>> 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)

>> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering Spark Summit East 2017. Brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to snowy Boston, everybody. This is theCUBE, the leader in live tech coverage. We're here at Spark Summit East, hashtag SparkSummit. Manish Gupta is here, he's the CMO at Redis Labs. Manish, welcome to theCUBE. >> Thank you, good to be here. >> So, you know, 10 years ago you say you're in the database business and everybody would yawn. Now you're the life of the party. >> Yeah, the world has changed. I think the party has lots and lots of players. We are happy to be on the top of that heap. >> It is a crowded space, so how does Redis Labs differentiate? >> Redis Labs is the company behind the massively popular open source Redis, and Redis became popular because of its performance primarily, and then simplicity. Developers could very easily run up an instance of Redis, solve some very hairy problems, and time to market was a big issue for them. Redis Enterprise took that forward and enabled it to be mission critical, ready for the largest workloads, ready for things that the enterprises need in a highly distributed clustered environment. So they have resilience and they benefit from the performance of Redis. >> And your claim to fame, as you say, is that top-gun performance, you guys will talk about some of the benchmarks later. We're talking about use cases like fraud detection, as example. Obviously ad serving would be another one. But add some color to that if you would. >> Redis is whatever you need to make real time real, Redis plays a very important role. It is able to deliver millions of operations per second with sub-millisecond latency, and that's the hallmark. With data structures that comprise Redis, you can solve the problems in a way, and the reason you can get that performance is because the data structures take some very complex issues and simplify the operation. Depending on the use case, you could use one of the data structures, you can mix and match the data structures, so that's the power of a Redis. We're used for ITO, for machine learning, for metering of billing and telecommunications environment, for personalization, for ad serving with companies like Groupon and others, and the list goes on and on. >> Yeah, you've got a big list on your website of all your customers, so you can check that out. Let's get the business model piece out of the way. Everybody's always fascinated. Okay, you got open source, how do you make money? How does Redis make money? >> Yeah, you know, we believe strategically fostering the growth of open source is foundational in our business model, and we invest heavily both R&D and marketing to do that. On top of that, to enable enterprise success and deployment of Redis, we have the mission critical, highly available Redis Enterprise offerings. Our monetization is entirely based on the Redis Enterprise platform, which takes advantage of the data structures and performance of core Redis, but layers on top management and the capabilities that make things like auto-recovery, auto-sorting, management much, much easier for the enterprise. We make that available in four deployment models. The enterprise can select us as Redis cloud, which runs on a public infrastructure on any of the four major platforms. We also allow for the enterprise to select a VPC environment in their own private clouds. They can also get software and self-manage that, or get our software and we can manage it for them. Four deployment options are the modalities in other ways where the enterprise customers help us monetize. >> When you said four major platforms, you meant cloud platforms? >> That's right. AWS, >> So, AWS, Azure >> Azure, Google, and IBM. >> Is IBM software, got there in the fourth, alright. >> That's right, all four. >> Go to the whip IBM. Go ahead, George. >> Along the lines of the business model, and we were sort of starting to talk about this earlier offline, you're just one component in building an application, and there's always this challenge of, well, I can manage my component better than anyone else, but it's got to fit with a bunch of other vendors' components. How do you make that seamless to the customer so that it's not defaulting over to a cloud vendor who has to build all the components themselves to make it work together? >> Certainly, you know, database is an integral part of your stack, of your application stack, but it is a stack, so there are other components. Redis and Redis Labs has a very, very large ecosystem within which we operate. We work closely with others for interfaces, for connectors, for interoperability, and that's a sustained environment that we invest in on a continuous basis. >> How do handle application consistency? A lot of in the no-SQL world, even in the AWS world, you hear about eventual consistency, but in the real-time world, there's a need for more rigorous, what's your philosophy there, how do you approach that? >> I think that's an issue that many no-SQL vendors have not been able to crack. Redis Labs has been at the forefront of that. We are taking an approach, and we are offering what we call tuneable consistency. Depending on the economics and the business model and the use case, the needs of consistency vary. In some cases, you do need immediate consistency. In other cases, you don't ever need consistency. And to give that flexibility to the customer is very important, so we've taken the approach where you can go from loose consistency to what we call strong eventual consistency. That approach is based on a fairly well trusted