about creating a data fabric, the data zone that they announced. I think all these things put together, if you're able to kind of connect the dots and drive the business processes, I think that's what we want to do for our customers. >> And the value to AWS, it just can't be underscored enough I would assume, because there's comfort there, there's confidence there. When you bring that to the table as well along with your services, what kind of magnitude are we talking about here? What kind of force do you think? How would you characterize that? >> Well I think, you know, firstly, I would say that most of our engagements are not just services. Ashish and team and the company have invested heavily in building IP that we pair with our services so that we bring non-linearity and more, I would say, certainty to the outcomes that our customers get. And I can share some examples in the course of the conversation, but to answer your question in terms of magnitude, what we are collaborating with AWS on for our clients ranges from helping customers build more resiliency. And I'm talking about life sciences companies build more resiliency in the manufacturing R and D processes. That's so critical. It was even more critical during the pandemic times because we were working with some of the pharma companies who were contributing to the efforts in the pandemic. That's one end of the spectrum. On the other side, we are helping streaming companies and media companies digitize their supply chain, and their supply chains, the media supply chain, so that it is more effective, it's more efficient, it's more real time, again, using the power of the cloud. We are helping pharmaceutical companies drive far greater speed in the R and D processes. We are helping banking companies drive far more compliance in their anti-money laundering efforts and all of those things. So if you look at the magnitude, we judge the magnitude by the business impact that it's creating and we are very excited about what AWS, LTI Mindtree, and the customer are able to create in terms of those business impacts. >> And these are such major decisions. >> That's right. >> For a company, right, to make, and there are a number of factors that come into play here. What are you hearing from the C-Suite with regard to what weighs the most in their mind and is there, is it a matter of, you know, fear missing out? Or is it about trying to stay ahead of your competition, catching up the competition? I mean, generally speaking, you know, where are the, where's the C-Suite weighing in on this? >> I think in the current times, I think there is a certain level of adoption of cloud that's already happened in most enterprises. So most CIOs in the C-suite- >> They already get it. They already get it. >> They kind of get it, but I would say that they're very cagey about a bunch of things. They're very cagey about, am I going to end up spending too much for too little? Am I going to be able to deliver this transformation at the speed that I'm hoping to achieve? What about security? Compliance? What about the cost of running in the cloud? So those are some really important factors that sometimes end up slowing the cloud transformation journeys down because customers end up solving for them or not knowing for them. So while there is a decent amount of awareness about what cloud can do, there are some, a whole bunch of important factors that they continue to solve for as they go down that journey. >> And so what kind of tools do you provide them then? >> Primarily, what we do is, to Siddharth's point, right? So on one end, we want to see to it that we are doing the business transformation and all our cloud journeys start with a business North Star. So we align, we have doubled down on, say, five to six business domains. And for each of these business domains industries, we have created business North Star. For these business North Star, we define the use cases. And these use cases then get lit up through our platform. So what we have done is we have codified everything onto our platform. We call it Infinity. So primarily business processes from level one, level two, level three, level, and then the KPIs which are associated with these business processes, the technical KPIs and the business KPIs, and then tying it back to what you have deployed on cloud. So we have end to end cloud transformation journeys enabled for customers through the business North Star. >> And Infinity is your product. >> Can I add something? >> Please do. Yeah, please. >> Yeah so, you know, Ashish covered the part about demystifying if I were to do this particular cloud initiative, it's not just modernizing the application. This is about demystifying what business benefit will accrue to you. Very rare to find unless you do a very deep dive assessment. But what the platform we built also accelerates, you talked about modernization early in the conversation, accelerates the modernization process by automating a whole bunch of activities that are often manual. It bakes insecurity and compliance into everything it does. It automates a whole bunch of cloud operations including things like finops. So this is a life cycle platform that essentially codifies best practices so that you are not getting success by coincidence, you're getting success by design. So that's really what, that's really how we've approached the topic of realizing the true power of cloud by making sure that it's repeatedly delivered. >> Right. You know, I want to hit on security too because you brought that up just a few moments ago. Obviously, you know, we all, and I'd say we, we can do a better job, right? I mean, there's still problems, there's still challenges, there are a lot of bad actors out there that are staying ahead of the game. So as people come to you, clients come to you, and they raise these security concerns, what's your advice to them in terms of, you know, what kind of environment they're going into and what precautions or protections they can put in place to try to give themselves a little bit of peace of mind about how they're going to operate? >> You want to take it? >> So I think primarily, if you are going to cloud, you are going with an assumption that you are moving out of your firewalls, right? You're putting something out of your network area. So and from that perspective, the parameter security from the cloud perspective is very, very important. And then each and every service or the interactions between the services and what you integrate out of your organization, everything needs to be secured through the right guard rates. And we integrate all those things into our platform so that whatever new apps that get deployed or build or any cost product that gets deployed on cloud, everything is secure from a 360 degree perspective. So primarily, maintaining a good security posture, which on a hybrid cloud, I would not say only cloud, but extending your on-prem security posture to cloud is very, very important to when you go to implementing anything on could. >> If you had a crystal ball and we were sitting down here a year from now, you know, what do you think we'd be talking about with regard to, you know, developing these end-to-end opportunities that you are, what's the, I wouldn't say missing piece, but a piece that you would like to have refined to the point where you come back next year and say, John, guess what we did? Look what we were able to accomplish. Anything that you're looking at that you want to tackle here in 2023? Or is there some fine tuning somewhere that you think could even tighten your game even more than it is already? >> We have a long, long way to go, I would say. I think my core takeaway in terms of where the world of technology is headed because cloud is, you know, is essentially a component of what customers want to achieve. It's a medium through which they want to achieve. I think we live in a highly change oriented economy. Every industry is what I call getting re-platformed, right? New processes, new experiences, new products, new efficiency. So a year from now, and I can tell you even for few years from now, we would be constantly looking at our success in terms of how did cloud move the needle on releasing products faster? How did cloud move the needle on driving better experience and better consumer loyalty, for example. How did cloud move the needle on a more efficient supply chain? So increasingly, the technology metrics like, you know, keeping the lights on, or solving tickets, or releasing code on time, would move towards business metrics because that's really the ultimate goal of technology or cloud. So I would say that my crystal ball says we will increasingly be talking business language and business outcomes. Jeff Bezos is an incredible example, right? One of his annual letters, he connected everything back into how much time did consumers save by using Amazon. And I think that's really where in the world, that's the world we are headed towards. >> Ashish, any thoughts on that? >> I think Siddharth put it quite well. I would say if you are able to make a real business impact for our customers in next one year, helping them in driving some of their newer services on cloud through cloud, that would be a success factor for us. >> Well gentlemen, congratulations on the merger. I said two weeks. Still very much in the honeymoon phase and I'm sure it's going to go very well and I look forward to seeing you back here in a year. We'll sit down, same spot, let's remember, fifth floor, and we'll give it a shot and see how accurate you were on that. >> Absolutely. >> Wonderful. It's been a pleasure. >> Thank you gentlemen. >> Thank you for joining us. >> Thank you. >> Very good. Ashish, good to see you, sir. >> Thank you. >> A pleasure. We'll continue here. We're at the Venetian at AWS re:Invent 22, continue at the AWS Global Showcase startup. I'm John Walls. You're watching theCUBE, the leader in high tech coverage. (gentle music)

>>Hey everyone. Welcome back to Chicago. Lisa Martin here with John Furrier. We're live with the Cube at Ansible Fest 2022. This is not only Ansible's 10th anniversary, John Wood. It's the first in-person event in three years. About 14 to 1500 people here talking about the evolution of automation, really the democratization opportunities. Ansible >>Is money, and this segment's gonna be great. Cub alumni are back, and we're gonna get an industry perspective on the automation journey. So it should be great. >>It will be great. We've got two alumni back for the price of wine. Scott Canine joins us, Director of Worldwide Automation at Kendra. A Nelson Shoe is back as well. Product marketing director at Red Hat. Guys, great to have you back on the, on the live cube. >>Oh, thank you for having us. And, and you know, it's really great to be back here live and in person and, and, you know, get a chance to see you guys again. >>Well, and also you get, you get such a sense of the actual Ansible community here. Yeah. And, and only a fraction of them that are here, but people are ready to be back. They're ready to collaborate in person. And I always can imagine the amount of innovation that happens at these events, just like off the show floor, people bumping into each other and go, Hey, I had this idea. What do you think, Scott? It's been just about a, a year since Kenel was formed. Talk to us about the last close to a year and what that's been like. Especially as the world has been so, chops >>The world been Yeah, exactly. Topsy turvy. People getting back to working in person and, and everything else. But, you know, you know, throw on that what we've done in the last year, taking Kendra, you know, outside of being a part of ibm Right. In our own company at this point, you know, and you know, you hear a lot of our executives and a lot of our people when we talk about it, like, Oh yeah, it's, you know, it's a $19 billion startup. We got freedom of action. We can do all these different things. But, you know, one of the ways I look at it is we are a $19 billion startup, which means we've got a lot of companies out there that are trusting us to, no matter what change we're doing, continue to deliver their operations, do it flawlessly, do it in a way so they can continue to, to service their clients effectively and, and don't break 'em. And, and so that to me, you know, the way we do that and the way I focusing on that is automation Ansible, obviously corridor strategy, getting there. >>Yeah. And I'd like to get your thoughts too, because we seeing a trend, we've been reporting on this with the cloud growth and the scale of cloud and distributed computing going cloud native, the automation is the front and piece center of all conversations. Automate this, make developers go faster. And with the pandemic, we're coming out of that pandemic. You post pandemic with large scale automation, system architecture, a lot more like architectural conversations and customers leaning on new things. Yeah. What are you seeing in this automation framework that you guys are talking about? What's been the hot playbook or recipe or, or architecture to, you know, play on words there, but I mean, this is kind of the, the key focus. >>Yeah. I mean, if you, one of the things that I com customer comp talks, I've been pulled into a lot recently, have all been around thinking about security, right? A lot in terms of security and compli, I think, I mean, think about the world environment as a whole, right here, everything that's been going on. So, so people are, are conscious of how much energy that's being used in their data centers, right? And people are conscious of how secure they are, right? Are they, you know, the, their end customers are trusting them with data information about them, right? And, and they're trusting us to make sure that those systems are secure to make sure that, you know, all that is taken care of in the right way. And so, you know that what's hot security and compliance, right? What can we do in the energy space, right? Can we do things to, to help clients understand better their energy consumption as, as, you know, especially as we get now in Europe to the winter months, can we do things there that'll help them also be better in that space, Right? Reduce their >>Costs and a lot more cloud rails obviously right there. You got closer and you got now Ansible, they're kind of there to help the customers put it together at scale. This has been the big conversation last year, remember was automate, automate, automate, right? This year it's automation everywhere, in every piece of the, the landscape edge. It's been big discussion tomorrow here about event driven stuff. This is kind of a change of focus and scope. Can you like, share your thoughts on how you see how big this is in terms of the, the, the customer journey >>In terms, I'm sorry, in terms of, >>In terms of their architecture, how they're rolling out automation, >>What's their Yeah, yeah. So, so in terms of their rolling out arch, arch in terms of them consuming architecture, right? And the architecture or consuming automation. Yeah. And rolling out the architecture for how they do that. You know, again, it, to me it's, it's a lot of, it's been focused around how do we do this in the most secure manner possible? How do we deliver the service to them and the most secure managers possible? How do they understand that it, that they can trust the automation and it's doing the right things on their environments, right? So it's not, you know, we're not pushing out or, or you know, it's not making bad policies >>And they're leaning on you guys. >>It's, it's not being putting malware out there, right? At the same time we're doing different things. And so they really rely on, on our customers, rely on us to really help them with that journey. >>I think a, a big part of that with Kendra as such a great partner and so many customers trusting them, is the fact that they really understand that enterprise. And so as, as Scott talks about the security aspect, we're not just talking to the IT operations people, right? We're talking across the enterprise, the security, the infrastructure, and the automation around that. So when we talk about hybrid cloud, we talk about network and security edge is a natural conversation to that, cuz absolutely at the edge network and security automation is critical. Otherwise, how are you gonna manage just the size of your edge as it grows? >>Yeah. And, and we've been, and that's another area that we've been having a a lot more conversations with clients on, is how do you do automation for IOT and edge based devices, right? We, you know, traditionally data center cloud, right? Kind of the core pieces of where we've been focusing on, but I, you know, recently I've been seeing a lot more opportunities and a lot more companies coming forward saying, you know, help us with the network space, help us with the iot space. We really wanna start getting to that level of automation and that part of our environments. And what >>Are some of the key barriers that customers are coming to you with saying, help us overcome these so that they can, you're smiling so that they can, can obviously attract and retain the right talent and also be able to determine what processes to automate to extract the most value and the most ROI for the organization. >>Yeah. And, and, and you know, that's, that's an interesting, the ROI conversation's always an interesting one, right? Because when you start having that with customers, some of the first things they think about, or the first, the natural place people go is, >>Oh, >>Labor takeout. I can do this with less people. Right? But that's not the end all be all of automation. In fact, you know, my personal view is that's, you know, maybe the, the the bottom 30%, right? That's kind of, then you have to think about the value you get above and beyond that standard operations, standardized processes, right? How are you gonna able to do those faster? How's that enabling your business, right? What's all the risks that's now been taken out by having these changes codified, right? By having them done in a manner that is repeatable, scalable, and, and, and really gets them to the point of, you know, what their business needs from an operational standpoint and >>Extracting that value. Nelson, talk about the automation journey from your perspective, How have you seen that evolve from your lens, especially over the last couple of years? >>It's a great question. You know, it's interesting because obviously all of our customers are at different stages of their automation journey. We have someone that just beginning looking at automation, they've been doing old scripts, if you will, the past. And then we have more that are embracing it, right? As a culture. So we have customers that are building cultures of automation, right? They have standups, they have automation guilds. It's, it's kind of a little bit of a, of a click. It's kind of, you know, building up steam in that momentum. And then we have, you know, the clients that Kindra works with, right? And they're very much focused on automation because they understand that they have a lack of resources, they don't have the expertise, they don't have the time to be able to deliver all this. Yeah. And that's really, Kendra really comes into effect to really help those customers accelerate their automation. Yeah. Right. And to that point, you know, we're doing a lot of innovation work with Kendra and we lean on them heavily because, you know, they're willing to make that commitment as a partner both on the, the, the day to day work that we do together as well as Ford looking at different architectures. >>Yeah. And, and the community aspect from our side internally has been tremendous in terms of us being able to expand what we'll be doing with automation and, and what a's been able to do with that community to get there. Right? Yeah. So to last month we did about 33 million day one, day two operations through automation, right? So that's what we've done. If you look at it, you know, if I break it down, it's really 80% of that standard global process stuff that we bring to the table. 