 Live from Las Vegas, it's theCUBE, covering AWS re-invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. Welcome back everyone to theCUBE's live coverage of AWS re-invent here at the Venetian in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We're joined by Clement Pang. He is the co-founder of Wavefront by VMware. Welcome. Thank you so much. Great to have you on the show. So I want you to tell our viewers a little bit about Wavefront. You were just purchased by VMware in May. Right. What do you do? What is Wavefront all about? Sure, we're actually purchased last year in May by VMware, yeah. So we are an operational analytics company, so monitoring, I think, is you could say what we do. And the way that I always introduce Wavefront is kind of a untold secret of Silicon Valley. The reason I said that is because in the valley, well, you just look at the floor, you know, there's so many monitoring companies, you know, doing logs, APM, metrics monitoring. And if you really want to look at, you know, what do the companies in the valley really use, right? I'm talking about companies such as Workday, Box, Groupon, Intuit, DoorDash, Lyft. You know, they're all companies that are customers of Wavefront today. So they've obviously looked at all the tools that are available on the market on the show floor, and they've, you know, decided to be with Wavefront. And they were with us before the acquisition, and they're still with us today. So yeah. They're the scale-up guys. They have large-scale, large-system- Container infrastructure running clouds, hybrid clouds. Some of them are still on-prem data centers, and so we just gobble up all that data. We're a platform. We're not really opinionated about how you get the data. We call them hardcore DevOps. Yes, hardcore DevOps is the right term. Pushing the envelope, a lot of new stuff. That's right. Building their own innovation. Even serverless and all the ML stuff that's been talked about, you know, they're, you know, very pioneering. All right, so VMware, they're very acquisitive on technology, very technology buyers. That's right, yeah. Take a minute to explain the tech under the covers. What's going on? Sure, sure. So Wavefront is an at-scale time series database with an analytics engine on top of it. So we have actually since expanded beyond just time series data. It could be distributed histograms. It could be tracing. It includes, you know, things like events. So anything that you could gather up from your operation stack and application metrics, business metrics, we'll take that data. Again, I just said we are unopininated. So any data that you have, like sometimes it could be from a script or it could be from your serverless functions, we'll take that data, we'll store it, we'll render it and visualize it. And of course, you don't have people looking at charts all day long. We'll alert you if something bad is going on. So, you know, teams just really love the ability to explore the data and just to figure out, you know, trends, correlations and just have a platform that scales and just runs reliably. Talk about it. You who is Switzerland. Yeah, yeah. Basically I think that's the reason why VMware is very interested is because we work with AWS, work with Azure, work with GCP and soon to be like Ali Cloud and IBM, right? So. Talk about why time series data is now more important. We've had this conversation with smart. That's right, yeah. We saw the new announcement by Amazon. Right, yes, that's right. So, because if you're doing real time, time matters. Yeah. And super important, why is it important now? Why are people coming to the realization as the early adopters, the pioneers? That's right, that's right. I think, you know, I used to work at Google and I think Google very early on realized that time series is a way to kind of understand complex systems, especially if you have ephemeral workloads. And so I think what companies have realized is that logs is just very voluminous, it's very difficult to wield, right? And then traditional APM products, they tend to just show you what they are, they want to show you, like what are the important pain points that you should be monitoring. And with Wayfront, it's just a tool that understand time series data. And if you think about it, most of the data that you gather out of your operational environment is time series data. You know, CPU, memory, network, right? How many people logging in? How many errors? How many people are signing up? You know, we certainly have our customer-like lift. You know, how many people are getting rise? How many credit cards are off? You know, all of the information drives, you know, should we page someone because, you know, at a certain city, you know, nobody is getting picked up, right? And that's kind of the dimension that you want to be monitoring on, not on the individual like, okay, this base, your network, even though we monitor those, of course. Yeah. You know, Clement, I got to talk to you about this important point because, you know, we've been covering real time. We've been covering IoT. We've been doing a ton of stuff around looking at the importance of data and having data be addressable in real time. That's right. And the database is part of the problem. That's right. And also the overall architecture of the holistic operating environment. That's right. So to have an actual understanding of time series is one. That's right. So then you got to actually operationalize it. That's right, yeah. Talk about how customers are implementing and getting value out of time series data and how they differentiate that with, you know, data lakes that they might spin up that has all this addup data in it. Some might not be valuable. Yeah, yeah. All this is like all now coming together. How do people do that? Right, right. So I think that there are a couple of dimensions to this. So scalability is a big piece. So you have to be able to take in enormous amount of data, understand data lakes can do that. It has to be real time. So our latency from ingestion to materialization on a chart is under a second, right? So if you're a DevOps team, you're spinning up containers. You know, you can't go blind, you know, for like even 10 seconds. Or else, you know, you don't know what's going on with your new service that you just launched. So real time is super important. And then it's analytics, right? So it's not, you can't, you can see all the data in real time. But if it's like millions of times you was coming in, you know, it's like the matrix, right? You need to have some way to actually gather some insights out of that data. So I think that that's what we are really good at. You know, a couple of years ago, we were doing an open compute summit that Facebook puts on. He mentioned you worked at Google. So obviously, he's talking about the cutting edge tech companies. There's so much data going on at scale. You need AI. You got to have machines do some processing. You can't have this manual process or even scripts. You got to have machines take care of it. Talk about the at scale component because as the tsunami of data continues to grow, Amazon's going to satellite you with Lockheed Martin. That's going to light up edge computing. Autonomous vehicles, petabytes coming into the cloud. Time series matters. How do people start thinking about machine learning and AI? What do you guys do? Post acquisition, I would say. We really double down on looking at AI and machine learning in our system. Because we don't down sample any of the data that we collect, we have actually