(upbeat music) >> Hello friends and welcome back to theCUBE's Live coverage of AWS re:Invent 2022 from the Venetian Expo in Vegas, baby. This show is absolutely packed. Lisa Martin with Dave Vellante, Dave this is day two, but really full day one of our wall to wall coverage on theCUBE. We've had great conversations the last half day this morning already, we've been talking with a lot of companies, a lot of Amazonians and some Amazonians that have left and gone on to interesting more things, which is what we're going to talk about next. >> Well, I'm excited about this segment because it's a really interesting space. You've got a search company who's gotten into observability and security and through our ETR partner our research, we do quarterly research and Elastic off the charts. Obviously they're the public company, so you can see how well they're doing. But the spending momentum on this platform is very, very strong and it has been consistently for quite some time. So really excited to learn more. >> The voice of the customer speaking loudly, from Elastic, its Chief Product Officer joins us, Ken Exner. Ken, welcome to the program. Hi, thank you, good to be here. >> Dave Vellante: Hey Ken. >> So a lot of us know about Elastic from Elastic Search but it's so much more than that these days. Talk about Elastic, what's going on now? What's the current product strategy? What's your vision? >> Yeah. So people know Elastic from the ELK Stack, you know Elastic Search, Logstash, Kibana. Very, very popular open source projects. They've been used by millions of developers for years and years. But one of the things that we started noticing over the years is that people were using it for all kinds of different use cases beyond just traditional search. So people started using Elastic Search to search through operational data, search through logs, search through all kinds of other types of data just to find different answers. And what we started realizing is the customers were taking us into different spaces. They took us into log analytics they started building log management solutions. And we said, cool, we can actually help these customers by providing solutions that already do this for them. So it took us into observability, they took us into security, and we started building solutions for security and observability based on what customers were starting to do with the platform. So customers can still use the platform for any number of different use cases for how do you get answers added data or they can use our pre-built packaged solutions for observability and security. >> So you were a longtime Amazonian. >> I was. I was. >> Talk a little bit about some of the things that you did there and what attracted you to Elastic? 'Cause it's only been a couple months, right? >> I've been here three months, I think three months as of yesterday. And I was at AWS for 16 years. So I was there a long, long time. I was there pretty much from the beginning. I was hired as one of the first product managers in AWS. Adam Selipsky hired me. And it was a great run. I had a ton of fun, I learned a lot. But you know, after 16 years I was kind of itching to do something new and it was going to take something special because I had a great gig and enjoyed the team at AWS. But I saw in Elastic sort of a great foundational technology they had a lot of momentum, a huge community behind it. I saw the business opportunity where they were going. I saw, you know the business opportunity of observability and security. These are massive industries with tons of business problems. Customers are excited about trying to get more answers out of data about their operational environment. And I saw, you know, that customers were struggling with their operating environments and things were becoming increasingly complicated. We used to talk in AWS about, you know how customers want to move from monolithic applications to monoliths, but one of the secrets was that things were increasingly complicated. Suddenly people had all these different microservices they had all these different managed services and their operating environment got complicated became this constellation of different systems, all emitting data. So companies like Elastic were helping people find answers in that data, find the problems with their systems so helping tame that complexity. So I saw that opportunity and I said I want to jump on that. Great foundational technology, good community and building solutions that actually helped solve real problems. >> Right. >> So, before you joined you probably looked back, and said, let think about the market, what's happening in the market space. What were the big trends that you saw that sort of informed your decision? >> Well, just sort of the mountain of data that was sort of emerging. Adam Selipsky in his talk this morning began by talking about how data is just multiplying constant. And I saw this, I saw how much data businesses were drowning in. Operational data, security data. You know, if you're trying to secure your business you have all these different endpoints you have all these different devices, you have