20% of that is what our, our account teams are bringing specifically to their clients based on their needs and what they need to get done. Right. You know, one of my favorite examples of of, of this, right? We have a automation example out there for a, a client we've got in Japan, right? They tie, you know, they're, they're obviously concerned, you know, security a everything else that we've been talking about. >>They're also concerned about resiliency, right? In the face of natural disasters. Yeah. So they took our automation, they said, Okay, we're gonna tie your platform to seismic data that's coming through, and we understand what seismic data's happening. Okay, it's hitting a certain event. Let's automatically start kicking off resiliency operations so we can be prepared and thus keeps serving our clients when that's happening. Right? And that's not something like when you talk about a global team coming in and, and saying, we're gonna do all this. It's that community aspect, getting, getting the account focus, getting to that level, right? That's really brings value to clients. And that's one of the use cases, you know, and aaps enabled us to do with the a the community approach. We've got >>Now talk about this partnership. I think earlier when we were talking to Stephanie and Tom, the bottoms up Ansible community with top down kind of business objectives kind of come into play. You guys have a partnership where it's, there's some game changing things happening because Ansible's growing, continuing to have that scope grow from a skill set standpoint, expand the horizons, doing more automation at scale, and then you got business objectives where people wanna move faster in their, in their digital transformation. So to me, it's interesting that this part kind of hits both. >>It does really hit both. I mean, you know, the community cloud that Kendra has is so critical, right? Because they build that c i CF architecture internally, but they follow that community mantra, if you will. And community is so important to us, right? And that's really where we find innovation. So together with what we were call discussing about validated content earlier today becomes critical to build that content to really help people get started, Right? Validated content, content they can depend on and deliver, right? So that becomes critical on the other side, as you mentioned, is the reality of how do we get this done? Yeah. Right? How do we mature, how do we accelerate? And without the ability to drive those solutions to them to fix, if you are the problems that the line of business has. Well, if you don't answer those questions with the innovation, with the community, and then with the ap, it's, it, it does, it's gotta all come >>Together as, I mean, that community framework is interesting. I think we hear a lot in the cube, you know, Hey, let's do this. Sounds good. Who's gonna do it? Someone who's the operator. So there's a little skills gap going on. It's also a transformation in the roles of the operators in particular, and the dev, So the DevOps equation's completely going to the next level, right? And this is where people wanna move faster. So you're seeing a lot more managed services, a lot more Yes. Services that's, I won't say so much top down, but more like, let's do it and here's a play to get it done, right? Then backfill on the hiring, whether it's taking on a little bit of technical debt or going a little faster to get the proof points, >>Right? And I think one of the critical aspects is, you know, Ansible has it certified collections, right? And oftentimes we, we don't, I don't, I meet with customers two, three times a week, right? There's not a single one that doesn't emphasize the importance of partners and the importance of certified collections, Right? And kindra is included in that, right? Because they bring a lot of those certified collections. Use them, leverage them, it's helps customers get a jumpstarter, right? It's a few, it's their easy button, right? But they only get that and they value that because of the support that's there. >>Yeah. Right? They get the with >>The cert. Yeah. I was gonna say, just adding on the certified collections, right? We, so, you know, it was, it was great to see the hub come out with those capabilities because, you know, as we've gone through the last 12 months and, and change, one of the things that we focused more in on is network devices, network support, right? And, and so, you know, some of the certified collections out there for Cisco for F five, right? Some of those things we've been able to take back in and now build on top of with the expertise that we, we have in that space as well. And then use that as a starting point to more value for our clients. >>How is Kentrell working together with, with Red Hat and with Ansible to help organizations like you mentioned Nelson, they're on the journey varies considerably. Some are well on their way, others aren't. But for those to really start developing an automation, first culture, we talked a lot about cultural ship, we talked about it this morning. You can feel the power of that community and driving it, but how do you guys work together to help companies and any industry kind of really start understanding what an automation first culture is and then building it internally and getting some grounds? Well, >>Well, it's interesting, right? One of the, one of the things that really is we found really helpful is assessments, right? So you have silos and pockets of automation, and that's that challenge, right? So to be able to bring that, if you are automation community within an enterprise together, we often go out and we'll do an assessment, right? An automation assessment to really understand holistically how the enterprise could leverage automation not just in the pockets, but to bring it together. And when they bring that automation together, they can share, playbooks can share their experiences, right? And with Kindra and the multiple and the practices they have, right? They really bring that home from an industry perspective. They also bring that home, if you will, from a technology perspective. And they bring that together. So, you know, Kindra in that respect is the glue for our customer success. >>What's news? What's the next big thing that you guys see? Because if this continues down the road, this path, people are gonna get, the winds gonna get the successes. The new beachhead, if you will, is established. You got the edge around the corner. What's next for you guys in the partnership? How do you see it developing? >>No, we're looking at >>No, it's all good. So really, you know, I, I mentioned it earlier and, and the jour the automation journey paralleled by innovation, right? Customers today are automating, they're doing a great job. There's multiple tools out there. We understand we're not gonna be the only tool in the shed, but Ansible can come in and integrate that entire environment. And in a hybrid cloud environment, you want that there, right? I think what next is obviously the hybrid cloud is critical. The edge is critical, right? And I think that, you know, the needs and the requirements that Kindra hears that we have is kind of that future. And, you know, we, we often, often in, in Red Hat, we talk about a north star, right? And when I work with partners, ikin, do we talk about the North Star, where we want to get to? And that is the acceleration of automation. And I think both by the practical aspect of working with our customers and the innovation as partners, as business partners, technology partners will help accelerate >>That. Yeah. Scott, your perspective to bridge to the future is obviously hybrid and edge, how you bringing your customers along? >>Yes. So, so we see, you know, when we talk about my, when I talk about my automation strategy, our automated strategy, right? It's about being automated, orchestrated and intelligent, right? Kind of those, those three layers of the stack. We've been building out a lot of work, what we call our integrated AIOps layer for actionable insights, right? We've got a, you know, a goal to integrate that and, and we have integrated into our automation service for how we're delivering the whole package to our clients so they can better see opportunities for automation. What's the best way to go about it? You know, what are the, what are some of the, the issues they have, vulnerabilities they have in their environment and really bringing it to them in, in a real holistic manner. In fact, we internally, we call it our F five steering wheel, right? Based on the, the race thing, right? >>Because you think about the, the racing cars, f fives know they're right there, right? They got everything they need in front of 'em. Yeah. So our goal is been to, to include that into our automation view and service and build that out, right? So that's one way we're doing it. The additional way is, is through some announcements you probably heard, hopefully heard the last couple weeks through something called Kendra Bridge, right? Kendra Bridge is more the digitization of, of the way we deliver services for our clients to make it easier for them to consume and, and to, to make the barrier to entry for things like getting automation, getting it more in their environment, right? Lower as much as possible, right? So really integrated AIOps kind bridge. Those are really the two ways we see it as, as going forward. >>It's interesting, you know, we live through a lot of these different inflection points in the industry. Every time there's a big inflection point, there's more complexity that needs to be tamed, you know? And so you got innovation. If you got innovation coming and you got the clients wanna simplify and tame the complexity, this is a big part of what you guys do. >>Absolutely. Yeah. I mean, how do we, you know, most, when the clients come to us, right? Like I said, one, it's about trust. They trust us to do it because we can make it easy for them to not have to worry about that, right? Yeah. They don't have to worry about what it takes to secure the environment, manage it, run it, design it, build it for the, the cloud. We give 'em the ability, we give them the ability to focus on their core business while we do the stuff that's important to them, which >>Is absolutely critical that you, you can't emphasize trust in this relationship enough. I wish we had more time, guys, you're gonna have to come back. I think that's basically what this is boil down to. But thanks so much guys for talking with John and me about how Kendra and and Ansible are working together, really enabling your customers to, to unlock the value of automation across their organization and really make some big business changes. We appreciate your insights and your time. Fantastic. Thank you. Happy to do it and happy to do it any time. All right. Our pleasure. Thank you so much for our guests and John Furrier. I'm Lisa Martin. You're watching The Cube Live from Chicago. This is day one of our coverage of Ansible Fest 22. Don't go anywhere. Our next guest joins us in just a minute.

foreign [Music] welcome to this Cube showcase accelerating business transformation with VMware Cloud on aw it's a solution Innovation conversation with two great guests Fred Ward and VP of Commercial Services at AWS and Narayan bardawaj who's the VP and general manager of cloud Solutions at VMware gentlemen thanks for uh joining me on the Showcase great to be here hey thanks for having us on it's a great topic you know we we've been covering this VMware Cloud on AWS since since the launch going back and it's been amazing to watch The Evolution from people saying oh it's the worst thing I've ever seen what's this mean uh and depressed we're we're kind of not really on board with kind of the vision but as it played out as you guys had announced together it did work out great for VMware it did work out great for a divs and it continues two years later and I want to just get an update from you guys on where you guys see this has been going obviously multiple years where is the evolution of the solution as we are right now coming off VMware Explorer just recently and going in to reinvent uh which is only a couple weeks away uh this feels like tomorrow but you know as we prepare a lot going on where are we with the evolution of the solution I mean the first thing I want to say is you know October 2016 was a seminal moment in the history of I.T right when bad girls singer and Andy jassy came together to announce this and I think John you were there at the time I was there it was a great great moment we launched the solution in 2017 the year after that at vmworld back when we called it vmworld I think we've gone from strength to strength one of the things that has really mattered to us is we've learned from AWS also and the process is this notion of working backwards so we're really really focused on customer feedback as we build the service offering now five years old pretty remarkable Journey uh you know in the first years we tried to get across all the regions you know that was a big Focus because there was so much demand for it in the second year we started going really on Enterprise great features we invented this pretty awesome feature called stretch clusters where you could stretch a vsphere cluster using vsan NSX across two azs in the same region pretty phenomenal for lines of availability that applications start started to get with that particular feature and we kept moving forward all kinds of integration with AWS direct connect Transit gateways with our own Advanced networking capabilities uh you know along the way Disaster Recovery we punched out you need two new Services just focused on that and then more recently we launched our outposts partnership we were up on the stage at reinvent again with Pat and Andy announcing AWS outposts and the VMware flavor of that VMware cloud and AWS outposts I think it's been significant growth in our federal sector as well the federal Empire certification more recently so all in all we're super excited we're five years old the customer momentum is really really strong we are scaling the service massively across all GEOS and industries that's great great update and I think one of the things that you mentioned was how the advantages you guys got from that relationship and this has kind of been the theme for AWS man since I can remember from day one Fred you guys do the heavy lifting as as it's always say for the customers here VMware comes on board takes advantage of the AWS and kind of just doesn't miss a beat continues to move their workloads that everyone's using you know vsphere and these are these are Big workloads on AWS what's the AWS perspective on this how do you see it yeah uh it's pretty fascinating to watch how fast customers can actually transform and move when you take the the skill set that they're familiar with and the advanced capabilities that they've been using on-prem and then overlay it on top of the AWS infrastructure that's that's evolving quickly and and building out new hardware and new instances we'll talk about uh but that combined experience between both of us on a jointly engineered solution uh to bring the best security and the best features that really matter for those workloads uh drive a lot of efficiency and speed for the for the customer so it's been well received and the partnership is stronger than ever from an engineering standpoint from a business standpoint and obviously it's been very interesting to look at just how we stay day one in terms of looking at new features and work and and responding to what customers want so pretty pretty excited about just seeing the transformation and the speed that which customers can move to uh BMC yeah that's a great value probably we've been talking about that in context to anyone building on top of the cloud they can have their own super cloud as we call it if you take advantage of all the capex and investment Amazon's made and AWS is made and and continues to make in performance I as and pass all great stuff I have to ask you guys both as you guys see this going to the next level what are some of the differentiations you see around the service compared to other options on the market what makes it different what's the combination you mentioned jointly engineered what are some of the key differentias of the service compared to others yeah I think one of the key things red talked about is this jointly engineered notion right from day one we were the earlier doctors of the AWS Nitro platform right the reinvention of ec2 back five years ago and so we've been you know having a very very strong engineering partnership at that level I think from uh we have a customer standpoint you get the full software-defined data center compute storage networking on ec2 bare metal across all regions you can scale that elastically up and down it's pretty phenomenal just having that consistency Global right on AWS ec2 Global regions now the other thing that's a real differentiator for us customers tell us about is this whole notion of a managed service right and this was somewhat new to VMware this undifferentiated heavy lifting where customers are to provision rack stack Hardware configure the software on top and then upgrade the software and the security patches on top so we took away all of that pain as customers transition to VMware cloud and AWS in fact my favorite story from last year when we were all going through the lock for Jay debacle the industry was just going through that right favorite proof point from customers was before they could even race uh this issue to us we sent them a notification saying uh we already patched all of your systems no action from you the customers were super thrilled I mean these are large Banks many other customers around the world super thrill they had to take no action for a pretty incredible industry challenge that we were all facing that's a great point you know the whole managed service piece brings up the security and you're kind of teasing at it but you know there's always vulnerabilities that emerge when you're doing complex logic and as you grow your Solutions there's more bits you know Fred we were commenting before we came on cameras more bits than ever before and and at the physics layer too as well as the software so you never know when there's going to be a zero day vulnerability out there just it happens we saw one with Fortinet this week um this came out of the woodwork but moving fast on those patches is huge this brings up the whole support angle I wanted to ask you about how you guys are doing that as well because to me we see the value when we when we talk to customers on the cube about this you know it was a real real easy understanding of how what the cloud means to them with VMware now with the AWS but the question that comes up that we want to get more clarity on is how do you guys handle the support together well what's interesting about this is that it's it's done mutually we have dedicated support teams on both sides that work together pretty seamlessly to make sure that whether there's a issue at any layer including all the way up into the app layer as you think about some of the other workloads like sap we'll go end to end and make sure that we support the customer regardless of where the particular issue might be for them uh and on top of that we look at where where we're improving reliability in as a first order of principle between both companies so from an availability and reliability standpoint it's it's top of mind and no matter where the particular item might land we're going to go help the customer resolve that works really well on the VMware side let's spend the feedback there what's the what's some of the updates same scene yeah yeah I think uh look I mean VMware owns and operates the service will be a phenomenal back in relationship with AWS customers call VMware for the service for any issues and then we have a awesome relationship with AWS in the back end for support issues for any hardware issues capacity management that we jointly do right all the hard problems that customers don't have to worry about uh I think on the front end we also have a really good group of solution Architects across the companies that help to really explain the solution do complex things like Cloud migration which is much much easier with VMware on AWS we're presenting that easy button to the public cloud in many ways and so we have a whole technical audience across the two companies that are working with customers every single day you know you had mentioned a list here some of the Innovations the you mentioned the stretch clustering you know getting the GEOS working Advanced Network disaster recovery um you know fed fed ramp public sector certifications outposts all good you guys are checking the boxes every year you got a good good accomplishments list there on the VMware AWS side here in this relationship the question that I'm interested in is what's next what uh recent Innovations are you doing are you making investments in what's on the list this year what items will be next year how do you see the the new things the list of the cosmos people want to know what's next they don't want to see stagnant uh growth here they want to see more action you know as as uh Cloud kind of continues to scale and modern applications Cloud native you're seeing more and more containers more and more you know more CF CI CD pipelining with with modern apps putting more pressure on the system what's new what's the new Innovations absolutely and I think as a five-year-old service offering uh Innovation is top of mind for us every single day so just to call out a few recent innovations that we announced in San Francisco at VMware Explorer um first of all uh our new platform i4i dot metal it's isolate based it's pretty awesome it's the latest and greatest uh all the speeds and beats that you would expect from VMware and AWS at this point in our relationship we announced two different storage options this notion of working from customer feedback allowing customers even more price reductions really take off that storage and park it externally right and you know separate that from compute so two different storage offerings there one is with AWS FSX NetApp on tap which brings in our NetApp partnership as well into the equation and really get that NetApp based really excited about this offering as well and the second storage offering called VMware Cloud Flex story vmware's own managed storage offering beyond that we've done a lot of other Innovations as well I really wanted to talk about VMware Cloud Flex compute where previously customers could only scale by hosts you know host is 36 to 48 cores give or take but with VMware cloudflex compute we are now allowing this notion of a resource defined compute model where customers can just get exactly the vcpu memory and storage that maps to the applications however small they might be so this notion of granularity is really a big innovation that that we are launching in the market this year and then last but not least topper ransomware of course it's a Hot Topic in the industry we are seeing many many customers ask for this we are happy to announce a new ransomware recovery with our VMware Cloud VR solution a lot of innovation there and the way we are able to do machine learning and make sure the workloads that are covered from snapshots backups are actually safe to use so there's a lot of differentiation on that front as well a lot of networking Innovations with project North Star the ability to have layer 4 through layer seven uh you know new SAS services in that area as well keep in mind that the service already supports managed kubernetes for containers it's built in to the same clusters that have virtual machines and so this notion of a single service with a great TCO for VMS and containers is sort of at the heart of our option the networking side certainly is a hot area to keep innovating on every year it's the same same conversation get better faster networking more more options there the flex computes interesting if you don't mind me getting a quick clarification could you explain the address between resource defined versus Hardware defined because this is kind of what we had saw at explore coming out that notion of resource defined versus Hardware defined what's that what does that mean yeah I mean I think we've been super successful in this Hardware defined notion where we're scaling by the hardware unit uh that we present as software-defined data centers right so that's been super successful but we you know customers wanted more especially customers in different parts of the world wanted to start even smaller and grow even more incrementally right lower the cost even more and so this is the part where resource defined starts to be very very interesting as a way to think about you know here's my bag of resources exactly based on what the customer's requested it would be for fiber machines five containers its size exactly for that and then as utilization grows we elastically behind the scenes were able to grow it through policies so that's a whole different dimension it's a whole different service offering that adds value when customers are comfortable they can go from one to the other they can go back to that post-based model if they so choose to and there's a jump off point across these two different economic models it's kind of cloud flexibility right there I like the name Fred let's get into some of the uh examples of customers if you don't mind let's get into some of these we have some time I want to unpack a little bit of what's going on with the customer deployments one of the things we've heard again on the cube is from customers is they like the clarity of the relationship they love the cloud positioning of it and then what happens is they lift and shift the workloads and it's like feels great it's just like we're running VMware on AWS and then they start consuming higher level Services kind of that adoption Next Level happens um and because it's in the cloud so so can you guys take us through some recent examples of customer wins or deployments where they're using VMware Cloud on AWS on getting started and then how do they progress once they're there how does it evolve