(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat 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)

(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

(upbeat music) >> Welcome back, everyone, to our Cube special programming on "Securing Compute, Engineered for the Hybrid World." We got Cole Humphreys who's with HPE, global server security product manager, and Mike Ferron-Jones with Intel. He's the product manager for data security technology. Gentlemen, thank you for coming on this special presentation. >> All right, thanks for having us. >> So, securing compute, I mean, compute, everyone wants more compute. You can't have enough compute as far as we're concerned. You know, more bits are flying around the internet. Hardware's mattering more than ever. Performance markets hot right now for next-gen solutions. When you're talking about security, it's at the center of every single conversation. And Gen11 for the HPE has been big-time focus here. So let's get into the story. What's the market for Gen11, Cole, on the security piece? What's going on? How do you see this impacting the marketplace? >> Hey, you know, thanks. I think this is, again, just a moment in time where we're all working towards solving a problem that doesn't stop. You know, because we are looking at data protection. You know, in compute, you're looking out there, there's international impacts, there's federal impacts, there's state-level impacts, and even regulation to protect the data. So, you know, how do we do this stuff in an environment that keeps changing? >> And on the Intel side, you guys are a Tier 1 combination partner, Better Together. HPE has a deep bench on security, Intel, We know what your history is. You guys have a real root of trust with your code, down to the silicon level, continuing to be, and you're on the 4th Gen Xeon here. Mike, take us through the Intel's relationship with HPE. Super important. You guys have been working together for many, many years. Data security, chips, HPE, Gen11. Take us through the relationship. What's the update? >> Yeah, thanks and I mean, HPE and Intel have been partners in delivering technology and delivering security for decades. And when a customer invests in an HPE server, like at one of the new Gen11s, they're getting the benefit of the combined investment that these two great companies are putting into product security. On the Intel side, for example, we invest heavily in the way that we develop our products for security from the ground up, and also continue to support them once they're in the market. You know, launching a product isn't the end of our security investment. You know, our Intel Red Teams continue to hammer on Intel products looking for any kind of security vulnerability for a platform that's in the field. As well as we invest heavily in the external research community through our bug bounty programs to harness the entire creativity of the security community to find those vulnerabilities, because that allows us to patch them and make sure our customers are staying safe throughout that platform's deployed lifecycle. You know, in 2021, between Intel's internal red teams and our investments in external research, we found 93% of our own vulnerabilities. Only a small percentage were found by unaffiliated external entities. >> Cole, HPE has a great track record and long history serving customers around security, actually, with the solutions you guys had. With Gen11, it's more important than ever. Can you share your thoughts on the talent gap out there? People want to move faster, breaches are happening at a higher velocity. They need more protection now than ever before. Can you share your thoughts on why these breaches are happening, and what you guys are doing, and how you guys see this happening from a customer standpoint? What you guys fill in with Gen11 with solution? >> You bet, you know, because when you hear about the relentless pursuit of innovation from our partners, and we in our engineering organizations in India, and Taiwan, and the Americas all collaborating together years in advance, are about delivering solutions that help protect our customer's environments. But what you hear Mike talking about is it's also about keeping 'em safe. Because you look to the market, right? What you see in, at least from our data from 2021, we have that breaches are still happening, and lot of it has to do with the fact that there is just a lack of adequate security staff with the necessary skills to protect the customer's application and ultimately the workloads. And then that's how these breaches are happening. Because ultimately you need to see some sort of control and visibility of what's going on out there. And what we were talking about earlier is you see time. Time to seeing some incident happen, the blast radius can be tremendous in today's technical, advanced world. And so you have to identify it and then correct it quickly, and that's why this continued innovation and partnership is so important, to help work together to keep up. >> You guys have had a great track record with Intel-based platforms with HPE. Gen11's a really big part of the story. Where do you see that impacting customers? Can you explain the benefits of what's going on with Gen11? What's the key story? What's the most important thing we should be paying attention to here? >> I think there's probably three areas as we look into this generation. And again, this is a point in time, we will continue to evolve. But at this particular point it's about, you know, a fundamental approach to our security enablement, right? Partnering as a Tier 1 OEM with one of the best in the industry, right? We can deliver systems that help protect some of the most critical infrastructure on earth, right? I know of some things that are required to have a non-disclosure because it is some of the most important jobs that you would see out there. And working together with Intel to protect those specific compute workloads, that's a serious deal that protects not only state, and local, and federal interests, but, really, a global one. >> This is a really- >> And then there's another one- Oh sorry. >> No, go ahead. Finish your thought. >> And then there's another one that I would call our uncompromising focus. We work in the industry, we lead and partner with those in the, I would say, in the good side. And we want to focus on enablement through a specific capability set, let's call it our global operations, and that ability to protect our supply chain and deliver infrastructure that can be trusted and into an operating environment. You put all those together and you see very significant and meaningful solutions together. >> The operating benefits are significant. I just want to go back to something you just said before about the joint NDAs and kind of the relationship you kind of unpacked, that to me, you know, I heard you guys say from sand to server, I love that phrase, because, you know, silicone into the server. But this is a combination you guys have with HPE and Intel supply-chain security. I mean, it's not just like you're getting chips and sticking them into a machine. This is, like, there's an in-depth relationship on the supply chain that has a very intricate piece to it. Can you guys just double down on that and share that, how that works and why it's important? >> Sure, so why don't I go ahead and start on that one. So, you know, as you mentioned the, you know, the supply chain that ultimately results in an end user pulling, you know, a new Gen11 HPE server out of the box, you know, started, you know, way, way back in it. And we've been, you know, Intel, from our part are, you know, invest heavily in making sure that all of our entire supply chain to deliver all of the Intel components that are inside that HPE platform have been protected and monitored ever since, you know, their inception at one of any of our 14,000, you know, Intel vendors that we monitor as part of our supply-chain assurance program. I mean we, you know, Intel, you know, invests heavily in compliance with guidelines from places like NIST and ISO, as well as, you know, doing best practices under things like the Transported Asset Protection Alliance, TAPA. You know, we have been intensely invested in making sure that when a customer gets an Intel processor, or any other Intel silicone product, that it has not been tampered with or altered during its trip through the supply chain. HPE then is able to pick up that, those components that we deliver, and add onto that their own supply-chain assurance when it comes down to delivering, you know, the final product to the customer. >> Cole, do you want to- >> That's exactly right. Yeah, I feel like that integration point is a really good segue into why we're talking today, right? Because that then comes into a global operations network that is pulling together these servers and able to deploy 'em all over the world. And as part of the Gen11 launch, we have security services that allow 'em to be hardened from our factories to that next stage into that trusted partner ecosystem for system integration, or directly to customers, right? So that ability to have that chain of trust. And it's not only about attestation and knowing what, you know, came from whom, because, obviously, you want to trust and make sure you're get getting the parts from Intel to build your technical solutions. But it's also about some of the provisioning we're doing in our global operations where we're putting cryptographic identities and manifests of the server and its components and moving it through that supply chain. So you talked about this common challenge we have of assuring no tampering of that device through the supply chain, and that's why this partnering is so important. We deliver secure solutions, we move them, you're able to see and control that information to verify they've not been tampered with, and you move on to your next stage of this very complicated and necessary chain of trust to build, you know, what some people are calling zero-trust type ecosystems. >> Yeah, it's interesting. You know, a lot goes on under the covers. That's good though, right? You want to have greater security and platform integrity, if you can abstract the way the complexity, that's key. Now one of the things I like about this conversation is that you mentioned this idea of a hardware-root-of-trust set of technologies. Can you guys just quickly touch on that, because that's one of the major benefits we see from this combination of the partnership, is that it's not just one, each party doing something, it's the combination. But this notion of hardware-root-of-trust technologies, what is that? >> Yeah, well let me, why don't I go ahead and start on that, and then, you know, Cole can take it from there. Because we provide some of the foundational technologies that underlie a root of trust. Now the idea behind a root of trust, of course, is that you want your platform to, you know, from the moment that first electron hits it from the power supply, that it has a chain of trust that all of the software, firmware, BIOS is loading, to bring that platform up into an operational state is trusted. If you have a breach in one of those lower-level code bases, like in the BIOS or in the system firmware, that can be a huge problem. It can undermine every other software-based security protection that you may have implemented up the stack. So, you know, Intel and HPE work together to coordinate our trusted boot and root-of-trust technologies to make sure that when a customer, you know, boots that platform up, it boots up into a known good state so that it is ready for the customer's workload. So on the Intel side, we've got technologies like our trusted execution technology, or Intel Boot Guard, that then feed into the HPE iLO system to help, you know, create that chain of trust that's rooted in silicon to be able to deliver that known good state to the customer so it's ready for workloads. >> All right, Cole, I got to ask you, with Gen11 HPE platforms that has 4th Gen Intel Xeon, what are the customers really getting? >> So, you know, what a great setup. I'm smiling because it's, like, it has a good answer, because one, this, you know, to be clear, this isn't the first time we've worked on this root-of-trust problem. You know, we have a construct that we call the HPE Silicon Root of Trust. You know, there are, it's an industry standard construct, it's not a proprietary solution to HPE, but it does follow some differentiated steps that we like to say make a little difference in how it's best implemented. And where you see that is that tight, you know, Intel Trusted Execution exchange. The Intel Trusted Execution exchange is a very important step to assuring that route of trust in that HPE Silicon Root of Trust construct, right? So they're not different things, right? We just have an umbrella that we pull under our ProLiant, because there's ILO, our BIOS team, CPLDs, firmware, but I'll tell you this, Gen11, you know, while all that, keeping that moving forward would be good enough, we are not holding to that. We are moving forward. Our uncompromising focus, we want to drive more visibility into that Gen11 server, specifically into the PCIE lanes. And now you're going to be able to see, and measure, and make policies to have control and visibility of the PCI devices, like storage controllers, NICs, direct connect, NVME drives, et cetera. You know, if you follow the trends of where the industry would like to go, all the components in a server would be able to be seen and attested for full infrastructure integrity, right? So, but this is a meaningful step forward between not only the greatness we do together, but, I would say, a little uncompromising focus on this problem and doing a little bit more to make Gen11 Intel's server just a little better for the challenges of the future. >> Yeah, the Tier 1 partnership is really kind of highlighted there. Great, great point. I got to ask you, Mike, on the 4th Gen Xeon Scalable capabilities, what does it do for the customer with Gen11 now that they have these breaches? Does it eliminate stuff? What's in it for the customer? What are some of the new things coming out with the Xeon? You're at Gen4, Gen11 for HP, but you guys have new stuff. What does it do for the customer? Does it help eliminate breaches? Are there things that are inherent in the product that HP is jointly working with you on or you were contributing in to the relationship that we should know about? What's new? >> Yeah, well there's so much great new stuff in our new 4th Gen Xeon Scalable processor. This is the one that was codenamed Sapphire Rapids. I mean, you know, more cores, more performance, AI acceleration, crypto acceleration, it's all in there. But one of my favorite security features, and it is one that's called Intel Control-Flow Enforcement Technology, or Intel CET. And why I like CET is because I find the attack that it is designed to mitigate is just evil genius. This type of attack, which is called a return, a jump, or a call-oriented programming attack, is designed to not bring a whole bunch of new identifiable malware into the system, you know, which could be picked up by security software. What it is designed to do is to look for little bits of existing, little bits of existing code already on the server. So if you're running, say, a web server, it's looking for little bits of that web-server code that it can then execute in a particular order to achieve a malicious outcome, something like open a command prompt, or escalate its privileges. Now in order to get those little code bits to execute in an order, it has a control mechanism. And there are different, each of the different types of attacks uses a different control mechanism. But what CET does is it gets in there and it disrupts those control mechanisms, uses hardware to prevent those particular techniques from being able to dig in and take effect. So CET can, you know, disrupt it and make sure that software behaves safely and as the programmer intended, rather than picking off these little arbitrary bits in one of these return, or jump, or call-oriented programming attacks. Now it is a technology that is included in every single one of the new 4th Gen Xeon Scalable processors. And so it's going to be an inherent characteristic the customers can benefit from when they buy a new Gen11 HPE server. >> Cole, more goodness from Intel there impacting Gen11 on the HPE side. What's your reaction to that? >> I mean, I feel like this is exactly why you do business with the big Tier 1 partners, because you can put, you know, trust in from where it comes from, through the global operations, literally, having it hardened from the factory it's finished in, moving into your operating environment, and then now protecting against attacks in your web hosting services, right? I mean, this is great. I mean, you'll always have an attack on data, you know, as you're seeing in the data. But the more contained, the more information, and the more control and trust we can give to our customers, it's going to make their job a little easier in protecting whatever job they're trying to do. >> Yeah, and enterprise customers, as you know, they're always trying to keep up to date on the skills and battle the threats. Having that built in under the covers is a real good way to kind of help them free up their time, and also protect them is really killer. This is a big, big part of the Gen11 story here. Securing the data, securing compute, that's the topic here for this special cube conversation, engineering for a hybrid world. Cole, I'll give you the final word. What should people pay attention to, Gen11 from HPE, bottom line, what's the story? >> You know, it's, you know, it's not the first time, it's not the last time, but it's our fundamental security approach to just helping customers through their digital transformation defend in an uncompromising focus to help protect our infrastructure in these technical solutions. >> Cole Humphreys is the global server security product manager at HPE. He's got his finger on the pulse and keeping everyone secure in the platform integrity there. Mike Ferron-Jones is the Intel product manager for data security technology. Gentlemen, thank you for this great conversation, getting into the weeds a little bit with Gen11, which is great. Love the hardware route-of-trust technologies, Better Together. Congratulations on Gen11 and your 4th Gen Xeon Scalable. Thanks for coming on. >> All right, thanks, John. >> Thank you very much, guys, appreciate it. Okay, you're watching "theCube's" special presentation, "Securing