>>Good afternoon everyone, and welcome back to the Cube. I am Savannah Peterson here, coming to you from Detroit, Michigan. We're at Cuban Day three. Such a series of exciting interviews. We've done over 30, but this conversation is gonna be extra special, don't you think, John? >>Yeah, this is gonna be a good one. Griffon Labs is here with us. We're getting the conversation of what's going on in the industry management, watching the Kubernetes clusters. This is large scale conversations this week. It's gonna be a good one. >>Yeah. Yeah. I'm very excited. He's also got a fantastic Twitter handle, twitchy. H Please welcome Richie Hartman, who is the director of community here at Griffon. Richie, thank you so much for joining us. Thanks >>For having me. >>How's the show been for you? >>Busy. I, I mean, I, I, >>In >>A word, I have a ton of talks at at like maintain a thing and like the covering board searches at the TLC panel. I run forme day. So it's, it's been busy. It, yeah. Monday, I didn't have to run anything. That was quite nice. But there >>You, you have your hands in a lot. I'm not even gonna cover it. Looking at your bio, there's, there's so many different things that you're working on. I know that Grafana specifically had some announcements this week. Yeah, >>Yeah, yeah. We had quite a few, like the, the two largest ones is a, we now have a field Kubernetes integration on Grafana Cloud. So our, our approach is generally extremely open source first. So we try to push stuff into the exporters, like into the open source exporters, into mixes into things which are out there as open source for anyone to use. But that's little bit like a tool set, not a ready made solution. So when we talk integrations, we actually talk about things where you get this like one click experience, You log into your Grafana cloud, you click, I have a Kubernetes, which probably most of us have, and things just work like you in just the data. You have to write dashboards, you have to write alerts, you have to write everything to just get started with extremely opinionated dashboards, SLOs, alerts, again, all those things made by experts, so anyone can use them. And you don't have to reinvent the view for every single user. So that's the one. The other is, >>It's a big deal. >>Oh yeah, it is. Yeah. It is. It, we, we has, its heavily in integrations course. While, I mean, I don't have to convince anyone that perme is a DD factor standard in everything. Cloudnative. But again, it's, it's, it's sometimes a little bit hard to handle or a little bit not easy to get into. So, so smoothing this, this, this path onto onboarding yourself onto this stack and onto those types of solutions. Yes. Is what a lot of people need. Course, if you, if you look at the statistics from coupon, and we just heard this in the governing board session yesterday. Yeah. Like 60% of the people here are first time attendees. So there's a lot of people who just come into this thing and who need, like, this is your path. This is where you should be going. Or at least if you want to go, go there. This is how to get there. >>Here's your runway for takeoff. Yes. Yeah. I think that's a really good point. And I love that you, you had those numbers. I was curious. I, I had seen on Twitter, speaking of Twitter, I had seen, I had seen that, that there were a lot of people here coming for the first time. You're a community guy. Are we at an inflection point where this community is about to continue to scale? >>That's a very good question. Which I can't really answer. So I mean, >>Obviously I bet you're gonna try. >>I covid changed a few things. Yeah. Probably most people, >>A couple things. I mean, you know, casually, it's like such a gentle way of putting that, that was >>Beautiful. I'm gonna say yes, just to explode. All these new ERs are gonna learn Prometheus. They're gonna roll in with a open, open metrics, open telemetry. I love it, >>You know, But, but at the same time, like Cuban is, is ramping back up. But if you look at the, if you look at the registration numbers between Valencia Andro, it was more or less the same. Interesting. Which, so it didn't go onto this, onto this flu trajectory, which it was on like, up to, up to 2019. I expect this to take up again. But also with the economic situation, everything, I, I don't think >>It's, I think the jury's still out on hybrid. I think there's a lot, lot more hybrid. Let's see how the projects are gonna go. That's what I think it's gonna be the tell sign. How many people are in participating? How are the project's advancing? Some of the momentum, >>I mean, from the project level, Most of this is online anyway. Of course. That's how open source, right. I've been working for >>Ages. That's >>Cause you don't have any trouble budget or, or any office or, It's >>Always been that way. >>Yeah, precisely. So the projects are arguably spearheading this, this development and the, the online numbers. I I, I have some numbers in my head, but I'm, I'm not a hundred percent certain to, but they're higher for this time in Detroit than in volunteer as far somewhere. Cool. So that is growing and it's grown in parallel, which also is great. Cause it's much more accessible, much more inclusive. You don't have to have a budget of at least, let's say, I don't know, two to five k to, to fly over the pond and, and attend this thing. You can just do it from your home. So that is, that's a lot more inclusive. And I expect this to, to basically be a second more or less orthogonal growth, growth path. But the best thing about coupon is the hallway track. I'm just meeting people, talking to people and that kind of thing is not really possible with, >>It's, it's great to see people >>In person. No, and it makes such a difference. I mean, yeah. Even and interviewing people in person too. I mean, it does a, it's, it's, and, and this, this whole, I mean cncf, this whole community, every company here is community first. It's how these projects come to be. I think it's awesome. I feel like you got something you're saying to say, Johnny. >>Yeah. And I love some of the advancements. Rich Richie, we talked last time about, you know, open telemetry, open metrics. You're involved in dashboards. Yeah. One of the themes here is ease of use, simplicity, developer productivity. Where do you see the ease of use going from a project standpoint? For me, as you mentions everywhere, it's pretty much, it is, it's almost all corners of the world. Yep. And new people coming in. How, how are you making it easier? What's going on? Give us the update on that. >>So we also, funnily enough at precisely this topic in the TC panel just a few hours ago, about ease of use and about how to, how to make things easier to, to handle how developers currently, like if they just want to get into the cloud native seen, they have like, like we, we did some neck and math, like maybe 10 tools at least, which you have to be somewhat proficient in to just get started, which is honestly horrendous. Yeah. Course. Like with a server, I just had my survey install my thing and it runs, maybe I need a database, but that's roughly it. And this needs to change again. Like it's, it's nice that everything is, is un unraveled. And you have, you, you, you, you don't have those service boundaries which you had before. You can do all the horizontal scaling, you can do all the automatic scaling, all those things that they're super nice. But at the same time, this complexity, which used to be nicely compartmentalized, was deliberately broken up. And so it's becoming a lot harder to, to, like, we, we need to find new ways to compartmentalize this complexity back to, to human understandable levels again, in particular, as we keep onboarding new and new and new, new people, of course it's just not good use of anyone's time to, to just like learn the basics again and again and again. This is something which should be just compartmentalized and automated away. We're >>The three, We were talking to Matt Klein earlier and he was talking about as projects become mature and all over the place and have reach and and usage, you gotta work on the boring stuff. Yes. And when it's boring, that means you have success. Yes. But then you gotta work on the plumbing. What are some of the things that you guys are working on? Because people are relying on the product. >>Oh yeah. So for with my premises head on, the highlight feature is exponential or native or spars. Histograms. There's like three different names for one single concept. If you know Prometheus, you ha you currently have hard bucket boundaries where I say my latency is lower equal two seconds, one second, a hundred milliseconds, what have you. And I can put stuff into those histogram buckets accordingly to those predefined levels, which is extremely efficient, but like on the, on the code level. But it's not very nice for the humans course you need to understand your system before you're able to, to, to choose good cutoff points. And if you, if you, if you add new ones, that's completely fine. But if you want to actually change them, course you, you figured out that you made a fundamental mistake, you're going to have a break in the continue continuity of your observability data. And you cannot undo this in, into the past. So this is just gone native histograms. On the other hand, allow me to, to, okay, I'm not going to get get into the math, but basically you define a single formula, which there comes a good default. If you have good reasons, then you can change it. But if you don't, just don't talk, >>The people are in the math, Hit him up on Twitter. Twitter, h you'll get you that math. >>So the, >>The thing is people want the math, believe me. >>Oh >>Yeah. I mean we don't have time, but hit him up. Yeah. >>There's ProCon in two weeks in Munich and there will be whole talk about like the, the dirty details of all of the stuff. But the, the high level answer is it just does what people would expect it to do. And with very little overhead, you become, you get highly, highly or high resolution histograms, which is really important for a lot of use cases. But this is not just Prometheus with my open metrics head on the 2.0 feature, like the breaking highlight feature of Open Metrics 2.0 will be you guested precisely the same with my open telemetry head on. Low and behold the same underlying technology is being put or has been put into open telemetry. And we've worked for month and month and month and even longer between all different projects to, to assert that we have one single standard which is actually compatible with each other course. One of the worst things which you can have in the cloud ecosystem is if you have soly different things and they break in subtly wrong ways, like it's much better to just not work than to break in a way, which is just a little bit wrong. Of course you won't figure this out until it's too late. So we spent, like with all three hats, we spent insane amounts of time on making this happen and, and making this nice. >>Savannah, one of the things we have so much going on at Cube Con. I mean just you're unpacking like probably another day of cube. We can't go four days, but open time. >>I know, I know. I'm the same >>Open telemetry >>Challenge acceptance open. >>Sorry, we're gonna stay here. All the, They >>Shut the lights off on us last night. >>They literally gonna pull the plug on us. Yeah, yeah, yeah, yeah. They've done that before. It's not the first time we go until they kick us out. We love, love doing this. But Open telemetry is got a lot of news too. So that's, We haven't really talked much about that. >>We haven't at >>All. So there's a lot of stuff going on that, I won't call it boring. That's like code word's. That's cube talk for, for it's working. Yeah. So it's not bad, but there's a lot of stuff going on. Like open telemetry, open metrics, This is the stuff that matters cuz when you go in large scale, that's key. It's just what, missing all the, all the stuff. >>No, >>What are we missing? What are people missing? What's going on in the show that you think that's not actually being reported on? I mean it's a lot of high web assembly for instance got a lot >>Of high. Oh yeah, I was gonna say, I'm glad you're asking this because you, you've already mentioned about seven different hats that you wear. I can only imagine how many hats are actually in your hat cabinet. But you, you are someone with your, with your fingers in a lot of different things. So you can kind of give us a state of the union. Yeah. So go ahead. Let's talk about >>It. So I think you already hit a few good points. Ease of use is definitely one of them. And, and improving the developer experience and not having this like a value of pain. Yeah. That is one of the really big ones. It's going to be interesting cause it is boring. It is janitorial and it needs a different type of persona. A lot of, or maybe not most, but a large fraction of developers like the shiny stuff. And we could see this in Prometheus where like initially the people who contributed this the most where like those restless people who need to fix that one thing, this is impossible, are going to do it. Which changed over the years where the people who now contribute the most are off the janitorial. Like keep things boring, keep things running, still have substantial changes. But but not like more on the maintenance level. >>Yeah. The maintainers. I was just gonna bring that >>Up. Yeah. On the, on the keep things boring while still pushing 'em forward. Yeah. And the thing about ease of use is a lot of this is boring. A lot of this is strategy. A lot of this is toil. A lot of this takes lots of research also in areas where developers are not really good at, like UX for example, and ui like most software developers are really bad at those cause they just think differently from normal humans, I guess. >>So that's an interesting observation that you just made. I we could unpack that on a whole nother show as well. >>So the, the thing is this is going to be interesting for the open source scene course. This needs deliberate investment by companies who assign people to those projects and say, okay, fix that one thing or make it easier to use what have you. That is a lot easier with, with first party products and projects from companies cuz they can invest directly into the thing and they see much more of a value prop. It's, it's kind of normal by now to, to allow developers or even assigned developers onto open source projects. That's not so much the case for the tpms, for the architects, for the UX and your I people like for the documentation people that there's not as much awareness of that this is also driving value for everyone. Yes. And also there's not much as much. >>Yeah, that's a great point. This whole workflow production system of open source, which has grown and keeps growing and we'll keep growing. These be funded. And one of the things we were talking earlier in another session about is about the recession potentially we're hitting and the global issues, macroeconomics that might force some of these projects or companies not to get VC >>Funding. It's such a theme at the show. So, >>So to me, I said it's just not about VC funding. There's other funding mechanisms that's community oriented. There's companies participating, there's other meccas. Richie, if you could have your wishlist of how things could progress an open source, what would you want to see happen in terms of how it's, how things are funded, how things are executed. Cuz developers are going to run businesses. Cuz ultimately if you follow digital transformation to completion, it and developers aren't a department serving the business. They are the business. And that's coming fast. You know, what has to happen in your opinion, if you had the wish magic wand, what would you, what would you snap your fingers to make happen? >>If I had a magic wand that's very different from, from what is achievable. But let, let's >>Go with, Okay, go with the magic wand first. Cause we'll, we'll, we'll we'll riff on that. So >>I'm here for dreams. Yeah, yeah, >>Yeah. I mean I, I've been in open source for more than two, two decades, but now, and most of the open source is being driven forward by people who are not being paid for those. So for example, Gana is the first time I'm actually paid by a company to do my com community work. It's always been on the side. Of course I believe in it and I like doing it. I'm also not bad at it. And so I just kept doing it. But it was like at night on the weekends and everything. And to be honest, it's still at night and in the weekends, but the majority of it is during paid company time, which is awesome. Yeah. Most of the people who have driven this space forward are not in this position. They're doing it at night, they're doing it on the weekends. They're doing it out of dedication to a cause. Yeah. >>The commitment is insane. >>Yeah. At the same time you have companies mostly hyperscalers and either they have really big cloud offerings or they have really big advertisement business or both. And they're extracting a huge amount of value, which has been created in large part elsewhere. Like yes, they employ a ton of developers, but a lot of the technologies they built on and the shoulders of the giants they stand upon it are really poorly paid. And there are some efforts to like, I think the core foundation like which redistribute a little bit of money and such. But if I had my magic wand, everyone who is an open source and actually drives things forwards, get, I don't know, 20% of the value which they create just magically somehow. Yeah. >>Or, or other companies don't extract as much value and, and redistribute more like put more full-time engineers onto projects or whichever, like that would be the ideal state where the people who actually make the thing out of dedication are not more or less left on the sideline. Of course they're too dedicated to just say, Okay, I'm, I'm not doing this anymore. You figure this stuff out and let things tremble and falter. So I mean, it's like with nurses and such who, who just like, they, they know they have something which is important and they keep doing it. Of course they believe in it. >>I think this, I think this is an opportunity to start messaging this narrative because yeah, absolutely. Now we're at an inflection point where there's a big community, there is a shared responsibility in my opinion, to not spread the wealth, but make sure that it's equally balanced and, and the, and I think there's a way to do that. I don't know how yet, but I see that more than ever, it's not just come in, raid the kingdom, steal all the jewels, monetize it, and throw some token token money around. >>Well, in the burnout. Yeah, I mean I, the other thing that I'm thinking about too is it's, you know, it's, it's the, it's the financial aspect of this. It's the cognitive load. And I'm curious actually, when I ask you this question, how do you avoid burnout? You do a million different things and we're, you know, I'm sure the open source community that passion the >>Coach. Yeah. So it's just write code, >>It's, oh, my, my, my software engineering days are firmly over. I'm, I'm, I'm like, I'm the cat herer and the janitor and like this type of thing. I, I don't really write code anymore. >>It's how do you avoid burnout? >>So a i I didn't curse ahead burnout a few years ago. I was not nice, but that was still when I had like a full day job and that day job was super intense and on top I did all the things. Part of being honest, a lot of the people who do this are really dedicated and are really bad at setting boundaries between work >>And process. That's why I bring it up. Yeah. Literally why I bring it up. Yeah. >>I I I'm firmly in that area and I'm, I'm, I don't claim I have this fully figured out yet. It's also even more risky to some extent per like, it's, it's good if you're paid for this and you can do it during your work time. But on the other hand, if it's so nice and like if your hobby and your job are almost completely intersectional, it >>Becomes really, the lines are blurry. >>Yeah. And then yeah, like have work from home. You, you don't even commute anything or anymore. You just sit down at your computer and you just have fun doing your stuff and all of a sudden it's deep at night and you're still like, I want to keep going. >>Sounds like God, something cute. I >>Know. I was gonna say, I was like, passion is something we all have in common here on this. >>That's the key. That is the key point There is a, the, the passion project becomes the job. But now the contribution is interesting because now yeah, this ecosystem is, is has a commercial aspect. Again, this is the, this is the balance between commercialization and keeping that organic production system that's called open source. I mean, it's so fascinating and this is amazing. I want to continue that conversation. It's >>Awesome. Yeah. Yeah. This is, this is great. Richard, this entire conversation has been excellent. Thank you so much for joining us. How can people find you? I mean, I give em your Twitter handle, but if they wanna find out more about Grafana Prometheus and the 1700 things you do >>For grafana grafana.com, for Prometheus, promeus.io for my own stuff, GitHub slash richie age slash talks. Of course I track all my talks in there and like, I don't, I currently don't have a personal website cause I stop bothering, but my, like that repository is, is very, you find what I do over, like for example, the recording link will be uploaded to this GitHub. >>Yeah. Great. Follow. You also run a lot of events and a lot of community activity. Congratulations for you. Also, I talked about this last time, the largest IRC network on earth. You ran, built a data center from scratch. What happened? You done >>That? >>Haven't done a, he even built a cloud hyperscale compete with Amazon. That's the next one. Why don't you put that on the >>Plate? We'll be sure to feature whatever Richie does next year on the cube. >>I'm game. Yeah. >>Fantastic. On that note, Richie, again, thank you so much for being here, John, always a pleasure. Thank you. And thank you for tuning in to us here live from Detroit, Michigan on the cube. My name is Savannah Peterson and here's to hoping that you find balance in your life this weekend.

