(upbeat jingle) >> Cameraman: Just look, Mike. >> Good afternoon everyone, and welcome back to Supercomputing. We're live here with theCUBE in Dallas. I'm joined by my cohost, David. Wonderful to be sharing the afternoon with you. And we are going to be kicking things off with a very thrilling discussion from two important thought leaders at Dell. Bhavesh and Shreya, thank you so much for being on the show. Welcome. How you doing? How does it feel to be at Supercomputing? >> Pretty good. We really enjoying the show and enjoying a lot of customer conversations ongoing. >> Yeah. Are most of your customers here? >> Yes. Most of the customers are, mostly in the Hyatt over there and a lot of discussions ongoing. >> Yeah. Must be nice to see everybody show off. Are you enjoying the show so far, Shreya? >> Yeah, I missed this for two years and so it's nice to be back and meeting people in person. >> Yeah, definitely. We all missed it. So, it's been a very exciting week for Dell. Do you want to talk about what you're most excited about in the announcement portfolio that we saw yesterday? >> Absolutely. >> Go for it, Shreya. >> Yeah, so, you know, before we get into the portfolio side of the house, you know, we really wanted to, kind of, share our thoughts, in terms of, you know, what is it that's, kind of, moving HPC and supercomputing, you know, for a long time- >> Stock trends >> For a long time HPC and supercomputing has been driven by packing the racks, you know, maximizing the performance. And as the work that Bhavesh and I have been doing over the last, you know, couple of generations, we're seeing an emerging trend and that is the thermal dissipated power is actually exploding. And so the idea of packing the racks is now turning into, how do you maximize your performance, but are able to deliver the infrastructure in that limited kilowatts per rack that you have in your data center. >> So I, it's been interesting walking around the show seeing how many businesses associated with cooling- >> Savannah: So many. >> are here. And it's funny to see, they open up the cabinet, and it's almost 19th-century-looking technology. It's pipes and pumps and- >> Savannah: And very industrial-like. >> Yeah, very, very industrial-looking. Yeah, and I think, so that's where the, the trends are more in the power and cooling. That is what everybody is trying to solve from an industry perspective. And what we did when we looked at our portfolio, what we want to bring up in this timeframe for targeting more the HPC and AI space. There are a couple of vectors we had to look at. We had to look at cooling, we had to look at power where the trends are happening. We had to look at, what are the data center needs showing up, be it in the cooler space, be it in the HPC space, be it in the large install happening out there. So, looking at those trends and then factoring in, how do you build a node out? We said, okay, we need to diversify and build out an infrastructure. And that's what me and Shreya looked into, not only looking at the silicon diversity showing up, but more looking at, okay, there is this power, there is this cooling, there is silicon diversity. Now, how do you start packing it up and bringing it to the marketplace? So, kind of, those are some of the trends that we captured. And that's what you see, kind of, in the exhibit floor today, even. >> And Dell technology supports both, liquid cooling, air cooling. Do you have a preference? Is it more just a customer-based? >> It is going to be, and Shreya can allude to it, it's more workload and application-focused. That is what we want to be thinking about. And it's not going to be siloed into, okay, is we going to be just targeting air-cooling, we wanted to target a breadth between air to liquid. And that's how we built into our portfolio when we looked at our GPUs. >> To add to that, if we look at our customer landscape, we see that there's a peak between 35 to 45 kilowatts per rack. We see another peak at 60, we see another peak at 80, and we've got selects, you know, very specialized customers above hundred kilowatts per rack. And so, if we take that 35 to 45 kilowatts per rack, you know, you can pack maybe three or four of these chassis, right? And so, to what Bhavesh is saying, we're really trying to provide the flexibility for what our customers can deliver in their data centers. Whether it be at the 35 end where air cooling may make complete sense. As you get above 45 and above, maybe that's the time to pivot to a liquid-cool solution. >> So, you said that there, so there are situations where you could have 90 kilowatts being consumed by a rack of equipment. So, I live in California where we are very, very closely attuned to things like the price for a kilowatt hour of electricity. >> Seriously. >> And I'm kind of an electric car nerd, so, for the folks who really aren't as attuned, 90 kilowatts, that's like over a hundred horsepower. So, think about a hundred horsepower worth of energy being used for compute in one of these racks. It's insane. So, we, you can kind of imagine a layperson can kind of imagine the variables that go into this equation of, you know, how do we, how do we bring the power and get the maximum bang for, per kilowatt hour. But, are there any, are there any kind of interesting odd twists in your equations that you find when you're trying to figure out. Do you have a- >> Yeah, and we, a lot of these trends when we look at it, okay, it's not, we think about it more from a power density that we want to try to go and solve. We are mindful about all the, from an energy perspective where the energy prices are moving. So, what we do is we try to be optimizing right at the node level and how we going to do our liquid-cooling and air cooled infrastructure. So, it's trying to, how do you keep a balance with it? That's what we are thinking about. And thinking about it is not just delivering or consuming the power that is maybe not needed for that particular node itself. So, that's what we are thinking about. The other way we optimize when we built this infrastructure out is we are thinking about, okay, how are we go going to deliver it at the rack level and more keeping in mind as to how this liquid-cooling plumbing will happen. Where is it coming into the data center? Is it coming in the bottom of the floor? Are we going to do it on the left hand side of your rack or the right hand side? It's a big thing. It's like it becomes, okay, yeah, it doesn't matter which side you put it on, but there is a piece of it going into our decision as to how we are going to build that, no doubt. So, there are multiple factors coming in and besides the power and cooling, which we all touched upon, But, Shreya and me also look at is where this whole GPU and accelerators are moving into. So, we're not just looking at the current set of GPUs and where they're moving from a power perspective. We are looking at this whole silicon diversity that is happening out there. So, we've been looking at multiple accelerators. There are multiple companies out there and we can tell you there'll be over three 30 to 50 silicon companies out there that we are actively engaged and looking into. So, our decision in building this particular portfolio out was being mindful about what the maturity curve is from a software point of view. From a hardware point of view and what can we deliver, what the customer really needs in it, yeah. >> It's a balancing act, yeah. >> Bhavesh: It is a balancing act. >> Let's, let's stay in that zone a little bit. What other trends, Shreya, let's go to you on this one. What other trends are you seeing in the acceleration landscape? >> Yeah, I think you know, to your point, the balancing act is actually a very interesting paradigm. One of the things that Bhavesh and I constantly think about, and we call it the Goldilocks syndrome, which is, you know, at that 90 and and a hundred, right? Density matters. >> Savannah: A lot. >> But, what we've done is we have really figured out what that optimal point is, 'cause we don't want to be the thinnest most possible. You lose a lot of power redundancy, you lose a lot of I/O capability, you lose a lot of storage capability. And so, from our portfolio perspective, we've really tried to think about the Goldilocks syndrome and where that sweet spot is. >> I love that. I love the thought of you all just standing around server racks, having a little bit of porridge and determining >> the porridge. Exactly the thickness that you want in terms of the density trade off there. Yeah, that's, I love that, though. I mean it's very digestible. Are you seeing anything else? >> No, I think that's pretty much, Shreya summed it up and we think about what we are thinking about, where the technology features are moving and what we are thinking, in terms of our portfolio, so it is, yeah. >> So, just a lesson, you know, Shreya, a lesson for us, a rudimentary lesson. You put power into a CPU or a GPU and you're getting something out and a lot of what we get out is heat. Is there a measure, is there an objective measure of efficiency in these devices that we look at? Because you could think of a 100 watt light bulb, an incandescent light bulb is going to give out a certain amount of light and a certain amount of heat. A 100 watt equivalent led, in terms of the lumens that it's putting out, in terms of light, a lot more light for the power going in, a lot less heat. We have led lights around us, thankfully, instead of incandescent lights. >> Savannah: Otherwise we would be melting. >> But, what is, when you put power into a CPU or a GPU, how do you measure that efficiency? 'Cause it's sort of funny, 'cause it's like, it's not moving, so it's not like measuring, putting power into a vehicle and measuring forward motion and heat. You're measuring this, sort of, esoteric thing, this processing thing that you can't see or touch. But, I mean, how much per watt of power, how do you, how do you measure it I guess? Help us out, from the base up understanding, 'cause people generally, most people have never been in a data center before. Maybe they've put their hand behind the fan in a personal computer or they've had a laptop feel warm on their lap. But, we're talking about massive amounts of heat being generated. Can you, kind of, explain the fundamentals of that? >> So, the way we think about it is, you know, there's a performance per dollar metric. There's a performance per dollar per watt metric and that's where the power kind of comes in. But, on the flip side, we have something called PUE, power utilization efficiency from a data center aspect. And so, we try to marry up those concepts together and really try to find that sweet spot. >> Is there anything in the way of harvesting that heat to do other worthwhile work, I mean? >> Yes. >> You know, it's like, hey, everybody that works in the data center, you all have your own personal shower now, water heated. >> Recirculating, too. >> Courtesy of Intel AMD. >> Or a heated swimming pool. >> Right, a heated swimming pool. >> I like the pool. >> So, that's the circulation of, or recycling of that thermal heat that you're talking about, absolutely. And we see that our customers in the, you know, in the Europe region, actually a lot more advanced in terms of taking that power and doing something that's valuable with