>>Hello, and welcome back to the cubes coverage of.com. Splunk's annual conference is virtual this year. I'm John furrier, host of the cube and a very special guest Sean vice president of product and technology cube, alumni, Sean, great to see you. Thanks for coming on the cube and chatting with us. Thanks. It's great to be here. It's been a while since we chatted, you were at AWS. Now it's Splunk heading up the entire products and technology group here, um, which we've been covering sponsors 2012. So we kinda know a lot about what's going on and, and followed your career. Um, your keynote, we kind of went into this cloud vision is hitting Splunk with the data because the cloud scale, which you know a lot about and data is now taking Splunk to a whole nother level. And that's the big theme you observability multi-cloud and security excuse has been for one there for a while. What's your, what's your assessment. >>Yeah, I mean, you know, uh, you and I have talked a number of times before, and what I found is that, you know, there's a lot of companies through this pandemic that, you know, some are thriving and some are not. And the ones that are really thriving, they have this strong data foundation. Like when you, when you talk to them, they're not stuck. Like they're there. When they talk about scaling or adding capacity or building new co uh, uh, customer experiences, they can, uh, their data platform allows that to happen. But the ones that are are stuck, you know, they just can't, they can't, they can't get to the data. They can't ask those questions that they otherwise, you know, love too. So that's, you know, I think Splunk is right in the middle of that. And that's the fun part of it. >>Yeah. You told me you have the strong foundation when thinking about Splunk is every inflection point in the industry. Over the past decade, you see Splunk do something new operationalized data, do something new, operationalize it. We saw security, I think around 2015, come on the radar at.com. And then since then a whole nother level of data, you've got edge. You have now cybersecurity, even, even more advanced than ever before. And then enterprise is just trying to develop modern applications. So you have this whole rapid scale of CICB pipeline, modern applications and the role of data. Isn't just storing it and managing it. It's like making it addressable. This is like, uh, the, the new current phenomenon of cloud. >>I mean, I liked the way you just put it, it, it really, you know, making data addressable, we put it in terms of like turn data into doing so, you know, if you have data that you're storing it, oh, that's one thing. If you don't, you don't want to leave data behind because you don't know what question you may want to ask. And when, but to your point making it addressable is if you and I decided, Hey, we want to build a new customer experience where we're thinking about doing this thing, and we're going to have a million questions to ask that data is going to help you be, uh, to know whether what you're trying to do for your customers is right or wrong. So it is a, it's remarkable to see how many customers are in pursuit of really turning data into >>Doing so. We've got to you, we had the formula one team on here, McLaren, um, Zach brown. I got a little selfie with, uh, the drivers that kind of cool. My son loved it, but that's an IOT application in my mind, first, the coolest of the sports. Awesome. But like the car going in real time, you know, driving that, driving an advantage with data. So it's an IOT IOT. Then you got just the blocking and tackling >>Data warehouse in the cloud. And then you got companies who are trying to transform a data. So I have to ask you as customers out there, look at Splunk and look at the next level of their architecture with multicloud coming around the corner. How should they be thinking about data? Get the foundation with Splunk. What's the next chapter in your mind? I mean, you know, a lot of customers that I meet they're in multiple clouds. They're not just in one. It means they've got data in Amazon or Google or Azure. A lot of them still have data on prem, you know, but when I talk to customers, they don't say things to me like, Hey, I'm in different clouds, I'm on prem. Can you make sure I have different observability and security experiences for each one? Like they don't, they really, at the end of the day, they're like, look, I need a consistent observability experience, consistent security, regardless of where my data is. >>So what that means to Splunk is, you know, wherever your data is, we're going to be Splunk will just work that that's kinda, as you know, it's how we think about it. And speaking that I had dinner with Lando the other night and it was, I hadn't met Lando before, but man, what an awesome, awesome person. We were just kind of hanging out, talking about data and I ask, this is the kind of stuff you wouldn't normally get. I asked him like, Hey, if you could, if technology could do anything to help you win formula one races, what would it be? A totally open-ended question. And I wasn't sure how he was going to answer it, but he didn't pause this guy. Like you talk about, you think of these scenarios. He's very quickly. He's like, oh man, if we had data, could help me do this and this and this and this because in his business, a millisecond can be the difference between winning or losing a race. And for some of you like, oh, that can't be, but for him, that's how his mind works. So it's crazy to see how excited he was to use tech, to get to data, ask questions that can ultimately help them. >>What was the number one thing pitting the right time or tires? What was he, what did he come up there? He is. >>You know, I can't, unfortunately >>I don't want to put you on the spot. I will be. >>This is like, you know, I, I wouldn't, uh, that would put him in a bad spot, but I will tell you though, I mean, this guy is, and that whole team is really about using data to win. >>Well, you know, I was joking. Um, but these guys can, they came on. Cause you know, I'm a big fan, obviously with the Netflix special driving two survives the name of the title. They become hugely popular to a new fan base, especially techies. Um, I said, Hey, you're driving the advantage with data kind of my little, little comeback to that, but that's really kind of a real encapsulates a real world scenario. I mean, well, there are 10,000 people working on McLaren. You have the driver in the car, you have the car itself with all this instrumentation that kind of encapsulates the enterprise experience right now. They don't have the right app doing the right thing with customers. It could be the difference between having a successful digital transformation or not. So it's kind of like parallel. I mean, I know that's kind of the tie in with the, with the sponsorship, but that's the real world now. >>Yeah, it is. And I mean, if you think about it, there's two drivers per car, 10 teams. There's so many races, there's a tremendous amount of money that they're all spending. But you know, when, when your season is really composed of a certain number of races and you got millions of people tuning in you're right. There's hundreds of people working behind the seat. Could you imagine if they didn't use data and you're trying to, you're, you're trying to race and formula one against the best drivers and the best engineers in the world. I just, you know, it goes to show you're right. It is, it's a perfect example of them transforming as any other enterprise, basically using data to get an advantage. >>And just before we move on to the next topic, the e-sports thing is fascinating as well, because now they're taking this memento verse kind of vibe where they're moving people on the e-sports, where they're having the shadow competition. It's a very interesting kind of bringing the fan base in, but there's probably gonna be a lot of data involved in that as well. Maybe identify the next driver who knows, hopefully, you know, good stuff. So Sean, you're in charge of process technology. I have to ask you, um, as customers look at all the different solutions out there, I'll say multicloud