>>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.

>> 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)