>>president >>so called big data promised to usher in a new era of innovation where companies competed on the basis of insights and agile decision making. There's little question that social media giants, search leaders and e commerce companies benefited. They had the engineering shops and the execution capabilities to take troves of data and turned them into piles of money. But many organizations were not as successful. They invested heavily in data architecture is tooling and hyper specialized experts to build out their data pipelines. Yet they still struggle today to truly realize they're busy. Did data in their lakes is plentiful but actionable insights aren't so much chaos. Search is a cloud based startup that wants to change this dynamic with a new approach designed to simplify and accelerate time to insights and dramatically lower cost and with us to discuss his company and its vision for the future is cuba Lem Ed Walsh had great to see you. Thanks for coming back in the cube. >>I always love to be here. Thank you very much. It's always a warm welcome. Thank you. >>Alright, so give us the update. You guys have had some big funding rounds, You're making real progress on the tech, taking it to market what's new with chaos surgery. >>Sure. Actually even a lot of good exciting things happen. In fact just this month we need some, you know, obviously announced some pretty exciting things. So we unveiled what we consider the industry first multi model data late platform that we allow you to take your data in S three. In fact, if you want to show the image you can, but basically we allow you to put your data in S three and then what we do is we activate that data and what we do is a full index of the data and makes it available through open a P. I. S. And the key thing about that is it allows your end users to use the tools are using today. So simply put your data in your cloud option charge, think Amazon S three and glacier think of all the different data. Is that a natural act? And then we do the hard work. And the key thing is to get one unified delic but it's a multi mode model access so we expose api like the elastic search aPI So you can do things like search or using cabana do log analytics but you can also do things like sequel, use Tableau looker or bring relational concepts into cabana. Things like joins in the data back end. But it allows you also to machine learning which is early next year. But what you get is that with that because of a data lake philosophy, we're not making new transformations without all the data movement. People typically land data in S. Three and we're on the shoulders of giants with us three. Um There's not a better more cost effective platform. More resilient. There's not a better queuing system out there and it's gonna cost curve that you can't beat. But basically so people store a lot of data in S. Three. Um But what their um But basically what you have to do is you E. T. L. Out to other locations. What we do is allow you to literally keep it in place. We index in place. We write our hot index to rewrite index, allow you to go after that but published an open aPI S. But what we avoid is the GTL process. So what our index does is look at the data and does full scheme of discovery normalization, were able to give sample sets. And then the refinery allows you to advance transformations using code. Think about using sequel or using rejects to change that data pull the dead apartheid things but use role based access to give that to the end user. But it's in a format that their tools understand cabana will use the elasticsearch ap or using elasticsearch calls but also sequel and go directly after data by doing that. You get a data lake but you haven't had to take the three weeks to three months to transform your data. Everyone else makes you. And you talk about the failure. The idea that Alex was put your data there in a very scalable resilient environment. Don't do transformation. It was too hard to structure for databases and data. Where else is put it there? We'll show you how value out Largely un delivered. But we're that last mile. We do exactly that. Just put it in s. three and we activated and activate it with a piece that the tools of your analysts use today or what they want to use in the future. That is what's so powerful. So basically we're on the shoulders of giants with street, put it there and we light it up and that's really the last mile. But it's this multi model but it's also this lack of transformation. We can do all the transformation that's all to virtually and available immediately. You're not doing extended GTL projects with big teams moving around a lot of data in the enterprise. In fact, most time they land and that's three and they move it somewhere and they move it again. What we're saying is now just leave in place well index and make it available. >>So the reason that it was interesting, so the reason they want to move in the S three was the original object storage cloud. It was, it was a cheap bucket. Okay. But it's become much more than that when you talk to customers like, hey, I have all this data in this three. I want to do something with it. I want to apply machine intelligence. I want to search it. I want to do all these things, but you're right. I have to move it. Oftentimes to do that. So that's a huge value. Now can I, are you available in the AWS marketplace yet? >>You know, in fact that was the other announcement to talk about. So our solution is one person available AWS marketplace, which is great for clients because they've been burned down their credits with amazon. >>Yeah, that's that super great news there. Now let's talk a little bit more about data. Like you know, the old joke of the tongue in cheek was data lakes become data