can you just walk us through a couple use cases sure um there's a well there's a couple one it's pretty interesting that you know like you said as there's more and more bids you need better and better hardware and networking and we're super excited about the I-4 uh and the capabilities there in terms of doubling and or tripling what we're doing around a lower variability on latency and just improving all the speeds but what customers are doing with it like the college in New Jersey they're accelerating their deployment on a on onboarding over like 7 400 students over a six to eight month period and they've really realized a ton of savings but what's interesting is where and how they can actually grow onto additional native Services too so connectivity to any other services is available as they start to move and migrate into this um the the options there obviously are tied to all the Innovation that we have across any Services whether it's containerized and with what they're doing with tanzu or with any other container and or services within AWS so so there's there's some pretty interesting scenarios where that data and or the processing which is moved quickly with full compliance whether it's in like health care or regulatory business is is allowed to then consume and use things for example with text extract or any other really cool service that has you know monthly and quarterly Innovations so there's things that you just can't could not do before that are coming out uh and saving customers money and building Innovative applications on top of their uh their current uh app base in in a rapid fashion so pretty excited about it there's a lot of examples I think I probably don't have time to go into too many here yeah but that's actually the best part is listening to customers and seeing how many net new services and new applications are they actually building on top of this platform now Ryan what's your perspective from the VMware psychics you know you guys have now a lot of head room to offer customers with Amazon's you know higher level services and or whatever's homegrown what is being rolled out because you now have a lot of hybrid too so so what's your what's your take on what what's happening and with customers I mean it's been phenomenal the customer adoption of this and you know Banks and many other highly sensitive verticals are running production grade applications tier one applications on the service over the last five years and so you know I have a couple of really good examples SNP Global is one of my favorite examples large Bank the merch with IHS Market big sort of conglomeration now both customers were using VMware cloud and AWS in different ways and with the uh with the use case one of their use cases was how do I just respond to these Global opportunities without having to invest in physical data centers and then how do I migrate and consolidate all my data centers across the globe of which there were many and so one specific example for this company was how they migrated thousand one thousand workloads to VMware cloud and AWS in just six weeks pretty phenomenal if you think about everything that goes into a cloud migration process people process technology and the beauty of the technology going from VMware point a to VMware point B the the lowest cost lowest risk approach to adopting we have our cloud in AWS so that's uh you know one of my favorite examples there are many other examples across other verticals that we continue to see the good thing is we're seeing rapid expansion across the globe we're constantly entering new markets uh with a limited number of regions and progressing our roadmap it's great to see I mean the data center migrations go from months many many months to weeks it's interesting to see some of those success stories so congratulations another one of the other uh interesting uh and fascinating uh uh benefits is the sustainability Improvement in terms of being green so the efficiency gains that we have both in current uh generation and New Generation processors and everything that we're doing to make sure that when a customer can be elastic they're also saving power which is really critical in a lot of regions worldwide at this point in time they're they're seeing those benefits if you're running really inefficiently in your own data center that is just a not a great use of power so the actual calculators and the benefits to these workloads is are pretty phenomenal just in being more green which I like we just all need to do our part there and and this is a big part of it here it's a huge it's a huge point about sustainability for everyone glad you called that out the other one I would say is supply chain issues another one you see that constrains I can't buy hardware and the third one is really obvious but no one really talks about it it's security right I mean um I remember interviewing Steven Schmidt with that AWS and many years ago this is like 2013 and um you know at that time people saying the Cloud's not secure and he's like listen it's more secure in the cloud than on premise and if you look at the security breaches it's all about the on-premise data center vulnerabilities not so much Hardware so there's a lot you gotta the the stay current on on the isolation there is hard so I think I think the security and supply chain threat is another one do you agree I I absolutely agree uh it's it's hard to manage supply chain nowadays we put a lot of effort into that and I think we have a great ability to forecast and make sure that we can lean in and have the resources that are available and run them run them more efficiently yeah and then like you said on the security Point Security is job one it is it is the only P1 and if you think of how we build our infrastructure from Nitro all the way up and how we respond and work with our partners and our customers there's nothing more important and Narayan your point earlier about the managed service patching and being on top of things is really going to get better all right final question I really want to thank you for your time on this showcase it's really been a great conversation uh Fred you had made a comment earlier I want to kind of end with the kind of a curveball and put you guys on the spot we're talking about a modern a new modern shift it's another we're seeing another inflection point we've been documenting it it's almost like Cloud hitting another inflection point um with application and open source growth significantly at the app layer continue to put a lot of pressure and innovation in the infrastructure side so the question is for you guys each to answer is what's the same and what's different in today's market so it's kind of like we want more of the same here but also things have changed radically and better here what are the what's what's changed for better and where what's still the same kind of thing hanging around that people are focused on can you share your perspective I'll I'll tackle it um you know uh businesses are complex and they're often unique uh that that's the same uh what's changed is how fast you can innovate the ability to combine manage services and new Innovative services and build new applications is so much faster today leveraging world-class Hardware uh that you don't have to worry about that's elastic you could not do that even five ten years ago to the degree you can today especially with the Innovation so Innovation is accelerating uh at a rate that most people can't even comprehend and understand the the set of services that are available to them it's really fascinating to see what a one pizza team of of Engineers can go actually develop in a week it is phenomenal so super excited about this space and it's only going to continue to accelerate that that's my take there I am you got a lot of platform to compete on with Amazon I got a lot to build on the memory which then you're right on your side what's your what's your answer to that question I think we're seeing a lot of innovation with new applications that customers [Music] I think uh what we see is this whole notion of how do you go from desktop to production to the secure supply chain and how can we truly uh you know build on the agility that developers desire and build all the security and the pipelines to energize that motor production quickly and efficiently I think we are seeing uh you know we're at the very start of that sort of uh of Journey um of course we have invested in kubernetes means to an end but it's so much more Beyond that's happening in the industry and I think we're at the very very beginning of this Transformations Enterprise transformation that many of our customers are going through and we're inherently part of it yeah well gentlemen I really appreciate that we're seeing the same things more the same here on you know solving these complexities with abstractions whether it's you know higher level services with large-scale infrastructure um at your fingertips infrastructure is code infrastructure to be provisioned serverless all the good stuff happening Fred with AWS on your side and we're seeing customers resonate with this idea of being an operator again being a cloud operator and developer so the developer Ops is kind of devops is kind of changing too so all for the better thank you for spending the time we're seeing again that traction with the VMware customer base and it was getting getting along great together so thanks for sharing your perspectives they appreciate it thank you so much okay thank you John okay this is thecube and AWS VMware showcase accelerating business transformation VMware Cloud on AWS jointly engineered solution bringing Innovation to the VMware customer base going to the cloud and Beyond I'm John Furrier your host thanks for watching [Music]

(upbeat music) >> Hello, everyone. Welcome to theCUBE here, in Palo Alto, California for a Cube Conversation. I'm John Furrier, host of theCUBE. This is a KubeCon preview conversation. We got a great guest here, in studio, Madhura Maskasky, Co-Founder and VP of Product, Head of Product at Platform9. Madhura, great to see you. Thank you for coming in and sharing this conversation about, this cube conversation about KubeCon, a Kubecon conversation. >> Thanks for having me. >> A light nice play on words there, a little word play, but the fun thing about theCUBE is, we were there at the beginning when OpenStack was kind of on its transition, Kubernetes was just starting. I remember talking to Lou Tucker back in, I think Seattle or some event and Craig McLuckie was still working at Google at the time. And Google was debating on putting the paper out and so much has happened. Being present at creation, you guys have been there too with Platform9. Present at creation of the Kubernetes wave was not obvious only a few insiders kind of got the big picture. We were one of 'em. We saw this as a big wave. Docker containers at that time was a unicorn funded company. Now they've went back to their roots a few years ago. I think four years ago, they went back and recapped and now they're all pure open source. Since then Docker containers and containers have really powered the Kubernetes wave. Combined with the amazing work of the CNCF and KubeCon which we've been covering every year. You saw the maturation, you saw the wave, the early days, end user projects being contributed. Like Envoy's been a huge success. And then the white spaces filling in on the map, you got observability, you've got run time, you got all the things, still some white spaces in there but it's really been great to watch this growth. So I have to ask you, what do you expect this year? You guys have some cutting edge technology. You got Arlo announced and a lot's going on Kubernetes this year. It's going mainstream. You're starting to see the traditional enterprises embrace and some are scaling faster than others, manage services, plethora of choices. What do you expect this year at KubeCon North America in Detroit? >> Yeah, so I think you summarize kind of that life cycle or lifeline of Kubernetes pretty well. I think I remember the times when, just at the very beginning of Kubernetes, after it was released we were sitting I think with box, box dot com and they were describing to us why they are early adopters of Kubernetes. And we were just sitting down taking notes trying to understand this new project and what value it adds, right? And then flash forward to today where there are Dilbert strips written about Kubernetes. That's how popular it has become. So, I think as that has happened, I think one of the things that's also happened is the enterprises that adopted it relatively early are running it at a massive scale or looking to run it at massive scale. And so I think at scale cloud-native is going to be the most important theme. At scale governance, at scale manageability are going to be top of the mind. And the third factor, I think that's going to be top of the mind is cost control at scale. >> Yeah, and one of the things that we've seen is that the incubated projects a lot more being incubated now and you got the combination of end user and company contributed open source. You guys are contributing RLO >> RLO. >> and open source. >> Yeah. >> That's been part of your game plan there. So you guys are no stranger open source. How do you see this year's momentum? Is it more white space being filled? What's new coming out of the block? What do you think is going to come out of this year? What's rising in terms of traction? What do you see emerging as more notable that might not have been there last year? >> Yeah, so I think it's all about filling that white space, some level of consolidation, et cetera. That's usually the trend in the cloud-native space. And I think it's going to continue to be on that and it's going to be tooling that lets users simplify their lives. Now that Kubernetes is part of your day to day. And so it is observability, et cetera, have always been top of the mind, but I think starting this year, et cetera it's going to be at the next level. Which is gone other times of just running your Prometheus at individual cluster level, just to take that as an example. Now you need a solution- >> Yep. >> that operates at this massive scale across different distributions and your edge locations. So, it's taking those same problems but taking them to that next order of management. >> I'm looking at my notes here and I see orchestration and service mesh, which Envoy does. And you're seeing other solutions come out as well like Linkerd and whatnot. Some are more popular than others. What areas do you see are most needed? If you could go in there and be program chair for a day and you've got a day job as VP of product at Platform9. So you kind of have to have that future view of the roadmap and looking back at where you've come, what would you want to prioritize if you could bring your VP of product skills to the open source and saying, hey, can I point out some needs here? What would you say? >> Yeah, I think just the more tooling that lets people make sense and reduce some of the chaos that this prowling ecosystem of cloud-native creates. Which is tooling, that is not adding more tooling that covers white space is great, but introducing abilities that let you better manage what you have today is probably absolutely top of the mind. And I think that's really not covered today in terms of tools that are around. >> You know, I've been watching the top five incubated projects in CNCF, Argo cracked the top five. I think they got close to 12,000 GitHub stars. They have a conference now, ArgoCon here in California. What is that about? >> Yeah. >> Why is that so popular? I mean, I know it's kind of about obviously workflows and dealing with good pipeline, but why is that so popular right now? >> I think it's very interesting and I think Argo's journey and it's just climbed up in terms of its Github stars for example. And I think it's because as these scale factors that we talk about on one end number of nodes and clusters growing, and on the other end number of sites you're managing grows. I think that CD or continuous deployment of applications it used to kind of be something that you want to get to, it's that north star, but most enterprises wouldn't quite be there. They would either think that they're not ready and it's not needed enough to get there. But now when you're operating at that level of scale and to still maintain consistency without sky rocketing your costs, in terms of ops people, CD almost becomes a necessity. You need some kind of manageable, predictable way of deploying apps without having to go out with new releases that are going out every six months or so you need to do that on a daily basis, even hourly basis. And that's why. >> Scales the theme again, >> Yep. >> back to scale. >> Yep. >> All right, final question. We'll wrap up this preview for KubeCon in Detroit. Whereas we start getting the lay of the land and the focus. If you had to kind of predict the psychology of the developer that's going to be attending in person and they're going to have a hybrid event. So, they will be not as good as being in person. Us, it's going to be the first time kind of post pandemic when I think everyone's going to be together in LA it was a weird time in the calendar and Valencia was the kind of the first international one but this is the first time in North America. So, we're expecting a big audience. >> Mhm. >> If you could predict or what's your view on the psychology of the attendee this year? Obviously pumped to be back. But what do you think they're going to be thinking about? what's on their mind? What are they going to be peaked on? What's the focus? Where will be the psychology? Where will be the mindset? What are people going to be looking for this year? If you had to make a prediction on what the attendees are going to be thinking about what would you say? >> Yeah. So there's always a curiosity in terms of what's new, what new cool tools that are coming out that's going to help address some of the gaps. What can I try out? That's always as I go back to my development roots, first in mind, but then very quickly it comes down to what's going to help me do my job easier, better, faster, at lower cost. And I think again, I keep going back to that theme of automation, declarative automation, automation at scale, governance at scale, these are going to be top of the mind for both developers and ops teams. >> We'll be there covering it like a blanket like we always do from day one, present at creation at KubeCon we are going to be covering again for the consecutive year in a row. We love the CNCF. We love what they do. We thank the developers this year, again continue going mainstream closer and closer to the front lines as the company is the application. As we say, here on theCUBE we'll be there bringing you all the signal. Thanks for coming in and sharing your thoughts on KubeCon 2022. >> Thank you for having me. >> Okay. I'm John Furrier here in theCUBE in Palo Alto, California. Thanks for watching. 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(upbeat music) >> Hello and welcome to this Cube Conversation. I'm here in Palo Alto, California. I'm John Furrier host of theCUBE, and I'm here with mparticle. With Chee Chew, Chief Product Officer. Thanks for joining us today. Thanks for coming on. >> Thank you. It's great to be here. >> So mparticle's doing some pretty amazing things around managing customer data end to end as a data platform. A lot of integrations. You guys are state of the art cloud scale for this new kind of use case of using the data for customer value in real time. A lot of good stuff going on. So I really want to dig into this whole prospect. So what is the company about first? Take a minute to explain what is mparticle for the folks watching? >> Yeah, absolutely. Well, if you think about the world today where it's like cloud computing and businesses are getting a lot of data from customers as consumers go online. And they have these cloud services that are collecting all this data about the customer. How do you get it organized? How do you have all that data that's in different departments, reconcile them and like give it to your departments. So they can really personalize the experiences. We've all had these experiences where, you know, like we're this loyal customer of a brand, we shop there a lot. And then we go over to like the customer service and they act like they have no idea who we are. Our job is to help businesses really understand the customer and be able to treat them in a personal way. To do the very best for every experience. >> Well, Chee you're in a really big spot there with the company, Chief Product... You got the keys to the kingdom over there. You're overseeing all the action. You got a platform, a bunch of solutions you're enabling. Customer data has been around for a long time. We hear big systems in the past, oh got to leverage the customer data. But why is the customer data more important now than ever as developers and cloud scale are emerging in. Why is customer data becoming more and more valuable to organizations? >> No. Well, customer data has been around for like decades and decades. The amount of customer data being generated online has just accelerated. It's been exponential. There's been more data collected in the past four years than the past 40 years. And like businesses are just starting to realize, how much of a goldmine that could be for them. If they could really harness it. And especially in today's world where treating it properly, respecting people's privacy, really doing well by the customer, earning the right to use that data is ever so important. The combination that brings the need for solutions like mparticle. >> Talk about some of the enablement that you guys offer your customers. You got a platform, you got a lot of moving parts in there. A lot of key components, a lot of integrations. With all the best platforms to connect to. We're in an API economy. So trust is huge. You got to have the data governance. Everything's got to work together. It's a really hard problem. How do you guys enable value there? What is the key product value that you guys are enabling? >> Yeah, it is a hard problem. And with the data being so important to businesses and treating it well and collecting it from all the different aspects, there are many places where we... Our customers really value the services we bring. As you mentioned, we have a large set of integrations. We can get data in from pretty much any system that you have. Even if you built it yourself, we have ways of enabling you to collect that data from all around the company. Then we reconcile them. So we create one single view of the customer. We adhere to all the privacy regulations around the world to make sure that you're compliant with not only laws but with the trust with your consumers. We clean that data and then we distribute it to all the systems where you really want to create personalized experiences. So the collection, the reconciliation, the cleaning, the conformance, and then the distribution. Those are all key events that we do to bring value to customers. >> It's funny in all these major shifts, you're seeing all