different systems that you need to monitor all tons of data. And companies like Elastic were helping companies sort of manage that complexity, helping them find answers in that. So, when you're trying to track intruders or trying to track you know, malicious activity, there's a ton of different systems you need to pay attention to. And you know, there's a bunch of data. It's different devices, laptops and phone devices and stuff that you need to pay attention to. And you find correlations across that to figure out what is going on in your network, what is going on in your business. And that was exciting to me. This is a company sort of tackling one of the hardest problems which is helping you understand your operating environment, helping you understand and secure your business. >> So everybody's getting into observability. >> Yep. >> Right, it's a very crowded space right now. First of all, you know it's like overnight it just became the hottest thing going. VCs were throwing money at it. Why was that and how were you guys different? >> Well, we began by focusing on log analytics because that was the core of what we were doing. But customers started using it beyond log analytics and started using it for APM and started using it for performance data. And what we realized is that we could do all this for customers. So we ended up, sort of overnight over the course of three years building that a complete observe observability suite. So you can do APM, you can do profiling, you can do tracing, sort of distributed tracing, you can do synthetic monitoring everything you want to do, wheel user wondering. >> Metrics? >> All of it, metrics, all of it. And you can use the same system for this. So this was sort of a powerful concept, not only is it the best in leading log system, it also provides everything you need for complete observability. And because it's based on this open platform you can extend it to a number of different scenarios. So this is important, a lot of the different observability companies provide you something that's sort of packaged and as long as you're trying to do what it wants to support, it's great. But with Elastic, you have this flexible data architecture that you can use for anything. So companies use it to monitor assembly lines, they use it to monitor dish networks, for example use it to not only manage their fleet of servers they also use it to manage all their devices. So 25 million desktop devices. So, you know, observability systems like that that can do a number of different scenarios, I think that's a powerful thing. It's not just about how do you manage your servers how do you manage the things that are simple. It's how do you manage anything? How do you get observability into anything. >> Multiple use cases. >> Sorry, when you say complete, okay you talked about all the different APM, log analytics tracing, metrics, and also end-to-end. >> Ken Exner: End-to-end, yeah. >> Could you talk about that component of complete? >> So, if you're trying to find an issue like you have some metric that goes into alarm. You want to have a metric system that has alarming. Once that metric goes in alarm you're going to want to dig into your log. So you're going to want it to take you to the area of your logs that has that issue. Once you gets to there, you're going to want to find the trace ID that takes you to your traces and looks at sort of profiling, distributed tracing information. So a system that can do all of that end-to-end is a powerful solution. So it not only helps you track things end-to-end across the different signals that you're monitoring, but it actually helps you remediate more quickly. And the other thing that Elastic does that is unique is a lot of ML in this. So not only helping you find the information but surfacing things before you even know of them. So anomaly detection for example, helps you know about something before you even realize that there was an issue. So you should pay attention to this because it's anomalous. So a lot of systems help you find something if you know what to look for. But we're trying to help you not only find the things that you know to look for, but help you find the things that you didn't even think to know about. >> And it's fair to say one of your differentiators is you're open, open source. I mean, maybe talk about the ELK stack a little bit and how that plays. >> Yeah, well, so the great thing about this is we've extended that openness to both security and to observability. An example of this on the security side is all the detection rules that you use for looking for intrusion all the detection rules are open source and there's an entire community around this. So if you wanted to create a detection rule you can publish an open source, there's a bunch in GitHub you can benefit from what the community is doing as well. So in the world of security you want to be supported by the entire community, everyone looking for the same kind of issues. And there's an entire community around Elastic that is helping support these detection rules. So that approach, you know wanting to focus on community is differentiating