(Introductory music) >> Hello everyone, welcome to theCUBE's coverage of Mobile World Congress (MWC) and also SiliconANGLEs news coverage. Welcome to SiliconANGLEs news update for MWC. I'm John Furrier, host of theCUBE and reporter with SiliconANGLE News Today. Google showcasing new updates for Android and wearables at MWC. Kind of going after the old Apple-like functionality. Google has announced some new updates for Android and wearables at MWC and Barcelona. The new features are aimed at enhancing user productivity, connectivity and overall enjoyment across various devices for Chromebooks and all their Android devices. This is their answer to be Apple-like. New features include updates to Google Keep, audio enhancements, instant pairing of Chromebooks, headphones, new emojis, smartphones, more wallet options, and greater accessibility options. These features designed to bridge the gap between different devices that people use together often such as watches and phones or laptops or headphones. Fast Pair, another feature which allows new Bluetooth headphones to be connected to a Chromebook with just one tap. If the headphones are already set up with Android phone, the Chromebook will automatically connect to them with no additional setup. And finally, Google Keep taking notes for you that app - very cool. New features include widgets for Android screens, making it easier for users to make to-do lists from their mobile devices and Smartwatches phones. So that's the big news there. And it's really about Apple-like functionality and they have added things to their meat, which is new backgrounds and then filters that's kind of a Zoom clone. So here you got Android, Google adding stuff to their wallet. They are really stepping up their game and they want to be more mobile in at a telecom conference like this. They can see them upping their game to try to compete with Apple. And that's the update from from Google, Android and Chromebook updates. Stay tuned for more coverage. Check out SiliconANGLE.com for our special report on Mobile World Congress and Barcelona. Got theCUBE team - Dave Vellante, Lisa Martin, the whole gang is there for four days of live coverage. Check that out on theCUBE.net (closing music)

(gentle music) >> Hello, everyone. Welcome to this Cube Conversation. I'm John Furrier, host of theCUBE here, in our Palo Alto Studios. We're featuring OctoML. I'm with the CEO, Luis Ceze. Chief Executive Officer, Co-founder of OctoML. I'm John Furrier of theCUBE. Thanks for joining us today. Luis, great to see you. Last time we spoke was at "re:MARS" Amazon's event. Kind of a joint event between (indistinct) and Amazon, kind of put a lot together. Great to see you. >> Great to see you again, John. I really have good memories of that interview. You know, that was definitely a great time. Great to chat with you again. >> The world of ML and AI, machine learning and AI is really hot. Everyone's talking about it. It's really great to see that advance. So I'm looking forward to this conversation but before we get started, introduce who you are in OctoML. >> Sure. I'm Luis Ceze, Co-founder and CEO at OctoML. I'm also professor of Computer Science at University of Washington. You know, OctoML grew out of our efforts on the Apache CVM project, which is a compiler in runtime system that enables folks to run machine learning models in a broad set of harder in the Edge and in the Cloud very efficiently. You know, we grew that project and grew that community, definitely saw there was something to pain point there. And then we built OctoML, OctoML is about three and a half years old now. And the mission, the company is to enable customers to deploy models very efficiently in the Cloud. And make them, you know, run. Do it quickly, run fast, and run at a low cost, which is something that's especially timely right now. >> I like to point out also for the folks 'casue they should know that you're also a professor in the Computer Science department at University of Washington. A great program there. This is a really an inflection point with AI machine learning. The computer science industry has been waiting for decades to advance AI with all this new cloud computing, all the hardware and silicon advancements, GPUs. This is the perfect storm. And you know, this the computer science now we we're seeing an acceleration. Can you share your view, and you're obviously a professor in that department but also, an entrepreneur. This is a great time for computer science. Explain why. >> Absolutely, yeah, no. Just like the confluence of you know, advances in what, you know, computers can do as devices to computer information. Plus, you know, advances in AI that enable applications that you know, we thought it was highly futuristic and now it's just right there today. You know, AI that can generate photo realistic images from descriptions, you know, can write text that's pretty good. Can help augment, you know, human creativity in a really meaningful way. So you see the confluence of capabilities and the creativity of humankind into new applications is just extremely exciting, both from a researcher point of view as well as an entrepreneur point of view, right. >> What should people know about these large language models we're seeing with ChatGPT and how Google has got a lot of work going on that air. There's been a lot of work recently. What's different now about these models, and why are they so popular and effective now? What's the difference between now, and say five years ago, that makes it more- >> Oh, yeah. It's a huge inflection on their capabilities, I always say like emergent behavior, right? So as these models got more complex and our ability to train and deploy them, you know, got to this point... You know, they really crossed a threshold into doing things that are truly surprising, right? In terms of generating, you know, exhalation for things generating tax, summarizing tax, expending tax. And you know, exhibiting what to some may look like reasoning. They're not quite reasoning fundamentally. They're generating tax that looks like they're reasoning, but they do it so well, that it feels like was done by a human, right. So I would say that the biggest changes that, you know, now, they can actually do things that are extremely useful for business in people's lives today. And that wasn't the case five years ago. So that's in the model capabilities and that is being paired with huge advances in computing that enabled this to be... Enables this to be, you know, actually see line of sites to be deployed at scale, right. And that's where we come in, by the way, but yeah. >> Yeah, I want to get into that. And also, you know, the fusion of data integrating data sets at scales. Another one we're seeing a lot of happening now. It's not just some, you know, siloed, pre-built data modeling. It's a lot of agility and a lot of new integration capabilities of data. How is that impacting the dynamics? >> Yeah, absolutely. So I'll say that the ability to either take the data that has that exists in training a model to do something useful with it, and more interestingly I would say, using baseline foundational models and with a little bit of data, turn them into something that can do a specialized task really, really well. Created this really fast proliferation of really impactful applications, right? >> If every company now is looking at this trend and I'm seeing a lot... And I think every company will rebuild their business with machine learning. If they're not already doing it. And the folks that aren't will probably be dinosaurs will be out of business. This is a real business transformation moment where machine learning and AI, as it goes mainstream. I think it's just the beginning. This is where you guys come in, and you guys are poised for handling this frenzy to change business with machine learning models. How do you guys help customers as they look at this, you know, transition to get, you know, concept to production with machine learning? >> Great. Great questions, yeah, so I would say that it's fair to say there's a bunch of models out there that can do useful things right off the box, right? So and also, the ability to create models improved quite a bit. So the challenge now shifted to customers, you know. Everyone is looking to incorporating AI into their applications. So what we do for them is to, first of all, how do you do that quickly, without needing highly specialized, difficult to find engineering? And very importantly, how do you do that at cost that's accessible, right? So all of these fantastic models that we just talked about, they use an amount of computing that's just astronomical compared to anything else we've done in the past. It means the costs that come with it, are also very, very high. So it's important to enable customers to, you know, incorporate AI into their applications, to their use cases in a way that they can do, with the people that they have, and the costs that they can afford, such that they can have, you know, the maximum impacting possibly have. And finally, you know, helping them deal with hardware availability, as you know, even though we made a lot of progress in making computing cheaper and cheaper. Even to this day, you know, you can never get enough. And getting an allocation, getting the right hardware to run these incredibly hungry models is hard. And we help customers deal with, you know, harder availability as well. >> Yeah, for the folks watching as a... If you search YouTube, there's an interview we did last year at "re:MARS," I mentioned that earlier, just a great interview. You talked about this hardware independence, this traction. I want to get into that, because if you look at all the foundation models that are out there right now, that are getting traction, you're seeing two trends. You're seeing proprietary and open source. And obviously, open source always wins in my opinion, but, you know, there's this iPhone moment and android moment that one of your investors John Torrey from Madrona, talked about was is iPhone versus Android moment, you know, one's proprietary hardware and they're very specialized high performance and then open source. This is an important distinction and you guys are hardware independent. What's the... Explain what all this means. >> Yeah. Great set of questions. First of all, yeah. So, you know, OpenAI, and of course, they create ChatGPT and they offer an API to run these models that does amazing things. But customers have to be able to go and send their data over to OpenAI, right? So, and run the model there and get the outputs. Now, there's open source models that can do amazing things as well, right? So they typically open source models, so they don't lag behind, you know, these proprietary closed models by more than say, you know, six months or so, let's say. And it means that enabling customers to take the models that they want and deploy under their control is something that's very valuable, because one, you don't have to expose your data to externally. Two, you can customize the model even more to the things that you wanted to do. And then three, you can run on an infrastructure that can be much more cost effective than having to, you know, pay somebody else's, you know, cost and markup, right? So, and where we help them is essentially help customers, enable customers to take machine learning models, say an open source model, and automate the process of putting them into production, optimize them to run with the right performance, and more importantly, give them the independence to run where they need to run, where they can run best, right? >> Yeah, and also, you know, I point out all the time that, you know, there's never any stopping the innovation of hardware silicon. You're seeing cloud computing more coming in there. So, you know, being hardware independent has some advantages. And if you look at OpenAI, for instance, you mentioned ChatGPT, I think this is interesting because I think everyone is scratching their head, going, "Okay, I need to move to this new generation." What's your pro tip and advice for folks who want to move to, or businesses that want to say move to machine learning? How do they get started? What are some of the considerations they need to think about to deploy these models into production? >> Yeah, great though. Great set of questions. First of all, I mean, I'm sure they're very aware of the kind of things that you want to do with AI, right? So you could be interacting with customers, you know, automating, interacting with customers. It could be, you know, finding issues in production lines. It could be, you know... Generating, you know, making it easier to produce content and so on. Like, you know, customers, users would have an idea what they want to do. You know, from that it can actually determine, what kind of machine learning models would solve the problem that would, you know, fits that use case. But then, that's when the hard thing begins, right? So when you find a model, identify the model that can do the thing that you wanted to do, you need to turn that into a thing that you can deploy. So how do you go from machine learning model that does a thing that you need to do, to a container with the right executor, the artifact they can actually go and deploy, right? So we've seen customers doing that on their own, right? So, and it's got a bit of work, and that's why we are excited about the automation that we can offer and then turn that into a turnkey problem, right? So a turnkey process. >> Luis, talk about the use cases. If I don't mind going and double down on the previous answer. You got existing services, and then there's new AI applications, AI for applications. What are the use cases with existing stuff, and the new applications that are being built? >> Yeah, I mean, existing itself is, for example, how do you do very smart search and auto completion, you know, when you are editing documents, for example. Very, very smart search of documents, summarization of tax, expanding bullets into pros in a way that, you know, don't have to spend as much human time. Just some of the existing applications, right? So some of the new ones are like truly AI native ways of producing content. Like there's a company that, you know, we share investors and love what they're doing called runwayyML, for example. It's sort of like an AI first way of editing and creating visual content, right? So you could say you have a video, you could say make this video look like, it's night as opposed to dark, or remove that dog in the corner. You can do that in a way that you couldn't do otherwise. So there's like definitely AI native use cases. And yet not only in life sciences, you know, there's quite a bit of advances on AI-based, you know, therapies and diagnostics processes that are designed using automated processes. And this is something that I feel like, we were just scratching the surface there. There's huge opportunities there, right? >> Talk about the inference and AI and production kind of angle here, because cost is a huge concern when you look at... And there's a hardware and that flexibility there. So I can see how that could help, but is there a cost freight train that can get out of control here if you don't deploy properly? Talk about the scale problem around cost in AI. >> Yeah, absolutely. So, you know, very quickly. One thing that people tend to think about is the cost is. You know, training has really high dollar amounts it tends over index on that. But what you have to think about is that for every model that's actually useful, you're going to train it once, and then run it a large number of times in inference. That means that over the lifetime of a model, the vast majority of the compute cycles and the cost are going to go to inference. And that's what we address, right? So, and to give you some idea, if you're talking about using large language model today, you know, you can say it's going to cost a couple of cents per, you know, 2,000 words output. If you have a million users active, you know, a day, you know, if you're lucky and you have that, you can, this cost can actually balloon very quickly to millions of dollars a month, just in inferencing costs. You know, assuming you know, that you actually have access to the infrastructure to run it, right? So means that if you don't pay attention to these inference costs and that's definitely going to be a surprise. And affects the economics of the product where this is embedded in, right? So this is something that, you know, if there's quite a bit of attention being put on right now on how do you do search with large language models and you don't pay attention to the economics, you know, you can have a surprise. You have to change the business model there. >> Yeah. I think that's important to call out, because you don't want it to be a runaway cost structure where you architected it wrong and then next thing you know, you got to unwind that. I mean, it's more than technical debt, it's actually real debt, it's real money. So, talk about some of the dynamics with the customers. How are they architecting this? How do they get ahead of that problem? What do you guys do specifically to solve that? >> Yeah, I mean, well, we help customers. So, it's first of all, be hyper aware, you know, understanding what's going to be the cost for them deploying the models into production and showing them the possibilities of how you can deploy the model with different cost structure, right? So that's where, you know, the ability to have hardware independence is so important because once you have hardware independence, after you optimize models, obviously, you have a new, you know, dimension of freedom to choose, you know, what is the right throughput per dollar for you. And then where, and what are the options? And once you make that decision, you want to automate the process of putting into production. So the way we help customers is showing very clearly in their use case, you know, how they can deploy their models in a much more cost-effective way. You know, when the cases... There's a case study that we put out recently, showing a 4x reduction in deployment costs, right? So this is by doing a mix optimization and choosing the right hardware. >> How do you address the concern that someone might say, Luis said, "Hey, you know, I don't want to degrade performance and latency, and I don't want the user experience to suffer." What's the answer there? >> Two things. So first of all, all of the manipulations that we do in the model is to turn the model to efficient code without changing the behavior of the models. We wouldn't degrade the experience of the user by having the model be wrong more often. And we don't change that at all. The model behaves the way it was validated for. And then the second thing is, you know, user experience with respect to latency, it's all about a maximum... Like, you could say, I want a model to run at 50 milliseconds or less. If it's much faster than 15 seconds, you're not going to notice the difference. But if it's lower, you're going to notice a difference. So the key here is that, how do you find a set of options to deploy, that you are not overshooting performance in a way that's going to lead to costs that has no additional benefits. And this provides a huge, a very significant margin of choices, set of choices that you can optimize for cost without degrading customer experience, right. End user experience. >> Yeah, and I also point out the large language models like the ChatGPTs of the world, they're coming out with Dave Moth and I were talking on this breaking analysis around, this being like, over 10X more computational intensive on capabilities. So this hardware independence is a huge thing. So, and also supply chain, some people can't get servers by the way, so, or hardware these days. >> Or even more interestingly, right? So they do not grow in trees, John. Like GPUs is not kind of stuff that you plant an orchard until you have a bunch and then you can increase it, but no, these things, you know, take a while. So, and you can't increase it overnight. So being able to live with those cycles that are available to you is not just important for all for cost, but also important for people to scale and serve more users at, you know, at whatever pace that they come, right? >> You know, it's really great to talk to you, and congratulations on OctaML. Looking forward to the startup showcase, we'll be featuring you guys there. But I want to get your personal opinion as someone in the industry and also, someone who's been in the computer science area for your career. You know, computer science has always been great, and there's more people enrolling in computer science, more diversity than ever before, but there's also more computer science related fields. How is this opening up computer science and where's AI going with the computers, with the science? Can you share your vision on, you know, the aperture, or the landscape of CompSci, or CS students, and opportunities. >> Yeah, no, absolutely. I think it's fair to say that computer has been embedded in pretty much every aspect of human life these days. Human life these days, right? So for everything. And AI has been a counterpart, it been an integral component of computer science for a while. And this medicines that happened in the last 10, 15 years in AI has shown, you know, new application has I think re-energized how people see what computers can do. And you, you know, there is this picture in our department that shows computer science at the center called the flower picture, and then all the different paddles like life sciences, social sciences, and then, you know, mechanical engineering, all these other things that, and I feel like it can replace that center with computer science. I put AI there as well, you see AI, you know touching all these applications. AI in healthcare, diagnostics. AI in discovery in the sciences, right? So, but then also AI doing things that, you know, the humans wouldn't have to do anymore. They can do better things with their brains, right? So it's permitting every single aspect of human life from intellectual endeavor to day-to-day work, right? >> Yeah. And I think the ChatGPT and OpenAI has really kind of created a mainstream view that everyone sees value in it. Like you could be in the data center, you could be in bio, you could be in healthcare. I mean, every industry sees value. So this brings up what I can call the horizontally scalable use constance. And so this opens up the conversation, what's going to change from this? Because if you go horizontally scalable, which is a cloud concept as you know, that's going to create a lot of opportunities and some shifting of how you think about architecture around data, for instance. What's your opinion on what this will do to change the inflection of the role of architecting platforms and the role of data specifically? >> Yeah, so good question. There is a lot in there, by the way, I should have added the previous question, that you can use AI to do better AI as well, which is what we do, and other folks are doing as well. And so the point I wanted to make here is that it's pretty clear that you have a cloud focus component with a nudge focused counterparts. Like you have AI models, but both in the Cloud and in the Edge, right? So the ability of being able to run your AI model where it runs best also has a data advantage to it from say, from a privacy point of view. That's inherently could say, "Hey, I want to run something, you know, locally, strictly locally, such that I don't expose the data to an infrastructure." And you know that the data never leaves you, right? Never leaves the device. Now you can imagine things that's already starting to happen, like you do some forms of training and model customization in the model architecture itself and the system architecture, such that you do this as close to the user as possible. And there's something called federated learning that has been around for some time now that's finally happening is, how do you get a data from butcher places, you do, you know, some common learning and then you send a model to the Edges, and they get refined for the final use in a way that you get the advantage of aggregating data but you don't get the disadvantage of privacy issues and so on. >> It's super exciting. >> And some of the considerations, yeah. >> It's super exciting area around data infrastructure, data science, computer science. Luis, congratulations on your success at OctaML. You're in the middle of it. And the best thing about its businesses are looking at this and really reinventing themselves and if a business isn't thinking about restructuring their business around AI, they're probably will be out of business. So this is a great time to be in the field. So thank you for sharing your insights here in theCUBE. >> Great. Thank you very much, John. Always a pleasure talking to you. Always have a lot of fun. And we both speak really fast, I can tell, you know, so. (both laughing) >> I know. We'll not the transcript available, we'll integrate it into our CubeGPT model that we have Luis. >> That's right. >> Great. >> Great. >> Great to talk to you, thank you, John. Thanks, man, bye. >> Hey, this is theCUBE. I'm John Furrier, here in Palo Alto, Cube Conversation. Thanks for watching. (gentle music)

(upbeat electronic music) >> Hello everyone, welcome to this CUBE conversation here from the studios in the CUBE in Palo Alto, California. I'm John Furrier, your host. We're featuring a startup, Astronomer. Astronomer.io is the URL, check it out. And we're going to have a great conversation around one of the most important topics hitting the industry, and that is the future of machine learning and AI, and the data that powers it underneath it. There's a lot of things that need to get done, and we're excited to have some of the co-founders of Astronomer here. Viraj Parekh, who is co-founder of Astronomer, and Paola Peraza Calderon, another co-founder, both with Astronomer. Thanks for coming on. First of all, how many co-founders do you guys have? >> You know, I think the answer's around six or seven. I forget the exact, but there's really been a lot of people around the table who've worked very hard to get this company to the point that it's at. We have long ways to go, right? But there's been a lot of people involved that have been absolutely necessary for the path we've been on so far. >> Thanks for that, Viraj, appreciate that. The first question I want to get out on the table, and then we'll get into some of the details, is take a minute to explain what you guys are doing. How did you guys get here? Obviously, multiple co-founders, sounds like a great project. The timing couldn't have been better. ChatGPT has essentially done so much public relations for the AI industry to kind of highlight this shift that's happening. It's real, we've been chronicalizing, take a minute to explain what you guys do. >> Yeah, sure, we can get started. So, yeah, when Viraj and I joined Astronomer in 2017, we really wanted to build a business around data, and we were using an open source project called Apache Airflow that we were just using sort of as customers ourselves. And over time, we realized that there was actually a market for companies who use Apache Airflow, which is a data pipeline management tool, which we'll get into, and that running Airflow is actually quite challenging, and that there's a big opportunity for us to create a set of commercial products and an opportunity to grow that open source community and actually build a company around that. So the crux of what we do is help companies run data pipelines with Apache Airflow. And certainly we've grown in our ambitions beyond that, but that's sort of the crux of what we do for folks. >> You know, data orchestration, data management has always been a big item in the old classic data infrastructure. But with AI, you're seeing a lot more emphasis on scale, tuning, training. Data orchestration is the center of the value proposition, when you're looking at coordinating resources, it's one of the most important things. Can you guys explain what data orchestration entails? What does it mean? Take us through the definition of what data orchestration entails. >> Yeah, for sure. I can take this one, and Viraj, feel free to jump in. So if you google data orchestration, here's what you're going to get. You're going to get something that says, "Data orchestration is the automated process" "for organizing silo data from numerous" "data storage points, standardizing it," "and making it accessible and prepared for data analysis." And you say, "Okay, but what does that actually mean," right, and so let's give sort of an an example. So let's say you're a business and you have sort of the following basic asks of your data team, right? Okay, give me a dashboard in Sigma, for example, for the number of customers or monthly active users, and then make sure that that gets updated on an hourly basis. And then number two, a consistent list of active customers that I have in HubSpot so that I can send them a monthly product newsletter, right? Two very basic asks for all sorts of companies and organizations. And when that data team, which has data engineers, data scientists, ML engineers, data analysts get that request, they're looking at an ecosystem of data sources that can help them get there, right? And that includes application databases, for example, that actually have in product user behavior and third party APIs from tools that the company uses that also has different attributes and qualities of those customers or users. And that data team needs to use tools like Fivetran to ingest data, a data warehouse, like Snowflake or Databricks to actually store that data and do analysis on top of it, a tool like DBT to do transformations and make sure that data is standardized in the way that it needs to be, a tool like Hightouch for reverse ETL. I mean, we could go on and on. There's so many partners of ours in this industry that are doing really, really exciting and critical things for those data movements. And the whole point here is that data teams have this plethora of tooling that they use to both ingest the right data and come up with the right interfaces to transform and interact with that data. And data orchestration, in our view, is really the heartbeat of all of those processes, right? And tangibly the unit of data orchestration is a data pipeline, a set of tasks or jobs that each do something with data over time and eventually run that on a schedule to make sure that those things are happening continuously as time moves on and the company advances. And so, for us, we're building a business around Apache Airflow, which is a workflow management tool that allows you to author, run, and monitor data pipelines. And so when we talk about data orchestration, we talk about sort of two things. One is that crux of data pipelines that, like I said, connect that large ecosystem of data tooling in your company. But number two, it's not just that data pipeline that needs to run every day, right? And Viraj will probably touch on this as we talk more about Astronomer and our value prop on top of Airflow. But then it's all the things that you need to actually run data and production and make sure that it's trustworthy, right? So it's actually not just that you're running things on a schedule, but it's also things like CICD tooling, secure secrets management, user permissions, monitoring, data lineage, documentation, things that enable other personas in your data team to actually use those tools. So long-winded way of saying that it's the heartbeat, we think, of of the data ecosystem, and certainly goes beyond scheduling, but again, data pipelines are really at the center of it. >> One of the things that jumped out, Viraj, if you can get into this, I'd like to hear more about how you guys look at all those little tools that are out. You mentioned a variety of things. You look at the data infrastructure, it's not just one stack. You've got an analytic stack, you've got a realtime stack, you've got a data lake stack, you got an AI stack potentially. I mean you have these stacks now emerging in the data world that are fundamental, that were once served by either a full package, old school software, and then a bunch of point solution. You mentioned Fivetran there, I would say in the analytics stack. Then you got S3, they're on the data lake stack. So all these things are kind of munged together. >> Yeah. >> How do you guys fit into that world? You make it easier, or like, what's the deal? >> Great question, right? And you know, I think that one of the biggest things we've found in working with customers over the last however many years is that if a data team is using a bunch of tools to get what they need done, and the number of tools they're using is growing exponentially and they're kind of roping things together here and there, that's actually a sign of a productive team, not a bad thing, right? It's because that team is moving fast. They have needs that are very specific to them, and they're trying to make something that's exactly