check, you guys have a good vision on that. Like that observability. I mean, that's the fashion right now. Let's talk about observability that there's so many companies out there doing quote observability. How should customers think about what that means in context to the decision of they make everyone's coming into the, the CSO or the CIO saying, um, your observability solution? >>Yeah, I mean first, um, you know, what is observability? I always like to just sort of map it back to things we might understand. So back in the day, monitoring really was connect to a machine. It has a monolith app, you know this and you just try to debug this one thing. That's not the world we live in today. Today when you're building apps in the cloud, you're you, you have hundreds of these services behind the scenes. Like no one person can actually comprehend all of it. So now all of a sudden tools become, they really matter. And what I would say is from a Splunk perspective, when we talk to customers, it's not like one person there, one team is quote, you know, working and making the whole system work. Oftentimes you have different teams like network teams, app teams, security teams, and they all kind of need to work together in one way shape or another. But this is why, you know, when rebuild our systems, it's off of shared data so that, you know, if I'm an operator, you're an app developer. And if I need to work with you, at least I can share something with you in context. So we, we, while there are individual tools to do certain things, our mental model is that they all do work together. That's super, super important for any observability thing you're looking at. You just want to make sure that you can see things end to end. Otherwise you get in trouble >>Quick. You know, I'd love to get your perspective being new to Splunk as you come in and new, the industry obviously has experienced that in the cloud has been well documented, certainly in the cube. What's it like there because as you come in, it's not a utility anymore. It's not a tool anymore. It's a platform and it's getting bigger and growing. So you have probably a lot of things going on. So you walk in and you, you say, okay, let me see the price of technology. Were you blown away? What was your reaction? What can you share some, uh, color around what's uh, what was it like when you open up the doors of the kingdom of the product? >>Yeah. Well, I mean, these t-shirts are real men and there's like ponies running around this. The Splunkers love to have fun. And you know, before I came to Splunk, the one thing I noticed, anytime I asked my thoughts long, they were fired up. Like they were really, really excited about the tech, but when I got into it, the truth is, you know, you don't know what you don't know until you see it, but I was just done to, to then sort of connect the dots like wow. Splunk is in the core data plane of tens of thousands of enterprises all over the world, like the data plane for all of their architecture and applications. So with that becomes a great responsibility, as you could imagine, but it is not just a tool. It is something that customers like. I dunno, the university of Illinois, you know, with COVID, they'll they'll track, uh, they'll track 3.2 million saliva tests just for contract tracing and behind the scenes, they're using Splunk for a real thing. Or we've talked about F1 or you think of slack, like we're all kind of using slack. These days, slack is using, um, uh, Splunk to make sure that their environment of slackers and everything's building it's all secure. So th it's those stories that go on and on are just incredible. When you learn that, >>I started at Teresa Carlson yesterday, and we were talking about the growth opportunity and I spent speculating that, you know, my opinion, my opinion, that's looking, hang on the cube is that Splunk's that this new inflection point that another elbow, another kickoff, the growth, the way it's positioned. If you look at kind of where it's been, kind of where it's going with security now as a platform with the enterprises, how do you describe that growth in your mind? Because obviously this market's changing an edge real time. All these things are happening. What's, what's the, where's the growth going to be? >>Yeah, I think it's in the cloud. I mean, if you think of Splunk, I think the company is about 18, 19 years old. So its history is an almost 20 years of on-premise software. In some sense, you might go, Hey, is that a liability? But Rio, the reality is it's a strength because we're already part of these enterprise infrastructures and application stacks. And then when you now move that group to the cloud, and then you got all others coming to the cloud, that's where they're, I mean, it is just the tip of what is happening. So, you know, if I'm a customer and I moved to the cloud in the cloud, it's like, I don't have to really scale or size anything. Like it just works. And it, to me, it's just an end point and I load data. So in that context, the number of new use cases that customers are able to get after is actually pretty awesome. But really at the end of the day it's cloud. >>Well, great to have you on, I know you've got to go. Thanks for coming on the queue. One final question. What's your vision for the next year or two, what's your to do items. What's the message to the marketplace. >>You know, I'm, I'm thrilled to be here, but at the end of the day, you know, my message to the marketplaces, we're all excited to work with our customers to really help them have that strong foundation so they can turn data into doing and actually pull off these digital transformation. >>One final final question for the companies that get the cloud scale combined with putting data into action for the, for the value what's the result going to be is they can put more competitive advantage. Is it more agility? What do you see happening when you combine the cloud scale with a great data plane? >>Yeah, I think at the end of the day, these companies would tell you that they can move faster than ever before. They're more competitive there. They have confidence that their environments secure, they can build new customer experiences. And when you put all of that together, honestly, that is what these digital transformations are all >>Great to be in the product and technology business these days. Isn't it a lot of fun, a lot of action. Thanks for coming on the cube. Really appreciate it. Yeah, you bet. Good to be here. It's the cube coverage here, here at the live studio for Splunk studios, for their virtual events, the cube bring you all the action. I'm John for a, your host. Thanks for watching.

>>from around the globe. It's the Cube with digital coverage of aws reinvent 2024 sponsored by Intel and AWS. Yeah. >>Welcome back here to our coverage here on the Cube of AWS reinvent 2020. It's now pleasure. Welcome. Sean. Vice to the program was the vice president of databases at AWS and Sean. Good day to you. How you doing, sir? >>I'm doing great. Thank you for having me. >>You bet. You bet. Thanks for carving out time. I know it was a very a busy couple of weeks for the A. W s team on DSO certainly was kicked off key notes today. We heard right away that there's some fairly significant announcements that I know certainly affect your world at AWS. Tell us a little bit about those announcements, and then we'll do a little deeper divers. You you go through >>sure, you know. And he made three big announcements this morning as it relates to databases, one of whom was around Aurora serverless V two on. Do you could just think of that as, uh um, no infrastructure whatsoever to manage and Aurora server list that can scale for, you know, from zero to hundreds of thousands of transactions in a fraction of a second, literally with no infrastructure to manage. So it's a really easy way to build applications in the cloud. Eso excited about that? Another big announcement WAAS related to a lot of our customers today are really they're using the right tool for the right job. In other words, they're not trying toe GM all of their data into one database management systems. They're breaking app down into