>>from around the >>globe. It's the >>cube with coverage of Kublai >>Khan and Cloud Native Con Europe 2021 >>virtual brought to >>you by red hat, the cloud native computing foundation and ecosystem partners. Hello, welcome back to the cubes coverage of coupon 21 Cloud Native Con 21 Virtual, I'm John Ferrier Host of the Cube. We're here with a great gas to break down one of the hottest trends going on in the industry and certainly around cloud native as this new modern architecture is evolving so fast. Richard Hartman, director of community at Griffon, a lab's involved with Prometheus as well um, expert and fun to have on and also is going to share a lot here. Richard, thanks for coming. I appreciate it. >>Thank you >>know, we were chatting before we came on camera about the human's ability to to handle all this new shift uh and the and the future of observe ability is what everyone has been talking about. But you know, some say the reserve abilities, just network management was just different, you know, scale Okay, I can buy that, but it's got a lot more than that. It involves data involves a new architecture, new levels of scale that cloud native has brought to the table that everyone is agreeing on. It scales their new capabilities, thus setting up new architectures, new expectations and new experiences are all happening. Take us through the future of observe ability. >>Mhm. Yes, so um 11 of the things which many people find when they onboard themselves onto the cloud native space is um you can scale along different and new axis, which you couldn't scale along before, uh which is great. Of course, it enables growth, it enables different operating models, it enables you to choose different or more modern engineering trade offs, like the underlying problems are still the same, but you just slice and dice your problems and compartmentalize your services differently. But the problem is um it becomes more spread out and the more classic tooling tends to be built for those more classic um setups and architectures as your architecture becomes more malleable and as you can can choose and pick how to grow it along with which access a lot more directly and you have to um that limits the ability of the humans actually operating that system to understand what is truly going on. Um Obviously everyone is is fully fully all in on A. I. M. L. And all those things. But one of the dirty secrets is you will keep needing domain specific experts who know what they're doing and what that thing should look like, what should be working hard to be working. But enable those people to actually to actually understand the current state of the system and compare this to the desired state of the system. Is highly nontrivial in particular, once you have not machine lifetimes of month or years which he had before, which came down to two sometimes hours and when you go to Microsoft to surveillance and such sometimes even into sub seconds. So a lot of this is about enabling this, this this higher volume of data, this higher scale of data, this higher cardinality of what what you actually attach as metadata on your data and then still be able to carry all this and makes sense of it at scale and at speed because if you just toss it into a data lake and do better analysis like half a day later no one cares about it anymore. It needs to be life it needs or at least the largest part of it needs to be life. You need to be able to alert right now if something is imminently customer facing. >>Well, that's awesome. I love totally agree this new observe ability horizontally scalable, more surface area, more axes, as you point out, changes the data equation on the automation plays a big role in mention machine learning and ai great, great grounds for that. I gotta ask you just well before we move on to the next topic around this is that the most people that come from the old world with the tooling and come from that old school vendor mentality or old soup architecture, old school architecture tend to kind of throw stones at the future and say, well the economics are all wrong and the performance metrics. So I want to ask you so I assume that we believe we do believe because assume that's going to happen. What is the economic picture? What's the impact that people are missing? When you look at the benefits of what this system is going to enable the impact? Specifically whether it's economics, productivity, efficient code, what are some of the things that maybe the VCS or other people in the naysayers side? Old school will, will throw stones at what's the, what's the big upside here? >>Mhm. So this will not be true for everyone and there will still be certain situations where it makes sense to choose different sets of of trade offs, but most everyone will be moving into the cloud for for convenience and speed reasons. And I'm deliberately not saying cost reasons. Um the reason being um usually or in the past you had simply different standard service delineations and all of the proserve, the consulting your hiring pool was all aligned with this old type of service delineation, which used to be a physical machine or a service or maybe even a service and you had a hot standby or something. If we, if we got like really a hugely respect from the same things still need to operate under laying what you do. But as we grow as an industry, more of more of this is commoditized and same as we commoditize service and storage network. We commoditized actually running off that machine and with service and such go even further. Um so it's not so much about about this fundamentally changing how it's built. It's just that a larger or a previously thing which was part of your value at and of what you did in your core is now just off the shelf infrastructure which you just by as much as you need again at certain scales and for certain specific use cases, this will not be true for the foreseeable future, but most everyone um will be moving there simply because where they actually add value and the people they can hire for and who are interested in that type of problem. I just mean that it's a lot more more sensical to to choose this different delineation but it's not cheaper >>and the commoditization and disintermediation is definitely happening, totally agree. And the complexity that's gonna be abstracted away with software is novell and it's also systematic. There's just it's new and there's some systems involved, so great insight there. I totally agree with you. The disruption is happening majority of almost all areas, so in all verticals and all industries, so so great point. I think this is where I think everyone's so excited and some people are paranoid actually frankly, but we cover that in depth on the Cuban other segments. But great point. We'll get back to what you're where you're spending your time right now. Um You're spending a lot of time on open metrics. What is that enabling take us through that? >>So um the super quick history of Prometheus, of course, we need that for open metrics. Promises was actually created in 2012. Um and the wire format which he used to in the exposition format, which he used to transport metrics into Prometheus is stable since 2014. Um But there is a large problem here. Um It carries the promise his name and a lot of competing projects and a lot of competing vendors of course there are vendors which compete with just the project. Um It's simply refused to to to take anything in which carried the promise his name. Of course, this doesn't align with their food um strategy, which they ran back then. So um together with scenes, the f we decided to just have a new different name for just that wire format for the underlying data model for everything which you need to make one complete exposition or a bunch of expositions towards towards permissions. So that's it at the corn, that's been ongoing since 2000 and 15 16 something. Um But there's also changes on the one hand, there is a super careful, a super super careful um Clean up and backwards compatible cleanup of a few things which the permit this exposition former serious here for didn't get right. But also we enable two features within this and as permitted chose open metrics as its official format. We also uplift committees and varying both heads. Obviously it's easier to get the synchronization. Um Ex employers stand out which is a completely new, at least outside of certain large search companies google. Um Who who used who use ex employers to do something different with with their traces. Um it was in 2017 when they told me that for them searching for traces didn't scale by labels. Uh and at that point I wanted to have both. I wanted to have traces and logs also with the same label set as permitting system. But when they tell you searching doesn't scale like they tell you you better listen. So uh the thing is this you have your index where you store all your data or your where you have the reference to enter your database and you have these label sets and they are super efficient and and quite powerful when compared to more traditional systems but they still carry a cost and that cost becomes non trivial at scale. So instead of storing the same labels for your metrics and your logs and your traces, the idea is to just store an I. D. For your trace which is super lightweight and it's literally just one idea. So your index is super tiny. Um And then you touch this information to your logs to your metrics and in the meantime also two year to year logs. Um So you know already that trace has certain properties because historically you have this needle estate problem. You have endless amounts of traces and you need to figure out what are the useful are they are the judicial and interesting aero state highlight and see some error occurring whatever if that information is already attached to your other signals. That's a lot easier. Of course. You see you're highlighting see bucket and you see a trace ID which is for that high latency bucket. So going into that trace, I already know it is a highlight and see trace for for a service which has a high latency, it has visited that labor. It was running this in that context, blah blah blah blah blah. Same for logs. There is an error. There is an exception, maybe a security breach, what have you and I can jump directly into a trace and I have all this mental context and the most expensive part is the humans. So enabling that human to not need to break mental uh train of thought to just jump directly from all the established state which they already have here in debugging just right into the trace, went back and just see why that thing behave that way. It's super powerful and it's also a lot cheaper to store this on the back and a four year traces which in our case internally we just run at 100% something. We do not throw data way, which means you don't have the super interesting thing. And by the way the trace just doesn't exist for us a good job. And that's the one thing to to from day one this intent to to marry those three pillars more closely. The other thing is by having a true lingua franca. It gave that concept of of of promises compatibility on the wire, its own name and it's its own distinct concept. And that is something which a lot of people simply attached to. So just by having that name, allow the completely different conversation over the last half decade or so and to close >>them close it >>up and to close that point because I come from the network, from the networking space and, and basically I T f r f C s are the currency within the networking space and how you force your vendors to support something, which is why I brought open metrics into the I. D. F. To to give it an official stamp of approval in Rfc number which is currently hopefully successful. Um So all of a sudden you can slip this into your tender and just tell your vendor, ex wife said okay, you need to support this. But I've seen all of a sudden by contract they're bound to to support communities native. So >>I support that Rfc yet or no, is that still coming? >>I, so at the last uh TF meeting, which was virtual, obviously I presented everything to the L. A W G. Um there was very good feedback. Um they want to adopt it as an informational uh I. D. Reason being it is most or it is a documentation of an already widely existed standard. So it gets different bits and pieces in the heather. Um Currently I'm waiting for a few rounds of feedback on specific wording how to make it more clear and such. Um looking >>good. It's looking good. >>Oh yes while presenting it. They actually told me that I have a conference with promises and performance. Well >>that's how you get things done in the old school internet. That's the way it was talking to Vince serving all of my friends and that generation we grew up, I mean I was telling a story on the clubhouse, just random that I grew up in the era. We used to pirate software used to deal software back in the old days. Pre open source. This is how things get done. So I gotta ask you the impact question. The, the deal with open metrics potentially could disrupt all those startups. So what, how does this impact all these stars because everyone is jockeying for land grabbing the observe ability space? Is that just because it's just too many people competing for one spot or do they all have differentiation? What happens to all those observe ability startups that got minted and funded? >>So I have, I think we have to split this into two answers, the first one open metrics and also Prometheus we're trying really hard to standardize what we're doing and to make this reusable as much as we possibly can um simply because premises itself does not have any any profit motivation or anything, it is just a project run by people. Um so we gain by, by users using our stuff and working in the way, which we think is a good way to operate. So anyone who just supports all those open standards, just on boards themselves onto a huge ecosystem of already installed base. And we're talking millions and millions and millions of installations, we don't have hard numbers, but the millions and millions I am certain of and thats installations, not users, so that's several orders of magnitude more. Um, so that that actually enables an ecosystem within which to move as to the second question. It is a super hot topic. So obviously that we see money starts coming in from all right. Um, I don't think that everyone will survive, but that is just how it usually is. There is a lot of of not very differentiated offerings, be the software, be they as a service, be their distributions? Well, you don't really see much much value and not not a lot of, not a lot of much anything in ways of innovation. So this is more about about making it easier to run or or taking that pain away, which obviously makes you open to attack by by all the hyper scale. Of course, they can just do this at a higher scale than you. Um, so unless you actually really in a way in that space and actually shape and lead in that space, at least to some extent, it will probably be relatively hard. That being said. >>Yeah, when you ride, when you ride the big waves like this, I mean, you you got to be on the right side of this. Uh, Pat Gelsinger's when he was that VM Where now is that intel told me on the cube one time. If you're not, you don't get it right on these waves, your driftwood, Right? So, so, you know, and we've seen this movie before, when you start to see the standards bodies like the I E T. F. Start to look at standards. You start to think there's a broader market opportunities, a need for some standards, which is good. It enables more value, right value creation, whether it's out in the open or if it's innovative from a commercialization standpoint, you know, these are good things and then you have everyone who's jockeying around from the land grab incomes, a standard momentum, you gotta be on the right side of these things. We know what we know it's gonna look like. If you're not on the right side of the standard, then your proprietary, >>precisely. >>And so that's the endgame. Okay, well, I really appreciate the impact. Final question. Um, as the world evolved post Covid as cloud Native goes mainstream, the enterprises in the cloud scale are demanding more things. Enterprises are are, you know, they want more stuff than just straight up in the cloud startups, for instance. So you start to see, you know, faster, more agility obviously, uh, with deploying modern apps, when you start getting into enterprise grade scale, you gotta start thinking, you know, this is an engineering and computer science discipline. Coming together, you've got to look at the architecture. What's your future vision of how the next gen programmable infrastructure looks like? >>You mean, as in actually manage those services or limited to observe ability to >>observe ability, role, observe ability. Just you're in the urine. The survivability speaks to the operating system of what's going on, distributed computing you're looking at, you gotta have a good observe ability if you want to deploy services. So, you know, as it evolves and this is not a fringe thing anymore. This is real deal. This observe abilities a key linchpin in the architecture. >>So, um, maybe to approach us from two sides. One of the things which, which, I mean I come from very much non cloud native background. One of the things which tends to be overlooked in cloud native is that not everything is green field. Matter of fact, legacy is the code word for makes actual money. Um, so a lot of brownfield installations, which still make money, which we keep making money and all of those existence, they will not go away anytime soon. And as soon as you go to actually industry trying to uplift themselves to industry that foreign, all those passwords you get a lot more complexity in, in just the availability of systems than just the cloud native scheme. So being able to to actually put all of those data types together and not just have you. Okay, nice. I have my micro service events fully instrumented and if anything happens on the layer below, I'm simply unable to make any any effort on debugging um things like for example, Prometheus course they are so widely adopted enable you to literally, and I did this myself um from the Diesel Genset of your data center over the network down to down to the office. If if someone is in there, if if if your station and your pager is is uh stepped in such to the database to the extra service which is facing your end customers, all of those use the same labels that use the same metadata to actually talk about this. So all of a sudden I can really drill down into my data, not only from you. Okay. I have my microservices, my database. Big deal. No, I can actually go down as deep in my infrastructure as my infrastructure is. And this is especially important for anyone who's from the more traditional enterprise because most of them will for the foreseeable future have tons and tons and tons of those installations and the ability to just marry all this data together no matter where it's coming from. Of course you have this lingual franklin, you have these widely adopted open standards. I think that is one of the main drivers in >>jail. I think you just nailed the hybrid and surprised use case, you know, operation at scale and integrating the systems. So great job Richard, thank you so much for coming on. Richard Hartman, Director of community Griffon A labs. I'm talking, observe ability here on the cube. I'm john for your host covering cube con 21 cognitive content. One virtual. Thanks for watching. Mhm Yeah. Mhm.

>>Live from San Diego, California. It's the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem. >>Welcome back to the queue bumps to men. And my cohost is John Troyer and you're watching the cube here at CubeCon, cloud-native con 2019 in beautiful and sunny San Diego today. Happy to welcome to the program a first time guest, Tom Willkie, who's vice president of product ECRO funnel labs. Thank you. Thank you so much for joining us. All right, so it's on your tee shirt. We've been hearing, uh, customers talking about it and the like, but, uh, why don't you introduce the company to our audience in a, where you fit in this broad landscape, uh, here at the CNCF show. Thank you. Yes. So Grafana is probably the most popular open source project for dashboarding and visualization. Um, started off focused on time series data on metrics, um, but really recently has branched out into log analysis and tracing and, and all, all of the kinds of aspects of your observability stack. >>Alright, so really big, uh, you know, broad topic there. Uh, we know many of the companies in that space. Uh, there's been many acquisitions, uh, you know, uh, recently in this, um, where, where do you fit in your system? I saw like databases, like a big focus, uh, when, when I, when I look at the company website, uh, bring us inside a little bit. Yeah. As a product to the offering. The customers most, um, >> most, most vendors in this space will sell you a monitoring product that includes the time series database normally includes visualization and some agent as well where pharma Lampson Griffon open source projects, very focused on the visualization aspects. So we are data source agnostic and we have back ends for more than 60 different data sources. So if you want to bring together data from let's say Datadog and combine it with some open source monitoring from, you can do that with. >>Uh, you can, you can have the dashboards and the individual panels in that dashboard combined data from multiple different data sources and we're pretty much the only game in town for that. You can, you can think of it like Tableau allows you to plug into a whole bunch of different databases for your BI with that. But for monitoring and for metrics. Well, so Tom, maybe let's, before we get into the exit products and more of the service and the, and the conference here, let's talk a little well on the front page of your website, you use the Oh 11, why word? So we've said where it's like monitoring here we use words like management, we use words like ops. Observability is a hot topic in the space and for people in a space that has some nuances. And so can you just maybe let the viewers and us know a little bit about what, how the space is looking at this and how you all feel about observability and what everybody here who's running some cloud native apps needs to actually function in production. >>Yeah. So I think, um, you can't talk about observability without either being pro or, or for, um, uh, the three pillars, right? So people talk about metrics, logs and traces. Um, I think what people miss here is that it's more about the experience for the developer, you know, Gruffalo and what we're trying to achieve is all about giving engineers and developers the tools they need to understand what their applications and their infrastructure doing, right? So we're not actually particularly picky about which pillars you use and which products you use to implement those pillars. But what we want to do is provide you with an experience that allows you to bring it all into a single, a single user interface and allows you to seamlessly move between the different sources of data and, and hopefully, uh, combine them in your analysis and in your root cause of any particular incident. >>And that for me is what observability means. It's about helping you understand the behavior of your application in particular. I mean, I'm, I'm a, I'm a software engineer by trade. I'm still on call. I still get paged at 3:00 AM occasionally. And, and having the right tools at 3:00 AM to allow me to as quickly as possible, figure out what happened and then dive into a fix. That's what we're about over funnel labs. All right. So Tom, one of the things we always need to understand and show here. There's the project and there's the company. Yep. Help us just kind of understand, you know, definitely a difference. The products, the, the, the mission of the company and how that fits with the project. So the Gruffalo project predates the company and it was started by taco. Um, he, you know, he saw a spot for like needing a much better kind of graphical editing of dashboards and making, making the kind of metrics way more accessible to your average human. >>Um, the final lab started really to focus on the it and, uh, monitoring observability use cases of profanity and, but the project itself is much broader than that. We see a lot of use cases in industrial, in IOT, even in BI as well. But Grafana labs is a company we're focused on the monitoring side of things. We're focused on the observability. So we also offer, we mean, like most companies, we have an enterprise version of. It has a few data sources for commercial vendors. So if you want to, you want to get your data dog or your Splunk into Grafana, then there's a commercial auction for that. But we also offer a hosted observability platform called Grafana clown. And this is where we take the best open source projects, the best tools that we think you need as an engineer to understand your applications and we host them for you and we operate them for you. >>We scale them, we upgrade them, we fix bugs, we sacrifice the clouds predominantly are hosted from atheists, our hosted graphite and our hosted Loki, our log aggregation system, um, all combined and brought together with uh, with the Gruffalo frontend. So yeah, like two products, a bunch of open source projects for final labs, employees, four of the promethium maintainers. And I'm one of the promethium maintainers. Um, we am employee graphite maintainers. Obviously a lot of Gryffindor maintainers, but also Loki. Um, I'm trying to think, like there's just so many open source projects. We, uh, we get involved with that. Really it's about synthesizing, uh, an observability platform out of those. And that's what we offer as a product. So you recently had an announcement that Loki is now GA. can you talk just a little bit about Loki and aggregation and logs and what Loki does? >>Yeah, I'd love to. Yeah. Um, a year ago in Seattle actually we announced the Loki project. Um, it was super early. I mean I just basically been finishing the code on the plane over and we announced it and no one I think could have predicted the response we had. Um, everyone was so keen and so hungry for alternative to traditional log aggregation systems. Um, so it's been a year and we've learned a hell of a lot. We've had so much feedback from the community. We've built a whole team internally around, around Loki. We now offer a hosted version of it and we've been running it in production now for over a year, um, doing some really great scale on it and we think it's ready for other people to do the same. One of the things we hear, especially at shows like this is I really, I really, you know, developers and the grassroots adopters come to us, say, we really love Loki. >>We really love what you're doing with it. Um, but my boss won't let me use it until it goes to be one. And so really yesterday we announced it's Don V. one, we think it's stable. We're not going to change any of the APS on you. We, uh, we would love you to use it and uh, and put it into production. All right. Uh, we'd like to hear a little bit more about the business side of things. So, um, I believe there was some news around funding, uh, uh, you know, how many people you have, how many, you know, can you parse for us, you know, how many customers have the projects versus how many customers have, uh, you know, the company's products. Well, we don't, we don't call them customers of the projects that users, yes, yes, we, uh, but I'm from a company where we have hundreds of customers. >>Um, I don't believe we make our revenue figures public and, uh, so I'm probably not going to dive into them, but I know, I know the CEO stands up at our, our yearly conference and, and discloses, you know, what our revenue the last year was. So I'll refer you to that. Um, the funding announcement, that was about a month ago. We, uh, we raised a great round from Lightspeed, um, 24 million I believe. Um, and we're gonna use that to really invest in the community, really invest in our projects and, and build a bit more of a commercial function. Um, the company is now about 110 people. I think, um, it's growing so quickly. I joined 18 months ago and we were 30 people and so we've almost quadrupled in size in, in the last year and a half. Um, so keeping up is quite a challenge. Uh, the two projects, uh, products I've already touched on a few hundred customers and I think we're, you know, we're really happy with the growth. >>We've been, uh, we've never had any institutional funding before this. The company is about five years old. So we've been growing based on organic revenue and, and, and, and, you know, barely profitable, uh, but reinvesting that into the company and, and it's, yeah, it's going really well. We're also one of the, I mean it's not that unique I guess, but we're remote first. We have a more than 50% of our employees work from home. I work from my basement in London. We have a few tiny like offices, one in Stockholm and one in New York, but, but we're really keen to hire the best people wherever they are. Um, and we invest a lot in travel. Uh, we invest a lot in, um, the, the right tools and getting the whole company together to really make that work. Actually a really fun place to work. What time? >>We're S we're still in the business here and I don't know how much time you've spent at the booth this year, but I don't, can you compare, I mean, we've been talking about the growth of this community and the growth of this conference. Can you compare say this year to last year, the, the people coming up, their maturity, the maturity of their production, et cetera. Are they, are they ready to buy? Are they still kicking? Are they still wondering what this Cooper Cooper need easy things is, you know, where, where is everybody this year and how does that, how has it changed? Yeah, and that's a good question where we're definitely seeing people with a lot more sophisticated questions. The, the, the conversations we're having at the booth are a lot longer than they've been in previous years. The um, you know, in particular people now know what key is. We only announced it a year ago and gonna have a lot of people asking us very detailed questions about what scale they can run it at. >>Um, otherwise, yeah, I think there is starting to be a bit more commercial intent at the conference, some few more buying decisions being made here. It's still predominantly a community oriented conference and I think the, the, I don't want that to go away. Like, that's one of the things that makes it attractive to me. And, and I bring my whole team here and that's one of the things that makes it attractive to them. But there is a little bit more, I'm a little more sales activity going on for sure. Any updates to the, to the tracing and monitoring observability stories of the projects here at CNCF this year since you as you're part of the promethium project? >> Yes. So we actually, we had the promethium conference in Munich two weeks ago and after each committee conference, the maintainers like to get together and kind of plan out the next six months of the project. >>So we started to talk about um, adding support for things like exemplars into Prometheus's. This is where each histogram bucket, you can associate an example trace that goes, that contributed towards that, that history and that latency. And then you can build nice user interfaces around that. So you can very quickly move from a latency graph to example traces that caused that. Um, so that's one of the things we're looking to do in Prometheus. And of course Jaeger graduated just a week ago. I think. Um, we're big users of Jaeger internally at for final amps. And actually on our booth right now, uh, we're showing a demo of how we're integrating, um, visualization of distributed tracing, integral foreigner. So you can, you know, using the same approach we do with metrics where we support multiple backends, we're going to support Yeager, we're going to support Zipkin, we're going to support as many open source tracing projects as we can with the Grafana UI experience and being able to seamlessly kind of switch between different data sources, metrics all the way to logs all the way to traces within one UI. >>And without ever having to copy and paste your query and make mistakes and kind of translate it in your head. Right. >> Tom, give us a little bit, look forward. Uh, you know, a lot of activities as the thing's going to, you know, graduating and pulling things together. So what should your users be looking for kind of over the next six to 12 months? >> That's a great question. Yeah, I think we do a yearly release cycle for foreigners. So the next one we're, we're aiming towards is for seven, like for me to find a seven's going to be all about tracing. So I really want to see the demo we're doing. I want to see that turned into like production ready code support for multiple different data sources, support for things like exemplars, which we're not showing yet. Um, I want to see all of that done in Grafana in the next year and we've also massively been flushing out the logging story. >>I'm with Loki, we've been adding support for uh, extracting metrics from the logs and I really think that's kind of where we're going to drive Loki forward in the future. And that really helps with systems that aren't really exposing metrics like legacy systems where the only kind of output you get from them is the logs. Um, beyond that. Yeah, I mean the welds are kind of oyster. I think I'm really keen to see the development of open telemetry and um, we've just starting to get involved to that project ourselves. Um, I'm really interested to kind of talk to people about what they need out of a tracing system. We, we see people asking for a hosted tracing systems. Um, but, but IMO is very much like pick the best open source ones. I don't think that's, that's emerged yet. I don't think people know which is the best one yet. >>So we're going to get involved in all of them. See which one's a C, which one's a community kind of coalesces around and maybe start offering a hosted version of that. >> You know, our final thing is, uh, you know, what advice do you have for users? Obviously, you know, you like the open source thing, but you know, they're hearing about observability everywhere there are, you know, the, the whole APM market is moving this direction. There's acquisitions as we talked about earlier. Um, there's so many moving pieces and a lot of different viewpoints out there. So just, you know, from a user, how do you know, how will things ma, what makes their lives easier and what advice would you give them? Yeah, no, definitely. I think a lot of vendors will tell you like to pick a, pick a vendor who's going to help you with this journey. >>Like I would say like, pick a vendor you trust who can help you make those decisions. Like find someone impartial who's gonna not make, not try and persuade you to buy their product. So we would, uh, you know, I would encourage you to try things out to dog food and to really like invest in experimentation. There's a lot going on in, uh, in, in the observability world and in the cloud native world. And you've got to, you've got to try it and see what fits. Like we embrace this, uh, composability of the, uh, of the observatory of, of the observability ecosystem. So like, try and find which, which choices work best for you. Like I, uh, whenever, whenever I talk to him, you still have to lick all the cupcakes in 2019. I think. I mean, I would, it depends on your level of kind of maturity, right? >>And sophistication. Like, I think if, uh, if, if this is really important to you, you should go down that approach. You should try them all. If this is not one of your core competencies that may be going with a vendor that helps you is a better approach. But, but I'm, I come from the open source world and, uh, you know, I like to see the, um, the whole ecosystem and all the different players and all the different, new and exciting ways to solve these problems. Um, so I'm, I'm always going to encourage people to have a play and try things out. All right, Tom, final word, Loki. Explain to us, uh, you know, when you're coming up with it, how you ended, uh, are you the God of mischief? Well, so the official line is the Loki is the, um, is the North mythology equivalent of Prometheus's, uh, in Greek mythology and, and lochia logging project is, is, is Prometheus's inspired logging. So we've tried to take the operational model from, from atheists, the query language from, from atheists and, and the kind of a cost efficiency from, from atheists and apply it to logs. Um, but I will admit to being a big fan of the Marvel movies. All right, Tom Willkie. Thank you so much for sharing the updates on, on the labs. Uh, we definitely look forward to hearing updates from you and thank you. All right, for, for John Troyer, I'm Stu Madmen back with more coverage here from San Diego. Thank you for watching. Thank you for watching the cube.