it, right? >> Cooking croissant and, and making lattes, probably right? >> (laughing) Or heating your home. >> Makes me want to go on >> vacation, a pool, croissants. >> That would be a good use. But, do you, it's more on the PUE aspect of it. It's more thinking about how are we more energy efficient in our design, even, so we are more thinking about what's the best efficiency we can get, but what's the amount of heat capture we can get? Are we just kind of wasting any heat out there? So, that's always the goal when designing these particular platforms, so that's something that we had kept in mind with a lot of our power and cooling experts within Dell. When thinking about, okay, is it, how much can we get, can we capture? If we are not capturing anything, then what are we, kind of, recirculating it back in order to get much better efficiency when we think about it at a rack level and for the other equipment which is going to be purely air-cooled out there and what can we do about it, so. >> Do you think both of these technologies are going to continue to work in tandem, air cooling and liquid cooling? Yeah, so we're not going to see- >> Yeah, we don't, kind of, when we think about our portfolio and what we see the trends moving in the future, I think so, air-cooling is definitely going to be there. There'll be a huge amount of usage for customers looking into air-cooling. Air-cooling is not going to go away. Liquid-cooling is definitely something that a lot of customers are looking into adopting. PUE become the bigger factor for it. How much can I heat capture with it? That's a bigger equation that is coming into the picture. And that's where we said, okay, we have a transition happening. And that's what you see in our portfolio now. >> Yeah, Intel is, Intel, excuse me, Dell is agnostic when it comes to things like Intel, AMD, Broadcom, Nvidia. So, you can look at this landscape and I think make a, you know, make a fair judgment. When we talk about GPU versus CPU, in terms of efficiency, do you see that as something that will live on into the future for some applications? Meaning look, GPU is the answer or is it simply a question of leveraging what we think of as CPU cores differently? Is this going to be, is this going to ebb and flow back and forth? Shreya, are things going to change? 'Cause right now, a lot of what's announced recently, in the high performance computer area, leverages GPUs. But, we're right in the season of AMD and Intel coming out with NextGen processor architectures. >> Savannah: Great point. >> Shreya: Yeah >> Any thoughts? >> Yeah, so what I'll tell you is that it is all application dependent. If you rewind, you know, a couple of generations you'll see that the journey for GPU just started, right? And so there is an ROI, a minimum threshold ROI that customers have to realize in order to move their workloads from CPU-based to GPU-based. As the technology evolves and matures, you'll have more and more applications that will fit within that bucket. Does that mean that everything will fit in that bucket? I don't believe so, but as, you know, the technology will continue to mature on the CPU side, but also on the GPU side. And so, depending on where the customer is in their journey, it's the same for air versus liquid. Liquid is not an if, but it's a when. And when the environment, the data center environment is ready to support that, and when you have that ROI that goes with it is when it makes sense to transition to one way or the other. >> That's awesome. All right, last question for you both in a succinct phrase, if possible, I won't character count. What do you hope that we get to talk about next year when we have you back on theCUBE? Shreya, we'll start with you. >> Ooh, that's a good one. I'm going to let Bhavesh go first. >> Savannah: Go for it. >> (laughs) >> What do you think, Bhavesh? Next year, I think so, what you'll see more, because I'm in the CTI group, more talking about where cache coherency is moving. So, that's what, I'll just leave it at that and we'll talk about it more. >> Savannah: All right. >> Dave: Tantalizing. >> I was going to say, a little window in there, yeah. And I think, to kind of add to that, I'm excited to see what the future holds with CPUs, GPUs, smart NICs and the integration of these technologies and where that all is headed and how that helps ultimately, you know, our customers being able to solve these really, really large and complex problems. >> The problems our globe faces. Wow, well it was absolutely fantastic to have you both on the show. Time just flew. David, wonderful questions, as always. Thank you all for tuning in to theCUBE. Here live from Dallas where we are broadcasting all about supercomputing, high-performance computing, and everything that a hardware nerd, like I, loves. My name is Savannah Peterson. We'll see you again soon. (upbeat jingle)

(electronic music) >> From New York City, it's theCUBE. Covering, New Relic Futurestack 2019. Brought to you by New Relic. >> Hi, I'm Stu Minamin and we're here at New Relic's Futurestack 2019 in the middle of Manhattan. Right next door to Grand Central Station at the Grand Hyatt. Right next door to Grand Central Station at the Grand Hyatt. Happy to welcome to the program, first time guest, Nadya Duke Boone, who's the vice president and general manager of application monitoring here at New Relic. Thanks so much for joining us. >> You're welcome, it's great to be here. >> All right, so, a lot of announcements this morning. Of course, observability front and center Lou talking about how that fits into this space. You have handled really kind of the APM product inside New Relic, so I'm hoping you can help us understand kind of the journey that New Relic's going on. And I've heard in the marketplace, you know, there's AI ops, and there's observability in all of these things. And, you know, APM was the old world for the monolith. So, you know, how does New Relic help live across all of these environments that customers are living in today, and you know, undergoing so much change and new things? >> So as Lou talked about this morning, we think to be an observability platform like New Relic 1, you've got to be open, connected and programmable. That is, we think about that within the application monitoring space, um, we really think it comes down to the matter and issue of like, what are the questions you need to ask. And that really depends on like what stacks you need to see and what are the questions you need to ask. And so, I think it's a false dichotomy to say you need to like, pick a side in observability or monitoring. I think it's really a yes/and. You don't have to pick a side. And with New Relic, what we're able to do whether using our agents and all the rich data they give you or they're using our open platform, the important thing is that we're able to bring it all together in one place. So you can get all your questions answered. >> Yeah, I spent lots of time in my career trying to help break down silos. You know, the traditional infrastructure world, the networking and storage and compute teams. >> Sure >> You know, virtualization helped pull some things together. Software tends to be a unifying factor, but when I look at, you know, the people that own application and the developers. I mean, you've got monoliths, you've got this containerization in microservices coming. You've got the new serverless environments here. You've got a lot of fragmentation inside the customers. How does that impact your business today and are we going to see those, you know, pulled together over time? >> Yeah, what we hear from customers is that, you know, they're going to be running heterogenius environments for a long time. If you're over a year old company, you're not running a single tech stack. You've made choices for your business needs and you need to be able to see across your whole estate. And where New Relic's adding value for our customers, is by bringing this all together and connecting it. So, you can actually see, let's say from a lambda function and our lambda agents, all the way back through your Java monolith and down to the server whether it's running containers or on bare metal, you can see all the way down. And then you can connect it out to you front end as well. And I think it's that ability to see across, is where we're playing. >> All right, uh, can you bring us inside your customers? What are some of the challenges they're facing? And how do you help them along those transformations that they're undergoing? Cause, as you've said, they're going to have this heterogenius environment for quite a long time. >> Yeah, well I think one of the thing they're saying is that they're trying to move faster. And one of the ways they're moving faster is by changing the process by which they build software. So, you know, we've been talking about DevOps for years. We've been talking about Agile for much longer than years. Um, but those changes bring about new needs also, for observability. Cause now, you've got a team that maybe wants to see very deeply with, um, the things they're on call for. But software refuses to break neatly at team boundaries. It just won't, it's going to break wherever it wants to break. So you need to be able to quickly assess, across your whole enterprise what's going on and help those teams talk to you. So, that's definitely a problem we're solving for our customers now. And if I were to pick one more, that I'm hearing, um, well, I'll pick one from this morning and that's cost management, right. As people move to the Cloud, um, its so powerful and easy to be able to start up new services in the Cloud but then, do you know what you have, do you know what is costs, do you know how to optimize? Um, we announced 12 new applications this morning. One of them is addressing exactly that point. >> Yeah, um, okay, what are some of the challenges customers have really monitoring across these different environments? I think cost, it's, well, the promise of Cloud is to help me understand and control my cost quite a bit. But, you know, I understand my data center cost and, in general, much more than I do what I have in the Cloud. >> So, you mean, trying to understand in their software? >> So, I guess, just, if they have these different environments that need to span from a monitoring standpoint what are some of the challenges that customers have and the differences and how does New Relic pull those together for them? >> Well, I think some of it is bringing their teams together. If you've got folks that have a Dev accent and an Ops accent, they may have different points of view about monitoring right? And so, a Dev team might be saying lets go all in on this method or this tool. But an Ops team might be saying something else. And then as you introduce new technologies and maybe now people don't always want to run an agent. They want to have complete visibility over their software. And so, with New Relic, we're giving them those choices. We're giving them, like, hey, you can run an agent, you can, if you've already got stuff at Zipkin, cause maybe, internally, you've got like a great Zipkin champion. Like, great, we're going to be there with you on that too. So, we want to be able to help these teams come together. Um, rather than forcing them to sort of live in silos. >> All right, uh, Lou