tailored to their business. So a lot of times what we find is that customers have some sort of base layer, right? That's kind of like, it might be they're running most of the things in AWS, right? And then on top of that, they'll be using some of the things AWS offers, things like SageMaker, Redshift, whatever, but they also might need things that their cloud can't provide. Something like Fivetran, or Hightouch, those are other tools. And where data orchestration really shines, and something that we've had the pleasure of helping our customers build, is how do you take all those requirements, all those different tools and whip them together into something that fulfills a business need? So that somebody can read a dashboard and trust the number that it says, or somebody can make sure that the right emails go out to their customers. And Airflow serves as this amazing kind of glue between that data stack, right? It's to make it so that for any use case, be it ELT pipelines, or machine learning, or whatever, you need different things to do them, and Airflow helps tie them together in a way that's really specific for a individual business' needs. >> Take a step back and share the journey of what you guys went through as a company startup. So you mentioned Apache, open source. I was just having an interview with a VC, we were talking about foundational models. You got a lot of proprietary and open source development going on. It's almost the iPhone/Android moment in this whole generative space and foundational side. This is kind of important, the open source piece of it. Can you share how you guys started? And I can imagine your customers probably have their hair on fire and are probably building stuff on their own. Are you guys helping them? Take us through, 'cause you guys are on the front end of a big, big wave, and that is to make sense of the chaos, rain it in. Take us through your journey and why this is important. >> Yeah, Paola, I can take a crack at this, then I'll kind of hand it over to you to fill in whatever I miss in details. But you know, like Paola is saying, the heart of our company is open source, because we started using Airflow as an end user and started to say like, "Hey wait a second," "more and more people need this." Airflow, for background, started at Airbnb, and they were actually using that as a foundation for their whole data stack. Kind of how they made it so that they could give you recommendations, and predictions, and all of the processes that needed orchestrated. Airbnb created Airflow, gave it away to the public, and then fast forward a couple years and we're building a company around it, and we're really excited about that. >> That's a beautiful thing. That's exactly why open source is so great. >> Yeah, yeah. And for us, it's really been about watching the community and our customers take these problems, find a solution to those problems, standardize those solutions, and then building on top of that, right? So we're reaching to a point where a lot of our earlier customers who started to just using Airflow to get the base of their BI stack down and their reporting in their ELP infrastructure, they've solved that problem and now they're moving on to things like doing machine learning with their data, because now that they've built that foundation, all the connective tissue for their data arriving on time and being orchestrated correctly is happening, they can build a layer on top of that. And it's just been really, really exciting kind of watching what customers do once they're empowered to pick all the tools that they need, tie them together in the way they need to, and really deliver real value to their business. >> Can you share some of the use cases of these customers? Because I think that's where you're starting to see the innovation. What are some of the companies that you're working with, what are they doing? >> Viraj, I'll let you take that one too. (group laughs) >> So you know, a lot of it is... It goes across the gamut, right? Because it doesn't matter what you are, what you're doing with data, it needs to be orchestrated. So there's a lot of customers using us for their ETL and ELT reporting, right? Just getting data from other disparate sources into one place and then building on top of that. Be it building dashboards, answering questions for the business, building other data products and so on and so forth. From there, these use cases evolve a lot. You do see folks doing things like fraud detection, because Airflow's orchestrating how transactions go, transactions get analyzed. They do things like analyzing marketing spend to see where your highest ROI is. And then you kind of can't not talk about all of the machine learning that goes on, right? Where customers are taking data about their own customers, kind of analyze and aggregating that at scale, and trying to automate decision making processes. So it goes from your most basic, what we call data plumbing, right? Just to make sure data's moving as needed, all the ways to your more exciting expansive use cases around automated decision making and machine learning. >> And I'd say, I mean, I'd say that's one of the things that I think gets me most excited about our future, is how critical Airflow is to all of those processes, and I think when you know a tool is valuable is when something goes wrong and one of those critical processes doesn't work. And we know that our system is so mission critical to answering basic questions about your business and the growth of your company for so many organizations that we work with. So it's, I think, one of the things that gets Viraj and I and the rest of our company up every single morning is knowing how important the work that we do for all of those use cases across industries, across company sizes, and it's really quite energizing. >> It was such a big focus this year at AWS re:Invent, the role of data. And I think one of the things that's exciting about the open AI and all the movement towards large language models is that you can integrate data into these models from outside. So you're starting to see the integration easier to deal with. Still a lot of plumbing issues. So a lot of things happening. So I have to ask you guys, what is the state of the data orchestration area? Is it ready for disruption? Has it already been disrupted? Would you categorize it as a new first inning kind of opportunity, or what's the state of the data orchestration area right now? Both technically and from a business model standpoint. How would you guys describe that state of the market? >> Yeah, I mean, I think in a lot of ways, in some ways I think we're category creating. Schedulers have been around for a long time. I released a data presentation sort of on the evolution of going from something like Kron, which I think was built in like the 1970s out of Carnegie Mellon. And that's a long time ago, that's 50 years ago. So sort of like the basic need to schedule and do something with your data on a schedule is not a new concept. But to our point earlier, I think everything that you need around your ecosystem, first of all, the number of data tools and developer tooling that has come out industry has 5X'd over the last 10 years. And so obviously as that ecosystem grows, and grows, and grows, and grows, the need for orchestration only increases. And I think, as Astronomer, I think we... And we work with so many different types of companies, companies that have been around for 50 years, and companies that got started not even 12 months ago. And so I think for us it's trying to, in a ways, category create and adjust sort of what we sell and the value that we can provide for companies all across that journey. There are folks who are just getting started with orchestration, and then there's folks who have such advanced use case, 'cause they're hitting sort of a ceiling and only want to go up from there. And so I think we, as a company, care about both ends of that spectrum, and certainly want to build and continue building products for companies of all sorts, regardless of where they are on the maturity curve of data orchestration. >> That's a really good point, Paola. And I think the other thing to really take into account is it's the companies themselves, but also individuals who have to do their jobs. If you rewind the clock like 5 or 10 years ago, data engineers would be the ones responsible for orchestrating data through their org. But when we look at our customers today, it's not just data engineers anymore. There's data analysts who sit a lot closer to the business, and the data scientists who want to automate things around their models. So this idea that orchestration is this new category is right on the money. And what we're finding is the need for it is spreading to all parts of the data team, naturally where Airflow's emerged as an open source standard and we're hoping to take things to the next level. >> That's awesome. We've been up saying that the data market's kind of like the SRE with servers, right? You're going to need one person to deal with a lot of data, and that's data engineering, and then you're got to have the practitioners, the democratization. Clearly that's coming in what you're seeing. So I have to ask, how do you guys fit in from a value proposition standpoint? What's the pitch that you have to customers, or is it more inbound coming into you guys? Are you guys doing a lot of outreach, customer engagements? I'm sure they're getting a lot of great requirements from customers. What's the current value proposition? How do you guys engage? >> Yeah, I mean, there's so many... Sorry, Viraj, you can jump in. So there's so many companies using Airflow, right? So the baseline is that the open source project that is Airflow that came out of Airbnb, over five years ago at this point, has grown exponentially in users and continues to grow. And so the folks that we sell to primarily are folks who are already committed to using Apache Airflow, need data orchestration in their organization, and just want to do it better, want to do it more efficiently, want to do it without managing that infrastructure. And so our baseline proposition is for those organizations. Now to Viraj's point, obviously I think our ambitions go beyond that, both in terms of the personas that we addressed and going beyond that data engineer, but really it's to start at the baseline, as we continue to grow our our company, it's really making sure that we're adding value to folks using Airflow and help them do so in a better way, in a larger way, in a more efficient way, and that's really the crux of who we sell to. And so to answer your question on, we get a lot of inbound because they're... >> You have a built in audience. (laughs) >> The world that use it. Those are the folks who we talk to and come to our website and chat with us and get value from our content. I mean, the power of the opensource community is really just so, so big, and I think that's also one of the things that makes this job fun. >> And you guys are in a great position. Viraj, you can comment a little, get your reaction. There's been a big successful business model to starting a company around these big projects for a lot of reasons. One is open source is continuing to be great, but there's also supply chain challenges in there. There's also we want to continue more innovation and more code and keeping it free and and flowing. And then there's the commercialization of productizing it, operationalizing it. This is a huge new dynamic, I mean, in the past 5 or so years, 10 years, it's been happening all on CNCF from other areas like Apache, Linux Foundation, they're all implementing this. This is a huge opportunity for entrepreneurs to do this. >> Yeah, yeah. Open source is always going to be core to what we do, because we wouldn't exist without the open source community around us. They are huge in numbers. Oftentimes they're nameless people who are working on making something better in a way that everybody benefits from it. But open source is really hard, especially if you're a company whose core competency is running a business, right? Maybe you're running an e-commerce business, or maybe you're running, I don't know, some sort of like, any sort of business, especially if you're a company running a business, you don't really want to spend your time figuring out how to run open source software. You just want to use it, you want to use the best of it, you want to use the community around it, you want to be able to google something and get answers for it, you want the benefits of open source. You don't have the time or the resources to invest in becoming an expert in open source, right? And I think that dynamic is really what's given companies like us an ability to kind of form businesses around that in the sense that we'll make it so people get the best of both worlds. You'll get this vast open ecosystem that you can build on top of, that you can benefit from, that you can learn from. But you won't have to spend your time doing undifferentiated heavy lifting. You can do things that are just specific to your business. >> It's always been great to see that business model evolve. We used a debate 10 years ago, can there be another Red Hat? And we said, not really the same, but there'll be a lot of little ones that'll grow up to be big soon. Great stuff. Final question, can you guys share the history of the company? The milestones of Astromer's journey in data orchestration? >> Yeah, we could. So yeah, I mean, I think, so Viraj and I have obviously been at Astronomer along with our other founding team and leadership folks for over five years now. And it's been such an incredible journey of learning, of hiring really amazing people, solving, again, mission critical problems for so many types of organizations. We've had some funding that has allowed us to invest in the team that we have and in the software that we have, and that's been really phenomenal. And so that investment, I think, keeps us confident, even despite these sort of macroeconomic conditions that we're finding ourselves in. And so honestly, the milestones for us are focusing on our product, focusing on our customers over the next year, focusing on that market for us that we know can get valuable out of what we do, and making developers' lives better, and growing the open source community and making sure that everything that we're doing makes it easier for folks to get started, to contribute to the project and to feel a part of the community that we're cultivating here. >> You guys raised a little bit of money. How much have you guys raised? >> Don't know what the total is, but it's in the ballpark over $200 million. It feels good to... >> A little bit of capital. Got a little bit of cap to work with there. Great success. I know as a Series C Financing, you guys have been down. So you're up and running, what's next? What are you guys looking to do? What's the big horizon look like for you from a vision standpoint, more hiring, more product, what is some of the key things you're looking at doing? >> Yeah, it's really a little of all of the above, right? Kind of one of the best and worst things about working at earlier stage startups is there's always so much to do and you often have to just kind of figure out a way to get everything done. But really investing our product over the next, at least over the course of our company lifetime. And there's a lot of ways we want to make it more accessible to users, easier to get started with, easier to use, kind of on all areas there. And really, we really want to do more for the community, right, like I was saying, we wouldn't be anything without the large open source community around us. And we want to figure out ways to give back more in more creative ways, in more code driven ways, in more kind of events and everything else that we can keep those folks galvanized and just keep them happy using Airflow. >> Paola, any final words as we close out? >> No, I mean, I'm super excited. I think we'll keep growing the team this year. We've got a couple of offices in the the US, which we're excited about, and a fully global team that will only continue to grow. So Viraj and I are both here in New York, and we're excited to be engaging with our coworkers in person finally, after years of not doing so. We've got a bustling office in San Francisco as well. So growing those teams and continuing to hire all over the world, and really focusing on our product and the open source community is where our heads are at this year. So, excited. >> Congratulations. 200 million in funding, plus. Good runway, put that money in the bank, squirrel it away. It's a good time to kind of get some good interest on it, but still grow. Congratulations on all the work you guys do. We appreciate you and the open source community does, and good luck with the venture, continue to be successful, and we'll see you at the Startup Showcase. >> Thank you. >> Yeah, thanks so much, John. Appreciate it. >> Okay, that's the CUBE Conversation featuring astronomer.io, that's the website. Astronomer is doing well. Multiple rounds of funding, over 200 million in funding. Open source continues to lead the way in innovation. Great business model, good solution for the next gen cloud scale data operations, data stacks that are emerging. I'm John Furrier, your host, thanks for watching. (soft upbeat music)

>> John: Hello and welcome to this cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great conversation on making machine learning easier and more affordable in an era where everybody wants more machine learning and AI. We're featuring Neural Magic with the CEO is also Cube alumni, Brian Steve. CEO, Great to see you Brian. Thanks for coming on this cube conversation. Talk about machine learning. >> Brian: Hey John, happy to be here again. >> John: What a buzz that's going on right now? Machine learning, one of the hottest topics, AI front and center, kind of going mainstream. We're seeing the success of the, of the kind of NextGen capabilities in the enterprise and in apps. It's a really exciting time. So perfect timing. Great, great to have this conversation. Let's start with taking a minute to explain what you guys are doing over there at Neural Magic. I know there's some history there, neural networks, MIT. But the, the convergence of what's going on, this big wave hitting, it's an exciting time for you guys. Take a minute to explain the company and your mission. >> Brian: Sure, sure, sure. So, as you said, the company's Neural Magic and spun out at MIT four plus years ago, along with some people and, and some intellectual property. And you summarize it better than I can cause you said, we're just trying to make, you know, AI that much easier. And so, but like another level of specificity around it is. You know, in the world you have a lot of like data scientists really focusing on making AI work for whatever their use case is. And then the next phase of that, then they're looking at optimizing the models that they built. And then it's not good enough just to work on models. You got to put 'em into production. So, what we do is we make it easier to optimize the models that have been developed and trained and then trying to make it super simple when it comes time to deploying those in production and managing them. >> Brian: You know, we've seen this movie before with the cloud. You start to see abstractions come out. Data science we saw like was like the, the secret art of being like a data scientist now democratization of data. You're kind of seeing a similar wave with machine learning models, foundational models, some call it developers are getting involved. Model complexity's still there, but, but it's getting easier. There's almost like the democratization happening. You got complexity, you got deployment, it's challenges, cost, you got developers involved. So it's like how do you grow it? How do you get more horsepower? And then how do you make developers productive, right? So like, this seems to be the thread. So, so where, where do you see this going? Because there's going to be a massive demand for, I want to do more with my machine learning. But what's the data source? What's the formatting? This kind of a stack develop, what, what are you guys doing to address this? Can you take us through and demystify this, this wave that's hitting, that everyone's seeing? >> Brian: Yeah. Now like you said, like, you know, the democratization of all of it. And that brings me all the way back to like the roots of open source, right? When you think about like, like back in the day you had to build your own tech stack yourself. A lot of people probably probably don't remember that. And then you went, you're building, you're always starting on a body of code or a module that was out there with open source. And I think that's what I equate to where AI has gotten to with what you were talking about the foundational models that didn't really exist years ago. So you really were like putting the layers of your models together in the formulas and it was a lot of heavy lifting. And so there was so much time spent on development. With far too few success cases, you know, to get into production to solve like a business stereo technical need. But as these, what's happening is as these models are becoming foundational. It's meaning people don't have to start from scratch. They're actually able to, you know, the avant-garde now is start with existing model that almost does what you want, but then applying your data set to it. So it's, you know, it's really the industry moving forward. And then we, you know, and, and the best thing about it is open source plays a new dimension, but this time, you know, in the, in the realm of AI. And so to us though, like, you know, I've been like, I spent a career focusing on, I think on like the, not just the technical side, but the consumption of the technology and how it's still way too hard for somebody to actually like, operationalize technology that all those vendors throw at them. So I've always been like empathetic the user around like, you know what their job is once you give them great technology. And so it's still too difficult even with the foundational models because what happens is there's really this impedance mismatch between the development of the model and then where, where the model has to live and run and be deployed and the life cycle of the model, if you will. And so what we've done in our research is we've developed techniques to introduce what's known as sparsity into a machine learning model. It's already been developed and trained. And what that sparsity does is that unlocks by making that model so much smaller. So in many cases we can make a model 90 to 95% smaller, even smaller than that in research. So, and, and so by doing that, we do that in a way that preserves all the accuracy out of the foundational model as you talked about. So now all of a sudden you get this much smaller model just as accurate. And then the even more exciting part about it is we developed a software-based engine called Deep Source. And what that, what the Inference Runtime does is takes that now sparsified model and it runs it, but because you sparsified it, it only needs a fraction of the compute that it, that it would've needed otherwise. So what we've done is make these models much faster, much smaller, and then by pairing that with an inference runtime, you now can actually deploy that model anywhere you want on commodity hardware, right? So X 86 in the cloud, X 86 in the data center arm at the edge, it's like this massive unlock that happens because you get the, the state-of-the-art models, but you get 'em, you know, on the IT assets and the commodity infrastructure. That is where all the applications are running today. >> John: I want to get into the inference piece and the deep sparse you mentioned, but I first have to ask, you mentioned open source, Dave and I with some fellow cube alumnis. We're having a chat about, you know, the iPhone and Android moment where you got proprietary versus open source. You got a similar thing happening with some of these machine learning modules where there's a lot of proprietary things happening and there's open source movement is growing. So is there a balance there? Are they all trying to do the same thing? Is it more like a chip, you know, silicons involved, all kinds of things going on that are really fascinating from a science. What's your, what's your reaction to that? >> Brian: I think it's like anything that, you know, the way we talk about AI you think had been around for decades, but the reality is it's been some of the deep learning models. When we first, when we first started taking models that the brain team was working on at Google and billing APIs around them on Google Cloud where the first cloud to even have AI services was 2015, 2016. So when you think about it, it's really been what, 6 years since like this thing is even getting lift off. So I think with that, everybody's throwing everything at it. You know, there's tons of funded hardware thrown at specialty for training or inference new companies. There's legacy companies that are getting into like AI now and whether it's a, you know, a CPU company that's now building specialized ASEX for training. There's new tech stacks proprietary software and there's a ton of asset service. So it really is, you know, what's gone from nascent 8 years ago is the wild, wild west out there. So there's a, there's a little bit of everything right now and I think that makes sense because at the early part of any industry it really becomes really specialized. And that's the, you know, showing my age of like, you know, the early pilot of the two thousands, you know, red Hat people weren't running X 86 in enterprise back then and they thought it was a toy and they certainly weren't running open source, but you really, and it made sense that they weren't because it didn't deliver what they needed to at that time. So they needed specialty stacks, they needed expensive, they needed expensive hardware that did what an Oracle database needed to do. They needed proprietary software. But what happens is that commoditizes through both hardware and through open source and the same thing's really just starting with with AI. >> John: Yeah. And I think that's a great point before we to call that out because in any industry timing's everything, right? I mean I remember back in the 80s, late 80s and 90s, AI, you know, stuff was going on and it just wasn't, there wasn't enough horsepower, there wasn't enough tech. >> Brian: Yep. >> John: You mentioned some of the processing. So AI is this industry that has all these experts who have been itch scratching that itch for decades. And now with cloud and custom silicon. The tech fundamental at the lower end of the stack, if you will, on the performance side is significantly more performant. It's there you got more capabilities. >> Brian: Yeah. >> John: Now you're kicking into more software, faster software. So it just seems like we're at a tipping point where finally it's here, like that AI moment or machine learning and now data is, is involved. So this is where organizations I see really jumping in with the CEO mandate. Hey team, make ML work for us. Go figure it out. It's got to be an advantage for us. >> Brian: Yeah. >> John: So now they go, okay boss, we will. So what, what do they do? What's the steps does an enterprise take to get machine learning into their organizations? Cause you know, it's coming down from the boards, you know, how does this work for rob? >> Brian: Yeah. Like the, you know, the, what we're seeing is it's like anything, like it's, whether that was source adoption or whether that was cloud adoption, it always starts usually with one person. And increasingly it is the CEO, which realizes they're getting further behind the competition because they're not leaning in, you know, faster. But typically it really comes down to like a really strong practitioner that's inside the organization, right? And, that realizes that the number one goal isn't doing more and just training more models and and necessarily being proprietary about it. It's really around understanding the art of the possible. Something that's grounded in the art of the possible, what, what deep learning can do today and what business outcomes you can deliver, you know, if you can employ. And then there's well proven paths through that. It's just that because of where it's been, it's not that industrialized today. It's very much, you know, you see ML project by ML project is very snowflakey, right? And that was kind of the early days of open source as well. And so, we're just starting to get to the point where it's getting easier, it's getting more industrialized, there's less steps, there's less burdensome on developers, there's less burdensome on, on the deployment side. And we're trying to bring that, that whole last mile by saying, you know what? Deploying deep learning and AI models should be as easy as the as to deploy your application, right? You shouldn't have to take an extra step to deploy an AI model. It shouldn't have to require a new hardware, it shouldn't require a new process, a new DevOps model. It should be as simple as what you're already doing. >> John: What is the best practice for companies to effectively bring an acceptable level of machine learning and performance into their organizations? >> Brian: Yeah, I think like the, the number one start is like what you hinted at before is they, they have to know the use case. They have to, in most cases, you're going to find across every industry you know, that that problem's been tackled by some company, right? And then you have to have the best practice around fine-tuning the models already exist. So fine tuning that existing model. That foundational model on your unique dataset. You, you know, if you are in medical instruments, it's not good enough to identify that it's a medical instrument in the picture. You got to know what type of medical instrument. So there's always a fine tuning step. And so we've created open source tools that make it easy for you to do two things at once. You can fine tune that existing foundational model, whether that's in the language space or whether that's in the vision space. You can fine tune that on your dataset. And at the same time you get an optimized model that comes out the other end. So you get kind of both things. So you, you no longer have to worry about you're, we're freeing you from worrying about the complexity of that transfer learning, if you will. And we're freeing you from worrying about, well where am I going to deploy the model? Where does it need to be? Does it need to be on a device, an edge, a data center, a cloud edge? What kind of hardware is it? Is there enough hardware there? We're liberating you from all of that. Because what you want, what you can count on is there'll always be commodity capability, commodity CPUs where you want to deploy in abundance cause that's where your application is. And so all of a sudden we're just freeing you of that, of that whole step. >> John: Okay. Let's get into deep sparse because you mentioned that earlier. What inspired the creation of deep sparse and how does it differ from any other solutions in the market that are out there? >> Brian: Sure. So, so where unique is it? It starts by, by two things. One is what the industry's pretty good at from the optimization side is they're good at like this thing called quantization, which turns like, you know, big numbers into small numbers, lower precision. So a 32 bit representation of a, of AI weight into a bit. And they're good at like cutting out layers, which also takes away accuracy. What we've figured out is to take those, the industry techniques for those that are best practice, but we combined it with unstructured varsity. So by reducing that model by 90 to 95% in size, that's great because it's made it smaller. But we've taken that when it's the deep sparse engine, when you deploy it that looks at that model and says, because it's so much smaller, I no longer have to run the part of the model that's been essentially sparsified. So what that's done is, it's meant that you no longer need a supercomputer to run models because there's not nearly as much math and processing as there was before the model was optimized. So now what happens is, every CPU platform out there has, has an enormous amount of compute because we've sparsified the rest of it away. So you can pick a, you can pick your, your laptop and you have enough compute to run state-of-the-art models. The second thing that, and you need a software engine to do that cause it ignores the parts of the models. It doesn't need to run, which is what like specialized hardware can't do. The second part is it's then turned into a memory efficiency problem. So it's really around just getting memory, getting the models loaded into the cash of the computer and keeping it there. Never having to go back out to memory. So, so our techniques are both, we reduce the model size and then we only run the part of the model that matters and then we keep it all in cash. And so