Compute, Engineered for the Hybrid World." I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>Good Morning Cloud community and welcome back to Fabulous Las Vegas, Nevada, where we are at AWS Reinvent. It is day four here on the Cube. I'm Savannah Peterson with Lisa Martin. You are looking fantastic. Day four, we've done 45 interviews. How are you feeling? Oh, >>Great. I can't believe it's day four. The cube will be producing over 100 interviews. >>Impressive. Right >>On this stage where there are two sets, and of course we have the set upstairs as well. It's amazing how much content we've created, how many great conversations we've had, right? And the excitement around AWS and the, and the community. >>Yeah. I feel like we've learned so much together. Love co-hosting with you, and so excited for our first conversation this morning with Wira. Welcome, Tim and Kashmira, welcome to the show. How you doing? You both look great for day four. Thank >>You. Yeah, we're doing good. Great. We're doing good. Ready to go. Day four, let's go. >>That's the spirit. That's exactly the energy we need here on the cube. So just in case someone in the audience is not familiar, tell us about Wipro. >>So Wipro is a global consulting company and we help transform our customers and their businesses. >>Transformation's been a super hot topic here at the show, quite frankly a big priority, especially with cost cutting and everything else that's going on. How, how do you do that? How do you help customers do that? Has >>Me run? So we, we, so we have our A strategy, which we call our full stride cloud strategy. So particularly from a cloud perspective here, obviously with aws, we have end to end client services. So from high end strategic consulting through customer journeys, technology implementation, all the way through to our managed services. So we help customers with the end to end journey, particularly as here we're talking about cloud, but also business transformation as well. And we have, you know, a whole host of technologies. So about a few years ago we made an announcement around a billion investment in cloud casual and that Yeah, absolutely. A cool billion and just a cool billion. Yeah. And that pocket >>Change. Exactly. >>Right. And that investment. Over the last few years, we've acquired a number of really exciting companies like Capco, which is a consulting company in the financial services space. We've acquired design companies, a company called Design it, looking at customer journeys and user experience, and then also technology companies called Rising, which looks after the whole SAP space. So we've kind of got the end to end solutions and technologies. And then we also invest in what we call Wipro Ventures. These are really innovative, exciting startups. We invest in those companies to really drive transformation. And the final thing that really brings the whole thing together is that we have decades of experience in engineering. That's kind of the heart of where we come from. So that experience all of that together really helps our clients to transform their business. And particularly as we're talking about cloud helps us to transform the cloud. Now what we are really hoping is that we can help our clients become what we call intelligent enterprises, and we are focusing more and more on customer outcomes and really helping them with those business outcomes. >>Yeah. It doesn't matter what we do if there isn't that business outcome. >>Yeah. That's what it's all about. I'm curious, Tim, to get your, as the America's cloud leader, one of the things that, that our boss, John Furrier, who is the co CEO of the Cube, was able to do every year, he gets to sit down with the head of AWS for a preview of reinvent, right? He's been doing this for 10 years now, and one of the things that Adam Olitsky said to him, this is something about a week or so ago, is CIOs and CEOs are not coming to me to talk about technology. They wanna talk about transformation. Sure, yeah. Business transformation, not an amorphous topic of digital transformation. Are you hearing the same? >>Absolutely. Right. So I think this is my seventh reinvent, right? And I think six, seven years ago, the majority of the conversations you would've had are about technology, right? Great technology, but kind of technology for it to solve it problems. You know, how do I, how do I migrate, how do I modernize, how do I use data? How do I make all this stuff happen? Right now it's about how do I drive new business opportunities, new revenue streams, how do I drive more efficiencies through the manufacturing 2.0 or what have you, right? Yeah. One really good example, like take, take medical devices, right? So like a connected defibrillator, right? Anytime you're building a, what they call an IOT device or a connected device, right? You have four competing an edge device in the space, an edge device, yeah. Right? You have four competing elements, right? >>You've got form factor, power, connectivity and intelligence, and all those things compete, right? I can have all the power if I want, if I can have something as biggest as a tape, right? You know, I can have satellite if I, it gets right off if I can plug it in somewhere. But when you're talking about an implanted defibrillator, right? That, that all competes. So you have an engineering problem, an engineering challenge that's based on a device, right? And then it's gotta connect to the cloud, right? So you have a lot of AWS services, I ot, core device shadowing, all sorts of things. That individual patient then, so, so there's the engineering challenge of, okay, I wanna build a device, I gotta prototype it, I gotta design it, I gotta build it at scale, I have to support it. Then you have a patient, right? Which is the end goal of the business is the patient care. >>They have a console at home that connects to that defibrillator via Bluetooth, let's say. And that's where you get your device updates, just like your laptop, right? You know, now push from where updates to your chest. Yes. Device, ot. It's like, okay, I'm just gonna do this every Thursday, right? So now you've very quickly move to a patient experience and that patient experience will very greatly, right? You know, based on age and exposure to technology and all other sorts of things, how diligent they are. Do they do the update every week Right. To their primary care provider? And then what we're, we're also hearing, okay, so like Kashmira mentioned, we, we can, we can have that design discussion, right? Yeah. We can have the engineering device discussion with our device, device lab. Then we can have our, you know, what's the, what's the patient experience, but then broader, what's the patient experience as they move, as we all do through a healthcare, that's a healthcare network, it's a provider network, it's a series of hospitals and providers. So what does that big picture and ecosystem look like? And it's, you haven't heard me mention server or data center or any of that stuff? No. Right? This is >>The most human anecdote we've had on >>Show. Fantastic. This >>Sidebar. Okay. I mean it great. Keep going. It's wonderful. And it's, and it's, it's fascinating because none of this happens or is possible without cloud and, and the type of services that AWS is, is releasing out into their, into their, into their, into the world, right? But it very quickly moves from technology to human. It very quickly moves from individual to ecosystem to to, to partner and culture and, you know, society, right? So, so these are the types of conversations we're having. I mean, this is kind of stuff that gets me outta bed in the morning. So it's great, right? It's great that, I love that. It's great that we've moved, we moved into that space. >>Well, it's, I mean the human element is so important. Every, every company has to be a data company. Hospitals, absolutely. Grocery stores, retailers, you name it. And what we're seeing is this, and we talk about data democratization all the time. Well, another thing that Adam Slosky told John Furrier is that the role of, of data analysts is gonna, is going to change, maybe go away or the, or the term because data needs to be everywhere. The doctors need the data. Absolutely. Every person in the organization needs to be able to analyze data to deliver outcomes. >>Yeah, absolutely. Yeah. And it's fundamental part of our strategies. And when we are looking at, you know, data is everywhere, you need to really think about how do you align to it. But we are looking at it from an industry perspective. So when we're looking at solutions for our clients, we're looking at how do we deliver data solutions for our bank? How do we deliver data solutions in healthcare? How do we deliver data solutions in various different industry? So >>Many different verticals that you're >>Touching. Yeah, all the different verticals. So that's, you know, we have like a four point strategy industry is the first one. So we have been really worked with a lot of clients around migrations and modernizations. What we're moving to now is really this industry play. So this week we've spent a lot of time with our energy and utilities clients and the AWS practice at banking and financial services, which is a very significant part of our business. Also cloud automotive. This is a really, really, you know, the fascinat, this is so exciting, but the fundamental part of that, it's very, is data, right? It's all hits on data. So it was really great to hear some of the announcements this week around the data piece announcements just for me, that's really exciting. Yeah. A couple of other things that when we're thinking about our overall focus and strategy is, you know, looking at business transformation is, as you mentioned, is the ecosystem. >>So how do we bring all this together? And it's really, we see ourselves as an ecosystem orchestrator, and we are really here to look at leveraging our relationship with the best partners. We've actually met 17 partners here this week and had client sessions with them. And that's, you know, working with the license of Snowflake and Data Break in the, in the data space, our long term partners like sap, ibm, VMware, and you know, and new partners like Con. And we are looking at how do we bring the best of this ecosystem orchestration so that to support those client business outcome. Sure. And then one final sort of pillar, sorry, is talent, right? So the biggest thing that everyone is thinking about and we all think about every single day is talent. So we've done two really exciting things this year. One has been around our own talent. >>So we've really looked at our own internal influences, people who are speaking to our clients every single day. Not so much the technology people, but the client people speaking to the client. And we've really raised the level of cloud fluency with these people so that they can really start to have that discussion. You know, and our clients, you know, they know this technology way better than us, you most of the time. And then secondly, we actually announced last week and, and you initiative, which we are calling skill skills, which is very well known to our AWS clients because AWS provide this skill, skill concept to their clients. But we are the first partner to do the skills. Skills Yeah. From a partnering perspective. And this is really gonna transform. So it's not just about training and enablement, it's actually about creating a journey for you to, you know, do your best work. >>Tim, what, how do you define cloud fluency? We were actually talking about it yesterday. Sure, sure. Yeah. And, and really kind of bringing that across an organization, but what, what does it take for an individual who may not be a technologist to become cloud fluent? >>Sure. Well, there's a couple, there's a couple angles to that, right? One is, one is how do you create cloud fluency for people who might already be technical, right? And that's, and that's, you know, I've spent over a decade with, you know, boutique disruptive consulting companies who live and die by whether they can attract and retain talent. And there's sort of four elements to that. It's, can you, can you show people they're gonna work on interesting stuff, right? Are they gonna be excited about what they do? Can you show that they're gonna expand their skill sets? Yep. Can you show them a career path? And you can, can you surround all of that with a supportive engineering first culture, right? That, you know, rewards for outcomes, but also creates this sort of community, right? Yeah. That's, that's one thing that sort of, you know, that that will be a natural entropy, people will be attracted to that. On the other side of it, as you create fluency, you kind of do it with the conversation that I just had, like around something like medical devices or something like the cloud car. When you just say, look, you start with something everybody already knows, right? We all know what patient care is like. We all know what autonomous vehicles is kind of like, right? And you work backwards from that and say, now here's, here's how all the pieces stitch together to create this end outcome for, for us and for our customers, for >>The, you know, I'm speaking my language, Tim. So I run a boutique consultancy, my talent go, I live and die on that. Quite frankly. It's everything, right? And, and it's so, wow, it's so important. I mean, in eliminating that churn at scale, how big is your team? Now I'm just thinking about this cause I'm sure you're, your talent retention has to be a challenge as well. Sure. >>So, so we have 25,000 woo professionals on aws trained on, you know, tech cloud technologies globally. Impressive. Yeah. And then we have, in terms of our go to market team, we've got 50 strong as well. Well, so we, these are people who are live and breathe aws, right? And speaking and working with the cloud. >>Let's hang out there a little bit. Tell us a little bit more about the partnership with aws. Cast me, >>Let's go to you. Yeah, so our partnership is, you know, it's 11 years strong. It's been an and a really, really great partnership's. >>How longs >>That's true. Yeah. >>No, is you, were, you're, you're like day ones there. That's Yeah. Real legacy it. >>Awesome. You know, this year excitingly, we actually won the APJ partner of dsi, partner of the year. Congratulations. >>Really casual. >>Yeah. Just like >>Married the lead there. Congratulations. >>Yeah. So that really is testament to how we're really knuckling down and working proactively to, to really support our clients. And, you know, the, the partnership is a really, really strong partnership. It's been there for many years with, you know, great solutions and engagement and many of the things I talked about in terms of our industry plays that we're driving. We've got a whole new set of competencies that we've launched, like a new energy competency this year. So we're focusing on industry and then also security, two new security competencies. And you know, what's really exciting on the security side, you saw the announcements around the security data lake, but we've been working over the last few months with Gary, me and his team, and actually are one of the first partners that are driving that initiative. So we're really proud to be part of that. So yeah. You know, and then there's a client engagement as well. So we have a dedicated team at AWS that works with our dedicated team. So we're supporting the client's needs day to day. >>Are you as customer obsessed as AWS is? Absolutely. I >>Figured so. Absolutely. Everything's about the customer. Nothing happens about >>That. Right? Well, you talked about outcomes, it's all about outcomes. >>Well, and I mean, quite literally going for the heart with the defibrillator analogy. No, I mean, you tell the customers at the heart of what you're doing, part of everything. Can't resist a good pun there. So as I warned you, we have a little challenge for you here on the cube. We're looking for your hot take your 32nd sound bite thought leadership. What's the biggest takeaway from the event and moving forward, looking into 2023? Tim, you're giving me that eye contact. I'm going to you first, >>Right? Okay, sure. Love to. So I don't know how hot a take it is, but I kind of see this transition as cloud, as the operating system, right? So, so let's take the, the what we call the cloud car project. We have the connected car. You know, a car is a durable good, and we all know, or there's been a lot of talk about the electric cars or the autonomous vehicles being like more of a computer than a vehicle, right? But a vehicle's supposed to last 10, 15, 20 years. Our laptops don't last 10, 15, 20 years. So there's this cell, there's this major challenge to say, how can I, how can I change the way the technology operates within the vehicle? So you see this transition to where instead of it being a car that, that has a computer, then it, the, the, the latest transition is to more of a computer that, that operates like a car. >>This new vehicle that's going to emerge is gonna be much like a cell phone, right? Where it, it traverses the world and depending on where it is, different things might be available, right? And, and how and how, how the actual technology, the software that is running will, will be, you know, sort of amorphous and move between different resources in the network on the car, everywhere else. And so that's a really different way of thinking about if, if we think about how quickly the Overton window, like what becomes normal, it changes over time. We're really getting to like a very fast movement of that into something like this vehicle's still gonna be something that we don't even maybe think of as a car anymore. Just the way that an iPhone isn't what we used to think of a phone at our >>Pocket computer. Yeah. What's in the mirror part? Great. >>That's kind my >>Take. Awesome. Right? Catch me man. >>Yeah, and I mean I, if I was to suggest that, you know, summarize it by simply, for me it's really focusing on industry solutions, delivering client outcomes, fundamentally underpinned by data security and sustainability. You know, I think Nailed it. >>Yeah. Knock it outta the park. Perfect little sound bite. That was fantastic. You both were a wonderful start to the day. Thank you so much for being here. Tim and Kashmir, absolute >>Pleasure. >>This is, this is a joy. We're gonna keep learning here on the cube. And thank all of you for tuning in to our fabulous AWS reinvent coverage here from Sin City with Lisa Martin. I'm Savannah Peterson and you are watching The Cube, the leader in high tech coverage.