smaller parts. They pick the right tool for the right job. And with that context, we announce glue elastic views, which just allows you to very easily write a sequel. Query most. There's a lot of developers that understand sequel. So if I could easily write a sequel query to reach out to the source databases and then materialize, um, that data into a different target, Um, that's a really simple way toe. Build new customer experiences and make the most of the databases you have. Aan den. The third big announcement remained today was called Babble Eso Babel. Babel Fish is really a a compatibility or a sequel server compatibility layer on Aurora post grass. So if you have ah sequel server application. You've been trying to migrate it to post grass, and you've been wishing for an easier way to get that done. Babel Fish allows you to take your T sequel or your Microsoft sequel server application connected to post grass. Using your same client drivers with little to no code change eso That's a big deal for those that are trying to migrate from commercial systems to open source. And then finally, we didn't stop there as we thought about Babel, Um, and talked to a lot of customers about it. We actually are open sourcing the technology, so it will be available later in 21. All the development will be done open transparently hosted on get hub and licensed under Apache 20 so those that's kind of one lap around the track, if you will, of the big announcements from today How big >>the open source announcement to me. I mean, that's fairly significant that that you're opening up this new opportunity thio the entire community, um, that you're willing to open it up, and I'm sure you're gonna have you know, I mean, this is this is gonna be I would imagine Ah, very popular destination for a lot of folks. >>Yeah, I think so, too. You know, I'm I'm personally, I'm a believer that every customer can use data to build a foundation for future innovation. And to me, a lot of things start and end with data. As we know, data really is a foundational component of at a swell A systems and, you know, and you know, what we found is not every customer can plan for every contingency that happens. But what they can do is build a strong foundation. So, you know, and with a strong foundation, you really stand the best chance to overcome whatever that next unexpected thing is or innovate new ways. And with that is a backdrop. We think this open source piece is a big deal. Why? I'll tell you, you know, it's just us right now. But if I told you the story behind the story, I have met so many customers over the last few years that you know, John, if you and I were sitting down with them, it kind of sounds like this. You sit down, you talk to somebody and they'll say things like, Hey, I've built, you know, we've built years and years and years of application development against sequel server. We really don't like the punitive commercial licensing and, you know, we're trying to get over Thio open source, but we need an easier way and, you know, and we thought about that long and hard and, you know, we came up with the team, came up with a wonderful solution for this, But to tell you the truth, as we were building Babel fish and talking to customers, what became really clear with the community enterprises in I S V s and s eyes is they all basically said, Hey, if there was a way where we could go and extend this, um for, you know, like it could be Boy, if this thing supported to more features, that would be awesome. But if it was open source, that would be even better, because then we could we could take things under our own control so that that's what truly motivated this decision to go open source and based on conversations we've had in the decisions we made, we actually think it's it's really big. It's really big for everybody who has been trying to move off of commercial systems and over toe open source. You. >>Let's talk about transforming your kind of your database mindset in general right now from a client's perspective, especially for somebody who was considering, you know, substantial moves, you know, a major reconfigurations off their processes. What's the process that you go through with them to evaluate their needs, to evaluate their capabilities, to evaluate their storage? All that, you know, that comes into play here and help them to get thio kind of the end of the rainbow >>because it z absolutely, you know, so it really depends on who you're talking Thio and no, at this stage of the game, the clouds been around now for 10, 14 years. I think it is something in that range, you know? So a lot of the early cloud adopters, you know, they've been here and they've been building in a certain way. Um and you know, you and I know early cloud adopters by way of watching streaming media, ordering rideshare, taking a selfie, you know, and you know, we have these great application experiences and we expect them to work all the time at Super Low. Leighton See, they should always be available. So you know, the single biggest thing we learned from Early Cloud builders was there's no such thing as one size football. There's one thing doesn't fit anything at all. Um, that's kind of the way data was, you know, 20 years ago. But today, if you take the learning from these early cloud builders, the journey that we go on with, let's say a mid to late stage cloud a doctor. We're all excited on, you know, sort of. If they can start now today, where Early Cloud Wilders have done a bunch of pioneering, they get excited. So So what happens is, um, there's usually to kind of conversations. One is how do we you know, we've got all these databases that we self managed on premise. How do we bring those into the cloud? And then how do we stop doing undifferentiated heavy lifting? In other words, what they're saying is, we don't want to do patching and back up and monitoring that Z instead, our precious resources should be working on innovations for the business. So in that context, you and I would end up talking to somebody about moving to fully managed services like an already s, for example, um and then the other conversation we have with customers is is the one about breaking free, which is hey, a burn on commercial. I wanna move for open source. And in that context, there are a lot of customers today that they'll move to the cloud. And then and then when they get there as a first step, their second step is to is to migrate over toe open source. And then that third piece is folks that are trying to build for the cloud, these modern APS. And in that context, they follow the playbook of these early cloud builders, which is what you take this big app. You break it into smaller parts and then they pick the right tool for the right job. So that's that's kind of the conversation that we go through there. And finally, what I would say is, most customers say that they'll say to me, What do you mean by picking the right tool for the right job? And the mindset is very different than the one that we all grew up in from 20 years ago. 20 years ago, you just bought a database platform. And then whatever the business was trying to do, you you you would try to support that access pattern on on that database choice. But today, the new world that we live in, it really is. Let's start with the business use case first, understand the access pattern and then pick the best optimized database storage for that. So that's that's kind of how those conversations go. >>You've got what, 15, 14, 15 different data based instruments, you know, like in your tool chest? Um, how how is that evolution occurred? Um because I'm sure, you know one, but got another big at another big at another, looking at different capabilities, different needs. So I mean, >>kind of walked me >>through that a little bit and how you've gotten to the point that you've got 15 >>Tonto eso. So one of the things that you know I'd start off with here, like the question is, Well, if there's 15 today, is there gonna be 100 tomorrow? The real answer is, I don't know, you know, And but what I do know is there's really a handful of categories around data models and access patterns that if you will kind of fill out the portfolio if you will. Um, the first one is around relation. Also, relational databases have been around for a long time. It has a certain set of characteristics that people have come to appreciate and understand and, you know, and we provide a set of services that provide fully managed relational services. Let it be for things like Oracle or sequel, server or open source, like Maria DB or my sequel or Post Press and even Aurora, which provides commercial grade performance availability and scale it about 1/10 the cost of commercial. So you know, there's a handful of different services in that context. But there's new services in this key value. And think of a key value access pattern along the lines of you. Imagine. We order you order a ride share and you're trying to track a vehicle every second. So on your phone you can see it moving across your phone. And now imagine if you were building that at our a million people going to do that all at the same time or 10. So in that kind of access pattern, a product like dynamodb is excellent because It's designed for basically unlimited scale, really high throughput. So developer doesn't have toe really worry about a million people. 