architecture and approach called CRDT, Conflict-free Replication Data Type. That approach allows us to, regardless of what the cluster magnitude or the distribution looks like geographically, we can deliver strong eventual consistency which meets the needs of majority of the customers. >> What are you seeing in terms of, you know, also in that a discussion about acid properties, and how many workloads really need acid properties. What are seeing now as you get more cloud native workloads and more no-SQL oriented workloads in terms of the requirement for those acid properties? >> First of all, we truly believe and agree that not all environments required acid support. Having said that, to be a truly credible database, you must support acid, and we do. Redis is acid-compli, supports acid, and Redis Labs certainly supports that. >> I remember on a stage once with Curt Monash, I'm sure you know Curt, right? Very famous database person. And he basically had a similar answer. But you would say that increasingly there are workloads that, the growth workloads don't necessarily require that, is that fair statement? >> That's a fair statement I would say. >> Dave: Great, good. >> There's a trade-off, though, when you talked about strong eventual consistency, potentially you have to wait for, presumably, a quorum of the partitions, I'm getting really technical here, but in other words, you've got a copy of the data here-- >> Dave: Good CMO question. (laughing) >> But your value proposition to the customers, we get this stuff done fast, but if you have to wait for a couple other servers to make sure that they've got the update, that can slow things way down. How does that trade-off work? >> I think that's part of the power of our architecture. We have a nothing shared, single proxy architecture where all of the replication, the disaster recovery, and the consistency management of the back end is handled by the proxy, and we ensure that the performance is not degraded when you are working through the consistency challenges, and that's where significant amount of IP is in the development of that proxy. >> I'll take that as a, let's go into it even more offline. >> Manish: Sounds good. >> And I have some other CMO questions, if I may. A lot of young companies like yours, especially in open source world, when they go to get the word out, they rely on their community, their open source community, and that's the core, and that makes a lot of sense, it's their peeps. As you become, grow more into enterprise grade apps and workloads, how do you extend beyond that? What is Redis Labs doing to sort of reach that C-Suite, are you even trying to reach that C-Suite up level to messaging? How do you as a CMO deal with those challenges? >> Maybe I'll begin by talking about our personas that matter to us in the ecosystem. The enterprise level, the architects, the developers, are the primary target, which we try to influence in early part of the decision cycle, it's at the architectural level. The ultimate teams that manage, run, and operate the infrastructure is certainly the DevOps, or the operations teams, and we spend time there. All along for some of the enterprise engagements, CIOs, chief data officers, and CTOs tend to play a very important role in the decisions and the selection process, and so, we do influence and interact with the C-Suite quite heavily. What the power of the open source gives us is that groundswell of love for Redis. Literally you can walk around a developer environment, such as the Spark Summit here, and you'll find people wearing Redis Geek shirts. And we get emails from Kazakhstan and strange, places from all over the world where we don't necessarily have salesforce, and requesting t-shirts, "send us stickers." Because people love Redis, and the word of mouth, that ground level love for the technology enables the decisions to be so much easier and smoother. We're not convincing, it's not a philosophical battle anymore. It's simply about the use case and the solution where Redis Enterprise fits or doesn't fit. >> Okay, so it really is that core developer community that are your advocates, and they're able to internally sell to the C-Suite. A lot of times the C-Suite, not the CTO so much, but certainly the CIO, CDO are like, "Yeah, yeah, they're geekin' out on some new hot thing. "What's the business impact?" Do you get that question a lot, and how do address it? >> I think then you get to some of the very basic tools, ROI calculators and the value proposition. For the C-level, the message is very simple. We are the least risky bet. We are the best long-term proposition, and we are the best cost answer for their implementation. Particularly as the needs are increasingly becoming more real-time in nature, they are not batch processed. Yes, there will always be some of that, but as the workloads are becoming, there is a need for faster processing, there is a need for quick insights, and real-time is not a moniker anymore, right. Real-time truly needs to be delivered today. And so, I think those three propositions for the C-Suite are resonating very well. >> Let's talk about ROI calculators for a second. I love talking about it because it underscores what a company feels as though its core value proposition is. I would think with Redis Labs part of the value proposition is you are enabling new types of workloads and new types of, whether it's sources of revenue or productivity. And these are generally telephone numbers as compared to some of the cost savings head to head to your competition, which of course you want to stress as well because the CFO cares about the cap-backs. What do you emphasize in that, and