the same things. You got to be a media company. You got to be a data company. Got to be a video company. Got to be a cloud company. So in the digital transformation, you know with machine learning and AI really at the center of the application value now, you can measure everything in a company. So, smart leadership saying, hey, if we can measure everything, don't we want to know what's going on with respect to our customer. The journey they call it. So, you know, there's the industry taglines of customer best in class experiences, capturing the moments that matter. Describe how you do that. Because moments that matter to me feel like something that's real time or something that's super important, that's contextualized. You got to get that context with that journey. How do you guys do that? This is something I'm intrigued about. >> Yeah, absolutely. And you know, I... This hearken backs to my experience when I was at Amazon doing retail and we really focus on personalization and the notion of when you go to one page or one screen on your mobile device and then you go to the very next page. That very next page has to be personalized with the things that you did on... Just seconds ago on the previous one. That idea of being at the interaction speed, keeping up with the customers. That's what, we've... What we provide for our brands. It's not enough to just collect the data, churn on it, do a bunch of like calculations and then tomorrow figure out what to do. Tomorrow figure out how to personalize it. It has to be in interaction time with our customers. >> John: It's interesting too. You'll have experience in big companies, hyperscalers with large, you know, media business and data. Bringing that to normal companies, enterprises, and mid-market, they have to then stand up their own staff. They have to operationalize this in a large data strategy that maximizes the value. How do brands do this effectively? Can you share best practice of what's the best way to stand up and operationalize the team, the developers, the strategy. >> Chee: Yeah and this is a great question. And right now with the world... The way the world and the industry is developing, businesses don't all do it the same way. Like at Amazon, we built our own. Now we had several hundred engineers in my team who are collecting the data, analyzing it, and really cleaning it. Not every company can afford a couple hundred engineers just to do this... Solve this one problem. Which is why I'm super excited about what we're doing at mparticle, where we're trying to make that available to every company in the world. Whether you're a huge brand, like an NBC, or you're a smaller, medium size startup. Like you have a lot of data and we can help make it accessible for you. Now, many companies do start and build it from scratch and the problems early on, seem very tractable. But then as new laws come out, as the platform changes, as Apple and iOS change the rules on what you can collect and what data you can't collect. That puts you on this treadmill of always like reinvesting and reinvesting in the data collection. And not as much at innovating on your business. And then many companies turn around and decide, oh I understand why you want a company like an mparticle, providing that service. >> It's interesting. You guys do a lot of that... The key value proposition that we hear a lot for successful companies. You take care of that the heavy differentiate... Undifferentiated heavy lifting. So the customer can focus on the value. This seems to be the theme of of the data problem that companies want to solve. There's a lot of grunt work that has to get done. A lot of, you know, get down and dirty and work on stuff. If you can just automate it, make it go faster, then you can apply more creative processes and tools onto getting more growth or more value out of the use case. Can you... Is that something that's happening here? >> Oh yeah, absolutely. You know, the dirty secret that if you talk to any like machine learning scientist data engineer, what they'll tell you is it seems like the world is sexy when you talk to new like computer science students about like building models. But when they go to industry they spend like 80 or 90% of their time cleaning data, getting access to data, like getting the right permissions. And they spend like 10 to 20% of the time actually building models and doing the really interesting things that you want your data science to do. That's a really expensive way of getting to your models. And that's why you're right. Services like, mparticle, like our core business is to take that grunt work and that... Things that might be less exciting and bespoke to your business. Like that's the stuff that we get excited about. And we want to provide the best op... Best in breed experience for our customers. >> Yeah. There's no doubt, every company will have to have this really complex, hard to solve platform problem. You either buy or build it. I mean, you're not... Not everyone's Amazon, right? So not everyone can do that. So you got to have the integrations, you got to have the personalizations, you got to have the data quality and you got to have the data governance in there too. You can't forget the fact that you'd be dealing with potentially trusted parties that don't work for you. Right? So this is a huge connection point that I want to just quickly get into. Quickly, APIs connects companies but now also connects data. How do you view that? How should customers think about the connection points when they start to share customer data with other companies? >> Yeah, you're totally right in that. Not only is it important for you to do this in terms of saving your time in engineering and all the amount of work you have, but the risk is super high. If you treat customers data incorrectly, you can break trust with your consumers. It takes a long time to build that trust and just a moment to lose it. And so it is more than just engineering time savings but it is also a risk to the business. Now... Then you go to down to like, how do you do it? Why APIs? The reason for us, our push on really the API platform is to give power to developers. Within your company, you may have some innovation that you want, some way you want to really differentiate yourself from the rest of the field. If we provided only standard UI. Standard ways of doing it, then our customers would all behave and have the same capabilities as every other customer. But by us providing APIs it allows our customers to really innovate and make the platform bend to their will. To support the unique ideas that they have. So that's our approach of why we really focus on the customer data infrastructure. >> John: Yeah, it's a great opportunity Chee, I really appreciate your time. Real final question for you, as folks look at this opportunity to have a data platform and mparticle, one that you have. They're going to probably ask you the question of, hey I got developers too. I'm hiring more and more cloud native developers. We're API first, obviously we're cloud native. We love that direction. We're distributed computing. All that great stuff at the edge. I got machine learning. But I really want to integrate, I want to control the experience. I want to be agile and fast. Can you help us? What's your answer to that question? >> Absolutely. If you look at the things that your engines are doing, and you ask them how much of what they're doing is similar to what you expect from other similar companies and how much is really unique to your business. You'll probably find that a minority of the work is really unique to that business. And the majority are things that are common problems that other companies struggle with. Our job is to help take that away. So you can really focus on what's unique, bespoke, and innovative for you. >> John: Follow up to that real quick, as you're the Chief Product Officer. Talk to the folks out there who are watching, who may not know what goes on in a product organization. You're making all kinds of trade offs. You got a product roadmap, you've got the 20 mile stare. You have a North Star. What should they know about mparticle, about the product that they... That's important for them to either pay attention to or they may not know about. >> You know, my... When I think about mparticle, it's not just a product but it's the whole offering. And what you want to know about mparticle is we really work hard to empower our customers, whether it's through the API platforms. So that you have the full flexibility to do whatever you want or through our customer service and our support teams. We are... Have a great reputation with our customers about really focusing on and unblocking them, enabling whatever the heart desires. >> John: Yeah and building on top of it. Sounds great. Chee, thanks for coming on. Appreciate the update on mparticle. Thanks for your time. Great to see you. >> Absolutely. Thank you for your time. >> Okay. This is theCUBE conversation. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage of AWS Summit 2022, here at Moscone Center live on the floor, I'm John Furry host of theCUBE, all the action day two, remember AWS Summit in New York City is coming in the summer. We'll be there as well. Got a great guest Murli Murli who's the VP and GM of Cloud Native Business Unit Portworx, been in theCUBE multiple times. We were just talking about the customer he had on Ford from Detroit, where kubernetes will be this year. >> That's right. >> Great to see you. >> Yeah, same here, John. Great to see. >> So, what's the update? Quickly this, before we get into the country, give the update on what's going on in the company, what's happening? >> Well, you know, we've been acquired by Pure Storage it's well over a year. So we've had one full year of being inside of Pure. It's been wonderful, right? So we've had a great ride so far, The products have been renewed. We've got a bunch of integrations with Pure. We more than doubled our business and more than doubled our head count. So things are going great. >> I always had a, congratulations by the way. And I was going to ask about the integration but before I get there, yeah, we've been always like play some jokes on theCUBE and because serverless is so hot, I've been using storage lists and actually saw a startup yesterday had the word networking lists in their title. So this idea of like making things easier, but me, I mean serverless of this is basically servers that make it easier. >> Yeah, yeah >> So this is kind of where we see Cloud Native going. Can you share your thoughts on how Pure and Portworx are bringing this together? Because you can almost connect the dots in my mind. So say specifically what is the Cloud Native angle with Pure? >> Yeah. So look, I'll kind of start by being captain an obvious, I guess. Just sort of stating some obvious stuff and then get to what I hope will be a little bit more new and interesting. So the obvious stuff to start with is just the fact that Cloud Native is exploding. Containers are exploding. It's kind of a well known fact that 85% of the enterprise organizations around the world are pretty much going to be deploying containers, if not already in the next couple of years, right? So one it's really happening. The, buzz is now, it's not just in the future, the hype is now. The second part of that is it's really part of that is things are going production. 56% of these organizations are in production already. And that's the number is going to climb to 80 fairly quickly. So not only is this stuff being deployed as being deployed in sort of fairly mission critical, especially Greenfield applications. So that's kind of one, right? Now, the second thing that we're seeing is as they go in into production, John, the migraines are starting, right? Customer migraines, right? It's always happens in stuff that they have not looked around the corner and anticipated. So one of them is, again, a fairly obvious one is as they go into production, they need to be able to kind of recover from some oops that happens, right? And the kinds of think about this, right? John, this stuff is rapidly changing, right? Look at how many versions of kubernetes come out on a regular basis. On top of that, you got all these app, virgins, new database virgins, new stuff, vendors like us, ourselves have new virgins. So with all these new virgins, when you put it all together the stack, sometimes misbehave. So you got to kind of, "Hey, let me go recover." Right? You have outages. So essentially the whole area of data protection becomes a lot more critical. That's the migraine that people are beginning to get now, right? They can feel the migraine coming on. The good news is this is not new stuff. People know on- >> John: The DevOps. >> Yeah. Well, and in fact it is that transition from DevOps to ITOps, right? People know that they're going into production, that they need backup and data protection and disaster recovery. So in a way it's kind of good news, bad news, the good news is they know that they need it. The bad news is, it turns out that it's kind of interesting as they go Cloud Native, the technology stack has changed. So 82% of customers who are kind of deploying Cloud Native are worried about data protection. And in fact, I'll go one step further 67% of those people have actually kind of looked at what they can get from existing vendors and are going, "Hey, this is not it. This is not going to do my stuff for me." >> And by the way, just to throw a little bit more gas on that fire is ransomware attacks. So any kind of vulnerability opening? Maybe make people are scared. >> Murli: Absolutely. >> So with- >> Murli: Its a board level topic, right? >> Yeah, and then you bring down the DevOps, which is we all know the innovation formula launch in iterate, pivot, iterate, pivot, then innovation you get the formula, all your metrics, but it's a system. >> Correct. >> Storage is part now of a system when you bring Cloud Native into it, you have a consequence if something changes. >> Murli: Correct. >> So I see that. And the question I have for you is, where are we in the stability side of it? Are we close to getting there and what's coming out to help that, is it more tooling? Because the trend is people are building tools around their Cloud Native thing. I was just talking to MongoDB and they got a database, now that's all tooling. Vertically integrate into the asset or the product, because it integrates with APIs, right? So that makes total sense. >> So I think there's kind of again, a good news, bad news there, right? There's a lot of good news, right? In the world of containers and kubernetes what are some of the good news items, right? A lot of the APIs have settled down have been defined well, CNCF has done a great job promoting that, right? So the APIs are stable, right? Second, the product feature set, have become more stable, particularly sort of the the core kubernetes product security kind of stuff, right? Now what's the bad news. The bad news is, while these things are stable they are not ready for scale in every case yet, right? And when you integrate at scale, so and typically the tipping point is around 20 to 30 nodes, right? So typically when you go beyond 20 to 30 nodes then the stuff starts to come a apart, right? Like, the wheels come off of the train and all of that. And that's typically because there's a lot of the products that were designed for DevOps, are not well suited for ITOps. So really there is a new- >> And the talent culture. >> Exactly. >> Talent and culture sometimes aren't ready or are changing. >> So it's a whole bunch of people trying to use kind of a maturing product set with skill sets that are pretty low, right? So when we get into production, then other factors come into play, high availability, right? Security, you talk about ransomware, disaster recovery backup. So these are things that are sort of, I would say not 101 problems, but 201 problems, so right? This is natural as we go to that part of the thing. And that's the kind of stuff that, Portworx and Pure Storage have been kind of focused on solving. And that's kind of been how we've made our mark in the industry, right? We've helped people really get to production on some of these different points. >> Expectation on both companies have been strong, high quality, obviously performance on Pure side from day one, just did a great job with the products. Now, when you go into Cloud Native you have now this connection okay. To the customer, again I think huge point on the changing landscape. How do you see that IT to DevOps emerging? Because the trend that we're seeing is, abstracting way the complexities of management. So I won't say managed services are more of a trend, they've always been around but the notion of making it easier for customers. >> Yep, absolutely right. >> Super important. So can you guys share what you guys are doing to make it easier because not everyone has a DevOps team. >> Yeah, so look, the number one way things are made more easy, is to make it more consumable by making it as a service. So this is one of the things, here we are, at AWS Summit, right? And delighted to be here by the way. And we have a strategic alliance with with AWS, and specifically, what we're here to announce really is that we're announcing a backup as a SaaS product. Coming up in a few weeks we're going to be giing running on AWS as a service integrated with AWS. So essentially what happens is, if you have a containerized set of applications you're deploying it on EKS, ECS, AWS, what have you. We will automatically provide the ability for that to be backed up scaled and to be very, very container granular, very app specific, right? Yeah, so it's designed specifically for kubernetes. Now here's the kind of key thing to say, right? Backup's been around for a long time. You've interviewed, tons of backup people in the past. But traditional backup is just not going to work for kubernetes. And it's very simple if you think about it, John. >> John: And why is that? >> It's a very simple thing, right? Traditional backup focuses on apps and data, right? Those are the two kind of legs of that. And they create catalogs and then do a great job there. Well, here's, what's happened with Cloud Native. You have a thing inserted in the middle called kubernetes. So when you take a snapshot, I'm now kind of going into a specific kind of, world of storage, right? When you take a snapshot, what Portworx does is we take a 3D snapshot. What you really need to recover, from a backup situation where, you want to go back to the earlier stage to be kubernetes specific, you need a app snapshot, snapshot of the kubernetes spec, pod spec, And third of snapshot of the data. Well, traditional, backup folks are not taking that middle snapshot. So we do a 3D snapshot and we recover all three which is really what you need to be able to kind of like get backed up, get recovered in minutes. >> Okay and so the alternative to not doing that is what? What will happen? >> You To do that, to do your old machine level backup? So what happens with traditional backups are typically VM level or machine level, right? So you're taking a snapshot of the whole kind of machine and server or VM setup and then you recover all of that, and then you run kubernetes on that and then you try to recover it- >> John: To either stand everything up again. >> Yeah, yeah. >> John: Pretty much. >> Yeah. Whereas, what do most people want to do? This is a very different use case, by the way, right? How does this work? What people are doing for kubernetes is they're not doing archival kind of backup. What they're doing is real time, right? You're running an ops. Like I said, you got an oops, "Hey, a new release for one of the new databases then work right? Boom! I want to just go back to like yesterday, right? So how do I do that? Well, here you can just go back for that one database, one app, and recover back to that. So it's operational backup and recovery as opposed to archival backup and recovery. So for that, to be able to recover in seconds, right? You need to be, he kind of want integrated with AWS which is what we are. So it's integrated, it's automated, and it's very, very container granular. And so these three things are the things that make it sort of, very specific way. >> I love the integration story. 'Cause I think that's the big mega trend we're seeing now is is that integrating in. And, but again, it's a systems concept. It's not standalone storage, detached storage. >> Murli: Exactly. >> It's always, even though it might be decoupled a little bit it's glued together through say- >> John, you said it right. The easy button is for the system, right? Not for the individual component. Look, all of us vendors in this ecosystem are going around framing, having a being easy. But when we say that, what do we mean? We mean, oh, I'm easy to use. Well that doesn't help the user. Who's got to put all this stuff together. So it's really kind of making that stack work. >> This is easy to use, but it made these things more complex. This is what we do in the enterprise solve complexity with more complexity. >> Putting the problem to the other guy. Yeah. So it's that end to end ease of use is kind of what I would say, is the number one benefit, right? One it's container specific and designed for kubernetes. And second, it really, really is easy. >> Well, I really like the whole thing and I want to get your thoughts as we close out, what should people know about Pure and Portworx's relationship now and in the Amazon integration, what's the new narrative the north Star's still the same? High performance store, backup, securely recover and deliver the data in whatever mechanism we can. That north Star's clear, never changes, which is great. I feel love about Pure and Cloud Native. It's just taking the blockers away- >> I think the single biggest thing I would say, is all of these things, what we're turning into it is as a service offering. So if we're going to backup as a service our Portworx product now is going to be the Portworx enterprise Pure Storage product is going to be offered as a service. So with, as a service, it's easy to consume. It's easy to deploy. It's fully automated. That's the kind of the single biggest aha! Especially for the folks who are deploying on AWS today, AWS is well known for being easy to use. It's kind of fully automated. Well here, now you have this functionality for Cloud Native workloads. >> Final question, real quick, customer reaction so far, I'm assuming marketplace integration, buying terms, join selling, go to market? >> So yeah, it is integrated billing and all of that is part of that kind of offering, right? So when we say easy, it's not just about being easy to use it's about being easy to buy. It's being easy to expand all of that and scaling. Yeah. And being able to kind of automatically or automagically as I like to say, scale it, right? So all of that is absolutely part of it, right? So it is really kind of... It's not about having the basics anymore. We've been in the market now for six, seven years, so right? We have sort of an advanced offering that not only knows what customer want but anticipates what ones can expect and that's a key difference. >> I was talking to Dr. Matt Wood real quick. I know we got to wrap up on the schedule, but earlier today about AI and business analytics division's running and we were talking about serverless and the impact of serverless. And he really kind of came down the same lines where you are with the storage and the cloud data which is, "Hey, some people just want storage and the elastic leap analytics without all the under the cover stuff." Some people want to look under the covers, fine whatever choice. So really two things, so. >> Yeah, yeah. All the way from you can buy the individual components or you can buy the as a service offering, which just packages it all up in a on easy to consume kind of solution, right? >> Final, final question. What's it like at Pure everything going well, things good? >> We love it, man. I'll tell you these folks have welcomed us with open arms. And look, I've been acquired twice before. And I say this, that one of the key linchpins to a successful integration or acquisition is not just the strategic intent that always exists but really around a common culture. And, we've been blessed. I think the two companies have a strong common culture of being customer first, product excellence, and team wins every time. And these three things kind of have pulled us together. It's been a pleasure. >> One of the benefits of doing the queue for 13 years is that you get the seats things. Scott came on the queue to announce Pure Storage on theCUBE, cuz he was a nobody else. There was, oh, you're never going to get escape Velocity, EMC's going to kill, you never owned you. Nope. >> Well, we're talking about marketplaces and theCUBE is the marketplace of big announcements, John. So this is, delighted- >> Announcements. >> Yeah. Yeah. Well that was the AWS announcement. Yeah. So that's, that is big >> Final words, share the audience. What's what to expect in the next year for you guys? What's the big come news coming down? What's coming around the corner? >> I think you can expect from from Pure and Portworx the as a service set of offerings around, HADR backup, but also a brand new stuff, keep an eye out. We'll be back with John. I hope that talking about this is data services. So we have a Portworx data service product that is going to be announced. And it's magic. It's allowing people to deploy databases in a very, very, it's the easy button for database deployment. >> Congratulations on all your success. The VP and General Manager of the Cloud Native Business Unit. >> You make it sound bigger than it actually is, John. >> Thanks for coming on. Appreciate it. >> Thanks. >> Okay theCUBE coverage be back for more coverage. You're watching theCUBE here, live in Moscone on the ground at an event AWS Summit 2022. I'm John Furrier. Thanks for watching. (upbeat music)