the raw data coming in from weather sensors, from machines, from infrastructure, from cloud. And we just is able to learn on that because we understand incidents. We understand anomalies. So we could take all of that data and crunch it through different kinds of algorithms and figures out maybe we could just have the computer look at the incoming time series data and tell you if it's anomalous. The holy grail for VMware I think is to have a self-driving data center. And what that means is you have systems that understand, well yesterday there's a reinforcement learning announcement by Amazon. Like how do we actually apply those techniques so that we have the observability piece and then we have some way to effect change against the environment. And then we figure out just let the computer just do it. I love this topic. You should come into our studio and follow out that we do a deep dive on this because there's so many implications to the data. Because if you have real-time data, you got to have the streaming data come in. You got to make sense of it. The old networking days, they call it differentiated services. You got to differentiate on the data because machine learning, the data's good, it works great. The data sucks, machine learning doesn't go well. So talk about that dynamic of managing the data so you don't have to do all this cleaning. How do people get that data verified? How do they set up the machine learning? Sure, it still requires clean data. Not dirty data, not dirty data. So, but the ability for us, for machine learning in general to understand anything kind of in a high dimensional space is for it to figure out what are the signals from a lot of the noise, right? A human may require it to be reduced in dimensionality so that they could understand a single line, a single chart that they could actually have insights out of. Machines can technically look at hundreds, even tens of thousands of series and figures out, okay, these are the two that are the signals and these are the knobs that I could turn that could affect those signals. So, I think with machine learning, it actually helps with just the voluminous nature of the data that we're gathering and figuring out what is the signal from the noise, yeah. It's a hard problem. So talk about the two functionalities you guys just launched. What's the news, what are you doing here at AWS? Yeah, so the most exciting thing that we launched is our distributed tracing offering. We call it three dimensional microservice observability. So we're the only platform that marry metrics, histograms, and distributed tracing in a single platform offering. So it's certainly at scale, as I said, it's reliable, it has all the analytical capabilities on top of it, but we basically give you a way for you to quickly dive down into a problem, realize what the root cause is, and to actually see the actual request at its context, whether it's troubleshooting, root cause analysis, performance optimization. So it's a single shop kind of experience. You put in our SDK, it goes ahead and figures out, you're running Java, you're running Jersey, or Drop Wizard, or Spring Boot, and then it figures out, okay, these are the key metrics you should be looking at. If there are any violations, we show you the actual request, including multiple services that are involved in that request, and just give you a out of the box turn key way to understand at scale microservice deployments, where are the pain points, where is the latency coming from, where are the errors coming from. So that's kind of our first offering that we're launching, same pricing model, all that. So how are companies going to use this? What kind of business problems is the solving? Yeah, so as the world transitions to a deployment architecture that mostly consists of microservices, it's no longer a monolithic app, it's no longer an NTR application. There are a lot of different heterogeneous languages, frameworks are involved, or even AWS, right, cloud services, SaaS services are involved, and you just have to have some way to understand what is going on, right? The classic example I have is, you could even trace things like an actual order how it goes through the entire pipeline. Someone placed the orders a couple days later, there's someone who the orders actually get shipped and it gets delivered. That's technically a trace, right? It could be that, you could send that trace to us, but you want to understand like, so what are the different pieces that was involved? It could be code, or it could be like a vendor, it could be like even a human process, right? All of that is a distributed tracing atom, and you could actually send it to Wavefront and we just help you stitch that picture together so you can understand what's really going on, yeah. What's next for you guys? Now you're part of VMware. That's right, yeah. What's the investment area? What are you guys looking at, building? Yeah. What's the next horizon? Right, right, right. So I think obviously the distributed tracing, we still have a lot to work on and just to help teams figure out what do they want to see kind of instantly from the data that we've gathered. Again, we just have gathered data for so long, for so many years, and at the full resolution. So what can we, what insights can develop out of it? And then as I said, we're working on AI and ML, so that's kind of the second launch offering that we have here where people have been telling us like, okay, it's great to have all the analytics, but if I don't have any statistical background or anything like that, can you just tell me, like, I have a chart, a whole bunch of lines, tell me just what I should be focusing on. So that's what we call the AI Genie, and so you just apply, call the Genie, I guess. And then you would basically just have the chart show you what is going wrong and the machines that are going wrong or maybe a particular service that's going wrong, a particular KPI that's in violation, and you could just go there and figure out what's- Get the Genie out of the bottom, that's the goal. So final question before we go, what's it like working for VMware, startup, culture, you raise a lot of money, billionaires so, crunch-based reports? That's right. VMware's cutting edge, they're partnering with Amazon. That's right. Big turnaround there. What's it like there? It's a, well, it's a very large company, obviously, but they're obviously, as with everything, there's always some good points and bad points. I'll focus on the good. So the good thing are, you know, there's just a lot of people, very smart people at VMware, they worked on the problem of virtualization, which was, you know, as a computer scientist, I just thought like, that's just so hard, right? How do you run it like the Matrix, right? It's kind of like, and so a lot of very smart people there, a lot of the stuff that we are actually launching includes like components that were built inside VMware based on their expertise over the years, and we're just able to, you know, pull, it's just a, as I said, a lot of fun toys, and how do we connect all of that together and just do an even better job than what we could have been as we were independent. Congratulations on the acquisition. VMware's got the radio event we covered. You were there. You had a lot of engineers, a lot of great scientists. So congratulations. Let me get to see you. Great. Thanks so much for coming on. Thank you so much, Rebecca. I'm Rebecca Knight for John Furrier. We will have more from AWS re-invent coming up in just a little bit.