what that does is it gets us to like these, these low, low latency faster and we're able to increase, you know, the CPU processing by an order magnitude. >> John: Yeah. That low latency is key. And you got developers, you know, co coding super fast. We'll get to the developer angle in a second. I want to just follow up on this, this motivation behind the, the deep sparse because you know, as we were talking earlier before we came on camera about the old days, I mean, not too long ago, virtualization and VMware abstracted away the os from, from the hardware rights and the server virtualization changed the game. >> Brian: Yeah. >> John: And that basically invented cloud computing as we know it today. So, so we see that abstraction. >> Brian: Yeah. >> John: There seems to be a motivation behind abstracting the way the machine learning models away from the hardware. And that seems to be bringing advantages to the AI growth. Can you elaborate on, is that true? And it's, what's your comment? >> Brian: It's true. I think it's true for us. I don't think the industry's there yet, honestly. Cause I think the industry still is of that mindset that if I took, if it took these expensive GPUs to train my model, then I want to run my model on those same expensive GPUs. Because there's often like not a separation between the people that are developing AI and the people that have to manage and deploy at where you need it. So the reality is, is that that's everything that we're after. Like, do we decrease the cost? Yes. Do we make the models smaller? Yes. Do we make them faster? A yes. But I think the most amazing power is that we've turned AI into a docker based microservice. And so like who in the industry wants to deploy their apps the old way on a os without virtualization, without docker, without Kubernetes, without microservices, without service mesh without serverless. You want all those tools for your apps by converting AI models. So they can be run inside a docker container with no apologies around latency and performance cause it's faster. You get the best of that whole world that you just talked about, which is, you know, what we're calling, you know, software delivered AI. So now the AI lives in the same world. Organizations that have gone through that digital cloud transformation with their app infrastructure. AI fits into that world. >> John: And this is where the abstraction concepts matter. When you have these inflection points, the convergence of compute data, machine learning that powers AI, it really becomes a developer opportunity. Because now applications and businesses, when they actually go through the digital transformation, their businesses are completely transformed. There is no IT. Developers are the application. They are the company, right? So AI will be part of whatever business or app will be out there. So there is a application developer angle here. Brian, can you explain >> Brian: Oh completely. >> John: how they're going to use this? Because you mentioned docker container microservice, I mean this really is an insane flipping of the script for developers. >> Brian: Yeah. >> John: So what's that look like? >> Brian: Well speak, it's because like AI's kind of, I mean, again, like it's come so fast. So you figure there's my app team and here's my AI team, right? And they're in different places and the AI team is dragging in specialized infrastructure in support of that as well. And that's not how app developers think. Like they've ran on fungible infrastructure that subtracted and virtualized forever, right? And so what we've done is we've, in addition to fitting into that world that they, that they like, we've also made it simple for them for they don't have to be a machine learning engineer to be able to experiment with these foundational models and transfer learning 'em. We've done that. So they can do that in a couple of commands and it has a simple API that they can either link to their application directly as a library to make difference calls or they can stand it up as a standalone, you know, scale up, scale out inference server. They get two choices. But it really fits into that, you know, you know that world that the modern developer, whether they're just using Python or C or otherwise, we made it just simple. So as opposed to like Go learn something else, they kind of don't have to. So in a way though, it's made it. It's almost made it hard because people expect when we talk to 'em for the first time to be the old way. Like, how do you look like a piece of hardware? Are you compatible with my existing hardware that runs ML? Like, no, we're, we're not. Because you don't need that stack anymore. All you need is a library called to make your prediction and that's it. That's it. >> John: Well, I mean, we were joking on Twitter the other day with someone saying, is AI a pet or a cattle? Right? Because they love their, their AI bots right now. So, so I'd say pet there. But you look at a lot of, there's going to be a lot of AI. So on a more serious note, you mentioned in microservices, will deep sparse have an API for developers? And how does that look like? What do I do? >> Brian: Yeah. >> John: tell me what my, as a developer, what's the roadmap look like? What's the >> Brian: Yeah, it, it really looks, it really can go in both modes. It can go in a standalone server mode where it handles, you know, rest API and it can scale out with ES as the workload comes up and scale back and like try to make hardware do that. Hardware may scale back, but it's just sitting there dormant, you know, so with this, it scales the same way your application needs to. And then for a developer, they basically just, they just, the PIP install de sparse, you know, has one commanded to do an install, and then they do two calls, really. The first call is a library call that the app makes to create the model. And models really already trained, but they, it's called a model create call. And the second command they do is they make a call to do a prediction. And it's as simple as that. So it's, it's AI's as simple as using any other library that the developers are already using, which I, which sounds hard to fathom because it is just so simplified. >> John: Software delivered AI. Okay, that's a cool thing. I believe in it personally. I think that's the way to go. I think there's going to be plenty of hardware options if you look at the advances of cloud players that got more silicon coming out. Yeah. More GPU. I mean, there's more instance, I mean, everything's out there right now. So the question is how does that evolve in your mind? Because that's seems to be key. You have open source projects emerging. What, what path does this take? Is there a parallel mental model that you see, Brian, that is similar? You mentioned open source earlier. Is it more like a VMware virtualization thing or is it more of a cloud thing? Is there Yeah. Is it going to evolve in a, in a trajectory that looks similar to what we might've seen in the past? >> Brian: Yeah, we're, you know, when I, when when I got involved with the company, what I, when I thought about it and I was reasoning about it, like, do you, you know, you want to, like, we all do when you want to join something full-time. I thought about it and said, where will the industry eventually get to? Right? To fully realize the value of, of deep learning and what's plausible as it evolves. And to me, like I, I know it's the old adage of, you know, you know, software, its hardware, cloudy software. But it truly was like, you know, we can solve these problems in software. Like there's nothing special that's happening at the hardware layer and the processing AI. The reality is that it's just early in the industry. So the view that that we had was like, this is eventually the best place where the industry will be, is the liberation of being able to run AI anywhere. Like you're really not democratizing, you democratize the model. But if you can't run the model anywhere you want because these models are getting bigger and bigger with these large language models, then you're kind of not democratizing. And if you got to go and like by a cluster to run this thing on. So the democratization comes by if all of a sudden that model can be consumed anywhere on demand without planning, without provisioning, wherever infrastructure is. And so I think that's with or without Neural Magic, that's where the industry will go and will get to. I think we're the leaders, leaders in getting it there. It's right because we're more advanced on these techniques. >> John: Yeah. And your background too. You've seen OpenStack, pre-cloud, you saw open source grow and still exponentially growing. And so you have the same similar dynamic with machine learning models growing. And they're also segmenting into almost a, an ML stack or foundational model as we talk about. So you're starting to see the formation of tooling inference. So a lot of components coming. It's almost a stack, it's almost a, it literally is like an operating system problem space, you know? How do you run things, how do you link things? How do you bring things together? Is that what's going on here? Is this like a data modeling operating environment kind of red hat type thing going on? Like. >> Brian: Yeah. Yeah. Like I think there is, you know, I thought about that too. And I think there is the role of like distribution, because the industrialization not happening fast enough of this. Like, can I go back to like every customers, every, every user does it in their own kind of way. Like it's not, everyone's a little bit of a snowflake. And I think that's okay. There's definitely plenty of companies that want to come in and say, well, this is the way it's going to be and we industrialize it as long as you do it our way. The reality is technology doesn't get industrialized by one company just saying, do it our way. And so that's why like we've taken the approach through open source by saying like, Hey, you haven't really industrialized it if you said. We made it simple, but you always got to run AI here. Yeah, right. You only like really industrialize it if you break it down into components that are simple to use and they work integrated in the stack the way you want them to. And so to me, that first principles was getting thing into microservices and dockers that could be run on VMware, OpenShare on the cloud in the edge. And so that's the, that's the real part that we're happening with. The other part, like I do agree, like I think it's going to quickly move into less about the model. Less about the training of the model and the transfer learning, you know, the data set of the model. We're taking away the complexity of optimization. Giving liberating deployment to be anywhere. And I think the last mile, John is going to be around the ML ops around that. Because it's easy to think of like soft now that it's just a software problem, we've turned it into a software problem. So it's easy to think of software as like kind of a point release, but that's not the reality, right? It's a life cycle. And it's, and so I think ML very much brings in the what is the lifecycle of that deployment? And, you know, you get into more interesting conversations, to be honest than like, once you've deployed in a docking container is around like model drift and accuracy and the dataset changes and the user changes is how do you become from an ML perspective of where of that sending signal back retraining. And, and that's where I think a lot of the, in more of the innovation's going to start to move there. >> John: Yeah. And software also, the software problem, the software opportunity as well is developer focused. And if you look at the cloud native landscape now, similar stacks developing a lot of components. A lot of things to, to stitch together a lot of things that are automating under the hood. A lot of developer productivity conversations. I think this is going to go down that same road. I want to get your thoughts because developers will set the pace. And this is something that's clear in this next wave developer productivity. They're the defacto standards bodies. They will decide what microservices check, API check. Now, skill gap is going to be a problem because it's relatively new. So model sprawl, model sizes, proprietary versus open. There has to be a way to kind of crunch that down into a, like a DevOps, like just make it, get the developer out of the, the muck. So what's your view? Are we early days like that? Or what's the young kid in college studying CS or whatever degree who comes into this with, with both feet? What are they doing? >> Brian: I'll probably say like the, the non-popular answer to that. A little bit is it's happening so fast that it's going to get kind of boring fast. Meaning like, yeah, you could go to school and go to MIT, right? Sorry. Like, and you could get a hold through end like becoming a model architect, like inventing the next model, right? And the layers and combining 'em and et cetera, et cetera. And then what operators and, and building a model that's bigger than the last one and trains faster, right? And there will be those people, right? That actually, like they're building the engines the same way. You know, I grew up as an infrastructure software developer. There's not a lot of companies that hire those anymore because they're all sitting inside of three big clouds. Yeah. Right? So you better be a good app developer, but I think what you're going to see is before you had to be everything, you had to be the, if you were going to use infrastructure, you had to know how to build infrastructure. And I think the same thing's true around is quickly exiting ML is to be able to use ML in your company, you better be like, great at every aspect of ML, including every intricacy inside of the model and every operation's doing, that's quickly changing. Like, you're going to start with a starting point. You know, in the future you're not going to be like cracking open these GPT models, you're going to just be pulling them off the shelf, fine tuning 'em and go. You don't have to invent it. You don't have to understand it. And I think that's going to be a pivot point, you know, in the industry between, you know, what's the future? What's, what's the future of a, a data scientist? ML engineer researcher look like? >> John: I think that's, the outcome's going to be determined. I mean, you mentioned, you know, doing it yourself what an SRE is for a Google with the servers scale's huge. So yeah, it might have to, at the beginning get boring, you get obsolete quickly, but that means it's progressing. So, The scale becomes huge. And that's where I think it's going to be interesting when we see that scale. >> Brian: Yep. Yeah, I think that's right. I think that's right. And we always, and, and what I've always said, and much the, again, the distribute into my ML team is that I want every developer to be as adept at being able take advantage of ML as non ML engineer, right? It's got to be that simple. And I think, I think it's getting there. I really do. >> John: Well, Brian, great, great to have you on theCUBE here on this cube conversation. As part of the startup showcase that's coming up. You're going to be featured. Or your company would featured on the upcoming ABRA startup showcase on making machine learning easier and more affordable as more machine learning models come in. You guys got deep sparse and some great technology. We're going to dig into that next time. I'll give you the final word right now. What do you see for the company? What are you guys looking for? Give a plug for the company right now. >> Brian: Oh, give a plug that I haven't already doubled in as the plug. >> John: You're hiring engineers, I assume from MIT and other places. >> Brian: Yep. I think like the, the biggest thing is like, like we're on the developer side. We're here to make this easy. The majority of inference today is, is on CPUs already, believe it or not, as much as kind of, we like to talk about hardware and specialized hardware. The majority is already on CPUs. We're basically bringing 95% cost savings to CPUs through this acceleration. So, but we're trying to do it in a way that makes it community first. So I think the, the shout out would be come find the Neural Magic community and engage with us and you'll find, you know, a thousand other like-minded people in Slack that are willing to help you as well as our engineers. And, and let's, let's go take on some successful AI deployments. >> John: Exciting times. This is, I think one of the pivotal moments, NextGen data, machine learning, and now starting to see AI not be that chat bot, just, you know, customer support or some basic natural language processing thing. You're starting to see real innovation. Brian Stevens, CEO of Neural Magic, bringing the magic here. Thanks for the time. Great conversation. >> Brian: Thanks John. >> John: Thanks for joining me. >> Brian: Cheers. Thank you. >> John: Okay. I'm John Furrier, host of theCUBE here in Palo Alto, California for this cube conversation with Brian Stevens. Thanks for watching.

(soft music) >> Hello everyone, welcome to this Cube conversation here from the studios of theCube in Palo Alto, California. John Furrier, your host. We're featuring a startup, Astronomer, astronomer.io is the url. Check it out. And we're going to have a great conversation around one of the most important topics hitting the industry, and that is the future of machine learning and AI and the data that powers it underneath it. There's a lot of things that need to get done, and we're excited to have some of the co-founders of Astronomer here. Viraj Parekh, who is co-founder and Paola Peraza Calderon, another co-founder, both with Astronomer. Thanks for coming on. First of all, how many co-founders do you guys have? >> You know, I think the answer's around six or seven. I forget the exact, but there's really been a lot of people around the table, who've worked very hard to get this company to the point that it's at. And we have long ways to go, right? But there's been a lot of people involved that are, have been absolutely necessary for the path we've been on so far. >> Thanks for that, Viraj, appreciate that. The first question I want to get out on the table, and then we'll get into some of the details, is take a minute to explain what you guys are doing. How did you guys get here? Obviously, multiple co-founders sounds like a great project. The timing couldn't have been better. ChatGPT has essentially done so much public relations for the AI industry. Kind of highlight this shift that's happening. It's real. We've been chronologicalizing, take a minute to explain what you guys do. >> Yeah, sure. We can get started. So yeah, when Astronomer, when Viraj and I joined Astronomer in 2017, we really wanted to build a business around data and we were using an open source project called Apache Airflow, that we were just using sort of as customers ourselves. And over time, we realized that there was actually a market for companies who use Apache Airflow, which is a data pipeline management tool, which we'll get into. And that running Airflow is actually quite challenging and that there's a lot of, a big opportunity for us to create a set of commercial products and opportunity to grow that open source community and actually build a company around that. So the crux of what we do is help companies run data pipelines with Apache Airflow. And certainly we've grown in our ambitions beyond that, but that's sort of the crux of what we do for folks. >> You know, data orchestration, data management has always been a big item, you know, in the old classic data infrastructure. But with AI you're seeing a lot more emphasis on scale, tuning, training. You know, data orchestration is the center of the value proposition when you're looking at coordinating resources, it's one of the most important things. Could you guys explain what data orchestration entails? What does it mean? Take us through the definition of what data orchestration entails. >> Yeah, for sure. I can take this one and Viraj feel free to jump in. So if you google data orchestration, you know, here's what you're going to get. You're going to get something that says, data orchestration is the automated process for organizing silo data from numerous data storage points to organizing it and making it accessible and prepared for data analysis. And you say, okay, but what does that actually mean, right? And so let's give sort of an example. So let's say you're a business and you have sort of the following basic asks of your data team, right? Hey, give me a dashboard in Sigma, for example, for the number of customers or monthly active users and then make sure that that gets updated on an hourly basis. And then number two, a consistent list of active customers that I have in HubSpot so that I can send them a monthly product newsletter, right? Two very basic asks for all sorts of companies and organizations. And when that data team, which has data engineers, data scientists, ML engineers, data analysts get that request, they're looking at an ecosystem of data sources that can help them get there, right? And that includes application databases, for example, that actually have end product user behavior and third party APIs from tools that the company uses that also has different attributes and qualities of those customers or users. And that data team needs to use tools like Fivetran, to ingest data, a data warehouse like Snowflake or Databricks to actually store that data and do analysis on top of it, a tool like DBT to do transformations and make sure that that data is standardized in the way that it needs to be, a tool like Hightouch for reverse ETL. I mean, we could go on and on. There's so many partners of ours in this industry that are doing really, really exciting and critical things for those data movements. And the whole point here is that, you know, data teams have this plethora of tooling that they use to both ingest the right data and come up with the right interfaces to transform and interact with that data. And data orchestration in our view is really the heartbeat of all of those processes, right? And tangibly the unit of data orchestration, you know, is a data pipeline, a set of tasks or jobs that each do something with data over time and eventually run that on a schedule to make sure that those things are happening continuously as time moves on. And, you know, the company advances. And so, you know, for us, we're building a business around Apache Airflow, which is a workflow management tool that allows you to author, run and monitor data pipelines. And so when we talk about data orchestration, we talk about sort of two things. One is that crux of data pipelines that, like I said, connect that large ecosystem of data tooling in your company. But number two, it's not just that data pipeline that needs to run every day, right? And Viraj will probably touch on this as we talk more about Astronomer and our value prop on top of Airflow. But then it's all the things that you need to actually run data and production and make sure that it's trustworthy, right? So it's actually not just that you're running things on a schedule, but it's also things like CI/CD tooling, right? Secure secrets management, user permissions, monitoring, data lineage, documentation, things that enable other personas in your data team to actually use those tools. So long-winded way of saying that, it's the heartbeat that we think of the data ecosystem and certainly goes beyond scheduling, but again, data pipelines are really at the center of it. >> You know, one of the things that jumped out Viraj, if you can get into this, I'd like to hear more about how you guys look at all those little tools that are out there. You mentioned a variety of things. You know, if you look at the data infrastructure, it's not just one stack. You've got an analytic stack, you've got a realtime stack, you've got a data lake stack, you got an AI stack potentially. I mean you have these stacks now emerging in the data world that are >> Yeah. - >> fundamental, but we're once served by either a full package, old school software, and then a bunch of point solution. You mentioned Fivetran there, I would say in the analytics stack. Then you got, you know, S3, they're on the data lake stack. So all these things are kind of munged together. >> Yeah. >> How do you guys fit into that world? You make it easier or like, what's the deal? >> Great question, right? And you know, I think that one of the biggest things we've found in working with customers over, you know, the last however many years, is that like if a data team is using a bunch of tools to get what they need done and the number of tools they're using is growing exponentially and they're kind of roping things together here and there, that's actually a sign of a productive team, not a bad thing, right? It's because that team is moving fast. They have needs that are very specific to them and they're trying to make something that's exactly tailored to their business. So a lot of times what we find is that customers have like some sort of base layer, right? That's kind of like, you know, it might be they're running most of the things in AWS, right? And then on top of that, they'll be using some of the things AWS offers, you know, things like SageMaker, Redshift, whatever. But they also might need things that their Cloud can't provide, you know, something like Fivetran or Hightouch or anything of those other tools and where data orchestration really shines, right? And something that we've had the pleasure of helping our customers build, is how do you take all those requirements, all those different tools and whip them together into something that fulfills a business need, right? Something that makes it so that somebody can read a dashboard and trust the number that it says or somebody can make sure that the right emails go out to their customers. And Airflow serves as this amazing kind of glue between that data stack, right? It's to make it so that for any use case, be it ELT pipelines or machine learning or whatever, you need different things to do them and Airflow helps tie them together in a way that's really specific for a individual business's needs. >> Take a step back and share the journey of what your guys went through as a company startup. So you mentioned Apache open source, you know, we were just, I was just having an interview with the VC, we were talking about foundational models. You got a lot of proprietary and open source development going on. It's almost the iPhone, Android moment in this whole generative space and foundational side. This is kind of important, the open source piece of it. Can you share how you guys started? And I can imagine your customers probably have their hair on fire and are probably building stuff on their own. How do you guys, are you guys helping them? Take us through, 'cuz you guys are on the front end of a big, big wave and that is to make sense of the chaos, reigning it in. Take us through your journey and why this is important. >> Yeah Paola, I can take a crack at this and then I'll kind of hand it over to you to fill in whatever I miss in details. But you know, like Paola is saying, the heart of our company is open source because we started using Airflow as an end user and started to say like, "Hey wait a second". Like more and more people need this. Airflow, for background, started at Airbnb and they were actually using that as the foundation for their whole data stack. Kind of how they made it so that they could give you recommendations and predictions and all of the processes that need to be or needed to be orchestrated. Airbnb created Airflow, gave it away to the public and then, you know, fast forward a couple years and you know, we're building a company around it and we're really excited about that. >> That's a beautiful thing. That's exactly why open source is so great. >> Yeah, yeah. And for us it's really been about like watching the community and our customers take these problems, find solution to those problems, build standardized solutions, and then building on top of that, right? So we're reaching to a point where a lot of our earlier customers who started to just using Airflow to get the base of their BI stack down and their reporting and their ELP infrastructure, you know, they've solved that problem and now they're moving onto things like doing machine learning with their data, right? Because now that they've built that foundation, all the connective tissue for their data arriving on time and being orchestrated correctly is happening, they can build the layer on top of that. And it's just been really, really exciting kind of watching what customers do once they're empowered to pick all the tools that they need, tie them together in the way they need to, and really deliver real value to their business. >> Can you share some of the use cases of these customers? Because I think that's where you're starting to see the innovation. What are some of the companies that you're working with, what are they doing? >> Raj, I'll let you take that one too. (all laughing) >> Yeah. (all laughing) So you know, a lot of it is, it goes across the gamut, right? Because all doesn't matter what you are, what you're doing with data, it needs to be orchestrated. So there's a lot of customers using us for their ETL and ELT reporting, right? Just getting data from all the disparate sources into one place and then building on top of that, be it building dashboards, answering questions for the business, building other data products and so on and so forth. From there, these use cases evolve a lot. You do see folks doing things like fraud detection because Airflow's orchestrating how transactions go. Transactions get analyzed, they do things like analyzing marketing spend to see where your highest ROI is. And then, you know, you kind of can't not talk about all of the machine learning that goes on, right? Where customers are taking data about their own customers kind of analyze and aggregating that at scale and trying to automate decision making processes. So it goes from your most basic, what we call like data plumbing, right? Just to make sure data's moving as needed. All the ways to your more exciting and sexy use cases around like automated decision making and machine learning. >> And I'd say, I mean, I'd say that's one of the things that I think gets me most excited about our future is how critical Airflow is to all of those processes, you know? And I think when, you know, you know a tool is valuable is when something goes wrong and one of those critical processes doesn't work. And we know that our system is so mission critical to answering basic, you know, questions about your business and the growth of your company for so many organizations that we work with. So it's, I think one of the things that gets Viraj and I, and the rest of our company up every single morning, is knowing how important the work that we do for all of those use cases across industries, across company sizes. And it's really quite energizing. >> It was such a big focus this year at AWS re:Invent, the role of data. And I think one of the things that's exciting about the open AI and all the movement towards large language models, is that you can integrate data into these models, right? From outside, right? So you're starting to see the integration easier to deal with, still a lot of plumbing issues. So a lot of things happening. So I have to ask you guys, what is the state of the data orchestration area? Is it ready for disruption? Is it already been disrupted? Would you categorize it as a new first inning kind of opportunity or what's the state of the data orchestration area right now? Both, you know, technically and from a business model standpoint, how would you guys describe that state of the market? >> Yeah, I mean I think, I think in a lot of ways we're, in some ways I think we're categoric rating, you know, schedulers have been around for a long time. I recently did a presentation sort of on the evolution of going from, you know, something like KRON, which I think was built in like the 1970s out of Carnegie Mellon. And you know, that's a long time ago. That's 50 years ago. So it's sort of like the basic need to schedule and do something with your data on a schedule is not a new concept. But to our point earlier, I think everything that you need around your ecosystem, first of all, the number of data tools and developer tooling that has come out the industry has, you know, has some 5X over the last 10 years. And so obviously as that ecosystem grows and grows and grows and grows, the need for orchestration only increases. And I think, you know, as Astronomer, I think we, and there's, we work with so many different types of companies, companies that have been around for 50 years and companies that got started, you know, not even 12 months ago. And so I think for us, it's trying to always category create and adjust sort of what we sell and the value that we can provide for companies all across that journey. There are folks who are just getting started with orchestration and then