10 million people are one. This thing can just scale inevitably. Yeah, it's just not an issue. And, you know, I'll give you one other example like, um, in Neptune, which is a graph database. So you and I would know graph databases by way of seeing a product recommendation, for example, Um, and you know, grab the beauty of a graph databases. It's optimized for highly connected data. In other words, as a developer, I can what I can do with a few lines of code and a graph database because it's optimized for all these different relationships. I might try to do that in a different system that I might write 1500 lines of codes and because it was never designed for something like highly connect the data like graph. So that's kind of the evolution of how things there's just these different categories that have to do with access patterns and data models. And our strategy is simple. In each category, we wanna have the very best AP is available for our customers. Let's >>talk about security here for a moment because you have, you know, these just these tremendous reservoirs now, right that you've built up in capabilities got, you know, new data centers going up every day. It seems like around around the country and around the world, security or securing data nevermore important on dnep ver mawr, I guess on the radar of the bad actors to at the same time because of the value of that data. So just if you would paint the picture in terms of security awareness three encryption devices that you're now deploying the stuff that's keeping you up at night, I would think probably falls into this category a little bit. Eso Let's just take it on security and the level of concern. And then what you at a w s are doing about that? >>Yeah. So, you know, when I talked to customers, I always remind people security is a shared responsibility on De So Amazon's piece of that is the infrastructure that we build the processes that we have, you know, from how people you know can enter a building toe, what they can do in an environment. The auditing to the encryption systems that rebuild. Um, there's there's three infrastructure responsibility, which, you know, we think about every second of every day. Um, Andi, it's, you know, yes, it's one of those things that keeps you up at night. But you have to kind of have this level of paranoia, if you will. There's bad actors everywhere. And, you know, that mindset is kind of, you know, kind of helps you stay focused on Ben. There's the customers responsibility to in in terms of how they think about security. So, you know, um and what that means is, uh, you know, best practices around how they how they integrate identity and access management into their solution. Um, you know how they use how they rotate encryption keys, how they apply encryption and all the safeguards that you would expect the customer do so together, you know, we work with our customers to ensure that our systems are are secure. Um, and the only other thing that I would add to this is that, you know, kind of in the old world. And I keep bringing up the old world because security in the old world was sort of one of those things. Like if you go back 20 years ago. You know, security sometimes is one of those things that you think about a little bit later in the cycle. And I've met a lot of customers that tryto bolt on security and it never works. It's just hard to just bolt it into an app. But the really nice thing about thes fully managed services in the cloud they have security built right in. So security, performance and availability is built right into these fully managed A p I s eso customer doesn't have to think about Well, how do I add this capability onto it? You know, in some sense, it could be a simple is turning a feature on or something like encryption being turned on by default, and they don't have to do anything. So, you know, there it's just a completely different world that we live in today, and we try to improve it every second of every day. >>Well, Sean, it's nice to know that you're experiencing the paranoia for all your customers. That Zaveri very gracious yesterday There. Hey, thanks for the time. I appreciate it. I know you're very busy the next couple of weeks with the number of leadership sessions and intermediate sessions as well with AWS reinvent. So thanks again for carving a little bit of time for us here today on the Cube. >>You bet, John. Thank you. I really appreciate it. >>Take care.

(futuristic electronic music) >> Live from Las Vegas, its theCUBE covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Hey welcome back everyone. Live here in Las Vegas with AWS, Amazon Web Services, re:Invent 2018's CUBE coverage. Two sets, wall-to-wall coverage here on the ground floor. I'm here with Dave Vellante. Dave, six years we've been coming to re:Invent. Every year except for the first year. What a progression. We got great news. Always raising the bar, as they say at Amazon. This year, big announcements. One of them is blockchain. Really kind of laying out early formation of how they're going to roll out, thinking about blockchain. We're here to talk about here, with Rahul Pathak, who's the GM of analytics, and data lakes, and blockchain. Managing that. And Shawn Bice who's the vice president of non-relational databases. Guys, welcome to theCUBE. >> Thank you. >> Thank you, it's great to be here. >> I wish my voice was a little bit stronger. I love this segment. You know, we've been doing blockchain. We've been following one of the big events in the industry. If you separate out the whole token ICO scam situation, token economics is actually a great business model opportunity. Blockchain is an infrastructure, a decentralized infrastructure, that's great. But it's early. Day one really for you guys in a literal sense. How are you guys doing blockchain? Take a minute to explain the announcement because there are use cases, low-hanging use cases, that look a lot like IoT and supply chain that people are interested in. So take a minute to explain the announcements and what it means. >> Absolutely, so when we began looking at blockchain and blockchain use cases, we really realized there are two things that customers are trying to do. One case is really keep an immutable record of transactions and in a scenario where centralized trust is okay. And for that we have Amazon QLDB, which is an immutable cryptographically verifiable ledger. And then in scenarios where customers really wanted the decentralized trust and the smart contracts, that's where blockchain frameworks like Hyperledger Fabric and Ethereum play a role. But they're just super complicated to use and that's why we built Managed Blockchain, to make it easy to stand up, scale, and monitor these networks, so customers can focus on building applications. And in terms of use cases on the decentralized side, it's really quite diverse. I mean, we've got a customer, Guardian Life Insurance, so they're looking at Managed Blockchain 'cause they have this distributed network