for us. Not just, we got you covered as long you use things from us you can use it from the entire community. >> Well there implies the name Elastic. >> Yeah >> Talk a little bit about the influence that the customer has in the product roadmap and the direction. You've talked a little bit in the beginning about customers were leading us in different directions. It sounds very Amazonian in terms of following the customers where they go. >> It does, it actually does, it was one of the things that resonated for me personally is the journey that Elastic took to observability and security was customer led. So, we started looking at what customers were doing and realized that they were taking us into log analytics they were taking us into APM, they were taking us into these different solutions, and yeah, it is an Amazonian thing, so it resonated for me personally. And they're going to continue taking us in new places. Like we love seeing all the novel things that customers do with the platform and it's sort of one of the hallmarks of a great platform is you can have all kinds of novel things that, novel use cases for how people use your platform and we'll continue to see things and we may get taken into other solutions as well as we start seeing things emerge, like common patterns. But for now we're really excited about security and observability. >> So what do you see, so security's a big space, right? >> Yep. >> You see the optiv taxonomy and it makes your eyes bleed 'cause there's so many tools in there. Where do you fit in that taxonomy? How do you see and think about the security space and the opportunity for your customers? >> Yeah, so we began with logs in the security space as well. So SIEM, which is intrusion detection is based on aggregating a bunch of logs and helping you do threat hunting on those logs. So looking for patterns of malicious behavior or intrusion. So we started there and we did both detections as well as just ad hoc threat hunting. But then we started expanding into endpoint protection. So if we were going to have agents on all these different devices they were gathering logs, what if we also started providing remediation. So if you had malicious activity that was happening on one of the servers, don't just grab the information quarantine it, isolate it. So that took us into sort of endpoint protection or XDR. And then beyond that, we recently got into cloud security as well. So similar to observability, we started with logs but expanded to a full suite so that you can do everything. You can have both endpoint protection, you can have cloud security, all of it from one solution. >> Security is a very crowded market as well. What's your superpower? >> Ken Exner: What's our super power? >> Yeah. >> I think it, a lot of it is just the openness. It's the open platform, there's the community around it. People know and love the, the Elastic Search ELK stack and use it, we go into businesses all the time and they're familiar, their security engineers are using our product for searching through logs. So they're familiar with the product already and the community behind it. So they were excited about being able to use detection rules from other businesses and stay on top of that and be part of that community. The transparency of that is important to the customers. So if you're trying to be the most secure place, the most secure business, you want to basically invest in a community that's going to support that and not be alone in that. >> Right, absolutely, so much that rides on that. Favorite customer example that you think really articulates the value of Elastic, its openness, its transparency. >> Well, there's a customer Dish Media Dish Networks that's going to present here at re:Invent tomorrow at 1:45 at Mandalay Bay. I'm excited about their example because they use it to manage, I think it's 10 billion records a day across 25 million devices. So it illustrates the scale that we can support for managing observability for a company but also just sort of the unique use cases. We can use this for set top boxes for all their customers and they can track the performance that those customers are having. It's a unique case that a lot of vendors couldn't support but we can support because of the openness of the platform, the open data architecture that we have. So I think it illustrates the scale that we support, the elasticity, but also the openness of the data platform. >> Awesome and folks can catch that tomorrow, 1:45 PM at the Mandalay Bay. Last question for you, Ken, is you have a bumper sticker. >> Ken Exner: A bumper sticker? >> A bumper sticker you're going to put it on your fancy sexy new car and it's about elastic, what does it say? >> Helping you get answers out of data. So yeah. >> Love it, love it. Brilliant. >> Ken Exner: Thank you. >> Short and sweet. Ken, it's been a pleasure. >> It's been a pleasure being here, thank you. >> Thank you so much for sharing your journey with us as an Amazonian now into Elastic what Elastic is doing from a product perspective. We will keep our eyes peeled as Dave was saying. >> Ken Exner: Fantastic. >> The data show