there's folks who have such advanced use case 'cuz they're hitting sort of a ceiling and only want to go up from there. And so I think we as a company, care about both ends of that spectrum and certainly have want to build and continue building products for companies of all sorts, regardless of where they are on the maturity curve of data orchestration. >> That's a really good point Paola. And I think the other thing to really take into account is it's the companies themselves, but also individuals who have to do their jobs. You know, if you rewind the clock like five or 10 years ago, data engineers would be the ones responsible for orchestrating data through their org. But when we look at our customers today, it's not just data engineers anymore. There's data analysts who sit a lot closer to the business and the data scientists who want to automate things around their models. So this idea that orchestration is this new category is spot on, is right on the money. And what we're finding is it's spreading, the need for it, is spreading to all parts of the data team naturally where Airflows have emerged as an open source standard and we're hoping to take things to the next level. >> That's awesome. You know, we've been up saying that the data market's kind of like the SRE with servers, right? You're going to need one person to deal with a lot of data and that's data engineering and then you're going to have the practitioners, the democratization. Clearly that's coming in what you're seeing. So I got to ask, how do you guys fit in from a value proposition standpoint? What's the pitch that you have to customers or is it more inbound coming into you guys? Are you guys doing a lot of outreach, customer engagements? I'm sure they're getting a lot of great requirements from customers. What's the current value proposition? How do you guys engage? >> Yeah, I mean we've, there's so many, there's so many. Sorry Raj, you can jump in. - >> It's okay. So there's so many companies using Airflow, right? So our, the baseline is that the open source project that is Airflow that was, that came out of Airbnb, you know, over five years ago at this point, has grown exponentially in users and continues to grow. And so the folks that we sell to primarily are folks who are already committed to using Apache Airflow, need data orchestration in the organization and just want to do it better, want to do it more efficiently, want to do it without managing that infrastructure. And so our baseline proposition is for those organizations. Now to Raj's point, obviously I think our ambitions go beyond that, both in terms of the personas that we addressed and going beyond that data engineer, but really it's for, to start at the baseline. You know, as we continue to grow our company, it's really making sure that we're adding value to folks using Airflow and help them do so in a better way, in a larger way and a more efficient way. And that's really the crux of who we sell to. And so to answer your question on, we actually, we get a lot of inbound because they're are so many - >> A built-in audience. >> In the world that use it, that those are the folks who we talk to and come to our website and chat with us and get value from our content. I mean the power of the open source community is really just so, so big. And I think that's also one of the things that makes this job fun, so. >> And you guys are in a great position, Viraj, you can comment, to get your reaction. There's been a big successful business model to starting a company around these big projects for a lot of reasons. One is open source is continuing to be great, but there's also supply chain challenges in there. There's also, you know, we want to continue more innovation and more code and keeping it free and and flowing. And then there's the commercialization of product-izing it, operationalizing it. This is a huge new dynamic. I mean, in the past, you know, five or so years, 10 years, it's been happening all on CNCF from other areas like Apache, Linux Foundation, they're all implementing this. This is a huge opportunity for entrepreneurs to do this. >> Yeah, yeah. Open source is always going to be core to what we do because, you know, we wouldn't exist without the open source community around us. They are huge in numbers. Oftentimes they're nameless people who are working on making something better in a way that everybody benefits from it. But open source is really hard, especially if you're a company whose core competency is running a business, right? Maybe you're running e-commerce business or maybe you're running, I don't know, some sort of like any sort of business, especially if you're a company running a business, you don't really want to spend your time figuring out how to run open source software. You just want to use it, you want to use the best of it, you want to use the community around it. You want to take, you want to be able to google something and get answers for it. You want the benefits of open source. You don't want to have, you don't have the time or the resources to invest in becoming an expert in open source, right? And I think that dynamic is really what's given companies like us an ability to kind of form businesses around that, in the sense that we'll make it so people get the best of both worlds. You'll get this vast open ecosystem that you can build on top of, you can benefit from, that you can learn from, but you won't have to spend your time doing undifferentiated heavy lifting. You can do things that are just specific to your business. >> It's always been great to see that business model evolved. We used to debate 10 years ago, can there be another red hat? And we said, not really the same, but there'll be a lot of little ones that'll grow up to be big soon. Great stuff. Final question, can you guys share the history of the company, the milestones of the Astronomer's journey in data orchestration? >> Yeah, we could. So yeah, I mean, I think, so Raj and I have obviously been at astronomer along with our other founding team and leadership folks, for over five years now. And it's been such an incredible journey of learning, of hiring really amazing people. Solving again, mission critical problems for so many types of organizations. You know, we've had some funding that has allowed us to invest in the team that we have and in the software that we have. And that's been really phenomenal. And so that investment, I think, keeps us confident even despite these sort of macroeconomic conditions that we're finding ourselves in. And so honestly, the milestones for us are focusing on our product, focusing on our customers over the next year, focusing on that market for us, that we know can get value out of what we do. And making developers' lives better and growing the open source community, you know, and making sure that everything that we're doing makes it easier for folks to get started to contribute to the project and to feel a part of the community that we're cultivating here. >> You guys raised a little bit of money. How much have you guys raised? >> I forget what the total is, but it's in the ballpark of 200, over $200 million. So it feels good - >> A little bit of capital. Got a little bit of cash to work with there. Great success. I know it's a Series C financing, you guys been down, so you're up and running. What's next? What are you guys looking to do? What's the big horizon look like for you? And from a vision standpoint, more hiring, more product, what is some of the key things you're looking at doing? >> Yeah, it's really a little of all of the above, right? Like, kind of one of the best and worst things about working at earlier stage startups is there's always so much to do and you often have to just kind of figure out a way to get everything done, but really invest in our product over the next, at least the next, over the course of our company lifetime. And there's a lot of ways we wanting to just make it more accessible to users, easier to get started with, easier to use all kind of on all areas there. And really, we really want to do more for the community, right? Like I was saying, we wouldn't be anything without the large open source community around us. And we want to figure out ways to give back more in more creative ways, in more code driven ways and more kind of events and everything else that we can do to keep those folks galvanized and just keeping them happy using Airflow. >> Paola, any final words as we close out? >> No, I mean, I'm super excited. You know, I think we'll keep growing the team this year. We've got a couple of offices in the US which we're excited about, and a fully global team that will only continue to grow. So Viraj and I are both here in New York and we're excited to be engaging with our coworkers in person. Finally, after years of not doing so, we've got a bustling office in San Francisco as well. So growing those teams and continuing to hire all over the world and really focusing on our product and the open source community is where our heads are at this year, so. >> Congratulations. - >> Excited. 200 million in funding plus good runway. Put that money in the bank, squirrel it away. You know, it's good to kind of get some good interest on it, but still grow. Congratulations on all the work you guys do. We appreciate you and the open sourced community does and good luck with the venture. Continue to be successful and we'll see you at the Startup Showcase. >> Thank you. - >> Yeah, thanks so much, John. Appreciate it. - >> It's theCube conversation, featuring astronomer.io, that's the website. Astronomer is doing well. Multiple rounds of funding, over 200 million in funding. Open source continues to lead the way in innovation. Great business model. Good solution for the next gen, Cloud, scale, data operations, data stacks that are emerging. I'm John Furrier, your host. Thanks for watching. (soft music)

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

(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)

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

>>Hey, everyone. Welcome back to San Francisco. Lisa Martin and John furrier live on the floor at VMware Explorer, 2022. This is our third day of wall to wall coverage on the cube. But you know that cuz you've been here the whole time. We're pleased to welcome up. First timer to the cubes we saw is here. The CEO and founder of ZDA. Saed welcome to the program. >>Thank you for having me >>Talk to me a little bit about what ZDA does in edge. >>Sure. So ZDA is a company purely focused in edge computing. I started a company about five years ago, go after edge. So what we do is we help customers with orchestrating their edge, helping them to deploy secure monitor application services and devices at the edge. >>What's the business model for you guys. We get that out there. So the targeting the edge, which is everything from telco to whatever. Yeah. What's the business model. Yeah. >>Maybe before we go there, let's talk about edge itself. Cuz edge is complex. There's a lot of companies. I call 'em lens company nowadays, if you're not a cloud company, you're probably an edge company at this point. So we are focusing something called the distributed edge. So distributed edge. When you start putting tiny servers in environments like factory floors, solar farms, wind farms, even inside machines or well sites, et cetera. And a question that people always ask me, like why, why would you want to put, you know, servers there on servers supposed to be in a data center in the cloud? And the answer to the question actually is data gravity. So traditionally wherever the data gets created is where your applications live. But as we're connecting more and more devices to the edge of the network, we basically customers now are required to push the applications to the edge cause they can't go all the data to the cloud. So basically that's where we focus on people call it the far edge as well. You know, that's the term we've heard in the past as well. And what we do in our business model is provide customers a, a software as a service solution where they can basically deploy and monitor these applications at these highly distributed environments. >>Data, gravity comes up a lot and I want you to take a minute to explain the definition as it is today. And people have used that term, you know, with big data, going back to 2010 leads when we covering the Hadoop wave, which ended up becoming, you know, data, data, bricks, and snowflake now, but, but a lots changed, but what does it mean to be data gravity? It means that staying local, it's just what specifically describe and, and define what data gravity is. >>Yeah. So for me, data gravity is where you need to process the data, right? It's where the data usually gets created. So if you think about a web app, where does the data get created? Where people click on buttons, they, they interface with it. They, they upload content to it, et cetera. So that's where the data gravity therefore is therefore that's where you do your analytics. That's where you do your visualization processing, machine learning and all of those pieces. So it's really where that data gets created is where the data gravity in my view says, >>What are some of the challenges that data and opportunities that data gravity presents to customers? >>Well, obviously I think every enterprise in this day is trying to take data and make it a competitive advantage, right? Like faster decisions, better decisions, outcompete your competition by, you know, being first with a product or being first with a product with the future, et cetera. So, so I think, you know, if you're not a data driven enterprise by now, then I think the future may be a little bit bleak. >>Okay. So you're targeting the market distributed edge business model, SAS technology, secret sauce. What's that piece. >>Yeah. So that's, that's what the interesting part comes in. I think, you know, if you kind of look at the data center in the cloud, we've had these virtualization and orchestration stacks create, I mean, we're here in VMware Explorer. And as an example, what we basically, what we saw is that the edge is so unique and so different than what we've seen in the data center, in the cloud that we needed to build a complete brand new purpose-built illustration and virtualization solution. So that's really what we, we set off to do. So there's two components that we do. One end is we built a purpose-built edge operating system for the edge and we actually open sourced it. And the reason we opensource it, we said, Hey, you know, edge is so diverse. You know, depending on the environment you're running in a machine or in a vehicle or in a well site, you have different hardware, different networks, different applications you need to enable. >>And we will never be able to support all of them ourselves. As a matter of fact, we actually think there's a need for standardization at the edge. We need to kind of cut through all these silos that have been created traditionally from the embedded way of thinking. So we created basically an open source project in the Linux foundation in LFS, which is a sister organization through the CNCF it's called project Eve. And the idea is to create the Android of the edge, basically what Android became for mobile computing, an a common operating system. So you build one app. You can run in any phone in the world that runs Android, build an architecture. You build one app. You can run in any Eve powered node in the world, >>So distributed edge and you get the tech here, get the secret sauce. We'll get more into that in a second, but I wanna just tie one kick quick point and get your clarification on edge is becoming much more about the physical side too. I mean, absolutely. So when you talk about Android, you're making the reference of a phone. I get that's metaphor to what you're doing at the edge, wind farms, factories, alarms, light bulbs, buildings. I mean, that's what you're talking about, right? Yes. We're getting down to that very, >>Very physical, dark distributed locations. >>We're gonna come back to the CISO CSO. We're gonna come back to the CISO versus CSO question because is the CISO or CIO or who runs that anyway? So that's true. What's the important thing that's happening because that sounds like old OT world, like yes. Operating technology, not it information technology, is it a complete reset of those worlds or is it a collision? >>It's a great question. So what we're seeing is first of all, there is already compute in these environments, industrial PCs of existed well beyond, you know, an industrial automation has been done for many, many decades. The point is that that stuff has been done. Collect data has been collected, but never connected, right? So with edge computing, we're connecting now this data from an industrial machine and industrial process to the cloud, right? And one of the problems is it's data that comes of that industrial process too much to upload to the cloud. So I gotta analyze, analyze it locally. So one of the, the things we saw early on in edge is there's a lot of brownfield. Most of our customers today actually have applications running on windows and they would love to make in Linux and containers and Kubernetes, but it took them 20, 30 years to build those apps. And they basically are the money makers of the enterprise. So they are in a, in a transitionary phase and they need something that can take them from the brown to the Greenfield. So to your point, you gotta support all of these types of unique brownfield applications. >>So you're, you're saying I don't really care if this is a customer, how you get the data, you wanna start new start fresh. That's cool. But if you wanna take your old data, you'll >>Take that. Yeah. You don't wanna rebuild the whole machine. You're >>Just, they can life cycle it out on their own timetable. Yeah. >>So we had to learn, first of all, how do we take and lift and shift windows based industrial application and make it run at the edge on, on our architecture. Right? And then the second step is how do we then Sen off that data that this application is generating and do we fuse it with cloud native capability? Like, >>So your cloud, so your staff is your open source that you're giving to the Linux foundation as part of that Eve project that's available to everybody. So they can, they can look at the code, which is great by the way. Yeah. So people wanna do that. Yeah. Your self source, I'm assuming, is your hardened version with support? >>Well, we took what we took, what the open source companies did, opensource companies traditionally have sold, you know, basically a support model around the open source. We actually saw another problem. Customers has like, okay, now I have this node running and I can, you know, do this data analytics, but what if I have 15 or 20,000 of these node? And they're all around the world in remote locations on satellite links or wireless connectivity, how do I orchestrate them? So we actually build an orchestration service for these nodes running this open source >>Software. So that's a key secret sauce right there. >>That is the business model that taking open store and a lot. >>And you're taking your own code that you have. Okay. Got it. Cool. And then the customer's customer piece is, is key. So that's the final piece, I guess who's using it. >>Yeah. Well, and, >>And, and one of the business outcomes that they're achieving. Oh >>Yeah. Well, so maybe start with that first. I mean, we are deployed in customers in all and gas, for instance, helping them with the transition to renewable energy, right? So basically we, we have customers for instance, that deploy us in the, how they drill Wells is one use case and doing that better, faster, and cheaper and, and less environmental impacting. But we also have customers that use us in wind farms. We have, and solar farms, like we, one of the leading solar energy companies in the world is using us to bring down the cost of power by predicting failures ahead of time, for >>Instance. And when you're working with customers to create the optimal solution at the distributed edge, who are you working with in, within an organization? Yeah. >>It's usually a mix of OT and it people. Okay. So the OT people typically they're >>Arm wrestling, well, or they're getting along, actually, >>I think they're getting along very well. Okay, good. But they also agree that they have to have swim lanes. The it folks, obviously their job is to make sure, you know, everything is secure. Everything is according to the compliance it's, it's, you know, the, the best TCO on the infrastructure, those type of things, the OT guy, they, they, or girl, they care about the application. They care about the services. They care about the support new business. So how can you create a model that too can coexist? And if you do that, they get along really well. >>You know, we had an event called Supercloud and@theurlsupercloud.world, if you're watching check it out, it's our version of what we think multicloud will merge into including edge cuz edge is just another node in the, in the, in the network. As far as we're concerned, hybrid is the steady state. That's distributed computing on premise, private cloud, public cloud. We know what that looks like. People love that things are happening. Edge is like a whole nother new area. That's blossoming and with disruption, yeah. There's a lot of existing market and incumbents that need to be disrupted. And there's also a new capabilities that are coming that we don't yet see. So we're seeing it with the super cloud idea that these new kinds of clouds are emerging. Like there could be an edge cloud. Yeah. Why isn't there a security cloud, whereas the financial services cloud, whereas the insurance cloud, whereas the, so these become super clouds where the CapEx could be done by the Amazon, whatnot you've been following them is edge cloud. Can you make that a cloud? Is that what you guys are trying to do? And if so, what does that look like? Cause we we're adding a new track to our super cloud site. I mentioned on edge specifically, we're trying to figure out you and if you share your opinion, it'd be great. Can the E can edge clouds exist and be run by companies? Yeah. Or is that what you guys are trying to do? >>I, I, I mean, I think first of all, there is no edge without cloud, right? So when I meet any customer who says, Hey, we're gonna do edge without cloud. Then I'm like, you're probably not gonna do edge computing. Right. And, and the way we built the company and the way we think about it, it's about extending the cloud experience all the way into these embedded distributed environments. That's really, I think what customers are looking for, cuz customers love the simplicity of the cloud. They love the ease of use agility, all of that greatness. And they're like, Hey, I want that. But not in a, you know, in an Amazon or Azure data center. I want that in my factories. I want that in my wealth sites, in my vehicles. And that's really what I think the future >>Is gonna. And how long have you guys been around? What's the, what's the history of the company because you might actually be that cloud. Yeah. And are you on AWS or Azure? You're building your own. What's the, >>Yeah. Yeah. So >>Take it through the, the architecture because yeah, yeah, sure. You're a modern startup. I mean you gotta, and the edges you're going after you gotta be geared up. Yeah. To win that. Yeah. >>So, so the company's about five years old. So we, when we started focusing on edge, people didn't necessarily talk as much about edge. We kind of identified the it's like, you know, how do you find a black hole in, in the universe? Cuz you can't see it, but you sort of look around that's why you in it. And so we were like looking at it, like there's something gonna happen here at the edge of the network, because everybody's saying we're connecting these vice upload the data to the cloud's never gonna work. My background is networking. I worked at companies like Juniper and Ericsson ran several products there. So I know how the internet networks have built. And it was very Evan to me. It's not gonna be possible. My co-founders come from open source companies like pivotal and Cloudera. My auto co-founder was a, an engineer at sun Microsystems built the first network stack in the solar is operating system. So a lot of experience that kind of came together to build this. >>Yeah. Cloudera is a big day. That's where the cube started by the way. Yeah. >>Yeah. So, so we, we, we have, I think a good view on the stack, the cloud stack and therefore a good view of what the ed stack needs to look like. And then I think, you know, to answer your other question, our orchestration service runs in the cloud. We have, we actually are multi-cloud company. So we offer customers choice where they want to orchestrate the node from the nodes themself, never sit in a data center. They always highly embedded. We have customers are putting machines or inside these factory lines, et cetera. Are >>You running your SAS on Amazon web services or which >>Cloud we're running it on several clouds, including Amazon, all of, pretty much the cloud. So some customers say, Hey, I'd prefer to be on the Amazon set. And others customers say, I wanna be on Azure set. >>And you leverage their CapEx on that side. Yes. On behalf of yeah. >>Yeah. We, yes. Yes. But the majority of the customer data and, and all the data that the nodes process, the customer send it to their clouds. They don't send it to us. We don't get a copy of the camera feed analytics or the machine data. We actually decouple those though. So basically the, the team production data go straight to the customer's cloud and that's why they love us. >>And they choose that they can control their own desktop. >>Yeah. So we separate the management plane from the data plane at the edge. Yeah. >>That's a good call >>Actually. Yeah. That was another very important part of the architecture early on. Cause customers don't want us to see their, you know, highly confidential production data and we don't wanna have it either. So >>We had a great chat with Chris Wolf who works with kit culvert about control plane, data, plane. So that seems to be the trend data, plane customers want full yeah. Management of that. Yeah. Control plane. Maybe give multiple >>Versions. Yeah. Yeah. So our cloud consumption what the data we stories about the apps, their behavior, the networking, the security, all of that. That's what we store in our cloud. And then customers can access that and monitor. But the actual machine that I go somewhere else >>Here we are at VMware. Explore. Talk a little bit about the VMware relationship. You just had some big news the other day. >>Yeah. So two days ago we actually made a big announcement with VMware. So we signed an OEM agreement with VMware. So we're part now of VMware's edge compute stack. So VMware customers, as they start using the recently announced edge compute stack 2.0, that was announced here. Basically it's powered by Edda technology. So it's a really exciting partnership as part of this, we actually building integrations with the VMware organization products. So that's basically now extending to more, you know, other groups inside VMware. >>So what's the value in it for VMware customers. >>Yeah. So I think the, the, the benefit of, of VMware customers, I think cus VMware customers want that multi-cloud multi edge orchestration experience. So they wanna be able to deploy workloads in the cloud. They wanna deploy the workloads in the data center. And of course also at the edge. So by us integrating in that vision customers now can have that unified experience from cloud to edge and anywhere in between. >>What's the big vision that you see happening at the edge. I mean, a lot of the VMware customers here, they're classic it that have evolved into ops now, dev ops. Now you've got second data ops coming. The edge is gonna right around the corner for them. They're dealing with it now, probably just kicking the tires, towing the water kind of thing. Where do you see the vision going? Cuz now, no matter what happens with VMware, the Broadcom, this wave is still here. You got AWS, got Azure, got Google cloud, you got Oracle, Alibaba internationally. And the cloud native surges here. How do you see that disrupting the existing edge? Because let's face it the O some of those OT players, a little bit old and antiquated, a little bit outdated. I mean, I was talking to a telco person. They, they puked the word open source. I mean, these people are so dogmatic on, on their architecture. Yeah. They're gonna get disrupted. It's a matter of time. Yeah. Where's the new guard come in. How do you see the configuration changing in the landscape? Because some people will cross over to the right side of the street here. Yeah. Some won't yeah. Open circle. Dominate cloud native will be key. Yeah. >>Well, I mean, I think, again, let's, let's take an example of a vertical that's heavily disrupted now as the automotive market, right? The, so look at Tesla and look at all these companies, they built, they built software first cars, right? Software, first delivery of capabilities and everything else. And the, and the incumbents. They have only two options, right? Either they try to respond by adopting open source cloud, native technologies. Like the, these new entrants have done and really, you know, compete with them at that level, or they can become commodity. Right. So, and I think that's the customers we're seeing the smart customers go like, we need to compete with these guys. We need to figure out how to take this technology in. And they need partners like us and partners like VMware for them. >>Do you see customers becoming cloud super cloud players? If they continue to keep leveraging the CapEx of the clouds and focus all their operational capital on top line revenue, generating activities. >>Yeah. I, so I think the CapEx model of the cloud is a great benefit of the cloud, but I think that is not, what's the longer term future of the cloud. I think the op the cloud operating model is the future. Like the agility, the ability imagine embedded software that, you know, you do an over the year update to fix a bug, but it's very hard to make a, an embedded device smarter over time. And then imagine if you can run cloud native software, you can roll out every two weeks new features and make that thing smarter, intelligent, and continue to help you in your business. That I think is what cloud did ultimately. And I think that is what really these customers are gonna need at their edge. >>Well, we talked about the value within it for customers with the VMware partnership, but what are some of your expectations? Obviously, this is a pretty powerful partnership for you guys. Yeah. What are some of the things that you're expecting that this is gonna drive? Yeah, >>So we, we, we have always operated at the more OT layer, distributed organizations in retail, energy, industrial automotive. Those are the verticals we, so we've developed. I think a lot of experience there, what, what we're seeing as we talk to those customers is they obviously have it organizations and the it organizations, Hey, that's great. You're looking at its computing, but how do we tie this into the existing investments we made with VMware? And how do we kind of take that also to this new environment? And I think that's the expectation I have is that I think we will be able to, to talk to the it folks and say, Hey, you can actually talk to the OT person. And both of you will speak the same language. You probably will both standardize on the same architecture and you'll be together deploying and enabling this new agility at the edge. >>What are some of the next things coming up for ZDA and the team? >>Well, so we've had a really amazing few quarters. We just close a series B round. So we've raised the companies raised over 55 million so far, we're growing very rapidly. We opened up no new international offices. I would say the, the early customers that we started deploying, wait a while back, they're now going into mass scale deployment. So we have now deployments underway in, you know, the 10 to hundred thousands of nodes at certain customers and in amazing environments. And so, so for us, it's continuing to prove the product in more and more verticals. Our, our product is really built for the largest of the largest. So, you know, for the size of the company, we are, we have a high concentration of fortune 500 global 500 customers, and some of them even invested in our rounds recently. So we we've been really, you know, honored with that support. Well, congratulations. Good stuff, edges popping. All right. Thank you. >>Thank you so much for joining us, talking about what you're doing in distributed edge. What's in it for customers, the VMware partnership, and by the way, congratulations on >>That too. Thank you. Thank you so much. Nice to meet you. Thank >>You. All right. Nice to meet you as well for our guest and John furrier. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 22, John and I will be right back with our next guest.

