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

(soft techno music) >> Good evening. Welcome back to the Kube. Live in Los Angeles. We are at KubeCon Cloud Native Con 2021. Lisa Martin with Dave Nicholson, rounding out our day. We're going to introduce you to a new company, a new company that's new to us. I should say, log DNA. Peter Choi joins us the VP of product. Peter, welcome to the program. >> Thanks for having me. >> (Lisa) Talk to us about log DNA. Who are you guys? What do you do? >> So, you know, log DNA is a log medicine platform. Traditionally, we've been focused on, you know, offering log analysis, log management capabilities to dev ops teams. So your classic kind of troubleshooting, debugging, getting into your systems. More recently, maybe in like the last year or so we've been focused on a lot of control functionality around log medicine. So what I mean by that is a lot of people typically think of kind of the analysis or the dashboards, but with the pandemic, we noticed that you see this kind of surge of data because all of the services are being used, but you also see a downward pressure on cost, right? Because all of a sudden you don't want to be spending two X on those digital experiences. So we've been focused really on kind of tamping down kind of controls on the volume of log data coming in and making sure that they have a higher kind of signal and noise ratio. And then, you know, I'll talk about it a little bit, but we've really been honing in on how can we take those capabilities and kind of form them more in a pipeline. So log management, dev ops, you know, focusing on log data, but moving forward really focused on that flow of data. >> (Dave) So, when you talk about the flow of data and logs that are being read, make this a little more real, bring it up, bring it up just to level in terms of data, from what? >> Yeah. >> What kind of logs? What things are generating logs? What's the relevant information that's being. Kept track of? >> Yeah, I mean, so from our perspective, we're actually agnostic to data source. So we have an assist log integration. We have kind of basic API's. We have, you know, agents for any sort of operating system. Funny enough people actually use those agents to install, log DNA on robots, right? And so we have a customer they're, you know, one of the largest E-commerce platforms on, in the, in the world and they have a warehouse robots division and they installed the agent on every single one of those robots. They're, you know, they're running like arm 64 processors and they will send the log data directly to us. Right? So to us, it's no different. A robot is no different from a server is no different from an application is no different from a router. We take in all that data. Traditionally though, to answer your question, I guess, in the simplest way, mostly applications, servers, firewalls, all the traditional stuff you'd expect kind of going into a log platform. >> So you mentioned a big name customer. I've got a guess as to who that is. I won't, I won't say, but talk to us about the observability pipeline. What is that? What are the benefits in it for customers? >> (Peter) Sure. So, like if we zoom out again, you know, you think about logs traditionally. I think a lot of folks say, okay, we'll ingest the logs. We'll analyze them. What we noticed is that there's a lot of value in the step before that. So I think in the earlier days it was really novel to say, Hey, we're going to get logs and we're going to put it into a system. We're going to analyze it. We're going to centralize. Right. And that had its merits. But I think over time it got a little chaotic. And so you saw a lot of the vendors over the last three years consolidating and doing more of a single pane of glass, all the pillars of observability and whatnot. But then the downside of that is you're seeing a lot of the teams that are using that then saying being constrained by single vendor for all the ways that you can access that data. So we decided that the control point being on the analysis side on, on the very far right side was constricting. So we said, okay, let's move the control point up into a pipeline where the logs are coming to a single point of ingress. And then what we'll do is we will offer views, but also allow you to stream into other systems. So we'll allow you to stream into like a SIM or a data warehouse or something, something like that. Right? So, and you know, we're still trying to like nail down the messaging. I'm sure our marketing person's going to roast me after this. But the simplest way to think of observability pipeline is it's the step before the analysis part, that kind of ingest processes and routes the data. >> (Dave) Yeah. This is the Kube, by the way, neither one of us is a weather reporter. (laughing) So, so the technical stuff is good with us. >> Yes. It is. What are, and talk to us about some of the key features and capabilities and maybe anything that's newly announced are going to be announced. >> Yeah. For sure. So what we recently announced early access on is our streaming capabilities. So it's something that we built in conjunction with IBM and with a couple of, you know, large major institutions that we were working with on the IBM cloud. And basically we realized as we were ingesting a log data, some of those consumers wanted to access subsets of that data and other systems such as Q radar or, you know, a security product. So we ended up taking, we filtered down a subset of that data and we stream it out into those