(bright music) >> Hi, everybody, this is Dave Vellante, welcome back to Supercloud 2. Last August, at the first Supercloud event, we invited the broader community to help further define Supercloud, we assessed its viability, and identified the critical elements and deployment models of the concept. The objectives here at Supercloud too are, first of all, to continue to tighten and test the concept, the second is, we want to get real world input from practitioners on the problems that they're facing and the viability of Supercloud in terms of applying it to their business. So on the program, we got companies like Walmart, Sachs, Western Union, Ionis Pharmaceuticals, NASDAQ, and others. And the third thing that we want to do is we want to drill into the intersection of cloud and data to project what the future looks like in the context of Supercloud. So in this segment, we want to explore the concept of data architectures and what's going to be required for Supercloud. And I'm pleased to welcome one of our Supercloud sponsors, ChaosSearch, Ed Walsh is the CEO of the company, with Thomas Hazel, who's the Founder, CTO, and Chief Scientist. Guys, good to see you again, thanks for coming into our Marlborough studio. >> Always great. >> Great to be here. >> Okay, so there's a little debate, I'm going to put you right in the spot. (Ed chuckling) A little debate going on in the community started by Bob Muglia, a former CEO of Snowflake, and he was at Microsoft for a long time, and he looked at the Supercloud definition, said, "I think you need to tighten it up a little bit." So, here's what he came up with. He said, "A Supercloud is a platform that provides a programmatically consistent set of services hosted on heterogeneous cloud providers." So he's calling it a platform, not an architecture, which was kind of interesting. And so presumably the platform owner is going to be responsible for the architecture, but Dr. Nelu Mihai, who's a computer scientist behind the Cloud of Clouds Project, he chimed in and responded with the following. He said, "Cloud is a programming paradigm supporting the entire lifecycle of applications with data and logic natively distributed. Supercloud is an open architecture that integrates heterogeneous clouds in an agnostic manner." So, Ed, words matter. Is this an architecture or is it a platform? >> Put us on the spot. So, I'm sure you have concepts, I would say it's an architectural or design principle. Listen, I look at Supercloud as a mega trend, just like cloud, just like data analytics. And some companies are using the principle, design principles, to literally get dramatically ahead of everyone else. I mean, things you couldn't possibly do if you didn't use cloud principles, right? So I think it's a Supercloud effect, you're able to do things you're not able to. So I think it's more a design principle, but if you do it right, you get dramatic effect as far as customer value. >> So the conversation that we were having with Muglia, and Tristan Handy of dbt Labs, was, I'll set it up as the following, and, Thomas, would love to get your thoughts, if you have a CRM, think about applications today, it's all about forms and codifying business processes, you type a bunch of stuff into Salesforce, and all the salespeople do it, and this machine generates a forecast. What if you have this new type of data app that pulls data from the transaction system, the e-commerce, the supply chain, the partner ecosystem, et cetera, and then, without humans, actually comes up with a plan. That's their vision. And Muglia was saying, in order to do that, you need to rethink data architectures and database architectures specifically, you need to get down to the level of how the data is stored on the disc. What are your thoughts on that? Well, first of all, I'm going to cop out, I think it's actually both. I do think it's a design principle, I think it's not open technology, but open APIs, open access, and you can build a platform on that design principle architecture. Now, I'm a database person, I love solving the database problems. >> I'm waited for you to launch into this. >> Yeah, so I mean, you know, Snowflake is a database, right? It's a distributed database. And we wanted to crack those codes, because, multi-region, multi-cloud, customers wanted access to their data, and their data is in a variety of forms, all these services that you're talked about. And so what I saw as a core principle was cloud object storage, everyone streams their data to cloud object storage. From there we said, well, how about we rethink database architecture, rethink file format, so that we can take each one of these services and bring them together, whether distributively or centrally, such that customers can access and get answers, whether it's operational data, whether it's business data, AKA search, or SQL, complex distributed joins. But we had to rethink the architecture. I like to say we're not a first generation, or a second, we're a third generation distributed database on pure, pure cloud storage, no caching, no SSDs. Why? Because all that availability, the cost of time, is a struggle, and cloud object storage, we think, is the answer. >> So when you're saying no caching, so when I think about how companies are solving some, you know, pretty hairy problems, take MySQL Heatwave, everybody thought Oracle was going to just forget about MySQL, well, they come out with Heatwave. And the way they solve problems, and you see their benchmarks against Amazon, "Oh, we crush everybody," is they put it all in memory. So you said no caching? You're not getting performance through caching? How is that true, and how are you getting performance? >> Well, so five, six years ago, right? When you realize that cloud object storage is going to be everywhere, and it's going to be a core foundational, if you will, fabric, what would you do? Well, a lot of times the second generation say, "We'll take it out of cloud storage, put in SSDs or something, and put into cache." And that adds a lot of time, adds a lot of costs. But I said, what if, what if we could actually make the first read hot, the first read distributed joins and searching? And so what we went out to do was said, we can't cache, because that's adds time, that adds cost. We have to make cloud object storage high performance, like it feels like a caching SSD. That's where our patents are, that's where our technology is, and we've spent many years working towards this. So, to me, if you can crack that code, a lot of these issues we're talking about, multi-region, multicloud, different services, everybody wants to send their data to the data lake, but then they move it out, we said, "Keep it right there." >> You nailed it, the data gravity. So, Bob's right, the data's coming in, and you need to get the data from everywhere, but you need an environment that you can deal with all that different schema, all the different type of technology, but also at scale. Bob's right, you cannot use memory or SSDs to cache that, that doesn't scale, it doesn't scale cost effectively. But if you could, and what you did, is you made object storage, S3 first, but object storage, the only persistence by doing that. And then we get performance, we should talk about it, it's literally, you know, hundreds of terabytes of queries, and it's done in seconds, it's done without memory caching. We have concepts of caching, but the only caching, the only persistence, is actually when we're doing caching, we're just keeping another side-eye track of things on the S3 itself. So we're using, actually, the object storage to be a database, which is kind of where Bob was saying, we agree, but that's what you started at, people thought you were crazy. >> And maybe make it live. Don't think of it as archival or temporary space, make it live, real time streaming, operational data. What we do is make it smart, we see the data coming in, we uniquely index it such that you can get your use cases, that are search, observability, security, or backend operational. But we don't have to have this, I dunno, static, fixed, siloed type of architecture technologies that were traditionally built prior to Supercloud thinking. >> And you don't have to move everything, essentially, you can do it wherever the data lands, whatever cloud across the globe, you're able to bring it together, you get the cost effectiveness, because the only persistence is the cheapest storage persistent layer you can buy. But the key thing is you cracked the code. >> We had to crack the code, right? That was the key thing. >> That's where the plans are. >> And then once you do that, then everything else gets easier to scale, your architecture, across regions, across cloud. >> Now, it's a general purpose database, as Bob was saying, but we use that database to solve a particular issue, which is around operational data, right? So, we agree with Bob's. >> Interesting. So this brings me to this concept of data, Jimata Gan is one of our speakers, you know, we talk about data fabric, which is a NetApp, originally NetApp concept, Gartner's kind of co-opted it. But so, the basic concept is, data lives everywhere, whether it's an S3 bucket, or a SQL database, or a data lake, it's just a node on the data mesh. So in your view, how does this fit in with Supercloud? Ed, you've said that you've built, essentially, an enabler for that, for the data mesh, I think you're an enabler for the Supercloud-like principles. This is a big, chewy opportunity, and it requires, you know, a team approach. There's got to be an ecosystem, there's not going to be one Supercloud to rule them all, so where does the ecosystem fit into the discussion, and where do you fit into the ecosystem? >> Right, so we agree completely, there's not one Supercloud in effect, but we use Supercloud principles to build our platform, and then, you know, the ecosystem's going to be built on leveraging what everyone else's secret powers are, right? So our power, our superpower, based upon what we built is, we deal with, if you're having any scale, or cost effective scale issues, with data, machine generated data, like business observability or security data, we are your force multiplier, we will take that in singularly, just let it, simply put it in your object storage wherever it sits, and we give you uniformity access to that using OpenAPI access, SQL, or you know, Elasticsearch API. So, that's what we do, that's our superpower. So I'll play it into data mesh, that's a perfect, we are a node on a data mesh, but I'll play it in the soup about how, the ecosystem, we see it kind of playing, and we talked about it in just in the last couple days, how we see this kind of possibly. Short term, our superpowers, we deal with this data that's coming at these environments, people, customers, building out observability or security environments, or vendors that are selling their own Supercloud, I do observability, the Datadogs of the world, dot dot dot, the Splunks of the world, dot dot dot, and security. So what we do is we fit in naturally. What we do is a cost effective scale, just land it anywhere in the world, we deal with ingest, and it's a cost effective, an order of magnitude, or two or three order magnitudes more cost effective. Allows them, their customers are asking them to do the impossible, "Give me fast monitoring alerting. I want it snappy, but I want it to keep two years of data, (laughs) and I want it cost effective." It doesn't work. They're good at the fast monitoring alerting, we're good at the long-term retention. And yet there's some gray area between those two, but one to one is actually cheaper, so we would partner. So the first ecosystem plays, who wants to have the ability to, really, all the data's in those same environments, the security observability players, they can literally, just through API, drag our data into their point to grab. We can make it seamless for customers. Right now, we make it helpful to customers. Your Datadog, we make a button, easy go from Datadog to us for logs, save you money. Same thing with Grafana. But you can also look at ecosystem, those same vendors, it used to be a year ago it was, you know, its all about how can you grow, like it's growth at all costs, now it's about cogs. So literally we can go an environment, you supply what your customer wants, but we can help with cogs. And one-on one in a partnership is better than you trying to build on your own. >> Thomas, you were saying you make the first read fast, so you think about Snowflake. Everybody wants to talk about Snowflake and Databricks. So, Snowflake, great, but you got to get the data in there. All right, so that's, can you help with that problem? >> I mean we want simple in, right? And if you have to have structure in, you're not simple. So the idea that you have a simple in, data lake, schema read type philosophy, but schema right type performance. And so what I wanted to do, what we have done, is have that simple lake, and stream that data real time, and those access points of Search or SQL, to go after whatever business case you need, security observability, warehouse integration. But the key thing is, how do I make that click, click, click answer, and do it quickly? And so what we want to do is, that first read has to be fast. Why? 'Cause then you're going to do all this siloing, layers, complexity. If your first read's not fast, you're at a disadvantage, particularly in cost. And nobody says I want less data, but everyone has to, whether they say we're going to shorten the window, we're going to use AI to choose, but in a security moment, when you don't have that answer, you're in trouble. And that's why we are this service, this Supercloud service, if you will, providing access, well-known search, well-known SQL type access, that if you just have one access point, you're at a disadvantage. >> We actually talked about Snowflake and BigQuery, and a different platform, Data Bricks. That's kind of where we see the phase two of ecosystem. One is easy, the low-hanging fruit is observability and security firms. But the next one is, what we do, our super power is dealing with this messy data that schema is changing like night and day. Pipelines are tough, and it's changing all the time, but you want these things fast, and it's big data around the world. That's the next point, just use us alongside, or inside, one of their platforms, and now we get the best of both worlds. Our superpower is keeping this messy data as a streaming, okay, not a batch thing, allow you to do that. So, that's the second one. And then to be honest, the third one, which plays you to Supercloud, it also plays perfectly in the data mesh, is if you really go to the ultimate thing, what we have done is made object storage, S3, GCS, and blob storage, we made it a database. Put, get, complex query with big joins. You know, so back to your original thing, and Muglia teed it up perfectly, we've done that. Now imagine if that's an ecosystem, who would want that? If it's, again, it's uniform available across all the regions, across all the clouds, and it's right next to where you are building a service, or a client's trying, that's where the ecosystem, I think people are going to use Superclouds for their superpowers. We're really good at this, allows that short term. I think the Snowflakes and the Data Bricks are the medium term, you know? And then I think eventually gets to, hey, listen if you can make object storage fast, you can just go after it with simple SQL queries, or elastic. Who would want that? I think that's where people are going to leverage it. It's not going to be one Supercloud, and we leverage the super clouds. >> Our viewpoint is smart object storage can be programmable, and so we agree with Bob, but we're not saying do it here, do it here. This core, fundamental layer across regions, across clouds, that everyone has? Simple in. Right now, it's hard to get data in for access for analysis. So we said, simply, we'll automate the entire process, give you API access across regions, across clouds. And again, how do you do a distributed join that's fast? How do you do a distributed join that doesn't cost you an arm or a leg? And how do you do it at scale? And that's where we've been focused. >> So prior, the cloud object store was a niche. >> Yeah. >> S3 obviously changed that. How standard is, essentially, object store across the different cloud platforms? Is that a problem for you? Is that an easy thing to solve? >> Well, let's talk about it. I mean we've fundamentally, yeah we've extracted it, but fundamentally, cloud object storage, put, get, and list. That's why it's so scalable, 'cause it doesn't have all these other components. That complexity is where we have moved up, and provide direct analytical API access. So because of its simplicity, and costs, and security, and reliability, it can scale naturally. I mean, really, distributed object storage is easy, it's put-get anywhere, now what we've done is we put a layer of intelligence, you know, call it smart object storage, where access is simple. So whether it's multi-region, do a query across, or multicloud, do a query across, or hunting, searching. >> We've had clients doing Amazon and Google, we have some Azure, but we see Amazon and Google more, and it's a consistent service across all of them. Just literally put your data in the bucket of choice, or folder of choice, click a couple buttons, literally click that to say "that's hot," and after that, it's hot, you can see it. But we're not moving data, the data gravity issue, that's the other. That it's already natively flowing to these pools of object storage across different regions and clouds. We don't move it, we index it right there, we're spinning up stateless compute, back to the Supercloud concept. But now that allows us to do all these other things, right? >> And it's no longer just cheap and deep object storage. Right? >> Yeah, we make it the same, like you have an analytic platform regardless of where you're at, you don't have to worry about that. Yeah, we deal with that, we deal with a stateless compute coming up -- >> And make it programmable. Be able to say, "I want this bucket to provide these answers." Right, that's really the hope, the vision. And the complexity to build the entire stack, and then connect them together, we said, the fabric is cloud storage, we just provide the intelligence on top. >> Let's bring it back to the customers, and one of the things we're exploring in Supercloud too is, you know, is Supercloud a solution looking for a problem? Is a multicloud really a problem? I mean, you hear, you know, a lot of the vendor marketing says, "Oh, it's a disaster, because it's all different across the clouds." And I talked to a lot of customers even as part of Supercloud too, they're like, "Well, I solved that problem by just going mono cloud." Well, but then you're not able to take advantage of a lot of the capabilities and the primitives that, you know, like Google's data, or you like Microsoft's simplicity, their RPA, whatever it is. So what are customers telling you, what are their near term problems that they're trying to solve today, and how are they thinking about the future? >> Listen, it's a real problem. I think it started, I think this is a a mega trend, just like cloud. Just, cloud data, and I always add, analytics, are the mega trends. If you're looking at those, if you're not considering using the Supercloud principles, in other words, leveraging what I have, abstracting it out, and getting the most out of that, and then build value on top, I think you're not going to be able to keep up, In fact, no way you're going to keep up with this data volume. It's a geometric challenge, and you're trying to do linear things. So clients aren't necessarily asking, hey, for Supercloud, but they're really saying, I need to have a better mechanism to simplify this and get value across it, and how do you abstract that out to do that? And that's where they're obviously, our conversations are more amazed what we're able to do, and what they're able to do with our platform, because if you think of what we've done, the S3, or GCS, or object storage, is they can't imagine the ingest, they can't imagine how easy, time to glass, one minute, no matter where it lands in the world, querying this in seconds for hundreds of terabytes squared. People are amazed, but that's kind of, so they're not asking for that, but they are amazed. And then when you start talking on it, if you're an enterprise person, you're building a big cloud data platform, or doing data or analytics, if you're not trying to leverage the public clouds, and somehow leverage all of them, and then build on top, then I think you're missing it. So they might not be asking for it, but they're doing it. >> And they're looking for a lens, you mentioned all these different services, how do I bring those together quickly? You know, our viewpoint, our service, is I have all these streams of data, create a lens where they want to go after it via search, go after via SQL, bring them together instantly, no e-tailing out, no define this table, put into this database. We said, let's have a service that creates a lens across all these streams, and then make those connections. I want to take my CRM with my Google AdWords, and maybe my Salesforce, how do I do analysis? Maybe I want to hunt first, maybe I want to join, maybe I want to add another stream to it. And so our viewpoint is, it's so natural to get into these lake platforms and then provide lenses to get that access. >> And they don't want it separate, they don't want something different here, and different there. They want it basically -- >> So this is our industry, right? If something new comes out, remember virtualization came out, "Oh my God, this is so great, it's going to solve all these problems." And all of a sudden it just got to be this big, more complex thing. Same thing with cloud, you know? It started out with S3, and then EC2, and now hundreds and hundreds of different services. So, it's a complex matter for a lot of people, and this creates problems for customers, especially when you got divisions that are using different clouds, and you're saying that the solution, or a solution for the part of the problem, is to really allow the data to stay in place on S3, use that standard, super simple, but then give it what, Ed, you've called superpower a couple of times, to make it fast, make it inexpensive, and allow you to do that across clouds. >> Yeah, yeah. >> I'll give you guys the last word on that. >> No, listen, I think, we think Supercloud allows you to do a lot more. And for us, data, everyone says more data, more problems, more budget issue, everyone knows more data is better, and we show you how to do it cost effectively at scale. And we couldn't have done it without the design principles of we're leveraging the Supercloud to get capabilities, and because we use super, just the object storage, we're able to get these capabilities of ingest, scale, cost effectiveness, and then we built on top of this. In the end, a database is a data platform that allows you to go after everything distributed, and to get one platform for analytics, no matter where it lands, that's where we think the Supercloud concepts are perfect, that's where our clients are seeing it, and we're kind of excited about it. >> Yeah a third generation database, Supercloud database, however we want to phrase it, and make it simple, but provide the value, and make it instant. >> Guys, thanks so much for coming into the studio today, I really thank you for your support of theCUBE, and theCUBE community, it allows us to provide events like this and free content. I really appreciate it. >> Oh, thank you. >> Thank you. >> All right, this is Dave Vellante for John Furrier in theCUBE community, thanks for being with us today. You're watching Supercloud 2, keep it right there for more thought provoking discussions around the future of cloud and data. (bright music)

hi everybody welcome back to re invent 2022. this is thecube's exclusive coverage we're here at the satellite set it's up on the fifth floor of the Venetian Conference Center and this is part of the global startup program the AWS startup showcase series that we've been running all through last year and and into this year with AWS and featuring some of its its Global Partners Ed wallson series the CEO of chaos search many times Cube Alum and Kevin Miller there's also a cube Alum vice president GM of S3 at AWS guys good to see you again yeah great to see you Dave hi Kevin this is we call this our Super Bowl so this must be like your I don't know uh World Cup it's a pretty big event yeah it's the World Cup for sure yeah so a lot of S3 talk you know I mean that's what got us all started in 2006 so absolutely what's new in S3 yeah it's been a great show we've had a number of really interesting launches over the last few weeks and a few at the show as well so you know we've been really focused on helping customers that are running Mass scale data Lakes including you know whether it's structured or unstructured data we actually announced just a few just an hour ago I think it was a new capability to give customers cross-account access points for sharing data securely with other parts of the organization and that's something that we'd heard from customers is as they are growing and have more data sets and they're looking to to get more out of their data they are increasingly looking to enable multiple teams across their businesses to access those data sets securely and that's what we provide with cross-count access points we also launched yesterday our multi-region access point failover capabilities and so again this is where customers have data sets and they're using multiple regions for certain critical workloads they're now able to to use that to fail to control the failover between different regions in AWS and then one other launch I would just highlight is some improvements we made to storage lens which is our really a very novel and you need capability to help customers really understand what storage they have where who's accessing it when it's being accessed and we added a bunch of new metrics storage lens has been pretty exciting for a lot of customers in fact we looked at the data and saw that customers who have adopted storage lens typically within six months they saved more than six times what they had invested in turning storage lens on and certainly in this environment right now we have a lot of customers who are it's pretty top of mind they're looking for ways to optimize their their costs in the cloud and take some of those savings and be able to reinvest them in new innovation so pretty exciting with the storage lens launch I think what's interesting about S3 is that you know pre-cloud Object Store was this kind of a niche right and then of course you guys announced you know S3 in 2006 as I said and okay great you know cheap and deep storage simple get put now the conversations about how to enable value from from data absolutely analytics and it's just a whole new world and Ed you've talked many times I love the term yeah we built chaos search on the on the shoulders of giants right and so the under underlying that is S3 but the value that you can build on top of that has been key and I don't think we've talked about his shoulders and Giants but we've talked about how we literally you know we have a big Vision right so hard to kind of solve the challenge to analytics at scale we really focus on the you know the you know Big Data coming environment get analytics so we talk about the on the shoulders Giants obviously Isaac Newton's you know metaphor of I learned from everything before and we layer on top so really when you talk about all the things come from S3 like I just smile because like we picked it up naturally we went all in an S3 and this is where I think you're going Dave but everyone is so let's just cut the chase like so any of the data platforms you're using S3 is what you're building but we did it a little bit differently so at first people using a cold storage like you said and then they ETL it up into a different platforms for analytics of different sorts now people are using it closer they're doing caching layers and cashing out and they're that's where but that's where the attributes of a scale or reliability are what we did is we actually make S3 a database so literally we have no persistence outside that three and that kind of comes in so it's working really well with clients because most of the thing is we pick up all these attributes of scale reliability and it shows up in the clients environments and so when you launch all these new scalable things we just see it like our clients constantly comment like one of our biggest customers fintech in uh Europe they go to Black Friday again black Friday's not one days and they lose scale from what is it 58 terabytes a day and they're going up to 187 terabytes a day and we don't Flinch they say how do you do that well we built our platform on S3 as long as you can stream it to S3 so they're saying I can't overrun S3 and it's a natural play so it's it's really nice that but we take out those attributes but same thing that's why we're able to you know help clients get you know really you know Equifax is a good example maybe they're able to consolidate 12 their divisions on one platform we couldn't have done that without