>>Hey, welcome back to live coverage in San Francisco, California, the cubes coverage of 80 west summit, 2022 here in SF and NYC New York city. Summit's coming up in the summer. We'll be there as well. Check it out. Okay. We've got a great guest here. C Bhan co and CEO RAI systems. Welcome to the cube, hot startup and growing company. And Kubernetes is great to see you. >>Yeah, John, thanks for having me. Appreciate it. >>Great to have you on. So Cubans coming up, you got cloud native here at AWS. You guys in the middle of it, take a minute to explain what your company does. Sure. >>So 50,000 enterprises are going to modernize in the next five to 10 years. They're all going to run into the exact same problem, which is they're gonna choose Kubernetes as the orchestra platform. And then they're gonna invest in building a platform essentially on top of Kubernetes so that their internal consumers, that developers can consume it. That requires a lot, a lot of effort. We, lot of people, a lot of time, a lot of effort, what we did was we thought about entire journey for Kubernetes operations that a team would go through and we package that as an offering. It's a SaaS product that you can consume. You can make it work with Amazon's Kubernetes, Azures Kubernetes, Googles, Kubernetes, upstream, Kubernetes, but then you can move significantly faster so that the goal of modernization can be achieved now versus two years more. >>What's the big, uh, opportunity that Kubernetes brings. And what are some of the pain points that are being removed or solved or blockers being removed and pain being reduced? Is it standing up Kubernetes? Is it running it in production? Is it the new revisions? I mean, honestly, it's huge. Yeah. What's the pain point. The customers that you guys solve. >>Yeah. Look, the, the paradox with Kubernetes is when it's working. It's awesome. It's great. And we can move it fast, but to get there, it's hard. Yeah. So simple things as a starting point, how do I provision my infrastructure repeatedly in the same way with the right blueprints? How do I make sure they all look the same? How do I make sure John can access certain things? And he cannot, how do I make sure the right policies are set up? How do I make sure consistent deployment is happening? Can I watch every we think, and I measure everything and we are not beyond basic things, right? Yeah. I need to back this up, you know, on and on. I need to do cost management. I need to network policy management. I need service management. You already built the team now. Right? Each of these is, is, is multiple people's jobs sometimes. Right? So it's really complicated. But again, everybody is investing. This is complexity. It's complexity. Yes. But people are investing in this because everybody understands now that once this is all working, the beauty, the, the P the pace at which you can run is exactly what we were promised five, six years ago when we were all told about modernization. Yeah. So the, when you get there, it's awesome. And we are helping companies get there significantly faster. Then they would've had, were they not working with a company? >>It's it really is a holy grail kind of orchestration layer if it works. And a lot of people, even myself, which a big fan of Kubernetes, caution, cautions over the oo problem, which is the clusters are up. I can't find talent to run them. They're too hard. Um, that's kind of in the back of people's minds and there's a lot of scar tissue around that. Uh, and then a little bit of open stack, you know, is it too hard, too hard? So the question is, is that what needs to happen to be successful with Kubernetes to make that go faster? So that's easy to deploy. Exactly. Yeah. And what what's your product do? Is it software open source? Yeah. What's, what's your product? >>The, the key here is repeatably usable automation. It's automation that it can use again and again, and it's flexible enough that it solves for many companies problem. You know, the funny thing is, and this is something that took me a while to figure out whether we have a financial services customer or an insurance company, or a healthcare company, or a high tech company, you know, what their problems are exactly the same. <laugh> when it comes to Kubernetes, it's all the same, right? So we figured out what it takes to build that automation in a repeatable fashion so that we could essentially sell it as a product. Our product is a SaaS product. Um, and once you have the right automation in place that you can ideally consume as a service, then now the beauty is that the people who are using it on a day to day basis, they don't need to be as expert at Kubernetes as today. Yeah. And that's the issue today? The issue is, you know, people, I've seen ads now where people say, you know, looking for Kubernetes expertise, 10 years, minimum experience, okay, that's ridiculous. Right. But you see these ads out there, right? Because people are rude about it, a tool like this makes it easy for you to take your existing skillset, existing resources and allow them to become Kubernetes. >>That's the key. I think that's the key in my mind is like hiring talent for these, I call DevOps glass eating projects, cuz it's hard. Yep. Some of this stuff's hard when you get down to the early stuff. And even in the hyperscalers, you look at the early hyperscalers, they were rolling their own and they were rock stars. And they were like the 1% of top developers. Right? Yep. And now you have general audiences who just want to code. Yep. They want abstractions. They want Kubernetes as a service. Uh, and they want all the benefits. And even if they could hire the Oddsly hiring the low level core people yeah. Is hard. Yeah. >>It takes time. Yeah. >>Absolutely. That's a core problem you guys solve. >>Absolutely. I think look, the, the one thing that every enterprise you think about is when the, the big companies, the hyperscale is that you mentioned that build this themselves when they us out 5, 6, 7, whatever years ago, when, you know, even some, some of them pre Kubernetes, it was a competitive advantage to roll this out because nobody else was doing it now as, as an enterprise who is trying to use software to move faster. Yeah. It's actually a competitive disadvantage because now you're building your own product. And now you're building this thing called Kubernetes that doesn't make any sense, focus your application, focus on your products, roll them out faster, and then essentially reuse the learnings from the market. Right. That's what we are doing. Really? What, what are we doing? We're taking the best practices of this industry and packaging that up into an easy to consume platform. That's awesome. That's it? >>Well, we'll see you in Cooper, Cuban, not Kubernetes contest in Valencia. Yep. Uh, and thanks for coming on. I know we didn't have a lot of time to drill into it, um, here, but great to meet you and the company. Final question as a co-founder what's your north star, you got, you got a company to run in. Bill got employees, you're managing and hiring inspiring. What's the north star for the company. >>So I'd say, I mean, the phrase that I, that, that I think about when, when you say north star is, is loyalty with urgency. >>So loyalty to whom? Yeah. It's to my team, right? My team comes first beyond before anything else. Right? And then my customers, right? My customers, many, many of our, our customers even now, right? We a four old company, they have my cell phone number and people call me at odd hours and I will show up. I will get people on a call. I will show up. Right. That's critical. But with urgency now my customer needs help. It needs to happen now. Not tomorrow, not next week. My team has heard me say this a thousand times, by the way, not tomorrow, not next week now. And this, if you do this in a startup, you will be successful. >>Yeah. I mean, you gotta make the market as the founder, inspiring people, product market fits huge. Yep. Getting that scale point. Yep. Where you're got the value proposition in position you're in mode to scale, you got visibility on unit economics. It's hard. Yep. It's super hard look. Good news is you get in a good area. Cloud native Kubernetes, automation, cloud, native modernization of apps. Super hot right now. Yeah. Big >>Time. Yeah. Look, I mean, you know, of course you, you want your teams to be topnotch. Right. But I gotta tell you there's a lot of luck and timing to everything. >>Exactly. >>Timing is in hindsight, nobody times anything. Right. So we have, time is perfect, but it's luck. Yeah. Right. We're very lucky. We're we have the right team. We're doing a great job. I think our customers are very happy. What we've rebuilt and uh, you know, look forward >>To Steve. You're humble. And you're a humble person. I can tell. I don't believe in luck. I think you make luck. I think luck is just part of the hustle, making those phone calls, doing those calls, doing the right things, grinding. And then when you get the shot, you're ready. Yeah. Yeah. So congratulations. Thanks for coming the queue. Appreciate it. Appreciate your time, sir. Nice to meet you coverage here in San Francisco, back with more day, two coverage. After this short break, stay with us.

>>Okay, okay, we're back, you're watching the cubes, continuous coverage of HBs Green Lake announcement. One of the things that we said on the Cuban. We first saw Green Lake was let's watch the pace at which H P E delivers new servants is what's that cadence like? Because that's a real signal as to the extent that the company's leading into the cloud and today we're covering that continued expansion. We're here with Tom Black, who was the general manager of HPC storage and Omar assad, who's the storage platform lead for cloud data services at Hewlett Packard Enterprise gentlemen welcome. It's good to see you. >>Thanks Dave. Thanks for having us today. Good to see you. >>Happy to be here. Dave. >>So obviously a lot has changed globally, but when you think of things like cyber threats, ransomware, uh, the acceleration of business transformation, uh, these are new things, a lot of it is unknown a lot of it was forced upon us tom what are you guys doing to address these trends? How are you helping customers? >>Sure, thanks for the question. So if you think back to what we launched in early May, kind of the initial cloud transformation of what was our traditional storage business. Um, we really focused on one key theme. Very customer and customer driven theme that the cloud operational model has one and that customers want that operational model, whether they're operating their workload in the cloud or whether they're operating that workload in their own facility or Nicolo kind of the same thing. So that was kind of our true north and that's what we launched out of the gate in May. But we did allude in May to the fact that we would have an ongoing series of new services coming out on the uh H B Green Lake edge to cloud platform. And just really excited today to be talking about somewhat that expansion looks like um we will continue uh through this month and through the quarters ahead to really add more and more services in that vein of focusing on bringing that true cloud services model to our customer. So we're really excited today to unveil kind of, we've entered the data protection as a service market with HP Green Lake. So this is really our expansion into a very top of mind topic and set of problems and solutions or headaches and aspirins, to quote an old friend um that Ceos faces, they think about how to manage data through its life cycle in their organization. >>When I talked to see IOS during the pandemic. Not that we're out yet, but really in the throes of it and asked them about things like business resilience that they said, you know, we really had to rethink our disaster recovery strategy. It was it was sort of geared toward a fire or a hurricane and we we just didn't even imagine this type of disaster if you will. So we really needed to rethink it. So when I, I see your disaster recovery as a service and capabilities like that. Is that the Xarelto acquisition? >>Yes. Dave thanks you. So we're super happy to have the Xarelto team now as part of our family. Um, just a brilliant team, a well respected technology, uh, kind of a blue chips at our customers and partners that really appreciate what zero has to offer. Um, as we looked at the data protection as a service market, one of the hardest problems is really in that disaster recovery space, I think Omar's gonna talk a little bit more about today. Um, but sort of really does bring the leading industry, what's called continuous data protection um, capability into our green lake platform. Um, we've just recently closed the acquisition and we're working on kind of integration plan as we speak now that we can actually talk to each other post close. Um, but you'll uh, you'll continue to see, you know, some really exciting milestones each and every quarter as we march forward with certain now as part of the family. >>So we all talk about how data is, is so important. We certainly learned during the pandemic that that if you weren't a digital business, you were out of business and a digital business is a data business. So things like backup data protection as a service become increasingly critical. I know you have some capabilities there maybe you could share with us. >>Absolutely. So you know, one of the things that we noticed was as we took the storage business through its transformation and we started can work you know, with the launch of the electron 90 and the six K platform. We really really brought the cloud operational model to our customers. So one of the things that you know, feedback that was coming loud and clear to us is that as we look at the storage portfolio where we look at file block and object, which are now being transformed into a cloud operational experience, data protection, disaster recovery coming back into business after a disaster snapshot management. All of those capabilities, we still have to rely on our partner technologies in order to do that now. It's not bad that we have great partners in the data protection world, but what we're really focused on is that cloud operational model and cloud operational experience and to and as tom mentioned through the data management life cycle. So as a result of that, we talked to a lot of our customers, we talked to a bunch of partners and one of the things that was coming back was that yes, there are many data protection backup offerings on the market. But that true as a service experience that is completely integrated to the services experience of the storage that the customers is experiencing that is not there. So what we looked at was especially to the largest ecosystem, which is the VM ware ecosystems. So we're launching data protection as a service or backup as a service for our VM ware customers offered from data services, cloud console as a SAAS portal. 100% SAs service, nothing to install. No media servers, no application servers, no catalog servers, no backup targets, no patching, no expansion, no capacity planning. None of that is needed. All that's needed is sign on click. Give your V center credentials and off you go, that's it. That is it three clicks and you're in business. So currently, you know, in our, in our analysis we offer five x faster recovery from any of the competitive offerings that there there there are 3.5 better de doop ratios. But for our customers is as simple as this. VM is protected as this many dollars per gig per month. That's it. No backup target, no media server, no catalogs are nothing nothing to manage total Turkey off of the portal. So that's the cadence of services that if you promise and this is one of the first ones when it comes to data management that is coming out into the open. >>So you may have just answered this question, but I want to pose it and get you maybe just summarize it because tom was talking earlier about the customer mandate for cloud in a cloud operational model. So I want you to explain to the audience how you're making that real >>actually can I start that one should be the test was monday morning. Getting ready for this chat with you Dave they got me on console and I'm not kidding three clicks, I got back up and running off the lab VM ware instance so I'll pass it off to you the real answer. But if I could do it three clicks >>as well as a convenience of this service, even tom can be your back, you might be able to do with this. Uh again, you know, a very important question the when you, when you look at the cloud operational model as you abstracts the hardware and and take the management model up into a SAS service, it gives our customers that access to that continuous delivery access that we have. We're going to continue to make the service medal better in the cloud model and automatically customers get the value of it without even reinstalling or going through a patch cycle or an upgrade cycle. But as we get into this cloud operational model, one of the things that was missing was uh if you if you if you if you start to talk about applications, how our application workloads going to be deployed, how are they going to be protected and how are they going to be expanded? So what we did was we, we expanded our info site offerings by merging them into the data services, cloud console and we're releasing a new service called app insects. It is going to be available to our customers at the end of the month. Uh It is, nothing has to change. They don't have to install any sort of agents or or host modifications, nothing like that. If their customers of electra nimble primary boxes and they're using info site and data services, cloud console, they will automatically get app insights. What Athens sites does is it really teases apart all that data that we have been collecting within foresight and now with the acquisition of HPV cloud physics, we're merging them together and relating the operational stacked top to bottom. So discovering all the way from your application usage, network usage, storage, use it. IOP usage VM values cross, collaborating them and presenting that to a customer from an app or an outcome perspective all in the data services, cloud console. So what this does for our customers is it really really transforms not only their operational experience but also buying experience. Because if you remember in one of the earlier releases of data services cloud console we released this application called, you know, intelligent intent based provisioning in which you just describe your workload and we go ahead and we provision that app insights and info site, feed that information directly into that and cloud physics generates and results and displays those analytics back to us to your partner of record and to the H. B. So we can all come together on a common data driven discussion point with our customers to continue to make their journey better >>tom where's all the boxes, traditional storage is changing. I've actually been waiting for this day for a long, long time. We've certainly seen glimpses of it from the cloud players, but they don't have, you know, super rich portfolio storage portfolio. They're growing now, but this is a really good strong example of a company with a large storage portfolio. That's, I mean I haven't heard the word three power once today. Right. And so what that says to me, that's an indication that you're thinking like a cloud player, can you maybe talk >>to that? Sure. Yeah, we're just tremendously excited about this transformation and really the reception we've got in the market from analysts, from partners, from customers because you're right, you haven't heard us talk about a box at all today. It's really about a block service, a file on the object service, a backup and recovery service, disaster recovery service. That that's that is the the language, if you will of the business problems of our customers not, do they need to pick this widget or that widget. And how many apps can I get here and there? And which did the h a cage protection scheme be that, is that, is our job to manage underneath are true North, which is the cloud operational model. And so that's going to be really how we we've set our course and how we will uh kind of deliver products solutions offers into the market underneath that umbrella, Ultimately, um getting our