systems. And so we're taking those capabilities and then bringing it into our direct product, you know, whatever you access via logging.com. That is what's essentially going to be the seed for the kind of observability pipeline moving forward. So when you start thinking about it, all of this stuff that I mentioned, where we say, we're focusing on control, like allowing you to exclude logs, allowing you to transform logs, you take those processing capabilities, you take the streaming capabilities, you put them together and all of a sudden that's the pipeline, right? So that's the biggest focus for us now. And then we also have supporting features such as, you know, control API's. We have index rate alerting so that you can get notified if you see aberrations in the amount of flow of data. We have things like variable retention. So when a certain subset of logs come in, if you want it store it for seven days or 30 days, you can go ahead and do that because we know that a large block of logs is going to have many different use cases and many different associated values, right? >> So let's pretend for a moment that a user, somebody who has spent their money on log DNA is putting together a Yelp review and they've given you five stars. >> Yup. >> What do they say about log DNA? Why did they give you that five star rating? >> Yeah. Absolutely. I think, you know, the most common one and it's funny it's Yelp because we actually religiously mine, our G2 crowd reviews. (all laughing) And so the thing that we hear most often is, it's ease of use, right? A lot of these tools. I mean, I'm sure, you know, you're talking to founders and product leaders every day with developers. Like the, the bar, the baseline is so low, you know, a lot of, a lot of organizations where like, we'll give them the, you know, their coders, they'll figure it out. We'll just give them docs and they'll figure it out. But we, we went a little bit extra in terms of like, how can we smooth that experience so that when you go to your computer and you type in QTPL, blah, blah, blah, two lines, and all of a sudden all your logs are shipping from your cluster to log DNA. So that's the constant theme for us in all of our views is, Hey, I showed up, I signed up and within 30 minutes I had everything going that I needed to get. >> (Lisa) So fast time to value. >> Yes. >> Which is critical these days. >> Absolutely. >> Talk to me. So here we are at, at KubeCon, the CNCF community is huge. I think I, the number I saw yesterday was 138,000 contributors. Lots of activity, because we're in person, which is great. We can have those hallway networking conversations that we haven't been able to have in a year and a half. What are some of the things that you guys have heard at the booth in terms of being able to engage with the community again? >> You know, the thing that we've heard most often is just like having a finger on the pulse. It's so hard to do that because you know, when we're all at our computers, we just go from zoom to zoom. And so it, it like, unless it punches you in the face, you're not aware of it. Right. But when you come here, you look around, you go, you can start to identify trends, you hear the conversations in the hallway, you see the sessions. It's just that, that sense of, it's almost like a Phantom limb that, that sense of community and being kind of connected. I think that's the thing that we've heard most often that people are excited. And, you know, I think a lot of us are just kind of treating this like a dry run. Like we're kind of easing our way back in. And so it, you know, it felt good to be back. >> Well, they've done a great job here, right? I mean, you have to show your proof of vaccination. They're doing temperature checks, or you can show your clear health pass. So they're making it. We were talking to the executive director of CNCF earlier today and you're making it, it's not rocket science. We have enough data to know that this can be done carefully and safely. >> (David) Don't forget the wristbands. >> That's right. And, and did you see the wristbands? >> (Peter) Oh yeah. >> Yeah, yeah that's great. >> Yep, it is great. >> I was, I was on the fence by the way. I was like, I was a green or yellow, depending on the person. >> (both) Yeah. >> Yeah. But giving, giving everybody the opportunity to socialize again and to have those, those conversations that you just can't have by zoom, because you have somebody you've seen someone and it jogs your memory and also the control of do I want to shake someone's hand or do I not. They've done a great job. And I think hopefully this is a good test in the water for others, other organizations to learn. This can be done safely because of the community. You can't replicate that on video. >> (Peter) Absolutely. And I'll tell you this one for us, this is our, this is our event. This is the event for us every single year. We, we it's the only event we care about at the end of the day. So. >> What are some of the things that you've seen in the last year, in terms of where, we were talking a lot about the, the adoption of Kubernetes, kind of, where is it in its maturation state, but we've seen so much acceleration and digital transformation in the last 18 months for every industry businesses rapidly pivoting multiple times to try to, to survive one and then figure out a new way to thrive in this, this new I'll call it the new. Now I'll borrow that from a friend at Citrix, the new now, not the new normal, the new now, what are some of the things that you've seen in