(upbeat music) >> Hello, and welcome to this CUBE Conversation. I'm John Furrier, host of theCUBE here in Palo Alto, California for a great remote interview with Ameya Talwalkar, CEO of Cequence Security. Protecting APIs is the name of the game. Ameya thanks for coming on this CUBE Conversation. >> Thank you, John. Thanks for having us. >> So, I mean, obviously APIs, cloud, it runs everything. It's only going to get better, faster, more containers, more Kubernetes, more cloud-native action, APIs are at the center of it. Quick history, Cequence, how you guys saw the problem and where is it today? >> Yeah, so we started building the company or the product, the first product of the company focused on abuse or business logic abuse on APIs. We had design partners in large finance FinTech companies that are now customers of Cequence that were sort of API first, if you will. There were products in the market that were, you know, solving this problem for them on the web and in some cases mobile applications, but since these were API first very modern FinTech and finance companies that deal with lot of large enterprises, merchants, you have it, you name it. They were struggling to protect their APIs while they had protection on web and mobile applications. So that's the genesis. The problem has evolved exponentially in terms of volume size, pain, the ultimate financial losses from those problems. So it has, it's been a interesting journey and I think we timed it perfectly in terms of when we got started with the problem we started with. >> Yeah, I'm sure if you look at the growth of APIs, they're just exponentially growing because of the development, cloud-native development wave plus open source driving a lot of action. I was talking to a developer the other day and he's like, "Just give me a bag of Lego blocks and I'll build whatever application." I mean, this essentially- >> Yeah. >> API first is, has got us here, and that's standard. >> Yeah. >> Everyone's building on top of APIs, but the infrastructure going cloud-native is growing as well. So how do you secure APIs without slowing down the application velocity? Which everyone's trying to make go faster. So you got faster velocity on the developer side and (chuckles) more APIs coming. How do you secure the API infrastructure without slowing down the apps? >> Yeah, I'll come to the how part of it but I'll give you a little bit of commentary on what the problem really is. It's what has happened in the last few years is as you mentioned, the sort of journey to the cloud whether it's a public cloud or a private cloud, some enterprises have gone to a multi-cloud strategy. What really has happened is two things. One is because of that multi-environment deployment there is no defined parameter anymore to your applications or APIs. And so the parameter where people typically used to have maybe a CDN or WAF or other security controls at the parameter and then you have your infrastructure hosting these apps and APIs is completely gone away, that just doesn't exist anymore. And even more so for APIs which really doesn't have a whole lot of content to be cashed. They don't use CDN. So they are behind whatever API gateways whether they're in the cloud or whatever, they're hosting their APIs. And that has become your micro parameter, if you will, as these APIs are getting spread. And so the security teams are struggling with, how do I protect such a diverse set of environments that I am supposed to manage and protect where I don't have a unified view. I don't have even, like a complete view, if you will, of these APIs. And back in the days when phones or the modern iPhones and Android phones became popular, there used to be a sort of ad campaign I remember that said, "There is an app for that." >> Yeah. >> So the fast forward today, it's like, "There's an API for that." So everything you wanted to do today as a consumer or a business- >> John: Yeah. >> You can call an API and get your business done. And that's the challenge that's the explosion in APIs. >> Yeah. >> (laughs) Go ahead. >> It's interesting you have the API life cycle concept developing. Now you got, everyone knows- >> Right. >> The application life cycle, you know CI/CD pipelining, shifting left, but the surface area, you got web app firewalls which everyone knows is kind of like outdated, but you got API gateways. >> Yep. >> The surface area- >> Yeah. >> Is only increasing. So I have to ask you, do the existing API security tools out there bring that full application- >> Yeah. >> And API life cycle together? 'Cause you got to discover- >> Yep. >> The environment, you got to know what to protect and then also net new functionality. Can you comment? >> Right. Yeah. So that actually goes to your how question from, you know, previous section which is really what Cequence has defined is a API protection life cycle. And it's this concrete six-step process in which you protect your APIs. And the reason why we say it's a life cycle is it's not something that you do once and forget about it. It's a continuous process that you have to keep doing because your DevOps teams are publishing new APIs almost every day, every other day, if you will. So the start of that journey of that life cycle is really about discovering your external facing API attack surface which is where we highlight new hosting environments. We highlight accidental exposures. People are exposing their staging APIs. They might have access to production data. They are exposing Prometheus or performance monitoring servers. We find PKCS 7 files. We find Log4j vulnerabilities. These are things that you can just get a view of from outside looking in and then go about prioritizing which API environments you want to protect. So that's step number one. Step number two, really quick is do an inventory of all your APIs once you figure out which environments you want to protect or prioritize. And so that inventory includes a runtime inventory. Also creating specifications for these APIs. In lot of places, we find unmanaged APIs, shadow APIs and we create the API inventory and also push them towards sort of a central API management program. The third step is really looking at the risk of these APIs. Make sure they are using appropriate security controls. They're not leaking any sensitive information, PCI, PHI, PII, or other sort of industry-specific sensitive information. They are conforming to their schema. So sometimes the APIs dba.runtime from their schema and then that can cause a risk. So that's the first, sort of first half of this life cycle, if you will, which is really making sure your APIs are secure, they're using proper hygiene. The second half is about attack detection and prevention. So the fourth step is attack detection. And here again, we don't stop just at the OWASP Top 10 category of threats, a lot of other vendors do. They just do the OWASP API Top 10, but we think it's more than that. And we go deeper into business logic abuse, bots, and all the way to fraud. And that's sort of the attack detection piece of this journey. Once you detect these attacks, you start about, think about prevention of these attacks, also natively with Cequence. And the last step is about testing and making sure your APIs are secure even before they go live. >> What's- >> So that's a journey. Yeah. >> What's the secret sauce? What makes you different? 'Cause you got two sides to that coin. You got the auditing, kind of figure things out, and then you got the in-built attacks. >> Yeah. >> What makes you guys different? >> Yeah. So the way we are different is, first of all, Cequence is the only vendor that can, that has all these six steps in a single platform. We talked about security teams just lacking that complete view or consistent and uniform view of all your, you know, parameter, all your API infrastructure. We are combining that into a single platform with all the six steps that you can do in just one platform. >> John: Yeah. >> Number two is the outside looking in view which is the external discovery. It's something Cequence is unique in this space, uniquely doing this in this space. The third piece is the depth of our detection which is we don't just stop at the OWASP API Top 10, we go to fraud, business logic abuse, and bot attacks. And the mitigation, this will be interesting to you, which is a lot of the API security vendors say you come into existence because your WAF is not protecting your APIs, but they turn around when they detect the attacks to rely on a WAF to mitigate this or prevent these threats. And how can you sort of comprehend all that, right? >> Yeah. >> So we are unique in the sense we can prevent the attacks that we detect in the same platform without reliance on any other third-party solution. >> Yeah, I mean we- >> The last part is, sorry, just one last. >> Go ahead. Go ahead. >> Which is the scale. So we are serving largest of the large Fortune 100, Fortune 50 enterprises. We are processing 6 billion API calls per day. And one of the large customers of ours is processing 1 billion API calls per day with Cequence. So scale of APIs that we can process and how we can scale is also unique to Cequence. >> Yeah, I think the scale thing's a huge message. There, just, I put a little accent on that. I got to comment because we had an event last week called Supercloud which we were trying to talking about, you know, as clouds become more multicloud, you get more super capabilities. But automation, with super cloud comes super hackers. So as things advance, you're seeing the step function, the bad guys are getting better too. You mentioned bots. So I have to ask you what are some of the sophisticated attacks that you see that look like legitimate traffic or transactions? Can you comment on what your scale and your patterns are showing? Because the attacks are coming in fast and furious >> Correct. So APIs make the attack easier because APIs are well documented. So you want your partners and, you know, programmers to use your API ecosystem, but at the same time the attackers are getting the same information and they can program against those APIs very easily which means what? They are going to write a bunch of bots and automation to cause a lot of pain. The kind of sophistication we have seen is I'll just give a few examples. Ulta Beauty is one of our customers, very popular retailer in the US. And we recently found an interesting attack. They were selling some high-end hair curling high ends which are very high-end demand, very expensive, very hard to find. And so this links sort of physical path to API security, think about it, which is the bad guys were using a bot to scrape a third-party service which was giving local inventory information available to people who wanted to search for these items which are high in demand, low in supply. And they wrote a bot to find where, which locations have these items in supply, and they went and sort of broke into these showrooms and stole those items. So not only we say are saving them from physical theft and all the other problems that they have- >> Yeah. >> But also, they were paying about $25,000 per month extra- >> Yeah. >> For this geo-location service that was looking at their inventory. So that's the kind of abuse that can go on with APIs. Even when the APIs are perfectly secure, they're using appropriate security controls, these can go on. >> You know, that's a really great example. I'm glad you brought that up because I observed at AWS re:Inforce in Boston that Steven Schmidt has changed his title from chief information security officer to just chief security officer, to the point when asked he said, "Physical security is now tied together with the online." So to your point- >> Yeah. >> About the surveillance and attack setup- >> Yeah. >> For the physical, you got warehouses- >> Yep. >> You've got brick and mortar. This is the convergence of security. >> Correct. Absolutely. I mean, we do deal with many other, sort of a governance case. We help a Fortune 50 finance company which operates worldwide. And their gets concern is if an API is hosted in a certain country in Europe which has the most sort of aggressive data privacy and data regulations that they have to deal with, they want to make sure the consumer of that API is within a certain geo location whereby they're not subject to liabilities from GDPR and other data residency regulation. And we are the ones that are giving them that view. And we can have even restrict and make sure they're compliant with that regulation that they have to sort of comply with. >> I could only imagine that that geo-regional view and the intelligence and the scale gives you insights- >> Yeah. >> Into attacks that aren't really kind of, aren't supposed to be there. In other words, if you can keep the data in the geo, then you could look- >> Yep. >> At anything else as that, you know, you don't belong here kind of track. >> You don't belong here. Exactly. Yeah, yeah. >> All right. So let's get to the API. >> Yeah, I mean- >> So the API visibility is an issue, right? So I can see that, check, sold me on that, protection is key, but if, what's the current security team makeup? Are they buying into this or are they just kind of the hair on fire? What are security development teams doing? 'Cause they're under a lot of pressure to do the hardcore security work. And APIs, again, surface area's wide open, they're part of everyone's access. >> Yeah. So I mentioned about the six-step journey of the life cycle. Right? We see customers come to us with very acute pain point and they say, "Our hair is on, our hair on fire. (John laughing) Solve this problem for us." Like one large US telco company came to us to, just a simple problem, do the inventory and risk assessment of all our APIs. That's our number one pain point. Ended up starting with them on those two pain points or those two stops on their life cycle. And then we ended up solving all the six steps with them because once we started creating an inventory and looking at the risk profile, we also observed that these same APIs were target by bots and fraudsters doing all kinds of bad things. So once we discovered those problems we expanded the scope to sort of have the whole life cycle covered with the Cequence platform. And that's the typical experience which is, it's typically the security team. There are developer communities that are coming to us with sort of the testing aspect of it which integrated into DevOps toolchains and CI/CD pipelines. But otherwise, it's all about security challenges, acute pain points, and then expanding into the whole journey. >> All right. So you got the detection, you got the alerting, you got the protection, you got the mitigation. What's the advice- >> Yeah. >> To the customer or the right approach to set up with Cequence so that they can have the best protection. What the motion? What's the initial engagement look like? How do they engage? How do they operationalize? >> Yeah. >> You guys take me through that. >> Yeah. The simple way of engaging with Cequence is get that external assessment which will map your APIs for you, it'll create a assessment for you. We'll present that assessment, you know, to your security team. And like 90% of the times customers have an aha moment, (John chuckles) that they didn't know something that we are showing them. They find APIs that were not supposed to be public. They will find hosting environments that they didn't know about. They will find API gateways that were, like not commissioned, but being used. And so start there, start their journey with an assessment with Cequence, and then work with us to prioritize what problems you want to solve next once you have that assessment. >> So really making sure that their inventory of API is legit. >> Yep. Yep, absolutely. >> It's basically- >> Yep. >> I mean, you're starting to see more of this in the cloud-native, you know, Sbot, they call 'em, you know, (indistinct) materials. >> (Ameya faintly speaking). What do you got out there, kind of full understanding of what's being instrumented out there, big time. >> Yeah. The thing is a lot of analysts say that APIs is the number one attack vector this year and going forward, but you'll be surprised to see that it's not the APIs that get targeted that are poorly secured. Actually, the APIs that are completely not secured are the ones that are attacked the most because there are plenty of them. So start with the assessment, figure out the APIs that are out there and then start your journey. That's sort of my recommendation. >> So based on your advice what you're saying is there's a, most people make the mistake of having a lot of undocumented or unauthorized APIs out there that are unsecured. >> Yeah. And security teams are unaware of those APIs. So how do you protect something that you don't know even exists? >> Yeah. >> Right? So that's the challenge. >> Okay. You know, the APIs have to be secure. And as applications connect too, there's the other side of the APIs, whether that's credential passing, so much is at stake here relative to the security. It's not just access it's what's behind it. There's a lot of trust coming in. So, you know, I got to ask you a final question. You got zero trust and you got trust kind of coming together. What's (laughs), how do you respond to that? >> Yeah. Zero trust is part of it in the sense that you have to not trust sort of any API consumer as a completely trusted entity. Just like I gave you the Ultra Beauty example. They had trusted this third party to be absolutely safe and secure, you know, no controls necessary to sort of monitor their traffic, whereas they can be abused by their end consumers and cause you a lot of pain. So there is a sort of a linkage between zero trust. Never trusts anybody until you verify, that's the sort of angle, that's sort of the connection between APIs security and zero trust. >> Ameya, thank you for coming on theCUBE. Really appreciate the conversation. I'll give you the final word. What should people know about Cequence Security? How would you give the pitch? You go, you know, quick summary, what's going on? >> Yeah. So very excited to be in this space. We sort of are the largest security of API security vendor in the space in terms of revenue, the largest volume of API traffic that we process. And we are just getting started. This is a exciting journey we are on, we are very happy to serve the, you know, Fortune 50, you know, global 200 customers that we have, and we are expanding into many geographies and locations. And so look for some exciting updates from us in the coming days. >> Well, congratulations on your success. Love the approach, love the scale. I think scale's a new competitive advantage. I think that's the new lock-in if you're good, and your scaling providing a lot of benefits. So Ameya, thank you for coming, sharing the story. Looking forward to chatting again soon. >> Thank you very much. Thanks for having us. >> Okay. This is a CUBE Conversation. I'm John Furrier, here at Palo Alto, California. Thanks for watching. (cheerful music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante at our inaugural super cloud 22 event we further refined the concept of a super cloud iterating on the definition the salient attributes and some examples of what is and what is not a super cloud welcome to this week's wikibon cube insights powered by etr you know snowflake has always been what we feel is one of the strongest examples of a super cloud and in this breaking analysis from our studios in palo alto we unpack our interview with benoit de javille co-founder and president of products at snowflake and we test our super cloud definition on the company's data cloud platform and we're really looking forward to your feedback first let's examine how we defl find super cloudant very importantly one of the goals of super cloud 22 was to get the community's input on the definition and iterate on previous work super cloud is an emerging computing architecture that comprises a set of services which are abstracted from the underlying primitives of hyperscale clouds we're talking about services such as compute storage networking security and other native tooling like machine learning and developer tools to create a global system that spans more than one cloud super cloud as shown on this slide has five essential properties x number of deployment models and y number of service models we're looking for community input on x and y and on the first point as well so please weigh in and contribute now we've identified these five essential elements of a super cloud let's talk about these first the super cloud has to run its services on more than one cloud leveraging the cloud native tools offered by each of the cloud providers the builder of the super cloud platform is responsible for optimizing the underlying primitives of each cloud and optimizing for the specific needs be it cost or performance or latency or governance data sharing security etc but those primitives must be abstracted such that a common experience is delivered across the clouds for both users and developers the super cloud has a metadata intelligence layer that can maximize efficiency for the specific purpose of the super cloud i.e the purpose that the super cloud is intended for and it does so in a federated model and it includes what we call a super pass this is a prerequisite that is a purpose-built component and enables ecosystem partners to customize and monetize incremental services while at the same time ensuring that the common experiences exist across clouds now in terms of deployment models we'd really like to get more feedback on this piece but here's where we are so far based on the feedback we got at super cloud 22. we see three deployment models the first is one where a control plane may run on one cloud but supports data plane interactions with more than one other cloud the second model instantiates the super cloud services on each individual cloud and within regions and can support interactions across more than one cloud with a unified interface connecting those instantiations those instances to create a common experience and the third model superimposes its services as a layer or in the case of snowflake they call it a mesh on top of the cloud on top of the cloud providers region or regions with a single global instantiation a single global instantiation of those services which spans multiple cloud providers this is our understanding from a comfort the conversation with benoit dejaville as to how snowflake approaches its solutions and for now we're going to park the service models we need to more time to flesh that out and we'll propose something shortly for you to comment on now we peppered benoit dejaville at super cloud 22 to test how the snowflake data cloud aligns to our concepts and our definition let me also say that snowflake doesn't use the term data cloud they really want to respect and they want to denigrate the importance of their hyperscale partners nor do we but we do think the hyperscalers today anyway are building or not building what we call super clouds but they are but but people who bar are building super clouds are building on top of hyperscale clouds that is a prerequisite so here are the questions that we tested with snowflake first question how does snowflake architect its data cloud and what is its deployment model listen to deja ville talk about how snowflake has architected a single system play the clip there are several ways to do this you know uh super cloud as as you name them the way we we we picked is is to create you know one single system and that's very important right the the the um [Music] there are several ways right you can instantiate you know your solution uh in every region of a cloud and and you know potentially that region could be a ws that region could be gcp so you are indeed a multi-cloud solution but snowflake we did it differently we are really creating cloud regions which are superposed on top of the cloud provider you know region infrastructure region so we are building our regions but but where where it's very different is that each region of snowflake is not one in instantiation of our service our service is global by nature we can move data from one region to the other when you land in snowflake you land into one region but but you can grow from there and you can you know exist in multiple clouds at the same time and that's very important right it's not one single i mean different instantiation of a system is one single instantiation which covers many cloud regions and many cloud providers snowflake chose the most advanced level of our three deployment models dodgeville talked about too presumably so it could maintain maximum control and ensure that common experience like the iphone model next we probed about the technical enablers of the data cloud listen to deja ville talk about snow grid he uses the term mesh and then this can get confusing with the jamaicani's data mesh concept but listen to benoit's explanation well as i said you know first we start by building you know snowflake regions we have today furry region that spawn you know the world so it's a worldwide worldwide system with many regions but all these regions are connected together they are you know meshed together with our technology we name it snow grid and that makes it hard because you know regions you know azure region can talk to a ws region or gcp regions and and as a as a user of our cloud you you don't see really these regional differences that you know regions are in different you know potentially clown when you use snowflake you can exist your your presence as an organization can be in several regions several clouds if you want geographic and and and both geographic and cloud provider so i can share data irrespective of the the cloud and i'm in the snowflake data cloud is that correct i can do that today exactly and and that's very critical right what we wanted is to remove data silos and and when you instantiate a system in one single region and that system is locked in that region you cannot communicate with other parts of the world you are locking the data in one region right and we didn't want to do that we wanted you know data to be distributed the way customer wants it to be distributed across the world and potentially sharing data at world scale now maybe there are many ways to skin the other cat meaning perhaps if a platform does instantiate in multiple places there are ways to share data but this is how snowflake chose to approach the problem next question how do you deal with latency in this big global system this is really important to us because while snowflake has some really smart people working as engineers and and the like we don't think they've solved for the speed of light problem the best people working on it as we often joke listen to benoit deja ville's comments on this topic so yes and no the the way we do it it's very expensive to do that because generally if you want to join you know data which is in which are in different regions and different cloud it's going to be very expensive because you need to move you know data every time you join it so the way we do it is that you replicate the subset of data that you want to access from one region from other regions so you can create this data mesh but data is replicated to make it very cheap and very performant too and is the snow grid does that have the metadata intelligence yes to actually can you describe that a little bit yeah snow grid is both uh a way to to exchange you know metadata about so each region of snowflake knows about all the other regions of snowflake every time we create a new region diary you know the metadata is distributed over our data cloud not only you know region knows all the regions but knows you know every organization that exists in our clouds where this organization is where data can be replicated by this organization and then of course it's it's also used as a way to uh uh exchange data right so you can exchange you know beta by scale of data size and we just had i was just receiving an email from one of our customers who moved more than four petabytes of data cross-region cross you know cloud providers in you know few days and you know it's a lot of data so it takes you know some time to move but they were able to do that online completely online and and switch over you know to the diff to the other region which is failover is very important also so yes and no probably means typically no he says yes and no probably means no so it sounds like snowflake is selectively pulling small amounts of data and replicating it where necessary but you also heard him talk about the metadata layer which is one of the essential aspects of super cloud okay next we dug into security it's one of the most important issues and we think one of the hardest parts related to deploying super cloud so we've talked about how the cloud has become the first line of defense for the cso but now with multi-cloud you have multiple first lines of defense and that means multiple shared responsibility models and multiple tool sets from different cloud providers and an expanded threat surface so listen to benoit's explanation here please play the clip this is a great question uh security has always been the most important aspect of snowflake since day one right this is the question that every customer of ours has you know how you can you guarantee the security of my data and so we secure data really tightly in region we have several layers of security it starts by by encrypting it every data at rest and that's very important a lot of customers are not doing that right you hear these attacks for example on on cloud you know where someone left you know their buckets uh uh open and then you know you can access the data because it's a non-encrypted uh so we are encrypting everything at rest we are encrypting everything in transit so a region is very secure now you know you never from one region you never access data from another region in snowflake that's why also we replicate data now the replication of that data across region or the metadata for that matter is is really highly secure so snow grits ensure that everything is encrypted everything is you know we have multiple you know encryption keys and it's you know stored in hardware you know secure modules so we we we built you know snow grids such that it's secure and it allows very secure movement of data so when we heard this explanation we immediately went to the lowest common denominator question meaning when you think about how aws for instance deals with data in motion or data and rest it might be different from how another cloud provider deals with it so how does aws uh uh uh differences for example in the aws maturity model for various you know cloud capabilities you know let's say they've got a faster nitro or graviton does it do do you have to how does snowflake deal with that do they have to slow everything else down like imagine a caravan cruising you know across the desert so you know every truck can keep up let's listen it's a great question i mean of course our software is abstracting you know all the cloud providers you know infrastructure so that when you run in one region let's say aws or azure it doesn't make any difference as far as the applications are concerned and and this abstraction of course is a lot of work i mean really really a lot of work because it needs to be secure it needs to be performance and you know every cloud and it has you know to expose apis which are uniform and and you know cloud providers even though they have potentially the same concept let's say blob storage apis are completely different the way you know these systems are secure it's completely different the errors that you can get and and the retry you know mechanism is very different from one cloud to the other performance is also different we discovered that when we were starting to port our software and and and you know we had to completely rethink how to leverage blob storage in that cloud versus that cloud because just of performance too so we had you know for example to you know stripe data so all this work is work that's you know you don't need as an application because our vision really is that applications which are running in our data cloud can you know be abstracted of all this difference and and we provide all the services all the workload that this application need whether it's transactional access to data analytical access to data you know managing you know logs managing you know metrics all of these is abstracted too such that they are not you know tied to one you know particular service of one cloud and and distributing this application across you know many regions many cloud is very seamless so from that answer we know that snowflake takes care of everything but we really don't understand the performance implications in you know in that specific case but we feel pretty certain that the promises that snowflake makes around governance and security within their data sharing construct construct will be kept now another criterion that we've proposed for super cloud is a super pass layer to create a common developer experience and an enabler for ecosystem partners to monetize please play the clip let's listen we build it you know a custom build because because as you said you know what exists in one cloud might not exist in another cloud provider right so so we have to build you know on this all these this components that modern application mode and that application need and and and and that you know goes to machine learning as i say transactional uh analytical system and the entire thing so such that they can run in isolation basically and the objective is the developer experience will be identical across those clouds yes right the developers doesn't need to worry about cloud provider and actually our system we have we didn't talk about it but the marketplace that we have which allows actually to deliver we're getting there yeah okay now we're not going to go deep into ecosystem today we've talked about snowflakes strengths in this regard but snowflake they pretty much ticked all the boxes on our super cloud attributes and definition we asked benoit dejaville to confirm that this is all