of partners, providers, patients, and customers, and they want to provide decentralized verifiable records of what's taking place. And it's just a broad set of use cases. >> And then we saw in the video this morning, I think it was Indonesian farmers, right? Wasn't that before the keynote? Did you see that? It was good. >> I missed that one. >> Yeah, so they don't have bank accounts. >> Oh, got it. >> And they got a reward system, so they're using the blockchain to reward farmers to participate. >> So a lot of people ask the question is, why do I need blockchain? Why don't you just put in database? So there are unique, which is true by the way, 'cause latency's an issue. (chuckles) Certainly, you might want to avoid blockchain in the short term, until that gets fixed. Assume that every one will get fixed over time, but what are some of the use cases where blockchain actually is relevant? Can you be specific because that's really people starting to make their selection criteria on. Look, I still use a database. I'm going to have all kinds of token and models around, but in a database. Where is the blockchain specifically resonating right now? >> I'll take a shot at this or we can do it together, but when you think of QLDB, it's not that customers are asking us for a ledger database. What they were really saying is, hey, we'd like to have this complete immutable, cryptographically verifiable trail of data. And it wasn't necessarily a blockchain conversation, wasn't necessarily a database conversation, it was like, I really would like to have this complete cyrptographic verifiable trail of data. And it turns out, as you sort of look at the use cases, in particular, the centralized trust scenario, QLDB does exactly that. It's not about decentralized trust. It's really about simply being able to have a database that when you write to that database, you write a transaction to the database, you can't change it. You know, a typical database people are like, well, hey, wait a second, what does immutable really mean? And once you get people to understand that once that transaction is written to a journal, it cannot be changed at all and attached, then all of a sudden there's that breakthrough moment of it being immutable and having that cryptographic trail. >> And the advantage relative to a distributive blockchain is performance, scale, and all the challenges that people always say. >> Yeah, exactly. Like with QLDB, you can find it's going to be two to three times faster cause you're not doing that distributing consensus. >> How about data lakes? Let's talk about data lakes. What problem were you guys trying to solve with the data lakes? There's a lot of them, but. (chuckles) >> That's a great question. So, essentially it's been hard for customers to set up data lakes 'cause you have to figure out where to get data from, you have to land it in S3, you've got to secure it, you've then got to secure every analytic service that you've got, you might have to clean your data. So with lake formation, what we're trying to do is make it super easy to set up data lakes. So we have blueprints for common databases and data sources. We bring that data into an S3 data lake and we've created a central catalog for that data where customers can define granular access policies with the table, and the column, and the row level. We've also got ML-based data cleansing and data deduplication. And so now customers can just use lake formation, set up data lakes, curate their data, protect it in a single place, and have those policies that enforce across all of the analytic services that they might use. >> So does it help solve the data swamp problem, get more value out of the data lake? And if so, how? >> Absolutely, so the way it does that is by automatically cataloging all datas that comes in. So we can recognize what the data is and then we allow customers to add business metadata to that so they can tag this as customer data, or PII data, or this is my table of sales history. And that then becomes searchable. So we automatically generate a catalog as data comes in and that addresses the, what do I have in my data lake problem. >> Okay, so-- >> Go ahead. >> So, Rahul, you're the general manager. Shawn, what's your job, what do you do? >> So our team builds all the non-relational databases at Amazon. So DynamoDB, Neptune, ElastiCache, Timestream, which you'll hear about today, QLDB, et cetera. So all those things-- >> Beanstalk too, Elastic Beanstalk? >> No we do not build Beanstalk. >> Okay, we're a customer of DynamoDB, by the way. >> Great! >> We're happy customers. >> That's great! >> And we use ElastiCache, right? >> Yup, the elastic >> There you go! >> surge still has it. >> So-- >> Haven't used Neptune yet. >> What's the biggest problem stigmas that you guys are trying to raise the bar on? What's the key focus as you get this new worlds and use cases coming together? These are new use cases. How are you guys evaluating it? How are you guys raising the bar? >> You know, that's a really good question you ask. What I've found in my experience is developers that have been building apps for a long time, most people are familiar with relational databases. For years we've been building apps in that context, but when you kind of look at how people are building apps today, it's very different than how they did in the past. Today developer do what they do best. They take an application, a big application, break it down into smaller parts, and they pick the right tool for the right job. >> I think the game developer mark is going to be a canary in the coal mine for developers, and it's a good spot for data formation in these kind of unstructured, non-relational scenarios. Okay, now all this engagement data, could be first person shooter, whatever it is, just throw it, I need to throw it somewhere, and I'll get to it and let it be ready to be worked on by analytics. >> Well, yeah, if you think about that gamer scenario, think about if you and I are building a game, who knows if there's going to be one user, ten players, or 10 million, or 100 million. And if we had 100 million, it's all about the performance being steady. At 100 million or ten. >> You need a fleet of servers. (John laughing) >> And a fleet of servers! >> Have you guys played Fortnite? Or do you have kids that play? >> I look over my kid's shoulder. I might play it. >> I've played, but-- >> They run all their analytics on us. They've got about 14 petabytes in S3 using S3 as their data lake, with EMR and Athena for analytics. >> We got a season-- >> I mean, think about that F1 example on keynotes today. Great example of insights. We apply that kind of concept to Fortnite, by the way, Fortnite has theCUBE in there. It's always a popular term. We noticed that, the hastag, #wherestheCUBEtoday. (Rahul chuckling) I couldn't resist. But the analytics you could get out of all that data, every interaction, all that gesture data. I mean, what are some of the things they're doing? Can you share how they're using the new tech to scale up and get these insights? >> Yeah, absolutely. So they're doing a bunch of things. I mean, one is just the health of the systems when you've got hundreds of millions of players. You need to know if you're up and it's working. The second is around engagement. What games, what collection of people work well together. And then it's what incentives they create in the game, what power ups people buy that lead to continued engagement, 'cause that defines success over the long term. What gets people coming back? And then they have an offline analytics process where they're looking at reporting, and history, and telemetry, so it's very comprehensive. So you're exactly right about gaming and analytics being a huge consumer of databases. >> Now, Shawn, didn't you guys have hard news today on DynamoDB, or? >> Yeah today we announce DynamoDB On-Demand, so customers that basically have workloads that could spike up and then all of a sudden drop off, a lot of these customers basically don't even want to think about capacity planning. They don't want to guess. They just want to basically pay only for what they're using. So we announced DynamoDB On-Demand. The