is really strong spending momentum so well done. >> Thank you very much, good to meet you. >> Our pleasure. For our guest and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Hey, everybody, this is Dave Vellante, and welcome to this CXO Series. As you know, I've been running this series discussing major trends and CXOs, how they've navigated through the pandemic. And we've got some good news and some bad news today. And Ed Walsh is here to talk about that. Ed, how you doing? Great to see you. >> Great seeing you, thank you for having me on. I really appreciate it. So the bad news is Ed Walsh is leaving IBM as the head of the storage division (indistinct). But the good news is, he's joining a new startup as CEO, and we're going to talk about that, but Ed, always a pleasure to have you. You're quite a run at at IBM. You really have done a great job there. So, let's start there if we can before we get into the other part of the news. So, you give us the update. You're coming off another strong quarter for the storage business. >> I would say listen, they're sweet, heartily, but to be honest, we're leaving them in a really good position where they have sustainable growth. So they're actually IBM storage in a very good position. I think you're seeing it in the numbers as well. So, yeah, listen, I think the team... I'm very proud of what they were able to pull off. Four years ago, they kind of brought me in, hey, can we get IBM storage back to leadership? They were kind of on their heels, not quite growing, or not growing but falling back in market share. You know, kind of a distant third place finisher, and basically through real innovation that mattered to clients which that's a big deal. It's the right innovation that matters to the clients. We really were able to dramatically grow, grow all different four segments of the portfolio. But also get things like profitability growing, but also NPS growing. It really allowed us to go into a sustainable model. And it's really about the team. You heard I've talked about team all the time, which is you get a good team and they really nailed great client experiences. And they take the right offerings and go to market and merge it. And I'll tell you, I'm very proud of what the IBM team put together. And I'm still the number one fan and inside or outside IBM. So it might be bittersweet, but I actually think they're ready for quite some growth. >> You know Ed, when you came in theCUBE, right after you had joined IBM, a lot of people are saying, Ed Walsh joined an IBM storage division to sell the division. And I asked you on theCUBE, are you there to sell division? And you said, no, absolutely not. So it's always it seemed to me, well, hey, it's good. It's a good business, good cash flow business, got a big customer base, so why would IBM sell it? Never really made sense to me. >> I think it's integral to what IBM does, I think it places their client base in a big way. And under my leadership, really, we got more aligned with what IBM is doing from the big IBM right. What we're doing around Red Hat hybrid multi cloud and what we're doing with AI. And those are big focuses of the storage portfolio. So listen, I think IBM as a company is in a position where they're really innovating and thriving, and really customer centric. And I think IBM storage is benefiting from that. And vice versa. I think it's a good match. >> So one of the thing I want to bring up before we move on. So you had said you were seeing a number. So I want to bring up a chart here. As you know, we've been using a lot of data and sharing data reporting from our partner. ETR, Enterprise Technology Research, they do quarterly surveys. They have a very tight methodology, it's similar to NPS. But it's a net score, we call it methodology. And every quarter they go out and what we're showing here is the results from the last three quarter, specific to IBM storage and IBM net score in storage. And net scores is essentially, we ask people are you spending more, are you spending less, we subtract the less from the more and that's the net score. And you can see when you go back to the October 19, survey, you know, low single digits and then it dipped in the April survey, which was the height of the pandemic. So this was this is forward looking. So in the height of the pa, the lockdown people were saying, maybe I'm going to hold off on budgets. But then now look at the July survey. Huge, huge up check. And I think this is testament to a couple of things. One is, as you mentioned, the team. But the other is, you guys have done a good job of taking R&D, building a product pipeline and getting it into the field. And I think that shows up in the numbers. That was really a one of the hallmarks of your leadership. >> Yeah, I mean, they're the innovation. IBM is there's almost an embarrassment of riches inside. It's how do you get in the pipeline? We went from a typically about for four years, four and a half year cycles, not a two year cycle product cycle. So we're able to innovate and bring it to market much quicker. And I think that's what clients are looking for. >> Yeah, so I mean, you