shipping and available today and he also gave us a glimpse of the future play the clip and we are still developing it you know the transactional you know unistore as we call it was announced in last summit so so they are still you know working properly but but but that's the vision right and and and that's important because we talk about the infrastructure right you mentioned a lot about storage and compute but it's not only that right when you think about application they need to use the transactional database they need to use an analytical system they need to use you know machine learning so you need to provide also all these services which are consistent across all the cloud providers so you can hear deja ville talking about expanding beyond taking advantage of the core infrastructure storage and networking et cetera and bringing intelligence to the data through machine learning and ai so of course there's more to come and there better be at this company's valuation despite the recent sharp pullback in a tightening fed environment okay so i know it's cliche but everyone's comparing snowflakes and data bricks databricks has been pretty vocal about its open source posture compared to snowflakes and it just so happens that we had aligotsy on at super cloud 22 as well he wasn't in studio he had to do remote because i guess he's presenting at an investor conference this week so we had to bring him in remotely now i didn't get to do this interview john furrier did but i listened to it and captured this clip about how data bricks sees super cloud and the importance of open source take a listen to goatzee yeah i mean let me start by saying we just we're big fans of open source we think that open source is a force in software that's going to continue for you know decades hundreds of years and it's going to slowly replace all proprietary code in its way we saw that you know it could do that with the most advanced technology windows you know proprietary operating system very complicated got replaced with linux so open source can pretty much do anything and what we're seeing with the data lake house is that slowly the open source community is building a replacement for the proprietary data warehouse you know data lake machine learning real-time stack in open source and we're excited to be part of it for us delta lake is a very important project that really helps you standardize how you lay out your data in the cloud and with it comes a really important protocol called delta sharing that enables you in an open way actually for the first time ever share large data sets between organizations but it uses an open protocol so the great thing about that is you don't need to be a database customer you don't even like databricks you just need to use this open source project and you can now securely share data sets between organizations across clouds and it actually does so really efficiently just one copy of the data so you don't have to copy it if you're within the same cloud so the implication of ellie gotzi's comments is that databricks with delta sharing as john implied is playing a long game now i don't know if enough about the databricks architecture to comment in detail i got to do more research there so i reached out to my two analyst friends tony bear and sanji mohan to see what they thought because they cover these companies pretty closely here's what tony bear said quote i've viewed the divergent lake house strategies of data bricks and snowflake in the context of their roots prior to delta lake databrick's prime focus was the compute not the storage layer and more specifically they were a compute engine not a database snowflake approached from the opposite end of the pool as they originally fit the mold of the classic database company rather than a specific compute engine per se the lake house pushes both companies outside of their original comfort zones data bricks to storage snowflake to compute engine so it makes perfect sense for databricks to embrace the open source narrative at the storage layer and for snowflake to continue its walled garden approach but in the long run their strategies are already overlapping databricks is not a 100 open source company its practitioner experience has always been proprietary and now so is its sql query engine likewise snowflake has had to open up with the support of iceberg for open data lake format the question really becomes how serious snowflake will be in making iceberg a first-class citizen in its environment that is not necessarily officially branding a lake house but effectively is and likewise can databricks deliver the service levels associated with walled gardens through a more brute force approach that relies heavily on the query engine at the end of the day those are the key requirements that will matter to data bricks and snowflake customers end quote that was some deep thought by by tony thank you for that sanjay mohan added the following quote open source is a slippery slope people buy mobile phones based on open source android but it's not fully open similarly databricks delta lake was not originally fully open source and even today its photon execution engine is not we are always going to live in a hybrid world snowflake and databricks will support whatever model works best for them and their customers the big question is do customers care as deeply about which vendor has a higher degree of openness as we technology people do i believe customers evaluation criteria is far more nuanced than just to decipher each vendor's open source claims end quote okay so i had to ask dodgeville about their so-called wall garden approach and what their strategy is with apache iceberg here's what he said iceberg is is very important so just to to give some context iceberg is an open you know table format right which was you know first you know developed by netflix and netflix you know put it open source in the apache community so we embrace that's that open source standard because because it's widely used by by many um many you know companies and also many companies have you know really invested a lot of effort in building you know big data hadoop solution or data like solution and they want to use snowflake and they couldn't really use snowflake because all their data were in open you know formats so we are embracing icebergs to help these companies move through the cloud but why we have been relentless with direct access to data direct access to data is a little bit of a problem for us and and the reason is when you direct access to data now you have direct access to storage now you have to understand for example the specificity of one cloud versus the other so as soon as you start to have direct access to data you lose your you know your cloud diagnostic layer you don't access data with api when you have direct access to data it's very hard to secure data because you need to grant access direct access to tools which are not you know protected and you see a lot of you know hacking of of data you know because of that so so that was not you know direct access to data is not serving well our customers and that's why we have been relented to do that because it's it's cr it's it's not cloud diagnostic it's it's you you have to code that you have to you you you need a lot of intelligence while apis access so we want open apis that's that's i guess the way we embrace you know openness is is by open api versus you know you access directly data here's my take snowflake is hedging its bets because enough people care about open source that they have to have some open data format options and it's good optics and you heard benoit deja ville talk about the risks of directly accessing the data and the complexities it brings now is that maybe a little fud against databricks maybe but same can be said for ollie's comments maybe flooding the proprietaryness of snowflake but as both analysts pointed out open is a spectrum hey i remember unix used to equal open systems okay let's end with some etr spending data and why not compare snowflake and data bricks spending profiles this is an xy graph with net score or spending momentum on the y-axis and pervasiveness or overlap in the data set on the x-axis this is data from the january survey when snowflake was holding above 80 percent net score off the charts databricks was also very strong in the upper 60s now let's fast forward to this next chart and show you the july etr survey data and you can see snowflake has come back down to earth now remember anything above 40 net score is highly elevated so both companies are doing well but snowflake is well off its highs and data bricks has come down somewhat as well databricks is inching to the right snowflake rocketed to the right post its ipo and as we know databricks wasn't able to get to ipo during the covet bubble ali gotzi is at the morgan stanley ceo conference this week they got plenty of cash to withstand a long-term recession i'm told and they've started the message that they're a billion dollars in annualized revenue i'm not sure exactly what that means i've seen some numbers on their gross margins i'm not sure what that means i've seen some numbers on their net retention revenue or net revenue retention again i'll reserve judgment until we see an s1 but it's clear both of these companies have momentum and they're out competing in the market well as always be the ultimate arbiter different philosophies perhaps is it like democrats and republicans well it could be but they're both going after a solving data problem both companies are trying to help customers get more value out of their data and both companies are highly valued so they have to perform for their investors to paraphrase ralph nader the similarities may be greater than the differences okay that's it for today thanks to the team from palo alto for this awesome super cloud studio build alex myerson and ken shiffman are on production in the palo alto studios today kristin martin and sheryl knight get the word out to our community rob hoff is our editor-in-chief over at siliconangle thanks to all please check out etr.ai for all the survey data remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcasts i publish each week on wikibon.com and siliconangle.com and you can email me at david.vellante at siliconangle.com or dm me at devellante or comment on my linkedin posts and please as i say etr has got some of the best survey data in the business we track it every quarter and really excited to be partners with them this is dave vellante for the cube insights powered by etr thanks for watching and we'll see you next time on breaking analysis [Music] you

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> After a two-year epic run in tech, 2022 has been an epically bad year. Through yesterday, The NASDAQ composite is down 30%. The S$P 500 is off 21%. And the Dow Jones Industrial average 16% down. And the poor holders at Bitcoin have had to endure a nearly 60% decline year to date. But judging by the attendance and enthusiasm, in major in-person tech events this spring. You'd never know that tech was in the tank. Moreover, walking around the streets of Las Vegas, where most tech conferences are held these days. One can't help but notice that the good folks of Main Street, don't seem the least bit concerned that the economy is headed for a recession. Hello, and welcome to this weeks Wiki Bond Cube Insights powered by ETR. In this Breaking Analysis we'll share our main takeaways from the first half of 2022. And talk about the outlook for tech going forward, and why despite some pretty concerning headwinds we remain sanguine about tech generally, but especially enterprise tech. Look, here's the bumper sticker on why many folks are really bearish at the moment. Of course, inflation is high, other than last year, the previous inflation high this century was in July of 2008, it was 5.6%. Inflation has proven to be very, very hard to tame. You got gas at $7 dollars a gallon. Energy prices they're not going to suddenly drop. Interest rates are climbing, which will eventually damage housing. Going to have that ripple effect, no doubt. We're seeing layoffs at companies like Tesla and the crypto names are also trimming staff. Workers, however are still in short supply. So wages are going up. Companies in retail are really struggling with the right inventory, and they can't even accurately guide on their earnings. We've seen a version of this movie before. Now, as it pertains to tech, Crawford Del Prete, who's the CEO of IDC explained this on theCUBE this very week. And I thought he did a really good job. He said the following, >> Matt, you have a great statistic that 80% of companies used COVID as their point to pivot into digital transformation. And to invest in a different way. And so what we saw now is that tech is now where I think companies need to focus. They need to invest in tech. They need to make people more productive with tech and it played out in the numbers. Now so this year what's fascinating is we're looking at two vastly different markets. We got gasoline at $7 a gallon. We've got that affecting food prices. Interesting fun fact recently it now costs over $1,000 to fill an 18 wheeler. All right, based on, I mean, this just kind of can't continue. So you think about it. >> Don't put the boat in the water. >> Yeah, yeah, yeah. Good luck if ya, yeah exactly. So a family has kind of this bag of money, and that bag of money goes up by maybe three, 4% every year, depending upon earnings. So that is sort of sloshing around. So if food and fuel and rent is taking up more, gadgets and consumer tech are not, you're going to use that iPhone a little longer. You're going to use that Android phone a little longer. You're going to use that TV a little longer. So consumer tech is getting crushed, really it's very, very, and you saw it immediately in ad spending. You've seen it in Meta, you've seen it in Facebook. Consumer tech is doing very, very, it is tough. Enterprise tech, we haven't been in the office for two and a half years. We haven't upgraded whether that be campus wifi, whether that be servers, whether that be commercial PCs as much as we would have. So enterprise tech, we're seeing double digit order rates. We're seeing strong, strong demand. We have combined that with a component shortage, and you're seeing some enterprise companies with a quarter of backlog, I mean that's really unheard of. >> And higher prices, which also profit. >> And therefore that drives up the prices. >> And this is a theme that we've heard this year at major tech events, they've really come roaring back. Last year, theCUBE had a huge presence at AWS Reinvent. The first Reinvent since 2019, it was really well attended. Now this was before the effects of the omicron variant, before they were really well understood. And in the first quarter of 2022, things were pretty quiet as far as tech events go But theCUBE'a been really busy this spring and early into the summer. We did 12 physical events as we're showing here in the slide. Coupa, did Women in Data Science at Stanford, Coupa Inspire was in Las Vegas. Now these are both smaller events, but they were well attended and beat expectations. San Francisco Summit, the AWS San Francisco Summit was a bit off, frankly 'cause of the COVID concerns. They were on the rise, then we hit Dell Tech World which was packed, it had probably around 7,000 attendees. Now Dockercon was virtual, but we decided to include it here because it was a huge global event with watch parties and many, many tens of thousands of people attending. Now the Red Hat Summit was really interesting. The choice that Red Hat made this year. It was purposefully scaled down and turned into a smaller VIP event in Boston at the Western, a couple thousand people only. It was very intimate with a much larger virtual presence. VeeamON was very well attended, not as large as previous VeeamON events, but again beat expectations. KubeCon and Cloud Native Con was really successful in Spain, Valencia, Spain. PagerDuty Summit was again a smaller intimate event in San Francisco. And then MongoDB World was at the new Javits Center and really well attended over the three day period. There were lots of developers there, lots of business people, lots of ecosystem partners. And then the Snowflake summit in Las Vegas, it was the most vibrant from the standpoint of the ecosystem with nearly 10,000 attendees. And I'll come back to that in a moment. Amazon re:Mars is the Amazon AI robotic event, it's smaller but very, very cool, a lot of innovation. And just last week we were at HPE Discover. They had around 8,000 people attending which was really good. Now I've been to over a dozen HPE or HPE Discover events, within Europe and the United States over the past decade. And this was by far the most vibrant, lot of action. HPE had a little spring in its step because the company's much more focused now but people was really well attended and people were excited to be there, not only to be back at physical events, but also to hear about some of the new innovations that are coming and HPE has a long way to go in terms of building out that ecosystem, but it's starting to form. So we saw that last week. So tech events are back, but they are smaller. And of course now a virtual overlay, they're hybrid. And just to give you some context, theCUBE did, as I said 12 physical events in the first half of 2022. Just to compare that in 2019, through June of that year we had done 35 physical events. Yeah, 35. And what's perhaps more interesting is we had our largest first half ever in our 12 year history because we're doing so much hybrid and virtual to compliment the physical. So that's the new format is CUBE plus digital or sometimes just digital but that's really what's happening in our business. So I think it's a reflection of what's happening in the broader tech community. So everyone's still trying to figure that out but it's clear that events are back and there's no replacing face to face. Or as I like to say, belly to belly, because deals are done at physical events. All these events we've been to, the sales people are so excited. They're saying we're closing business. Pipelines coming out of these events are much stronger, than they are out of the virtual events but the post virtual event continues to deliver that long tail effect. So that's not going to go away. The bottom line is hybrid is the new model. Okay let's look at some of the big themes that we've taken away from the first half of 2022. Now of course, this is all happening under the umbrella of digital transformation. I'm not going to talk about that too much, you've had plenty of DX Kool-Aid injected into your veins over the last 27 months. But one of the first observations I'll share is that the so-called big data ecosystem that was forming during the hoop and around, the hadoop infrastructure days and years. then remember it dispersed, right when the cloud came in and kind of you know, not wiped out but definitely dampened the hadoop enthusiasm for on-prem, the ecosystem dispersed, but now it's reforming. There are large pockets that are obviously seen in the various clouds. And we definitely see a ecosystem forming around MongoDB and the open source community gathering in the data bricks ecosystem. But the most notable momentum is within the Snowflake ecosystem. Snowflake is moving fast to win the day in the data ecosystem. They're providing a single platform that's bringing different data types together. Live data from systems of record, systems of engagement together with so-called systems of insight. These are converging and while others notably, Oracle are architecting for this new reality, Snowflake is leading with the ecosystem momentum and a new stack is emerging that comprises cloud infrastructure at the bottom layer. Data PaaS layer for app dev and is enabling an ecosystem of partners to build data products and data services that can be monetized. That's the key, that's the top of the stack. So let's dig into that further in a moment but you're seeing machine intelligence and data being driven into applications and the data and application stacks they're coming together to support the acceleration of physical into digital. It's happening right before our eyes in every industry. We're also seeing the evolution of cloud. It started with the SaaS-ification of the enterprise where organizations realized that they didn't have to run their own software on-prem and it made sense to move to SaaS for CRM or HR, certainly email and collaboration and certain parts of ERP and early IS was really about getting out of the data center infrastructure management business called that cloud 1.0, and then 2.0 was really about changing the operating model. And now we're seeing that operating model spill into on-prem workloads finally. We're talking about here about initiatives like HPE's Green Lake, which we heard a lot about last week at Discover and Dell's Apex, which we heard about in May, in Las Vegas. John Furrier had a really interesting observation that basically this is HPE's and Dell's version of outposts. And I found that interesting because outpost was kind of a wake up call in 2018 and a shot across the bow at the legacy enterprise infrastructure players. And they initially responded with these flexible financial schemes, but finally we're seeing real platforms emerge. Again, we saw this at Discover and at Dell Tech World, early implementations of the cloud operating model on-prem. I mean, honestly, you're seeing things like consoles and billing, similar to AWS circa 2014, but players like Dell and HPE they have a distinct advantage with respect to their customer bases, their service organizations, their very large portfolios, especially in the case of Dell and the fact that they have more mature stacks and knowhow to run mission critical enterprise applications on-prem. So John's comment was quite interesting that these firms are basically building their own version of outposts. Outposts obviously came into their wheelhouse and now they've finally responded. And this is setting up cloud 3.0 or Supercloud, as we like to call it, an abstraction layer, that sits above the clouds that serves as a unifying experience across a continuum of on-prem across clouds, whether it's AWS, Azure, or Google. And out to both the near and far edge, near edge being a Lowes or a Home Depot, but far edge could be space. And that edge again is fragmented. You've got the examples like the retail stores at the near edge. Outer space maybe is the far edge and IOT devices is perhaps the tiny edge. No one really knows how the tiny edge is going to play out but it's pretty clear that it's not going to comprise traditional X86 systems with a cool name tossed out to the edge. Rather, it's likely going to require a new low cost, low power, high performance architecture, most likely RM based that will enable things like realtime AI inferencing at that edge. Now we've talked about this a lot on Breaking Analysis, so I'm not going to double click on it. But suffice to say that it's very possible that new innovations are going to emerge from the tiny edge that could really disrupt the enterprise in terms of price performance. Okay, two other quick observations. One is that data protection is becoming a much closer cohort to the security stack where data immutability and air gaps and fast recovery are increasingly becoming a fundamental component of the security strategy to combat ransomware and recover from other potential hacks or disasters. And I got to say from our observation, Veeam is leading the pack here. It's now claiming the number one revenue spot in a statistical dead heat with the Dell's data protection business. That's according to Veeam, according to IDC. And so that space continues to be of interest. And finally, Broadcom's acquisition of Dell. It's going to have ripple effects throughout the enterprise technology business. And there of course, there are a lot of questions that remain, but the one other thing that John Furrier and I were discussing last night John looked at me and said, "Dave imagine if VMware runs better on Broadcom components and OEMs that use Broadcom run VMware better, maybe Broadcom doesn't even have to raise prices on on VMware licenses. Maybe they'll just raise prices on the OEMs and let them raise prices to the end customer." Interesting thought, I think because Broadcom is so P&L focused that it's probably not going to be the prevailing model but we'll see what happens to some of the strategic projects rather like Monterey and Capitola and Thunder. We've talked a lot about project Monterey, the others we'll see if they can make the cut. That's one of the big concerns because it's how OEMs like the ones that are building their versions of outposts are going to compete with the cloud vendors, namely AWS in the future. I want to come back to the comment on the data stack for a moment that we were talking about earlier, we talked about how the big data ecosystem that was once coalescing around hadoop dispersed. Well, the data value chain is reforming and we think it looks something like this picture, where cloud infrastructure lives at the bottom. We've said many times the cloud is expanding and evolving. And if companies like Dell and HPE can truly build a super cloud infrastructure experience then they will be in a position to capture more of the data value. If not, then it's going to go to the cloud players. And there's a live data layer that is increasingly being converged into platforms that not only simplify the movement in ELTing of data but also allow organizations to compress the time to value. Now there's a layer above that, we sometimes call it the super PaaS layer if you will, that must comprise open source tooling, partners are going to write applications and leverage platform APIs and build data products and services that can be monetized at the top of the stack. So when you observe the battle for the data future it's unlikely that any one company is going to be able to do this all on their own, which is why I often joke that the 2020s version of a sweaty Steve Bomber running around the stage, screaming, developers, developers developers, and getting the whole audience into it is now about ecosystem ecosystem ecosystem. Because when you need to fill gaps and accelerate features and provide optionality a list of capabilities on the left hand side of this chart, that's going to come from a variety of different companies and places, we're talking about catalogs and AI tools and data science capabilities, data quality, governance tools and it should be of no surprise to followers of Breaking Analysis that on the right hand side of this chart we're including the four principles of data mesh, which of course were popularized by Zhamak Dehghani. So decentralized data ownership, data as products, self-serve platform and automated or computational governance. Now whether this vision becomes a reality via a proprietary platform like Snowflake or somehow is replicated by an open source remains to be seen but history generally shows that a defacto standard for more complex problems like this is often going to emerge prior to an open source alternative. And that would be where I would place my bets. Although even that proprietary platform has to include open source optionality. But it's not a winner take all market. It's plenty of room for multiple players and ecosystem innovators, but winner will definitely take more in my opinion. Okay, let's close with some ETR data that looks at some of those major platform plays who talk a lot about digital transformation and world changing impactful missions. And they have the resources really to compete. This is an XY graphic. It's a view that we often show, it's got net score on the vertical access. That's a measure of spending momentum, and overlap or presence in the ETR survey. That red, that's the horizontal access. The red dotted line at 40% indicates that the platform is among the highest in terms of spending velocity. Which is why I always point out how impressive that makes AWS and Azure because not only are they large on the horizontal axis, the spending momentum on those two platforms rivals even that of Snowflake which continues to lead all on the vertical access. Now, while Google has momentum, given its goals and resources, it's well behind the two leaders. We've added Service Now and Salesforce, two platform names that have become the next great software companies. Joining likes of Oracle, which we show here and SAP not shown along with IBM, you can see them on this chart. We've also plotted MongoDB, which we think has real momentum as a company generally but also with Atlas, it's managed cloud database as a service specifically and Red Hat with trying to become the standard for app dev in Kubernetes environments, which is the hottest trend right now in application development and application modernization. Everybody's doing something with Kubernetes and of course, Red Hat with OpenShift wants to make that a better experience than do it yourself. The DYI brings a lot more complexity. And finally, we've got HPE and Dell both of which we've talked about pretty extensively here and VMware and Cisco. Now Cisco is executing on its portfolio strategy. It's got a lot of diverse components to its company. And it's coming at the cloud of course from a networking and security perspective. And that's their position of strength. And VMware is a staple of the enterprise. Yes, there's some uncertainty with regards to the Broadcom acquisition, but one thing is clear vSphere isn't going anywhere. It's entrenched and will continue to run lots of IT for years to come because it's the best platform on the planet. Now, of course, these are just some of the players in the mix. We expect that numerous non-traditional technology companies this is important to emerge as new cloud players. We've put a lot of emphasis on the data ecosystem because to us that's really going to be the main spring of digital, i.e., a digital company is a data company and that means an ecosystem of data partners that can advance outcomes like better healthcare, faster drug discovery, less fraud, cleaner energy, autonomous vehicles that are safer, smarter, more efficient grids and factories, better government and virtually endless litany of societal improvements that can be addressed. And these companies will be building innovations on top of cloud platforms creating their own super clouds, if you will. And they'll come from non-traditional places, industries, finance that take their data, their software, their tooling bring them to their customers and run them on various clouds. Okay, that's it for today. Thanks to Alex Myerson, who is on production and does the podcast for Breaking Analysis, Kristin Martin and Cheryl Knight, they help get the word out. And Rob Hoofe is our editor and chief over at Silicon Angle who helps edit our posts. Remember all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me at dvellante, or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE's Insights powered by ETR. Thanks for watching be well. And we'll see you next time on Breaking Analysis. (upbeat music)