developer experience is simple. You create a table and you putyour read/write capacity in the on-demand mode, and you literally only pay for the request that your workload puts through system. >> It's a great service actually. Again, making life easier for customers. Lower the bill, manage capacity, make things go better, faster, enables value. >> It's all about improving the customer experience. >> Alright, guys, I really appreciate you coming in. I'm really interested in following what you guys do in the future. I'm sure a lot of people watching will be as well, as analytics and AI become a real part of, as you guys move the stack and create that API model for, what you did for infrastructure, for apps. A total game changer, we believe. We're interested in following you guys, I'm sure others are. Where are you going to be this year? What's your focus? Where can people find out more besides going to Amazon site? Is there certain events you're going to be at? How do people get more information and what's the plans? >> There's actually some sessions on lake formation, blockchain that we're doing here. We'll have a continuous stream of summits, so as the AWS Summit calendar for 2019 gets published that's a great place to go for more information. And then just engage with us either on social media or through the web and we'll be happy to follow up. >> Alright, well, we'll do a good job on amplifying. A lot of people are interested, certainly blockchain, super hot. But people want better, stronger, more stable, but they want the decentralized immutable database model. >> Cryptographically verifiable! >> And see as everyone knows. >> Scalable! >> Anyone who wants to keep those, they talk about CUBE coins but I haven't said CUBE coin once on this episode. Wait for those tokens to be released soon. More coverage after this short break, stay with us. I'm John Furrier, and Dave Vellante, we'll be right back. 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(upbeat music) >> Hello everyone, welcome back to theCUBE's coverage of splunk.com 2021 virtual. We're here live in the Splunk studios. We're all here gettin all the action, all the stories. Garth Fort, senior vice president, Chief Product Officer at Splunk is here with me. CUBE alumni. Great to see you. Last time I saw you, we were at AWS now here at Splunk. Congratulations on the new role. >> Thank you. Great to see you again. >> Great keynote and great team. Congratulations. >> Thank you. Thank you. It's a lot of fun. >> So let's get into the keynote a little bit on the product. You're the Chief Product Officer. We interviewed Shawn Bice, who's also working with you as well. He's your boss. Talk about the, the next level, cause you're seeing some new enhancements. Let's get to the news first. Talk about the new enhancements. >> Yeah, this was actually a really fun keynote for me. So I think there was a lot of great stuff that came out of the rest of it. But I had the honor to actually showcase a lot of the product innovation, you know, since we did .conf last year, we've actually closed four different acquisitions. We shipped 43 major releases and we've done hundreds of small enhancements, like we're shipping code in the cloud every six weeks and we're shipping new versions twice a year for our Splunk Enterprise customers. And so this was kind of like if you've seen that movie Sophie's Choice, you know, where you have to pick one of your children, like this was a really hard, hard thing to pick. Cause we only had about 25 minutes, but we did like four demos that I think landed really well. The first was what we call ingest actions and you know, there's customers that are using, they start small with gigabytes and they go to terabytes and up to petabytes of data per day. And so they wanted tools that allow them to kind of modify filter and then route data to different sort of parts of their infrastructure. So that was the first demo. We did another demo on our, our visual playbook editor for SOAR, which has improved quite a bit. You know, a lot of the analysts that are in the, in the, in the SOC trying to figure out how to automate responses and reduce sort of time to resolution, like they're not Python experts. And so having a visual playbook editor that lets them drag and drop and sort of with a few simple gestures create complex playbooks was pretty cool. We showed some new capabilities in our APM tool. Last year, we announced we acquired a company called Plumbr, which has expertise in basically like code level analysis and, and we're calling it "Always On" profiling. So we, we did that demo and gosh, we did one more, four, but four total demos. I think, you know, people were really happy to see, you know, the thing that we really tried to do was ground all of our sort of like tech talk and stuff that was like real and today, like this is not some futuristic vision. I mean, Shawn did lay out some, some great visions, visionary kind of pillars. But, what we showed in the keynote was I it's all shipping code. >> I mean, there's plenty of head room in this market when it comes to data as value and data in motion, all these things. But we were talking before you came on camera earlier in the morning about actually how good Splunk product and broad and deep the product portfolio as well. >> Garth: Yeah. >> I mean, it's, I mean, it's not a utility and a tooling, it's a platform with tools and utilities. >> Garth: Yeah >> It's a fully blown out platform. >> Yeah. Yeah. It is a platform and, and, you know, it's, it's one that's quite interesting. I've had the pleasure to meet a couple of big customers and it's kind of amazing, like what they do with Splunk. Like I was meeting with a large telco on the east coast and you know, they actually, for their set top boxes, they actually have to figure out in real time, which ads to display and the only tool they could find to process 15 million events in real time, to decide what ad to display, was Splunk. So that was, that was like really cool to hear. Like we never set out to be like an ad tech kind of platform and yet we're the only tool that operates at that level of scale and that kind of data. >> You know, it's funny, Doug Merritt mentioned this in my interview with him earlier today about, you know, and he wasn't shy about it, which was great. He was like, we're an enabling platform. We don't have to be experts in all these vertical industries >> Garth: Yep >> because AI takes care of that. That's where the machine learning >> Garth: Yeah >> and the applications get built. So others are trying to build fully vertically integrated stacks into these verticals when in reality they don't have to, if they don't want it. >> Yeah, and Splunk's kind of, it's quite interesting when you look across our top 100 customers, you know, Doug talks about like the, you know, 92 of the fortune 100 are kind of using Splunk today, but the diversity across industries and, you know, we have government agencies, we have, you know, you name the retail or the vertical, you know, we've got really big customers, they're using Splunk. And the other thing that I kind of, I was excited about, we announced the last demo I forgot was TruSTAR integration with Enterprise Security. That's pretty cool. We're calling that Splunk Threat Intelligence. And so That was really fun and we only acquired, we closed the acquisition to TruSTAR in May, but the good news is they've been a partner with us like for 18 months before we actually bought em. And so they'd already done a lot of the work to integrate. And so they had a running start in that regard, But other, one other one that was kind of a, it was a small thing. I didn't get to demo it, but we talked about the, the content pack for application performance monitoring. And so, you know, in some ways we compete in the APM level, but in many ways there's a ton of great APM vendors out there that customers are using. But what they wanted us to do was like, hey, if I'm using APM for that one app, I still want to get data out of that and into Splunk because Splunk ends up being like the core repository for observability, security, IT ops, Dev Sec Ops, et cetera. It's kind of like where