we don't have to get into the calculator itself, but in the conceptual model, what's the emphasis? Is it on those sort of business value attributes, is it on the sort of cost-savings? How do you translate performance into that business value? A lot of questions there, but if you could summarize, that'd be great. >> Well, I think you can think of it in three dimensions. The very first one is, does the performance support the use case or the solution that is required? That's the very first one. The second piece that fits in it, and that's in our books, that's operations per second and the latency. The second piece is the cost side, and that has two components to it. The first component is, what are the compute requirements? So, what is the infrastructure underneath that has to support it? And the efficiency that Redis and Redis Enterprise has is dramatically superior to the alternatives. And so, the economics show up. To run a million operations per second, we can do that on two nodes as opposed to alternative, which might need 50 nodes or 300 nodes. >> You can utilize your assets on the floor much better than maybe the competition can. >> This is where the data structures come into play quite a bit. That's one part of-- >> Dave: That's one part of the cost. >> Yeah. The other part of the cost is the human cost. >> Dave: People, yeah. >> And because, and this goes back to the open source, because the people available with the talent and the competency and appreciation for Redis, it's easy to procure those people, and your cost of acquisition and deploying goes down quite a bit. So, there's a human cost to it. The third dimension to this whole equation is time to market. And time to market is measured in many ways. Is it lost revenue if it takes you longer to get there? And Redis consistently from multiple analysts' reports gets top ranking for fastest way to get to market because of how simple it is. Beyond performance, simplicity is a second hallmark. >> That's a benefit acceleration, and you can quantify that. >> Absolutely, absolutely. And that's a revenue parameter, right. >> For years, people have been saying this Cambrian explosion of databases is unsustainable, and sort of in response we've gotten a squaring of the Cambrian explosion. The question is, with your sort of very flexible, I don't want to get too geeky, 'cause Dave'll cut me off, but the idea that you can accommodate time series and all these different ways of, all these different types of data, are we approaching a situation where customers can start consolidating their database choices and have fewer vendors, fewer products in their landscape? >> I think not only are we getting there, but we must get there. You've got over 300 databases in the marketplace, and imagine a CIO or an architect trying to have to sort through that to make a decision, it's difficult, and you certainly cannot support it from a trading standpoint or from an investment, cap-backs, and all that standpoint. What we have done with Redis is introduce something called Redis Modules. We released that at the last RedisConf in May in San Francisco. And the Redis Module is a very simple concept but a very powerful concept. It's an API which can be utilized to take an existing development effort, written as CC++, that can be ported onto the Redis data structures. This gives you the flexibility without having to reinvent the wheel every single time to take that investment, port it on top of Redis, and you get the performance, and you can make now Redis becomes a multi-model database. And I'm going to get to your answer of how do you address the multiple needs so you don't need multiple databases. To give you some examples, since the introduction of Redis Modules, we have now over 50 modules that have been published by a variety of places, not just Redis Labs. To indicate how simple and how powerful this model is. We took Lucene and developed the world's fastest full-text search engine as a module. We have very recently introduced Redis machine learning as a module that works with Spark ML and serves as a great serving layer in the machine learning domain. Just two very simple examples, but work that's being done ported over onto Redis data structures and now you have ability to do some very powerful things because of what Redis is. And this is the way future's going to be. I think every database is trying to offer multi-functionality to be multi-model in nature, but instead of doing it one step at a time, this approach gives us the ability to leverage the entire ecosystem. >> Your point being consolidation's inevitable in this business as well. >> Manish: Architectural consolidation. >> Yes, but also you would think, company consolidation, isn't that going to follow? What do you make of the market, and tell me, if you look back on the database market and what Oracle was able to achieve in the face of, maybe not as many players, but you had Sybase and Informix, and certainly DB2's still around, and SQL Server's still around, but Oracle won, and maybe it was SQL standards that. It's great to be lucky and good. Can we learn from that, or is this a whole different world? Are there similarities, and how do you, how do you see that consolidation potentially shaking out, if you agree that there will be consolidation? >> Yeah, there has to be, first and foremost, an architectural approach that solves the OPEX, CAPEX challenge for the enterprise. But beyond that, no industry can sustain the diversity and the fragmentation that exists in database world. I think there will always be new things coming out, of universities particularly. There's great innovation and research