(upbeat electronic music) >> Hello, and welcome to this Cube Conversation. My name is Dave Vellante, and we're here with Raj Gossain, who's the Chief Product Officer at Alation. We have some news. Hello, Raj. Thanks for coming on. >> Dave, it's great to be with you on theCUBE again. >> Yeah, good to see you. So, okay, we're going to talk about Alation Connected Sheets. You know, what is that? Talk to us about what it is, what it does, what it brings to customers. >> So we recognize, spreadsheets are really the dark matter of the data universe. And they're used by, over 78 million people use spreadsheets on a regular basis to drive critical business analysis. But there's a lot of challenges with spreadsheet usage. It brings risk to the organization. There's no visibility into where data comes from. And so we wanted to bring the power of the Alation Data Intelligence Platform to business users where they spend most of their time. And that's in a tool that they love, and that's spreadsheets. And so we're launching a brand new product next week called Alation Connected Sheets. >> So talk more about that. So yes, I get the lineage issue, like where did-- who did this, where's this data come from? I got different data. But talk more about the problems that Alation Connected Sheets solves, specifically for customers. >> Yeah, so the big challenges that we see when we talk to data organizations is how do they understand where the data came from? Is it trusted? Is it reusable? Should it be used in this format? And if you look at where most users that use spreadsheets get the data to power their spreadsheets, maybe it's a CSV download from a database, and then you have no idea where the data came from and where it's going. Or even worse, it's copying and pasting data from other spreadsheets. And so if you take those problems, how can we bring trusted data from governed sources like Snowflake and Redshift and put it in the hands of spreadsheet users, and give them the power and flexibility of Google Sheets or Microsoft Excel, but use trusted, reliable, well-governed data so that the data office feels great about them using spreadsheets and the end users, the business users, can take advantage of the tool that they know and love and do the work that they need to do quickly. >> So, okay. So I'm inferring from your comments there that you've got the ability to take data from you mentioned a couple, Snowflake and Redshift, other popular data warehouses. >> Yep. >> So talk about the key capabilities that you have, any specific features that we should know about. >> Sure. So, we built the leading data intelligence platform and the leading data catalog. And one of the benefits of that catalog is where you have visibility into all of the trusted, governed data sources that a data organization cares about, whether it's enterprise warehouses like Snowflake or Redshift, databases like SQL Server, Google BigQuery, what have you. So what we've done is we've brought the power of that data catalog directly into both Google Sheets as well as Excel. And the idea there is a user can log into their application, authenticate to Alation using the Alation Connected Sheets plugin into their spreadsheet tool, and browse those trusted data sets that are surfaced in the Alation catalog. They get trust signals, they get visibility into where this data came from. So lineage, insights, descriptive information. And then with one or two clicks, they can choose a data set from their warehouse, basically apply filtering conditions. So let's say I'm looking for customer data in Snowflake. I can find the right customer table. If I only want it for say, 2022, I can apply some filter conditions, I can reorder columns, push one button, authenticate to that data source. We want to maintain and ensure security is being applied, so only those users that have access to the warehouse can actually download that data set. But once they've authenticated, that data gets downloaded into their spreadsheet and there's a live connection that's maintained to that spreadsheet. So anytime you need to refresh the data, one push of a button and that data set gets updated. I can schedule the updates. So, you know, if I have to produce a report every Monday morning, I could have that data set refreshed at 8:00 a.m. Monday morning, or whatever schedule the user wants. And so it gives the user the data set they need, but the data organization, they can see where that data came from and they understand the lineage of the data as it is used in analysis in those spreadsheets themselves. >> So Raj, I know you're at the Super Bowl this week, a.k.a. re:Invent. >> Yes. >> And I know you got very close relationships with Snowflake, you've mentioned them a couple times with the data summit last spring. And I know you've done some integration work with those platforms and I'm sure others. So should we think of this as you're extending that sort of trust and governance out to spreadsheets, is that right? And stretching that out? >> That's exactly right. The way we talk about it is how do we bring data intelligence to business users in the tool that they know and love, which is the spreadsheet. And so, the data catalog and data intelligence platforms in general have really primarily been focused on servicing the needs of data users: data analysts, data scientists, data engineers. But you know, our vision, our aspiration at Alation is to really bring data intelligence to any business user. And so it's a big part of our strategy to make sure that the insights from the Alation catalog and platform can find their way into tools like Excel and Google Sheets. And so that's, what you highlighted, Dave, is exactly correct. We want to maximize the likelihood that a business user can have self-service access to trusted, governed data, do the work that they need to do, and ensure that the organization has a set of data assets in spreadsheets, frankly as opposed to liabilities, which is the way most data organizations look at spreadsheets is it's almost like a risk factor. We want to convert that risk, that liability, into an asset so that people can reuse data sets and they understand where this analysis is actually coming from. >> It's something that we've talked about for well over a decade on theCUBE. Is data an asset or is it a liability? >> Yeah, yeah. >> You obviously want to get value out of it, but if you can't share it, it's not trusted. So what people do is they lock it down and then that constricts value creation. >> Exactly. >> My understanding is this tech came out of an acquisition from a company, Kloudio. >> That's correct. >> Tell us about Kloudio. Why Kloudio? What's the fit there? >> Yeah, so Kloudio is a company, it's about five years old. We closed the acquisition of the company in March of this past year. And they had about 20 customers, 10 engineers. And we saw an opportunity with the spreadsheet tool that they'd created to really compliment our data intelligence strategy. And as you said, Dave, extend the value of data intelligence to business users. And so, we brought the Kloudio team into the fold. The thing I'm most excited about as a product guy, is within seven months of them joining Alation, we're actually shipping a brand new product that's going to drive revenue and meet the needs of tens of millions of users, ultimately. Like that's really our aspiration. And so, the tech they had was extremely modern. It reinforces the platform position that we have. You know, this microservices architecture that we've built Alation around, made it easy for that new team to come in and leverage existing APIs and capabilities from our platform and the tech that they brought into Alation to essentially connect the dots and deliver a brand new set of capabilities to an entirely new audience, to help our customers achieve their business objectives, which is really creating a data culture across their entire organization, inclusive of business users, not just, like I said, the data X users that are already taking advantage of solutions like Alation and cloud warehouses, et cetera. >> So I have two questions, follow up questions by me, and I think you might have answered the second one. The first one is what's the secret sauce behind Kloudio? How does the tech work? The second question is how does it fit into the Alation portfolio? How were you able to integrate it so quickly? Maybe that's the microservices architecture. But start with the secret sauce. What is it, what can you share with me? >> I think the thing that we saw with Kloudio that got us excited, and the fact that they, even though it was a small company, they had 20 customers, they were generating revenue, and they were delivering real value to business users, by really enabling business users to tap into the value of trusted, governed data, and frankly, get IT out of the way. You know, we almost refer to it as like smart self-service, which is, they could find a data asset and connect to that source, and just with a couple quick clicks, almost a low-code, no-code type of an experience, bring that sort of data into their spreadsheet so they could do the work that they needed to do. That opportunity, that tech that the Kloudio team had built out, the big gap that they had is, my goodness, what does it take to actually be aware of all the data sources that exist across an organization and connect to them? And that's what Alation does, right? That's why we built the platform that we built, so that we can basically understand all of a customer's data assets, whether they're on-prem or in the cloud. And so it was a little bit of, you know, that Reese's Peanut Butter Cup analogy. The chocolate and the peanut butter coming together. The Alation platform, the Alation catalog, coupled with the technology that Kloudio brought to us really was sort of a match made in heaven. And it's allowed us to bring this new capability to market that really is value-add on top of the platform and catalog investments that our customers have already made. >> Yeah, so they had this magic pixie dust, but it was sort of isolated, and then you've integrated it into your catalog. And that's the second part of my question. How were you able to do that so quickly? >> So, we've been on this evolution, enhancing the the Alation data intelligence platform. We've moved to a microservices architecture, we're fully multi-tenant in the cloud. And the fact that we'd made those investments over the past few years gave us the opportunity to make it easy for an acquired business like Kloudio, or you know, perhaps a future acquisition, or third party developers leveraging APIs that we expose to make it easy for them to integrate into the Alation platform. And so, I think it's a bit of foresight. We recognize that in starting with the catalog, the opportunity was much bigger than just providing a data catalog. We've added data governance, we've built out this platform and we recognize that more and more users can and should be benefiting from data intelligence. And so I think those platform investments have paid significant dividends and accelerated our ability to deliver Alation Connected Sheets as quickly as we have. >> Sounds like a great acquisition, like a diamond in the rough. I mean, I love big these big mega acquisitions 'cause the media company can write about 'em, but I really love the high, high return. You know, low denominator, high value. So, congratulations. >> Thank you. >> Where can people learn more about this? Maybe play around a little bit with it? >> Yeah, so we're going to be demoing Alation Connected Sheets at AWS re:Invent next week. And it's going to be available starting next week, so the 28th of November. And obviously you'll see it online, on social media, on our website as well. But folks that are going to be in Las Vegas next week, come to the Alation booth and you'll get a chance to see it directly. >> Awesome. Okay, Raj. Hey, thanks for spending some time with us today. Really appreciate it. >> Great, thanks so much, Dave. Great to see you. >> Hey, you're very welcome. And thank you for watching. This is Dave Vellante for theCUBE, your leader in enterprise and emerging tech coverage.

>>Hello everyone, and welcome back to the Cube's Live coverage of Cuban here in Motor City, Michigan. My name is Savannah Peterson and I'm delighted to be joined for this segment by my co-host Lisa Martin. Lisa, how you doing? Good. >>We are, we've had such great energy for three days, especially on a Friday. Yeah, that's challenging to do for a tech conference. Go all week, push through the end of day Friday. But we're here, We're excited. We have a great conversation coming up. Absolutely. A little of our alumni is back with us. Love it. We have a great conversation about learning. >>There's been a lot of learning this week, and I cannot wait to hear what these folks have to say. Please welcome Tom and Victoria from Cast by Beam. You guys are swag up very well. You've got the Fanny pack. You've got the vest. You even were nice enough to give me a Carhartt Beanie. Carhartt being a Michigan company, we've had so much love for Detroit and, and locally sourced swag here. I've never seen that before. How has the week been for you? >>The week has been amazing, as you can say by my voice probably. >>So the mic helps. Don't worry. You're good. >>Yeah, so, So we've been talking to tons and tons of people, obviously some vendors, partners of ours. That was great seeing all those people face to face again, because in the past years we haven't really been able to meet up with those people. But then of course, also a lot of end users and most importantly, we've met a lot of people that wanted to learn Kubernetes, that came here to learn Kubernetes, and we've been able to help them. So feel very satisfied about that. >>When we were at VMware explorer, Tom, you were on the program with us, just, I guess that was a couple of months ago. I'm listening track. So many events are coming up. >>Time is a loop. It's >>Okay. It really is. You, you teased some new things coming from a learning perspective. What is going on there? >>All right. So I'm happy that you link back to VMware explorer there because Yeah, I was so excited to talk about it, but I couldn't, and it was frustrating. I knew it was coming up. That was was gonna be awesome. So just before Cuban, we launched Cube Campus, which is the rebrand of learning dot cast io. And Victoria is the great mind behind all of this, but what the gist of it, and then I'll let Victoria talk a little bit. The gist of Cube Campus is this all started as a small webpage in our own domain to bring some hands on lab online and let people use them. But we saw so many people who were interested in those labs that we thought, okay, we have to make this its own community, and this should not be a branded community or a company branded community. >>This needs to be its own thing because people, they like to be in just a community environment without the brand from the company being there. So we made it completely independent. It's a Cube campus, it's still a hundred percent free and it's still the That's right. Only platform where you actually learn Kubernetes with hands on labs. We have 14 labs today. We've been creating one per month and we have a lot of people on there. The most exciting part this week is that we had our first learning day, but before we go there, I suggest we let Victoria talk a little bit about that user experience of Cube Campus. >>Oh, absolutely. So Cube Campus is, and Tom mentioned it's a one year old platform, and we rebranded it specifically to welcome more and, you know, embrace this Kubernetes space total as one year anniversary. We have over 11,000 students and they've been taking labs Wow. Over 7,000. Yes. Labs taken. And per each user, if you actually count approximation, it's over three labs, three point 29. And I believe we're growing as per user if you look at the numbers. So it's a huge success and it's very easy to use overall. If you look at this, it's a number one free Kubernetes learning platform. So for you user journey for your Kubernetes journey, if you start from scratch, don't be afraid. That's we, we got, we got it all. We got you back. >>It's so important and, and I'm sure most of our audience knows this, but the, the number one challenge according to Gartner, according to everyone with Kubernetes, is the complexity. Especially when you're getting harder. I think it's incredibly awesome that you've decided to do this. 11,000 students. I just wanna settle on that. I mean, in your first year is really impressive. How did this become, and I'm sure this was a conversation you two probably had. How did this become a priority for CAST and by Beam? >>I have to go back for that. To the last virtual only Cuban where we were lucky enough to have set up a campaign. It was actually, we had an artist that was doing caricatures in a Zoom room, and it gave us an opportunity to actually talk to people because the challenge back in the days was that everything virtual, it's very hard to talk to people. Every single conversation we had with people asking them, Why are you at cu com virtual was to learn Kubernetes every single conversation. Yeah. And so that was, that is one data point. The other data point is we had one lab to, to use our software, and that was extremely popular. So as a team, we decided we should make more labs and not just about our product, but also about Kubernetes. So that initial page that I talked about that we built, we had three labs at launch. >>One was to learn install Kubernetes. One was to build a first application on Kubernetes, and then a third one was to learn how to back up and restore your application. So there was still a little bit of promoting our technology in there, but pretty soon we decided, okay, this has to become even more. So we added storage, we added security and, and a lot more labs. So today, 14 labs, and we're still adding one every month. The next step for the labs is going to be to involve other partners and have them bring their technologies in the lab. So that's our user base can actually learn more about Kubernetes related technologies and then hopefully with links to open source tools or free software tools. And it's, it's gonna continue to be a, a learning experience for Kubernetes. I >>Love how this seems to be, have been born out of the pandemic in terms of the inability to, to connect with customers, end users, to really understand what their challenges are, how do we help you best? But you saw the demand organically and built this, and then in, in the first year, not only 11,000 as Victoria mentioned, 11,000 users, but you've almost quadrupled the number of labs that you have on the platform in such a short time period. But you did hands on lab here, which I know was a major success. Talk to us about that and what, what surprised you about Yeah, the appetite to learn that's >>Here. Yeah. So actually I'm glad that you relay this back to the pandemic because yes, it was all online because it was still the, the tail end of the pandemic, but then for this event we're like, okay, it's time to do this in person. This is the next step, right? So we organized our first learning day as a co-located event. We were hoping to get 60 people together in a room. We did two labs, a rookie and a pro. So we said two times 30 people. That's our goal because it's really, it's competitive here with the collocated events. It's difficult >>Bringing people lots going on. >>And why don't I, why don't I let Victoria talk about the success of that learning day, because it was big part also her help for that. >>You know, our main goal is to meet expectations and actually see the challenges of our end user. So we actually, it also goes back to what we started doing research. We saw the pain points and yes, it's absolutely reflecting, reflecting on how we deal with this and what we see. And people very appreciative and they love platform because it's not only prerequisites, but also hands on lab practice. So, and it's free again, it's applied, which is great. Yes. So we thought about the user experience, user flow, also based, you know, the product when it's successful and you see the result. And that's where we, can you say the numbers? So our expectation was 60 >>People. You're kinda, you I feel like a suspense is starting killing. How many people came? >>We had over 350 people in our room. Whoa. >>Wow. Wow. >>And small disclaimer, we had a little bit of a technical issue in the beginning because of the success. There was a wireless problem in the hotel amongst others. Oh geez. So we were getting a little bit nervous because we were delayed 20 minutes. Nobody left that, that's, I was standing at the door while people were solving the issues and I was like, Okay, now people are gonna walk out. Right. Nobody left. Kind >>Of gives me >>Ose bump wearing that. We had a little reception afterwards and I talked to people, sorry about the, the disruption that we had under like, no, we, we are so happy that you're doing this. This was such a great experience. Castin also threw party later this week at the party. We had people come up to us like, I was at your learning day and this was so good. Thank you so much for doing this. I'm gonna take the rest of the classes online now. They love it. Really? >>Yeah. We had our instructors leading the program as well, so if they had any questions, it was also address immediately. So it was a, it was amazing event actually. I'm really grateful for people to come actually unappreciated. >>But now your boss knows how you can blow out metrics though. >>Yeah, yeah, yeah, yeah. Gonna >>Raise Victoria. >>Very good point. It's a very >>Good point. I can >>Tell. It's, it's actually, it's very tough to, for me personally, to analyze where the success came from. Because first of all, the team did an amazing job at setting the whole thing up. There was food and drinks for everybody, and it was really a very nice location in a hotel nearby. We made it a colocated event and we saw a lot of people register through the Cuban registration website. But we've done colocated events before and you typically see a very high no-show rate. And this was not the case right now. The a lot of, I mean the, the no-show was actually very low. Obviously we did our own campaign to our own database. Right. But it's hard to say like, we have a lot of people all over the world and how many people are actually gonna be in Detroit. Yeah. One element that also helped, I'm actually very proud of that, One of the people on our team, Thomas Keenan, he reached out to the local universities. Yes. And he invited students to come to learning day as well. I don't think it was very full with students. It was a good chunk of them. So there was a lot of people from here, but it was a good mix. And that way, I mean, we're giving back a little bit to the universities versus students. >>Absolutely. Much. >>I need to, >>There's a lot of love for Detroit this week. I'm all about it. >>It's amazing. But, but from a STEM perspective, that's huge. We're reaching down into that community and really giving them the opportunity to >>Learn. Well, and what a gateway for Castin. I mean, I can easily say, I mean, you are the number, we haven't really talked about casting at all, but before we do, what are those pins in front of you? >>So this is a physical pain. These are physical pins that we gave away for different programs. So people who took labs, for example, rookie level, they would get this p it's a rookie. >>Yes. I'm gonna hold this up just so they can do a little close shot on if you want. Yeah. >>And this is PR for, it's a, it's a next level program. So we have a program actually for IS to beginners inter intermediate and then pro. So three, three different levels. And this one is for Helman. It's actually from previous. >>No, Helmsman is someone who has taken the first three labs, right? >>Yes, it is. But we actually had it already before. So this one is, yeah, this one is, So we built two new labs for this event and it was very, very great, you know, to, to have a ready absolutely new before this event. So we launched the whole website, the whole platform with new labs, additional labs, and >>Before an event, honestly. Yeah. >>Yeah. We also had such >>Your expression just said it all. Exactly. >>You're a vacation and your future. I >>Hope so. >>We've had a couple of rough freaks. Yeah. This is part of it. Yeah. So, but about those labs. So in the classroom we had two, right? We had the, the, the rookie and the pro. And like I said, we wanted an audience for both. Most people stayed for both. And there were people at the venue one hour before we started because they did not want to miss it. Right. And what that chose to me is that even though Cuban has been around for a long time, and people have been coming back to this, there is a huge audience that considers themselves still very early on in their Kubernetes journey and wants to take and, and is not too proud to go to a rookie class for Kubernetes. So for us, that was like, okay, we're doing the right thing because yeah, with the website as well, more rookie users will keep, keep coming. And the big goal for us is just to accelerate their Kubernetes journey. Right. There's a lot of platforms out there. One platform I like as well is called the tech world with nana, she has a lot of instructional for >>You. Oh, she's a wonderful YouTuber. >>She, she's, yeah, her following is amazing. But what we add to this is the hands on part. Right? And, and there's a lot of auto resources as well where you have like papers and books and everything. We try to add those as well, but we feel that you can only learn it by doing it. And that is what we offer. >>Absolutely. Totally. Something like >>Kubernetes, and it sounds like you're demystifying it. You talked about one of the biggest things that everyone talks about with respect to Kubernetes adoption and some of the barriers is the complexity. But it sounds to me like at the, we talked about the demand being there for the hands on labs, the the cube campus.io, but also the fact that people were waiting an hour early, they're recognizing it's okay to raise, go. I don't really understand this. Yeah. In fact, another thing that I heard speaking of, of the rookies is that about 60% of the attendees at this year's cube con are Yeah, we heard that >>Out new. >>Yeah. So maybe that's smell a lot of those rookies showed up saying, >>Well, so even >>These guys are gonna help us really demystify and start learning this at a pace that works for me as an individual. >>There's some crazy macro data to support this. Just to echo this. So 85% of enterprise companies are about to start making this transition in leveraging Kubernetes. That means there's only 15% of a very healthy, substantial market that has adopted the technology at scale. You are teaching that group of people. Let's talk about casting a little bit. Number one, Kubernetes backup, 900% growth recently. How, how are we managing that? What's next for you, you guys? >>Yeah, so growth last year was amazing. Yeah. This year we're seeing very good numbers as well. I think part of the explanation is because people are going into production, you cannot sell back up to a company that is not in production with their right. With their applications. Right? So what we are starting to see is people are finally going into production with their Kubernetes applications and are realizing we have to back this up. The other trend that we're seeing is, I think still in LA last year we were having a lot of stateless first estate full conversations. Remember containers were created for stateless applications. That's no longer the case. Absolutely. But now the acceptance is there. We're not having those. Oh. But we're stateless conversations because everybody runs at least a database with some user data or application data, whatever. So all Kubernetes applications need to be backed up. Absolutely. And we're the number one product for that. >>And you guys just had recently had a new release. Yes. Talk to us a little bit about that before we wrap. It's new in the platform and, and also what gives you, what gives cast. And by being that competitive advantage in this new release, >>The competitive advantage is really simple. Our solution was built for Kubernetes. With Kubernetes. There are other products. >>Talk about dog fooding. Yeah. Yeah. >>That's great. Exactly. Yeah. And you know what, one of our successes at the show is also because we're using Kubernetes to build our application. People love to come to our booth to talk to our engineers, who we always bring to the show because they, they have so much experience to share. That also helps us with ems, by the way, to, to, to build those labs, Right? You need to have the, the experience. So the big competitive advantage is really that we're Kubernetes native. And then to talk about 5.5, I was going like, what was the other part of the question? So yeah, we had 5.5 launched also during the show. So it was really a busy week. The big focus for five five was simplicity. To make it even easier to use our product. We really want people to, to find it easy. We, we were using, we were using new helm charts and, and, and things like that. The second part of the launch was to do even more partner integrations. Because if you look at the space, this cloud native space, it's, you can also attest to that with, with Cube campus, when you build an application, you need so many different tools, right? And we are trying to integrate with all of those tools in the most easy and most efficient way so that it becomes easy for our customers to use our technology in their Kubernetes stack. >>I love it. Tom Victoria, one final question for you before we wrap up. You mentioned that you have a fantastic team. I can tell just from the energy you two have. That's probably the truth. You also mentioned that you bring the party everywhere you go. Where are we all going after this? Where's the party tonight? Yeah. >>Well, let's first go to a ballgame tonight. >>The party's on the court. I love it. Go Pistons. >>And, and then we'll end up somewhere downtown in a, in a good club, I guess. >>Yeah. Yeah. Well, we'll see how the show down with the hawks goes. I hope you guys make it to the game. Tom Victoria, thank you so much for being here. We're excited about what you're doing. Lisa, always a joy sharing the stage with you. My love. And to all of you who are watching, thank you so much for tuning into the cube. We are wrapping up here with one segment left in Detroit, Michigan. My name's Savannah Peterson. Thanks for being here.