put a real emphasis talking about platform. And he said platform with a capital 'P'. >> Yeah >> Help us understand a little bit about that and the impact that's going to have for your customers. >> Yeah, absolutely, I think, you know, anyone can say I've got more than one product, therefore I have a platform I think. When we talk about a Platform, we think of software engineers, a Platform is something I can build on. So, I think a capital 'p' Platform is the ability to build apps, to be able to extend it, to be able to add data because you're open. Um, and then the power that we bring, you know, I got to put in my plug, is by connecting it all together. Um, but I think the power of the Platform, um, has been really showing off in the work that we've been doing with our customers to build these new applications. >> All right, um, you mentioned open, which was one of the three features of the Platform itself. Uh, there's open and with API'S and then there's open source can you help us tease through a little bit because there's the openness and then there's some open source pieces. How do those go together and um, I guess, more importantly, what does it mean for the customers? >> Mhmm, thanks for asking, cause I do think those words kind of got tumbled up. So, let's first, let me like tease it apart a little bit. So, first part of open, you sort of already mentioned this, is like, we're open to all data. So, metrics, vents, logs, traces, you can send that data. That's, that's the first thing. You don't have to be running a New Relic agent to use New Relic. The second part though, uh, is that we are actually building and contributing to the open source community software development kits and exporters to make it easy for our customers. And so, we've shipped, we're shipping Open Census and Drop Wizard and Micrometer and exporters and Prometheus scrapers so that these are open source tools that our customers can get, can extend if they need to, to get that data in. So, we're making it easy to get the open data in by providing these open source tools. Um, and we're in there with the communities contributing to the communities as well. And then, finally, you know, the last one is with our new programmable Platform, we are also all in on open source on that. So, we're contributing to open source for folks building on New Relic and our customers are telling us that they're excited to also be able to do that and to share and exchange with each other. >> There's value to the customer and I guess the question is, your relationship with your customer is going to change though. As they're building applications not just, you know, more than just a tool. And I've heard from many of the customers that use New Relic, is, they talk about the partnership. And it really is taking that partnership to the next level. What I say is, New Relic is not coming out and saying oh, we're an open source company and we're building our company around open source. So, you know, it seems that somewhat a maturation of the model but not open source being the be all and end all of New Relic's mission. >> Our mission is to help customers build more perfect software. I mean, that's why we come to work. Is to help them do that and we think this is the right step. Um, to be able to do that and our community around New Relic, as you said, is excited and dynamic. It's great to be here at Futurestack and hear them talking to each other and hear the buzz. I was at our customer advisory board meeting yesterday which is 11 execs from some of our biggest customers and they were talking about how excited they are to see how this is going to help them with their business cause they can connect, now their telemetry data to sort of higher order business problems. Um, and they're also excited to share. So, I think it's the right step for New Relic and our customers. >> There's a lot of startups out there that attack pieces of what New Relic's trying to deliver. Um, you know, how does New Relic look at the landscape out there and the challenge when you're trying to be a platform is, are you providing good enough solutions? Or, you know, are you providing, you know, best solutions across all of these environments? >> Yeah, I think any of our point solutions could go head to head with anything on the market. Um, you know, and the fact that the market is so dynamic is because it's a real problem space for people who are building software. So, folks are going to keep innovating and coming up with new ideas and my mission is to make sure that everyone writing software, is instrumenting it and able to observe it. So I think, I love that more and more folks are joining this conversation. I think it's a great time to be working on monitoring observability. >> Okay, uh, let's start at the top talking a little bit about observability, what should customers be looking at, should they be thinking about that? What feedback are you getting from some of your key customers? Uh, in the space in general and how New Relic's looking to address it? >> Yep, well I think comes down to, a little bit of what we talked about earlier, visibility and answerability and if I were talking to an exec or if I was talking to an engineer, and I was looking at their tools, you know, whatever level you're at and saying, what do you need to monitor how can you get that data in and can you answer the questions? Do you have the tools, the ability to query, to connect the data. Um, to see, hey there's an event that happened and how did my systems change? So I think a lot of it comes down to, is it visible, can I ask the questions? And then for every stack, and no matter what job I'm doing. >> All right, um, when we look at this broad term which gets overused some, but, digital transformation Um, the comment I've made is the long pole in the tent of going through that transformation, really is the application portfolio. You know, I can modernize my platform, I can go to Cloud, but, you know, changing my applications, especially the ones that run my business, is really tough you know. If I'm a company that's been around 15-20 years, you know, I probably have applications that are as old as the company, if not longer. >> Yep. >> Uh, just broadly, how are your customers doing, uh, are they being able to kind of, you know, move along that modernization journey of the application uh, better today than they might have a couple of years ago, or just kind of macro level? >> I think so, I think, you know, between what the Cloud vendors are doing and what we're doing, folks are getting both tools and they're also getting support. I think, you know, the community, the software engineering community is really leaning into this moment. And talking about how to do these types of trasnformations. So I think there's a lot of just, knowledge sharing going on, there's a lot of advice and consulting that you can get. And then I think the tools are lending themselves to being able to do, you know, some people move to the Cloud or lift and shift. Some people use it as an excuse to re-architect. A lot of folks pick and choose. Because not every apps work the same and some apps are, you know, are, um. For some given app, it might be a more relevant time to change it, a more relevant time to let it stay put and you can make those choices. And I think people are approaching it with a certain rational sense. >> Yeah, uh, one last question for you, New Relic's a leader in, according to, the analyst firms that look at the APM market. New Relic's doing a lot of the things that I hear from, you know, the startups getting lots of money thrown at them, so, how should customers think of New Relic today? >> I think, we're the best leading APM product on the market for a reason. And we can never rest our laurel. So I think customers should at us as a trusted partner. Who's going to continue to grow and meet them wherever they are. Our customers are going to Cloud, we want to be there first to meet them there and welcome them in the door. And that comes back to how do we help customers through digital transformation? We're a big software company. We get it, like, we are going through the same, we go through these same questions ourselves. Um, and we talk to our customers all the time. So I think for our customers, it's like, we're the platform and the right partner. Because we're never going to stop. >> Nadya, thank you so much for sharing the updates. Congratulations on the launch today and, uh, best of luck going forward. >> Thanks a bunch. >> All right, lots more here at New Relic Futurestack 2019, I'm Stu Minamin, thanks for watching theCUBE. (electronic music)

>> Narrator: From New York City It's theCUBE covering New Relic FutureStack 2019. Brought to you by New Relic. >> Hi, I'm Stu Minimen and we're here at New Relic's FutureStack 2019 at the Grand Hyatt, next to Grand Central Station, here in New York City. Happy to welcome to the program a first time guest, Roger Scott who's the Chief Customer Officer at New Relic. Roger, thanks so much for joining us. >> Thanks, Stu. Thanks for having me on. Good to be here. >> Alright so, I love this morning actually in addition to hearing all of the announcements, my first hand full of guests on theCUBE were customers. So I got to hear from them and we know your team is always excited about the announcements, but definitely enthusiasm from the customers, things in the keynote that got people. >> Fired up! Yeah. >> Clapping, and fired up. >> Great to see. >> Things like, oh wait! 10 terabytes of data, pressure thing, refresh for like a second, and >>oh my gosh! There's results. Yeah >> Pretty impressive so maybe give us a little bit of insight into customer engagement and how it's let to the bevy of announcements here at the show. >> Oh it's a great question actually and I think in my capacity as Chief Customer Officer and the functions I'm responsible for, we're continually engaging with customers as you can imagine. And one of the things we take a lot of pride in is being a proxy for the voice of the customer back into the organization. So we have a pretty rigid process. Not rigid, a pretty discipline process, I would argue, that allows us to get feedback from the field, listen to our customers, understand what's important to them, and reflect that in our product roadmap. And I'll let you know that's on a weekly cadence we do that. Now we're not doing that in a reactive fashion such that our roadmap diverts every single week in there, but we hear that constant feedback from the field as to what our customers are lacking. So lot of what you hear today, in terms of those six great announcements that we have were a combination of feedback that we've had over the last couple of years, I would argue. Because it's a dramatic shift to go from what we were previously, which was essentially six individual products that work really well together. But through the release of New Relic 1 in May earlier this year and what we announced today has truly developed us in to a observability platform. So monitoring with six different products to a true observably platform that's open, connected and programmable is a dramatic shift. And that's a combination of a bunch of feedback from our customers over the years. >> Yeah. I'm sure it's pretty much feedback from all customers. They're not asking for more tools and