customers wherever their data is Dave to be able to interact at that service level instead of at that infrastructure box >>level, you've got my attention wherever the data. So that's the north star here is this is, you know, you're not done today obviously, but you've got a vision to bring that to the cloud across clouds on prem out to the edge. That's the abstraction layer that you're gonna build, your hiding all that complexity. That's correct. And that's cloud. The definition of cloud is changing. >>Yeah, >>it's no longer started, it's no longer a remote set of services. Somewhere up in the cloud. It's expanding on prem hybrid across clouds edge >>everywhere. You're exactly right. Dave it is, cloud is more about the experience and the outcome. It gives a customer than actually where the compute or storage is. We've chosen to take a very customer an agnostic position of whether it's, you know, data in your premise, data in your cloud. We're going to help you manage that data and deliver, you know, that data to workloads and analytics, uh, wherever the, wherever the compute needs to be, where the data needs to be. Again, technologies like Xarelto giving instability and move data across clouds from facilities and clouds back and forth. So it's a really exciting new day for HP. Green Lake were just so super happy to bring these technologies out and really continue to follow on the course of doing what we said, we would do >>the new mindset starts there, I guess it's obviously knew certainly new technologies, uh, you're talking about machine intelligence is a metadata challenge. Absolutely. Big time, you know, long term that North Star that we talked about and applying that machine intelligence, all the experience that you gather data that you're gathering is, I think ultimately how customers want you to solve this problem >>in the middle of info site data services, cloud console and the instrumentation that is already shipping on our appliances, both in edge appliances and the data center appliances were collecting more than a trillion data points over the period of a quarter. Right at the end of the day. So it's harnessing that at the back end to cross relate and then using the cloud physics accusation. What we're doing is we can now simulate these things on behalf of our customers into the future timeline. So at the end of the day, it's really about listening to the customer and what outcomes that they want to achieve with their data storage is there we provide excellent persistence layers where customers can store their data safely. But at the end of the day it's customers choice, They can store their data out of the edge in compute servers, commodity servers, X 86 servers, they can have their data in the data center which they are privately owned or their data can be in a service provider or it can be in a hyper secular. The infrastructure of the persistence layer is independent from the data services. Cloud console data services. Cloud console provides our customers with a SAS based industry leading metadata rich management experience, which then allows you to draw conclusions. So services like cloud physics services like uh enforce it, provide the analytics and richness of the metadata, backup and recovery service allows us to index our customers data and add a rich metadata to that and then combine that with xylitol, which is our disaster recovery as a service offering. Going to start over here. That gives the customer a very simple slider as to where they want their protection levels to be, they want their protection to be instant or they want their protection to be lazy eight hours window. But the thing is at the end of the day, it's about choice without managing the complexities of the hardware >>underneath because programmable completely right I come in, what I'm hearing is file object blocks of your multi protocol. I got a full stack so data data reduction, my snaps might replicate whatever whatever I need it in there as a service. I can I can access latency sensitive storage if I need to or I can push it out to cheaper stores. I could push it out to the cloud, presumably I could someday I air gap it uh and it's all done as infrastructure as code and then different protection levels where I see this going. It really gets exciting is you're now a data company and you're bringing ai machine intelligence and driving data products, data services for your customers who are going to monetize that at their end of the value >>chain. That's right. That's right. And safely insecurity. Keeping in mind that was their toes technology. We can give you, you know, small second recovery points to protect against ransomware. So all of that operational elegance, all those insights and intelligence to help you build a more agile, um you know, workloads centric organization, but then to do it safely and securely against ransomware, that's kind of the storm, if you will. That's brewing. And we're just really excited to be at the eye of it. >>I'm excited to. This is uh I've been waiting for this day for a long time and we're not talking about envy, Emmy and Atomic Rights and I love that stuff by the way and I'm sure it's all under the covers, but that's not what drives business value guys. Thanks so much for coming on the Cuban. David. >>Thanks for having us. It's been great. Thank you. >>All right. We're seeing a transformation all through the stack and keep it right there. This is Dave Volonte for the Cuban. Our coverage of HBs Green Lake announcements right back mm mhm

(bright music) >> Hello everyone, welcome today's session for the "AWS Startup Showcase" presented by theCUBE, featuring ThoughtSpot for this track and data and analytics. I'm John Furrier, your host. Today, we're joined by Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development for ThoughtSpot. Victor, thanks for coming on and thanks for presenting. Talking about this building interactive data apps through ThoughtSpot Everywhere. Thanks for coming on. >> Thank you, it's my pleasure to be here. >> So digital transformation is reality. We're seeing it large-scale. More and more reports are being told fast. People are moving with modern application development and if you don't have AI, you don't have automation, you don't have the analytics, you're going to get slowed down by other forces and even inside companies. So data is driving everything, data is everywhere. What's the pitch to customers that you guys are doing as everyone realizes, "I got to go faster, I got to be more secure," (laughs) "And I don't want to get slowed down." What's the- >> Yeah, thank you John. No, it's true. I think with digital transformation, what we're seeing basically is everything is done in the cloud, everything gets done in applications, and everything has a lot of data. So basically what we're seeing is if you look at companies today, whether you are a SaaS emerging growth startup, or if you're a traditional company, the way you engage with your customers, first impression is usually through some kind of an application, right? And the application collects a lot of data from the users and the users have to engage with that. So for most of the companies out there, one of the key things that really have to do is find a way to make sense and get value for the users out of their data and create a delightful and engaging experience. And usually, that's pretty difficult these days. You know, if you are an application company, whether it doesn't really matter what you do, if you're hotel management, you're productivity application, analytics is not typically your strong suit, and where ThoughtSpot Everywhere comes in is instead of you having to build your own analytics and interactivity experience with a data, ThoughtSpot Everywhere helps deliver a really self-service interactive experience and transform your application into a data application. And with digital transformation these days, all applications have to engage, all applications have to delight, and all applications have to be self-service. And with analytics, ThoughtSpot Everywhere brings that for you to your customers and your users. >> So a lot of the mainstream enterprises and even businesses from SMB, small businesses that are in the cloud are scaling up, they're seeing the benefits. What's the problem that you guys are targeting? What's the use case? When does a potential customer or customer know they get that ThoughtSpot is needed to be called in and to work with? Is it that they want low code, no code? Is it more democratization? What's the problem statement and how do you guys turn that problem being solved into an opportunity and benefit? >> I think the key problem we're trying to solve is that most applications today, when they try to deliver analytics, really when they're delivering, is usually a static representation of some data, some answers, and some insights that are created by someone else. So usually the company would present, you know, if you think about it, if you go to your banking application, they usually show some pretty charts for you and then it sparks your curiosity about your credit card transactions or your banking transactions over the last month. Naturally, usually for me, I would then want to click in and ask the next question, which transactions fall into this category, what time, you know, change the categories a bit, usually you're stuck. So what happens with most applications? The challenge is because someone else is asking the questions and then the user is just consuming static insights, you wet their appetite and you don't satisfy it. So application users typically get stunted, they're not satisfied, and then leave application. Where ThoughtSpot comes in, ThoughtSpots through differentiation is our ability to create an interactive curiosity journey with the user. So ThoughtSpot in general, if you buy a standalone, that's the experience that we really stand by, now you can deliberate your application where the user, any user, business user, untrained, without the help of an analyst can ask their own questions. So if you see, going back to my example, if it's in your banking app, you see some kind of visualization around expense actions, you can dig in. What about last month? What about last week? Which transactions? Which merchant? You know, all those things you can continue your curiosity journey so that the business user and the app user ask their questions instead of an analyst who's sitting in the company behind a desk kind of asking your questions for you. >> And that's the outcome that everyone wants. I totally see that and everyone kind of acknowledges that, but I got to then ask you, okay, how do you make that happen? Because you've got the developers who have essentially make that happen and so, the cloud is essentially SaaS, right? So you got a SaaS kind of marketplace here. The apps can be deployed very quickly, but in order to do that, you kind of need self-service and you got to have good analytics, right? So self-service, you guys have that. Now on the analytics side, most people have to build their own or use an existing tool and tools become specialists, you know what I'm saying? So you're in this kind of like weird cycle of, "Okay, I got to deploy and spend resource to build my own, which could be long and tiresome." >> Yeah. >> "And or rely on other tools that could be good, but then I have too many tools but that creates specialism kind of silos." These seems to be trends. Do you agree with that? And if customers have this situation, you guys come in, can you help there? >> Absolutely, absolutely. So, you know, if you think about the two options that you just laid out, that you could either roll your own, kind of build your own, and that's really hard. If you think about analyst industry, where 20, $30 billion industry with a lot of companies that specialize in building analytics so it's a really tough thing to do. So it doesn't really matter how big of a company you are, even if you're a Microsoft or an Amazon, it's really hard for them to actually build analytics internally. So for a company to try to do it on their own, hire the talent and also to come up with that interactive experience, most companies fail. So what ends up happening is you deliver the budget and the time to market ends up taking much longer, and then the experience is engaging for the users and they still end up leaving your app, having a bad impression. Now you can also buy something. They are our competitors who offer embedded analytics options as well, but the mainstream paradigm today with analytics is delivering. We talked about earlier static visualizations of insights that are created by someone else. So that certainly is an option. You know, where ThoughtSpot Everywhere really stands out above everything else is our technology is fundamentally built for search and interactive and cloud-scale data kind of an experience that the static visualizations today can't really deliver. So you could deliver a static dashboard purchase from one of our competitors, or if you really want to engage your users again, today is all about self-service, it's all about interactivity, and only ThoughtSpot's architecture can deliver that embedded in a data app for you. >> You know, one of the things I'm really impressed with you guys at ThoughtSpot is that you see data as I see strategic advantage for companies and people say that it's kind of a cliche but, or a punchline, and some sort of like business statement. But when you start getting into new kinds of workflows, that's the intellectual property. If you can enable people to essentially with very little low-code, no-code, or just roll their own analysis and insights from a platform, you're then creating intellectual property for the company. So this is kind of a new paradigm. And so a lot of CIO's that I talked to, or even CSOs on the security side of like, they kind of want this but maybe can't get there overnight. So if I'm a CIO, Victor, who do I, how do I point to on my team to engage with you guys? Like, okay, you sold me on it, I love the vision. This is definitely where we want to go. Who do I bring into the meeting? >> I think that in any application, in any company actually, there's usually product leaders and developers that create applications. So, you know, if you are a SaaS company, obviously your core product, your core product team would be the right team we want to talk to. If you're a traditional enterprise, you'd be surprised actually, how many traditional enterprises that been around for 50, 100 years, you might think of them selling a different product but actually, they have a lot of visual applications and product teams within their company as well. For example, you know, we have customers like a big tractor company. You can probably imagine who they might be. They actually have visual applications that they use ThoughtSpot to offer to the dealers so that they can look at their businesses with the tractors. We also have a big telecom company, for example, that you would think about telecom as a whole service but they have a building application that they offer to their merchants to track their billing. So what I'm saying is really, whether you're a software company where that's your core product, or you're a traditional enterprise that has visual applications underneath to support your core product, there's usually product teams, product leaders, and developers. Those are the ones that we want to talk to and we can help them realize a better vision for the product that they're responsible for. >> I mean, the reality is all applications need analytics, right, at some level. >> Yes. >> Full instrumentation at a minimum log everything and then the ability to roll that up, that's where I see people always telling me like that's where the challenge seems to be. Okay, I can log everything, but now how do I have a... And then after the fact that they say, "Give me a report, what's happening?" >> That's right. >> They get stuck. >> They get stuck 'cause you get that report and you know, someone else asked that question for you and you're probably a curious person. I'm a curious person. You always have that next question, and then usually if you're in a company, let's just say, you're a CIO. You're probably used to having a team of analysts at your fingertip so at least if you have a question, you don't like the report, you can find two people, five people they'll respond to your request. But if you're a business application user, you're sitting there, I don't know about you, but I don't remember the last time I actually went through and really found a support ticket in my application, or I really read a detailed documentation describing features in application. Users like to be self-taught, self-service and they like to explore it on their own. And there's no analyst there, there's no IT guy that they can lean on so if they get a static report of the data, they'll naturally always want to ask more questions, then they're stuck. So it's that kind of unsatisfying where, "I have some curiosity, you sparked by questions, I can't answer them." That's where I think a lot of companies struggle with. That's why a lot of applications, they're data intensive but they don't deliver any insights. >> It's interesting and I like this anywhere idea because you think about like what you guys do, applications can be, they always start small, right? I mean, applications got to be built. So you guys, your solution really fits for small startups and business all the way up to large enterprises which in a large enterprise, they could have hundreds and thousands of applications which look like small startups. >> Absolutely, absolutely. You know, that's a great thing about the sort of ThoughtSpot Everywhere which takes the engine around ThoughtSpot that we built over the last eight or nine years and could deliver in any kind of a context. 'Cause nowadays, as opposed to 10, 15, 20 years ago, everything does run in applications these days. We talk about visual transformation at the beginning of the call. That's really what it means is today, the workflows of business are conducted in applications no matter who you're interacting with. And so we have all these applications. A lot of times, yes, if you have big analytical problems, you can take the data and put into a different context like ThoughtSpot's own UI and do a lot of analytics, but we also understand that a lot of times customers and users, they like to analyze in the context the workflow of the application they're actually working in. And so with that situation, actually having the analytics embedded within right next to their workflow is something that I think a lot of, especially business users that are less trained, they'd like to do that right in the context of their business productivity workflow. And so that's where ThoughtSpot Everywhere, I know the terminology is a little self-serving, but ThoughtSpot Everywhere, we think ThoughtSpot could actually be everywhere in your business workflow. >> That's great value proposition. I'm going to put my skeptic hat on challenge you and say, Okay, I don't want to... Prove it to me, what's in it for me? And how much is it going to cost me, how do I engage? So, you know- >> Yeah. >> What's in it for me as the buyer? If people want to buy this, I want to use it, I'm going to get engaged with ThoughtSpot and how much does it cost and what's the engagements look like? >> So, what's in it for you is easy. So if you have data in the cloud and you have an application, you should use ThoughtSpot Everywhere to deliver a much more valuable, interactive experience for your user's data. So that's clear. How do you engage? So we have a very flexible pricing models. If your data's in the cloud, we can either, you can purchase with us, we'll land small and then grow with your consumption. You know, that's always the kind of thing, "Hey, allow us to prove it to you, right?" We start, and then if a user starts to consume, you don't really have to pay a big bill until we see the consumption increase. So we have consumption and data capacity-based types of pricing models. And you know, one of the real advantages that we have for cloud applications is if you're a developer, often, even in the past for ThoughtSpot, we haven't always made that development experience very easy. You have to embed a relatively heavy product but the beauty for ThoughtSpot is from the beginning, we were designed with a modern API-based kind of architecture. Now, a lot of our BI competitors were designed and developed in the desktop server kind of era where everything you embed is very monolithic. But because we have an API driven architecture, we invest a lot of time now to wrap a seamless developer SDK, plus very easy to use REST APIs, plus an interactive kind of a portal to make that development experience also really simple. So if you're a developer, now you really can get from zero to an easy app for ThoughtSpot embedded in your data app in just often in less than 60 minutes. >> John: Yeah. >> So that's also a very great proposition where modern leaders is your data's in the cloud, you've got developers with an SDK, it can get you into an app very quickly. >> All right so bottom line, if you're in the cloud, you got to get the data embed in the apps, data everywhere with ThoughtSpot. >> Yes. >> All right, so let's unpack it a little bit because I think you just highlighted I think what I think is the critical factor for companies as they evaluate their plethora of tools that they have and figuring out how to streamline and be cloud native in scale. You mentioned static and old BI competitors to the cloud. They also have a team of analysts as well that just can make the executives feel like the all of the reports are dynamic but they're not, they're just static. But look at, I know you guys have a relation with Snowflake, and not to kind of bring them into this but to highlight this, Snowflake disrupted the data warehouse. >> Yes. >> Because they're in the cloud and then they refactored leveraging cloud scale to provide a really easy, fast type of value for their product and then the rest is history. They're public, they're worth a lot of money. That's kind of an example of what's coming for every category of companies. There's going to be that. In fact, Jerry Chen, who was just given the keynote here at the event, had just had a big talk called "Castles In The Cloud", you can build a moat in the cloud with your application if you have the right architecture. >> Absolutely. >> So this is kind of a new, this is a new thing and it's almost like beachfront property, whoever gets there first wins the category. >> Exactly, exactly. And we think the timing is right now. You know, Snowflake, and even earlier, obviously we had the best conference with Redshift, which really started the whole cloud data warehouse wave, and now you're seeing Databricks even with their Delta Lake and trying to get into that kind of swim lane as well. Right now, all of a sudden, all these things that have been brewing in the background in the data architecture has to becoming mainstream. We're now seeing even large financial institutions starting to always have to test and think about moving their