>>The cube presents HPE discover 2022 brought to you by HPE. >>Welcome back to the Cube's coverage. We're wrapping up day two, John furrier and Dave ante. We got some friends and colleagues, longtime friends, Crawford Del Pret is the president of IDC. Matt Eastwood is the senior vice president of infrastructure and cloud guys. Thanks for coming on spending time. Great to you guys. >>That's fun to do it. Awesome. >>Cravin I want to ask you, I, I think this correct me if I'm wrong, but this was your first physical directions as, as president. Is that true or did you do one in 2019? >>Uh, no, we did one in 20. We did, we did one in 20. I was president at the time and then, and then everything started, >>Well, how was directions this year? You must have been stoked to get back together. Yeah, >>It was great. I mean, it was actually pretty emotional, you know, it's, it's a community, right? I mean, we have a lot of customers that have been coming to that event for a long, long time and to stand up on the stage and look out and see people, you know, getting a little bit emotional and a lot of hugs and a lot of bringing people together. And this year in Boston, we were the first event really of any size that kind of came back. And when I kind of didn't see that coming in terms of how people, how ready people were to be together. Cause >>When did you did it April >>In Boston? Yeah, we did it March in March. Yeah, it was, it was, it was, it was a game day decision. I mean, we were, we had negotiated it, we were going back and forth and then I kind of made the call at the last minute, say, let's go and do it. And in Santa Clara, I felt like we were kind of opening up the crypt at the convention center. I mean, all the production people said, you know what? You guys were really the first event to be back. And attendance was really strong. You know, we, we, we got over a thousand. It was, it was really good. >>Good. It's always a fun when I was there. It was, it's a big deal. You guys prepare for it. Yeah. Some new faces up on the stage. Yeah. So, so Matt, um, you've been doing the circuit. I take it like, like all top analysts, super busy. Right. This is kind of end of the spring. I mean, I know it's summer, right. That's right. But, um, how do you look at, at discover relative some, some of the other events you've been at? >>So I think if you go back to what Crawford was just talking about our event in March, I mean, March was sort of the, the reopening and there was, I think people just felt so happy to be, to be back out there. You still get a little bit at, at these events. I mean, cuz for each, each company it's their first time back at it, but I think we're starting to get down what these events are gonna feel like going forward. Um, and it, I mean, there's good energy here. There's been a good attendance. I think the, the interest in getting back live and having face to face meetings is clearly strong. >>Yeah. I mean, this definitely shows that hybrids, the steady state, both events cloud. Yeah. Virtualization remotes. So what are you guys seeing with that hybrid mode? Just from a workforce, certainly people excited to get back together, but it's gonna continue. You're starting to see that digital piece. How is that impacting some of the, some of the customers you're tracking, who's winning and who's losing, coming out of the pandemic. What's the big picture look like? >>Yeah. I mean, if you, if you take a look at hybrid work, um, people are testing many, many, many different models. And I think as we move from a pandemic to an em, we're gonna have just waves and waves and waves of people needing that flexibility for a lot of different reasons, whether they have, uh, you know, preexisting conditions, whether they're just not comfortable, whether they have people who can't be vaccinated at home. So I think we're gonna be in this hybrid work for a long, long time. I do think though that we are gonna transition back into some kind of a normal, um, and I, and I think the big difference is that I think leaders back in the day, a long time ago, when people weren't coming into work, it was kind of like, oh, I know nothing's going on there. People aren't getting worked. And I think we're over that stage. Yeah. I think we're now into a stage where we know people can be productive. We know people can effectively work from home and now we're into the reason to be in the office. And the reason to be in the office is that collaboration, it's that mentoring it's that, you know, think about your 25 year old self. Do you wanna be staring at a windshield all day long and not kind of building those relationships? People want face to face, it's difficult. They want face >>To face and I would, and you guys had a great culture and it's a young culture. How are you handling it as an executive in terms of, is there a policy for hybrid or >>Yeah, so, so, so at IDC, what we did is we're in a pilot period and we've kind of said that the summertime is gonna be a pilot period and we've asked people, we're actually serving shocker, we're >>Serving, >>But we're, but we're, but, but we're actually asking people to work with their manager on what works for them. And then we'll come up with, you know, whether you are in, out of the office worker, which will be less than two days a hybrid worker, which will be three days or, uh, in, in the office, which is more than three days a week. And you know, we all know there's, there's, there's limitation, there's, there's, there's variability in that, but that's kind of what we're shooting for. And we'd like to be able to have that in place in the fall. >>Are you pretty much there? >>Yeah, I am. I, I am there three days a week. I I, Mondays and Fridays, unless, >>Because you got the CEO radius, right? Yeah. >><laugh>, <laugh> >>The same way I'm in the office, the smaller, smaller office. But so, uh, let's talk a little bit about the, the numbers we were chatting earlier, trying to squint through you guys are, you know, obviously the gold standard for what the market does, what happened in, you know, during the pandemic, what happened in 2021 and what do you expect to happen in, in 2022 in terms of it spending growth? >>Yeah. So this is, this is a crazy time, right? We've never seen this. You and I have a long history of, uh, of tracking this. So we saw in, in, in, in 2020, the market decelerated dramatically, um, the GDP went down to a negative like it always does in these cases, it was, you know, probably negative six in that, in that, in that kind of range for the first time, since I've been tracking it, which goes back over 30 years, tech didn't go negative tech went to about just under 3%. And then as we went to 2021, we saw, you know, everything kind of snap back, we saw tech go up to about 11% growth. And then of course we saw, you know, GDP come back to about a 4%, you know, ki kind of range growth. Now what's I think the story there is that companies and you saw this anecdotally everywhere companies leaned into tech, uh, company. >>You know, I think, you know, Matt, you have a great statistic that, you know, 80% of companies used COVID as their point to pivot into digital transformation, right. And to invest in a different way. And so what we saw now is that tech is now where I think companies need to focus. They need to invest in tech. They need to make people more productive with tech and it played out in the numbers now. So this year what's fascinating is we're looking at two Fastly different markets. We've got gasoline at $7 a gallon. We've got that affecting food prices. Uh, interesting fun fact recently it now costs over $1,000 to fill an 18 Wheeler. All right. Based on, I mean this just kind of can't continue. So you think about it, don't put the boat >>In the wall. Yeah. Yeah. >>Good, good, good, good luck. It's good. Yeah, exactly. <laugh> so a family has kind of this bag of money, right? And that bag of money goes up by maybe three, 4% every year, depending upon earnings. So that is sort of sloshing around. So if food and fuel and rent is taking up more gadgets and consumer tech are not, you know, you're gonna use that iPhone a little longer. You're gonna use that Android phone a little longer. You're gonna use that TV a little longer. So consumer tech is getting crushed, you know, really it's very, very, and you saw it immediately and ad spending, you've seen it in meta. You've seen it in Facebook. Consumer tech is doing very, very it's tough enterprise tech. We haven't been in the office for two and a half years. We haven't upgraded whether that be campus wifi, whether that be, uh, servers, whether that be, uh, commercial PCs, as much as we would have. So enterprise tech, we're seeing double digit order rates. We're seeing strong, strong demand. Um, we have combined that with a component shortage and you're seeing some enterprise companies with a quarter of backlog. I mean, that's, you know, really unheard at higher >>Prices, which >>Also, and therefore that drives that >>Drives. It shouldn't be that way. If there's a shortage of chips, it shouldn't be that way, >>But it is, but it is, but it is. And then you look at software and we saw this, you know, we've seen this in previous cycles, but we really saw it in the COVID downturn where, uh, in software, the stickiness of SaaS means that you just, you're not gonna take that stuff out. So the, the second half of last year we saw double digit rates in software surprise. We're seeing high single digit revenue growth in software now, so that we think is gonna sustain, which means that overall it demand. We expect to be between five and 6% this year. Okay, fine. We have a war going on. We have, you know, potentially, uh, a recession. We think if we do, it'll be with a lower case, R maybe you see a banded down to maybe 4% growth, but it's gonna grow this. >>Is it, is it both the structural change of the disruption of COVID plus the digital transformation yeah. Together? Or is it, >>I, I think you make a great point. Um, I, I, I think that we are entering a new era for tech. I think that, you know, Andrew's famous wall street journal oped 10 years ago, software is even world was absolutely correct. And now we're finding that software is, is eing into every nook and cranny people have to invest. They, they know disruptors are coming around every single corner. And if I'm not leaning into digital transformation, I'm dead. So >>The number of players in tech is, is growing, >>Cuz there's well, the number of players in tech number >>Industry's coming >>In. Yeah. The industry's coming in. So I think the interesting dynamic you're gonna see there is now we have high interest rates. Yeah. Which means that the price of funding these companies and buying them and putting data on is gonna get higher and higher, which means that I think you could, you could see another wave of consolidation. Mm-hmm <affirmative> because tech large install based tech companies are saying, oh, you know what? I like that now >>4 0 9 S are being reset too. That's another point. >>Yeah. I mean, so if you think about this, this transformation, right. So it's all about apps, absent data and differentiating and absent data. What the, the big winner the last couple years was cloud. And I would just say that if this is the first potential recession that we're talking about, where the cloud service providers. So I think a cloud as an operating model, not necessarily a destination, but for these cloud service providers, they've actually never experienced a slowdown. So how, and, and if you think about the numbers, 30% of, of the typical it budget is now quote, unquote cloud and 30% of all expenditures are it related. So there's a lot of exposure there. And I think you're gonna see a lot of, a lot of focus on how we can rationalize some of those investments. >>Well, that's a great point. I want to just double click on that. So yeah, the cloud did well during the pandemic. We saw that with SAS, have you guys tracked like the Tams of what got pulled forward? So the bit, a big discussion about something that pulled forward because of the pandemic, um, like zoom, for instance, obviously everyone's using zoom. Yeah, yeah, yeah. Was there fake Tams? There was one, uh, couple analysts who were pointing out that some companies were hot during the pandemic will go away that that Tam doesn't really exist, but there's some that got pulled forward early. That's where the growth is. So is there a, is there a line between the, I call fake Tam or pulled forward TA that was only for the pandemic situationally, um, devices might be like virtual event, virtual event. Software was one, I know Hoppin got laid a lot of layoffs. And so that was kind of gone coming, coming and going. And you got SAS which got pulled forward. Yep. And it's not going away, but it's >>Sustaining. Yeah. Yeah. But it's, but, but it's sustaining, um, you know, I definitely think there was a, there was a lot of spending that absolutely got pulled forward. And I think it's really about CEO's ability to control expectations and to kind of message what it, what it looks like. Um, you know, I think I look, I, I, I think virtual event platforms probably have a role. I think you can, you can definitely, you know, raise your margins in the event, business, significantly using those platforms. There's a role for them. But if you were out there thinking that this thing was gonna continue, then you know, that that was unrealistic, you know, Dave, to, to your point on devices, I'm not necessarily, you know. Sure. I think, I think we definitely got ahead of our expectations and things like consumer PCs, those things will go back to historical growth >>Rates. Yeah. I mean, you got the install base is pretty young right now, but I think the one way to look at it too, is there was some technical debt brought in because people didn't necessarily expect that we'd be moving to a permanent hybrid state two years ago. So now we have to actually invest on both. We have to make, create a little bit more permanency around the hybrid world. And then also like Crawford's talking about the permanency of, of having an office and having people work in, in multiple modes. Yeah. It actually requires investment in both the office. And >>Also, so you're saying operationally, you gotta run the company and do the digital transformation to level up the hybrid. >>Yeah. Yeah. Just the way people work. Right. So, so, you know, you basically have to, I mean, even for like us internally, Crawford was saying, we're experimenting with what works for us. My team before the pandemic was like one third virtual. Now it's two third virtual, which means that all of our internal meetings are gonna be on, on teams or zoom. Right. Yeah. They're not gonna necessarily be, Hey, just coming to the office today, cuz two thirds of people aren't in the Boston area. >>Right. Matt, you said if you see cloud as an operating model, not necessarily a place. I remember when you were out, I was in the, on the, on the, on the zoom when, when first met Adam Celski yeah. Um, he said, you were asking him about, you know, the, the on-prem guys and he's like, nah, it's not cloud. And he kind of was very dismissive of it. Yeah. Yeah. I wanna get your take on, you know, what we're seeing with as Azure service GreenLake, apex, Cisco's got their version. IBM. Fewer is doing it. Is that cloud. >>I think if it's, I, I don't think all of it is by default. I think it is. If I actually think what HPE is doing is cloud, because it's really about how you present the services and how you allow customers to engage with the platform. So they're actually creating a cloud model. I think a lot of people get lost in the transition from, you know, CapEx to OPEX and the financing element of this. But the reality is what HPE is doing and they're sort of setting the standard. I think for the industry here is actually setting up what I would consider a cloud model. >>Well, in the early days of, of GreenLake, for sure it was more of a financial, you >>Know, it was kind of bespoke, right. But now you've got 70 services. And so you can, you can build that out. But >>You know, we were talking to Keith Townsend right after the keynote and we were sort of UN unpacking it a little bit. And I, I asked the question, you know, if you, if you had to pin this in terms of AWS's maturity, where are we? And the consensus was 2014 console filling, is that fair or unfair? >>Oh, that's a good question. I mean, um, I think it's, well, clouds come a long way, right? So it'd be, I, I, I think 20, fourteen's probably a little bit too far back because >>You have more modern tools I Kubernetes is. Yeah. >>And, but you also have, I would say the market still getting to a point of, of, of readiness and in terms of buying this way. So if you think about the HP's kind of strategy around edge, the core platform as a, as a service, you know, we're all big believers in edge and the apps follow the data and the data's being created in new locations and you gotta put the infrastructure there. And for an end user, there's a lot of risk there because they don't know how to actually plan for capacity at the edge. So they're gonna look to offload that, but this is a long term play to actually, uh, build out and deploy at the edge. It's not gonna happen tomorrow. It's a five, 10 year play. >>Yeah. I mean, I like the operating model. I'd agree with you, Matt, that if it's, if it's cloud operations, DevSecOps and all that, all that jazz it's cloud it's cloud operating and, and, and public cloud is a public cloud hyperscaler on premise. And the storage folks were presented. That's a single pane of glass. That's old school concepts, but cloud based. Yep. Shipping hardwares, auto figures. Yeah. That's the kind of consumption they're going for now. I like it. Then I, then they got the partner led thing is the partner piece. How do you guys see that? Because if I'm a partner, there's two things, wait a minute, am I at bottleneck to the direct self-service? Or is that an enabler to get more cash, to make more money? If I'm a partner. Cause you see what Essentia's doing with what they do with Amazon and Deloitte and et C. Yeah. You know, it's interesting, right? Like they've a channel partner, I'm making more cash. >>Yeah. I mean, well, and those channel partners are all in transition too. They're trying to yeah. Right. Figure out. Right, right. Are they, you know, what are their managed services gonna look like? You know, what kind of applications are they gonna stand up? They're they're not gonna just be >>Reselling, bought a big house in a boat. The box is not selling. I wanna ask you guys about growth because you know, the big three cloud, big four growing pick a number, I dunno, 30, 35% revenue big. And like you said, it's 30% of the business now. I think Dell's growing double digits. I don't know how much of that is sustainable. A lot of that is PCs, but still strong growth. Yep. I think Cisco has promised 9% >>In, in that. Right, right. >>About that. Something like that. I think IBM Arvin is at 6%. Yep. And I think HPE has said, Hey, we're gonna do three to 4%. Right. Which is so really sort of lagging and which I think a lot of people in wall street is like, okay, well that's not necessarily so compelling. Right. What does HPE have to do to double that growth? Or even triple that growth. >>Yeah. So they're gonna need, so, so obviously you're right. I mean, being able to show growth is Tanem out to this company getting, you know, more attention, more heat from, from investors. I think that they're rightly pointing to the triple digit growth that they've seen on green lake. I think if you look at the trailing, you know, 12 month bookings, you got over, you know, 7 billion, which means that in a year, you're gonna have a significant portion of the company is as a service. And you're gonna see that revenue that's rat being, you know, recognized over a series of months. So I think that this is sort of the classic SAS trough that we've seen applied to an infrastructure company where you're basically have to kind of be in the desert for a long time. But if they can, I think the most important number for HPE right now is that GreenLake booking snow. >>And if you look at that number and you see that number, you know, rapidly come down, which it hasn't, I mean off a very large number, you're still in triple digits. They will ultimately start to show revenue growth, um, in the business. And I think the one thing people are missing about HPE is there aren't, there are a lot of companies that want to build a platform, but they're small and nobody cares. And nobody let's say they throw a party and nobody comes. HP has such a significant installed base that if they do build a platform, they can attract partners to that platform. What I mean by that is partners that deliver services on GreenLake that they're not delivering. They have the girth to really start to change an industry and change the way stuff is being built. And that's the be they're making. And frankly, they are showing progress in that direction. >>So I buy that. But the one thing that concerns me is they kind of hide the ball on services. Right. And I, and I worry about that is like, is this a services kind of just, you know, same wine, new bottle or, >>Or, yeah. So, so I, I, I would argue that it's not about hiding the ball. It's about eliminating confusion of the marketplace. This is the company that bought EDS only to spin it off <laugh>. Okay. And so you don't wanna have a situation where you're getting back into services. >>Yeah. They're the only one >>They're product, not the only ones who does, I mean, look at the way IBM used to count and still >>I get it. I get it. But I think it's, it's really about clarity of mission. Well, I point next they are in the Ts business, absolutely. Point of it. It's important prop >>Drive for them at the top. Right. The global 50 say there's still a lot of uniqueness in what they want to buy. So there's definitely a lot of bespoke kind of delivery. That's still happening there. The real promise here is when you get into the global 2000 and yeah. And can start them to getting them to consume very standardized offers. And then the margins are, are healthy >>And they got they're what? Below 30, 33, 30 3%. I think 34% last quarter gross margin. Yeah. That that's solid. Just compare that with Dell is, I don't know. They're happy with 20, 21% of correct. You get that, which is, you know, I I'll come back. Go ahead. I want, I wanna ask >>Guys. No, I wanna, I wanna just, he said one thing I like, which was, I think he nailed it. They have such, um, big install base. They have a great channel. They know how to use it. Right. That's a real asset. Yeah. And Microsoft, I remember when their stock was trading at 26 when Baltimore was CEO. Yep. What they did with no, they had office and windows, so a little bit different. Yep. But similar strategy, leverage our install base, bring something up to them. That's what you're kind of connecting the >>Absolutely. You have this velocity, uh, machine with a significant girth that you can now move to a new model. They move that to a new model. To Matt's point. They lead the industry, they change the way large swath the customers buy and you will see it in steady revenue growth over time. Okay. So I just in that, well, >>So your point is the focus and there the right it's the right focus. And I would agree what's >>What's the other move. What's their other move, >>The problem. Triple digit booking growth off a number that gets bigger >>Inspired. Okay. >>Whats what's the scoreboard. Okay. Now they're go at the growth. That's the scoreboard. What are the signals? Are you looking at on the scoreboard Crawford and Matt in terms of success? What are the benchmarks? Is it ecosystem growth, number of services, triple growth. Yeah. What's the, what are some of the metrics that you guys are gonna be watching and we should be watching? >>Yeah. I mean, I dunno if >>You wanna jump in, I mean, I think ecosystem's really critical. Yeah. You want to, you want to have well and, and you need to sell both ways like HPE needs to be selling their technology on other cloud providers and vice versa. You need to have the VMs of the world on, you know, offering services on your platform and, and kind of capturing some, some motion off that. I think that's pretty critical. The channel definitely. I mean, you have to help and what you're gonna see happen there is there will be channel partners that succeed in transforming and succeeding and there'll be a lot that go away and that some, some of that's, uh, generational there'll be people that just kind of age outta the system and, and just go home. >>Yeah. Yeah. So I would argue it's, it's, it's, it's gonna be, uh, bookings growth rate. It's gonna be retention rate of the, of, of, of the customers, uh, that they have. And then it's gonna be that, that, um, you know, ultimately you're gonna see revenue, um, growth, and which is that revenue growth is gonna have to be correlated to the booking's growth for green lake cross. >>What's the Achilles heel on, on HPE. If you had to do the SWAT, what's the, what's the w for HPE that they really need to pay >>Attention to. I mean, they, they need to continue their relentless focus on cost, particularly in the, in the core compute, you know, segment they need to be, they need to be able to be as cost effective as possible while the higher profit dollars associated with GreenLake and other services come in and then increase the overall operating margin and gross margin >>Picture for the, I mean, I think the biggest thing is they just have, they have to continue the motion that they've been on. Right. And they've been consistent about that. Mm-hmm, <affirmative> what you see where others have, have kind of slipped up is when you go to, to customers and you present the, the OPEX as a service and the traditional CapEx side by side, and the customers put in this position of trying to detangle what's in that OPEX service, you don't wanna do that obviously. And, and HP has not done that, but we've seen others kind of slip up. And, but >>A lot of companies still wanna buy CapEx. Right. Absolutely liquid. And, and I think, >>But you shouldn't do a, you shouldn't do that bake off by putting those two offers out. You should basically ascertain what they want to do. >>What's kind of what Dell does. Right. Hey, how, what do you want? We got this, we got >>This on one hand, we got this, the, we got that, right. Uh, the two hand sales rep, no, this CapEx. Thing's interesting. And if you're Amazon and Azure and, and GCP, what are they thinking right now? Cause remember what, four years ago outpost was launched, which essentially hardware. Yeah. This is cloud operating model. Yep. Yeah. They're essentially bringing outpost. This is what they got basically is Amazon and Azure, like, is this ABL on the radar for them? How would you, what, what are they thinking in your mind if we're on, if we're in their office, in their brain trust, are they laughing? Are they like saying, oh, they're scared. Is this real threat >>Opportunity? I, I, I mean, I wouldn't say they're laughing at all. I, I would say they're probably discounting a little bit and saying, okay, fine. You know, that's a strategy that a traditional hardware company is moving to. But I think if you look underneath the covers, you know, two years ago it was, you know, pretty basic stuff they were offering. But now when you start getting into some, you know, HPC is a service, you start getting into data fabric, you start getting into some of the more, um, sophisticated services that they're offering. And, and I think what's interesting about HP. What my, my take is that they're not gonna go after the 250 services the Amazon's offering, they're gonna basically have a portfolio of services that really focus on the core use cases of their infrastructure set. And, and I think one of the danger things, one, one of the, one of the red flags would be, if they start going way up the stack and wanting to offer the entire application stack, that would be like a big flashing warning sign, cuz it's not their sweet spot. It's not, not what they have. >>So machine learning, machine learning and quantum, okay. One you can argue might be up the stack machine learning quantum should be in their wheelhouse. >>I would argue machine learning is not up the stack because what they would focus on is inference. They'd focus on learning. If they came out and said, machine learning all the way up to the, you know, what a, what, what a drug discovery company needs to do. >>So they're bringing it down. >>Yeah. Yeah. Well, no, I think they're focusing on that middle layer, right? That, that, that data layer. And I think that helping companies manage their data make more sense outta their data structure, their data that's core to what they wanna do. >>I, I feel as though what they're doing now is table stakes. Honestly, I do. I do feel like, okay, Hey finally, you know, I say the same thing about apex, you >>Know, we finally got, >>It's like, okay guys, the >>Party. Great. Welcome to the, >>But the one thing I would just say about, about AWS and the other big clouds is whether they might be a little dismissive of what's truly gonna happen at the edge. I think the traditional OEMs that are transforming are really betting on that edge, being a huge play and a huge differentiator for them where the public cloud obviously have their own bets there. But I think they were pretty dismissive initially about how big that went. >>I don't, and I don't think anybody's really figured out the edge yet. >>Well, that's an, it's a battleground. That's what he's saying. I think you're >>Saying, but on the ecosystem, I wanna say up the stack, I think it's the ecosystem. That's gotta fill that out. You gotta see more governance tools and catalogs and AI tools and, and >>It immediately goes more, it goes more vertical when you go edge, you're gonna have different conversations and >>They're >>Lacking. Yeah. And they, but they're in there though. They're in the verticals. HP's in the, yeah, >>For sure. But they gotta build out an ego. Like you walk around here, the data, the number of data companies here. I mean, Starburst is here. I'm actually impressed that Starburst is here. Cause I think they're a forward thinking company. I wanna see that times a hundred. Right. I mean, that's >>You see HP's in all the verticals. That's I think the point here, >>So they should be able to attract that ecosystem and build that, that flywheel that's the, that's the hallmark of a cloud that marketplace. >>Yeah, it is. But I think there's a, again, I go back to, they really gotta stay focused on that infrastructure and data management. Yeah. >>But they'll be focused on that, but, but their ecosystem, >>Their ecosystem will then take it up from there. And I think that's the next stage >>And that ecosystem's gotta include OT players and communications technologies players as well. Right. Because that stuff gets kind of sucked up in that, in that edge play. Do >>You feel like HPE has a, has a leg up on that or like a little, a little bit of a lead or is it pretty much, you know, even raced right now? >>I think they've, I think the big infrastructure companies have all had OEM businesses and they've all played there. It's it's, it's also helping those OT players actually convert their own needs into more of a software play and, and not so much of >>Physical. You've been, you've been following and you guys both have been following HP and HPE for years. They've been on the edge for a long time. I've been focused on this edge. Yeah. Now they might not have the product traction that's right. Or they might not develop as fast, but industrial OT and IOT they've been talking about it, focused on it. I think Amazon was mostly like, okay, we gotta get to the edge and like the enterprise. And, and I think HP's got a leg up in my opinion on that. Well, I question is can they execute? >>Yeah. I mean, PTC was here years ago on stage talking >>About, but I mean, you think about, if you think about the edge, right. I mean, I would argue one of the best acquisitions this company ever did was Aruba. Right. I mean, it basically changed the whole conversation of the edge changed the whole conversation. >>If >>Became GreenLake, it was GreenLake. >>Well, it became a big department. They gave a big, but, but, but I mean, you know, I mean they, they, they went after going selling edge line servers and frankly it's very difficult to gain traction there. Yeah. Aruba, huge area. And I think the March announcement was when they brought Aruba management into. Yeah. Yeah. >>Totally. >>Last question. Love >>That. >>What are you guys saying about the, the Broadcom VMware acquisition? What's the, what are the implications for the ecosystem for companies like HPE and just generally for the it business? >>Yeah. So >>You start. Yeah, sure. I'll start, I'll start there. So look, you know, we've, you know, spent some time, uh, going through it spent some time, you know, speaking, uh, to the, to the, to the folks involved and, and, and I gotta tell you, I think this is a really interesting moment for Broadcom. This is Broadcom's opportunity to basically build a different kind of a conversation with developers to, uh, try to invest in. I mean, just for perspective, right? These numbers may not be exact. And I know a dollar is not a dollar, but in 2001, anybody, remember what HP paid for? Compact >>8,000,000,020, >>So 25 billion, 25 billion. Wow. VMware just got sold for 61 billion. Wow. Okay. Unbill dollars. Okay. That gives you a perspective. No, again, I know a dollar is not a dollar 2000. >>It's still big numbers, >>2022. So having said that, if you just did it to, to, to basically build your DCF model and say, okay, over this amount of time, I'll pay you this. And I'll take the money out of this period of time, which is what people have criticized them for. I think that's a little shortsighted. I, yeah, I think this is Broadcom's opportunity to invest in that product and really try to figure out how to get a seat at the table in software and pivot their company to enterprise software in a different way. They have to prove that they're willing to do that. And then frankly, that they can develop the skills to do that over time. But I do believe this is a, a different, this is a pivot point. This is not >>CA this is not CA >>It's not CA >>In my, in my mind, it can't be CA they would, they would destroy too much. Now you and I, Dave had some, had some conversations on Twitter. I, I don't think it's the step up to them sort of thinking differently about semiconductor, dying, doing some custom semi I, I don't think that's. Yeah. I agree with that. Yeah. I think I, I think this is really about, I got two aspiration for them pivoting the company. They could >>Justify the >>Price to the, getting a seat at the adults table in software is, >>Well, if, if Broadcom has been squeezing their supplies, we all hear the scutle butt. Yeah. If they're squeezing, they can use VMware to justify the prices. Yeah. Maybe use that hostage. And that installed base. That's kind of Mike conspiracy. >>I think they've told us what they're gonna do. >><laugh> I do. >>Maybe it's not like C what's your conspiracy theory like Symantec, but what >>Do you think? Well, I mean, there's still, I mean, so VMware there's really nobody that can do all the things that VMware does say. So really impossible for an enterprise to just rip 'em out. But obviously you can, you can sour people's taste and you can very much influence the direction they head in with the collection of, of providers. One thing, interesting thing here is, was the 37% of VMware's revenues sold through Dell. So there's, there's lots of dependencies. It's not, it's not as simple as I think John, you you're right. You can't just pull the CA playbook out and rerun it here. This is a lot more complex. Yeah. It's a lot more volume of, of, of distribution, but a fair amount of VMware's install >>Base Dell's influence is still there basically >>Is in the mid-market. It's not, it's not something that they're gonna touch directly. >>You think about what VMware did. I mean, they kept adding new businesses, buying new businesses. I mean, is security business gonna stay >>Networking security, I think are interesting. >>Same >>Customers >>Over and over. Haven't done anything. VMware has the same customers. What new >>Customers. So imagine simplifying VMware. Right, right. Becomes a different equation. It's really interesting. And to your point, yeah. I mean, I think Broadcom is, I mean, Tom Crouse knows how to run a business. >>Yeah. He knows how to run a business. He's gonna, I, I think it's gonna be, you know, it's gonna be an efficient business. It's gonna be a well run business, but I think it's a pivot point for >>Broadcom. It's amazing to me, Broadcom sells to HPE. They sell it to Dell and they've got a market cap. That's 10 X, you know? Yes. Yeah. All we gotta go guys. Awesome. Great conversation guys. >>A lot. Thanks for having us on. >>Okay. Listen, uh, day two is a, is a wrap. We'll be here tomorrow, all day. Dave ante, John furrier, Lisa Martin, Lisa. Hope you're feeling okay. We'll see you tomorrow. Thanks for watching the cube, your leader in enterprise tech, live coverage.