more interfaces and more things that they need to learn. >> Roger: Not at all, right. >> In many ways software can be a unifying feature especially that term platform who spend a bunch of time emphasizing what's needed from platform. >> Maybe, what were your costumers struggling with that kind of New Relic 1 in general is looking to solve as well as the observability piece? What went into that launch that was costumer pinpoints and things that they'd been asking for. >> Yeah maybe to stand back a little bit and understand some of the challenges that costumers had and then why they were asking for different solutions or evolution of our solution. If you think about today's world, there's this rapid development an deployment of software, so it's almost got to the point of continuous software deployment. And so your speed of needing to be able to react to problems in your environment, your costumer experience are degrading, ect. Being able to respond to that really quickly is essential, understanding the costumer experience is essential. You talked about operational efficiency of reducing the number of tooling sets or data sets that I'm looking at continually. So anything that we could provide to our costumers that allowed them to get to answers quicker, understand the why, and then be able to remediate that really easily so that the costumers have a greater experience. And at the same time reduces this friction that's unnecessarily introduced when you're going from one product to another, one tool to another and you're spending too much time rationalizing data sets across those tool sets. So consolidation is a big theme, ability to get to your answers really quickly is a big theme and that's really been the genesis of being able to create a platform. But not just a platform for consolidation, for better visibility, and observability but we believe it's not truly a platform until you can develop on it. If you think back in technology history of all the different peradams we've had throughout the history of technology, those who've won the platform wars over the years have been really good at being able to provide tools and ease of adoption of the platform by virtue of being able to build things on top of it. The ability to give people tools that allow them to build technology is really a therasense of the platform as well. >> You know, Roger, there's a certain trust level that costumers have to have if they're going to be building on top of your platform. >> When I've talked to costumers in New Relic they do talk about a partnership >> and the good back and forth but there's definitely a certain amount of stickiness once they've built something on your platform. >> Roger: Right, yeah. >> Any concerns from them as to, you know there's that term lock in out there as to the how do I know that this is going to work for me, and that I'm not going to have my pricing kind of crank up over time and be like oh my gosh, a year or two later, what did I get myself into? >> Right. It's a really important point that I'd like to start off by actually reemphasizing the point you made. I think we pride ourselves on the relationship we have with our costumers. It truly is the heart of everything at my organization does. We have this saying that we are because they are. In the realization that if we don't serve our costumers really well they have choices frequently, we're a saas vendor, the contracts come up for renewal frequently. And if you're unable to deliver on the promises that you made in the sales process, once they implement your solutions and try to use those in production, environments and everyday work if you can't deliver on those promises then you're going to breakdown that level of trust. And trust is at the center of all relationships as you know. Whether it's a personal relationship, you're playing on a sports team, whether you're working with your costumers. And so we want to make sure that we can deliver on those promises once we've sold them the product. So I haven't heard any specific concerns about lock in or anything, I think what they regularly come to us though with is they want us to have a really strong point of view, want us to be opinionated, tell them how this should work effectively together, what does best practice look like, what's the gold standard, what are some of the artifacts, tools, frameworks, reusable templates that we can share with them that accelerates their time to value. So I think the value significantly outweighs the concerns around lock in or reduction of the number of vendors that they're working with. >> If I look at really the enterprise space, you've got costumers working through their application modernization. They've got their modelist their going after micro services. I heard a stat that only about five to ten percent of apps are monitored at the app level today. >> Yeah, pretty scary, isn't it? >> Yeah, how many of your costumers are dealing with the installed state versus new deployments and what are some of the challenges you're hearing from costumers there? >> Yeah and I think it's important to pause that number because I think it's five to ten percent or growing to twenty percent as I think got indicated. If you look at those organizations Born In The Cloud or Born Digital it's significantly higher percentage of that which is possibly an indictment of the low level of instrumentation we see in a lot of legacy software technology stacks. And so I think in today's world we're tryna get that level of instrumentation observability up as much as possible. But maybe to link back to your previous question as well I think there's an important aspect here of when we move to a platform. When you're a product company your differentiation comes through product, comes through the capability of that product features and functions and we've certainly found ourselves in a significant number of those battles against competition where it's feature and function based. That's not a great comfort for the costumer. I think when you move to a platform it's very much around the networks differentiation. When I say network differentiation I think it's about getting the users of your service access to third party applications to third party data sources be they open source data emitters, opentelementry, open sensors, Zipkin any of those data sets that we are now in support for today. Giving them access to those data sets and being able to enrich the experience that we provide them that network effects and that's really where we see the opportunity to deliver significantly more value to our costumers with the ability to then build your own applications on top of the platform. That's second to none in the industry in my opinion. >> Roger, what's New Relic's role in helping costumers as really they're modernizing their work force? When I talk to so many companies it's like they need to retrain and they have to have new skill sets they need to make sure as certain cloud in automation changes where they focus on things and embrace devops and new ways of doing things. There are a lot of challenges there. Where does New Relic play in that modernization for costumers? >> You know what I think it's in a couple ways. The ways that we, my organization, can help the costumer in terms of just sheer understanding of the capability of the platform, what are best practices, how we can drive better accountability as you move to these new technology stacks and new ways of working much more agile environments. And so I think we can do a combination of that just sheer skills development, working really tightly with the likes of AWS you would've heard Dave McCann this morning talking about how when costumers migrate the application work goes to the AWS cloud environment. Hopefully they're not just doing that by way of compute lift and shift but they were actually looking at modernizing and refactoring those applications and when they do that, you heard Dave talk through a number of assets and frameworks and models and reusable best practices that we're trying to work with them on that we can give to our costumers that accelerate their journey 'cause it's not easy. We were talking to Chris Dillon this morning from Cox Automotive and when you think of an organization like that that's forty, fifty years old and has had to transform itself in terms of digital experience for it's costumer base, it's a significant cultural adjustment quite often to get teams to work in fundamentally different ways. So it's not an insignificant challenge but that's partly why we've invested so heavily in costumer success. Taking the costumers on the journey, thinking about their maturity over time, and constantly look for them to get better value from the platform. >> Roger, there are a number of things that have jumped out at me. Things like oh hey, we can save you potentially millions of dollars on your AWS cloud bill. You've already got costumers building on top of the platform, you had the future Haka event just a couple of weeks ago. Any other kind of interesting or exemplary costumer outcomes that you might be able to share? Either doesn't have to be about the new stuff but just that you've recently with your costumers. >> You know, one of the things that's most gratifying for me when talking to costumers is when we've been able to see when you work with older, more traditional companies that are undergoing some form of digital transformation and they're trying to shift a lot of the applications into a more modern stack and environment, become more agile, etc. they frequently sort of peel off part of the business and will have a digital division that will build some innovative, typically mobile based, apps. We've seen a number of different retailers that we've worked with. Number of different travel organizations where we've started out intrumenting the mobile application because they've built a new application to give their consumers or costumers access through to their services, and at some point that application is going to merge into the backend and have to connect back into older technology. And it's been the beauty of being able to connect those two different environments together. Not starting off at what we would've got as slightly easier place to start which was the more modern application environment where we are really well suited to. But then seeing the full value of being able to instrument the front end all the way through to the backend, link that back to the costumer's experience and to the impact on the business in terms of funnel analysis from number of people using the mobile application to actually ordering something to once they've ordered it, feeling satisfied in actually receiving the goods that they ordered. Being able to instrument all of that and understand the impact of performance and availability on the overall business arcam, that's when it's been truly transformational in working with costumers and that's certainly where we'd love to help more of our costumers in that fashion. >> Alright, Roger, want to give you the final word. Of course you bring together a number of costumers here at FutureStack in the U.S as well there's a few of those run in other geographical areas but throughout the year, any other key things you want to highlight as to how costumers can get engaged even more. >> Yeah, I mean, we've