(upbeat music) >> Announcer: theCUBE presents UiPath Forward5. Brought to you by UiPath. >> Hi everybody, we're back in Las Vegas. We're live with Cube's coverage of Forward 5 2022. Dave Vellante with Dave Nicholson Ted Kumer this year is the Executive Vice President, product and engineering at UiPath. Brought on to do a lot of the integration and bring on new capabilities for the platform and we've seen that over the last several years. And he's joined by Dr. Edward Challis, who's the co-founder of the recent acquisition that UiPath made, company called Re:infer. We're going to learn about those guys. Gents, welcome to theCUBE. Ted, good to see you again. Ed, welcome. >> Good to be here. >> First time. >> Thank you. >> Yeah, great to be here with you. >> Yeah, so we have seen, as I said, this platform expanding. I think you used the term business automation platform. It's kind of a new term you guys introduced at the conference. Where'd that come from? What is that? What are the characteristics that are salient to the platform? >> Well, I see the, the evolution of our platform in three chapters. You understand the first chapter, we call that the RPA chapter. And that's where we saw the power of UI automation applied to the old problems of how do I integrate apps? How do I automate processes? That was chapter one. You know, chapter two gets us to Forward3 in 2019, and the definition of this end-to-end automation platform you know, with the capabilities from discover to measure, and building out that core platform. And as the platform's progressed, what we've seen happen with our customers is the use of it goes from being very heavy in automating the repetitive and routine to being more balanced, to now where they're implementing new brought business process, new capability for their organization. So that's where the name, Business Automation Platform, came from. Reflecting now that it's got this central role, as a strategic tool, sitting between their application landscape, their processes, their people, helping that move forward at the rate that it needs to. >> And process mining and task mining, that was sort of the enabler of chapter two, is that right? >> Well, I'd say chapter two was, you know, first the robots got bigger in terms of what they could cover and do. API integration, long running workflows, AI and ML skills integrated document processing, citizen development in addition to professional development, engaging end users with things like user interfaces built with UiPath apps. And then the discovery. >> So, more robustness of the? Yeah, okay. >> Yeah. Just an expansion of the whole surface area which opened up a lot of things for our customers to do. That went much broader than where core RPA started. And so, and the other thing about this progression to the business automation platform is, you know, we see customers now talking more about outcomes. Early on they talk a lot about hours saved and that's great, but then what about the business outcomes it's enabling? The transformations in their business. And the other thing we're doing in the platform is thinking about, well, where can we land with solutions capabilities that more directly land on business, measurable business outcomes? And so we had started, for example, offering an email automation solution, big business problem for a lot of our customers last year. And we'd started encountering this company Re:infer as we were working with customers. And then, and we encountered Re:infer being used with our platform together. And we saw we can accelerate this. And what that is giving us now is a solution now that aligns with a very defined business outcome. And this way, you know, we can help you process communications and do it efficiently and provide better service for your customers. And that's beginning of another important progression for us in our platform. >> So that's a nice segue, Ed. Tell about Re:infer. Why did you start the company? >> Right, yeah, so my whole career has been in machine learning and AI and I finished my PhD around 2013, it was a very exciting time in AI. And me and my co-founders come from UCL, this university in London, and Deep Mind, this company which Google acquired a few years later, came from our same university. So very exciting time amongst the people that really knew about machine learning and AI. And everyone was thinking, you know, how do we, these are just really big breakthroughs. And you could just see there was going to be a whole bunch of subsequent breakthroughs and we thought NLP would be the next breakthrough. So we were really focused on machine reading problems. And, but we also knew as people that had like built machine learning production systems. 'Cause I'd also worked in industry that built that journey from having a hypothesis that machine learning can solve a problem to getting machine learning into production. That journey is of painful, painful journey and that, you know, you can see that you've got these advances, but getting into broad is just way too hard. >> So where do you fit in the platform? >> Yeah, so I think when you look in the enterprise just so many processes start with a message start with a no, start with a case ticket or, you know, some other kind of request from a colleague or a customer. And so it's super exciting to be able to, you know, take automation one step higher in that process chain. So, you could automatically read that request, interpret it, get all the structured data you need to drive that process forward. So it's about bringing automation into these human channels. >> So I want to give the audience a sense here. So we do a lot of events at the Venetian Conference Center, and it's usually very booth heavy, you know, brands and big giant booths. And here the booths are all very small. They're like kiosks, and they're all pretty much the same size. So it's not like one vendor trying to compete with the other. And there are all these elements, you know I feel like there's clouds and there's, you know, of course orange is the color here. And one of the spots is, it has this really kind of cool sitting area around customer stories. And I was in there last night reading about Deutsche Bank. Deutsche Bank was also up on stage. Deutsche Bank, you guys were talking about a Re:infer. So share with our audience what Deutsche Bank are doing with UiPath and Re:infer. >> Yeah, so I mean, you know, before we automate something, we often like to do what we call communications mining. Which is really understanding what all of these messages are about that might be hitting a part of the business. And at Deutsche Bank and in many, you know, like many large financial services businesses, huge volumes of messages coming in from the clients. We analyze those, interpret the high volume query types and then it's about automating against those to free up capacity. Which ultimately means you can provide faster, higher quality service because you've got more time to do it. And you're not dealing with all of those mundane tasks. So it's that whole journey of mining to automation of the coms that come into the corporate bank. >> So how do I invoke the service? So is it mother module or what's the customer onboarding experience like? >> So, I think the first thing that we do is we generate some understanding of actually the communications data they want to observe, right? And we call it mining, but you know, what we're trying to understand is like what are these communications about? What's the intent? What are they trying to accomplish? Tone can be interesting, like what's the sentiment of this customer? And once you understand that, you essentially then understand categories of conversations you're having and then you apply automations to that. And so then essentially those individual automations can be pointed to sets of emails for them to automate the processing of. And so what we've seen is customers go from things they're handling a hundred percent manual to now 95% of them are handled basically with completely automated processing. The other thing I think is super interesting here and why communications mining and automation are so powerful together is communications about your business can be very, very dynamic. So like, new conversations can emerge, something happens right in your business, you have an outage, whatever, and the automation platform, being a very rapid development platform, can help you adapt quickly to that in an automated way. Which is another reason why this is such a powerful thing to put the two things together. >> So, you can build that event into the automation very quickly you're saying? >> Speaker 1: Yeah. >> Speaker 2: That's totally right. >> Cool. >> So Ed, on the subject of natural language processing and machine learning versus machine teaching. If I text my wife and ask her would you like to go to an Italian restaurant tonight? And she replies, fine. Okay, how smart is your machine? And, of course, context usually literally denotes things within the text, and a short response like that's very difficult to do this. But how do you go through this process? Let's say you're implementing this for a given customer. And we were just talking about, you know, the specific customer requirements that they might have. What does that process look like? Do you have an auditor that goes through? And I mean do you get like 20% accuracy, and then you do a pass, and now you're at 80% accuracy, and you do a pass? What does that look? >> Yeah, so I mean, you know when I was talking about the pain of getting a machine learning model into production one of the principle drivers of that is this process of training the machine learning model. And so what we use is a technique called active learning which is effectively where the AI and ML model queries the user to say, teach me about this data point, teach me about this sentence. And that's a dynamic iterative process. And by doing it in that way you make that training process much, much faster. But critically that means that the user has, when you train the model the user defines how you want to encode that interpretation. So when you were training it you would say fine from my wife is not good, right? >> Sure, so it might be fine, do you have a better suggestion? >> Yeah, but that's actually a very serious point because one of the things we do is track the quality of service. Our customers use us to attract the quality of service they deliver to their clients. And in many industries people don't use flowery language, like, thank you so much, or you know, I'm upset with you, you know. What they might say is fine, and you know, the person that manages that client, that is not good, right? Or they might say I'd like to remind you that we've been late the last three times, you know. >> This is urgent. >> Yeah, you know, so it's important that the client, our client, the user of Re:infer, can encode what their notions of good and bad are. >> Sorry, quick follow up on that. Differences between British English and American English. In the U.K., if you're thinking about becoming an elected politician, you stand for office, right? Here in the U.S., you run for office. That's just the beginning of the vagaries and differences. >> Yeah, well, I've now got a lot more American colleagues and I realize my English phrasing often goes amiss. So I'm really aware of the problem. We have customers that have contact centers, some of them are in the U.K., some of them are in America, and they see big differences in the way that the customers get treated based on where the customer is based. So we've actually done analysis in Re:infer to look at how agents and customers interact and how you should route customers to the contact centers to be culturally matched. Because sometimes there can be a little bit of friction just for that cultural mapping. >> Ted, what's the what's the general philosophy when you make an acquisition like this and you bring in new features? Do you just wake up one day and all of a sudden there's this new capability? Is it a separate sort of for pay module? Does it depend? >> I think it depends. You know, in this case we were really led here by customers. We saw a very high value opportunity and the beginnings of a strategy and really being able to mine all forms of communication and drive automated processing of all forms of communication. And in this case we found a fantastic team and a fantastic piece of software that we can move very quickly to get in the hands of our customer's via UiPath. We're in private preview now, we're going to be GA in the cloud right after the first of the year and it's going to continue forward from there. But it's definitely not one size fits all. Every single one of 'em is different and it's important to approach 'em that way. >> Right, right. So some announcements, StudioWeb was one that I think you could. So I think it came out today. Can't remember what was today. I think we talked about it yesterday on the keynotes anyway. Why is that important? What is it all about? >> Well we talked, you know, at a very top level. I think every development platform thinks about two things for developers. They think, how do I make it more expressive so you can do other things, richer scenarios. And how do I make it simpler? 'Cause fast is always better, and lower learning curves is always better, and those sorts of things. So, Re:infer's a great example of look the runtime is becoming more and more expressive and now you can buy in communications state as part of your automation, which is super cool. And then, you know StudioWeb is about kind of that second point and Studios and Studio X are already low code visual, but they're desktop. And part of our strategy here is to elevate all of that experience into the web. Now we didn't elevate all of studio there, it's a subset. It is API integration and web based application automation, Which is a great foundation for a lot of apps. It's a complete reimagining of the studio user interface and most importantly it's our first cross-platform developer strategy. And so that's been another piece of our strategy, is to say to the customers we want to be everywhere you need us to be. We did cross-platform deployment with the automation suite. We got cross-platform robots with linear robots, serverless robots, Mac support and now we got a cross-platform devs story. So we're starting out with a subset of capabilities maybe oriented toward what you would associate with citizen scenarios. But you're going to see more roadmap, bringing more and more of that. But it's pretty exciting for us. We've been working on this thing for a couple years now and like this is a huge milestone for the team to get to this, this point. >> I think my first conversation on theCUBE with a customer was six years ago maybe at one of the earlier Forwards, I think Forward2. And the pattern that I saw was basically people taking existing processes and making them better, you know taking the mundane away. I remember asking customers, yeah, aren't you kind of paving the cow path? Aren't there sort of new things that you can do, new process? And they're like, yeah, that's sort of the next wave. So what are you seeing in terms of automating existing processes versus new processes? I would see Re:infer is going to open up a whole new vector of new processes. How should we think about that? >> Yeah, I think, you know, I mean in some ways RPA has this reputation because there's so much value that's been provided in the automating of the repetitive and routine. But I'd say in my whole time, I've been at the company now for two and a half years, I've seen lots of new novel stuff stood up. I mean just in Covid we saw the platform being used in PPP loan processing. We saw it in new clinical workflows for COVID testing. We see it and we've just seen more and more progression and it's been exciting that the conference, to see customers now talking about things they built with UiPath apps. So app experiences they've been delivering, you know. I talked about one in healthcare yesterday and basically how they've improved their patient intake processing and that sort of thing. And I think this is just the front end. I truly believe that we are seeing the convergence happen and it's happening already of categories we've talked about separately, iPass, BPM, low-code, RPA. It's happening and it's good for customers 'cause they want one thing to cover more stuff and you know, I think it just creates more opportunity for developers to do more things. >> Your background at Microsoft probably well prepared you for a company that you know, was born on-prem and then went all in on the cloud and had, you know, multiple code bases to deal with. UiPath has gone through a similar transformation and we talked to Daniel last night about this and you're now cloud first. So how is that going just in terms of managing multiple code bases? >> Well it's actually not multiple Code bases. >> Oh, it's the same one, Right, deployment models I should say. >> Is the first thing, Yeah, the deployment models. Another thing we did along the way was basically replatform at an infrastructure level. So we now can deploy into a Kubernetes Docker world, what you'd call the cloud native platform. And that allows us to have much more of a shared infrastructure layer as we look to deliver to the automation cloud. The same workload to the automation cloud that we now deliver in the automation suite for deployment on-prem or deploying a public cloud for a customer to manage. Interesting and enough, that's how Re:infer was built, which is it was built also in the cloud native platform. So it's going to be pretty easy. Well, pretty easy, there's some work to do, but it's going to be pretty easy for us to then bring that into the platform 'cause they're already working on that same platform and provide those same services both on premises and in the cloud without having your developers have to think too much about both. >> Okay, I got to ask you, so I could wrap my stack in a container and put it into AWS or Azure or Google and it'll run great. As well, I could tap some of the underlying primitives of those respective clouds, which are different and I could run them just fine. Or/and I could create an abstraction layer that could hide those underlying primitives and then take the best of each and create an automation cloud, my own cloud. Does that resonate? Is that what you're doing architecturally? Is that a roadmap, or? >> Certainly going forward, you know, in the automation cloud. The automation cloud, we announced a great partnership or a continued partnership with Microsoft. And just Azure and our platform. We obviously take advantage of anything we can to make that great and native capabilities. And I think you're going to see in the Automation Suite us doing more and more to be in a deployment model on Azure, be more and more optimized to using those infrastructure services. So if you deploy automation suite on-prem we'll use our embedded distro then when we deploy it say on Azure, we'll use some of their higher level managed services instead of our embedded distro. And that will just give customers a better optimized experience. >> Interesting to see how that'll develop. Last question is, you know what should we expect going forward? Can you show us a little leg on on the future? >> Well, we've talked about a number of directions. This idea of semantic automation is a place where you know, you're going to, I think, continue to see things, shoots, green shoots, come up in our platform. And you know, it's somewhat of an abstract idea but the idea that the platform is just going to become semantically smarter. You know, I had to serve Re:infer as a way, we're semantically smarter now about communications data and forms of communications data. We're getting semantically smarter about documents, screens you know, so developers aren't dealing with, like, this low level stuff. They can focus on business problem and get out of having to deal with all this lower level mechanism. That is one of many areas I'm excited about, but I think that's an area you're going to see a lot from us in the next coming years. >> All right guys, hey, thanks so much for coming to theCUBE. Really appreciate you taking us through this. Awesome >> Yeah Always a pleasure. >> Platform extension. Ed. All right, keep it right there, everybody. Dave Nicholson, I will be back right after this short break from UiPath Forward5, Las Vegas. (upbeat music)