data into cloud data warehouse. But once you're in the cloud data warehouse, all the benefits of its elasticity, performance, that can really get realized at the analytics layer. And what ThoughtSpot really can bring to the table is we've always, because we're a search-based paradigm and when you think about search. Search is all about, doesn't really matter what kind of search you're doing, it's about digging really deep into a lot of data and delivering interactive performance. Those things have always... Doesn't really matter what data architecture we sit on, I've always been really fundamental to how we build our product. And that translates extremely well when you have your data in a Snowflake or Redshift have billions of rows in the cloud. We're the only company, we think, that can deliver interactive performance on all the data you have in a cloud data warehouse. >> Well, I want to congratulate you, guys. I'm really a big fan of the company. I think a lot of companies are misunderstood until they become big and there was, "Why didn't everyone else do that search? Well, I thought they were a search engine?" Being search centric is an architectural philosophy. I know as a North Star for your company but that creates value, right? So if you look at like say, Snowflake, Redshift and Databricks, you mentioned a few of those, you have kind of a couple of things going on. You have multiple personas kind of living well together and the developers like the data people. Normally, they hated each other, right? (giggles) Or maybe they didn't hate each other but there's conflict, there's always cultural tension between the data people and the developers. Now, you have developers who are becoming data native, if you will, just by embedding that in. So what Snowflake, these guys, are doing is interesting. You can be a developer and program and get great results and have great performance. The developers love Snowflake, they love Databricks, they love Redshift. >> Absolutely. >> And it's not that hard and the results are powerful. This is a new dynamic. What's your reaction to that? >> Yeah, no, I absolutely believe that. I think, part of the beauty of the cloud is I like your kind of analogy of bringing people together. So being in the cloud, first of all, the data is accessible by everyone, everywhere. You just need a browser and the right permissions, you can get your data, and also different kind of roles. They all kind of come together. Things best of breed tools get blended together through APIs. Everything just becomes a lot more accessible and collaborative and I know that sounds kind of little kumbaya, but the great thing about the cloud is it does blur the lines between goals. Everyone can do a little bit of everything and everyone can access a little bit more of their data and get more value out of it. >> Yeah. >> So all of that, I think that's the... If you talk about digital transformation, you know, that's really at the crux of it. >> Yeah, and I think at the end of the day, speed and high quality applications is a result and I think, the speed game if automation being built in on data plays a big role in that, it's super valuable and people will get slowed down. People get kind of angry. Like I don't want to get, I want to go faster, because automations and AI is going to make things go faster on the dev side, certainly with DevOps, clouds proven that. But if you're like an old school IT department (giggles) or data department, you're talking to weeks not minutes for results. >> Yes. >> I mean, that's the powerful scale we're talking about here. >> Absolutely. And you know, if you think about it, you know, if it's days to minutes, it sounds like a lot but if you think about like also each question, 'cause usually when you're thinking about questions, they come in minutes. Every minute you have a new question and if each one then adds days to your journey, that over time is just amplified, it's just not sustainable. >> Okay- >> So now in the cloud world, you need to have things delivered on demand as you think about it. >> Yeah, and of course you need the data from a security standpoint as well and build that in. Chances is people shift left. I got to ask you if I'm a customer, I want to just run this by you. You mentioned you have an SDK and obviously talking to developers. So I'm working with ThoughtSpot, I'm the leader of the organization. I'm like, "Okay, what's the headroom? What's going to happen as a bridge, the future gets built so I'm going to ride with ThoughtSpot." You mentioned SDK, how much more can I do to build and wrap around ThoughtSpot? Because obviously, this kind of value proposition is enabling value. >> Yes. >> So I want to build around it. How do I get started and where does it go? >> Yeah, well, you can get started as easy as starting with our free trial and just play around with it. And you know, the beauty of SDK and when I talk about how ThoughtSpot is built with API-driven architecture is, hey, there's a lot of magic and features built into ThoughtSpot core pod. You could embed all of that into an application if you would like or you could also use our SDK and our APIs to say, "I just want to embed a couple of visualizations," start with that and allow the users to take into that. You could also embed the whole search feature and allow users to ask repetitive questions, or you can have different role-based kind of experiences. So all of that is very flexible and very dynamic and with SDK, it's low-code in the sense where it creates a JavaScript portal for you and even for me who's haven't coded in a long time. I can just copy and paste some JavaScript code and I can see my applications reflecting in real time. So it's really kind of a modern experience that developers in today's world appreciate, and because all the data's in the cloud and in the cloud, applications are built as services connected through APIs, we really think that this is the modern way that developers would get started. And analysts, even analysts who don't have strong developer training can get started with our developer portal. So really, it's a very easy experience and you can customize it in whichever way you want that suits your application's needs. >> Yeah, I think it's, you don't have to be a developer to really understand the basic value of reuse and discovery of services. I think that's one of these we hear from developers all the time, "I had no idea that Victor did that code. Why do I have to rewrite that?" So you see, reuse come up a lot around automation where code is building with code, right? So you have this new vibe and you need data to discover that search paradigm mindset. How prevalent is that on the minds of customers? Are they just trying to like hold on and survive through the pandemic? (giggles) >> Well, customers are definitely thinking about it. You know, the challenge is change is always hard, you know? So it takes time for people to see the possibilities and then have to go through especially in larger organizations, but even in smaller organizations, people think about, "Well, how do I change my workflow?" and then, "How do I change my data pipeline?" You know, those are the kinds of things where, you know, it takes time, and that's why Redshift has been around since 2012 or I believe, but it took years before enterprises really are now saying, "The benefits are so profound that we really have to change the workflows, change the data pipelines to make it work because we can't hold on to the old ways." So it takes time but when the benefits are so clear, it's really kind of a snowball effect, you know? Once you change a data warehouse, you got to think about, "Do I need to change my application architecture?" Then, "Do I need to change the analytics layer?" And then, "Do I need to change the workflow?" And then you start seeing new possibilities because it's all more flexible that you can add more features to your application and it's just kind of a virtuous cycle, but it starts with taking that first step to your point of considering migrating your data into the cloud and we're seeing that across all kinds of industries now. I think nobody's holding back anymore. It just takes time, sometimes some are slower and some are faster. >> Well, all apps or data apps and it's interesting, I wrote a blog post in 2017 called, "Data Is The New Developer Kit" meaning it was just like a vision statement around data will be part of how apps, like software, it'll be data as code. And you guys are doing that. You're allowing data to be a key ingredient for interactivity with analytics. This is really important. Can you just give us a use case example of how someone builds an interactive data app with ThoughtSpot Everywhere? >> Yeah, absolutely. So I think there are certain applications that when naturally things relates to data, you know, I talk about bending or those kinds of things. Like when you use it, you just kind of inherently know, "Hey, there's tons of data and then can I get some?" But a lot of times we're seeing, you know, for example, one of our customers is a very small company that provides software for personal trainers and small fitness studios. You know, you would think like, "Oh well, these are small businesses. They don't have a ton of data. A lot of them would probably just run on QuickBooks or Excel and all of that." But they could see the value is kind of, once a personal trainer conducts his business on a cloud software, then he'll realize, "Oh, I don't need to download any more data. I don't need to run Excel anymore, the data is already there in a software." And hey, on top of that, wouldn't it be great if you have an analytics layer that can analyze how your clients paid you, where your appointments are, and so forth? And that's even just for, again like I said, no disrespect to personal trainers, but even for one or two personal trainers, hey, they can be an analytics and they could be an analyst on their business data. >> Yeah, why not? Everyone's got a Fitbits and watches and they could have that built into their studio APIs for the trainers. They can get collaboration. >> That's right. So there's no application you can think that's too simple or you might think too traditional or whatnot for analytics. Every application now can become a very engaging data application. >> Well Victor, it's great to have you on. Obviously, great conversation around ThoughtSpot anywhere. And as someone who runs corp dev for ThoughtSpot, for the folks watching that aren't customers yet for ThoughtSpot, what should they know about you guys as a company that they might not know about or they should know about? And what are people talking about ThoughtsSpot, what are they saying about it? So what should they know that know that's not being talked about or they may not understand? And what are other people saying about ThoughtSpot? >> So a couple of things. One is there's a lot of fun out there. I think about search in general, search is generally a very broad term but I think it, you know, I go back to what I was saying earlier is really what differentiates ThoughtSpot is not just that we have a search bar that's put on some kind of analytics UI. Really, it's the fundamental technical architecture underlying that is from the ground up built for search large data, granular, and detailed exploration of your data. That makes us truly unique and nobody else can really do search if you're not built with a technical foundation. The second thing is, we're very much a cloud first company now, and a ton of our over the past few years because of the growth of these highly performing data warehouses like Snowflake and Redshift, we're able to really focus on what we do best which is the search and the query processing performance on the front end and we're fully engaged with cloud platforms now. So if you have data in the cloud, we are the best analytics front end for that. >> Awesome, well, thanks for coming on. Great the feature you guys here in the "Startup Showcase", great conversation, ThoughtSpot leading company, hot startup. We did their event with them with theCUBE a couple of months ago. Congratulations on all your success. Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development here on theCUBE and "AWS Startup Showcase". Go to awsstartups.com and be part of the community, we're doing these quarterly featuring the hottest startups in the cloud. I'm John Furrier, thanks for watching. >> Victor: Thank you so much. (bright music)

>>Welcome back to the cubes, continuous coverage of AWS storage day. We're in beautiful downtown Seattle in the great Northwest. My name is Dave Vellante and we're going to talk about file systems. File systems are really tricky and making those file systems elastic is even harder. They've got a long history of serving a variety of use cases as with me as Duncan Lennox. Who's the general manager of Amazon elastic file system. Dunkin. Good to see you again, Dave. Good to see you. So tell me more around the specifically, uh, Amazon's elastic file system EFS you, you know, broad file portfolio, but, but let's narrow in on that. What do we need to know? >>Yeah, well, Amazon elastic file system or EFS as we call it is our simple serverless set and forget elastic file system service. So what we mean by that is we deliver something that's extremely simple for customers to use. There's not a lot of knobs and levers. They need to turn or pull to make it work or manage it on an ongoing basis. The serverless part of it is there's absolutely no infrastructure for customers to manage. We handled that entirely for them. The elastic part then is the file system automatically grows and shrinks as they add and delete data. So they never have to provision storage or risk running out of storage and they pay only for the storage they're actually using. >>What are the sort of use cases and workloads that you see EFS supporting? >>Yeah. Yeah. It has to support a broad set of customer workloads. So it's everything from, you know, serial, highly latency, sensitive applications that customers might be running on-prem today and want to move to the AWS cloud up to massively parallel scale-out workloads that they have as well. >>So. Okay. Are there any industry patterns that you see around that? Are there other industries that sort of lean in more or is it more across the board? We >>See it across the board, although I'd have to say that we see a lot of adoption within compliance and regulated industries. And a lot of that is because of not only our simplicity, but the high levels of availability and durability that we bring to the file system as well. The data is designed for 11 nines of durability. So essentially you don't need to be worrying about your anything happening into your data. And it's a regional service meaning that your file system is available from all availability zones in a particular region for high availability. >>So as part of storage data, we, we saw some, some new tiering announcements. W w w what can you tell us about those >>Super excited to be announcing EFS intelligent tiering? And this is a capability that we're bringing to EFS that allows customers to automatically get the best of both worlds and get cost optimization for their workloads and how it works is the customer can select, uh, using our lifecycle management capability, a policy for how long they want their data to remain active in one of our active storage classes, seven days, for example, or 30 days. And what we do is we automatically monitor every access to every file they have. And if we see no access to a file for their policy period, like seven days or 30 days, we automatically and transparently move that file to one of our cost optimized, optimized storage classes. So they can save up to 92% on their storage costs. Um, one of the really cool things about intelligent tiering then is if that data ever becomes active again and their workload or their application, or their users need to access it, it's automatically moved back to a performance optimized storage class, and this is all completely transparent to their applications and users. >>So, so how, how does that work? Are you using some kind of machine intelligence to sort of monitor things and just learn over time? And like, what if I policy, what if I don't get it quite right? Or maybe I have some quarter end or maybe twice a year, you know, I need access to that. Can you, can the system help me figure >>That out? Yeah. The beauty of it is you don't need to know how your application or workload is accessing the file system or worry about those access patterns changing. So we'll take care of monitoring every access to every file and move the file either to the cost optimized storage class or back to the performance optimized class as needed by your application. >>And then optimized storage classes is again, selected by the system. I don't have to >>It that's right. It's completely transparent. So we will take care of that for you. So you'll set the policy by which you want active data to be moved to the infrequent access cost optimized storage class, like 30 or seven days. And then you can set a policy that says if that data is ever touched again, to move it back to the performance optimized storage class. So that's then all happened automatically by the service on our side. You don't need to do anything >>It's, it's it's serverless, which means what I don't have to provision any, any compute infrastructure. >>That's right. What you get is an end point, the ability to Mount your file system using NFS, or you can also manage your file system from any of our compute services in AWS. So not only directly on an instance, but also from our serverless compute models like AWS Lambda and far gays, and from our container services like ECS and EKS, and all of the infrastructure is completely managed by us. You don't see it, you don't need to worry about it. We scale it automatically for you. >>What was the catalyst for all this? I mean, you know, you got to tell me it's customers, but maybe you could give me some, some insight and add some, some color. Like, what would you decoded sort of what the customers were saying? Did you get inputs from a lot of different places, you know, and you had to put that together and shape it. Uh, tell us, uh, take us inside that sort of how you came to where you are >>Today. Well, you know, I guess at the end of the day, when you think about storage and particularly file system storage, customers always want more performance and they want lower costs. So we're constantly optimizing on both of those dimensions. How can we find a way to deliver more value and lower cost to customers, but also meet the performance needs that their workloads have. And what we found in talking to customers, particularly the customers that EFS targets, they are application administrators, their dev ops practitioners, their data scientists, they have a job they want to do. They're not typically storage specialists. They don't want to have know or learn a lot about the bowels of storage architecture, and how to optimize for what their applications need. They want to focus on solving the business problems. They're focused on whatever those are >>You meaning, for instance. So you took tiering is obvious. You're tiering to lower cost storage, serverless. I'm not provisioning, you know, servers, myself, the system I'm just paying for what I use. The elasticity is a factor. So I'm not having to over provision. And I think I'm hearing, I don't have to spend my time turning knobs. You've talked about that before, because I don't know how much time is spent, you know, tuning systems, but it's gotta be at least 15 to 20% of the storage admins time. You're eliminating that as well. Is that what you mean by sort of cost optimum? Absolutely. >>So we're, we're providing the scale of capacity of performance that customer applications need as they needed without the customer needing to know exactly how to configure the service, to get what they need. We're dealing with changing workloads and changing access patterns. And we're optimizing their storage costs. As at the same time, >>When you guys step back, you get to the whiteboard out, say, okay, what's the north star that you're working because you know, you set the north star. You don't want to keep revisiting that, right? This is we're moving in this direction. How do we get there might change, but what's your north star? Where do you see the future? >>Yeah, it's really all about delivering simple file system storage that just works. And that sounds really easy, but there's a lot of nuance and complexity behind it, but customers don't want to have to worry about how it works. They just need it to work. And we, our goal is to deliver that for a super broad cross section of applications so that customers don't need to worry about how they performance tune or how they cost optimize. We deliver that value for them. >>Yeah. So I'm going to actually follow up on that because I feel like, you know, when you listen to Werner Vogels talk, he gives takes you inside. It's a plumbing sometimes. So what is the, what is that because you're right. That it, it sounds simple, but it's not. And as I said up front file systems, getting that right is really, really challenging. So technically what's the challenges, is it doing this at scale? And, and, and, and, and, and having some, a consistent experience for customers, there's >>Always a challenge to doing what we do at scale. I mean, the elasticity is something that we provide to our customers, but ultimately we have to take their data as bits and put them into Adams at some point. So we're managing infrastructure on the backend to support that. And we also have to do that in a way that delivers something that's cost-effective for customers. So there's a balance and a natural tension there between things like elasticity and simplicity, performance, cost, availability, and durability, and getting that balance right. And being able to cover the maximum cross section of all those things. So for the widest set of workloads, we see that as our job and we're delivering value, and we're doing that >>For our customers. Then of course, it was a big part of that. And of course, when we talk about, you know, the taking away the, the need for tuning, but, but you got to get it right. I mean, you, you, you can't, you can't optimize for every single use case. Right. But you can give great granularity to allow those use cases to be supported. And that seems to be sort of the balancing act that you guys so >>Well, absolutely. It's focused on being a general purpose file system. That's going to work for a broad cross section of, of applications and workloads. >>Right. Right. And that's, that's what customers want. You know, generally speaking, you go after that, that metal Dunkin, I'll give you the last word. >>I just encourage people to come and try out EFS it's as simple as a single click in our console to create a file system and get started. So come give it a, try the >>Button Duncan. Thanks so much for coming back to the cube. It's great to see you again. Thanks, Dave. All right. And keep it right there for more great content from AWS storage day from Seattle.