swamps. You sort of know, see no schema on, right. Oh great. I can put everything into the lake and then it's like, okay, what? Um, so maybe double click on that a little bit and provide a little bit more details to your, your vision there and your philosophy. >>So if you could put things that data can get after it with your own tools on elastic or search, of course you do that. If you don't have to go through that. But everyone thinks it's a status quo. Everyone is using, you know, everyone has to put it in some sort of schema in a database before they can get access to what everyone does. They move it some place to do it. Now. They're using 1970s and maybe 1980s technology. And they're saying, I'm gonna put it in this database, it works on the cloud and you can go after it. But you have to do all the same pain of transformation, which is what takes human. We use time, cost and complexity. It takes time to do that to do a transformation for an user. It takes a lot of time. But it also takes a teams time to do it with dBS and data scientists to do exactly that. And it's not one thing going on. So it takes three weeks to three months in enterprise. It's a cost complexity. But all these pipelines for every data request, you're trying to give them their own data set. It ends up being data puddles all over this. It might be in your data lake, but it's all separated. Hard to govern. Hard to manage. What we do is we stop that. What we do is we index in place. Your dad is already necessary. Typically retailing it out. You can continue doing that. We really are just one more use of the data. We do read only access. We do not change that data and you give us a place in. You're going to write our index. It's a full rewrite index. Once we did that that allows you with the refinery to make that we just we activate that data. It will immediately fully index was performant from cabana. So you no longer have to take your data and move it and do a pipeline into elasticsearch which becomes kind of brittle at scale. You have the scale of S. Three but use the exact same tools you do today. And what we find for like log analytics is it's a slightly different use case for large analytics or value prop than Be I or what we're doing with private companies but the logs were saving clients 50 to 80% on the hard dollars a day in the month. They're going from very limited data sets to unlimited data sets. Whatever they want to keep an S. Three and glacier. But also they're getting away from the brittle data layer which is the loosen environment which any of the data layers hold you back because it takes time to put it there. But more importantly It becomes brittle at scale where you don't have any of that scale issue when using S. three. Is your dad like. So what what >>are the big use cases Ed you mentioned log analytics? Maybe you can talk about that. And are there any others that are sort of forming in the marketplace? Any patterns that you see >>Because of the multi model we can do a lot of different use cases but we always work with clients on high R. O. I use cases why the Big Bang theory of Due dad like and put everything in it. It's just proven not to work right? So what we're focusing first use cases, log analytics, why as by way with everything had a tipping point, right? People were buying model, save money here, invested here. It went quickly to no, no we're going cloud native and we have to and then on top of it it was how do we efficiently innovate? So they got the tipping point happens, everyone's going cloud native. Once you go cloud native, the amount of machine generated data that you have that comes from the environment dramatically. It just explodes. You're not managing hundreds or thousands or maybe 10,000 endpoints, you're dealing with millions or billions and also you need this insight to get inside out. So logs become one of the things you can't keep up with it. I think I mentioned uh we went to a group of end users, it was only 60 enterprise clients but we asked him what's your capture rate on logs And they said what do you want it to be 80%, actually 78 said listen we want eight captured 80 200 of our logs. That would be the ideal not everything but we need most of it. And then the same group, what are you doing? Well 82 had less than 50%. They just can't keep up with it and every everything including elastic and Splunk. They work harder to the process to narrow and keep less and less data. Why? Because they can't handle the scale, we just say landed there don't transform will make it all available to you. So for log analytics, especially with cloud native, you need this type of technology and you need to stop, it's like uh it feels so good when you stop hitting your head against the wall. Right? This detail process that this type of scale just doesn't work. So that's exactly we're delivering the second use case uh and that's with using elastic KPI but also using sequel to go after the same data representation. And we come out with machine learning. You can also do anomaly detection on the same data representation. So for a log uh analytic use case series devops setups. It's a huge value problem now the same platform because it has sequel exposed. You can do just what we use the term is agile B. I people are using you think about look or tableau power bi I uh metabolic. I think of all these toolsets that people want to give and uh and use your business or coming back to the centralized team every single week asking for new datasets. And they have to be set up like a data set. They have to do an e tail process that give access to that data where because of the way just landed in the bucket. If you have access to that with role based access, I can literally get you access that with your tool set, let's