(upbeat music) >> Hey everyone. And welcome to this CUBE Conversation which is part of the AWS startup showcase. Season two, episode four of our ongoing series. The theme of this episode is cybersecurity, detect and protect against threats. I'm your host, Lisa Martin, and I'm pleased to be joined by the founder and CEO of Hunters.AI, Uri May. Uri, welcome to theCUBE. It's great to have you here. >> Thank you, Lisa. It's great to be here. >> Tell me a little bit about your background and the founders story. This company was only founded in 2018, so you're quite young. But gimme that backstory about what you saw in the market that really determined, this is needed. >> Yeah, absolutely. So, I mean, I think the biggest thing for us was the understanding that significant things have happened in the cybersecurity landscape for customers and technology stayed the same. I mean, we tried on solving the same... We tried on solving a big problem with the same old tools when we actually noticed that the problem has changed significantly. And we saw that change happening in two different dimensions. The first is the types of attacks that we're defending against. A decade ago, we were mostly focused on these highly sophisticated nation state efforts that included unknown techniques and tactics and highly sophisticated kind of methods. Nowadays, we're talking a lot about cyber crime gangs, whoops of people that are financially motivated or using off the shelf tools, of the shelf malware, coordinating in the dark web, attacking for money and ransom basically, versus sophisticated intelligence kind of objectives. And in the same time of that happening, we also saw what we like to refer to as explosion of the securities stack. So some of our customers are using more than 60 or 70 different security tools that are generating sometimes tens of terabytes a day of flows. That explosion of data, together with a very persistent and consistent threat that is continuously affecting customers, create a very different environment, where you need to analyze a big variety of data and you need to constantly defend yourself against stuff that are happening all the time. And that was kind of like our wake moment when we understand that the tools that are out there now might have been the right tools a decade ago, they are probably not the right tools to solve the problem now. So yeah, I think that that was kind of what led us to Hunters. And in the same time, and I think that that's my personal kind of story behind it. We used to talk a lot about the fact that we want to solve a fundamental problem. And we, as part of the ideation around Hunters and us zooming in on exactly the areas that we want to focus on in security, we talked with a lot of CSOs, we talked with a lot of industry experts, everyone directed us to the security operation center. I mean the notion that there's a lot of tools and there's always going to be a lot of tools, but eventually decisions are being made by people that are running security operation center, that are actually acting as the first line of defense. And that's where you feel that the processes are woke. That's where you feel that that technology doesn't really meet the rabel, and the rabel doesn't really meet the hold. And for us, it was a very clear sign that this is where we need to focus on. And that set us on a journey to explore red hunting and then understand that we can solve something bigger than that. And then eventually get to where we are today, which is go to market around. So holistic a platform that can help SOC analysts doing the day to day job defending the organizations. >> So you saw back in 2018, probably even before that that the SIEM market was prime and right for disruption. And only in a four year time period, there's been some pretty significant milestones and accomplishment that the team at Hunters has made in that short timeframe. Talk to me about some of those big milestones that the company has reached in just four years. >> Yeah, I think that the biggest thing and I know that it's going to sound like a cliche, but we're actually believing that I think it's the team. I mean, we're able to go to an organization of around 150 employees. All over the world, the course, I think I mean the last time that I checked, like 15 countries. That's the most amazing feeling that you can have. That ability to attract people to a single mission from all over the world and to get them collaborate and do amazing things and achieve unbelievable accomplishment. I think that's the biggest thing. The other thing for us was customers. I mean, think about it like, SIEM it's such a central and critical system. So for us as a young startup from Tel Aviv to go out to Enterprise America and convince the biggest enterprise around the world to rip and replace the the existing solutions that are being built by the biggest software brands out there and install Hunters instead, that's a huge leap of trust, that we are very grateful for, and we're trying to handle with a lot of care and a lot of responsibility. And obviously, I think that other than that, is all of the investors that we were able to attract that basically enabled all of that customer acquisition and team building and product development. And we're very fortunate to work with the biggest names out there, both from a strategic perspective and also from tier one VCs from mainly from the U.S., but from all over the world, actually that are backing us. >> Great customers, solid foundation. Hunters is built for the clouds, is powered by Snowflake. This is AWS built. Talk to me about what's in it for me from an AWS customer perspective. What's that value in it for them? >> Yeah, so I think that the most important thing, in my opinion, at least, is the security value that you're getting from it. Other than the fact that Hunters is a multi-tenant SaaS application running in AWS, it's also a system that is highly tuned and specifically built to be very effective against detecting threats inside AWS environments. So we invested a lot of time in research, in analyzing the way attackers are operating inside cloud environments, specifically in AWS. And then we model these techniques and tactics and procedures into the system. We're leveraging data sets like AWS CloudRail and CloudWatch and VPC Flow Logs, obviously AWS GuardDuty which is an amazing detection system that AWS offer to its customer, and we're able to leverage it, correlate it with other signals. And at the same time, there's also the commercial aspect and the business aspect. I mean, we're allowing AWS customers to leverage the AWS credits to the marketplace to fund same projects like Hunters that comes with a lot of efficiencies also. And with a lot of additional capabilities like I mentioned earlier. >> So let's crack open Hunters.AI. What makes this approach different? You talked about the challenges that you guys saw in the market that were gaps there, and why technology needed to come in from a disruption standpoint. But describe the differentiators. When you're talking to perspective customers, what are those key differentiators that Hunters brings to the table? >> Yeah, absolutely. So we like to divide it into three main pillars. The first pillar is everything that we do with data, that is very different from our competitors. We believe that data should be completely liberated from the analytical layer. And that's why we're storing data in a dedicated data warehouse. Snowflake, as you mentioned earlier, is one of our go to data warehouses. And that give customers the ability to own their own data. So you as a customer can opt in into using Hunters on top of your Snowflake. It's not the only way. You can also get Snowflake bundled as part of that, your Hunter subscription, but for some customers that ability to reduce vendor lock risk on data on your own and also level security data for other kind of workflows is something that is really huge. So that's the first thing that is very different. The second thing is what we like to call security engineering as a service. So when you buy Hunters, you don't just buy a data platform. You actually buy a system, a SOC platform that is already populated with use cases. So what we are saying is that in today's world the threats that we're handling as a SOC, as security operations center professionals are actually shared by 80% of the customers out there. So 80% of the customers share around 80% of the threat. And what we're basically saying is let us as a vendor, solve the detection response around that 80%. So you as a customer could focus on the 20% that is unique to your environment. Then in a lot of cases generate 80% of the impact. So that means that you are getting a lot of rebuilt tools and detections, data modeling to your integrations, automatic investigations, scoring correlations. All of these things are being continuously deployed and delivered by us because we're multi tenant SaaS. And also allowing you again to get this effortless tail key kind of solution that is very different from your experience with your current SIEM tools that usually involves a lot of tuning, professional services, configuration, et cetera. And the last aspect of it, is everything that we're doing around automation. We're leveraging very unique graph technology and what we call automatic investigation enrichments that allows us to take all of these signals that we're extracting from all over the attacks, of say AWS included, but also the endpoint and the email and the network and IOT environments and whatever automatically investigate them, load them into a graph and then automatically correlate them to what we call stones, which are basically representation of incidents that are happening across your tax office. And that's a very unique capability that we bring into the table that demonstrates our focus on the analytical lens. So it's not just log aggregation, and querying and dashboarding kind of system. It's actually a security analytic system that is able to drive real insights on top of the data that you're plugging into it. >> So talk to me, Uri, when you're in customer conversations these days the market is there's so many dynamics and flux that customers are dealing with. Obviously, the threat landscape continues to expand and really become quite amorphous as that perimeter blends. What are some of the specific challenges that security operation center or SOC teams come to you saying, help us eliminate this. We have so many tools, we've probably got limited resources. What are those challenges and how does Hunters really wipe those off the plate? >> Yeah, so I think the first and foremost has to do with the second pillar that I mentioned earlier and that's security engineering. So for most security operations centers and most organizations around the world, the feeling is that they're kind of like stuck on this third wheel. They keep on buying tools and then implementing these tools and then writing rules and then generating noise and then fine tuning the rules. And then testing the rules and understanding that the fine tuning actually generated misdetections. And they're kind of like stuck on this vicious side. And no one can really help because a lot of the stuff that they're building, they're building it in their environment. And what we're saying is that, let us do it for you. Well, that 80% that we've mentioned earlier and allows you to really focus on the stuff that you're doing and even offset your talent. So, we're not talking about really a talent reduction. Because everyone needs more talent in cybersecurity nowadays but we're talking a lot about offset. I mean, if we had a team of five people investing efforts in building walls, building automation, and now three or four of these people can go and do advanced investigations, instant response, threat hunting interval, that's meaningful. For a lot of SOCs, in a lot of cases that means either identifying and analyzing a threat in time or missing it. So, I mean, I think that that's the biggest thing. And the other thing has to do with the first thing that I mentioned earlier, and these are the data challenges. Data challenges in terms of cost, performance, the ability to absorb data sets that today's tools can't really support. I mean, for example, one of the biggest data sets that we're loading that is tremendously helpful is raw data for EDR products. Raw data for EDR products in large enterprises can get to 10, 15, 20 terabytes a day. In today's SIEMs and SOC platforms that the customers are using, this thing is just as prohibited from SOC. They can't really analyze it because it's so costly. So what we're saying is a lot of what we're seeing is a lot of customers, either not analyzing it at all, or saving it for a very little amount of time, account of days. Because they can't support the retention around it. So the ability to store huge data sets for longer period of time makes it something that a lot of big enterprises need. And to be honest, I think that in the next couple of years they would also be forced to have these kind of capabilities, even from a compliance perspective. >> So in terms of outcomes, I'm hearing reduction in costs really helping security teams utilize their resources, the ability to analyze growing volumes of data. That's only going to continue to increase as we know. Is there a customer story, Uri that you have that really, where the value proposition of Hunters really shines through? >> Yeah, I think that one thing comes to mind from those hospitality vertical and actually it's a reference customer. I mean, we can share the name. His name is booking.com. It's also publicly shown on our website. And they think the coolest thing that we were able to do with booking is give them that capability to stay up to date with the threats that they're facing. So it's not just that we saved a lot of efforts from them because we came with a lot of out of the box capabilities that they can use. We also kept them up to date with everything that they were facing. And there was a couple of cases, where we were able to detect threats that were very recently from threat perspective. Based on our ability to invest research time and efforts in everything that is going on in the ecosystem and the feedback that we got from the customer, and it's not a single of feedback. Like we're getting it a lot, is that, without you guys we wouldn't be able to do the effective research and then the implementation of this and the threat modeling and the implementation of these things in time. And walking with you kind of like made the difference between analyzing it and reacting in time and potentially blocking like a very serious bridge versus maybe finding out when it's too late. >> Huge impact there. And I'm kind of thinking, Hunters aim, might be one of the reasons that booking.com's tagline it's booking.com, booking.yeah. Yeah, we're secure. We know if we can demonstrate that to everyone that uses our service. I noticed kind of wrapping things up here, Uri. I noticed that back in I think it was January of 2022, Hunters raised about 60 million in series C. You talked about kind of being in the GTM phase, where are some of those strategic investments? What have you been doing, focusing on this year and what's to come as we round out 22? >> Yeah, absolutely. So, I mean, there's a lot of building going on. Yeah. Still, right. I mean, we're getting into that scale mode and scale phase but we're very much also building our capabilities, building our infrastructure, building our teams, building our business processes. So there's a lot of efforts going into that, but in the same time, I mean, we've being able to vary, to depending our relationship with DataBlitz which is a very important partner of us. And we got some big news coming up on that. And they were a strategic investor that participated in our series C. And in the same time we're walking in the air market which is a very interesting market for us. And we get a lot of support from one other strategic investor that joined the series C, Deutsche Telekom. And they are a huge provider in IT and security in email, other than doing a lot of other things and including T-systems and T-Mobile and everything that has to do with that. So we're getting a lot of support from them. And regardless, I think, and that ties back to what we've mentioned earlier, the ability for us to come to really big customers with the quality of investors that we have is a very important external validation. It's basically saying like this company is here to stay. We're aiming at disrupting the market. We're building something big. You can count on us by replacing this critical system that we're talking about. And sometimes it makes a difference, like sometimes for some of the customers, it means that this is something that I can rely on. Like it's not a startup that is going to be sold two months after I'm deploying it. And it's not a founder that is going to disappear on me. And for a lot of customers, these things happen, especially in an ecosystem like cybersecurity, that is so big with such a huge variety of different systems. So, yeah, I think that we're getting ready for that scale mode and hopefully it'll happen sooner than what we think. >> A lot of growth already as we mentioned in the beginning of the program. Since just 2018 it sounds like from a foundation perspective, you guys are strong, you're rocking away and ready to really take things into 2023 with such force. Uri, thank you so much for joining me on the program, talking about what Hunters.AI is up to and how you're different and why you're disrupting the SIEM market. We appreciate your insights and your time. >> Absolutely. Lisa, the pleasure was all mine. Thank you for having me. >> Likewise. For Uri May, I'm Lisa Martin. Thank you for watching our CUBE Conversation as part of the AWS startup showcase. Keep it right here for more actions on theCUBE, your leader in tech coverage. (upbeat music)