the truth, the operational truth of how your systems works, lives in Splunk. >> It's so funny. The Splunk business model has actually been replicated. They call it data lake, whatever you want to call it. People are bringing up all these different metaphors. But at the end of the day, if you guys can create a value proposition where you can have data just be, you know, stored and dumped and dumped into whatever they call it stored in a way >> Garth: We call it ingest >> Ingested, ingested. >> Garth: Not dumped. >> Data dump. >> Garth: It's ingested. >> Well, I mean, well you given me a plan, but you don't have to do a lot of work to store just, okay, we can only get to it later, >> Garth: Yep. >> But let the machines take over >> Garth: Yep. >> With the machine learning. I totally get that. Now, as a pro, as a product leader, I have to ask you your, your mindset around optimization. What do you optimize for? Because a lot of times these use cases are emerging. They just pop out of nowhere. It's a net new use case that you want to operationalize. So balancing the headroom >> Yep. >> Or not to foreclose those new opportunities for customers. How are customers deciding what's important to them? How do you, because you're trying to read the tea leaves for the future >> Garth: A little bit, yeah. >> and then go, okay, what do our customers need, but you don't want to foreclose anything. How do you think about product strategy around that? >> There's a ton of opportunity to interact with customers. We have this thing called the Customer Advisory Board. We run, I think, four of them and we run a monthly. And so we got an opportunity to kind of get that anecdotal data and the direct contact. We also have a portal called ideas.splunk.com where customers can come tell us what they want us to build next. And we look at that every month, you know, and there's no way that we could ever build everything that they're asking us to, but we look at that monthly and we use it in sort of our sprint planning to decide where we're going to prioritize engineering resources. And it's just, it's kind of like customers say the darndest things, right? Sometimes they ask us for stuff and we never imagined building it in a million years, >> John: Yeah. >> Like that use case around ads on the set top box, but it's, it's kind of a fun place to be like, we, we just, before this event, we kind of laid out internally what, you know, Shawn and I kind of put together this doc, actually Shawn wrote the bulk of it, but it was about sort of what do we think? Where, where can we take Splunk to the next three to five years? And we talked about these, we referred to them as waves of innovation. Cause you know, like when you think about waves, there's multiple waves that are heading towards the beach >> John: Yeah. >> in parallel, right? It's not like a series of phases that are going to be serialized. It's about making a set of investments. that'll kind of land over time. And, and the first wave is really about, you know, what I would say is sort of, you know, really delivering on the promise of Splunk and some of that's around integration, single sign-on things about like making all of the Splunk Splunk products work together more easily. We've talked a lot in the Q and a about like edge and hybrid. And that's really where our customers are. If you watch the Koby Avital's sort of customer keynote, you know, Walmart by necessity, given their geographic breadth and the customers they serve has to have their own infrastructure. They use Google, they use Azure and they have this abstraction layer that Koby's team has built on top. And they use Splunk to manage kind of, operate basically all of their infrastructure across those three clouds. So that's the hybrid edge scenario. We were thinking a lot about, you mentioned data lakes. You know, if you go back to 2002, when Splunk was founded, you know, the thing we were trying to do is help people make sense of log files. But now if you talk to customers that are moving to cloud, everybody's building a data lake and there's like billions of objects flowing into millions of these S3 buckets all over the place. And we're kind of trying to think about, hey, is there an opportunity for us to point our indexing and analytics capability against structured and unstructured data and those data lakes. So that that'll be something we're going to >> Yeah. >> at least start prototyping pretty soon. And then lastly, machine learning, you know, I'd say, you know, to use a baseball metaphor, like in terms of like how we apply machine learning, we're like in the bottom of the second inning, >> Yeah. >> you know, we've been doing it for a number of years, but there's so much more. >> There's so, I mean, machine learning is only as good as the data you put into the machine learning. >> Exactly, exactly. >> And so if you have, if you have gap in the data, the machine learning is going to have gaps in it. >> Yeah. And we have, we announced a feature today called auto detect. And I won't go into the gory details, but effectively what it does is it runs a real-time analytics job over whatever metrics you want to look at and you can do what I would consider more statistics versus machine learning. You can say, hey, if in a 10 minute period, like, you know, we see more errors than we see on average over the last week, throw an alert so I can go investigate and take a look. Imagine if you didn't have to figure out what the right thresholds were, if we could just watch those metrics for you and automatically understand the seasonality, the timing, is it a weekly thing? Is it a monthly thing? And then like tell you like use machine learning to do the anomaly detection, but do it in a way that's more intelligent than just the static threshold. >> Yeah. >> And so I think you'll see things like auto detect, which we announced this week will evolve to take advantage of machine learning kind of under the covers, if you will. >> Yeah. It was interesting with cloud scale and the data velocity, automations become super important. >> Oh yeah. >> You don't have a lot of new disciplines emerge, like explainable AI is hot right now. So you got, the puck is coming. You can see where the puck is going. >> Yeah >> And that is automation at the app edge or the application layer where the data has got to be free-flowing or addressable. >> Garth: Yeah. >> This is something that is being talked about. And we talked about data divide with, with Chris earlier about the policy side of things. And now data is part of everything. It's part of the apps. >> Garth: Yeah. >> It's not just stored stuff. So it's always in flight. It should be addressable. This is what people want. What do you think about all of that? >> No, I think it's great. I actually just can I, I'll quote from Steve Schmidt in, in sort of the keynote, he said, look like security at the end of the day is a human problem, but it kind of manifests itself through data. And so being able to understand what's happening in the data will tell you, like, is there a bad actor, like wreaking havoc inside of my systems? And like, you can use that, the data trail if you will, of the bad actor to chase them down and sort of isolate em. >> The digital footprints, if you will, looking at a trail. >> Yeah. >> All right, what's the coolest thing that you like right now, when you look at the treasure trove of, of a value, as you look at it, and this is a range of value, Splunk, Splunk has had customers come in with, with the early product, but they keep the customers and they always do new things and they operationalize it >> Garth: Yep. >> and another new thing comes, they operationalize it. What's the next new thing that's coming, that's the next big thing. >> Dude that is like asking me which one of my daughters do I love the most, like that is so unfair. (laughing) I'm not going to answer that one. Next question please. >> Okay. All right. Okay. What's your goals for the next year or two? >> Yeah, so I just kind of finished roughly my first 100 days and it's been great to, you know, I had a whole plan, 30, 60, 90, and I had a bunch of stuff I wanted to do. Like I'm really hoping, sort