the scale and the performance of what you can get S3 but also they saved 90 I'm able to do that but that's really because the only persistence is S3 and what you guys are delivering but and then we really for focus on shoulders Giants we're doing on top of that innovating on top of your platforms and bringing that out so things like you know we have a unique data representation that makes it easy to ingest this data because it's kind of coming at you four v's of big data we allow you to do that make it performant on s3h so now you're doing hot analytics on S3 as if it's just a native database in memory but there's no memory SSC caching and then multi-model once you get it there don't move it leverage it in place so you know elasticsearch access you know Cabana grafana access or SQL access with your tools so we're seeing that constantly but we always talk about on the shoulders of giants but even this week I get comments from our customers like how did you do that and most of it is because we built on top of what you guys provided so it's really working out pretty well and you know we talk a lot about digital transformation of course we had the pleasure sitting down with Adam solipski prior John Furrier flew to Seattle sits down his annual one-on-one with the AWS CEO which is kind of cool yeah it was it's good it's like study for the test you know and uh and so but but one of the interesting things he said was you know we're one of our challenges going forward is is how do we go Beyond digital transformation into business transformation like okay well that's that's interesting I was talking to a customer today AWS customer and obviously others because they're 100 year old company and they're basically their business was they call them like the Uber for for servicing appliances when your Appliance breaks you got to get a person to serve it a service if it's out of warranty you know these guys do that so they got to basically have a you know a network of technicians yeah and they gotta deal with the customers no phone right so they had a completely you know that was a business transformation right they're becoming you know everybody says they're coming a software company but they're building it of course yeah right on the cloud so wonder if you guys could each talk about what's what you're seeing in terms of changing not only in the sort of I.T and the digital transformation but also the business transformation yeah I know I I 100 agree that I think business transformation is probably that one of the top themes I'm hearing from customers of all sizes right now even in this environment I think customers are looking for what can I do to drive top line or you know improve bottom line or just improve my customer experience and really you know sort of have that effect where I'm helping customers get more done and you know it is it is very tricky because to do that successfully the customers that are doing that successfully I think are really getting into the lines of businesses and figuring out you know it's probably a different skill set possibly a different culture different norms and practices and process and so it's it's a lot more than just a like you said a lot more than just the technology involved but when it you know we sort of liquidate it down into the data that's where absolutely we see that as a critical function for lines of businesses to become more comfortable first off knowing what data sets they have what data they they could access but possibly aren't today and then starting to tap into those data sources and then as as that progresses figuring out how to share and collaborate with data sets across a company to you know to correlate across those data sets and and drive more insights and then as all that's being done of course it's important to measure the results and be able to really see is this what what effect is this having and proving that effect and certainly I've seen plenty of customers be able to show you know this is a percentage increase in top or bottom line and uh so that pattern is playing out a lot and actually a lot of how we think about where we're going with S3 is related to how do we make it easier for customers to to do everything that I just described to have to understand what data they have to make it accessible and you know it's great to have such a great ecosystem of partners that are then building on top of that and innovating to help customers connect really directly with the businesses that they're running and driving those insights well and customers are hours today one of the things I loved that Adam said he said where Amazon is strategically very very patient but tactically we're really impatient and the customers out there like how are you going to help me increase Revenue how are you going to help me cut costs you know we were talking about how off off camera how you know software can actually help do that yeah it's deflationary I love the quote right so software's deflationary as costs come up how do you go drive it also free up the team and you nail it it's like okay everyone wants to save money but they're not putting off these projects in fact the digital transformation or the business it's actually moving forward but they're getting a little bit bigger but everyone's looking for creative ways to look at their architecture and it becomes larger larger we talked about a couple of those examples but like even like uh things like observability they want to give this tool set this data to all the developers all their sres same data to all the security team and then to do that they need to find a way an architect should do that scale and save money simultaneously so we see constantly people who are pairing us up with some of these larger firms like uh or like keep your data dog keep your Splunk use us to reduce the cost that one and one is actually cheaper than what you have but then they use it either to save money we're saving 50 to 80 hard dollars but more importantly to free up your team from the toil and then they they turn around and make that budget neutral and then allowed to get the same tools to more people across the org because they're sometimes constrained of getting the access to everyone explain that a little bit more let's say I got a Splunk or data dog I'm sifting through you know logs how exactly do you help so it's pretty simple I'll use dad dog example so let's say using data dog preservability so it's just your developers your sres managing environments all these platforms are really good at being a monitoring alerting type of tool what they're not necessarily great at is keeping the data for longer periods like the log data the bigger data that's where we're strong what you see is like a data dog let's say you're using it for a minister for to keep 30 days of logs which is not enough like let's say you're running environment you're finding that performance issue you kind of want to look to last quarter in last month in or maybe last Black Friday so 30 days is not enough but will charge you two eighty two dollars and eighty cents a gigabyte don't focus on just 280 and then if you just turn the knob and keep seven days but keep two years of data on us which is on S3 it goes down to 22 cents plus our list price of 80 cents goes to a dollar two compared to 280. so here's the thing what they're able to do is just turn a knob get more data we do an integration so you can go right from data dog or grafana directly into our platform so the user doesn't see it but they save money A lot of times they don't just save the money now they use that to go fund and get data dog to a lot more people make sense so it's a creativity they're looking at it and they're looking at tools we see the same thing with a grafana if you look at the whole grafana play which is hey you can't put it in one place but put Prometheus for metrics or traces we fit well with logs but they're using that to bring down their costs because a lot of this data just really bogs down these applications the alerting monitoring are good at small data they're not good at the big data which is what we're really good at and then the one and one is actually less than you paid for the one so it and it works pretty well so things are really unpredictable right now in the economy you know during the pandemic we've sort of lockdown and then the stock market went crazy we're like okay it's going to end it's going to end and then it looked like it was going to end and then it you know but last year it reinvented just just in that sweet spot before Omicron so we we tucked it in which which was awesome right it was a great great event we really really missed one physical reinvent you know which was very rare so that's cool but I've called it the slingshot economy it feels like you know you're driving down the highway and you got to hit the brakes and then all of a sudden you're going okay we're through it Oh no you're gonna hit the brakes again yeah so it's very very hard to predict and I was listening to jassy this morning he was talking about yeah consumers they're still spending but what they're doing is they're they're shopping for more features they might be you know buying a TV that's less expensive you know more value for the money so okay so hopefully the consumer spending will get us out of this but you don't really know you know and I don't yeah you know we don't seem to have the algorithms we've never been through something like this before so what are you guys seeing in terms of customer Behavior given that uncertainty well one thing I would highlight that I think particularly going back to what we were just talking about as far as business and digital transformation I think some customers are still appreciating the fact that where you know yesterday you may have had to to buy some Capital put out some capital and commit to something for a large upfront expenditure is that you know today the value of being able to experiment and scale up and then most importantly scale down and dynamically based on is the experiment working out am I seeing real value from it and doing that on a time scale of a day or a week or a few months that is so important right now because again it gets to I am looking for a ways to innovate and to drive Top Line growth but I I can't commit to a multi-year sort of uh set of costs to to do that so and I think plenty of customers are finding that even a few months of experimentation gives them some really valuable insight as far as is this going to be successful or not and so I think that again just of course with S3 and storage from day one we've been elastic pay for what you use if you're not using the storage you don't get charged for it and I think that particularly right now having the applications and the rest of the ecosystem around the storage and the data be able to scale up and scale down is is just ever more important and when people see that like typically they're looking to do more with it so if they find you usually find these little Department projects but they see a way to actually move faster and save money I think it is a mix of those two they're looking to expand it which can be a nightmare for sales Cycles because they take longer but people are looking well why don't you leverage this and go across division so we do see people trying to leverage it because they're still I don't think digital transformation is slowing down but a lot more to be honest a lot more approvals at this point for everything it is you know Adam and another great quote in his in his keynote he said if you want to save money the Cloud's a place to do it absolutely and I read an article recently and I was looking through and I said this is the first time you know AWS has ever seen a downturn because the cloud was too early back then I'm like you weren't paying attention in 2008 because that was the first major inflection point for cloud adoption where CFO said okay stop the capex we're going to Opex and you saw the cloud take off and then 2010 started this you know amazing cycle that we really haven't seen anything like it where they were doubling down in Investments and they were real hardcore investment it wasn't like 1998 99 was all just going out the door for no clear reason yeah so that Foundation is now in place and I think it makes a lot of sense and it could be here for for a while where people are saying Hey I want to optimize and I'm going to do that on the cloud yeah no I mean I've obviously I certainly agree with Adam's quote I think really that's been in aws's DNA from from day one right is that ability to scale costs with with the actual consumption and paying for what you use and I think that you know certainly moments like now are ones that can really motivate change in an organization in a way that might not have been as palatable when it just it didn't feel like it was as necessary yeah all right we got to go give you a last word uh I think it's been a great event I love all your announcements I think this is wonderful uh it's been a great show I love uh in fact how many people are here at reinvent north of 50 000. yeah I mean I feel like it was it's as big if not bigger than 2019. people have said ah 2019 was a record when you count out all the professors I don't know it feels it feels as big if not bigger so there's great energy yeah it's quite amazing and uh and we're thrilled to be part of it guys thanks for coming on thecube again really appreciate it face to face all right thank you for watching this is Dave vellante for the cube your leader in Enterprise and emerging Tech coverage we'll be right back foreign

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

>>Good afternoon, friends. My name is Savannah Peterson here in the Cube Studios live from Detroit, Michigan, where we're at Cuban and Cloud Native Foundation, Cloud Native Con all week. Our last interview of the day served me a real treat and one that I wasn't expecting. It turns out that I am in the presence of two caddies. It's a literal episode of Caddy Shack up here on Cube. John Furrier. I don't think the audience knows that you were a caddy. Tell us about your caddy days. >>I used to caddy when I was a kid at the local country club every weekend. This is amazing. Double loops every weekend. Make some bang, two bags on each shoulder. Caddying for the members where you're going. Now I'm >>On show. Just, just really impressive >>Now. Now I'm caddying for the cube where I caddy all this great content out to the audience. >>He's carrying the story of emerging brands and established companies on their cloud journey. I love it. John, well played. I don't wanna waste any more of this really wonderful individual's time, but since we now have a new trend of talking about everyone's Twitter handle here on the cube, this may be my favorite one of the day, if not Q4 so far. Drew, not reply. AKA Drew ne Drew Nielsen, excuse me, there is here with us from Teleport. Drew, thanks so much for being here. >>Oh, thanks for having me. It's great to be here. >>And so you were a caddy on a whole different level. Can you tell us >>About that? Yeah, so I was in university and I got tired after two years and didn't have a car in LA and met a pro golfer at a golf course and took two years off and traveled around caddying for him and tried to get 'em through Q School. >>This is, this is fantastic. So if you're in school and your parents are telling you to continue going to school, know that you can drop out and be a caddy and still be a very successful television personality. Like both of the gentlemen at some point. >>Well, I never said my parents like >>That decision, but we'll keep our day jobs. Yeah, exactly. And one of them is Cloud Native Security. The hottest topic here at the show. Yep. I want to get into it. You guys are doing some really cool things. Are we? We hear Zero Trust, you know, ransomware and we even, I even talked with the CEO of Dockets morning about container security issues. Sure. There's a lot going on. So you guys are in the middle of teleport. You guys have a unique solution. Tell us what you guys got going on. What do you guys do? What's the solution and what's the problem you solve? >>So Teleport is the first and only identity native infrastructure access solution in the market. So breaking that down, what that really means is identity native being the combination of secret list, getting rid of passwords, Pam Vaults, Key Vaults, Yeah. Passwords written down. Basically the number one source of breach. And 50 to 80% of breaches, depending on whose numbers you want to believe are how organizations get hacked. >>But it's not password 1 23 isn't protecting >>Cisco >>Right >>Now. Well, if you think about when you're securing infrastructure and the second component being zero trust, which assumes the network is completely insecure, right? But everything is validated. Resource to resource security is validated, You know, it assumes work from anywhere. It assumes the security comes back to that resource. And we take the combination of those two into identity, native access where we cryptographically ev, validate identity, but more importantly, we make an absolutely frictionless experience. So engineers can access infrastructure from anywhere at any time. >>I'm just flashing on my roommates, checking their little code, changing Bob login, you know, dongle essentially, and how frustrating that always was. I mean, talk about interrupting workflow was something that's obviously necessary, but >>Well, I mean, talk about frustration if I'm an engineer. Yeah, absolutely. You know, back in the day when you had these three tier monolithic applications, it was kind of simple. But now as you've got modern application development environments Yeah, multi-cloud, hybrid cloud, whatever marketing term around how you talk about this, expanding sort of disparate infrastructure. Engineers are sitting there going from system to system to machine to database to application. I mean, not even a conversation on Kubernetes yet. Yeah. And it's just, you know, every time you pull an engineer or a developer to go to a vault to pull something out, you're pulling them out for 10 minutes. Now, applications today have hundreds of systems, hundreds of microservices. I mean 30 of these a day and nine minutes, 270 minutes times 60. And they also >>Do the math. Well, there's not only that, there's also the breach from manual error. I forgot to change the password. What is that password? I left it open, I left it on >>Cognitive load. >>I mean, it's the manual piece. But even think about it, TR security has to be transparent and engineers are really smart people. And I've talked to a number of organizations who are like, yeah, we've tried to implement security solutions and they fail. Why? They're too disruptive. They're not transparent. And engineers will work their way around them. They'll write it down, they'll do a workaround, they'll backdoor it something. >>All right. So talk about how it works. But I, I mean, I'm getting the big picture here. I love this. Breaking down the silos, making engineers lives easier, more productive. Clearly the theme, everyone they want, they be gonna need. Whoever does that will win it all. How's it work? I mean, you deploying something, is it code, is it in line? It's, >>It's two binaries that you download and really it starts with the core being the identity native access proxy. Okay. So that proxy, I mean, if you look at like the zero trust principles, it all starts with a proxy. Everything connects into that proxy where all the access is gated, it's validated. And you know, from there we have an authorization engine. So we will be the single source of truth for all access across your entire infrastructure. So we bring machines, engineers, databases, applications, Kubernetes, Linux, Windows, we don't care. And we basically take that into a single architecture and single access platform that essentially secures your entire infrastructure. But more importantly, you can do audit. So for all of the organizations that are dealing with FedRAMP, pci, hipaa, we have a complete audit trail down to a YouTube style playback. >>Oh, interesting. We're we're California and ccpa. >>Oh, gdpr. >>Yeah, exactly. It, it, it's, it's a whole shebang. So I, I love, and John, maybe you've heard this term a lot more than I have, but identity native is relatively new to me as as a term. And I suspect you have a very distinct way of defining identity. How do you guys define identity internally? >>So identity is something that is cryptographically validated. It is something you have. So it's not enough. If you look at, you know, credentials today, everyone's like, Oh, I log into my computer, but that's my identity. No, it's not. Right. Those are attributes. Those are something that is secret for a period of time until you write it down. But I can't change my fingerprint. Right. And now I >>Was just >>Thinking of, well no, perfect case in point with touch ID on your meth there. Yeah. It's like when we deliver that cryptographically validated identity, we use these secure modules in like modern laptops or servers. Yeah. To store that identity so that even if you're sitting in front of your computer, you can't get to it. But more importantly, if somebody were to take that and try to be you and try to log in with your fingerprint, it's >>Not, I'm not gonna lie, I love the apple finger thing, you know, it's like, you know, space recognition, like it's really awesome. >>It save me a lot of time. I mean, even when you go through customs and they do the face scan now it actually knows who you are, which is pretty wild in the last time you wanna provide ones. But it just shifted over like maybe three months ago. Well, >>As long as no one chops your finger off like they do in the James Bond movies. >>I mean, we try and keep it a light and fluffy here on the queue, but you know, do a finger teams, we can talk about that >>Too. >>Gabby, I was thinking more minority report, >>But you >>Knows that's exactly what I, what I think of >>Hit that one outta bounds. So I gotta ask, because you said you're targeting engineers, not IT departments. What's, is that, because I in your mind it is now the engineers or what's the, is always the solution more >>Targeted? Well, if you really look at who's dealing with infrastructure on a day-to-day basis, those are DevOps individuals. Those are infrastructure teams, Those are site reliability engineering. And when it, they're the ones who are not only managing the infrastructure, but they're also dealing with the code on it and everything else. And for us, that is who is our primary customer and that's who's doing >>It. What's the biggest problem that you're solving in this use case? Because you guys are nailing it. What's the problem that your identity native solution solves? >>You know, right out of the backs we remove the number one source of breach. And that is taking passwords, secrets and, and keys off the board. That deals with most of the problem right there. But there are really two problems that organizations face. One is scaling. So as you scale, you get more secrets, you get more keys, you get all these things that is all increasing your attack vector in real time. Oh >>Yeah. Across teams locations. I can't even >>Take your pick. Yeah, it's across clouds, right? Any of it >>On-prem doesn't. >>Yeah. Any of it. We, and we allow you to scale, but do it securely and the security is transparent and your engineers will absolutely love it. What's the most important thing about this product Engineers. Absolutely. >>What are they saying? What are some of those examples? Anecdotally, pull boats out from engineering. >>You're too, we should have invent, we should have invented this ourselves. Or you know, we have run into a lot of customers who have tried to home brew this and they're like, you know, we spend an in nor not of hours on it >>And IT or they got legacy from like Microsoft or other solutions. >>Sure, yeah. Any, but a lot of 'em is just like, I wish I had done it myself. Or you know, this is what security should be. >>It makes so much sense and it gives that the team such a peace of mind. I mean, you never know when a breach is gonna come, especially >>It's peace of mind. But I think for engineers, a lot of times it deals with the security problem. Yeah. Takes it off the table so they can do their jobs. Yeah. With zero friction. Yeah. And you know, it's all about speed. It's all about velocity. You know, go fast, go fast, go fast. And that's what we enable >>Some of the benefits to them is they get to save time, focus more on, on task that they need to work on. >>Exactly. >>And get the >>Job done. And on top of it, they answer the audit and compliance mail every time it comes. >>Yeah. Why are people huge? Honestly, why are people doing this? Because, I mean, identity is just such an hard nut to crack. Everyone's got their silos, Vendors having clouds have 'em. Identity is the most fragmented thing on >>The planet. And it has been fragmented ever since my first RSA conference. >>I know. So will we ever get this do over? Is there a driver? Is there a market force? Is this the time? >>I think the move to modern applications and to multi-cloud is driving this because as those application stacks get more verticalized, you just, you cannot deal with the productivity >>Here. And of course the next big thing is super cloud and that's coming fast. Savannah, you know, You know that's Rocket. >>John is gonna be the thought leader and keyword leader of the word super cloud. >>Super Cloud is enabling super services as the cloud cast. Brian Gracely pointed out on his Sunday podcast of which if that happens, Super Cloud will enable super apps in a new architectural >>List. Please don't, and it'll be super, just don't. >>Okay. Right. So what are you guys up to next? What's the big hot spot for the company? What are you guys doing? What are you guys, What's the idea guys hiring? You put the plug in. >>You know, right now we are focused on delivering the best identity, native access platform that we can. And we will continue to support our customers that want to use Kubernetes, that want to use any different type of infrastructure. Whether that's Linux, Windows applications or databases. Wherever they are. >>Are, are your customers all of a similar DNA or are you >>No, they're all over the map. They range everything from tech companies to financial services to, you know, fractional property. >>You seem like someone everyone would need. >>Absolutely. >>And I'm not just saying that to be a really clean endorsement from the Cube, but >>If you were doing DevOps Yeah. And any type of forward-leaning shift, left engineering, you need us because we are basically making security as code a reality across your entire infrastructure. >>Love this. What about the team dna? Are you in a scale growth stage right now? What's going on? Absolutely. Sounds I was gonna say, but I feel like you would have >>To be. Yeah, we're doing, we're, we have a very positive outlook and you know, even though the economic time is what it is, we're doing very well meeting. >>How's the location? Where's the location of the headquarters now? With remote work is pretty much virtual. >>Probably. We're based in downtown Oakland, California. >>Woohoo. Bay area representing on this stage right now. >>Nice. Yeah, we have a beautiful office right in downtown Oakland and yeah, it's been great. Awesome. >>Love that. And are you hiring right now? I bet people might be. I feel like some of our cube watchers are here waiting to figure out their next big play. So love to hear that. Absolutely love to hear that. Besides Drew, not reply, if people want to join your team or say hello to you and tell you how brilliant you looked up here, or ask about your caddy days and maybe venture a guest to who that golfer may have been that you were CAD Inc. For, what are the best ways for them to get in touch with you? >>You can find me on LinkedIn. >>Great. Fantastic. John, anything else >>From you? Yeah, I mean, I just think security is paramount. This is just another example of where the innovation has to kind of break through without good identity, everything could cripple. Then you start getting into the silos and you can start getting into, you know, tracking it. You got error user errors, you got, you know, one of the biggest security risks. People just leave systems open, they don't even know it's there. So like, I mean this is just, just identity is the critical linchpin to, to solve for in security to me. And that's totally >>Agree. We even have a lot of customers who use us just to access basic cloud consoles. Yeah. >>So I was actually just gonna drive there a little bit because I think that, I'm curious, it feels like a solution for obviously complex systems and stacks, but given the utility and what sounds like an extreme ease of use, I would imagine people use this for day-to-day stuff within their, >>We have customers who use it to access their AWS consoles. We have customers who use it to access Grafana dashboards. You know, for, since we're sitting here at coupon accessing a Lens Rancher, all of the amazing DevOps tools that are out there. >>Well, I mean true. I mean, you think about all the reasons why people don't adopt this new federated approach or is because the IT guys did it and the world we're moving into, the developers are in charge. And so we're seeing the trend where developers are taking the DevOps and the data and the security teams are now starting to reset the guardrails. What's your >>Reaction to that? Well, you know, I would say that >>Over the top, >>Well I would say that you know, your DevOps teams and your infrastructure teams and your engineers, they are the new king makers. Yeah. Straight up. Full stop. >>You heard it first folks. >>And that's >>A headline right >>There. That is a headline. I mean, they are the new king makers and, but they are being forced to do it as securely as possible. And our job is really to make that as easy and as frictionless as possible. >>Awesome. >>And it sounds like you're absolutely nailing it. Drew, thank you so much for being on the show. Thanks for having today. This has been an absolute pleasure, John, as usual a joy. And thank all of you for tuning in to the Cube Live here at CU Con from Detroit, Michigan. We look forward to catching you for day two tomorrow.