(upbeat music) >> Hello, welcome to this Cube Conversation here in Palo Alto, California. I'm John Furrier, host of theCube. We have a returning Cube alumni, Ramesh Prabagan, who is the co-founder and CEO of Prosimo.io. Great to see you, Ramesh. Thanks for coming in to our studio, and welcome to the new layout. >> Thanks for having me here, John. After a series of Zoom conversations, it's great to be live and in the flesh! >> Great to be in person. We also got a new stage for our Supercloud event, which we've been opening up to the community, looking forward to getting your perspective on that soon as well. But I want to keep the conversation really about you guys. I want to get the story down. You guys came out of stealth, Multicloud, Supercloud is right in your wheelhouse. >> Exactly. >> You got to love Supercloud. >> Yeah. As I walked in, I saw Supercloud all over the place, and it just gives you a jolt of energy. >> Well, you guys are in the middle of the action. Your company, I want you to explain this in a minute, is in the middle of this next wave. Because we had the structural change I called Cloud One. Amazon, use case, developers, no need to build a data center, all that goodness happens, higher level service of abstractions are happening, and then Azure comes in. More PaaS, and then more install base, now they're nipping at the heels. So full on hyperscale, Cap Backs growth, great for everybody. Now comes new use cases. Cloud to cloud, app to app, you see Databricks, Snowflake, MongoDB, all doing extremely well by leveraging the Cap Backs, now it's an ops problem. >> Exactly. >> Now ops and security. >> Yeah. It's speed of applications. >> How are you guys vectoring into that? Explain what you guys do. >> Absolutely. So let me take kind of the customer pain point first, right? Because it's always easier to explain that, and then we explain what is it that we do. So, it's no surprise. Applications are moving into the cloud, or people are building apps in the cloud in masses. The infrastructure that's sitting in front of these applications, cutting across networking, security, the operational piece associated with that, does not move at the same speed. The apps sometimes get upgraded two, three times a day, the infrastructure gets touched one time a week at best. And so increasingly, the cloud platform teams, the developers are all like, "Hey, why? Why? Why?" Right? "I thought things were supposed to move fast in the cloud." It doesn't. Now, if you double click on that, really, it's two reasons. One, those that won't have consistency across the stack that they hired in the data center, they bring a virtual form factor of that stack and line it up in the cloud, and before you know it, it's cost, it's operation complexity, there are multiple single panes of glass, all the fun stuff associated... >> Just to interject real quick. It is fast in the cloud if you're a developer. >> Exactly. >> So it's kind of like, hurry up, slow down, wait. >> Correct. >> So the developers are shifting left, open source is booming. Things are fine for developers right now. If you're a developer, things are good. >> But the guy sitting in front of that... >> The ops guys, they've got to deal with things like lock-in, choice, security. >> Exactly. And those are really the key challenges. We've seen some that actually said, "Hey, know what, I don't want to bring my data center stack into the cloud. Let me go cloud-native. And they start to build it up. 14 services from AWS, 15 from iGR, 14 more from GCP, even if you are in a single cloud. They just keep it to that. I need to know how to put this together. Because all these services are great, but how do I put this together. And enterprises don't have just one application, they have hundreds of these applications. So the requirements of a database is different than a service mesh, different than a serverless application, different than a web application. And before you know it, "How do I put all these things together?" And so we looked at this problem, and we said, "Okay. We subscribe to the fact that cloud-native is the way to go, right, but something needs to be there to make this simple." Right? And so, first thing that we did was bring all these cloud-native services together, we help orchestrate that, and we said, "okay, know what, Mr. Enterprise? We got you covered." Right? But now, it doesn't stop there. That's like, 10% of the value, right? What do you really need? What do you care about now? Because the apps are in the center of the universe, and who's talking to it? It's another application sitting either in the same cloud, or in a different cloud, or it's a user connecting into the application. So now, let's talk about what are the networking security operational requirements required for these apps to talk to each other, or the user to talk to the application. That's really what we focus on. >> Yeah. And I think one of the things that's driving this opportunity for you, and I want to get your reaction to this, is that the modern application movement is all about cloud-native. Okay, they're obviously doing great. Now, kind of the kumbaya moment in enterprise is that the security team and ops teams have to play ball and be friends with the developer, and vice versa. So harmony's coming there. So the little harmony. And two, the business is driving apps. IT is transforming over. This is why the Supercloud idea is interesting to Dave and I. Because when we coined that term, multi-cloud was not a market. Everyone has multiple clouds, 'cause they have Microsoft Office, that's now in the cloud, they got SQL Server, I mean it's really kind of Microsoft Cloud. >> Exactly. >> So you have a cloud. But do you have ops teams building on the stack? What about the network layer? This is where the rubber meets the road. >> Absolutely, yeah. And if you look at the challenges there, if you just focus on networking and security, right? When applications need to talk to each other, you have a whole bunch of underlying services, but somebody needs to put this thing on top. Because what you care about is "can these group of users talk to these class of applications." Or, "these group of applications, can they talk to each other," right? This whole notion of connectivity is just table stakes. Everybody just assumes it's there, right? It's the next layer up, which is, "how do I bring Zero Trust access? How do I get the observability?" And observability is not just a bunch of pretty donut chats. I have had people look to me in my previous company, the start-up, and said, "okay, give me all these nice donut chats, but so what? What do you want me to do with this?" And so you have to translate that into real actions, right? "How do I bring Zero Trust capabilities? How do I bring the observability capabilities? How do I understand cloud-native and networking and bring those things together so that you can help solve for the problem." >> It's interesting, one of the questions I had here to ask you was "what does it mean to be cloud-native, and why now?" And you brought up Zero Trust, trust and verify, these are security concepts. But if you look at what's going on at KubeKon and CNCF and Linux Foundation, software supply chain's a huge issue, where trust is the issue. They want trust there, so you got Zero Trust here. What is it? Zero Trust or trust? I mean, what's there? Is one hardware based, perimeter, networking? That kind of perimeter's dead, ton of... >> No, the whole- >> Trust or Zero Trust. >> The whole concept of Zero Trust is don't trust what is underlying, just trust what you're talking to. So if you and I talking to each other, John, you need to trust me, I need to trust you, and be able to have this conversation. >> You've been verified. >> Exactly, right? But in the application world, if you talk about two apps that are talking to each other, let's say there is a web application in one AWS region talking to a database in a different region, right? Now, do you want to make sure you are able to build that trust all the way from the application to the application? Or do you want to move the trust boundary to the two entities that are talking to each other so that irrespective of what they go on underneath the covers, you can be always sure that these two things are trusted. >> So, Ramesh, I was on LinkedIn yesterday, I wrote a comment, Dave Vallante wrote a post on Supercloud, we're talking about it, and I wrote, "Cloud as a commodity," question, and then a bunch of other stuff that we're going to talk about, and Keith Townsend jumped on that, and got on Twitter, put a poll, "Is cloud a commodity? Source: me." So, it started a big thread. And the reaction was interesting. And my point was to be provocative on "Cloud isn't commodity, but there's commodity elements." EC2 and S3, you can look at that and say, "that's commodity IaaS," but Amazon Web Services has done an amazing job for higher level services. Okay, so how does that translate into the use cases that you see that you guys are going after and solving, because it's the same kind of concept. IaaS and SaaS have to work together to solve problems, but that's in an integrated environment, say, in a native-cloud. How does that work across clouds? >> Yeah, no, you bring up a great point, John. So, let's take the simple use case, right? Let's keep the user to app thing to the side. Let us say two apps need to talk to each other, right? There are multiple ways in which you can solve this problem. You can build highways. That's what our customers call it. I'll build highways. I don't care what goes on those highways, I'll just build highways. You bring any kind of application workload on it, I just make sure that the highways are good, right? That's kind of the lowest common denominator. It's the path to least resistance. You can get stuff done, but it's not going to move the needle, right? Then you have really modern, kind of service networking, where, okay, I'm looking at every single HTTP, API, n:point, whatnot, and I'm optimizing for that. Right? Great if you know what you're doing, but, like, if you have thousands of these applications, it's not going to be really feasible to do that. And so, what we have seen customers do, actually, is employ a mixed approach, where they say, "I'm going to build these highways, the highways are going to make sure that I can go from one place to another, and maybe within regions, across clouds, whatnot, but then, I have specific requirements that my business needs, that actually needs tweaking, right? And so I'm going to tweak those things. That's why, what we call as like, full stack transit, is exactly that, right, which is, I'll build you the guts of it so that hey, you know what, if somebody screams at you, "Hey, why is my application not accessible?" You don't have that problem. It is always accessible. But then, the requirements for performance, the requirements for Zero Trust, the requirements for segmentation, and all of that are things that... >> That's a hard problem. >> That's a hard problem to solve. >> And you guys are solving that? >> Absolutely, exactly. >> So, let me throw this at you. So, okay, I get that. And by the way, that's exactly what we're seeing. Dave and I were also debating about multi-cloud as what it is. Now, the nirvana definition is, "Well, I have a workload, that's going to work the same, and just magically just shift to Azure." (Ramesh laughs) >> Like, 'cause there's better resources. >> There is no magic there. >> So, but this brings up the point of operations. Now, Databricks and Snowflake, they're building their software to run on multi-cloud seamlessly. Now they can do that, 'cause it's their application. What is the multi-cloud use case, so that's a Supercloud use case in your mind, because right now it's not yet there. What is the Supercloud use case that's going to allow this seamless management or workloads. What's your view? >> Yeah, so if you take enterprise, right? Large enterprise in particular. They invariably have some workloads that are on, let's say, if the primary cloud is AWS, there are some workloads in Azure. Maybe they have acquired a new company, maybe a start-up that uses GCP, whatnot. So they have sprinkles of workloads in other clouds. >> So that's the breed kind of thing. >> Yeah, exactly. That's not what causes anybody to wake up in the morning and say, "I need to have a Supercloud strategy." That's not the thing, right? But now, increasingly you're seeing "pick the right cloud for the appropriate workload." That is going to change quite a bit. Because I have my infrastructure heavy workloads in AWS. I have quite a bit of like, analytics and mining type of applications that are better on GCP. I have all of my package applications work well on Azure, right? How do I make sure all of this. And it's not apps of this kind. Even simple things like VDI. VDI always used to be, "I have this instance I run up" and whatnot. Now every single cloud provider is giving you their own flavor of virtual desktop. And so, how do you make sure all of these things work together, right? And once again, what we have seen customers do is they settle on one cloud as their primary, but then you always have sprinkles of workloads across all of the clouds. Now, you could also go down the path, and you're increasingly seeing this, you could go down the path of, "Hey, I'm using cloud as backbone," right? Cloud providers have invested massive amounts of dollars to make sure that the infrastructure reaches there. Literally almost to the extent that every user in a metro city is ten milliseconds from the public cloud. And so they have allowed for that. Now, you can actually use cloud backbones to get the availability, the liability and whatnot. So these are some new use cases that we have seen actually blew up in customers. I was just doing an interview, and the topic was the innovator's dilemma. And one of the panelists said, "It's not the innovator's dilemma, it's the integrator dilemma." Because if you have commodity, and you have choices on, say, backbones and whatnot for transit, the integration is the key glue now. What's your reaction to that? >> Absolutely. And we have seen, we used to spend quite a bit of time in kind of what is the day zero problem, right? Like, how do I put this together? Conversations are moved past that, because there are multiple ways in which you can do that right now, right? Conversations are moving to kind of, "this is more of an operational problem for me." It's not just operations in the form of "Hey, I need to find out where the problem is, troubleshoot it, and so forth. But I need to make like really high quality decisions." And those decisions are going to be guided by data. We have enterprise customers that acquire new companies. Or they have a new site that they open up. >> It's a mishmash. >> Yeah, exactly. It's a New York based company and they acquire a team out in Sidney, Australia, right? Does your cloud tell you today that you have new users, or new applications that are in Sidney, and naturally just extend? No, it doesn't. Somebody has to look at the macro problem, look at "Where are all my workloads?" Do a bunch of engineering to make that work, right? We took it upon ourselves to say "Hey, you know what, twenty-four hours later, you're going to get a recommendation in the platform that says, 'okay, you have new set of applications, a new set of users coming from Sidney, Australia, what have you done about it?' Click a button, and then you expand on it. >> It's kind of like how IT became the easy way to run the data center. Before IT you had to be a PhD, and roll out, I mean, you know how it was, right? So you're kind of taking that same approach. Okay, well, Ramesh, great stuff. I want to do a followup, certainly with you on this. 'Cause you're in the middle of where this wave is going, this structural change, and certainly can participate in that Supercloud conversation. But for your company, what's going on there? Give us an update, customer activity, what's it like, you guys came out of stealth, what's been the reaction, give a plug for the company, who you going to hire, take a minute to plug it. >> Oh, wonderful, thank you. So, primary use cases are really around cloud networking. How do you go within the cloud, and across clouds, and to the cloud, right? So those are really the key use cases. We go after large enterprises predominantly, but any kind of mid enterprise that is extremely cloud oriented, has lot of workloads in the cloud, equally applicable, applicable there. So we have about 60 of the Fortune 500s that we are engaged in right now. Many of them are paying customers as well. >> How are they buying, service? Is it... >> Yeah. So we provide software that actually sits inside the customer's own administrative control, delivered as a service, that they can use to go- >> So on-premise hosting or in the cloud? >> Entirely in the cloud, delivered as a service, so they didn't need to take care of the maintenance and whatnot, but they just consume it from the cloud directly, okay? And so, where we are right now is essentially, I have a branch of repeatable use cases that many customers are employing us for. So again, building highways, many different ways to build highways, at the same time take care of the micro-segmentation requirements, and then importantly, this whole NetDevOps, right? This whole NetDevOps is a cultural shift that we have seen. So if you are a network engineer, NetDevOps seems like it's a foreign term, right? But if you are an operational engineer, then NetDevOps, you know exactly what to do. So bringing all those principles together, making sure that the networking teams are empowered to essentially embrace the cloud that I created, the single biggest thing that we have done, I would say done well, is we have built very well on top of the cloud provider. So we don't go against cloud-native services. They have done that really, really well. It makes no sense to go say, "I have a better transit gateway than you." No. Hands down, an AWS transit gateway, or an Azure V1 and whatnot, are some of the best services that they have provided. But what does that mean? >> How do you build software into it? >> Exactly, right? And so how can you build a layer of software on top, so that when you attach that into the applications, right, that you can actually get the experience required, you can get the security requirements and so forth. So that's kind of where we are. We're also humbled by essentially some of the mega partners that have taken a bet on us, sometimes to the extent that, we're a 70% company, and some of the partners that we are talking to actually are quite humbling, right? >> Hey, lot more resource. >> Exactly, yeah. >> And how many rounds of financing have you done? >> So we have done two rounds of financing, we have raised about 55,000,000 in capital, again, really great set of investors backing us up, and a strong sense of conviction, on kind of where we are going. >> Do you think you're early, or not? 'Cause, that's always probably the biggest scary, I can see the smile, is that what keeps you up at night? >> So, yeah, exactly, I go through these phases internally in my head. >> The vision's right on the money, no doubt about it. >> So when you win an opportunity, and we have like, a few dozen of these, right, when you win an opportunity, you're like, "Yes, absolutely, this is where it is," right, and you go for a week and you don't win something, and you're like, "Hey man, why are we not seeing this?" Right, and so you go through these cycles, but I'll tell you with conviction, the fact that customers are moving workloads into the public cloud, not in dozens but in like, the hundreds and the thousands, essentially means that they need something like this. >> And the cloud-native wave is driving big time. >> Exactly, right. And so, when the customer as a conversation with AWS, Azure, GCP, and they are privy to all the services, and we go in after that and talk about, "How do I put this together and help you focus on your outcomes?" That mentally moves them. >> It's a day zero opportunity, and then you got headroom beyond that. >> Exactly. So that's the positive side of it, and enterprises certainly are sometimes a little cautious about when they're up new technologies and so forth. It's a natural cycle. Fortunately, again we are humbled by the fact that we have a few dozen of the pioneering customers that are using our platform. That gives you the legitimacy for a start-up. >> You got great pedigree on clients. Real quick, final question. 30 seconds. What's the pain point, for people watching, when do they call you in? What's their environment look like, what are some of the things that give the signals that you guys got to get the call? >> If you have more than, let's say five or ten VPCs in the cloud, and you have not invested in building a networking platform that gives you the connectivity, the security, the observability, and the performance requirements, you absolutely have to do that, right? Because we have seen many, many customers, it goes from 5 to 50 to 100 within a week, and so you don't want to be caught essentially in the midst of that. >> One more final final question. Since you're a seasoned entrepreneur, you've been there, done that previous times, >> Yeah, I've got scars. (laughs) >> Yes, we've all got scar tissue. We've been doing theCube for 12 years, we've seen a lot of stuff. What's the difference now in this market that's different than before? What's exciting you? What's the big change? What's, in your opinion, happening now that's really important that people should pay attention to? >> Absolutely. A lot of it is driven by one, the focus on the cloud itself, right? That's driving a sense of speed like never before. Because in the infrastructure world, yeah you do it today, oh, you do it six months from now, you had some leeway. Here, networking security teams are being yelled at almost every single day, by the cloud guy saying, "You guys are not moving fast enough, fast enough, fast enough." So that thing is different. So it helps, going to shrink the sale cycle for us. So second big one is, nobody knows, essentially, the new set of use cases that are coming about. We are seeing patterns emerge in terms of new use cases almost every single day. Some days it's like completely on the other end of the spectrum. Like, "I'm only serverless and service mesh." On the other end, it's like, "I have a package application, I'm moving it to the cloud." Right? And so, we're learning a lot as well. >> A great time for Supercloud. >> Exactly. >> Do the cloud really well, make it super, bring it to other use cases, stitch it all together, make it easy to use, reduce the complexity, it's just evolution. >> Yeah. And our goal is essentially, enterprise customers should not be focused so much on building infrastructure this way, right? They should focus on users, application services, let vendors like us worry about the nitty-gritty underneath. >> Ramesh, thank you for this conversation. It's a great Cube conversation. In the middle of all the action, Supercloud, multi-cloud, the future is going to be very much cloud-based, IaaS, SaaS, connecting environments. This is the cloud 2.0, Superclouds. And this is what people are going to be working on. I'm John Furrier with theCube, thanks for watching. (soft music)