of, we get past this current kind of COVID scare and we get to back to normal. Cause I'm really looking forward to getting back on the road and sort of meeting with customers, you know, you can meet over Zoom and that's great, but what I've learned over time, you know, I used to go, I'd fly to Wichita, Kansas and actually go sit down with the operators like at their desk and watch how they use my tools. And that actually teaches you. Like you, you come up with things when you see, you know, your product in the hands of your customer, that you don't get from like a CAB meeting or from a Zoom call, you know? >> John: Yeah, yeah. >> And so being able to visit customers where they live, where they work and kind of like understand what we can do to make their lives better. Like that's going to, I'm actually really excited to gettin back to travel. >> If you could give advice to CTO, CISO, or CIO or a practitioner out there who are, who is who's sitting at their virtual desk or their physical desk thinking, okay, the pandemic, were coming through the pandemic. I want to come out with a growth strategy, with a plan that's going to be expansive, not restrictive. The pandemic has shown what's what works, what doesn't work. >> Garth: Sure. >> So it's going to be some projects that might not get renewed, but there's doubling down on, certainly with cloud scale. What would advice would you give that person when they start thinking about, okay, I got to get my architecture right. >> Yeah. >> I got to get my playbooks in place. I got to get my people aligned. >> Yeah >> What's what do you see as a best practice for kind of the mindset to actual implementation of data, managing the data? >> Yeah, and again, I'm, I'm, this is not an original Garth thought. It actually came from one of our customers. You know, the, I think we all, like you think back to March and April of 2020 as this thing was really getting real. Everybody moved as fast as they could to either scale up or scale scaled on operations. If you were in travel and hospitality, you know, that was, you know, you had to figure how to scale down quickly and like what you could shut down safely. If you were like in the food delivery business, you had to figure out how you could scale up, like Chipotle hit two, what is it? $2 billion run rate on delivery last year. And so people scrambled as fast as they could to sort of adapt to this new world. And I think we're all coming to the realization that as we sort of exit and get back to some sense of new normal, there's a lot of what we're doing today that's going to persist. Like, I think we're going to have like flexible rules. I don't think everybody's going to want to come back into the office. And so I think, I think the thing to do is you think about returning to whatever this new normal looks like is like, what did we learn that was good. And like the pandemic had a silver lining for folks in many ways. And it sucked for a lot. I'm not saying it was a good thing, but you know, there were things that we did to adapt that I think actually made like the workplace, like stronger and better. And, and sort of. >> It showed that data's important, internet is important. Didn't break, the internet didn't break. >> Garth: Correct. >> Zoom was amazing. And the teleconferencing with other tools. >> But that's kind of, just to sort of like, what did you learn over the last 18 months that you're going to take for it into the next 18 years? You know what I mean? Cause there was a lot of good and I think people were creative and they figured out like how to adapt super quickly and take the best of the pandemic and turn it into like a better place to work. >> Hybrid, hybrid events, hybrid workforce, hybrid workflows. What's what's your vision on Splunk as a tier one enterprise? Because a lot of the news that I'm seeing that's, that's the tell sign to me in terms of this next growth wave is big SI deals, Accenture and others are yours working with and you still got the other Partnerverse going. You have the ecosystems emerging. >> Garth: Yep. >> That's a good, that means your product's enabling people to make money. >> Garth: Yeah. Yeah, yeah, yeah. >> And that's a good thing. >> Yeah, BlueVoyant was a great example in the keynote yesterday and they, you know, they've really, they've kind of figured out how, you know, most of their customers, they serve customers in heavily regulated industries kind of, and you know, those customers actually want their data in a Splunk tenant that they own and control and they want to have that secure boundary around that. But BlueVoyant's figured out how they can come in and say, hey, I'm going to take care of the heavy lifting of the day-to-day operations, the monitoring of that environment with the security. So, so BlueVoyant has done a great job sort of pivoting and figuring out how they can add value to customers and do, you know, because they they're managing not just one Splunk instance, but they're managing 100s of Splunk cloud instances. And so they've got best practices and automation that they can play across their entire client base. And I think you're going to see a lot more of that. And, and Teresa's just, Teresa is just, she loves Partners, absolutely loves Partners. And that was just obvious. You could, you could hear it in her voice. You could see it in her body language, you know, when she talked about Partnerverse. So I think you'll see us start to really get a lot more serious. Cause as big as Splunk is like our pro serve and support teams are not going to scale for the next 10,000, 100,000 Splunk customers. And we really need to like really think about how we use Partners. >> There's a real growth wave. And I, and I love the multiples wave in parallel because I think that's what everyone's consensus on. So I have to ask you as a final question, what's your takeaway? Obviously, there's been a virtual studio here where all the Splunk executives and, and, and customers and partners are here. TheCUBE's here doing all the presentations, live by the way. It was awesome. What would you say the takeaway is for this .conf, for the people watching and consuming all the content online? A lot of asynchronous consumption would be happening. >> Sure. >> What's your takeaway from this year's Splunk .conf? >> You know, I, it's hard cause you know, you get so close to it and we've rehearsed this thing so many times, you know, the feedback that I got and if you look at Twitter and you look at my Slack and everything else, like this felt like a conf that was like kind of like a really genuine, almost like a Splunk two dot O. But it's sort of true to the roots of what Splunk was true to the product reality. I mean, you know, I was really careful with my team and to avoid any whiff of vaporware, like what were, what we wanted to show was like, look, this is Splunk, we're acquiring companies, you know, 43 major releases, you know, 100s of small ones. Like we're continuing to innovate on your behalf as fast as we can. And hopefully this is the last virtual conf. But even when we go back, like there was so much good about the way we did this this week, that, you know, when we, when we broke yesterday on the keynote and we were sitting around with the crew and it kind of looking at that stage and everything, we were like, wow, there is a lot of this that we want to bring to an in-person event as well. Cause so for those that want to travel and come sit in the room with us, we're super excited to do that as soon as we can. But, but then, you know, there may be 25, 50, 100,000 that don't want to travel, but can access us via this virtual event. >> It's like a time. It's a moment in time that becomes a timeless moment. That could be, >> Wow, did you make that up right now? >> that could be an NFT. >> Yeah >> We can make a global cryptocurrency. Garth, great to see you. Of course I made it up right then. So, great to see you. >> Air bump, air bump? Okay, good. >> Okay. Garth Fort, senior vice president, Chief Product Officer. In theCUBE here, we're live on site at Splunk Studio for the .conf virtual event. I'm John Furrier. Thanks for watching. >> All right. Thank you guys. (upbeat music)