brought a startup mentality to the division and of course now, cause your startup guy, let's face it. Now you're going back to the startup world. So the other part of the news is Ed Walsh is joining ChaosSearch as the CEO. ChaosSearches is a local Boston company, they're focused on log analytics but more on we're going to talk about that. So first of all, congratulations. And tell us about your decision. Why ChaosSearch? And you know where you're out there? >> Yeah, listen, if you can tell from the way I describe IBM, I mean, it was a hard decision to leave IBM, but it was a very, very easy decision to go to Chaos, right. So I knew the founder, I knew what he was working on for the last seven years, right. Last five years as a company, and I was just blown away at their fundamental innovation, and how they're really driving like how to get insights at scale from your data lake in the cloud. But also and also instead, and statements slash cost dramatically. And they make it so simple. Simply put your data in your S3 or really Cloud object storage. But right now, it's, Amazon, they'll go the rest of clouds, but just put your data in S3. And what we'll do is we'll index it, give you API so you can search it and query it. And it literally brings a way to do at scale data analysts. And also login analytics on everything you just put into S3 basically bucket. It makes it very simple. And because they're really fundamental, we can go through it. Fundamental on hard technology that data layer, but they kept all the API. So you're using your normal tools that we did for Elastic Search API's. You want to do Glyfada, you want to do Cabana, or you want to do SQL or you want to do use Looker, Tableau, all those work. Which is that's a part of it. It's really revolutionary what they're doing as far as the value prop and we can explain it. But also they made it evolution, it's very easy for clients to go. Just run in parallel, and then they basically turn off what they currently have running. >> So data lakes, really the term became popular during the sort of early big data, Hadoop era. And, Hadoop obviously brought a lot of innovation, you know, leave the data where it is. Bring the compute to the data, really launched the Big Data initiative, but it was very complicated. You had, MapReduce and and elastic MapReduce in the cloud. And, it really was a big batch job, where storage was really kind of a second class citizen, if you will. There wasn't a lot of real time stuff going on. And then, Spark comes in. And still there's this very complicated situation. So it's sounds like, ChaosSearch is really attacking that problem. And the first use case, it's really going after is log analytics. Explain that a little bit more, please. >> Yeah, so listen, they finally went after it with this, it's called a data lake engine for scalable and we'll say log analytics firstly. It was the first use case to go after it. But basically, they allows for log analytics people, everyone does it, and everyone's kind of getting to scale with it, right. But if you asked your IT department, are you even challenged with scale, or cost, or retention levels, but also management overlay of what they're doing on log analytics or security log analytics, or all this machine data they're collecting? The answer be absolutely no, it's a nightmare. It starts easy and becomes a big, very costly application for our environments. And what Chaos does is because they deal with a real issue, which is the data layer, but keep the API's on top. And so people easily use the data insights at scale, what they're able to do is very simply run in parallel and we'll save 80% of your cost, but also get better data retention. Cause there's typically a trade off. Clients basically have this trade off, or it gets really expensive. It gets to scale. So I should just retain less. We have clients that went from nine day retention and security logs to literally four and five days. If they didn't catch it in that time, it was too late. Now what they're able to do is, they're able to go to our solution. Not change what they're doing applications, because you're using the same API's, but literally save 80% and this is millions and 10s of millions of dollars of savings, but also basically get 90 day retention. There's really limitless, whatever you put into your S3 bucket, we're going to give you access to. So that alone shows you that it's literally revolutions that CFO wins because they save money. The IT department wins because they don't that wrestle with this data technology that wasn't really built. It is really built 30 years ago, wasn't built for this volume and velocity of data coming in. And then the data analytics guys, hey, I keep my tool set but I get all the retention I want. No one's limiting me anymore. So it's kind of an easy win win. And it makes it really easy for clients to have this really big benefit for them. And dramatic cost savings. But also you get the scale, which really means a lot in security login or anything else. >> So let's dig into that a little bit. So Cloud Object Storage has kind of become the de facto bucket, if