(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)

(upbeat music) >> Hey, everyone. Welcome to this CUBE Conversation that's part of the AWS startup showcase Season Two, Episode Four. I'm your host Lisa Martin. Chase Doelling joins me, the principles strategist at JumpCloud. Chase, welcome to theCUBE. It's great to have you. >> Chase: Perfect. Well, thank you so much, Lisa. I really appreciate the opportunity to come and hang out. >> Let's talk about JumpCloud. First of all, love the name. This is an open directory platform. Talk to the audience about what the platform is, obviously, the evolution of the domain controller. But give us that backstory? >> Yeah, absolutely. And so, company was started, and I think, from serial entrepreneurs, and after kind of last exit, taking a look around and saying, "Why is this piece of hardware still the dominant force when you're thinking about identities, especially when the world is moving to cloud, and all the different pieces that have been around it?" And so, over the years, we've evolved JumpCloud into an open directory platform. And what that is, is we're managing your identities, the devices that are associated to that, all the access points that employees need just to get their job done. And the best part is, is we're able to do that no matter where they are within the world. >> It seems like kind of a reinvention of how modern IT teams are getting worked done, especially in these days of remote work. Talk to me a little bit about the last couple of years particularly as remote work exploded, and here we are still probably, permanently, in that situation? >> Yeah, absolutely. And I think it's probably going to be one of those situations where we stick with it for quite a while. We had a very abrupt force in making sure that essentially every IT and security team could grapple with the fact of their users are no longer coming into the office. You know, how do we VPN into all of our different resources? Those are very common and unfortunate pain points that we've had over the last couple years. And so, now, people have starting to kind of get into the motion of it, working from home, having background and setups and other pieces. But one of the main areas of concern, especially as you're thinking about that, is how does it relate to my security infrastructure, or kind of my approach to my organization. And making sure that too, on the tail end, that a user's access and making sure that they can get into everything that they need to do in order to get work done, is still happening? And so, what we've done, is we've really taken, evolving and really kind of ripping apart this notion of what a directory was. 'Cause originally, it was just like, great, almost like a phone directory. It's where people lived they're going into all those different pieces. But it wasn't set up for the modern world, and kind of how we're approaching it, and how organizations now are started with a credit card and have all of their infrastructure. And essentially, all of their IP, is now hosted somewhere else. And so, we wanted to take a different approach where we're thinking about, not only managing that identity, but taking an open approach. So, matter where the identity's coming from, we can integrate that into the platform but then we're also managing and securing those devices, which is often the most important piece that we have sitting right in front of us in order to get into that. But then, also that final question, of when you're accessing networks applications, can you create the conditions for trust, right? And so, if you're looking at zero trust, or kind of going after different levels of compliance, ISO, SOC2, whatever that might be, making sure that you have all that put in place no matter where your employees are. So, in that way, as we kind of moved into this remote, now hybrid world, it wasn't the office as the gating point anymore, right? So, key cards, as much as we love 'em, final part, whereas the new perimeter, the kind of the new barrier for organizations especially how they're thinking about security, is the people's identities behind that. And so, that's the approach that we really wanted to take as we continue to evolve and really open up what a directory platform can do. >> Yeah. Zero trust security, remote work. Two things that have exploded in the last couple of years. But as employees, we expected to be able to still have the access that we needed to apps, to the network, to WiFi, et cetera. And, of course, on the security side, we saw massive changes in the threat landscape that really, obviously, security elevates to a board level conversation. So, I imagine zero trust security, remote work, probably compliance, you mentioned SOC2, are some of the the key use cases that you're helping organizations with? >> Those are a lot of the drivers. And what we do, is we're able to combine a lot of different aspects that you need for each one of those. And so, now you're thinking about essentially, the use case of someone joins an organization, they need access to all these different things. But behind the scenes, it's a combination of identity access management, device management, applications, networks, everything else, and creating those conditions for them to do their roles. But the other piece of that, is you also don't want to be overly cumbersome. I think a lot of us think about security as like great biometrics, so I'm going to add in these keys, I'm going to do everything else to kind of get into these secured resources. But the reality of it now, is those secure resources might be AWS infrastructure. It might be other Salesforce reporting tools. It might be other pieces, or kind of IP within the organization. And those are now your crown jewel. And so, if you're not thinking about the identities behind them and the security that you have in order to facilitate that transaction, it becomes a board level conversation very quickly. But you want to do it in a way that people can move forward with their lives, and they're not spending a ton of time battling the systems and procedures you put in place to protect it, but that it's working together seamlessly. And so, that's where, kind of this notion for us of bringing all these different technologies into one platform. You're able to consolidate a lot of those and remove a lot of the friction while maintaining the visibility, and answering the question, of who has access to what? And when did they do that? Those are the most critical pieces that IT and security teams are asking themselves when something happens. And hopefully, on the preventative side and not so much on the redacted side. >> Have you seen the escalation up the C-Suite change of the board in terms of really focusing on how do we do identity management? How do we do single sign on? How do we do device management and network access? Is that all the way up to the C-Suite board level as well? >> It certainly can be. And we've seen it in a lot of different conversations, because now you are thinking about all different portions of the organization. And then, two, as we're thinking about times we're currently in, there's also a cost associated to that. And so, when you start to consolidate all of those technologies into one area, now it becomes much more of total cost optimization types of story while you're still maintaining a lot of the security and basic blocking and tackling that you need for most organizations. So, everything you just mentioned, those are now table stakes for a lot of small, medium, startups to be at the table. So, how do you have access to enterprise level, essentially technology, without the cost that's associated to it. And that's a lot of the trade offs that organizations are facing and having those types of conversations as it relates to business preparedness and how we're making sure that we are putting our best foot forward, and we're able to be resilient in no matter what type, of either economic or security threat that the organization might be looking at. >> So, let's talk about the go-to market, the strategy from a sales and marketing perspective. Where are the customer conversations happening? Are they at the IT level? Are they higher up the stack? >> It's really at, I'd say the IT level. And so, by that, I mean the builders, the implementers, everyone that's responsible for putting devices in people's hands, and making sure that they can do their job effectively. And so, those are their, I'd say the IT admins the world as well as the managed service providers who support those organizations, making sure that we can enable them to making sure that their organizations or their client organizations have all the tools that their disposable to make sure that they have the security or the policies, and the technology behind them to enable all those different practices. >> Let's unpack the benefits from an IT perspective? Obviously, they're getting one console that they can manage at all. One user identity for email, and devices, and apps, and things. You mentioned regardless of location, but this is also regardless of operating system, correct? >> That's correct. And so, part of taking an open approach, is also the devices that you're running on. And so, we take a cross OS approach. So, Mac, Windows, Linux, iPhone, whatever it might be, we can make sure that, that device is secure. And so, it does a couple different things. So, one, is the employees have device choice, right? So, I'm a Mac person coming in. If forced into a Windows, it'd be an interesting experience. But then, also too, from the back end, now you have essentially one platform to manage your entire fleet. And also give visibility and data behind what's happening behind those. And then, from the end user perspective as well, everything's tied together. And so, instead of having, what we'll call user ID schizophrenia, it might be one employee, but hundreds of different identities and logins just to get their work done. We can now centralize that into one person, making sure you have one password to get into your advice, get into the network, to get into your single sign on. We also have push MFA associated with that. So, you can actually create the conditions for your most secured access, or you understand, say, "Hey, I'm actually in the office. I'm going to be a hybrid employee. Maybe I can actually relax some of those security concerns I might have for people outside of the network." And all we do, is making sure that we give all that optionality to our IT admins, manage service providers of the world to enable that type of work for their employees to happen. >> So, they have the ability to toggle that, is critically important in this day and age of the hybrid work model, that's probably here to stay? >> It is, yeah. And it's something that organizations change, right? Our own organizations, they grow, they change different. New threats might emerge, or same old existing threats continue to come back. And we need to just have better processes and automations put within that. And it's when you start to consolidate all of those technologies, not only are you thinking about the visibility behind that, but then you're automating a lot of those different pieces that are already tightly coupled together. And that actually is truly powerful for a lot of the IT admins of the world, because that's where they spend a lot of time, and they're able to spend more time helping users tackling big projects instead of run rate security, and blocking, and tackling. That should be enabled from the organization from the get go. >> You mentioned automation. And I think that there's got to be a TCO reduction aspect here with respect to security and IT practices. Can you talk about that a little bit? >> Yeah, absolutely. Let's think about the opposite of that. Let's say we have a laundry list of technology that we need to go out and source. One is, great, where the identity is, so we have an identity provider. Now, we need to make sure that we have application access that might look like single sign on. Now, we need to make sure, you are who you are no matter where you are in the world. Well, now we need multifactor authentication and that might involve either a push button, or biometrics. And then, well, great the device's in front of us, that's a huge component, making sure that I can understand, not only who's on the device, but that the device is secure, that there's certificates there, that there's policies that ensure the proper use of that wherever it might be. Especially, if I'm an employee, either, it used to be on the the jet center going between flying anywhere you need. Now, it's kind of cross country, cross domain, all those different areas. And when you start to have that, it really unlocks, essentially IT sprawl. You have a lot of different pieces, a lot of different contracts, trying to figure out one technology works, but the other might not. And you're now you're creating workarounds for all these different pieces. So, the opposite of that, is essentially, let's take all those technologies and consolidate that into one platform. So, not only is it cheaper essentially, looking after that and understanding all the different technologies, but now it's all the other soft costs around it that many people don't think about. It's all the other automations. It's all the workarounds that you didn't have to do in the first place. It's all the other pieces that you'd spend a lot of time trying to wire it together. Into the hopes of that, it creates some security model. But then again, you lose a lot of the visibility. So, you might have an incident happen over here, or a trigger, or alert, but it's not tied to the rest of the stack. And so, now you're spending a lot of time, especially, either trying to understand. And worse timing, is if you have an incident and you're trying to understand what's happening? Unraveling all of that as it happens, becomes impossible, especially if it's not consolidated with one platform. So, there's not only the hard cost aspect of bringing all that together, but also the soft costs of thinking about how your business can perform, or at least optimize for a lot of those different standard processes, including onboarding, offboarding, and everything else in between. >> Yeah. On the soft cost side, I can imagine. I can see huge benefits for HR onboarding, offboarding. I can see benefits for the employee experience period, which directly relates to the customer experience. So, in terms of the business impact that JumpCloud can make, it seems to be pretty horizontal across any type of organization? >> It is, and especially as you mentioned HR. Because when you think about, where does the origin of someone's identity start? Well, typically, it starts with a resume and that might be in applicant tracking software. Now, we're going to get hired, so we're going to move into HR, because, well, everyone likes payroll, and we need that in our lives, right? But now you get into the second phase, of great, now I've joined the organization. Now, I need access to all of these different pieces. But when you look at it, essentially horizontally, from HR, all the way into the employee experience, and their whole life cycle within the organization, now you're touching multiple different teams And that's one of the other, I'd say benefits of that, is now you're actually bringing in HR, and IT, and security, and everyone else that might be related within these kind of larger use cases of making work happen all coming under. And when they're tightly integrated, it's also a lot more secure, right? So, you're not passing notes along. You're not having a checklist of other stuff, especially when it relates to something as important as someone's identity, which is more often than not, the most common attack vector for people to go after. Because they know it's the keys to the kingdom. There's going to be a lot of different attempts, maybe malware and other pieces, but a lot of it comes back into, can I impersonate, or become the person that I want within the organization, because it's the identity allows you to access all those different pieces. And so, if it's coming from a disjointed process or something that's not as tightly as it could be, that's where it really opens up a lot of different vectors that organizations don't think about. >> Right, and those vectors are only growing and multiplying as we know, and here to stay. When you're in customer conversations what do you describe as maybe the top three differentiators of JumpCloud compared to the competition? >> Well, I think a lot of it is we take an open approach. And so, by that, I mean, it's one we're not locking into, I'd say different vendors or other areas. We're really looking into making sure that we can work within your environment as it stands today, or where you want to migrate in the future. And so, this could be a combination of on-prem resources, cloud resources, or nothing if you're starting a company from today. And the second, is again, coming back into how we're looking at devices. So, we take a cross OS approach that way, no matter what you're operating on, it all comes back from the same dashboard. But then, finally, we leverage a ton of different protocols to make sure it works with everything within your current technology stack, as well as it continues to elevate and evolve over time. So, it could be LD app and Radius, and Sam, and skim, and open ID Connect, and open APIs. And whatever that might be, we are able to tie in all those different pieces. So, now, all of a sudden, it's not just one platform, but you have your whole business tied into as that gives you some flexibility too, to evolve. Because even during the pandemic and the shift for remote, there's a lot of technology choices that shifted. A lot of people are like, "Okay, now's the time to go to the cloud." There might be other events that organizations change. There's other things that might happen. So, creating that flexibility for organizations to move and make those calls, is essentially how we're differentiating ourselves. And we're not locking you into this, walled garden of technology that's just our own. We really want to make sure that we can operate, and be that glue, so that way, no matter what you're trying to do and making sure that your work is being done, we can help facilitate that. >> Nice. No matter what happens. Because boy, at this day, anything's possible. One more question for you about your AWS partnership. Talk to me a little bit about that? >> Yeah, absolutely. So, we are preferred ADP identity provider and SSO provider for AWS. And so, now rebranded under their identity center. But it's crucial for a lot of our organizations and joint customers because again, when we think about a lot of organization IP and how they operate as a business, is tied into AWS. And so, really understanding, who has the right level of access? Who should be in there or not? And when too, you should challenge in making sure that actually there's something fishy there. Like let's make sure that they're not just traveling to Europe on a sabbatical, and it's really who they are instead of a threat actor. Those are some of the pieces when we're thinking about creating that authentication, but then also, the right authorization into those AWS resources. And so, that's actually something that we've been very close to, especially, I'd say that the origins of a company. Because a lot of startups, that's where they go. That's where they begin their journey. And so, we meet them where they are, and making sure that we're protecting not only everything else within their organization, but also what they're trying to get into, which is typically AWS >> Meeting customers where they are. It's all about that. Chase, thank you so much for joining me on the program talking about JumpCloud, it's open directory platform. The benefits, the capabilities, what's in it for IT, HR, security, et cetera. We appreciate all of your insights and time. Where do you want to point folks to go to learn more? >> Well, absolutely. Well, thank you so much for having us. And I'd say, if you're curious about any and all these different technologies, the best part is everything I talked about is free up to 10 users, 10 devices. So, just go to jumpcloud.com. You can create an organization, and it's great for startups, people at home. Any size company that you're at, we can help support all of those different facets in bringing in those different types of technologies all into one roof. >> Awesome. Chase, thank you so much. This is awesome, go to jumpcloud.com. For Chase Doelling, I'm Lisa Martin. We want to thank you so much for giving us some of your time and watching this CUBE Conversation. (upbeat music)