the last year and a half from, from your customer base in terms of what have they been coming to you saying help? >> (Peter) You know, I think going back to the earlier point about time to value, that's the thing that a lot. So a lot of our customers are, you know, very big Kubernetes, you know, they're, they're big consumers of Kubernetes. I would say, you know, for me, when I do the, we do our, our QBRs with our top customers, I would say 80% of them are huge Kubernetes shops. Right. And the biggest bottleneck for them actually is onboarding new engineers because a lot of the, and you know, we have a customer, we have better mortgage. We have, IBM, we have Rappi is a customer of ours. They're like Latin American version of Instacart. They double their engineering base and you, you know, like 18 in 18 months. And so that's, you know, I think it was maybe from 1500 to 3000 developers or so, so their thing is like, we need to get people on board as soon as possible. We need to get them in these tools, getting access to, to, to their longs, to whatever they need. And so that's been the biggest thing that we've heard over and over again is A, how can we hire? And then B when we hire them, how do we onboard them as quickly as possible, so that they're ramped up and they're adding value. >> How do you help with that onboarding, making it faster, seamless so that they can get value faster? >> So for us, you know, we really lean in on our, our customer success teams. So they do, you know, they do trainings, they do best practices. Basically. We kind of think of ourselves given how much Kubernetes contradiction we have, we think of ourselves as cross pollinators. So a lot of the times we'll go into those decks and we'll try to learn just as much as we're trying to try to teach. And then we'll go and repeat that process through every single set of our customers. So a lot of the patterns that we'll see are, well, you know, what kinds of tools are you using for orchestration? What kind of tools are you using for deployment? How are you thinking about X, Y, and Z? And then, you know, even our own SRE teams will kind of get into the mix and, you know, provide tips and feedback. >> (Lisa) Customer centricity is key. We've heard that a lot today. We hear that from a lot of companies. It's one thing to hear it. It's another thing to see it. And it sounds like the Yelp review that you would have given, or, or what you're hearing through G2 crowd. I mean, that voice of the customer is valid. That's, that's the only validation. I think that really matters because analysts are paid. >> Yeah. >> But hearing that validation through the voice of the customer consistently lets you know, we're going in the right direction here. >> Absolutely. >> I think it's, it's interesting that ease of use comes up. You wonder if those are only anonymous reviews, you don't necessarily associate open source community with cutting edge, you know, we're the people on the pirate ship. >> (Peter) Yeah. And so when, when, when people start to finally admit, you know, some ease of use would be nice. I think that's an indication of maturity at a certain point. It's saying, okay, not everyone is going to come in and sit behind a keyboard and program things in machine language. Every time we want to do some simple tasks, let's automate, let's get some ease of use into this. >> And I'll tell you in the early days it drove me and our, our CEO talker. It drove us nuts that people would say easiest to be like, that's so shallow. It doesn't mean anything. Well, you know, all of that. However, but to your point, if we don't meet the use case, if we don't have the power behind it, the ease of use is abstracting away. It's like an iceberg, right. It's abstracting away a lot. So we can't even have the ease of use conversation unless we're able to meet the use case. So, so what we've been doing is digging into that more, be like, okay, ease of use, but what were you trying to do? What, what is it that we enabled? Because ease of use, if it's a very shallow set of use cases is not as valid as ease of use for petabytes of data for an organization like IBM. Right? >> That's a great, I'm glad that you dug into that because ease of use is one of those things that you'll see it in marketing materials, but to your point, you want to know what does this actually mean? What are we delivering? >> Right. >> And now, you know what you're delivering with Peter, thank you for sharing with us about logged in and what you guys are doing, how you're helping your community of customers and hearing the voice of the customer through G2 and others. Good work. >> Thank you. And by the way, I'll be remiss if I, if I don't say this, if you're interested in learning more about some of the stuff that we're working on, just go to logging in dot com. We've got, I think we've got a banner for the early access programs that I mentioned earlier. So, you know, at the end of the day, to your point about customer centricity, everything we prioritize is based on our customers, what they need, what they tell us about. And so, you know, whatever engagement that we get from the people at the show and prospects, like that's how we drive a roadmap. >> (Lisa) Yup. That's why we're all here. Log dna.com. Peter, thank you for joining Dave and me today. We appreciate it. >> Thanks for having me. >> Our pleasure for Dave Nicholson. I'm Lisa Martin signing off from Los Angeles today. The Kubes coverage of KubeCon clouding of con 21 continues tomorrow. We'll see then. (soft techno music)