got a sort of what I would argue is a tiered approach to costumer success. At the very high end of our engagement model we have a significant number of resources. Solution architects, costumer success managers that we can deploy directly with our costumers. We typically do that in conjunction with them, build out success plans, etc. What we looking at investing Heavily at the moment is also having a good understanding of what the ideal costumer journey is like. Realizing that a costumer can come to an event like this and learn about our product but the best way for them to experience that is in the course of using the product. So heavy focus on product lead growth and how we actually deliver better value through the product itself, remove friction and adoption and getting to better value. We want to automate some of that costumer journey so that we know that if you've just signed up and, for instance, you've configured you're agent and you've done your learning policy but you haven't yet configured a custom apdex on that application or you haven't understood what your key transactions are, we've got all that data in the backend. So we're working really hard to understand how we get that information back out to costumers and go hey we know you haven't necessarily done this yet, here's some access to great assets. A short video clip, a self paced learn guide that somebody can get on demand from an LMS system. So trying to use a combination of direct resource investment, events like this where it's great to make announcements like we did about the six grade innovations and then increasingly using digital through the products but also through just the general costumer journey to say hey this is really important content and information, you should look at this now 'cause it's going to add value in what you're doing today. >> Alright, well Roger Scott, Chief Customer Officer at New Relic, thanks so much for joining us. >> Thanks so much, it's been great talking to you. >> All right. I'm Stu Minimen back with lots more here at New Relic FutureStack 2019 in New York City. Thanks for watching theCUBE. (outro music)

>> Reporter: From New York City, it's theCUBE, covering New Relic FutureStack 2019, brought to you by New Relic. >> I'm Stu Miniman, we're here in New York City right next door to Grand Central Station, at the Grand Hyatt. This first year of theCUBE, attending New Relic's Futurestack, the seventh year of the show, and happy to welcome to the program, Guy Fighel, who's the vice president and general manager of New Relic AI of course, CEO was up on stage this morning announcing New Relic AI, it's in beta, Lew said expect early 2020 for to come out, so thank you so much for joining us. >> Thank you for being here. >> All right, so Guy, you came to New Relic by way of the acquisition of signifAI. And that ends in AI of course, even though we pronounce it signify, so help us understand is this a repackaging, rebranding you know, New Relic-izing the product that was through the acquisition, tell us how we've gotten here. >> Yes, sure, so New Relic AI is a whole new set of capabilities, it's a suite of capabilities that we are launching today in beta that pretty much augments the site reliability engineers with AI and ML capabilities. It runs on top of the New Relic One platform, which is the first observability platform that is connected, open, and programmable so you have all of the existing information and data that you already have inside New Relic. And we've incorporated a lot of the technologies and the techniques that we have developed as part of signifAI with existing capabilities that New Relic already had, and pretty much integrated all of that into single user experience and single type of capabilities across the stack. >> All right so, Guy, AI is a really broad category you know, you got your AI and ML and cognitive and you know all these things, what was kind of the core IP of SignifAI when they came in. >> Sure, so we really focused on correlating and reducing the noise of all of your different alerts and incidents but not just that, we've actually built a recommendation engine on top of that, to provide you much faster context to get into potential root cause of all of your different information focused on events. And now we're combining that with all the time series data that New Relic as a platform has to offer, so you're getting a much broader capabilities for understanding. >> Yeah, you know, definitely there's that promise of AI as we know that humans alone or my traditional tooling just can't keep up, you know, talk about all the different sources of data, the volume of data. I just saw Lew talking about the amount of the millions of items being ingested into the New Relic database, and the billions of items that are being read basically per second. So, help us understand. You say we love, we talk about our videos or extracting the signal from the noise, so, did I hear it was like 80, 85% your early customers are helping to reduce that noise Bring us in a little bit more. >> True, yes, so definitely early results shows us over 80% noise reduction for some of the customers and it is important to understand this is automatic relations, so this is truly based on the engines with no human interaction. Now, we actually have even greater results when some user input is driven into the system and that raises the capabilities as well. In terms of the number of events, yes, we are dealing with huge amount of events and information in the platform and I think it's, all around, not replacing the humans, but actually augmenting the site reliability engineers, so you talked about how systems, you know, there is a great promise for those capabilities. We believe that applied intelligence is a much better term, because it gives really enabling the augmentation for the site reliability engineers. We don't believe that site reliability engineers needs to go away or can even be replaced anytime soon. We definitely think that we can help them understand better and faster, what is the type of problems that they see in their production environments, and then help them resolve that much faster and better. >> Yeah, absolutely, we're huge supporters of really, the best solutions are when you have the people plus machines, there are certain things the machines are going to do on their own, but it's the marrying, so help us understand who's going to be using New Relic AI how is it going to change their day-to-day life and maybe even kind of organizationally, what the impact will be. >> Sure, so if you're a site reliability engineer, or a DevOps themed depending on, how you want to call yourself and, you know, there's a big debate in the industry, whether it's DevOps or site reliability engineers. Pretty much anyone who is responsible for Op time in the digital production environments you're a relevant user, If you carry the pager, if you're on call, you're a relevant user, so you're going to be interacting with the system to be able to actually see what are the problems with potential recommendations and then, you can infuse the system with your own logic. Whether it's based on the logic, we also provide very easy user experience we'd like thumbs up, thumbs down, different types of feedbacks as part of the workflow and I think the most important piece is that we're connecting to users where they are. Meaning, we don't believe we need to change the workflows so, if you're a user and you're already using with a specific internet management providers and you've already connected some of the additional monitoring tools to those providers, we now offer you a streamline of syncing to those instant management platforms and then, in reaching them with all of the information that we already have on the platform. >> So Guy, we've talked about AI but, let's talk a little bit about AI Ops. So, you know I've talked to the number of the vendors I actually went to an AI ops conference earlier this year and some of the talk track was, APM is the old way, AI ops is going to replace what you were doing before Let's take all your scattered tools and consolidate them down. some of the messaging reminds me of what I heard this morning, the New Relic One platform is going to replace a number of tools, pull everything together. Help us kind of, you know, square that circle of APM and AI ops and where you see New Relic compared to some of those competitors out there today. >> Sure, so APM is application performance monitoring. it's all about monitor and have that visibility to your application layer, it has nothing to do with AI ops it has nothing to do with replacing the tools. We believe that everyone should have visibility into their application, and that's, a lot of that messaging came through Lew's key note this morning, and opening it up to any type of open source instrumentation so we can bring it to the platform whether you want to drop an agent, whether you want to use any other open source SDK, we allow you to do that. Pretty much opening up the platform and giving you the option. AI ops is a term coined by Gardner actually, and it is pretty much applying some automation, AI capabilities, ML capabilities, statistical analysis capabilities on huge amount of data that you have in a centralized place. It has nothing to do with the monitoring, per se, so, I definitely think that the industry's going into a new space, where there is a consolidation obviously with different vendors. I believe that New Relic is giving customers the choice to make, whether they want to go and continue using their old tools, and that's okay, and we are an open platform so we will sync up with their data as part of New Relic AI we'll be able to bring in the new data whether by, again inter-connecting with their incident management platform or through a rest API or native integrations or if customer choose to do that, they can just send us all of the data directly and then, we apply the AI ops capabilities on top of the existing platforms. So, it's really opening up for the choice of the customer. >> All right it's been less than a year since the acquisition of SignifAI we know that some of the things when you do an acquisition it's an area of investment, you're going to get more resources, more people but, you've mentioned customers a couple of times, maybe give us a little bit of insight as to how the customer conversations have changed now working for New Relic, as opposed to being a customer understanding that piece of the New Relic ecosystem. >> Oh absolutely, I think, you know, as you transition from a small start up into a company like New Relic you get much more exposure to enterprise customer, your scaling capabilities are much better so we're in serious conversations with a lot of the enterprises customers that have a lot of interest in what we do. A lot of it is part of the branding recognition and all of the great capabilities that New Relic has already, and then marinade that with all of the capabilities that we're bringing or that we brought into New Relic as a young start-up with all of the latest technologies and a lot of the AI capabilities which are truly innovative ones, so definitely see a lot of traction from the enterprise customers, the more sophisticated ones as well. >> All right, so the solution announced today is in beta give us a