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> This past week we saw two of the Big 3 cloud providers present the latest update on their respective cloud visions, their business progress, their announcements and innovations. The content at these events had many overlapping themes, including modern cloud infrastructure at global scale, applying advanced machine intelligence, AKA AI, end-to-end data platforms, collaboration software. They talked a lot about the future of work automation. And they gave us a little taste, each company of the Metaverse Web 3.0 and much more. Despite these striking similarities, the differences between these two cloud platforms and that of AWS remains significant. With Microsoft leveraging its massive application software footprint to dominate virtually all markets and Google doing everything in its power to keep up with the frenetic pace of today's cloud innovation, which was set into motion a decade and a half ago by AWS. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we unpack the immense amount of content presented by the CEOs of Microsoft and Google Cloud at Microsoft Ignite and Google Cloud Next. We'll also quantify with ETR survey data the relative position of these two cloud giants in four key sectors: cloud IaaS, BI analytics, data platforms and collaboration software. Now one thing was clear this past week, hybrid events are the thing. Google Cloud Next took place live over a 24-hour period in six cities around the world, with the main gathering in New York City. Microsoft Ignite, which normally is attended by 30,000 people, had a smaller event in Seattle, in person with a virtual audience around the world. AWS re:Invent, of course, is much different. Yes, there's a virtual component at re:Invent, but it's all about a big live audience gathering the week after Thanksgiving, in the first week of December in Las Vegas. Regardless, Satya Nadella keynote address was prerecorded. It was highly produced and substantive. It was visionary, energetic with a strong message that Azure was a platform to allow customers to build their digital businesses. Doing more with less, which was a key theme of his. Nadella covered a lot of ground, starting with infrastructure from the compute, highlighting a collaboration with Arm-based, Ampere processors. New block storage, 60 regions, 175,000 miles of fiber cables around the world. He presented a meaningful multi-cloud message with Azure Arc to support on-prem and edge workloads, as well as of course the public cloud. And talked about confidential computing at the infrastructure level, a theme we hear from all cloud vendors. He then went deeper into the end-to-end data platform that Microsoft is building from the core data stores to analytics, to governance and the myriad tooling Microsoft offers. AI was next with a big focus on automation, AI, training models. He showed demos of machines coding and fixing code and machines automatically creating designs for creative workers and how Power Automate, Microsoft's RPA tooling, would combine with Microsoft Syntex to understand documents and provide standard ways for organizations to communicate with those documents. There was of course a big focus on Azure as developer cloud platform with GitHub Copilot as a linchpin using AI to assist coders in low-code and no-code innovations that are coming down the pipe. And another giant theme was a workforce transformation and how Microsoft is using its heritage and collaboration and productivity software to move beyond what Nadella called productivity paranoia, i.e., are remote workers doing their jobs? In a world where collaboration is built into intelligent workflows, and he even showed a glimpse of the future with AI-powered avatars and partnerships with Meta and Cisco with Teams of all firms. And finally, security with a bevy of tools from identity, endpoint, governance, et cetera, stressing a suite of tools from a single provider, i.e., Microsoft. So a couple points here. One, Microsoft is following in the footsteps of AWS with silicon advancements and didn't really emphasize that trend much except for the Ampere announcement. But it's building out cloud infrastructure at a massive scale, there is no debate about that. Its plan on data is to try and provide a somewhat more abstracted and simplified solutions, which differs a little bit from AWS's approach of the right database tool, for example, for the right job. Microsoft's automation play appears to provide simple individual productivity tools, kind of a ground up approach and make it really easy for users to drive these bottoms up initiatives. We heard from UiPath that forward five last month, a little bit of a different approach of horizontal automation, end-to-end across platforms. So quite a different play there. Microsoft's angle on workforce transformation is visionary and will continue to solidify in our view its dominant position with Teams and Microsoft 365, and it will drive cloud infrastructure consumption by default. On security as well as a cloud player, it has to have world-class security, and Azure does. There's not a lot of debate about that, but the knock on Microsoft is Patch Tuesday becomes Hack Wednesday because Microsoft releases so many patches, it's got so much Swiss cheese in its legacy estate and patching frequently, it becomes a roadmap and a trigger for hackers. Hey, patch Tuesday, these are all the exploits that you can go after so you can act before the patches are implemented. And so it's really become a problem for users. As well Microsoft is competing with many of the best-of-breed platforms like CrowdStrike and Okta, which have market momentum and appear to be more attractive horizontal plays for customers outside of just the Microsoft cloud. But again, it's Microsoft. They make it easy and very inexpensive to adopt. Now, despite the outstanding presentation by Satya Nadella, there are a couple of statements that should raise eyebrows. Here are two of them. First, as he said, Azure is the only cloud that supports all organizations and all workloads from enterprises to startups, to highly regulated industries. I had a conversation with Sarbjeet Johal about this, to make sure I wasn't just missing something and we were both surprised, somewhat, by this claim. I mean most certainly AWS supports more certifications for example, and we would think it has a reasonable case to dispute that claim. And the other statement, Nadella made, Azure is the only cloud provider enabling highly regulated industries to bring their most sensitive applications to the cloud. Now, reasonable people can debate whether AWS is there yet, but very clearly Oracle and IBM would have something to say about that statement. Now maybe it's not just, would say, "Oh, they're not real clouds, you know, they're just going to hosting in the cloud if you will." But still, when it comes to mission-critical applications, you would think Oracle is really the the leader there. Oh, and Satya also mentioned the claim that the Edge browser, the Microsoft Edge browser, no questions asked, he said, is the best browser for business. And we could see some people having some questions about that. Like isn't Edge based on Chrome? Anyway, so we just had to question these statements and challenge Microsoft to defend them because to us it's a little bit of BS and makes one wonder what else in such as awesome keynote and it was awesome, it was hyperbole. Okay, moving on to Google Cloud Next. The keynote started with Sundar Pichai doing a virtual session, he was remote, stressing the importance of Google Cloud. He mentioned that Google Cloud from its Q2 earnings was on a $25-billion annual run rate. What he didn't mention is that it's also on a 3.6 billion annual operating loss run rate based on its first half performance. Just saying. And we'll dig into that issue a little bit more later in this episode. He also stressed that the investments that Google has made to support its core business and search, like its global network of 22 subsea cables to support things like, YouTube video, great performance obviously that we all rely on, those innovations there. Innovations in BigQuery to support its search business and its threat analysis that it's always had and its AI, it's always been an AI-first company, he's stressed, that they're all leveraged by the Google Cloud Platform, GCP. This is all true by the way. Google has absolutely awesome tech and the talk, as well as his talk, Pichai, but also Kurian's was forward thinking and laid out a vision of the future. But it didn't address in our view, and I talked to Sarbjeet Johal about this as well, today's challenges to the degree that Microsoft did and we expect AWS will at re:Invent this year, it was more out there, more forward thinking, what's possible in the future, somewhat less about today's problem, so I think it's resonates less with today's enterprise players. Thomas Kurian then took over from Sundar Pichai and did a really good job of highlighting customers, and I think he has to, right? He has to say, "Look, we are in this game. We have customers, 9 out of the top 10 media firms use Google Cloud. 8 out of the top 10 manufacturers. 9 out of the top 10 retailers. Same for telecom, same for healthcare. 8 out of the top 10 retail banks." He and Sundar specifically referenced a number of companies, customers, including Avery Dennison, Groupe Renault, H&M, John Hopkins, Prudential, Minna Bank out of Japan, ANZ bank and many, many others during the session. So you know, they had some proof points and you got to give 'em props for that. Now like Microsoft, Google talked about infrastructure, they referenced training processors and regions and compute optionality and storage and how new workloads were emerging, particularly data-driven workloads in AI that required new infrastructure. He explicitly highlighted partnerships within Nvidia and Intel. I didn't see anything on Arm, which somewhat surprised me 'cause I believe Google's working on that or at least has come following in AWS's suit if you will, but maybe that's why they're not mentioning it or maybe I got to do more research there, but let's park that for a minute. But again, as we've extensively discussed in Breaking Analysis in our view when it comes to compute, AWS via its Annapurna acquisition is well ahead of the pack in this area. Arm is making its way into the enterprise, but all three companies are heavily investing in infrastructure, which is great news for customers and the ecosystem. We'll come back to that. Data and AI go hand in hand, and there was no shortage of data talk. Google didn't mention Snowflake or Databricks specifically, but it did mention, by the way, it mentioned Mongo a couple of times, but it did mention Google's, quote, Open Data cloud. Now maybe Google has used that term before, but Snowflake has been marketing the data cloud concept for a couple of years now. So that struck as a shot across the bow to one of its partners and obviously competitor, Snowflake. At BigQuery is a main centerpiece of Google's data strategy. Kurian talked about how they can take any data from any source in any format from any cloud provider with BigQuery Omni and aggregate and understand it. And with the support of Apache Iceberg and Delta and Hudi coming in the future and its open Data Cloud Alliance, they talked a lot about that. So without specifically mentioning Snowflake or Databricks, Kurian co-opted a lot of messaging from these two players, such as life and tech. Kurian also talked about Google Workspace and how it's now at 8 million users up from 6 million just two years ago. There's a lot of discussion on developer optionality and several details on tools supported and the open mantra of Google. And finally on security, Google brought out Kevin Mandian, he's a CUBE alum, extremely impressive individual who's CEO of Mandiant, a leading security service provider and consultancy that Google recently acquired for around 5.3 billion. They talked about moving from a shared responsibility model to a shared fate model, which is again, it's kind of a shot across AWS's bow, kind of shared responsibility model. It's unclear that Google will pay the same penalty if a customer doesn't live up to its portion of the shared responsibility, but we can probably assume that the customer is still going to bear the brunt of the pain, nonetheless. Mandiant is really interesting because it's a services play and Google has stated that it is not a services company, it's going to give partners in the channel plenty of room to play. So we'll see what it does with Mandiant. But Mandiant is a very strong enterprise capability and in the single most important area security. So interesting acquisition by Google. Now as well, unlike Microsoft, Google is not competing with security leaders like Okta and CrowdStrike. Rather, it's partnering aggressively with those firms and prominently putting them forth. All right. Let's get into the ETR survey data and see how Microsoft and Google are positioned in four key markets that we've mentioned before, IaaS, BI analytics, database data platforms and collaboration software. First, let's look at the IaaS cloud. ETR is just about to release its October survey, so I cannot share the that data yet. I can only show July data, but we're going to give you some directional hints throughout this conversation. This chart shows net score or spending momentum on the vertical axis and overlap or presence in the data, i.e., how pervasive the platform is. That's on the horizontal axis. And we've inserted the Wikibon estimates of IaaS revenue for the companies, the Big 3. Actually the Big 4, we included Alibaba. So a couple of points in this somewhat busy data chart. First, Microsoft and AWS as always are dominant on both axes. The red dotted line there at 40% on the vertical axis. That represents a highly elevated spending velocity and all of the Big 3 are above the line. Now at the same time, GCP is well behind the two leaders on the horizontal axis and you can see that in the table insert as well in our revenue estimates. Now why is Azure bigger in the ETR survey when AWS is larger according to the Wikibon revenue estimates? And the answer is because Microsoft with products like 365 and Teams will often be considered by respondents in the survey as cloud by customers, so they fit into that ETR category. But in the insert data we're stripping out applications and SaaS from Microsoft and Google and we're only isolating on IaaS. The other point is when you take a look at the early October returns, you see downward pressure as signified by those dotted arrows on every name. The only exception was Dell, or Dell and IBM, which showing slightly improved momentum. So the survey data generally confirms what we know that AWS and Azure have a massive lead and strong momentum in the marketplace. But the real story is below the line. Unlike Google Cloud, which is on pace to lose well over 3 billion on an operating basis this year, AWS's operating profit is around $20 billion annually. Microsoft's Intelligent Cloud generated more than $30 billion in operating income last fiscal year. Let that sink in for a moment. Now again, that's not to say Google doesn't have traction, it does and Kurian gave some nice proof points and customer examples in his keynote presentation, but the data underscores the lead that Microsoft and AWS have on Google in cloud. And here's a breakdown of ETR's proprietary net score methodology, that vertical axis that we showed you in the previous chart. It asks customers, are you adopting the platform new? That's that lime green. Are you spending 6% or more? That's the forest green. Is you're spending flat? That's the gray. Is you're spending down 6% or worse? That's the pinkest color. Or are you replacing the platform, defecting? That's the bright red. You subtract the reds from the greens and you get a net score. Now one caveat here, which actually is really favorable from Microsoft, the Microsoft data that we're showing here is across the entire Microsoft portfolio. The other point is, this is July data, we'll have an update for you once ETR releases its October results. But we're talking about meaningful samples here, the ends. 620 for AWS over a thousand from Microsoft in more than 450 respondents in the survey for Google. So the real tell is replacements, that bright red. There is virtually no churn for AWS and Microsoft, but Google's churn is 5x, those two in the survey. Now 5% churn is not high, but you'd like to see three things for Google given it's smaller size. One is less churn, two is much, much higher adoption rates in the lime green. Three is a higher percentage of those spending more, the forest green. And four is a lower percentage of those spending less. And none of these conditions really applies here for Google. GCP is still not growing fast enough in our opinion, and doesn't have nearly the traction of the two leaders and that shows up in the survey data. All right, let's look at the next sector, BI analytics. Here we have that same XY dimension. Again, Microsoft dominating the picture. AWS very strong also in both axes. Tableau, very popular and respectable of course acquired by Salesforce on the vertical axis, still looking pretty good there. And again on the horizontal axis, big presence there for Tableau. And Google with Looker and its other platforms is also respectable, but it again, has some work to do. Now notice Streamlit, that's a recent Snowflake acquisition. It's strong in the vertical axis and because of Snowflake's go-to-market (indistinct), it's likely going to move to the right overtime. Grafana is also prominent in the Y axis, but a glimpse at the most recent survey data shows them slightly declining while Looker actually improves a bit. As does Cloudera, which we'll move up slightly. Again, Microsoft just blows you away, doesn't it? All right, now let's get into database and data platform. Same X Y dimensions, but now database and data warehouse. Snowflake as usual takes the top spot on the vertical axis and it is actually keeps moving to the right as well with again, Microsoft and AWS is dominant in the market, as is Oracle on the X axis, albeit it's got less spending velocity, but of course it's the database king. Google is well behind on the X axis but solidly above the 40% line on the vertical axis. Note that virtually all platforms will see pressure in the next survey due to the macro environment. Microsoft might even dip below the 40% line for the first time in a while. Lastly, let's look at the collaboration and productivity software market. This is such an important area for both Microsoft and Google. And just look at Microsoft with 365 and Teams up into the right. I mean just so impressive in ubiquitous. And we've highlighted Google. It's in the pack. It certainly is a nice base with 174 N, which I can tell you that N will rise in the next survey, which is an indication that more people are adopting. But given the investment and the tech behind it and all the AI and Google's resources, you'd really like to see Google in this space above the 40% line, given the importance of this market, of this collaboration area to Google's success and the degree to which they emphasize it in their pitch. And look, this brings up something that we've talked about before on Breaking Analysis. Google doesn't have a tech problem. This is a go-to-market and marketing challenge that Google faces and it's up against two go-to-market champs and Microsoft and AWS. And Google doesn't have the enterprise sales culture. It's trying, it's making progress, but it's like that racehorse that has all the potential in the world, but it's just missing some kind of key ingredient to put it over at the top. It's always coming in third, (chuckles) but we're watching and Google's obviously, making some investments as we shared with earlier. All right. Some final thoughts on what we learned this week and in this research: customers and partners should be thrilled that both Microsoft and Google along with AWS are spending so much money on innovation and building out global platforms. This is a gift to the industry and we should be thankful frankly because it's good for business, it's good for competitiveness and future innovation as a platform that can be built upon. Now we didn't talk much about multi-cloud, we haven't even mentioned supercloud, but both Microsoft and Google have a story that resonates with customers in cross cloud capabilities, unlike AWS at this time. But we never say never when it comes to AWS. They sometimes and oftentimes surprise you. One of the other things that Sarbjeet Johal and John Furrier and I have discussed is that each of the Big 3 is positioning to their respective strengths. AWS is the best IaaS. Microsoft is building out the kind of, quote, we-make-it-easy-for-you cloud, and Google is trying to be the open data cloud with its open-source chops and excellent tech. And that puts added pressure on Snowflake, doesn't it? You know, Thomas Kurian made some comments according to CRN, something to the effect that, we are the only company that can do the data cloud thing across clouds, which again, if I'm being honest is not really accurate. Now I haven't clarified these statements with Google and often things get misquoted, but there's little question that, as AWS has done in the past with Redshift, Google is taking a page out of Snowflake, Databricks as well. A big difference in the Big 3 is that AWS doesn't have this big emphasis on the up-the-stack collaboration software that both Microsoft and Google have, and that for Microsoft and Google will drive captive IaaS consumption. AWS obviously does some of that in database, a lot of that in database, but ISVs that compete with Microsoft and Google should have a greater affinity, one would think, to AWS for competitive reasons. and the same thing could be said in security, we would think because, as I mentioned before, Microsoft competes very directly with CrowdStrike and Okta and others. One of the big thing that Sarbjeet mentioned that I want to call out here, I'd love to have your opinion. AWS specifically, but also Microsoft with Azure have successfully created what Sarbjeet calls brand distance. AWS from the Amazon Retail, and even though AWS all the time talks about Amazon X and Amazon Y is in their product portfolio, but you don't really consider it part of the retail organization 'cause it's not. Azure, same thing, has created its own identity. And it seems that Google still struggles to do that. It's still very highly linked to the sort of core of Google. Now, maybe that's by design, but for enterprise customers, there's still some potential confusion with Google, what's its intentions? How long will they continue to lose money and invest? Are they going to pull the plug like they do on so many other tools? So you know, maybe some rethinking of the marketing there and the positioning. Now we didn't talk much about ecosystem, but it's vital for any cloud player, and Google again has some work to do relative to the leaders. Which brings us to supercloud. The ecosystem and end customers are now in a position this decade to digitally transform. And we're talking here about building out their own clouds, not by putting in and building data centers and installing racks of servers and storage devices, no. Rather to build value on top of the hyperscaler gift that has been presented. And that is a mega trend that we're watching closely in theCUBE community. While there's debate about the supercloud name and so forth, there little question in our minds that the next decade of cloud will not be like the last. All right, we're going to leave it there today. Many thanks to Sarbjeet Johal, and my business partner, John Furrier, for their input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast and Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does some wonderful editing. And check out SiliconANGLE, a lot of coverage on Google Cloud Next and Microsoft Ignite. Remember, all these episodes are available as podcast wherever you listen. Just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can always get in touch with me via email, david.vellante@siliconangle.com or you can DM me at dvellante or comment on my LinkedIn posts. And please do check out etr.ai, the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle music)

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