(upbeat music) >> Hello, welcome to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We're here with Tim Everson, CISO at Kalahari Resorts & Conventions. Tim, great to see you. Thanks for coming on theCUBE. >> Thank you for having me. Looking forward to it. >> So, you know, RSA is going on this week. We're talking a lot about security. You've got a lot of conferences. Security is a big scale now across all enterprises, all businesses. You're in the hospitality, you got conventions. You're in the middle of it. You have an interesting environment. You've got a lot of diverse use cases. And you've got a lot of needs. They're always changing. I mean, you talk about change. You've got a network that has to be responsive, robust and support a lot of tough customers who want to have fun or do business. >> Exactly, yeah. We have customers that come in, that we were talking about this before the segment. And we have customers that come in that bring their own Roku Sticks their own Amazon devices. All these different things they bring in. You know, our resort customers need dedicated bandwidth. So they need dedicated network segments stood up at a moment's notice to do the things they're doing and run the shows they're showing. So it's never, never ending. It's constantly changing in our business. And there's just data galore to keep an eye on. So it's really interesting. >> Can you scope the scale of the current cybersecurity challenges these days in the industry? Because they're wide and far, they're deep. You got zero trust on one end, which is essentially don't trust anything. And then you got now on the software supply chain, things like more trust. So you got the conflict between a direction that's more trusted and then zero trust, and everything in between. From, endpoint protection. It's a lot going on. What's the scale of this situation right now in cyber? >> You know, right now everything's very, very up in the air. You talk about zero trust. And zero trust can be defined a lot of ways depending on what security person you talk to today. So, I won't go into my long discussion about zero trust but suffice to say, like I said zero trust can be perceived so many different ways. From a user perspective, from a network perspective, from an end point. I look more broadly at the regulatory side of things and how that affects things too. Because, regulations are changing daily. You've got your GDPRs, your CCPAs, your HIPAA regulations, PCI. All these different things that affect businesses, and affect businesses different ways. I mean, at Kalahari we're vulnerable or we're not vulnerable, but we're subject to a lot of these different regulations, more so than other people. You wouldn't expect a lot of hotels to have HIPAA regulations for instance. We have health people at our resorts. So we actually are subject to HIPAA in a lot of cases. So there's a lot of these broad scenarios that apply and they come into play with all different industries. And again, things you don't expect. So, when you see these threats coming, when you see all the hacks coming. Even today I got an email that the Marriott breach data from a few years ago, or the MGM breach from a few years ago. We've got all these breaches out there in the world, are coming back to the surface and being looked at again. And our users and our guests and our corporate partners, and all these different people see those things and they rely on us to protect them. So it makes that scope just exponentially bigger. >> Yeah, there's so many threads to pull on here. One is, you know we've observed certainly with the pandemic and then now going forward is that if you weren't modern in your infrastructure, in your environment, you are exposed. Even, I'm not talking old and antiquated like in the dark ages IT. We're talking like really state of the art, current. If you're lagging just by a few years, the hackers have an advantage. So, the constant bar raising, leveling up on technology is part of this arms race against the bad guys. >> Absolutely. And you said it, you talked earlier about the supply chain. Supply chain, these attacks that have come through the SolarWinds attacks and some of these other supply chain attacks that are coming out right now. Everybody's doing their best to stay on top of the latest, greatest. And the problem with that is, when you rely on other vendors and other companies to be able to help you do that. And you're relying on all these different tool sets, the supply chain attack is hugely critical. It makes it really, really important that you're watching where you're getting your software from, what they're doing with it, how they secure it. And that when you're dealing with your vendors and your different suppliers, you're making sure that they're securing things as well as you are. And it just, it adds to the complexity, it adds to the footprint and it adds to the headache that a lot of these security teams have. Especially small teams where they don't have the people to manage those kind of contacts. >> It's so interesting, I think zero trust is a knee jerk reaction to the perimeter being gone. It's like, you got to People love the zero trust. Oh it's like, "We're going to protect this that nobody, and then vet them in." But once you're trusted, trust also is coming in to play here. And in your environment, you're a hotel, you're a convention. You have a lot of rotation of guests coming in. Very much high velocity. And spear phishing and phishing, I could be watching and socially engineering someone that could be on your property at any given time. You got to be prepared for that. Or, you got ransomware coming around the corners or heavily. So, you got the ransomware threat and you got potentially spear phishing that could be possible at your place. These are things that are going on, right? That you got to protect for. What's your reaction to that? >> Absolutely. We see all those kind of attacks on a daily basis. I see spear phishing attacks. I see, web links and I chase them down and see what's going on. I see that there's ransomware trying to come in. We see these things every single day. And the problem you have with it is not only, especially in a space where you have a high volume of customers and a high turnover of customers like you're talking about that are in and out of our resorts, in and out of our facilities. Those attacks aren't just coming from our executives and their email. We can have a guest sitting on a guest network, on a wireless network. Or on one of our business center machines, or using our resort network for any one of a number of the conference things that they're doing and the different ports that we have to open and the different bandwidth scenarios that you've got dealing with. All of these things come into play because if any attack comes from any of those channels you have to make sure that segmentation is right, that your tooling is proper and that your team is aware and watching for it. And so it does. It makes it a very challenging environment to be in. >> You know, I don't want to bring up the budget issue but I'll bring up the budget issue. You can have unlimited budget because there's so many tools out there and platforms now. I mean, if you've look at the ecosystem map of the cybersecurity landscape that you have to navigate through as a customer. You've got a lot of people knocking on your door to sell you stuff. So I have to ask you, what is the scale? I mean, you can't have unlimited budget. But the reality is you have to kind of, do the right thing. What's the most helpful kind of tools and platforms for you that you've seen that you've had experience with? Where's this going in terms of the most effective mechanisms and software and platforms that are available out there? >> From the security perspective specifically, the three things that are most important to me are visibility. Whether it's asset visibility or log visibility. You know, being able to see the data, being able to see what's going on. End user. Making sure that the end user has been trained, is aware and that you're watching them. Because the end user, the human is always the weakest link. The human doesn't have digital controls that can be hard set and absolutely followed. The human changes every day. And then our endpoint security solutions. Those are the three biggest things for me. You know, you have your network perimeter, your firewalls. But attackers aren't always looking for those. They're coming from the inside, they're finding a way around those. The biggest three things for me are endpoint, visibility and the end user. >> Yeah, it's awesome. And a lot of companies are really looking at their posture right now. So I would ask you as a CISO, who's in the front end of all this great stuff and protecting your networks and all your environments and the endpoints and assets. What advice would you have for other CISOs who are kind of trying to level up to where you're at, in terms of rethinking their security posture? What advice would you give them? >> The advice I would give you is surround yourself with people that are like-minded on the security side. Make sure that these people are aware but that they're willing to grow. Because security's always changing. If you get a security person that's dead set that they're going to be a network security person and that's all they're going to do. You know, you may have that need and you may fill it. But at the end of the day, you need somebody who's open rounded and ready to change. And then you need to make sure that you can have somebody, and the team that you work with is able to talk to your executives. It never fails, the executives. They understand security from the standpoint of the business, but they don't necessarily understand security from the technical side. So you have to make sure that you can cross those two boundaries. And when you grow your team you have to make sure that that's the biggest focus. >> I have to ask the pandemic question, but I know cybersecurity hasn't changed. In fact, it's gotten more aggressive in the pandemic. How has the post pandemic or kind of like towards the tail end of where we're at now, affect the cybersecurity landscape? Has it increased velocity? Has it changed any kind of threat vectors? Has it changed in any way? Can you share your thoughts on what happened during the pandemic and now has we come out of it into the next, well post pandemic? >> Absolutely. It affected hospitality in a kind of unique way. Because, a lot of the different governments, state, federal. I'm in Ohio. I work out of our Ohio resort. A lot of the governments literally shut us down or limited severely how many guests we could have in. So on the one hand you've got less traffic internal over the network. So you've got a little bit of a slow down there. But on the flip side it also meant a lot of our workers were working from home. So now you've got a lot of remote access coming in. You've got people that are trying to get in from home and work machines. You have to transition call centers and call volume and all of the things that come along with that. And you have to make sure that that human element is accounted for. Because, again, you've got people working from home, you no longer know if the person that's calling you today, if it's not somebody you're familiar with you don't know if that person is Joe Blow from the front desk or if that person's a vendor or who they are. And so when you deal with a company with 5,000 ish employees or 10,000 that some of these bigger companies are. 15,000, whatever the case may be. You know, the pandemic really put a shift in there because now you're protecting not only against the technologies, but you're dealing with all of the scams, all of the phishing attempts that are coming through that are COVID related. All of these various things. And it really did. It threw a crazy mix into cybersecurity. >> I can imagine that the brain trust over there is prior thinking, "Hey, we were a hybrid experience." Now, if people who have come and experienced our resorts and conventions can come in remotely, even in a hybrid experience with folks that are there. You've seen a lot of hybrid events for instance go on, where there's shared experience. I can almost imagine your service area is now extending to the homes of those guests. That you got to start thinking differently. Has that been something that you guys are looking at? >> We're looking at it from the standpoint of trying to broaden some of the events. In the case of a lot of our conventions, things of that nature. The conventions that aren't actually Kalahari's run conventions, we host them, we manage them. But it does... When you talk about workers coming from home to attend these conventions. Or these telecommuters that are attending these conventions. It does affect us in the stance that, like I said we have to provision network for these various events. And we have to make sure that the network and the security around the network are tight. So it does. It makes a big deal as far as how Kalahari does its business. Being able to still operate these different meetings and different conventions, and being able to host remotely as well. You know, making sure that telecommunications are available to them. Making sure that network access and room access are available to them. You know for places where we can't gather heavily in meetings. You know, these people still being able to be near each other, still being able to talk, but making sure that that technology is there between them. >> Well, Tim is great to have you on for this CUBE Conversation. CISO from the middle of all the action. You're seeing a lot. There's a lot of surface area you got to watch. There's a lot of data you got to observe. You got to get that visibility. You can only protect what you can see, and the more you see the better it is. The better the machine learning. You brought up the the common area about like-minded individuals. I want to just ask you on the final point here, on hiring and talent coming into the marketplace. I mean, this younger generation coming out of university and college is, or not even going to school. There's no cyber degree. I mean, there are now. But I mean, the world's changing. It's easy to level up. So, skill sets you can't get a degree in certain things. I mean, you got to have a broad set. What do you look for in talent? Is there a trend you see in terms of what makes a good cybersecurity professional, developer, analyst? Is there roles that you see emerging that you think people should pay attention to? What's your take on this as someone who's looking at the future? And- >> You know, it's very interesting that you bring this up. I actually have two of my team members, one directly working for me and another team member at Kalahari that are currently going through college degree programs for cybersecurity. And I wrote recommendations for them. I've worked with them, I'm helping them study. But as you bring people up, you know the other thing I do is I mentor at a couple of the local technical schools as well. I go in, I talk to people, I help them design their programs. And the biggest thing I try to get across to them is, number one, if you're in the learning side of it. Not even talking about the hiring side of it. If you're in the learning side of it, you need to come into it with a kind of an understanding to begin with to where you want to fit into security. You know, do you want to be an attacker, a defender, a manager? Where do you want to be? And then you also need to look at the market and talk to the businesses in the area. You know, I talk to these kids regularly about what their need is. Because if you're in school and you're taking Cisco classes, and focusing on firewalls and what an organization needs as somebody who can read log and do things like that. Or somebody who can do pen testing. You know, that's a huge thing. So I would say if you're on the hiring side of that equation, you know. Like you said, there's no super degrees that I can speak to. There's a lot of certifications. There's a lot of different things like that. The goal for me is finding somebody who can put hands to the ground and feet to the ground, and show me that they know what they know. You know, I'll pull somebody in, I'll ask them to show me a certain specific or I'll ask them for specific information and try to feel that out. Because at the end of the day, there's no degree that's going to protect my network. There's no degree that's a hundred percent going to understand Kalahari, for instance. So I want to make sure that the people I talk to, I get a broad interview scope, I get a number of people to talk to. And really get a feel for what it is they know, and what tools they want to work with and make sure it's going to align with us. >> Well, Tim, that's great that you do that. I think the industry needs that. And I think that's really paying it forward, by getting in and using your time to help shape the young curriculums and the young guns out there. It's interesting you know, like David Vellante and I talk on theCUBE all the time. Cyber is like sports. If you're playing football, you got to know the game. If you're playing football and you come in as a baseball player, the skills might not translate, right? So it's really more of, categorically cyber has a certain pattern to it. Math, open mindedness, connecting dots, seeing things around corners. Maybe it's more holistic views, if you're at the visibility level or getting the weeds with data. A lot of different skill sets needed. The aperture of the job requirements are changing a lot. >> They are. And you know, you touched on that really well. You know, they talk about hacking and the hacker mindset. You know, all the security stuff revolves around hacker. And people mislabel hacker. Hacking in general is making something do something that it wasn't originally designed to do. And when I hire people in security, I want people that have that mindset. I want people that not only are going to work with the tool set we have, and use that mathematical ability and that logic and that reasoning. But I want them to use a reasoning of, "Hey, we have this tool here today. How can this tool do what I want it do but what else can it do for me?" Because like any other industry we have to stretch our dollar. So if I have a tool set that can meet five different needs for me today, rather than investing in 16 different tool sets and spreading that data out and spreading all the control around. Let's focus on those tool sets and let's focus on using that knowledge and that adaptive ability that the human people have on the security side, and put that to use. Make them use the tools that work for them but make 'em develop things, new tools, new methods, new techniques that help us get things across. >> Grow the capabilities, protect, trust all things coming in. And Tim, you're a tech athlete, as we say and you've got a great thing going on over there. And again, congratulations on the work you're doing on the higher ed and the education side and the Kalahari Resorts & Conventions. Thanks for coming on theCUBE. I really appreciate the insight you're sharing. Thank you. >> Thanks for having me. >> Okay. I'm John Furrier here in Palo Alto for theCUBE. Thanks for watching. (somber music)