little bit of a look forward as to what we should expect to see and what feedback you're hoping to get from customers along the way and how they might get engaged if they want to. >> Yeah so definitely we are in beta today. We've engaged with customers prior to the beta, so, we already got a lot of feedback and great feedback and we make some tweaks to the product based on that. We're actually announcing AGI of a small feature today which is enhanced incident context, which provides you active detection for time series data all the way to your slack channels but the overall solution is currently in beta and as we are progressing, within every month we're going to get more and more customers engaging with the platform, and then we're going to release a much more advanced capabilities even than what we have today in GA coming early next year. >> All right great, last thing, big mention and push about observability this morning, help us understand where AI fits into the broader discussion of observability. >> So again, as I mentioned before the observability will allow you to see all of your data in a centralized place. So, it's combining matrix, events, logs and traces in a specific place that now algorithms and different techniques such as AI and ML based algorithms really, really be successful in gathering, understanding, because you have all of that different information for the human brain, it's very hard to actually go and crawl and kind of ingest all of that vast amount of different data points for machines, they're very good at that. They're starving for broad amount of data and so having that capability, building on top of a true observability platform is what makes the AI and ML so successful and drive value to customers in really understanding what the data means. >> All right well, Guy thank you so much for sharing best of luck on the journey towards GA for the the full New Relic AI in the future. We look forward to, launching it. >> Thank you so much. >> All right and once more here, walking through at the New Relic Futurestack 2019, here in New York City. I'm Stu Miniman and thanks for watching theCUBE. (upbeat music)

(upbeat music) [Narrator] - From New York City, it's theCUBE. Covering New Relic Futurestack 2019. Brought to you by New Relic. >> Hi, I'm Stu Miniman and the theCUBE's exclusive coverage of New Relic Futurestack 2019. We're here at the Grand Hyatt which is right next to Grand Central Station in beautiful Manhattan, New York City. We're going to be speaking to a number of customers as well as the executives. It's the seventh year of the show, our first year here and helping me kick off the event, always happy to have a customer on. Dirkie Gertenbach, who's the global B2B engineering lead at AB InBev, a local customer here. Dirkie, thank you so much for joining us. >> Thanks, nice to be here. >> Alright, so, nothing better than getting together with a bunch of your peers you know, downtown New York City, talk about, you know, some cool technology. Before we get into the tech though, I think most people understand AB InBev you know, global beverage brand, really well-known, I know I saw beer trucks when I was making my way through New York City. But tell us a little bit about, kind of, the company and your role inside it. >> Yeah, sure. So, yeah, we're a global beer company, we sell beer. My main focus is the engineering lead at InBev and we look specifically at the e-commerce side of it. So, the digital sales. We've been going through a large transformation these last couple of years, where we flew from more traditional sales to like, digital sales, and we've been implementing our e-commerce platform in a couple of countries the last couple of years. >> So, transformation, it's not just that AB InBev goes from a couple of the largest known brands, you know, in the beverage to "oh boy, now there's so many different micro beers and and different things, I know I can't keep up with all the locals", but even a large brewery like your company - has all little brands, a similar thing I guess is happening on the technology side. >> Yeah. >> Yeah, maybe, tell us a little bit about you know, what that transformation, you know, what's causing that transformation and what is happening inside your or, to support this transformation? >> Yeah sure. So when we started off the digital transformation obviously, it was much simpler. We had a couple of applications in only one or two countries. And in these last two years we've been expanding and we've been implementing it in other countries and we've started moving from a monolithic to a more micro service central so obviously it's like not only one application now, it's like, it's hundreds of applications. In the beginning, it was quite tough because we were moving, we were developing stuff much more quicker than what we could support and that's when we started talking to New Relic. And we looked at their product and looking at a couple of ways of streamlining this operational and having more of a a stability on our products overall. Like, there's still have a lot, we are still immature in a lot of spaces. >> Yeah, so bring us in. You talked about you applications. You know, so many customers are going from their monolist to their micro services but they usually have, you know, that transition is not something that's done overnight, and they need to be able to manage all of that environment. Give us a little bit of view into, you know, what you can about your application portfolio, where you are on that journey and then, you know, what tool sets are you using to be able to manage, monitor and you know, the word of the day of course is observability, so you know, what that means to you and your-- >> Yeah sure, so like I said we've (mumbles) into micro services which is (mumbles) There's a lot of different applications that's running and the main thing that it showed is just having visibility on infrastructure as well as application performance. And application where it's optimal or not. So those are the most basic. We got New Relic involved and that's one of the main tools that we use for observability today. We were using a couple more but we are, like, putting everything into one bucket now. So, it's interesting, the new stuff. What they announced today, that's one of the stuff that we've been missing that's really going to help us. Especially the data base monitoring and the network monitoring. That's something to all our stuff is on Azure so we rely a lot on Azure monitoring. But it doesn't always give you that granularity of like, observability. One of the other things that we are excited about is the, what's the other thing? Sorry, I forgot, I'll come back to you. >> That's all right. So first of all, you know, are you using New Relic One from New Relic? >> We're starting to use it now, so we still use-- >> So walk us through a little bit the journey with New Relic. What products were you using? And tell us where you are with the platform and what you think of the vision of, as Lou said, it's a capital P platform in certain characteristics, that New Relic built when they had in mind? >> Okay, yeah, sure. So in the beginning we were using the browser and Sonetics just like a normal looking whether that website is up or down, and then we started looking so we've got ABM running on every single server we've got now, that gives us like a lot of visibility and we use the insights a lot, so just dashboards. What we found in the new One platform is the dashboard so we can create the linear of data and the visibility that we can give to our stakeholders. It's much better. Just the visibility on the different. I can give you a couple of used cases that we've gone through in these last couple of weeks. So for example, on one of our applications we're having like, login failure, a lot of login failures. And we are really struggling to look at locks and stuff and just pinpointing with that. So on all the data that's coming into New Relic, we started creating dashboards where we can actually see what's the different causes of these login failures and we can actually pinpoint where do we need to put our focus? So it was a good example. And then the other nice thing that I like about the one that we are using actively is the Kubernetes monitoring. It gives you visibility of your entire cluster every single product that's on there and you can just quickly see if there's a part that's struggling or not. >> All right. If you can, I was wondering if you could bring us inside your Kubernetes? How long have you been using it? Do you build your own or using one of the cloud or some other solutions? Tell us a little bit about your stack. What that solution, and where New Relic fits into it? >> Yeah, we started off the Kubernetes just over a year ago. We're using Azure AKAs. So all our stuff is in Azure. And so yeah, in the beginning, we built all of the applications and everything ourselves, so it's all out set. And again, just coming back to monitoring within Kubernetes, it's all controlled. Like (murmurs) It's difficult to have clear visibility so yeah, when they brought out the Kubernetes like monitoring that was like a life changer for us. It's just operations, we're being much more productive now in terms of if we need to scale up and whether our reports are healthy or not that definitely helps a lot. And I think that we've been working (mumbles) It's just the DevOps, we're very new in DevOps, and just the visibility that New Relic gives us helps us a lot in like, pinpointing where we need to focus our DevOps effort. So that's also a good help. >> Stu: Okay. You'd mentioned that there was some things announced that had you excited, things that you'd been looking for. Maybe you can explain, you know, which items jumped off the board at you this morning. >> Yeah, so again, just the database monitoring and the network traffic. That's very important. And then the one thing that's, we were just busy investigating a lock analyser. And the lock ingestion that they announced today that's very exciting. So I mean, we're already in New Relic so I think we're definitely going to look at that. That's going to be a big help. And then it just brings all our data together. And after you've used different tools for locks and monitoring, that's something that makes me very excited. And the other thing is, we're also use SAP in (murmers) and the partnership that New Relic is staring with SAP now, that's also very exciting. Something I'm seeing forward to. >> Stu: Okay. Was there anything you were hoping for that you haven't seen yet? Or anything on your wishlist that you want from either New Relic or from Azure, or from the industry as a whole? >> Nothing yet. I mean, like I said, we're still at early stages. I think maybe in the next year or so we're probably going to start saying, "Hey guys, maybe you need to build this as well" but for now it's just like they keep delivering stuff that before we can even think about it. So that's great. >> Uh, Dirkie, it's your first year coming to Futurestack. What specifically bought you hear? What are you hoping to get our of the day? >> Yeah, it's my first time here. Hopefully I'll come, like I said, I've only got a couple of hours today but I think just in terms of seeing the new stuff that can help us in our operations, our business operations