(upbeat music) >> Hello, everyone. Welcome to the theCUBE's presentation of the AWS Startup Showcase. The theme, this showcase is MarTech, the emerging cloud scale customer experiences. Season two of episode three, the ongoing series covering the startups, the hot startups, talking about analytics, data, all things MarTech. I'm your host, John Furrier, here joined by Christian Wiklund, founder and CEO of unitQ here, talk about harnessing the power of user feedback to empower marketing. Thanks for joining us today. >> Thank you so much, John. Happy to be here. >> In these new shifts in the market, when you got cloud scale, open source software is completely changing the software business. We know that. There's no longer a software category. It's cloud, integration, data. That's the new normal. That's the new category, right? So as companies are building their products, and want to do a good job, it used to be, you send out surveys, you try to get the product market fit. And if you were smart, you got it right the third, fourth, 10th time. If you were lucky, like some companies, you get it right the first time. But the holy grail is to get it right the first time. And now, this new data acquisition opportunities that you guys in the middle of that can tap customers or prospects or end users to get data before things are shipped, or built, or to iterate on products. This is the customer feedback loop or data, voice of the customer journey. It's a gold mine. And it's you guys, it's your secret weapon. Take us through what this is about now. I mean, it's not just surveys. What's different? >> So yeah, if we go back to why are we building unitQ? Which is we want to build a quality company. Which is basically, how do we enable other companies to build higher quality experiences by tapping into all of the existing data assets? And the one we are in particularly excited about is user feedback. So me and my co-founder, Nik, and we're doing now the second company together. We spent 14 years. So we're like an old married couple. We accept each other, and we don't fight anymore, which is great. We did a consumer company called Skout, which was sold five years ago. And Skout was kind of early in the whole mobile first. I guess, we were actually mobile first company. And when we launched this one, we immediately had the entire world as our marketplace, right? Like any modern company. We launch a product, we have support for many languages. It's multiple platforms. We have Android, iOS, web, big screens, small screens, and that brings some complexities as it relates to staying on top of the quality of the experience because how do I test everything? >> John: Yeah. >> Pre-production. How do I make sure that our Polish Android users are having a good day? And we found at Skout, personally, like I could discover million dollar bugs by just drinking coffee and reading feedback. And we're like, "Well, there's got to be a better way to actually harness the end user feedback. That they are leaving in so many different places." So, you know what, what unitQ does is that we basically aggregate all different sources of user feedback, which can be app store reviews, Reddit posts, Tweets, comments on your Facebook ads. It can be better Business Bureau Reports. We don't like to get to many of those, of course. But really, anything on the public domain that mentions or refers to your product, we want to ingest that data in this machine, and then all the private sources. So you probably have a support system deployed, a Zendesk, or an Intercom. You might have a chatbot like an Ada, or and so forth. And your end user is going to leave a lot of feedback there as well. So we take all of these channels, plug it into the machine, and then we're able to take this qualitative data. Which and I actually think like, when an end user leaves a piece of feedback, it's an act of love. They took time out of the day, and they're going to tell you, "Hey, this is not working for me," or, "Hey, this is working for me," and they're giving you feedback. But how do we package these very messy, multi-channel, multiple languages, all over the place data? How can we distill it into something that's quantifiable? Because I want to be able to monitor these different signals. So I want to turn user feedback into time series. 'Cause with time series, I can now treat this the same way as Datadog treats machine logs. I want to be able to see anomalies, and I want to know when something breaks. So what we do here is that we break down your data in something called quality monitors, which is basically machine learning models that can aggregate the same type of feedback data in this very fine grained and discrete buckets. And we deploy up to a thousand of these quality monitors per product. And so we can get down to the root cause. Let's say, passive reset link is not working. And it's in that root cause, the granularity that we see that companies take action on the data. And I think historically, there has been like the workflow between marketing and support, and engineering and product has been a bit broken. They've been siloed from a data perspective. They've been siloed from a workflow perspective, where support will get a bunch of tickets around some issue in production. And they're trained to copy and paste some examples, and throw it over the wall, file a Jira ticket, and then they don't know what happens. So what we see with the platform we built is that these teams are able to rally around the single source of troop or like, yes, passive recent link seems to have broken. This is not a user error. It's not a fix later, or I can't reproduce. We're looking at the data, and yes, something broke. We need to fix it. >> I mean, the data silos a huge issue. Different channels, omnichannel. Now, there's more and more channels that people are talking in. So that's huge. I want to get to that. But also, you said that it's a labor of love to leave a comment or a feedback. But also, I remember from my early days, breaking into the business at IBM and Hewlett-Packard, where I worked. People who complain are the most loyal customers, if you service them. So it's complaints. >> Christian: Yeah. >> It's leaving feedback. And then, there's also reading between the lines with app errors or potentially what's going on under the covers that people may not be complaining about, but they're leaving maybe gesture data or some sort of digital trail. >> Yeah. >> So this is the confluence of the multitude of data sources. And then you got the siloed locations. >> Siloed locations. >> It's complicated problem. >> It's very complicated. And when you think about, so I started, I came to Bay Area in 2005. My dream was to be a quant analyst on Wall Street, and I ended up in QA at VMware. So I started at VMware in Palo Alto, and didn't have a driver's license. I had to bike around, which was super exciting. And we were shipping box software, right? This was literally a box with a DVD that's been burned, and if that DVD had bugs in it, guess what it'll be very costly to then have to ship out, and everything. So I love the VMware example because the test cycles were long and brutal. It was like a six month deal to get through all these different cases, and they couldn't be any bugs. But then as the industry moved into the cloud, CI/CD, ship at will. And if you look at the modern company, you'll have at least 20 plus integrations into your product. Analytics, add that's the case, authentication, that's the case, and so forth. And these integrations, they morph, and they break. And you have connectivity issues. Is your product working as well on Caltrain, when you're driving up and down, versus wifi? You have language specific bugs that happen. Android is also quite a fragmented market. The binary may not perform as well on that device, or is that device. So how do we make sure that we test everything before we ship? The answer is, we can't. There's no company today that can test everything before the ship. In particular, in consumer. And the epiphany we had at our last company, Skout, was that, "Hey, wait a minute. The end user, they're testing every configuration." They're sitting on the latest device, the oldest device. They're sitting on Japanese language, on Swedish language. >> John: Yeah. >> They are in different code paths because our product executed differently, depending on if you were a paid user, or a freemium user, or if you were certain demographical data. There's so many ways that you would have to test. And PagerDuty actually had a study they came out with recently, where they said 51% of all end user impacting issues are discovered first by the end user, when they serve with a bunch of customers. And again, like the cool part is, they will tell you what's not working. So now, how do we tap into that? >> Yeah. >> So what I'd like to say is, "Hey, your end user is like your ultimate test group, and unitQ is the layer that converts them into your extended test team." Now, the signals they're producing, it's making it through to the different teams in the organization. >> I think that's the script that you guys are flipping. If I could just interject. Because to me, when I hear you talking, I hear, "Okay, you're letting the customers be an input into the product development process." And there's many different pipelines of that development. And that could be whether you're iterating, or geography, releases, all kinds of different pipelines to get to the market. But in the old days, it was like just customer satisfaction. Complain in a call center. >> Christian: Yeah. >> Or I'm complaining, how do I get support? Nothing made itself into the product improvement, except for slow moving, waterfall-based processes. And then, maybe six months later, a small tweak could be improved. >> Yes. >> Here, you're taking direct input from collective intelligence. Okay. >> Is that have input and on timing is very important here, right? So how do you know if the product is working as it should in all these different flavors and configurations right now? How do you know if it's working well? And how do you know if you're improving or not improving over time? And I think the industry, what can we look at, as far as when it relates to quality? So I can look at star ratings, right? So what's the star rating in the app store? Well, star ratings, that's an average over time. So that's something that you may have a lot of issues in production today, and you're going to get dinged on star ratings over the next few months. And then, it brings down the score. NPS is another one, where we're not going to run NPS surveys every day. We're going to run it once a quarter, maybe once a month, if we're really, really aggressive. That's also a snapshot in time. And we need to have the finger on the pulse of product quality today. I need to know if this release is good or not good. I need to know if anything broke. And I think that real time aspect, what we see as stuff sort of bubbles up the stack, and not into production, we see up to a 50% reduction in time to fix these end user impacting issues. And I think, we also need to appreciate when someone takes time out of the day to write an app review, or email support, or write that Reddit post, it's pretty serious. It's not going to be like, "Oh, I don't like the shade of blue on this button." It's going to be something like, "I got double billed," or "Hey, someone took over my account," or, "I can't reset my password anymore. The CAPTCHA, I'm solving it, but I can't get through to the next phase." And we see a lot of these trajectory impacting bugs and quality issues in these work, these flows in the product that you're not testing every day. So if you work at Snapchat, your employees probably going to use Snapchat every day. Are they going to sign up every day? No. Are they going to do passive reset every day? No. And these things are very hard to instrument, lower in the stack. >> Yeah, I think this is, and again, back to these big problems. It's smoke before fire, and you're essentially seeing it early with your process. Can you give an example of how this new focus or new mindset of user feedback data can help customers increase their experience? Can you give some examples, 'cause folks watching and be like, "Okay, I love this value. Sell me on this idea, I'm sold. Okay, I want to tap into my prospects, and my customers, my end users to help me improve my product." 'Cause again, we can measure everything now with data. >> Yeah. We can measure everything. we can even measure quality these days. So when we started this company, I went out to talk to a bunch of friends, who are entrepreneurs, and VCs, and board members, and I asked them this very simple question. So in your board meetings, or on all hands, how do you talk about quality of the product? Do you have a metric? And everyone said, no. Okay. So are you data driven company? Yes, we're very data driven. >> John: Yeah. Go data driven. >> But you're not really sure if quality, how do you compare against competition? Are you doing as good as them, worse, better? Are you improving over time, and how do you measure it? And they're like, "Well, it's kind of like a blind spot of the company." And then you ask, "Well, do you think quality of experience is important?" And they say, "Yeah." "Well, why?" "Well, top of fund and growth. Higher quality products going to spread faster organically, we're going to make better store ratings. We're going to have the storefronts going to look better." And of course, more importantly, they said the different conversion cycles in the product box itself. That if you have bugs and friction, or an interface that's hard to use, then the inputs, the signups, it's not going to convert as well. So you're going to get dinged on retention, engagement, conversion to paid, and so forth. And that's what we've seen with the companies we work with. It is that poor quality acts as a filter function for the entire business, if you're a product led company. So if you think about product led company, where the product is really the centerpiece. And if it performs really, really well, then it allows you to hire more engineers, you can spend more on marketing. Everything is fed by this product at them in the middle, and then quality can make that thing perform worse or better. And we developed a metric actually called the unitQ Score. So if you go to our website, unitq.com, we have indexed the 5,000 largest apps in the world. And we're able to then, on a daily basis, update the score. Because the score is not something you do once a month or once a quarter. It's something that changes continuously. So now, you can get a score between zero and 100. If you get the score 100, that means that our AI doesn't find any quality issues reported in that data set. And if your score is 90, that means that 10% will be a quality issue. So now you can do a lot of fun stuff. You can start benchmarking against competition. So you can see, "Well, I'm Spotify. How do I rank against Deezer, or SoundCloud, or others in my space?" And what we've seen is that as the score goes up, we see this real big impact on KPI, such as conversion, organic growth, retention, ultimately, revenue, right? And so that was very satisfying for us, when we launched it. quality actually still really, really matters. >> Yeah. >> And I think we all agree at test, but how do we make a science out of it? And that's so what we've done. And when we were very lucky early on to get some incredible brands that we work with. So Pinterest is a big customer of ours. We have Spotify. We just signed new bank, Chime. So like we even signed BetterHelp recently, and the world's largest Bible app. So when you look at the types of businesses that we work with, it's truly a universal, very broad field, where if you have a digital exhaust or feedback, I can guarantee you, there are insights in there that are being neglected. >> John: So Chris, I got to. >> So these manual workflows. Yeah, please go ahead. >> I got to ask you, because this is a really great example of this new shift, right? The new shift of leveraging data, flipping the script. Everything's flipping the script here, right? >> Yeah. >> So you're talking about, what the value proposition is? "Hey, board example's a good one. How do you measure quality? There's no KPI for that." So it's almost category creating in its own way. In that, this net new things, it's okay to be new, it's just new. So the question is, if I'm a customer, I buy it. I can see my product teams engaging with this. I can see how it can changes my marketing, and customer experience teams. How do I operationalize this? Okay. So what do I do? So do I reorganize my marketing team? So take me through the impact to the customer that you're seeing. What are they resonating towards? Obviously, getting that data is key, and that's holy gray, we all know that. But what do I got to do to change my environment? What's my operationalization piece of it? >> Yeah, and that's one of the coolest parts I think, and that is, let's start with your user base. We're not going to ask your users to ask your users to do something differently. They're already producing this data every day. They are tweeting about it. They're putting in app produce. They're emailing support. They're engaging with your support chatbot. They're already doing it. And every day that you're not leveraging that data, the data that was produced today is less valuable tomorrow. And in 30 days, I would argue, it's probably useless. >> John: Unless it's same guy commenting. >> Yeah. (Christian and John laughing) The first, we need to make everyone understand. Well, yeah, the data is there, and we don't need to do anything differently with the end user. And then, what we do is we ask the customer to tell us, "Where should we listen in the public domain? So do you want the Reddit post, the Trustpilot? What channels should we listen to?" And then, our machine basically starts ingesting that data. So we have integration with all these different sites. And then, to get access to private data, it'll be, if you're on Zendesk, you have to issue a Zendesk token, right? So you don't need any engineering hours, except your IT person will have to grant us access to the data source. And then, when we go live. We basically build up this taxonomy with the customers. So we don't we don't want to try and impose our view of the world, of how do you describe the product with these buckets, these quality monitors? So we work with the company to then build out this taxonomy. So it's almost like a bespoke solution that we can bootstrap with previous work we've done, where you don't have these very, very fine buckets of where stuff could go wrong. And then what we do is there are different ways to hook this into the workflow. So one is just to use our products. It's a SaaS product as anything else. So you log in, and you can then get this overview of how is quality trending in different markets, on different platforms, different languages, and what is impacting them? What is driving this unitQ Score that's not good enough? And all of these different signals, we can then hook into Jira for instance. We have a Jira integration. We have a PagerDuty integration. We can wake up engineers if certain things break. We also tag tickets in your support system, which is actually quite cool. Where, let's say, you have 200 people, who wrote into support, saying, "I got double billed on Android." It turns out, there are some bugs that double billed them. Well, now we can tag all of these users in Zendesk, and then the support team can then reach out to that segment of users and say, "Hey, we heard that you had this bug with double billing. We're so sorry. We're working on it." And then when we push fix, we can then email the same group again, and maybe give them a little gift card or something, for the thank you. So you can have, even big companies can have that small company experience. So, so it's groups that use us, like at Pinterest, we have 800 accounts. So it's really through marketing has vested interest because they want to know what is impacting the end user. Because brand and product, the lines are basically gone, right? >> John: Yeah. >> So if the product is not working, then my spend into this machine is going to be less efficient. The reputation of our company is going to be worse. And the challenge for marketers before unitQ was, how do I engage with engineering and product? I'm dealing with anecdotal data, and my own experience of like, "Hey, I've never seen these type of complaints before. I think something is going on." >> John: Yeah. >> And then engineering will be like, "Ah, you know, well, I have 5,000 bugs in Jira. Why does this one matter? When did it start? Is this a growing issue?" >> John: You have to replicate the problem, right? >> Replicate it then. >> And then it goes on and on and on. >> And a lot of times, reproducing bugs, it's really hard because it works on my device. Because you don't sit on that device that it happened on. >> Yup. >> So now, when marketing can come with indisputable data, and say, "Hey, something broke here." And we see the same with support. Product engineering, of course, for them, we talk about, "Hey, listen, you you've invested a lot in observability of your stack, haven't you?" "Yeah, yeah, yeah." "So you have a Datadog in the bottom?" "Absolutely." "And you have an APP D on the client?" "Absolutely." "Well, what about the last mile? How the product manifests itself? Shouldn't you monitor that as well using machines?" They're like, "Yeah, that'd be really cool." (John laughs) And we see this. There's no way to instrument everything, lowering the stack to capture these bugs that leak out. So it resonates really well there. And even for the engineers who's going to fix it. >> Yeah. >> I call it like empathy data. >> Yup. >> Where I get assigned a bug to fix. Well, now, I can read all the feedback. I can actually see, and I can see the feedback coming in. >> Yeah. >> Oh, there's users out there, suffering from this bug. And then when I fix it and I deploy the fix, and I see the trend go down to zero, and then I can celebrate it. So that whole feedback loop is (indistinct). >> And that's real time. It's usually missed too. This is the power of user feedback. You guys got a great product, unitQ. Great to have you on. Founder and CEO, Christian Wiklund. Thanks for coming on and sharing, and showcase. >> Thank you, John. For the last 30 seconds, the minute we have left, put a plug in for the company. What are you guys looking for? Give a quick pitch for the company, real quick, for the folks out there. Looking for more people, funding status, number of employees. Give a quick plug. >> Yes. So we raised our A Round from Google, and then we raised our B from Excel that we closed late last year. So we're not raising money. We are hiring across go-to-markets, engineering. And we love to work with people, who are passionate about quality and data. We're always, of course, looking for customers, who are interested in upping their game. And hey, listen, competing with features is really hard because you can copy features very quickly. Competing with content. Content is commodity. You're going to get the same movies more or less on all these different providers. And competing on price, we're not willing to do. You're going to pay 10 bucks a month for music. So how do you compete today? And if your competitor has a better fine tuned piano than your competitor will have better efficiencies, and they're going to retain customers and users better. And you don't want to lose on quality because it is actually a deterministic and fixable problem. So yeah, come talk to us if you want to up the game there. >> Great stuff. The iteration lean startup model, some say took craft out of building the product. But this is now bringing the craftsmanship into the product cycle, when you can get that data from customers and users. >> Yeah. >> Who are going to be happy that you fixed it, that you're listening. >> Yeah. >> And that the product got better. So it's a flywheel of loyalty, quality, brand, all off you can figure it out. It's the holy grail. >> I think it is. It's a gold mine. And every day you're not leveraging this assets, your use of feedback that's there, is a missed opportunity. >> Christian, thanks so much for coming on. Congratulations to you and your startup. You guys back together. The band is back together, up into the right, doing well. >> Yeah. We we'll check in with you later. Thanks for coming on this showcase. Appreciate it. >> Thank you, John. Appreciate it very much. >> Okay. AWS Startup Showcase. This is season two, episode three, the ongoing series. This one's about MarTech, cloud experiences are scaling. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>Welcome everyone to this cube conversation featuring unit Q. I'm your host, Lisa Martin. And we are excited to be joined by Christian Vickle, the founder and CEO of unit Q Christian. Thank you so much for joining me today. >>Thank you so much, Lisa pleasure to be here. >>Let's talk a little bit about unit Q. You guys were founded in 2018, so pretty recent. What is it that unit Q does. And what were some of the gaps in the market that led you to founding the company? >>Yep. So me and my co-founder Nick, we're actually doing our second company now is the unit Q is number two, and our first company was called scout years ago. We were back ES wicks and it was very different from unit Q. It's a social network for meeting people. And it was really during that experience where we saw the impact that quality of the experience quality of the product can have on your growth trajectory and the challenges we faced. How do we test everything before we ship it? And in reality, a modern company will have, let's say, 20 languages supported you support Android, Iowas, web big screen, small screen, you have 20 plus integrations and you have lots of different devices out there that might run your binary a little differently. So who is the ultimate test group of all of these different permutation and that's the end user. >>And we, we saw the, the big gap in the market, sort of the dream platform for us was unit queue. So if, if this would've existed back in the day, we would've been a, a happy purchaser and customer, and it really comes down to how do we, how do we harness the power of user feedback? You know, the end user, that's testing your product every single day in all different configurations. And then they're telling you that, Hey, something didn't work for me. I got double build or the passive recent link didn't work, or I couldn't, you know, when music, when the ad is finished playing on, on my app, the music doesn't resume. So how do we capture those signals into something that the company and different teams can align on? So that's where, you know, unit Q the, the vision here is to build a quality company, to help other companies build higher quality products. >>So really empowering companies to take a data driven approach to product quality. I was looking on your website and noticed that Pandora is one of your customers, but talk to me a little bit about a customer example that you think really articulates the value of what Q unit he was delivering. >>Right? So maybe we should just go back one little step and talk about what is quality. And I think quality is something that is, is a bit subjective. It's something that we live and breathe every day. It's something that can be formed in an instant first impressions. Last it's something that can be built over time that, Hey, I'm using this product and it's just not working for me. Maybe it's missing features. Maybe there are performance related bots. Maybe there is there's even fulfillment related issues. Like we work with Uber and hello, fresh and, and other types of more hybrid type companies in addition to the Pandoras and, and Pinterest and, and Spotify, and these more digital, only products, but the, the end users I'm producing this data, the reporting, what is working and not working out there in many different channels. So they will leave app produce. >>They will write into support. They might engage with a chat support bot. They will post stuff on Reddit on Twitter. They will comment on Facebook ads. So like this data is dispersed everywhere. The end user is not gonna fill out a perfect bug report in a form somewhere that gets filed into gr like they're, they're producing this content everywhere in different languages. So the first value of what we do is to just ingest all of that data. So all the entire surface area of use of feedback, we ingest into a machine and then we clean the data. We normalize it, and then we translate everything into English. And it was actually a surprise to us when we started this company, that there are quite a few companies out there that they're only looking at feedback in English. So what about my Spanish speaking users? What about my French speaking users? >>And when, when, when that is done, like when all of that data is, is need to organized, we extract signals from that around what is impacting the user experience right now. So we break these, all of this data down into something called quality monitors. So quality monitor is basically a topic which can be again, passive reset, link noting, or really anything that that's impacting the end user. And the important part here is that we need to have specific actionable data. For instance, if I tell you, Hey, Lisa music stops playing is a growing trend that our users are reporting. You will tell me, well, what can I do with that? Like what specifically is breaking? So we deploy up to 1500 unique quality monitors per customer. So we can then alert different teams inside of the organization of like, Hey, something broke and you should take a look at it. >>So it's really breaking down data silos within the company. It aligns cross-functional teams to agree on what should be fixed next. Cause there's typically a lot of confusion, you know, marketing, they might say, Hey, we want this fixed engineering. They're like, well, I can't reproduce, or that's not a high priority for us. The support teams might also have stuff that they want to get fixed. And what we've seen is that these teams, they struggle to communicate. So how do we align them around the single source of truth? And I think that's for unit two is early identification of stuff. That's not working in production and it's also aligning the teams so they can quickly triage and say, yes, we gotta fix this right before it snowballs into something. We say, you know, we wanna, we wanna cap catch issues before you go into crisis PR mode, right? So we want to get this, we wanna address it early in the cycle. >>Talk to me about when you're in customer conversations, Christian, the MarTech landscape is competitive. There's nearly 10,000 different solutions out there, and it's growing really quickly quality monitors that you just described is that one of the key things that, that you talk to customers about, that's a differentiator for unit Q. >>Yeah. So I mean, it, it, it comes down to, as you're building your product, right, you, you have, you have a few different options. One is to build new features and we need to build new features and innovate and, and, and that's all great. We also need to make sure that the foundation of the product is working and that we keep improving quality and what, what we see with, with basically every customer that we work with, that, that when quality goes up, it's supercharges the growth machine. So quality goes up, you're gonna see less support tickets. You're gonna see less one star reviews, less one star reviews is of course good for making the store front convert better. You know, I, I want install a 4.5 star app, not a 3.9 star app. We also see that sentiment. So for those who are interested in getting that NPS score up for the next time we measure it, we see that quality is of course a very important piece of that. >>And maybe even more importantly, so sort of inside of the product machine, the different conversion steps, let's say sign up to activate it to coming back in second day, 30 day, 90 day, and so forth. We see a dramatic impact on how quality sort of moves that up and down the retention function, if you will. So it, it really, if you think about a modern company, like the product is sort of the center of the existence of the company, and if the product performs really well, then you can spend more money in marketing because it converts really good. You can hire more engineers, you can hire, you can hire more support people and so forth. So it's, it's really cool to see that when quality improves its supercharges, everything else I think for marketing it's how do you know if you're spending into a broken product or not? >>And I, and I, I feel like marketing has, they have their insights, but it's, it's not deep enough where they can go to engineering and say, Hey, these 10 issues are impacting my MPS score and they're impacting my conversion and I would love for you to fix it. And when you can bring tangible impact, when you can bring real data to, to engineering and product, they move on it cause they also wanna help build the company. And, and so I think that's, that's how we stand out from the more traditional MarTech, because we need to fix the core of, of sort of this growth engine, which is the quality of the product >>Quality of the product. And obviously that's directly related to the customer experience. And we know these days, one of the things I think that's been in short supply the last couple of years is patience. We know when customers are unhappy with the product or service, and you talked about it a minute ago, they're gonna go right to, to Reddit or other sources to complain about that. So being able to, for uniq, to help companies to improve the customer experience, isn't I think table stakes for businesses it's mission critical these days. Yeah, >>It is mission critical. So if you look at the, let's say that we were gonna start a, a music app. Okay. So how do we, how do we compete as a music app? Well, if you, if you were to analyze all different music apps out there, they have more or less the same features app. Like they, the feature differentiation is minimal. And, and if you launch a new cool feature than your competitor will probably copy that pretty quickly as well. So competing with features is really hard. What about content? Well, I'm gonna get the same content on Spotify as apple SD. So competing with content is also really hard. What about price? So it turns out you'll pay 9 99 a month for music, but there's no, there's no 1 99. It's gonna be 9 99. So quality of the experience is one of the like last vectors or areas where you can actually compete. >>And we see consistently that if you' beating your competition on quality, you will do better. Like the best companies out there also have the highest quality experience. So it's, it's been, you know, for us at our last company, measuring quality was something that was very hard. How do we talk about it? And when we started this company, I went out and talked to a bunch of CEOs and product leaders and board members. And I said, how do you talk about quality in a board meeting? And they were, they said, well, we don't, we don't have any metrics. So actually the first thing we did was to define a metrics. We have, we have this thing called this unit Q score, which is on our website as well, where we can base it's like the credit score. So you can see your score between zero and a hundred. >>And if your score is 100, it means that we're finding no quality issues in the public domain. If your score is 90, it means that 10% of the data we look at refers to a quality issue. And the definition of a quality issue is quite simple. It is when the user experience doesn't match the user expectation. There is a gap in between, and we've actually indexed the 5,000 largest apps out there. So we're then looking at all the public review. So on our website, you can go in and, and look up the unit Q score for the 5,000 largest products. And we republish these every night. So it's an operational metric that changes all the time. >>Hugely impactful. Christian, thank you so much for joining me today, talking to the audience about unit Q, how you're turning qualitative feedback into pretty significant product improvements for your customers. We appreciate your insights. >>Thank you, Lisa, have a great day. >>You as well, per Christian Lin, I'm Lisa Martin. You're watching a cube conversation.