you will. Everybody wants it, because it's simple. It's a get put kind of paradigm. And it's cheap, but it's also got performance issues. So people will throw cash at the problem, they'll have to move data around. So is that the problem that you're solving? Is it a performance? You know, problem is it a cause problem or both? And explain that a little bit. >> Yeah, so it's all over. So basically, if you were building a data lake, they would like to just put all their data in one very cost effective, scalable, resilient environment. And that is Cloud Object Storage, or S3, or every cloud has around, right? You can do also on prem, everyone would love to do that. And then literally get their insights out of it. But they want to go after it with our tools. Is it Search or is it SQL, they want to go after their own tools. That's the vision everyone wants. But what everyone does now is because this is where the core special sauce what ChaosSearch provides, is we built from the ground up. The database, the indexing technology, the database technology, how to actually make your Cloud object storage a database. We don't move it somewhere, we don't cash it. You put it in the inside the bucket, we literally make the Cloud object storage, the database. And then around it, we basically built a Chaos fabric that allows you to spin up compute nodes to go at the data in different ways. We truly have separated that the data from the compute, but also if a worker nodes, beautiful, beauty of like containerization technology, a worker nodes goes away, nothing happens. It's not like what you do on Prem. And all sudden you have to rebuild clusters. So by fundamentally solving that data layer, but really what was interesting is they just published API's, you mentioned put and get. So the API's you're using cloud obvious sources of put and get. Imagine we just added to that API, your Search API from elastic, or your SQL interface. It's just all we're doing is extending. You put it in the bucket will extend your ability to get after it. Really is an API company, but it's a hard tech, putting that data layer together. So you have cost effectiveness, and scale simultaneously. But we can ask for instance, log analytics. We don't cash, nothing's on the SSD, nothing's on local storage. And we're as fast as you're running Elastic Search on SSDs. So we've solved the performance and scale issues simultaneously. And that's really the core fundamental technology. >> And you do that with math, with algorithms, with machine learning, what's the secret sauce? Yeah, we should really have I'll tell you, my founder, just has the right interesting way of looking at problems. And he really looked at this differently and went after how do you make a both, going after data. He really did it in a different way, and really a modern way. And the reason it differentiates itself is he built from the ground up to do this on object storage. Where basically everyone else is using 30 year old technology, right? So even really new up and coming companies, they're using Tableau, Looker, or Snowflake could be another example. They're not changing how the data stored, they always have to move it ETL at somewhere to go after it. We avoid all that. In fact, we're probably a pretty good ecosystem players for all those partners as we go forward. >> So your talking about Tom Hazel, you're founder and CTO and he's brought in the team and they've been working on this for a while. What's his background? >> Launched Telkom, building out God boxes. So he's always been in the database space. I can't do his in my first day of the job, I can't do justice to his deep technology. There's a really good white paper on our website that does that pretty well. But literally the patent technology is a Chaos index, which is a database that it makes your object storage, the database. And then it's really the chaos fabric that puts around in the chaos refinery that gives you virtual views. But that's one solution. And if you look for log analytics, you come in log in and you get all the tools you're used to. But underneath the covers, were just saving about 80% of overall cost, but also almost limitless retention. We see people going from literally have been reduced the number of logs are keeping because of cost, and complexity, and scale, down to literally a very small amount and going right back at nine days. You could do longer, but that's what we see most people go into when they go to our service. >> Let's talk about the market. I mean, as a startup person, you always look for large markets. Obviously, you got to have good tech, a great team. And you want large markets. So the, space that you're in, I mean, I would think it started, early days and kind of the decision support. Sort of morphed into the data warehouse, you mentioned ETL, that's kind of part of it. Business Intelligence, it's sort of all in there. If you look at the EDW market, it's probably around 18 to 20 billion. Small slice of that is data lakes, maybe a billion or a billion plus. And then you got this sort of BI layer on top, you mentioned a lot of those. You got ETL, you probably