say Tableau looker. You know um these different data sets literally in five minutes and now you're off and running and if you want a new dataset they give another virtual and you're off and running. But with full governance so we can use to be in B I either had self service or centralized. Self service is kind of out of control, but we can move fast and the centralized team is it takes me months but at least I'm in control. We allow you do both fully governed but self service. Right. I got to >>have lower. I gotta excel. All right. And it's like and that's the trade off on each of the pieces of the triangle. Right. >>And they make it easy, we'll just put in a data source and you're done. But the problem is you have to E T L the data source. And that's what takes the three weeks to three months in enterprise and we do it virtually in five minutes. So now the third is actually think about um it's kind of a combination of the two. Think about uh you love the beers and diaper stories. So you know, think about early days of terror data where they look at sales out data for business and they were able to look at all the sales out data, large relational environment, look at it, they crunch all these numbers and they figured out by different location of products and the start of they sell more sticker things and they came up with an analogy which everyone talked about beers and diapers. If you put it together, you sell more from why? Because afternoon for anyone that has kids, you picked up diapers and you might want to grab a beer of your home with the kids. But that analogy 30 years ago, it's now well we're what's the shelf space now for approximate company? You know it is the website, it's actually what's the data coming from there. It's actually the app logs and you're not capturing them because you can't in these environments or you're capturing the data. But everyone's telling, you know, you've got to do an E. T. L. Process to keep less data. You've got to select, you got to be very specific because it's going to kill your budget. You can't do that with elastic or Splunk, you gotta keep less data and you don't even know what the questions are gonna ask with us, Bring all the app logs just land in S. three or glacier which is the most it's really shoulders of giants right? There's not a better platform cost effectively security resilience or through but to think about what you can stream and the it's the best queuing platform I've ever seen in the industry just landed there. And it's also very cost effective. We also compress the data. So by doing that now you match that up with actually relatively small amount of relational data and now you have the vaccine being data. But instead it's like this users using that use case and our top users are always, they start with this one then they use that feature and that feature. Hey, we just did new pricing is affecting these clients and that clients by doing this. We get that. But you need that data and people aren't able to capture it with the current platforms. A data lake. As long as you can make it available. Hot is a way to do it. And that's what we're doing. But we're unique in that. Other people are making GTL IT and put it in a in 19 seventies and 19 eighties data format called a schema. And we avoided that because we basically make S three a hot and elected. >>So okay. So I gotta I want to, I want to land on that for a second because I think sometimes people get confused. I know I do sometimes without chaos or it's like sometimes don't know where to put you. I'm like okay observe ability that seems to be a hot space. You know of course log analytics as part of that B. I. Agile B. I. You called it but there's players like elastic search their star burst. There's data, dogs, data bricks. Dream EOS Snowflake. I mean where do you fit where what's the category and how do you differentiate from players like that? >>Yeah. So we went about it fundamentally different than everyone else. Six years ago. Um Tom hazel and his band of merry men and women came up and designed it from scratch. They may basically yesterday they purposely built make s free hot analytic environment with open A. P. I. S. By doing that. They kind of changed the game so we deliver upon the true promises. Just put it there and I'll give you access to it. No one else does that. Everyone else makes you move the data and put it in schema of some format to get to it. And they try to put so if you look at elasticsearch, why are we going after? Like it just happens to be an easy logs are overwhelming. You once you go to cloud native, you can't afford to put it in a loose seen the elk stack. L is for loosen its inverted index. Start small. Great. But once you now grow it's now not one server. Five servers, 15 servers, you lose a server, you're down for three days because you have to rebuild the whole thing. It becomes brittle at scale and expensive. So you trade off I'm going to keep less or keep less either from retention or data. So basically by doing that so elastic we're not we have no elastic on that covers but we allow you to well index the data in S. Tree and you can access it directly through a cabana interface or an open search interface. Api >>out it's just a P. >>It's open A P. I. S. It's And by doing that you've avoided a whole bunch of time cost, complexity, time of your team to do it. But also the time to results the delays of doing that cost. It's crazy. We're saving 50-80 hard dollars while giving you unlimited retention where you were dramatically limited before us. And as a managed service you have to manage that Kind of Clunky. Not when it starts small, when it starts small, it's great once at scale. That's a terrible environment to manage the scale. That's why you end up with not