>> TheCUBE presents HPE Discover 2022 brought to you by HPE. >> Welcome back to theCUBE's coverage of HPE Discover 2022. We're here at the Venetian convention center in Las Vegas Dave Vellante for John Furrier. Cam Amirs here is the director of technical alliances at Cribl'. Cam, good to see you. >> Good to see you too. >> Cribl'. Cool name. Tell us about it. >> So let's see. Cribl' has been around now for about five years selling products for the last two years. Fantastic company, lots of growth, started there 2020 and we're roughly 400 employees now. >> And what do you do? Tell us more. >> Yeah, sure. So I run the technical alliances team and what we do is we basically look to build integrations into platforms such as HPE GreenLake and Ezmeral. And we also work with a lot of other companies to help get data from various sources into their destinations or, you know other enrichments of data in that data pipeline. >> You know, you guys have been on theCUBE. Clint's been on many times, Ed Bailey was on our startup showcase. You guys are successful in this overfunded observability space. So, so you guys have a unique approach. Tell us about why you guys are successful in the product and some of the things you've been doing there. >> Yeah, absolutely. So our product is very complimentary to a lot of the technologies that already exist. And I used to joke around that everyone has these like pretty dashboards and reports but they completely glaze over the fact that it's not easy to get the data from those sources to their destinations. So for us, it's this capability with Cribl' Stream to get that data easily and repeatably into these destinations. >> Yeah. You know, Cam, you and I are both at the Snowflake Summit to John's point. They were like a dozen observability companies there. >> Oh yeah. >> And really beginning to be a crowded space. So explain what value you bring to that ecosystem. >> Yeah, sure. So the ecosystem that we see there is there are a lot of people that are kind of sticking to like effectively getting data and showing you dashboards reports about monitoring and things of that sort. For us, the value is how can we help customers kind of accelerate their adoption of these platforms, how to go from like your legacy SIM or your legacy monitoring solution to like the next-gen observability platform or next-gen security platform >> and what you do really well is the integration and bringing those other toolings to, to do that? >> Correct, correct. And we make it repeatable. >> How'd you end up here? >> HP? So we actually had a customer that actually deployed our software on the HPS world platform. And it was kind of a light bulb moment that, okay this is actually a different approach than going to your traditional, you know, AWS, Google, et cetera. So we decided to kind of hunt this down and figure out how we could be a bigger player in this space. >> You saw the data fabric announcement? I'm not crazy about the term, data fabric is an old NetApp term, and then Gartner kind of twisted it. I like data mesh, but anyway, it doesn't matter. We kind of know what it is, but but when you see an announcement like that how do you look at it? You know, what does it mean to to Cribl' and your customers? >> Yeah. So what we've seen is that, so we work with the data fabric team and we're able to kind of route our data to their, as a data lake, so we can actually route the data from, again all these very sources into this data lake and then have it available for whatever customers want to do with it. So one of the big things that I know Clint talks about is we give customers this, we sell choice. So we give them the ability to choose where they want to send their data, whether that's, you know HP's data lake and data fabric or some other object store or some other destination. They have that choice to do so. >> So you're saying that you can stream with any destination the customer wants? What are some examples? What are the popular destinations? >> Yeah so a lot of the popular destinations are your typical object stores. So any of your cloud object stores, whether it be AWS three, Google cloud storage or Azure blob storage. >> Okay. And so, and you can pull data from any source? >> Laughter: I'd be very careful, but absolutely. What we've seen is that a lot of people like to kind of look at traditional data sources like Syslog and they want to get it to us, a next-gen SIM, but to do so it needs to be converted to like a web hook or some sort of API call. And so, or vice versa, they have this brand new Zscaler for example, and they want to get that data into their SIM but there's no way to do it 'cause a SIM only accepts it as a Syslog event. So what we can do is we actually transform the data and make it so that it lands into that SIM in the format that it needs to be and easily make that a repeatable process >> So, okay. So wait, so not as a Syslog event but in whatever format the destination requires? >> Correct, correct. >> Okay. What are the limits on that? I mean, is this- >> Yeah. So what we've seen is that customers will be able to take, for example they'll take this Syslog event, it's unstructured data but they need to put it into say common information model for Splunk or Elastic common schema for Elastic search or just JSON format for Elastic. And so what we can do is we can actually convert those events so that they land in that transformed state, but we can also route a copy of that event in unharmed fashion, to like an S3 bucket for object store for that long term compliance user >> You can route it to any, basically any object store. Is that right? Is that always the sort of target? >> Correct, correct. >> So on the message here at HPE, first of all I'll get to the marketplace point in a second, but it's cloud to edge is kind of their theme. So data streaming sounds expensive. I mean, you know so how do you guys deal with the streaming egress issue? What does that mean to customers? You guys claim that you can save money on that piece. It's a hotly contested discussion point. >> Laughter: So one of the things that we actually just announced in our 350 release yesterday is the capability of getting data from Windows events, or from Windows hosts, I'm sorry. So a product that we also have is called Cribl' Edge. So our capability of being able to collect data from the edge and then transit it out to whether it be an on-prem, or self-hosted deployment of Cribl', or or maybe some sort of other destination object store. What we do is we actually take the data in in transit and reduce the volume of events. So we can do things like remove white space or remove events that are not really needed and compress or optimize that data so that the egress cost to your point are actually lowered. >> And your data reduction approach is, is compression? It's a compression algorithm? >> So it is a combination, yeah, so it's a combination. So there's some people what they'll do is they'll aggregate the events. So sometimes for example, VPC flow logs are very chatty and you don't need to have all those events. So instead you convert those to metrics. So suddenly you reduced those events from, you know high volume events to metrics that are so small and you still get the same value 'cause you still see the trends and everything. And if later on down the road, you need to reinvestigate those events, you can rehydrate that data with Cribl' replay >> And you'll do the streaming in real time, is that right? >> Yeah. >> So Kafka, is that what you would use? Or other tooling? >> Laughter: So we are complimentary to a Kafka deployment. Customer's already deployed and they've invested in Kafka, We can read off of Kafka and feed back into Kafka. >> If not, you can use your tooling? >> If not, we can be replacing that. >> Okay talk about your observations in the multi-cloud hybrid world because hybrid obviously everyone knows it's a steady state now. On public cloud, on premise edge all one thing, cloud operations, DevOps, data as code all the things we talk about. What's the customer view? You guys have a unique position. What's going on in the customer base? How are they looking at hybrid and specifically multi-cloud, is it stitching together multiple hybrids? Or how do you guys work across those landscapes? >> So what we've seen is a lot of customers are in multiple clouds. That's, you know, that's going to happen. But what we've seen is that if they want to egress data from say one cloud to another the way that we've architected our solution is that we have these worker nodes that reside within these hybrid, these other cloud event these other clouds, I should say so that transmitting data, first egress costs are lowered, but being able to have this kind of, easy way to collect the data and also stitch it back together, join it back together, to a single place or single location is one option that we offer customers. Another solution that we've kind of announced recently is Search. So not having to move the data from all these disparate data sources and data lakes and actually just search the data in place. That's another capability that we think is kind of popular in this hybrid approach. >> And talk about now your relationship with HPE you guys obviously had customers that drove you to Greenlake, obviously what's your experience with them and also talk about the marketplace presence. Is that new? How long has that been going on? Have you seen any results? >> Yeah, so we've actually just started our, our journey into this HPE world. So the first thing was obviously the customer's bringing us into this ecosystem and now our capabilities of, I guess getting ready to be on the marketplace. So having a presence on the marketplace has been huge giving us kind of access to just people that don't even know who we are, being that we're, you know a five year old company. So it's really good to have that exposure. >> So you're going to get customers out of this? >> That's the idea. [Laughter] >> Bring in new market, that's the idea of their GreenLake is that partners fill in. What's your impression so far of GreenLake? Because there seems to be great momentum around HP and opening up their channel their sales force, their customer base. >> Yeah. So it's been very beneficial for us, again being a smaller company and we are a channel first company so that obviously helps, you know bring out the word with other channel partners. But HP has been very, you know open arm kind of getting us into the system into the ecosystem and obviously talking, or giving the good word about Cribl' to their customers. >> So, so you'll be monetizing on GreenLake, right? That's the, the goal. >> That's the goal. >> What do you have to do to get into a position? Obviously, you got a relationship you're in the marketplace. Do you have to, you know, write to their API's or do you just have to, is that a checkbox? Describe what you have to do to monetize. >> Sure. So we have to first get validated on the platform. So the validation process validates that we can work on the Ezmeral GreenLake platform. Once that's been completed, then the idea is to have our logo show up on the marketplace. So customers say, Hey, look, I need to have a way to get transit data or do stuff with data specifically around logs, metrics, and traces into my logging solution or my SIM. And then what we do with them on the back end is we'll see this transaction occur right to their API to basically say who this customer is. 'Cause again, the idea is to have almost a zero touch kind of involvement, but we will actually have that information given to us. And then we can actually monetize on top of it. >> And the visualization component will come from the observability vendor. Is that right? Or is that somewhat, do you guys do some of that? >> So the visualization is right now we're basically just the glue that gets the data to the visualization engine. As we kind of grow and progress our search product that's what will probably have more of a visualization component. >> Do you think your customers are going to predominantly use an observability platform for that visualization? I mean, obviously you're going to get there. Are they going to use Grafana? Or some other tool? >> Or yeah, I think a lot of customers, obviously, depending on what data and what they're trying to accomplish they will have that choice now to choose, you know Grafana for their metrics, logs, et cetera or some sort of security product for their security events but same data, two different kind of use cases. And we can help enable that. >> Cam, I want to ask you a question. You mentioned you were at Splunk and Clint, the CEO and co-founder, was at Splunk too. That brings up the question I want to get your perspective on, we're seeing a modern network here with HPE, with Aruba, obviously clouds kind of going next level you got on premises, edge, all one thing, distributed computing basically, cyber security, a data problem that's solved a lot by you guys and people in this business, making sure data available machine learnings are growing and powering AI like you read about. What's changed in this business? Because you know, Splunking logs is kind of old hat you know, and now you got observability. Unification is a big topic. What's changed now? What's different about the market today around data and these platforms and, and tools? What's your perspective on that? >> I think one of the biggest things is people have seen the amount of volume of data that's coming in. When I was at Splunk, when we hit like a one terabyte deal that was a big deal. Now it's kind of standard. You're going to do a terabyte of data per day. So one of the big things I've seen is just the explosion of data growth, but getting value out of that data is very difficult. And that's kind of why we exist because getting all that volume of data is one thing. But being able to actually assert value from it, that's- >> And that's the streaming core product? That's the whole? >> Correct. >> Get data to where it needs to be for whatever application needs whether it's cyber or something else. >> Correct, correct. >> What's the customer uptake? What's the customer base like for you guys now? How many, how many customers you guys have? What are they doing with the data? What are some of the common things you're seeing? >> Yeah. I mean, it's, it's the basic blocking and tackling, we've significantly grown our customer base and they all have the same problem. They come to us and say, look, I just need to get data from here to there. And literally the routing use case is our biggest use case because it's simple and you take someone that's a an expensive engineer and operations engineer instead of having them going and doing the plumbing of data of just getting logs from one source to another, we come in and actually make that a repeatable process and make that easy. And so that's kind of just our very basic value add right from the get go. >> You can automate that, automate that, make it repeatable. Say what's in the name? Where'd the name come from? >> So Cribl', if you look it up, it's actually kind of an old shiv to get to siphon dirt from gold, right? So basically you just, that's kind of what we do. We filter out all the dirt and leave you the gold bits so you can get value. >> It's kind of what we do on theCUBE. >> It's kind of the gold nuggets. Get all these highlights, hitting Twitter, the golden, the gold nuggets. Great to have you on. >> Cam, thanks for, for coming on, explaining that sort of you guys are filling that gap between, Hey all the observability claims, which are all wonderful but then you got to get there. They got to have a route to get there. That's what got to do. Cribl' rhymes with tribble. Dave Vellante for John Furrier covering HPE Discover 2022. You're watching theCUBE. We'll be right back.

(upbeat music) >> Full stack observability is all the rage today. As businesses lean into digital, customer experience becomes ever more important. Why? Well, it's obvious, fickle consumers can switch brands in the blink of an eye or the click of a mouse. Technology companies have sprung into action and the observability space is getting pretty crowded in an effort to simplify the process of figuring out the root cause of application performance problems without an army of PhDs and lab coats, also known as endlessly digging through logs, for example. We see decades old software companies that have traditionally done monitoring or log analytics and or application performance management stepping up their game. These established players, you know, they typically have deep feature sets and sometimes purpose-built tools that attack one particular segment of the marketplace. And now they're pivoting through M&A and some organic development trying to fill gaps in their portfolio. And then, you got all these new entrants coming to the market, claiming end to end visibility across the so-called modern cloud and now edge native stacks. Meanwhile, cloud players are gaining traction and participating through a combination of native tooling combined with strong ecosystems to address this problem. But, you know, recent survey research from ETR confirms our thesis that no one company has it all. Here's the thing. Customers just want to figure out the root cause as quickly and as efficiently as possible. It's one thing to observe the stack end to end, but the question is who is automating the observers? And that's why we're here today. Hello, my name is Dave Vellante and welcome to this special Cube presentation where we dig into root cause analysis, and specifically, how one company, Zebrium, is using unsupervised machine learning to detect anomalies and pinpoint root causes and delivering it as an automated service. And in this session, we have two deep dives. First, we're going to dig into this exciting new field of RCaaS, Root Cause As A Service with two of the founders and technical experts behind Zebrium. And then we bring in two technical experts from Cisco, an early Zebrium customer who ran a POC with Zebrium's service, automating and identifying root cause problems within four very well established and well known Cisco product lines, including WebEx Client and UCS. I was pretty amazed at the results and I think you'll be impressed as well. So thanks for being here. Let's get started. With me right now is Larry Lancaster, who's a founder and CTO of Zebrium. And he's joined by Rod Bagg, who's the founder and vice president of engineering at the company. Gents, welcome. Thanks for coming on. >> Thanks. >> Okay. >> It's good to be here. >> It's good to be here >> All right Rod, talk to me. Talk to me about software downtime, what root cause means, all the buzzwords in your domain, MTTR and SLO. What do we need to know? >> Yeah, I mean, it's like you said. I mean, it's extremely important to our customers and to most businesses out there to drive uptime and avoid as much downtime as possible. So, you know, when you think about it, all of these businesses, most companies nowadays, either their product is software and it's running, you know, running on the web and that's how you get a point click. Or the business depends on, you know, internal systems to drive their business and to run it. When that is down, that is hugely impacting to them. So if you take a look, you know, way back, you know, 20, 30 years ago, software was simple. You know, there wasn't much to it. It was pretty monolithic and maybe it took a couple of people to maintain it and keep it running. There wasn't really anything complicated about it. It was a single tenant piece of software. Today's software is so complicated, often running, you know, maybe hundreds of services to keep that or to actually implement what that software is doing. So as you point out, you know, enter the sort of observability space and the tools that are now in use to help monitor that software and make sure when something goes wrong, they know about it But there's kind of an interesting stat around the observability space. So when you look at observability in the context or through the lens of the cost of downtime, it's really interesting. So observability tools are about a $20 billion market, okay? But the cost of downtime, even with that in place, is still hundreds of billions of dollars. So you're not taking much of a bite out of what the real problem is. You have to solve root cause and get to that fast. So it's all great to know that something went wrong but you got to know why. And it's our contention here that, you know, really, when you take a look at the observability space, you have metrics, that's a great tool. I mean, there's lots of great tools out there, you know, around metrics monitoring that's going to tell you when something went wrong. It's very rarely it's going to tell you why. Similarly for tracing, it's going to point you to where the issue is. It's going to take you through that stack and probably pinpoint where you're being, you know where it's happening or where something is running slow, potentially. So that's great. But again, the root cause of why it's happening is going to be buried in log files. And I can expand on that a little bit more but you know, when you're a software developer and you're writing your software, those log files are a wealth of information. It's just a set of breadcrumbs that are littered with facts about how the software is behaving and why it's doing what it's doing, or why it went wrong. And it's that that really gets you to the root cause very fast. And that's our contention, is that these software systems are so complex nowadays and that the root cause is lying in those logs. So how do you get there fast? You know, we would contend that you better automate that or you are just doomed for failure. And that's where we come in. >> Great. >> Getting to that root cause. >> Thank you, Rod. You know, it's interesting you talk about the $20 billion market. There's an analogy with security, right? We spend 80, $100 billion a year on securing our infrastructure, and yet we lose probably closer to a trillion dollars a year in breaches. And there's a similar analogy here. 20 billion could be 5X in downtime impacts or more. Okay, let's go to Larry. Tell us a little bit more about Zebrium. I'm interested always to ask a founder why you started the company. Rod touched on that a little bit. You guys have invented this concept of RCaaS. What does it mean? What problems does it solve, and how does it solve the problem? Let's get into it. >> Yeah. Hey, thanks, Dave. So I think when you said, you know, who's automating the observer, that that's a great way to think about it because what observability really means is it's a property of a system that means you can see into it. You can observe the internal state and that makes it easier to troubleshoot, right? But the problem is if it's too complicated, you just push the bottleneck up to your eyeball. There's only so much a person can filter through manually, right? And I love the way you put that. So that's a great way to think about it is automating the observer. Now, of course, it means that, you know, you reduce your MTTR, you meet your service level objectives, all that stuff, you improve customer experience. That's all true, but it's important to step back and realize like we have cracked a real nut here. People have been trying to figure out how to automate this part of sort of the troubleshooting experience, this human part of finding the root cause indicators for a long time. And until Zebrium came along, I would argue, no one's really done it right. So, you know, I think it's also important you know, as we step back, we can probably look forward five to 10 years and say, everyone's going to look back and say how did we do all this manually? You're going to see this sort of last mile of observability and troubleshooting is going to be automated everywhere because otherwise, you know, people are just... They're not going to be able to scale their business. So, you know, I think one more thing that's important to point out is, you know, I think Zebrium, you know, it's one thing to have the technology but we've learned we need to deliver it right where people are today. You can't just expect people to dive into a new tool. So, you know, we're looking at, you know, if you look at Zebrium, you'll put us on your dashboard and we don't care what kind of a dashboard it is. It could be, you know Datadog, New Relic, Elastic, Dynatrace, Grafana AppDynamics, ScienceLogic, we don't care. You know, they're all our friends. So we're more interested in getting to that root cause than trying to fight, you know, these incumbents and all that stuff. Yep. >> Yeah. So, interesting. Again, another analogy I think about. You know, you talked about automation. If we're to look back and say this is what... We're never going to do this again, it's like provisioning loans. Nobody provisions loans anymore, it's all automated. >> Larry: (chuckling) That's right. >> So Larry, I'll stay with you, then the skeptic in me says, this sounds amazing, but if I, you know... It might be too good to be true. Tell us how it works. >> Larry: (chuckling) Yeah. So that's interesting. So Cisco came along and they were equally skeptical. So what they did was they took a couple of months and they did a very detailed study. And they got together 192 incidents across four product lines, where they knew that the root cause was in the logs. And they knew what that root cause was because they had had their best engineers, you know work on those cases and take detailed notes of the incidents that had taken place. And so they ran that data through the Zebrium software. And what they found was that in more than 95% of those incidents, Zebrium reflected the correct root cause indicators at the correct time. Like that blew us away. When we saw that kind of evidence, Dave, I have to tell you, everyone was just jumping up and down. It was like, you know, it was like the Apollo command center, you know when they finally, you know, touchdown on the moon kind of thing. So, you know, it's really an exciting point in time to be at the company, like just seeing everything finally being proven out according to this vision. I'm going to tell you one more story which is actually one of my favorites, because we got a chance to work with Seagate Lyve Cloud. So they're, you know, a hyper modern, you know, SaaS business, they're an S3 competitor. Zoom has their files stored on Lyve Cloud, you know, to let you know who they are. So essentially, what happened was they were in alpha, their early access, and they had an outage, and it was pretty bad. I mean, it went on for longer than a day, actually, before they were completely restored. And it was, you know, fortunately for them, it was early access. So no one was expecting, you know, uptime, you know, service level objectives and so on. But they were scared, because they realized, if something like this happens in production, you know, they're screwed. So what they did was they saw Zebrium. They went and did some research, they saw Zebrium. They went in a staging environment, recreated the exact (indistinct) that they had had. And what they saw was immediately, Zebrium pops up a root cause report that tells them exactly the root cause that they took over a day to find. These are the kind of stories that let us know we're onto something transformational. >> Dave: Yeah. That's great. I mean, you guys are jumping up and down, I'm sure. We're going to hear from Cisco later. I bet you, they were jumping up and down too because they didn't have to do all that heavy lifting anymore. So Rod, Larry's just sort of implying that, or actually, you guys both talked about that your tool is agnostic. So how does one actually use the service? How do I deploy it? >> Yeah. So let me step back. So when we talk about logs right? Like, you know, all these bread crumbs being in logs and everything else? So, you know, they are a great wealth of you know, information, but people hate dealing with them. I mean, they hate having to go in and figure out what log to look at. In fact, you know, we had one of our... Or we've heard from several of our customers now prior to using Zebrium, when they, you know, have some issue, and they know there's something wrong, something on their dashboard has told them that something's wrong, maybe a metric has, you know, taken a blip or something's happened that they know there's a problem. We've heard from them that it can take like a number of hours just to get to the right set of logs, like figuring out over these hundreds of services where the logs are, to get to them, maybe searching in a log manager. Just to get into the right context, even, can take hours. So, you know, that's obviously the problem we solve but, you know, we don't want them just looking at logs. I mean, you know, we don't want to put them back in the thing they don't like doing because people don't do that. They don't like doing it. So we put it up on the dashboard. So if something is going wrong with your metrics and that's the indicator, or maybe it's something with tracing that you're sort of digging through that you know something's wrong, we will be right on that same dashboard. So we're deployed as a SaaS service. You send us your logs, you click on one of our integrations and we integrate with all these tools that Larry's talked about. And when we detect anything that is a root cause report, it will show up on your dashboard in the same timeline as those blips in your metrics. So when you see something going wrong and you know there's an issue, take a look at the portion of your dashboard that is us, and we're going to tell you why. We're going to get you to the why that went wrong. No other work could be... You can, you know, also click down and click through to us so that you land up in our portal, if you want to do some more digging around, if you need to or whatever, maybe to get some context what have you, but it's fair that if you ever need to do that, the answer should be right there on your dashboard. And that that's how we expect people to use it. We don't want them digging in logs and going through things, we want it to be right in their workflow. >> Great. Thank you, Larry. So Rod, we talked about Cisco. We're going to hear more from them in a moment in Seagate. I would think this is like a perfect solution for a SaaS provider, anybody doing AI ops. Do you have some examples of those types of firms leaning into this? >> Rod: Yeah, a couple of great ones. Well, I mean, we've got many of them, but a couple that I'll touch on. We have an actual AI ops company that was looking for, you know, sort of some complimentary technology and so on. And so they decided to just put us through our paces by having one of their own SREs sign up for our service in our SaaS environment, and send the logs from their system to us, you know, and just see how we did. So it turned out we ended up talking back to this SRE like a week after he had installed the product, you know signed up and then, you know, started sending us logs. And, you know, he was hewing and hawing, saying that he was busy, like every SRE is, and that he didn't have a chance to really do much with us yet. And, you know, we were just, you know, having this conversation on the phone, and he comes to tell us that, yeah I've been busy because we had this, you know, terrible outage, like, you know, five days ago. And we said like, "Okay did you actually look on the Zebrium dashboard?" (chuckles) And he goes, "You know what? I didn't even think to do it yet. I mean, I'd just been so busy and frazzled." So we have an integration with that company, he hadn't put that integration in, so it wasn't in his dashboard yet, but it was certainly on ours. So he went there, and he looks and he looks on the day, you know, on the time range of when he had had this incident. And right at the very top of the page on our portal was that incident with that root cause. And he was flabbergasted. It literally would've saved him hours and hours and hours. They had this issue going on for over 24 hours. And we had the answer right there in five minutes, and it was crazy. And we get that kind of stories. It's just like the Seagate one. If you use us and you have a problem, we're going to detect it. And you're going to hear from Cisco how successful we are at detecting things. I mean, it'll be there when you have a problem. In SaaS companies, you know, one of our customers is Alchera. They do cost optimizations for cloud properties, you know, for AWS optimization, Google, Google cloud, and so on. But they use our software, and they have a lot of interaction, obviously with these cloud vendors and the APIs of those cloud vendors. So, you know, in order to figure out your costing at AWS, they're using all those APIs. So it turned out, you know, they had some issue where their services were breaking. And we had that root cause report right on the screen, again within five minutes, that was pointing to an API problem with Google. And they had changed one of their APIs and Alchera was not aware of it. So their stuff was breaking because of a change downstream that we had caught. And I'll just tell you one last one because it's somewhat related to one of these cloud vendors. You know, it was a big cloud vendor who had an outage a couple of months ago. And it's interesting because, you know, a lot of our customers will set up shared Slack channels with us, where we're monitoring or seeing their incidents as well as they are. So we get a little Slack representation of the incident that we detected for them or the root cause that we detected for them, and that's in a shared community channel. So we could see this happening when that AWS outage happened. We could see our customers getting impacted by that AWS outage, and the root cause of what was going on there in AWS that was impacting our customers that was showing up in our incidents. Now we didn't obviously, you know, have the very root cause of what was going on in AWS, per se but we were getting to the root cause of why our customer's applications were failing. And that was because of issues going on at AWS. >> Very interesting. I mean, I think one of your biggest challenges is going to be getting people's attention because these SREs are so busy, their hair's on fire. >> Rod: That's it. Right. (chuckling). You know, when you say, hey, (indistinct). >> I tell you, if you get their attention, they love it. I mean, this AI ops company, I didn't even tell you the punchline there, but, you know, they had this incident that occurred that we found. And quite literally, the next week, they ended up signing up as a paid customer. So... >> Dave: that's great. And Larry, to give you the last word. I mean, you know, Rod was talking about, you know, changes in APIs and you know, there's still a lot of scripts out there. You guys, if I understand it correctly, run both as a service in the cloud and you can run on-prem, which is important because there's a lot of sensitive information in logs that people are trying not to leave. >> Larry: That's right. Absolutely. >> Dave: But close it out here. >> Yeah. I mean, that's right, you can run it on-prem. Just like we run it in our cloud, you can run it in your cloud or on your own infrastructure. Now that's all true. You know, I think the one hurdle now that we have left as a company is getting the word out and getting people to believe that this is actually possible and try it for themselves. You don't believe it, do a POC, try it yourself. And you know, people have become so jaded by the lack of, you know, real, sort of, innovation in the software industry for the last 10 years that it's hard to get people to... But guys, you got to give it a shot, I'm telling you. I'm telling you right now, it works. And you'll hear more about that from one of our customers in a minute. >> All right guys, thanks so much. Great story. Really appreciate you sharing. >> Thank you. >> Yeah. Thanks Dave. Appreciate the time. >> Okay. In a moment, we're going to hear from Cisco who is the customer in this case example and a company that has... Look, they have quite an impressive suite of observability tooling, and they've done a pretty compelling proof of concept with Zebrium using real data on some Cisco products that you've heard of, like WebEx. So stay tuned and learn about how you can really take advantage of this new technology called Root Cause As A Service. You're watching theCube, the leader in enterprise and emerging tech coverage. (upbeat music)