get up into the 30,35 billion just sort of off the top of my head and from my historical experience and looking at these markets. But I have to say these markets have traditionally failed to live up to the expectations. Things like 360 degree views of the customer, real time analytics, delivering insights and self service to the business. Those are promises that these industries made. And they ended up being cumbersome, slow, maybe requiring real experts, requiring a lot of infrastructure, the cloud is changing that. Is that right? Is that the way to look at the market that you're going after? You're a player inside of that very large team. >> Yeah, I think we're a key fundamental component underneath that whole ecosystem. And yes, you're seeing us build a full stack solution for log analytics, because there's really good way to prove just how game changing the technology is. But also how we publishing API's, and it's seamless for how you're using log analytics. Same thing can be applied as we go across the SQL and different BI and analytic type of platforms. So it's exactly how we're looking at the market. And it's those players that are all struggling with the same thing. How they add more value to clients? It's a big cost game, right? So if I can literally make your underlying how you store your data and mix it literally 80% more cost effective. that's a big deal or simultaneously saving 80% and give you much longer retention. Those two things are typically, Lily a trade off, you have to go through, and we don't have to do that. That's what really makes this kind of the underlying core technology. And really I look at log analytics is really the first application set. But or if you have any log analytics issues, if you talk to your teams and find out, scale, cost, management issues, it's a pretty we make it very easy. Just run in parallel, we'll do a PLC, and you'll see how easy it is you can just save 80% which is, 80% and better retention is really the value proposition you see at scale, right. >> So this is day zero for you. Give us the hundred day plan, what do you want to accomplish? Where are you going to focus your priorities? I mean, obviously, the company's been started, it's well funded, but where are you going to focus in the next 100 days? >> No, I think it's building out where are we taking the next? There's a lot of things we could do, there's degrees of freedom as far as where we'd go with this technology is pretty wide. You're going to see us be the best log analytic company there. We're getting, really a (mumbling) we, you saw the announcement, best quarter ever last quarter. And you're seeing this nice as a service ramp, you're going to see us go to VPC. So you can do as a service with us, but now we can put this same thing in your own virtual private data center. You're going to see us go to Google, Azure, and also IBM cloud. And the really, clients are driving this. It's not us driving it, but you're going to see actually the client. So we'll go into Google because we had a couple financial institutions that are saying they're driving us to go do exactly that. So it's more really working with our client sets and making sure we got the right roadmap to support what they're trying to do. And then the ecosystem is another play. How to, you know, my core technology is not necessarily competitive with anyone else. No one else is doing this. They're just kind of, hey, move it here, I'll put it on this, you know, a foundational DV or they'll put it on on a presto environment. They're not really worried about the bottom line economics, which is really that's the value prop and that's the hard tech and patented technology that we bring to this ecosystem. >> Well, people are definitely worried about their cloud bills. The the CFO saying, whoa, cause it's so easy to spin up, instances in the cloud. And so, Ed it really looks like you're going after a real problem. You got some great tech behind you. And of course, we love the fact that it's another Boston based company that you're joining, cause it's more Boston based startups. Better for us here at the East Coast Cube, so give us a give us your final thoughts. What should we look for? I'm sure we're going to be being touched and congratulations. >> No, hey, thank you for the time. I'm really excited about this. I really just think it's fundamental technology that allows us to get the most out of everything you're doing around analytics in the cloud. And if you look at a data lake model, I think that's our philosophy. And we're going to drive it pretty aggressively. And I think it's a good fundamental innovation for the space and that's the type of tech that I like. And I think we can also, do a lot of partnering across ecosystems to make it work for a lot of different people. So anyway, so I guess thank you very much for the time appreciate. >> Yeah, well, thanks for coming on theCUBE and best of luck. I'm sure we're going to be learning a lot more and hearing a lot more about ChaosSearch, Ed Walsh. This is Dave Vellante. Thank you for watching everybody, and we'll see you next time on theCUBE. (upbeat music)