one elasticsearch cluster, dozens. I just talked to someone yesterday had 125 elasticsearch clusters because of the scale. So anyway, that's where elastic we're not a Mhm. If you're using elastic it scale and you're having problems with the retired off of cost time in the, in the scale, we become a natural fit and you don't change what your end users do. >>So the thing, you know, they had people here, this will go, wow, that sounds so simple. Why doesn't everybody do this? The reason is it's not easy. You said tom and his merry band. This is really hard core tech. Um and it's and it's it's not trivial what you've built. Let's talk about your secret sauce. >>Yeah. So it is a patented technology. So if you look at our, you know, component for architecture is basically a large part of the 90% of value add is actually S. Three, I gotta give S three full kudos. They built a platform that we're on shoulders of giants. Um But what we did is we purpose built to make an object storage a hot alec database. So we have an index, like a database. Um And we basically the data you bring a refinery to be able to do all the advanced type of transformation but all virtually done because we're not changing the source of record, we're changing the virtual views And then a fabric allows you to manage and be fully elastic. So if we have a big queries because we have multiple clients with multiple use cases, each multiple petabytes, we're spending up 1800 different nodes after a particular environment. But even with all that we're saving them 58%. But it's really the patented technology to do this, it took us six years by the way, that's what it takes to come up with this. I come upon it, I knew the founder, I've known tom tom a stable for a while and uh you know his first thing was he figured out the math and the math worked out. Its deep tech, it's hard tech. But the key thing about it is we've been in market now for two years, multiple use cases in production at scale. Um Now what you do is roadmap, we're adding a P. I. So now we have elasticsearch natural proofpoint. Now you're adding sequel allows you open up new markets. But the idea for the person dealing with, you know, so we believe we deliver on the true promise of Data Lakes and the promise of Data lakes was put it there, don't focus on transferring. It's just too hard. I'll get insights out and that's exactly what we do. But we're the only ones that do that everyone else makes you E. T. L. At places. And that's the innovation of the index in the refinery that allows the index in place and give virtual views in place at scale. Um And then the open api is to be honest, uh I think that's a game. Give me an open api let me go after it. I don't know what tool I'm gonna use next week every time we go into account they're not a looker shop or Tableau Sharp or quick site shop there, all of them and they're just trying to keep up with the businesses. Um and then the ability to have role based access where actually can give, hey, get them their own bucket, give them their own refinery. As long as they have access to the data, they can go to their own manipulation ends up being >>just, >>that's the true promise of data lakes. Once we come out with machine learning next year, now you're gonna rip through the same embassy and the way we structured the data matrices. It's a natural fit for things like tensorflow pytorch, but that's, that's gonna be next year just because it's a different persona. But the underlining architecture has been built, what we're doing is trying to use case that time. So we worked, our clients say it's not a big bang. Let's nail a use case that works well. Great R. O. I great business value for a particular business unit and let's move to the next. And that's how I think it's gonna be really. That's what if you think about gardener talks about, if you think about what really got successful in data, where else in the past? That's exactly it wasn't the big bang, it was, let's go and nail it for particular users. And that's what we're doing now because it's multi model, there's a bunch of different use cases, but even then we're focusing on these core things that are really hard to do with other relational only environments. Yeah, I >>can see why you're still because you know, you haven't been well, you and I have talked about the api economy for forever and then you've been in the storage world so long. You know what a nightmare is to move data. We gotta, we gotta jump. But I want to ask you, I want to be clear on this. So you are your cloud cloud Native talked to frank's Lukman maybe a year ago and I asked him about on prem and he's like, no, we're never doing the halfway house. We are cloud all the >>way. I think >>you're, I think you have a similar answer. What what's your plan on Hybrid? >>Okay. We get, there's nothing about technology, we can't go on, but we are 100 cloud native or only in the public cloud. We believe that's a trend line. Everyone agrees with us, we're sticking there. That's for the opportunity. And if you can run analytics, There's nothing better than getting to the public cloud like Amazon and he was actually, that were 100 cloud native. Uh, we love S three and what would be a better place to put this is put the next three and we just let you light it up and then I guess if I'm gonna add the commercial and buy it through amazon marketplace, which we love that business model with amazon. It's >>great. Ed thanks so much for coming back in the cube and participating in the startup showcase. Love having you and best of luck. Really exciting. >>Hey, thanks again, appreciate it. >>All right, thank you for watching everybody. This is Dave Volonte for the cube. Keep it right there.

>> 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)