(bright intro music) >> Full stack observability is all the rage today. As businesses lean in to digital, customer experience becomes ever more important, why? Well, it's obvious. Fickle consumers can switch brands in the blink of an eye or the click of a mouse. Technology companies have sprung into action, and the observability space is getting pretty crowded in an effort to simplify the process of figuring out the root cause of application performance problems without an army of PhDs and lab coats, also known as endlessly digging through logs, for example. We see decades-old software companies that have traditionally done monitoring or log analytics and/or application performance management stepping up their game. These established players, you know, they typically have deep feature sets and sometimes purpose built tools that attack one particular segment of the marketplace, and now, they're pivoting through M&A and some organic development trying to fill gaps in their portfolio, and then you got all these new entrants coming to the market claiming end to end visibility across the so-called modern cloud and now edge-native stacks. Meanwhile, cloud players are gaining traction and participating through a combination of native tooling combined with strong ecosystems to address this problem, but, you know, recent survey research from ETR confirms our thesis that no one company has at all. Here's the thing. Customers just want to figure out the root cause as quickly and efficiently as possible. It's one thing to observe the stack end to end, but the question is who is automating the observers? And that's why we're here today. Hello, my name is Dave Vellante, and welcome to this special "CUBE" presentation where we dig into root cause analysis and, specifically, how one company, Zebrium, is using unsupervised machine learning to detect anomalies and pinpoint root causes and delivering it as an automated service. In this session, we have two deep dives. First, we're going to dig into this exciting new field of RCA, root cause as a service, with two of the founders and technical experts behind Zebrium, and then we bring in two technical experts from Cisco, an early Zebrium customer who ran a POC with Zebrium's service, automating and identifying root cause problems within four very well established and well-known Cisco product lines including Webex client and UCS. I was pretty amazed at the results, and I think you'll be impressed as well. So thanks for being here. Let's get started with me right now is Larry Lancaster who's a founder and CTO of Zebrium, and he's joined by Rod Bagg who's a founder and Vice-President of Engineering at the company. Gents, welcome, thanks for coming on. >> Thanks. >> (indistinct). >> To be here. >> Great to be here. >> All right, Rod, talk to me. Talk to me about software downtime, what root cause means, all the buzzwords in your domain, MTTR and SLO, what do we need to know? >> Yeah, I mean, it's like you said. I mean, it's extremely important to our customers and to most businesses out there to drive up time and avoid as much downtime as possible. So, you know, when you think about it, all of these businesses, most companies nowadays, either their product is software and it's running, you know, running on the web, and that that's how you get a point click or their business depends on it and, you know, internal systems to drive their business and to run it. Now, when that is down, that is hugely impacting to them. So if you take a look, you know, way back, you know, 20, 30 years ago, software was simple. You know, there wasn't much to it. It was pretty monolithic, and maybe it took a couple of people to maintain it and keep it running. It wasn't really anything complicated about it. It was a single tenant piece of software. Today's software is so complicated, often running, you know, maybe hundreds of services to keep that or to actually implement what that software is doing. So as you point out, you know, enter the sort of observability space and the tools that are now in use to help monitor that software and make sure when something goes wrong, they know about it, but there's kind of an interesting stat around the observability space. So when you look at observability in the context or through the lens of the cost of downtime, it's really interesting. So observability tools are about a $20 billion market, okay? But the cost of downtime, even with that in place, is still hundreds of billions of dollars. So you're not taking much of a bite out of what the real problem is. You have to solve root cause and get to that fast. So it's all great to know that something went wrong, but you got to know why, and it it's our contention here that, you know, really, when you take a look at the observability space, you have metrics. That's a great tool. I mean, there's lots of great tools out there, you know, around metrics monitoring that's going to tell you when something went wrong. It's very rarely it's going to tell you why. Similarly for tracing, it's going to point you to where the issue is. It's going to take you through that stack and probably pinpoint where you're being, you know, where it's happening or where something is running slow potentially. So that's great, but again, the root cause of why it's happening is going to be buried in log files, and I can expand on that a little bit more, but, you know, when you're a software developer, and you're writing your software, those log files are a wealth of information. It's just a set of breadcrumbs that are littered with facts about how the software is behaving and why it's doing what it's doing or why it went wrong, and it's that that really gets you to the root cause very fast, and that's, our contention is that these software systems are so complex nowadays, and that the root cause is lying in those logs. So how do you get there fast? You know, we would contend that you better automate that or you're just doomed for failure, and that's where we come in. >> Great. >> Getting to that request. >> Thank you, Rod. You know, it's interesting. You talk about the $20 billion market. There's an analogy with security, right? We spend 80, $100 billion a year on securing our infrastructure, and yet we lose, probably, closer to a trillion dollars a year in breaches, and there's a similar analogy here. 20 billion could be 5x in downtime impacts or more. Okay, let's go to Larry. Tell us a little bit more about Zebrium. I'm interested always to ask a founder why you started the company. Rod touched on that a little bit. You guys have invented this concept of RCAs. What does it mean? What problems does it solve? And how does it solve the problem? Let's get into it. >> Yeah, hey, thanks, Dave. So I think when you said, you know, who's automating the observer? That's a great way to think about it because what observability really means is it's a property of a system that means you can see into it. You can observe the internal state, and that makes it easier to troubleshoot, right? But the problem is if it's too complicated, you just push the bottleneck up to your eyeball. There's only so much a person can filter through manually, right? And I love the way you put that. So that's a great way to think about it is automating the observer. Now, of course, it means that, you know, you reduce your MTTR, you meet your service level objectives, all that stuff, you improve customer experience, that's all true, but it's important to step back and realize like we have cracked a real nut here. People have been trying to figure out how to automate this part of sort of the troubleshooting experience, this human part of finding the root cause indicators for a long time, and until Zebrium came along, I would argue no one's really done it right. So, you know, I think it's also important, you know, as we step back, we can probably look forward five to 10 years and say, "Everyone's going to look back and say, 'How did we do all this manually?'" You're going to see this sort of last mile of observability and troubleshooting is going to be automated everywhere because otherwise, you know, people are just, they're not going to be able to scale their business. So, you know, I think one more thing that's important to point out is, you know, I think Zebrium, you know, it's one thing to have the technology, but we've learned we need to deliver it right where people are today. You can't just expect people to dive into a new tool. So, you know, we're looking at, you know, if you look at Zebrium, you'll put us on your dashboard, and we don't care what kind of a dashboard it is. It could be, you know, Datadog, New Relic, Elastic, Dynatrace, Grafana, AppDynamics, ScienceLogic, we don't care. You know, they're all our friends. So we're more interested in getting to that root cause than trying to fight, you know, these incumbents and all that stuff, yeah. >> Yeah, so interesting. Again, another analogy I think about, you know, you talked about automation, where to look back, and say, "This is what- We're never going to do this again." It's like provisioning LANs. Nobody provisioned LANs anymore. It's all automated. >> That's correct. >> So, Larry, stay with you. The skeptic in me says, "This sounds amazing," but if, you know, it probably too good to be true. Tell us how it works. >> Yeah, so that's interesting. So Cisco came along and they were equally skeptical. So what they did was they took a couple of months, and they did a very detailed study, and they got together 192 incidents across four product lines where they knew that the root cause was in the logs, and they knew what that root cause was because they'd had their best engineers, you know, work on those cases and take detailed notes of the incidents that had taken place, and so they ran that data through the Zebrium software, and what they found was that in more than 95% of those incidents, Zebrium reflected the correct root cause indicators at the correct time. Like that blew us away. When we saw that kind of evidence, Dave, I have to tell you, everyone was just jumping up and down. It was like, you know, it was like the Apollo Command Center, you know, when they finally, (Dave laughs) you know, touchdown on the moon kind of thing. So, you know, it's really exciting at a point in time to be at the company, like just seeing everything finally being proven out according to this vision. I'm going to tell you one more story, which is actually one of my favorites, because we got a chance to work with Seagate Lyve Cloud. So they're, you know, a hyper modern, you know, SaaS business. They're an S3 competitor. Zoom has their files stored on Lyve Cloud to give, you know, to let you know who they are. So, essentially, what happened was they were in alpha, in their early access, and they had an outage, and it was pretty bad. I mean, it went on for longer than a day, actually, before they were completely restored, and it was, you know, fortunately, for them, it was early access. So no one was expecting, you know, uptime, you know, service level objectives and so on, but they were scared because they realized if something like this happens in production, you know, they're screwed. So what they did was they saw Zebrium, they did some research, they saw Zebrium. They went in a staging environment, recreated the exact (indistinct) that they'd had, and what they saw was, immediately, Zebrium pops up a root cause report that tells them exactly the root cause that they took over a day to find. These are the kind of stories that let us know we're onto something transformational. >> Yeah, that's great. I mean, you guys are jumping up and down. I'm sure, we're going to hear from Cisco later. I bet you, they were jumping up and down, too, 'cause they didn't have to do all that heavy lifting anymore. So Rod, Larry's just sort of implying that or, actually, you guys both talked about that your tool's agnostic. So how does one actually use the service? How do I deploy it? >> Yeah, so let me step back. So when we talk about logs, right? Like, you know, all these red crumbs being in logs and everything else. So, you know, they are a great wealth of, you know, information, but people hate dealing with them. I mean, they hate having to go in and figure out what log to look at. In fact, you know, we had one of our, or we've heard from several of our customers now prior to using Zebrium, but when they're, you know, have some issue, and they know there's something wrong, something on their dashboard has told them that something's wrong, maybe a metrics is, you know, taken a blip or something's happened that they know there's a problem, we've heard from them that it can take like a number of hours just to get to the right set of logs, like figuring out over these hundreds of services where the logs are to get to them, maybe searching in a log manager, just to get into the right context even can take hours. So, you know, that's obviously the problem we solve, but, you know, we don't want them just looking at logs. I mean, you know, we don't want to put 'em back in the thing they don't like doing 'cause people don't do what they don't like doing. So we put it up on the dashboard. So if something is going wrong with your metrics, and that's the indicator or maybe it's something with tracing that you're sort of digging through now that you know something's wrong, we will be right on that same dashboard. So we're deployed as a SaaS service. You send us your logs. You click on one of our integrations, and we integrate with all these tools that Larry's talked about, and when we detect anything that is a root cause report, it will show up on your dashboard in the same timeline as those blips in your metrics. So when you see something going wrong, and you know there's an issue, take a look at the portion of your dashboard that is us, and we're going to tell you why. We're going to get you to the why that went wrong. Not no other work could be- You can, you know, also click down and click through to us so that you land up in our portal if you want to do some more digging around if you need to or whatever, maybe to get some context, what have you, but it's fair that you ever need to do that. The answer should be right there on your dashboard, and that's how we expect people to use it. We don't want them digging in logs and going through things. We want it to be right in their workflow. >> Great, thank you, Larry. So Rod, we talked about Cisco. We're going to hear more from them in a moment and Seagate. I would think this is like a perfect solution for a SaaS provider, anybody doing AIOps, do you have some examples of those types of firms leaning into this? >> Yeah, a couple of great, well, I mean, we got many of them, but couple that I'll touch on. We have an actual AIOps company that was looking for, you know, sort of some complimentary technology and so on, and so they decided to just put us through our paces by having one of their own SREs sign up for our service in our SaaS environment and send the logs from their system to us, you know, and just see how we did. So it turned out we ended up talking back to this SRE like a week after he had installed the product, you know, signed up, and then, you know, started sending us logs, and, you know, he was hemming and hawing saying that he was busy like, you know, like every SRE is, and that he didn't have a chance to really do much with us yet, and, you know, we just, you know, having this conversation on the phone, and he comes to tell us that, "Yeah, I've been busy because we had this, you know, terrible outage like, you know, five days ago," and we said like, "Okay, did you actually look on the Zebrium dashboard?" (laughs) And he goes, "You know what? I didn't even think to do it yet. I mean, I'd just been so busy and frazzled." So we have an integration with that company. He hadn't put that integration in so it wasn't in his dashboard yet, but it was certainly on ours. So he went there and he looks on the day like, you know, on the time range of when he had this incident, and right at the very top of the page on our portal was the incident with the root cause, and he was flabbergasted. It literally would've saved him hours and hours and hours. They had this issue going on for over 24 hours, and we had the answer right there in five minutes, and it was crazy, and we get that kind of story. It's just like the Seagate one. If you use us and you have a problem, we're going to detect it, and you're going to hear from Cisco how successful we are at detecting things. I mean, it'll be there when you have a problem. In SaaS companies, you know, one of our customers is Archera. They do cost optimizations for cloud properties, you know, for AWS optimization, Google cloud, and so on, but they use our software, and they have a lot of interaction, obviously, with these cloud vendors and the APIs of those cloud vendors. So, you know, in order to figure out you're costing at AWS, they're using all those APIs. So it turned out, you know, they had some issue where their services were breaking and we had that root cause report right on the screen, again, within five minutes that was pointing to an API problem with Google, and they had changed one of their APIs, and Archera was not aware of it. So their stuff was breaking because of a change downstream that we had caught, and I'll just tell you one last one because it's somewhat related to one of these cloud vendors of, you know, big cloud vendor who had an outage couple of months ago, and it's interesting because, you know, lot of our customers will set up shared Slack channels with us where we're monitoring or seeing their incidents as well as they are. So we get a little Slack representation of the incident that we detected for them or the root cause that we've detected for them, and that's in a shared community channel. So we could see this happening when that AWS outage happened. We could see our customers getting impacted by that AWS outage and the root cause of what was going on there in AWS that was impacting our customers, that was showing up in our incidents. Now, we didn't obviously, you know, have the very root cause of what was going on in AWS per se, but we were getting to the root cause of why our customer's applications were failing, and that was because of issues going on at AWS. >> Very interesting. I mean, I think one of your biggest challenge is going to be getting people's attention because these SREs is so busy, their hair's on fire. (all laughs) You know, he's like, "Hey, chap, I'm going to show you, look at this." >> I tell you. You get their attention, they love it. I mean, this AIOps company, I didn't even tell you the punchline there, but, you know, they had this incident that occurred that we found and, quite literally, the next week, they ended up signing up as a paid customer, so. >> That's great, and Larry, give you the last word. I mean, you know, Rod was talking about, you know, changes in APIs, and, you know, there's still a lot of scripts out there. You guys, if I understand it correctly, run both as a service in the cloud and you can run on-prem, which is important because there's a lot of sensitive information in logs and people don't want to leave. >> That's right, absolutely. >> But, yeah, close it out here. >> Yeah, I mean, you can, that's right, you can run it on-prem, just like we run it in our cloud. You can run it in your cloud or on your own infrastructure. Now, that's all true. You know, I think the one hurdle now that we have left as a company is getting the word out and getting people to believe that this is actually possible and try it for themselves. You don't believe it? Do a POC, try it yourself. And, you know, people have become so jaded by the lack of, you know, real sort of innovation in the software industry for the last 10 years that it's hard to get people to... But guys, you got to give it a shot. I'm telling you. I'm telling you right now, it works, and you'll hear more about that from one of our customers in a minute. >> Alright guys, thanks so much. Great story, really appreciate you sharing. >> Thank you. >> Yeah, thanks, Dave. Appreciate the time. >> Okay, in a moment, we're going to hear from Cisco who is the customer in this case example, and a company that is... Look, they have quite an impressive suite of observability tooling, and they've done a pretty compelling proof of concept with Zebrium using real data on some Cisco products that you've heard of like Webex. So stay tuned and learn about how you can really take advantage of this new technology called root cause as a service. You're watching "theCUBE", the leader in enterprise and emerging tech coverage. (bright outro music)

>> Instructor: "theCUBE" presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome to Valencia, Spain and we're at KubeCon, CloudNativeCon Europe 2022. I'm Keith Townsend, and my co-host, Enrico Signoretti. Enrico's really proud of me. I've called him Enrico instead of Enrique every session. >> Every day. >> Senior IT analyst at GigaOm. We're talking to fantastic builders at KubeCon, CloudNativeCon Europe 2022 about the projects and their efforts. Enrico, up to this point, it's been all about provisioning, insecurity, what conversation have we been missing? >> Well, I mean, I think that we passed the point of having the conversation of deployment, of provisioning. Everybody's very skilled, actually everything is done at day two. They are discovering that, well, there is a security problem. There is an observability problem a and in fact, we are meeting with a lot of people and there are a lot of conversation with people really needing to understand what is happening. I mean, in their cluster work, why it is happening and all the questions that come with it. And the more I talk with people in the show floor here or even in the various sessions is about, we are growing so that our clusters are becoming bigger and bigger, applications are becoming bigger as well. So we need to now understand better what is happening. As it's not only about cost, it's about everything at the end. >> So I think that's a great set up for our guests, Matt Provo, founder and CEO of StormForge and Patrick Brixton? >> Bergstrom. >> Bergstrom. >> Yeah. >> I spelled it right, I didn't say it right, Bergstrom, CTO. We're at KubeCon, CloudNativeCon where projects are discussed, built and StormForge, I've heard the pitch before, so forgive me. And I'm kind of torn. I have service mesh. What do I need more, like what problem is StormForge solving? >> You want to take it? >> Sure, absolutely. So it's interesting because, my background is in the enterprise, right? I was an executive at UnitedHealth Group before that I worked at Best Buy and one of the issues that we always had was, especially as you migrate to the cloud, it seems like the CPU dial or the memory dial is your reliability dial. So it's like, oh, I just turned that all the way to the right and everything's hunky-dory, right? But then we run into the issue like you and I were just talking about, where it gets very very expensive very quickly. And so my first conversations with Matt and the StormForge group, and they were telling me about the product and what we're dealing with. I said, that is the problem statement that I have always struggled with and I wish this existed 10 years ago when I was dealing with EC2 costs, right? And now with Kubernetes, it's the same thing. It's so easy to provision. So realistically what it is, is we take your raw telemetry data and we essentially monitor the performance of your application, and then we can tell you using our machine learning algorithms, the exact configuration that you should be using for your application to achieve the results that you're looking for without over-provisioning. So we reduce your consumption of CPU, of memory and production which ultimately nine times out of 10, actually I would say 10 out of 10, reduces your cost significantly without sacrificing reliability. >> So can your solution also help to optimize the application in the long run? Because, yes, of course-- >> Yep. >> The lowering fluid as you know optimize the deployment. >> Yeah. >> But actually the long-term is optimizing the application. >> Yes. >> Which is the real problem. >> Yep. >> So, we're fine with the former of what you just said, but we exist to do the latter. And so, we're squarely and completely focused at the application layer. As long as you can track or understand the metrics you care about for your application, we can optimize against it. We love that we don't know your application, we don't know what the SLA and SLO requirements are for your app, you do, and so, in our world it's about empowering the developer into the process, not automating them out of it and I think sometimes AI and machine learning sort of gets a bad rap from that standpoint. And so, at this point the company's been around since 2016, kind of from the very early days of Kubernetes, we've always been, squarely focused on Kubernetes, using our core machine learning engine to optimize metrics at the application layer that people care about and need to go after. And the truth of the matter is today and over time, setting a cluster up on Kubernetes has largely been solved. And yet the promise of Kubernetes around portability and flexibility, downstream when you operationalize, the complexity smacks you in the face and that's where StormForge comes in. And so we're a vertical, kind of vertically oriented solution, that's absolutely focused on solving that problem. >> Well, I don't want to play, actually. I want to play the devils advocate here and-- >> You wouldn't be a good analyst if you didn't. >> So the problem is when you talk with clients, users, there are many of them still working with Java, something that is really tough. I mean, all of us loved Java. >> Yeah, absolutely. >> Maybe 20 years ago. Yeah, but not anymore, but still they have developers, they have porting applications, microservices. Yes, but not very optimized, et cetera, cetera, et cetera. So it's becoming tough. So how you can interact with this kind of old hybrid or anyway, not well engineered applications. >> Yeah. >> We do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage and we, like Matt was saying, we can use any metric that you care about and we can work with any configuration for that