and as well as Stave Apps, it's exciting to see how this can transform our business going forward. In terms of what else I want to see, I don't have high expectations at this stage. Like I said again, they keep delivering before we can actually say what we want so that's just great. >> You mentioned that you're early in your DevOps journey inside the company. Any other color you want to share about, just kind of organizationally what's changing in your business? You know, there's so many new things coming on. You know, you've watched Kubernete's a year ago, you're getting into logging, so the roles and responsibilities that your team members have, and keeping up with all of these various technologies, how's that impacting the work force and the jobs that they do? >> Yeah that's great. So again on our services that we've got we've got a lot of new teams as well, and we've been in a kind of a hyper growth stage, and we're building a lot of micro servers and stuff. We struggle to know whether the performance of that micro service is good enough or not. So that's one thing that our developers struggle with and that's something that New Relics has helped us with. Every single service that we've built, we put it on New Relic, and we've got a, like, you can see three days ago what has been average performance of this API. And that helps us also to type back to (murmurs). So we've got this arranged with each of our services, for our API inpoints , and this gives us a easy way to see whether we're on track or not, and it then translates back to the developer on whether they need to do something to increase that. Another great thing that we've been doing with New Relic with the VP of engineering is they've been helping us a lot in setting up our sites reliability teams. So we've had a couple of discussions with them these last couple of weeks and they've helped us a lot in just identifying what's the different teams that we need to bring to our organization to keep operating in this way and the growth that we are. Also something that's great that we've been looking at, and New Relic has also helped us a lot there, we had a lot of monitoring, we're monitoring everything, but the data doesn't, we don't make a lot of use with the data. So what we've started doing now is to say, "Okay, what's the most (murmurs) path on our application?" "What is it the customer needs to do?" "What's the journey he needs to go to get his (murmurs)" And that's our most critical. So then we went and we worked with New Relic to say that, "Okay guys." "so help us map this to what's the infrastructure." "What's the application that needs to be up to support this journey?" and we created thresholds on that, and alerting. We're almost at a place now where we've got all the stuff mapped and alerted, and proper actions on that, which is also great. It's helping us to be more pro-active and we rely less and less on our customers to tell us, "Hey, there's a problem on the application." >> Stu: Alright. Lou was talking about all the applications that can be built on top of this platform. I saw the network flows, do we think we're going to see the beer flows by the time we come back a year from now? >> The network flows is great. So I need to do a little bit more deep dive onto the application build, but I can start thinking of a couple of examples where we can really use that to deep a little bit deeper into what the data that we've got day to day. So yeah, that's also exciting in the future. >> Well Dirkie Gertenbach, thank you so much for sharing what your groups going through at InBev. Thanks so much for joining us. >> Great. Thank you Stu. >> Alright, and lot's more coverage here at New Relic, Futurestack, in New York city. I'm Stu Miniman and thanks for watching theCUBE.

>> from Burlingame, California It's the Cube covering Suma logic Illuminate 2019. Brought to You by Sumer Logic. >> Hey, welcome back, everybody. Jeffrey here with the Cube worth, assume a logic illuminate 2019 of it. It's at the Hyatt Regency San Francisco airport. About 809 100 people are second year. It's a 30 year of the event, excited to be here and watch it grow. We've seen a bunch of these things grow from little to bigger over a number of years, and it's always funded kind of beer for the zenith. We're excited to do it by our next guest. She's an analyst. It's Nancy Goering, senior analyst for 4 51 research. Nancy, great to see you. >> Thank you for having me. >> Absolutely so first off, Just kind of impressions of the event here. >> Yeah, good stuff. You know, like he's definitely trying to, you know, get on top of some of the big trends. You know, The big news here was their new Cooper nineties monitoring, also obviously kind of staying on the the leading edge of the cloud. Native Technologies. >> It's it's amazing how fast it's growing, you know, doing some research for this. Then I found some of your stuff out on the Internet and just one quote. I think it's from years ago, but just for people to kind of understand the scale, I think, he said, Google was launching four billion containers a week. Twitter had 12,000. Service is uber 4000. Micro service is Yelp and Justin 25 million data points per minute. I think this is like a two or three year old presentation. I mean, the scale in which the data is moving is astronomical. >> Yeah, well, I mean, if you think of Google launching four billion containers every week, they're collecting a number of different data points about a container spinning up about the operation of that container while it's alive about the container spinning down. So it's not even just four billion pieces of data. It's, you know, multiply that by 10 20 or many more. So, yeah, So the volume of operations dated that people are faced with is just, you know, out of this world, and some of that is beginning to get abstracted away, terms of what you need to look at. So, you know, Kubernetes is an orchestration engine so that's helping move things around. You still need to collect that data to inform automation tools, right? So even if you was, even if humans aren't really looking at it, it's being used to drive automation, right? It still has to be collected, >> right, And they're still configurations and settings and and dials. And it seems like a lot of the breaches that we hear about today are people just miss configuring something on us. It's human error. And so how do we kind of square the circle? Because the date is only growing. The quantity sources, the complexity, Yeah, the lack of structure. And that's before we had a I ot And now we got edge devices and they're all reporting in from from home. Yeah, crazy problem. It's >> really, I think, driving a lot of the investments in the focus and more sophisticated analytics, right? So that's why you're hearing a lot more about machine learning. And a I in this space is because humans can't just look at that huge volume >> of data and >> figure out what it means. So the development of machine learning tools, for instance, is gonna pull out a piece of data that's important. Here is the anomaly. This is the thing you should be paying attention to. Andi, obviously getting increasingly sophisticated, right? In terms of correlating data from different parts of your infrastructure in order to yet make sense of it, >> right? And then, Oh, by the way, they're all made up of micro service is a literal interconnected in AP eyes. The third party providers. Yeah. I mean, the complexity is ridicu >> and then, you know, and I've been actually thinking and talking a lot recently about organizational issues within companies that exacerbate some of these challenges. So you mentioned Micro Service is so ah, lot of times, you know, you've got Dev ops groups and an individual Dev Ops group is responsible for a or multiple. Micro service is right. They're all running, sort of autonomous. They're doing their own thing, right? So they could move quickly. But is there anybody overseeing the application that's made up of maybe 1000 Micro Service's? And in some cases, the answer is no. And so it may look like all the Micro Service's are operating well, but the user experience actually is not good, and no one really notices until the user starts complaining. So it's like things start. You know, you have to think about organizational things. Who's responsible for that, right? You know, if you're on a Dev ops team and your job has been to support the certain service's and not the whole, like who's responsible for the whole application and that's it's a challenge, it's something. Actually, in our surveys, we're hearing from people that they're looking for people that skill set, someone who understands how to look at Micro Service's as they work together to deliver a service, right? It's it's a It's a pain point. Shouldn't >> the project the product manager for that application would hopefully have some instances abilities to kind of what they're trying to optimize for? >> In some cases, they're not technical enough, right? A product manager doesn't necessarily have the depth to know that, or they're not used to using the types of tools that the Dev Ops team or the operations team would use to track the performance of an application. So sometimes it's just a matter of having the right tooling in front of them, >> and then even the performance I was like What do you optimizing four you optimized for security up the mind thing for speed are optimizing for yeah, you can optimize for everything if you got a stack rank order at some point in time. So that would also then drive in a different prioritization or the way that you look at those doctorsservices performance. Yeah, interesting. It's another big topic that comes up often is the vision of a single pane of glass in You know, I can't help but think is in my work day. You know how often I'm tabbing between, you know, sales force and email and slack and asana and, um, a couple of browsers air open. I mean, it's it's it's bananas, you know, it's no longer just that that email is the only thing that's open on my desk all day and only imagine the Dev Ops world. No, we saw just crazy complexity around again, managing all the micro service's of the AP eyes. So what's kind of the story? What are you seeing in kind of the development of that? And there's so many vendors now, and so many service is yeah, it's not just we're just gonna put in HB open view, and that's the standard, and that's what we're all right on. >> So if you're looking at it from the lens of of monitoring or observe ability or performance. Traditionally, you had different tools that looked at, say, different layers of a service, so you had a tool that was looking at infrastructure. Was your infrastructure monitoring tool. You had an application performance monitoring tool. You might have a network performance monitoring tool. You might have point tools that are looking just at the data base layer. But as things get more complicated, Azadliq ations are getting much more complex. Looking at that data in a silo tool tends to obscure the bigger picture. You don't understand when you're looking at the's separate tools how some piece of infrastructure might be impacting the application, for instance. And so the idea is to bring all of that operations data about the performance of an application in tow. One spot where you can