happening, and that is required to augment. But at the end of the day, the commercial enterprises cannot be of the fragmented volume that we have today in the database world, so there is going to be some consolidation, and it's not unnatural. I think it's natural, it's expected, time will tell what that looks like. We've seen some of our competitors acquire smaller companies to add graph functionality, to add search functionality. We just don't think that's the level of consolidation that really moves the needle for the industry. It's got to be at a higher level of consolidation. >> I don't want to, don't take this the wrong way, don't hate me for saying it, but is Oracle sort of the enemy, if I can say that. I mean, it's like, no, okay. >> Depends how you define enemy. >> I'm not going to go do many of the workloads that you're talking about on Oracle, despite what Larry tells me at Oracle OpenWorld. And I'm not going to make Oracle my choice for any of the workloads that you guys are working on. I guess in terms, I mean, everybody who's in the database business looks at that and say, "Hey, we can do it cheaper, better, "more productively," but, could you respond to that, and what do you make of Amazon's moves in the database world? Does that concern you? >> We think of Amazon and Oracle as two very different philosophies, if you can use that word. The approach we have taken is really a forward-looking approach and philosophy. We believe that the needs of the market need to be solved in new ways, and new ways should not be encumbered by old approaches. We're not trying to go and replicate what was done in the SQL world or in a relational database world. Our approach is how do you deliver a multi-model database that has the real-time attribute attached to it in a way that requires very limited computer force power and very few resources to manage? You take all of those things as kind of the core philosophy, which is a forward-looking philosophy. We are definitely not trying to replicate what an Oracle used to be. AWS I think is a very different animal. >> Dave: Interesting, though. >> They have defined the cloud, and I think play a very important role. We are a strong partner of theirs, much of our traffic runs on AWS infrastructure, certainly also on other clouds. I think AWS is one to watch in how they evolve. They have database offerings, including Redis offerings. However, we fully recognize, and the industry recognizes that that's not to the same capability as Redis Enterprise. It's open sourced Redis managed by AWS, and that's fine as a cache, but you cannot persist, and you really cannot have a multi-model capability that's a full database in that approach. >> And you're in the marketplace. >> Manish: We are in the marketplace. >> Obviously. >> And actually, we announced earlier, a few weeks ago, that you can buy and get Redis cloud access, which is Redis Enterprise cloud, on AWS through the integrated billing approach on their marketplace. You can have an AWS account and get our service, the true Redis Enterprise service. >> And as a software company, you'd figure, okay, the cloud infrastructures are service, we don't care what infrastructure it runs on. Whatever the customer wants, but you see AWS making these moves up-market, you got to obviously be paying attention to that. >> Manish: Certainly, certainly. >> Go ahead, last question. >> Interesting that you were saying that to solve this problem of proliferation of choice it has to be multi-model with speed and low resource requirement. If I were to interpret that from an old-style database perspective, it would be you're going to get, the multi-model is something you are addressing now, with the extensibility, but the speed means taking out that abstraction layer that was the query optimizer sort of and working almost at the storage layer, or having an option to do that. Would that be a fair way to say? >> No, I don't think that necessarily needs to be the case. For us, speed translates from the simplicity and the power of the data structures. Instead of having to serialize, deserialize before you process data in a Spark context, or instead of having to look for data that is perhaps not put in sorted sets for a use case that you might be doing, running a query on, if the data is already handled through one of the data structures, you now have a much faster query time, you now have the ability to reach the data in the right approach. And again, this is no-SQL, right, so it's a schema lesson write and it sets your scheme as you want it be on read. We marry that with the data structures, and that gives you the ultimate speed. >> We have to leave it there, but Manish, I'll give you the last word. Things we should be paying attention to for Redis Labs this year, events, announcements? >> I think the big thing I would leave the audience with is RedisConf 2017. It's May 31 to June 2 in San Francisco. We are expecting over 1,000 people. The brightest minds around Redis of the database world will be there, and anybody who is considering deploying the next generation database should attend. >> Dave: Where are you doing that? >> It's the Marriott Marquis in San Franciso. >> Great, is that on Howard Street, across from the--? >> It is right across from Moscone. >> Great, awesome location. People know it, easy to get to. Well, congratulations on the success. We'll be lookin' for outputs from that event, and hope to see you again on theCUBE. >> Thank you, enjoyed the conversation. >> Alright, good. Keep it right there, everybody, we'll be back with our next guest. This is theCUBE, we're live from Spark Summit East. Be right back. (upbeat electronic rock music)