>> 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 Thank you so much. >> It's great to have you on the show. So, I want you 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 were actually purchased last year in May by VMware, yeah. 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, well, just look at the floor. You know, there's so many monitoring companies doing logs, APM, metrics monitoring. And if you really want to look at what do the companies in the Valley really use, right? I'm talking about companies such as Workday, Watts, Groupon, Intuit, DoorDash, Lyft, 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 decided to be with Wavefront, and they were with us before the acquisition, and they're still with us today, so. >> And they're the scale-up guys, they have large scale >> That's right, yeah, 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 are platform, we're not really opinionated about how you get the data. >> You call them hardcore devops. >> Yes, hardcore devops is the right word, yeah. >> Pushing the envelope, lot of new stuff. >> That's right. >> Doing their own innovation >> So even serverless and all the ML stuff that that's been talked about. They're very pioneering. >> Alright, so VMware, they're very inquisitive on technology, very technology buyers. Take a minute to explain the tech under the covers. What's going on. >> Sure, so Wavefront is a 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 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 that we are unopinionated so any data that you have. Like sometimes it could be from a script , 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 we don't have people looking at charts all day long. We'll alert you if something bad is going on. So teams just really allow the ability to explore the data and just to figure out trends, correlations and just have a platform that scales and just runs reliably. >> With you is Switzerland. >> Yeah, basically I think that's the reason why VMware is very interested, is cause we work with AWS, work with Azure, work with GCP and soon to be AliCloud and IBM, right. >> Talk about why time series data is now more on board. We've got, we've had this conversation with Smug, we saw the new announcement by Amazon. So 'cause if you 're doing real-time, time matters and super important. Why is it important now, why are people coming to the realization as the early adopters, the pioneers. >> That's right, I think I used to work at Google and I think Google, very early on I realized that time series is a way to understand complex systems, especially if you have FMR workloads and so I think what companies have realized is that logs is just very voluminous, it's very difficulty to wield and then traditional APM products, they tend to just show you what they want to show you, like what are the important paying points that you should be monitoring and with Wavefront, it's just a tool that understands time series data and if you think about it, most of the data that you gather out of your operational environment is timer series data. CPU, memory, network, how many people logging in, how many errors, how many people are signing up. We certainly have our customer like Lyft. You know, how many of you are getting Rise, how many credit cards are off. You know all of that information drives, should we pay someone because a certain city, nobody is getting picked up and that's kind of the dimension that you want to be monitoring on, not on the individual like, okay this base, no network even though we monitor those of course. >> You know, Clement, I got to talk to you about the supporting point because 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. And the database is part of the problem and also the overall architecture of the holistic operating environment. So to have an actual understanding of time series is one. Then you actually got to operationalize it. Talk about how customers are implementing and getting value out of time series data and how they differentiate that with data leagues that they might spin up as well as the new dupe data in it. Some might not be valuable. All this is like all now coming together. How do people do that? >> So I think there were a couple of dimensions to that. So it's scalability is a big piece. So you have to be able to take in enormous amount of data, (mumbles) data leagues can do that. It has to be real-time, so our latency from ingestion to maturalization on a chart is under our second So if you're a devops team, you're spinning up containers, you can't go blind for even 10 seconds or else you don't know what's going on with your new service that you just launched. So real-time is super important and then there's analytics. So you can't, you can see all the data in real-time but if it's like millions of time series coming in, it's like the matrix, you need to have some way to actually gather some insights out of that data. SO I think that's what we are good at. >> You know a couple of years ago, we were doing Open Compute, a summit that Facebook puts on, you eventually worked with Google so I see he's talking about the cutting edge tech companies. There's so much data going onto the scale, you need AI, you got to have machines so some of the processing, you can't have this manual process or even scrips, you got to have machines that take care of it. Talk about the at-scale component because as the tsunami of data continues to grow, I mean Amazon's got a satellite, Lockheed Martin, that's going to light up edge computing, autonomous vehicles, pentabytes moving to the cloud, time series matters. How do people start thinking about machine learning and AI, what do you guys do. >> So I think post-acquisition I would say, we really double down on looking at AI and machine learning in our system. We, 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 incidence, we understand anomalies. So we can take all of that data and punch 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 its anomalist, right. The holy grail for VMware I think, is to have a self-driving data center and what that means is you have systems that understands, well yesterday there was a reinforcement learning announcement by Amazon. How do we actually apply those techniques so that we have the observability piece and then we have some way to in fact change against the environment and then we figure out, you know, just let the computer just do it. >> I love this topic, you should come into our studio, if I'm allowed to, we'll 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, we call it differentiate services. You got to differentiate of the data. Machine learning, if the data's good, it works great, but data sucks, machine learning doesn't go well so if I want 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 required clean data because I mean, it's garbage in, garbage out >> Not dirty data >> So, but the ability for us, for machine learning in general to understand anything in a high dimensional space is for it to figure out, what are the signals from a lot of the noise. A human may require to be reduces 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 or 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. >> 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. >> So the most exciting thing that we launched is our distributed tracing offering. We call it a three-dimensional micro service 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 to quickly dive down into a problem and realize what the root cause is and to actually see the actual request at it's 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, okay you're running Java, you're running Jersey or Job 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 an out of the box turn keyway to understand at scale, microservice deployments, where are the pain points, where is latency coming from, where are the errors coming from. So that's kind of our first offering that we're launching. Same pricing mode, all that. >> So how are companies going to use this? What kind of business problem is this solving. >> So as the world transitions to a deployment architecture that mostly consists of Microservices, it's no longer a monolytic app, it's no longer an end-tier application. There are a lot of different heterogeneous languages, frameworks are involved, or even AWS. Cloud services, SAS services are involved and you just have to have some way to understand what is goin on. The classic example I have is you could even trace things like an actual order and how it goes through the entire pipeline. Someone places the orders, a couple days later there's someone who, the orders actually get shipped and then it gets delivered. You know, that's technically a trace. It could be that too. You could send that trace to us but you want to understand, so what are the different pieces that was involved. It could be code or it could be like a vendor. I could be like even a human process. 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 could understand what's really going on. >> What's next for you guys. Now you're part of VMware. What's the investment area, what are you guys looking at building, what's the next horizon? >> So I think, obviously the (mumbles) 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 why can't 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 you know, people have been telling us, it's great to have all the analytics but if I don't have any statistical background to 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 it a 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-- >> Yeah, the genie in the bottle. >> That's right (crosstalk) >> So final question before we go. What's it like working for VMware start-up culture. You raised a lot of money doing your so crunch based reports. VMware's cutting edge, they're a part with Amazon, bit turn around there, what's it like there? >> 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 things are there's just a lot of people, very smart people at VMware. They've worked on the problem of virtualization which was, as a computer scientist, I just thought, that's just so hard. How do you run it like the matrix, right, it's kind of like and a lot of very smart people there. A lot of the stuff that we're actually launching includes components that were built inside VMware based on their expertise over the years and we're just able to pull, it's just 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. >> Well congratulations on the acquisition. VMware's got the radio event we've covered. We were there, you got a lot of engineers, a lot of great scientists so congratulations. >> Thank you so much. >> Great, Clement thanks so much for coming on theCUBE. >> 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. (light electronic music)