(gentle music) >> We all know that virtually every organization is using the cloud in some way, shape or form. But those same organizations are building or maybe buying abstraction layers that attempt to hide the underlying complexity of these clouds. Which are now connected to on-prem workloads, they're in hybrid models, spanning across multiple clouds, and bleeding out to the edge. Now, while such an approach is extensively simplifies technology, provisioning and management, it brings challenges. And these challenges are fundamentally data problems. For example, with the sprawling clouds, how do you track sensitive data and know where it lives? How do you ensure compliance and privacy protections in a world of ever-changing regulations? How can you securely share data in an increasingly decentralized environment? How can you identify gaps in security policies and how can organizations identify and stop exfiltration in this complex environment? And, oh, by the way, very importantly, how can this all be automated? Because the number one challenges that CSOs face is a lack of talent. Hello everyone, this is Dave Vellante, and welcome to this CUBE conversation where we profile emerging technologies, innovative startups and disruptive trends in enterprise tech. And today we're pleased to welcome two guests from a really interesting firm trying to solve many of these problems. With us are Dr. Noah Johnson, who was the co-founder and CTO. And April Mitchell, head of engineering, both from Dasera. Folks, welcome April. Great to see you again. >> It's a pleasure to be here. Thanks, Dave. >> Okay Noah, let me start with you. I got to ask you, is security in your mind a do-over? >> Hey Dave. Thanks for having us. Great to be here. So yeah, you hear the adage a lot today, security is broken. And certainly if you look at the number of data breaches and misuses of data in the last few years, clearly something isn't working, right? Now, our view actually is that data security needs to be rethought, and kind of designed from the ground up for the modern way that the data is used. And that's exactly what we're doing. So we don't say, do over, so much as data security re-imagined, especially for the cloud. >> Yeah, you can't just rip and replace, but it's a little tongue in cheek there. But tell me more about the background of the company. Why did you and your co-founders start the firm? Are those challenges that I laid out upfront, the ones that you're directly attacking? >> Yeah, we're attacking all of them. So the background of the company, our technology originally came from PhD work that I did while I was studying at UC Berkeley. So I've spent most of the last decade or so looking at different cybersecurity problems. And my dissertation specifically focused on, how do you secure sensitive data while still allowing people to access it in a flexible way? As part of that work, I was able to collaborate with a big tech company, Fortune 500 company, who were facing very similar problems internally. They needed to get a handle on their data. And so through that kind of research collaboration, we built a platform that was able to track data and monitor how the data was used to better protect it while still allowing the company to be data-driven. They ended up deploying the system at scale. And so this was really strong, at least initial validation. The approach that we're using at Dasera actually is quite effective and sound. So since then, we've talked to hundreds of other CSOs and security teams, and really sort of gotten a deeper appreciation for the magnitude of the problem today. No person that we've spoken to has high confidence in their data security. And we can dig into the reasons for that. It's not for lack of effort, it's that this is a very hard problem, especially with the moving to the cloud. >> Yeah, I mean, trust is popping up on the NPS surveys. It's like the number one factor today. April, let's bring you into the discussion. You and I met early last decade and we've followed your career since then. What attracted you to Dasera? >> Yeah, that's a great question, Dave. I've spent my career at Fortune 10 companies with 15,000 plus employees. What made me take this step to go to all the magnitude, a smaller company and team? And I would say it was an easy choice and I was driven by a bold vision, the right team and an innovators heart. When I had a conversation with Ani, the CEO and Noah's co-founder, Ani and I crossed paths back at HP, and he had the opportunity to work with myself and one of my collaborators. And I'd say at the time we were the two co-founders running our own little two-person startup within HP labs, delivering consumer web services. And Ani and I connected then. And we knew we wanted to have that chance to work together in the future. And I was blessed with the opportunity to go from analytics, to programmability at HP at Cisco. And when Ani called me up just a little over two months ago, and he told me about Dasera, immediately I was interested. Data security is a wonderful hot space with so many challenges, and that innovation and the challenge from a real research perspective is what drew me to Dasera. And I had the conversation with Noah. And we went deep into differential privacy and the cracks of his PhD research. And I understood there, this company is built on very strong bonds. And really, to be successful, it's about the team. You have to have a diverse team with great experience. And when I talked to every single one of the team members, they shared a vision and they shared a passion. And you know me, I love being a part of a strong team and I love building strong teams. And that's exactly what we're doing here at Dasera. >> Thank you for that April. So Noah, give us the north star. Like early on, you guys got to focus in on where you're headed. What is that north star? >> Our goal is to really solve data security. You know, we touched on earlier, clearly current solutions aren't working. We think we have a very innovative solution that is designed specifically for where data lives today, which is the cloud. We see ourselves as being the kind of gold standard for tracking and managing and securing data in the cloud across the entire life cycle. You know, from the point the data is created to all of the different ways that data is used, to when the data is deleted, we want to build a system that lets companies for the first time, get that visibility, create that feedback loop between the data users, the different security stakeholders, the legal teams. Help them make better, more informed security decisions by providing that visibility. >> So April, I use this chart sometimes when I do segments on security. I think it's from Optiv and it's this, it shows all the different segments and this is a very fragmented market space. So I'm wondering, like for first of all, like who's the enemy, I mean, who you're trying to attack? But it's so fragmented, maybe there isn't one. But from an engineering standpoint, part two of the question is, what are the really gnarly problems that you're focusing on? But talk about part A first, if you would. Who are you targeting here? >> Absolutely. I would say the best defense is a really good offense. And how are we approaching this problem differently? And there are many data security tools out there. Many processes, from access control to DLP, but we still had 4,000 events, 4,000 breaches in the last year alone. So we can't continue to expect different outcomes by using these same approaches. So that's where we are changing the story. And we have a bold purpose. We don't want to be a typical existing cybersecurity company. We want to take the approach of treating data security as sacred, we want to make the world a safer place, and we want to do that by securing data across its life cycle. Creation to deletion. You asked about the gnarly challenges that are out there. To do that right, you have to do it at speed. You've got to do it in real time and you have to do it at scale. And those are definitely the challenges that we're running into right now from an engineering perspective. >> So Noah, when you looked at the landscape, you saw, as April said, it was just so many different tools out there. How do you describe your difference in the marketplace? And April, please chime in as well. >> Sure. Yeah. So everyone has a slightly different approach. April touched on this earlier. We want to fix data security. So in some sense, we're all on the same team. We have different views of the most effective way of solving this problem, but ultimately everyone wants to solve the same problem. I would say, we're the only ones that give a comprehensive look at the entire data life cycle. So if you look at other similar security offerings, a lot of players are focused on just access control, right? Or data loss prevention, or specific features like encryption. And these are all really important technologies, but they're not sufficient, right? These are technologies that have been in use for the last decade and yet we still see data breaches on a daily basis. And the reason for that is, even if you have those systems in place, there's a lot that can go wrong between when someone is granted access to all of the different ways they consume and share the data. And so where we're unique is we give this holistic picture of the data end to end. And we don't necessarily replace those other solutions. Actually, we compliment them. Our system can tell you, if you have an encryption solution in place, are you encrypting the right data, right? Are you using it the right way? So you get more value out of those tools. Or if you have access control, our platform can be a set of guard rails or kind of a backstop that can let you know, are those access control rules properly configured, are certain users over privileged, and so forth. So really providing that context, like I said earlier, to make better security decisions. That's where we're differentiated. That's kind of our unique view of how to solve the problem. >> April, anything you'd add to that? It sounds like you're a platform for all these tools. I feel like I need that for my apps. But what's the secret sauce there? >> Yeah. I think the secret sauce is that we've learned from the challenges that our customers are facing. We have an approach where we want to rapidly innovate and rapidly validate. And our team is doing that. Noah mentioned a couple of the key features. I'm going to add a few more, because really when you're making a choice, what should I use, you've got to start with, what do you want to protect? Your data and your people? How can we help you protect that? Well, we can help you manage data sprawl. You'd be surprised by how many customers on the cloud are really interested, or use our product for the first time and go, "Oh my gosh, I did not know that that was there. When did that get there? How did it get copied there? Why is it there?" You know, and they're asking these questions. So we want to help you track that sprawl of your data. We want to monitor the data when it's in use. How are people growing it? How are your employees accessing it? How are they using it? Are they using it in the right way? Are they using it in the right way today? Are they using it on the right way tomorrow based the permissions? And we can give you that risk analysis and that perspective. We also want to let you know that when the data's sprawls, when there's a new copy that's stored in the new data store, is it configured the right way? Are you protecting it the right way? We can analyze that for you as well. So really the completeness of the features from the end to end solution, you can't protect across the entire data life cycle from creation to deletion, unless you're truly connecting and understanding how the data is being used. >> Great. Thank you. Noah, what's the ideal customer look like? Big, small, different industries? Will you give us the ICP? >> So as far as industries, our view is, a data breaches is a data breach. So any company that collects data and needs to protect it would benefit from our solution. I will say specifically, organizations that are cloud first and data-driven. Meaning they collect a lot of data and need to use that data, especially if that data is sensitive. So think B to C companies, retail, e-commerce, social media, finance, any company that collects consumer data, there are legal obligations, security obligations, kind of a higher standard of care that's required for that data. And that's where we can really help. So we're seeing traction actually from all of these industries. As far as the ideal user profile, we are targeting data security professionals. But we are a platform. We are a collaboration platform. Our system is designed to let different stakeholders within the organization, work together. From the security team, to the legal team, to the different data custodians, they can all collaborate seamlessly within the platform using that context that we're stitching together about the data flow. >> That last point is important because it used to be, it was the SecOps team. It was their problem. And now it's IT, it's security, it's legal, it's the line of business. And then the first point you made about cloud first and data-driven, that's good news for your term. Because if you're not cloud first and data-driven, you're probably not going to be in business by the end of the decade. So, how about the business case? You know, your startup, the ideal startup situation is you're 10X the value at 1/10 the price. Now, maybe in your case it's a little different 'cause you're taking that holistic view as opposed to one narrow view. But what's the justification? Lay out the ROI. >> Yeah. So we've designed the platform actually to be very quick time to value and easy to deploy. The platform is fully automated, has built in policies and machine learning. So you spin it up and it will automatically discover the data stores, it will go and crawl the data to automatically classify it. And so now you've already solved the problem of just data sprawl, knowing what data is out there. And then we can show customers, here's how the databases are configured, is the data sufficiently protected, here's how employees are interacting with the data. And then finally optionally to write policies and workflows to make sure that there's a process in place to protect the data across its entire life cycle. So there's sort of an evolution of different features. So there's kind of a maturity evolution from just, number one, identifying the data, or like we say, you can't protect what you don't know exists, to protecting it and identifying whether there are any security risks and compliance gaps. And then finally automatic proactive protection and remediation by security policies. >> So where are you guys in terms of the maturity? Obviously it's early days, but where are you in terms of product market fit? Have you nailed that? Still trying to figure that out? I know you've raised around 9 million, you're out of stealth. You give us a sense of the maturity curve. >> I can jump in on that one and speak a bit about our first customers. And then Noah can add more detail as well. But we're seeing these cloud first organizations, the CSOs, the chief security or privacy officers coming to us because they know that traditional approaches aren't working. We are here, we are ready to engage. We aren't just grabbing, what's coming, we're talking about what we have now. And we will sit arm to arm with you and made sure that we are solving the challenges that your team is facing right now. And that's where we're getting early feedback. That's where we've really been able to showcase some new innovations and to validate and move from there. But I would say, if you're interested in talking to us, please call, please visit the website and make that connection. Because we're not stealth, we're not hiding. We're engaging and definitely have a offering that is ready to be used. >> So okay, so you're in market with that offering. What do I buy from you? Is it a SAS, is it a subscription, is it a service? >> Yeah, so we have a few different product offerings and deployment models, depending on where the data's stored and the environment that the company wants to run the software. So we support on-prem, we also have a SAS offering as well. >> Okay, and that runs in the cloud, obviously, the SAS offering, or you can sort of put it as in a require on appliance? How do I deploy it on on-prem? >> No appliance runs. Runs purely in the cloud. And within an hour to onboard, to connect to the environment and to get a scan up and running. >> And it's status of the company, am I right, I think you've raised like $9 million. Head count, anything you can share in that regard? Are you hiring? I'm sure you are. >> We're growing very quickly. There's been tremendous traction as April mentioned earlier, and we're super excited about the opportunity ahead of us. It's clear we've tackled the very big problem that is still unsolved. So we have big plans and we fortunately have been able to raise some capital to help us build out the team, to add the capabilities that we need to fully solve this problem end to end. So we're well on our way, but it's a journey. This is an unsolved problem for a reason, right? It's quite complex. And we've got a great headstart, we've got a great approach, we've got some great early customers, but there's a way to go still. >> And I'll use that opportunity to say, yes, we are hiring. And if you're interested in this space, if you want to learn from a team of experts, but also grow your skills and take on some new challenges, then please go to the website and check out the current positions that we have. Dropped me ping through any of the social media networks, 'cause we'd love to hear from you. >> Great. Website is Dasera, D-A-S-E-R-A. All right. So check it out. Guys. great to have you on. Thanks so much. Best of luck. We'll be tracking you, and really congratulations on getting to this point. And I know you have a lot more work to do, but really exciting times, I'm sure, for you. >> Thanks Dave. >> It's a pleasure to see you this way and hopefully in person soon. >> Hopefully. Yeah, absolutely. Hopefully in '21. We'll see. We'll see. Thank you for watching everybody. This is Dave Vellante for the CUBE, we'll see you next time. (gentle music)