application. So perfect example is Java, you have to worry about your heap size, your garbage collection tuning and one of the things that really struck me very early on about the StormForge product is because it is true machine learning. You remove the human bias from that. So like a lot of what I did in the past, especially around SRE and performance tuning, we were only as good as our humans were because of what they knew. And so, we kind of got stuck in these paths of making the same configuration adjustments, making the same changes to the application, hoping for different results. But then when you apply machine learning capability to that the machine will recommend things you never would've dreamed of. And you get amazing results out of that. >> So both me and Enrico have been doing this for a long time. Like, I have battled to my last breath the argument when it's a bare metal or a VM, look, I cannot give you any more memory. >> Yeah. >> And the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith you're cheap, my developer resources are expensive, buy bigger box. >> Yeah. >> Yap. >> Buying a bigger box in the cloud to your point is no longer a option because it's just expensive. >> Yeah. >> Talk to me about the carrot or the stick as developers are realizing that they have to be more responsible. Where's the culture change coming from? Is it the shift in responsibility? >> I think the center of the bullseye for us is within those sets of decisions, not in a static way, but in an ongoing way, especially as the development of applications becomes more and more rapid and the management of them. Our charge and our belief wholeheartedly is that you shouldn't have to choose. You should not have to choose between costs or performance. You should not have to choose where your applications live, in a public private or hybrid cloud environment. And so, we want to empower people to be able to sit in the middle of all of that chaos and for those trade offs and those difficult interactions to no longer be a thing. We're at a place now where we've done hundreds of deployments and never once have we met a developer who said, "I'm really excited to get out of bed and come to work every day and manually tune my application." One side, secondly, we've never met, a manager or someone with budget that said, please don't increase the value of my investment that I've made to lift and shift us over to the cloud or to Kubernetes or some combination of both. And so what we're seeing is the converging of these groups, their happy place is the lack of needing to be able to make those trade offs, and that's been exciting for us. >> So, I'm listening and looks like that your solution is right in the middle in application performance, management, observability. >> Yeah. >> And, monitoring. >> Yeah. >> So it's a little bit of all of this. >> Yeah, so we want to be, the intel inside of all of that, we often get lumped into one of those categories, it used to be APM a lot, we sometimes get, are you observability or and we're really not any of those things, in and of themselves, but we instead we've invested in deep integrations and partnerships with a lot of that tooling 'cause in a lot of ways, the tool chain is hardening in a cloud native and in Kubernetes world. And so, integrating in intelligently, staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for our users who have already invested likely, in a APM or observability. >> So to go a little bit deeper. What does it mean integration? I mean, do you provide data to this, other applications in the environment or are they supporting you in the work that you do. >> Yeah, we're a data consumer for the most part. In fact, one of our big taglines is take your observability and turn it into action ability, right? Like how do you take that, it's one thing to collect all of the data, but then how do you know what to do with it, right? So to Matt's point, we integrate with folks like Datadog, we integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. >> But also we want Datadog customers, for example, we have a very close partnership with Datadog so that in your existing Datadog dashboard, now you have-- >> Yeah. >> The StormForge capability showing up in the same location. >> Yep. >> And so you don't have to switch out. >> So I was just going to ask, is it a push pull? What is the developer experience when you say you provide developer this resolve ML learnings about performance, how do they receive it? Like, what's the developer experience. >> They can receive it, for a while we were CLI only, like any good developer tool. >> Right. >> And, we have our own UI. And so it is a push in a lot of cases where I can come to one spot, I've got my applications and every time I'm going to release or plan for a release or I have released and I want to pull in observability data from a production standpoint, I can visualize all of that within the StormForge UI and platform, make decisions, we allow you to set your, kind of comfort level of automation that you're okay with. You can be completely set and forget or you can be somewhere along that spectrum and you can say, as long as it's within, these thresholds, go ahead and release the application or go ahead and apply the configuration. But we also allow you to experience the same, a lot of the same functionality right now, in Grafana, in Datadog and a bunch of others that are coming. >> So I've talked to Tim Crawford who talks to a lot of CIOs and he's saying one of the biggest challenges or if not, one of the biggest challenges CIOs are facing are resource constraints. >> Yeah. >> They cannot find the developers to begin with to get this feedback. How are you hoping to address this biggest pain point for CIOs-- >> Yeah.6 >> And developers? >> You should take that one. >> Yeah, absolutely. So like my background, like I said at UnitedHealth Group, right. It's not always just about cost savings. In fact, the way that I look about at some of these tech challenges, especially when we talk about scalability there's kind of three pillars that I consider, right? There's the tech scalability, how am I solving those challenges? There's the financial piece 'cause you can only throw money at a problem for so long and it's the same thing with the human piece. I can only find so many bodies and right now that pool is very small, and so, we are absolutely squarely in that footprint of we enable your team to focus on the things that they matter, not manual tuning like Matt said. And then there are other resource constraints that I think that a lot of folks don't talk about too. Like, you were talking about private cloud for instance and so having a physical data center, I've worked with physical data centers that companies I've worked for have owned where it is literally full, wall to wall. You can't rack any more servers in it, and so their biggest option is, well, I could spend $1.2 billion to build a new one if I wanted to, or if you had a capability to truly optimize your compute to what you needed and free up 30% of your capacity of that data center. So you can deploy additional name spaces into your cluster, like that's a huge opportunity. >> So I have another question. I mean, maybe it doesn't sound very intelligent at this point, but, so is it an ongoing process or is it something that you do at the very beginning, I mean you start deploying this. >> Yeah. >> And maybe as a service. >> Yep. >> Once in a year I say, okay, let's do it again and see if something change it. >> Sure. >> So one spot, one single.. >> Yeah, would you recommend somebody performance test just once a year? Like, so that's my thing is, at previous roles, my role was to do performance test every single release, and that was at a minimum once a week and if your thing did not get faster, you had to have an executive exception to get it into production and that's the space that we want to live in as well as part of your CICD process, like this should be continuous verification, every time you deploy, we want to make sure that we're recommending the perfect configuration for your application in the name space that you're deploying into. >> And I would be as bold as to say that we believe that we can be a part of adding, actually adding a step in the CICD process that's connected to optimization and that no application should be released, monitored, and sort of analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, but for cost and performance. >> Almost a couple of hundred vendors on this floor. You mentioned some of the big ones Datadog, et cetera, but what happens when one of the up and comings out of nowhere, completely new data structure, some imaginative way to click to telemetry data. >> Yeah. >> How do, how do you react to that? >> Yeah, to us it's zeros and ones. >> Yeah. >> And, we really are data agnostic from the standpoint of, we're fortunate enough from the design of our algorithm standpoint, it doesn't get caught up on data structure issues, as long as you can capture it and make it available through one of a series of inputs, one would be load or performance tests, could be telemetry, could be observability, if we have access to it. Honestly, the messier the better from time to time from a machine learning standpoint, it's pretty powerful to see. We've never had a deployment where we saved less than 30%, while also improving performance by at least 10%. But the typical results for us are 40 to 60% savings and 30 to 40% improvement in performance. >> And what happens if the application is, I mean, yes Kubernetes is the best thing of the world but sometimes we have to, external data sources or, we have to connect with external services anyway. >> Yeah. >> So, can you provide an indication also on this particular application, like, where the problem could be? >> Yeah. >> Yeah, and that's absolutely one of the things that we look at too, 'cause it's, especially when you talk about resource consumption it's never a flat line, right? Like depending on your application, depending on the workloads that you're running it varies from sometimes minute to minute, day to day, or it could be week to week even. And so, especially with some of the products that we have coming out with what we want to do, integrating heavily with the HPA and being able to handle some of those bumps and not necessarily bumps, but bursts and being able to do it in a way that's intelligent so that we can make sure that, like I said, it's the perfect configuration for the application regardless of the time of day that you're operating in or what your traffic patterns look like, or, what your disc looks like, right. Like 'cause with our low environment testing, any metric you throw at us, we can optimize for. >> So Matt and Patrick, thank you for stopping by. >> Yeah. >> Yes. >> We can go all day because day two is I think the biggest challenge right now, not just in Kubernetes but application re-platforming and transformation, very, very difficult. Most CTOs and EASs that I talked to, this is the challenge space. From Valencia, Spain, I'm Keith Townsend, along with my host Enrico Signoretti and you're watching "theCube" the leader in high-tech coverage. (whimsical music)

(calm music) >> Hi everybody. We're here at VeeamON 2022. This is day two of the CUBE's continuous coverage. I'm Dave Vellante. My co-host is Dave Nicholson. A ton of energy. The keynotes, day two keynotes are all about products at Veeam. Veeam, the color of green, same color as money. And so, and it flows in this ecosystem. I'll tell you right now, Michael Cade is here. He's the senior technologist for product strategy at Veeam. Michael, fresh off the keynotes. >> Yeah, yeah. >> Welcome. Danny Allen's keynote was fantastic. I mean, that story he told blew me away. I can't wait to have him back. Stay tuned for that one. But we're going to talk about protecting containers, Kasten. You guys got announcements of Kasten by Veeam, you call it K10 version five, I think? >> Yeah. So just rolled into 5.0 release this week. Now, it's a bit different to what we see from a VBR release cycle kind of thing, cause we're constantly working on a two week sprint cycle. So as much as 5.0's been launched and announced, we're going to see that trickling out over the next couple of months until we get round to Cube (indistinct) and we do all of this again, right? >> So let's back up. I first bumped into Kasten, gosh, it was several years ago at VeeamON. Like, wow this is a really interesting company. I had deep conversations with them. They had a sheer, sheer cat grin, like something was going on and okay finally you acquire them, but go back a little bit of history. Like why the need for this? Containers used to be ephemeral. You know, you didn't have to persist them. That changed, but you guys are way ahead of that trend. Talk a little bit more about the history there and then we'll get into current day. >> Yeah, I think the need for stateful workloads within Kubernetes is absolutely grown. I think we just saw 1.24 of Kubernetes get released last week or a couple of weeks ago now. And really the focus there, you can see, at least three of the big ticket items in that release are focused around storage and data. So it just encourages that the community is wanting to put these data services within that. But it's also common, right? It's great to think about a stateless... If you've got stateless application but even a web server's got some state, right? There's always going to be some data associated to an application. And if there isn't then like, great but that doesn't really work- >> You're right. Where'd they click, where'd they go? I mean little things like that, right? >> Yeah. Yeah, exactly. So one of the things that we are seeing from that is like obviously the requirement to back up and put in a lot of data services in there, and taking full like exposure of the Kubernetes ecosystem, HA, and very tiny containers versus these large like virtual machines that we've always had the story at Veeam around the portability and being able to move them left, right, here, there, and everywhere. But from a K10 point of view, the ability to not only protect them, but also move those applications or move that data wherever they need to be. >> Okay. So, and Kubernetes of course has evolved. I mean the early days of Kubernetes, they kept it simple, kind of like Veeam actually. Right? >> Yeah. >> And then, you know, even though Mesosphere and even Docker Swarm, they were trying to do more sophisticated cluster management. Kubernetes has now got projects getting much more complicated. So more complicated workloads mean more data, more critical data means more protection. Okay, so you acquire Kasten, we know that's a small part of your business today but it's going to be growing. We know this cause everybody's developing applications. So what's different about protecting containers? Danny talks about modern data protection. Okay, when I first heard that, I'm like, eh, nice tagline, but then he peel the onion. He explains how in virtualization, you went from agents to backing up of VMware instance, a virtual instance. What's different about containers? What constitutes modern data protection for containers? >> Yeah, so I think the story that Danny tells as well, is so when we had our physical agents and virtualization came along and a lot of... And this is really where Veeam was born, right, we went into the virtualization API, the VMware API, and we started leveraging that to be more storage efficient. The admin overhead around those agents weren't there then, we could just back up using the API. Whereas obviously a lot of our competition would use agents still and put that resource overhead on top of that. So that's where Veeam initially got the kickstart in that world. I think it's very similar to when it comes to Kubernetes because K10 is deployed within the Kubernetes cluster and it leverages the Kubernetes API to pull out that data in a more efficient way. You could use image based backups or traditional NAS based backups to protect some of the data, and backup's kind of the... It's only one of the ticks in the boxes, right? You have to be able to restore and know what that data is. >> But wait, your competitors aren't as fat, dumb and happy today as they were back then, right? So it can't... They use the same APIs and- >> Yeah. >> So what makes you guys different? >> So I think that's testament to the Kubernetes and the community behind that and things like the CSI driver, which enables the storage vendors to take that CSI abstraction layer and then integrate their storage components, their snapshot technologies, and other efficiency models in there, and be able to leverage that as part of a universal data protection API. So really that's one tick in the box and you're absolutely right, there's open source tools that can do exactly what we're doing to a degree on that backup and recovery. Where it gets really interesting is the mobility of data and how we're protecting that. Because as much as stateful workloads are seen within the Kubernetes environments now, they're also seen outside. So things like Amazon RDS, but the front end lives in Kubernetes going to that stateless point. But being able to protect the whole application and being very application aware means that we can capture everything and restore wherever we want that to go as well. Like, so the demo that I just did was actually a Postgres database in AWS, and us being able to clone or migrate that out into an EKS cluster as a staple set. So again, we're not leveraging RDS at that point, but it gives us the freedom of movement of that data. >> Yeah, I want to talk about that, what you actually demoed. One of the interesting things, we were talking earlier, I didn't see any CLI when you were going through the integration of K10 V5 and V12. >> Yeah. >> That was very interesting, but I'm more skeptical of this concept, of the single pane of glass and how useful that is. Who is this integration targeting? Are you targeting the sort of traditional Veeam user who is now adding as a responsibility, the management of protecting these Kubernetes environments? Or are you at the same time targeting the current owners of those environments? Cause I know you talk about shift left and- >> Yeah. >> You know, nobody needs Kubernetes if you only have one container and one thing you're doing. So at some point it's all about automation, it's about blueprints, it's about getting those things in early. So you get up, you talk about this integration, who cares about that kind of integration? >> Yeah, so I think it's a bit of both, right? So we're definitely focused around the DevOps focused engineer. Let's just call it that. And under an umbrella, the cloud engineer that's looking after Kubernetes, from an application delivery perspective. But I think more and more as we get further up the mountain, CIS admin, obviously who we speak to the tech decision makers, the solutions architects systems engineers, they're going to inherit and be that platform operator around the Kubernetes clusters. And they're probably going to land with the requirement around data management as well. So the specific VBR centralized management is very much for the backup admin, the infrastructure admin or the cloud based engineer that's looking after the Kubernetes cluster and the data within that. Still we speak to app developers who are conscious of what their database looks like, because that's an external data service. And the biggest question that we have or the biggest conversation we have with them is that the source code, the GitHub or the source repository, that's fine, that will get your... That'll get some of the way back up and running, but when it comes to a Postgres database or some sort of data service, oh, that's out of the CI/CD pipeline. So it's whether they're interested in that or whether that gets farmed out into another pre-operations, the traditional operations team. >> So I want to unpack your press release a little bit. It's full of all the acronyms, so maybe you can help us- >> Sure. >> Cipher. You got security everywhere enhance platform hardening, including KMS. That's key- >> Yeah, key management service, yeah. >> System, okay. With AWS, KMS and HashiCorp vault. Awesome, love to see HashiCorp company. >> Yeah. >> RBAC objects in UI dashboards, ransomware attacks, AWS S3. So anyway, security everywhere. What do you mean by that? >> So I think traditionally at Veeam, and continue that, right? From a security perspective, if you think about the failure scenario and ransomware's, the hot topic, right, when it comes to security, but we can think about security as, if we think about that as the bang, right, the bang is something bad's happen, fire, flood, blood, type stuff. And we tend to be that right hand side of that, we tend to be the remediation. We're definitely the one, the last line of defense to get stuff back when something really bad happens. And I think what we've done from a K10 point of view, is not only enhance that, so with the likes of being able to... We're not going to reinvent the wheel, let's use the services that HashiCorp have done from a HashiCorp vault point of view and integrate from a key management system. But then also things like S3 or ransomware prevention. So I want to know if something bad's happened and Kasten actually did something more generic from a Veeam ONE perspective, but one of the pieces that we've seen since we've then started to send our backups to an immutable object storage, is let's be more of that left as well and start looking at the preventative tasks that we can help with. Now, we're not going to be a security company, but you heard all the way through Danny's like keynote, and probably when he is been on here, is that it's always, we're always mindful of that security focus. >> On that point, what was being looked for? A spike in CPU utilization that would be associated with encryption? >> Yeah, exactly that. >> Is that what was being looked- >> That could be... Yeah, exactly that. So that could be from a virtual machine point of view but from a K10, and it specifically is that we're going to look at the S3 bucket or the object storage, we're going to see if there's a rate of change that's out of the normal. It's an abnormal rate. And then with that, we can say, okay, that doesn't look right, alert us through observability tools, again, around the cloud native ecosystem, Prometheus Grafana. And then we're going to get insight into that before the bang happens, hopefully before the bang. >> So that's an interesting when we talk about adjacencies and moving into this area of security- >> We're talking to Zeus about that too. >> Exactly. That's that sort of creep where you can actually add value. It's interesting. >> So, okay. So we talked about shift left, get that, and then expanded ecosystem, industry leading technologies. By the way, one of them is the Red Hat Marketplace. And I think, I heard Anton's... Anton was amazing. He is the head of product management at Veeam. Is been to every VeeamON. He's got family in Ukraine. He's based in Switzerland. >> Yeah. >> But he chose not to come here because he's obviously supporting, you know, the carnage that's going on in Ukraine. But anyway, I think he said the Red Hat team is actually in Ukraine developing, you know, while the bombs are dropping. That's amazing. But anyway, back to our interview here, expanded ecosystem, Red Hat, SUSE with Rancher, they've got some momentum. vSphere with Tanzu, they're in the game. Talk about that ecosystem and its importance. >> Yeah, and I think, and it goes back to your point around the CLI, right? Is that it feels like the next stage of Kubernetes is going to be very much focused towards the operator or the operations team. The CIS admin of today is going to have to look after that. And at the moment it's all very command line, it's all CLI driven. And I think the marketplace is OpenShift, being our biggest foothold around our customer base, is definitely around OpenShift. But things like, obviously we are a longstanding alliance partner with VMware as well. So their Tanzu operations actually there's support for TKGS, so vSphere Tanzu grid services is another part of the big release of 5.0. But all three of those and the common marketplace gives us a UI, gives us a way of being able to see and visualize that rather than having to go and hunt down the commands and get our information through some- >> Oh, some people are going to be unhappy about that. >> Yeah. >> But I contend the human eye has evolved to see in color for a very good reason. So I want to see things in red, yellow, and green at times. >> There you go, yeah. >> So when we hear a company like Veeam talk about, look we have no platform agenda, we don't care which cloud it's in. We don't care if it's on-prem or Google Azure, AWS. We had Wasabi on, we have... Great, they got an S3 compatible, you know, target, and others as well. When we hear them, companies like you, talk about that consistent experience, single pane of glass that you're skeptical of, maybe cause it's technically challenging, one of the things, we call it super cloud, right, that's come up. Danny and I were riffing on that the other day and we'll do that more this afternoon. But it brings up something that we were talking about with Zeus, Dave, which is the edge, right? And it seems like Kubernetes, and we think about OpenShift. >> Yeah. >> We were there last week at Red Hat Summit. It's like 50% of the conversation, if not more, was the edge. Right, and really true edge, worst cases, use cases. Two weeks ago we were at Dell Tech, there was a lot of edge talk, but it was retail stores, like Lowe's. Okay, that's kind of near edge, but the far edge, we're talking space, right? So seems like Kubernetes fits there and OpenShift, you know, particularly, as well as some of the others that we mentioned. What about edge? How much of what you're doing with container data protection do you see as informing you about the edge opportunity? Are you seeing any patterns there? Nobody's really talking about it in data protection yet. >> So yeah, large scale numbers of these very small clusters that are out there on farms or in wind turbines, and that is definitely something that is being spoken about. There's not much mention actually in this 5.0 release because we actually support things like K3s,(indistinct), that all came in 4.5, but I think, to your first point as well, David, is that, look, we don't really care what that Kubernetes distribution is. So you've got K3s lightweight Kubernetes distribution, we support it, because it uses the same native Kubernetes APIs, and we get deployed inside of that. I think where we've got these large scale and large numbers of edge deployments of Kubernetes and that you require potentially some data management down there, and they might want to send everything into a centralized location or a more centralized location than a farm shed out in the country. I think we're going to see a big number of that. But then we also have our multi cluster dashboard that gives us the ability to centralize all of the control plane. So we don't have to go into each individual K10 deployment to manage those policies. We can have one big centralized management multi cluster dashboard, and we can set global policies there. So if you're running a database and maybe it's the same one across all of your different edge locations, where you could just set one policy to say I want to protect that data on an hourly basis, a daily basis, whatever that needs to be, rather than having to go into each individual one. >> And then send it back to that central repository. So that's the model that you see, you don't see the opportunity, at least at this point in time, of actually persisting it at the edge? >> So I think it depends. I think we see both, but again, that's the footprint. And maybe like you mentioned about up in space having a Kubernetes cluster up there. You don't really want to be sending up a NAS device or a storage device, right, to have to sit alongside it. So it's probably, but then equally, what's the art of the possible to get that back down to our planet, like as part of a consistent copy of data? >> Or even a farm or other remote locations. The question is, I mean, EVs, you know, we believe there's going to be tons of data, we just don't.. You think about Tesla as a use case, they don't persist a ton of their data. Maybe if a deer runs across, you know, the front of the car, oh, persist that, send that back to the cloud. >> I don't want anyone knowing my Tesla data. I'll tell you that right now. (all laughing) >> Well, there you go, that one too. All right, well, that's future discussion, we're still trying to squint through those patterns. I got so many questions for you, Michael, but we got to go. Thanks so much for coming to theCUBE. >> Always. >> Great job on the keynote today and good luck. >> Thank you. Thanks for having me. >> All right, keep it right there. We got a ton of product talk today. As I said, Danny Allan's coming back, we got the ecosystem coming, a bunch of the cloud providers. We have, well, iland was up on stage. They were just recently acquired by 11:11 Systems. They were an example today of a cloud service provider. We're going to unpack it all here on theCUBE at VeeamON 2022 from Las Vegas at the Aria. Keep it right there. (calm music)