run again, these more sophisticated analytics so that you can understand the relationship between the different layers of the application stack also horizontally, right? So how micro service's that are dependent on each other? How one micro service might be impacting the performance of another. So that's conceptually the idea behind having a single pane of glass. Now the execution can happen in a bunch of different ways, so you can have one vendor. There are vendors that are growing horizontally, so they're collecting data across the stack. And there's other vendors that are positioning themselves as that sort of central data repositories, so they may not directly collect all of that data. But they might in just some data that another monitoring vendor has collected. So there's an end. You know, there's there's always going to be good arguments for best of breed tools, right? So, you know, in most cases, businesses are not going to settle on just one monitoring tool that does it all. But that's conceptually the reason, right, and you want to bring all of this data together. However you get it, however, it's being collected so that you can analyze it and understand that big picture performance of a complicated application, >> right? But then, even then, as you said, you don't even want, you're not really monitoring the application performance per se. You're just waiting for the you're waiting for some of those needles to fall out of the haystack because you just you just can't get that much stuff. And you know, it's where do you focus your priority? You know what's most critical? What needs attention now. And if without a machine to help kind of point you in the right direction, you're gonna have a hard time finding that needle. >> And there's a lot of different approaches that are beginning to develop. So one is this idea of SL owes or service level objectives. And so, for instance, a really common service level objective that teams are looking at is Leighton. See, So this Leighton see of the service should never drop under whatever ah 100 milliseconds. And and if it does, I want to be alerted. And also if it drops below that objective for a certain amount of time, that can actually help you as a team. Allocate, resource is so if you're not living up to that service level objective, maybe you should shift some people's time toe working on improving the application instead of developing a new feature, right? So it can really help you prioritize your time because you know what? There was a time when people in operations teams or Dev. Ops teams had a really hard time, and they still d'oh figuring out which problems are important because you've always people always have a lot of performance problems going on. So which do you focus your time on? And it's been pretty opaque. It's hard to see. Is this performance impacting the bottom line of my business? Is this impacting? You know, my customers? Are we losing business over this? Like that's That's a really common question that people I can't answer, right? So there you people are beginning to develop these approaches to try to figure out how to prioritize work on performance problems. It's >> interesting because the other one that and some of you mentioned before is kind of this post incident review instead of a post boredom. And, you know, you talked about culture and words matter, and I think that's a really interesting take because it's it's it implies we're gonna learn, and we're gonna go forward. It's dead. Um, yeah, you know, we're gonna yell at each other and someone's gonna get blamed. That's exactly it. And we're going to move on. So, you know, how is that kind of evolved in. And how does that really help organizations do a better job? >> There's, I mean, there's there's much more of a focus on setting aside time to do that kind of analysis, right? So look at how we're performing as a team. Look at how we responded to an incident so that you can find ways that you can do better next time and some of that Israel tactical right? It's tweaking alerts. Did we not get an alert? You know, did we not even know this problem was happening? So maybe you build new alerts or sport get rid of a bunch of alerts that did nothing. You know, there's there's a lot you can learn on again to To your point, I think part of the reason people have started calling in a post Incident review instead of a postmortem is because yet you don't want that to be a session where people are feeling like Blaine. You know, this is my fault. I screwed up. I spent way too long on this, so I >> had to >> set things out properly. It's it's meant to be productive. Let's find the weak points and fill them right. Fill those gaps. >> It's funny you had another. There's another thing I found where you were talking about not not necessarily the Post Borden, but you know, people, people being much more proactive, much more, you know, thoughtful as to how they are going to take care of these things. And it is really more of a social cultural change unnecessarily. The technical piece that culture pieces. So so >> it is and especially, you know, right now there's a lot of focus on on tooling and that can cause some, you know, interesting issues. So, you know, especially in an organization that has really adopted Dev ops practices like the idea of a Dev Ops team is that it's very autonomous. They do what they do, what they need to do right to move fast and to get the job done. And that often includes choosing your own tools, but that that has created a number of problems, especially in monitoring. So if you have 100 Dev ops teams and they all have chosen their own, monitoring tools like this is not efficient, so it's not. It's not a good idea because those tools aren't talking to each other, even though they're micro service's that are dependent on each other. It's inefficient. From a business perspective. You've got all these relationships with vendors, and in some cases, with a single vendor, you might have 50 instances of the same monitoring tool that you know you have 50 accounts with them, like that's just totally inefficient. And then you've got people on a Dev ops, an individual, all the all the individual Dev ops teams have a person who's supposed to be the resident expert in these tools, like maybe you should share that knowledge across. But my point is, you get into the situation where you have hundreds of monitoring tools, sometimes 40 50 monitoring tools. You realize that's a problem. How do you address that problem? Because you're gonna have to go out and tell people you can't use this tool that you love. That helps you do your job that you chose. And so again, this whole cultural question comes out like, How do you manage that transition in a way that's gonna be productive? >> Thea other one that you brought up that was interesting is where the the sport team basically tells the business team you only have X number of incidents. We're gonna give you a budget. Yeah, exceed the budget. We're not going to help you. It's a really different way to think about prioritization. I >> don't necessarily think that's a great approach, but I mean, there was somebody who did that, but I think it's kind of it's kind of >> an interesting thing. And you talked about it in that. I think it was one of your presentations or speeches where, you know, it makes you kind of rethink. You know, why do we have so many incidents? Yeah, and there shouldn't be that many incidents, and maybe some of the responsibility should be shifted to think about why in the how and is more of a systemic problem than a feature problem or a bug, right? It's a broken code. So again, I think there's so many kind of cultural opportunities to rethink this. In a world of continuous development, continuous publishing and continuous pushing out of new code. Yeah, yeah, sure. All right. Nancy will. Thanks for taking a few minutes, and it's really great to talk to you. Thanks >> for having me. >> Alright. She's Nancy. I'm Jeff. You're watching the Cube where it's Uma Logic illuminate 2019. Thanks for watching. We'll see next time

>> Announcer: From Burlingame, California, it's theCUBE, covering Sumo Logic Illuminate 2019. Brought to you by Sumo Logic. >> Hey, welcome back, everybody. Jeff Frick here, with theCUBE. We're at Sumo Logic Illuminate at the Hyatt Regency San Francisco Airport. About 800 people, 900 people, packed house in the keynote earlier this afternoon, really interesting space. And we're excited to have our next guest, kind of on the cutting edge of the IoT space on the consumer side. And he's Robert Parker, the CTO of Samsung SmartThings. Robert, great to see you. >> Hi, great to be here. >> Absolutely, so, before we get into the depth of the conversation, a little bit of a background on SmartThings. I was doing some research, getting ready for this, and the fact that it started as a Kickstarter a long time ago, not that long ago, and now is part of Samsung, a global electronics giant, what a fun adventure. >> Absolutely, I think it's been one of these things where it's great to be something where it's community-driven to begin with. So, Kickstarter was a big part of our launch, and we were one of the biggest Kickstarter launches at the time, really powered by our community around the website and early users. We got a lot of interest in IoT, and then moved on to the next stage of the vision, which is sort of encompassing all devices. And so, that meant we have more than 2,000 different Samsung devices on the platform now, which really allowed devices to talk to each other in ways that are really exciting, and that breadth has been a really great thing to be part of. >> Right, it's really funny, we went to the Samsung Developer Conference a couple years ago, and it was funny to see the living room guys fighting with the kitchen guys as to, what was the center? Is it the TV, or is it the refrigerator? Or is the the washing machine, for that bit? And Samsung's really got a foot in all those places. >> Absolutely, this is one of the things that the SmartThing platform has really enabled Samsung to transition across, as then it's no longer a conversation with the washing machine person or the dryer. All the devices are part of the SmartThings cloud. The SmartThings cloud is a one way that you can talk to Samsung devices, and it's an open ecosystem. So, it's not just Samsung devices, we're equally comfortable with manufacturers, any manufacturer, bringing those devices because home is a multi-vendor environment. You are not going to have all of your home from any one vendor. >> Right. >> And that's been one of the exciting parts of the vision, is that's been part, the open ecosystem has been something that's been part of the SmartThings story forever. To really immortalize that in a platform for Samsung has been a great transition. >> Right, so we're here at Sumo Logic Illuminate, and in preparing for this, I saw an interview with you, you made a really interesting comment. You said that we are a pervasive user of Sumo Logic, and then you said 90% of the team are using Sumo Logic. It's fascinating to me, because I think a lot of companies are chasing innovation, and I think one of the ways to get innovation is you enable more people to have more access to more data, and the tools to actually operate that data so that they can do their jobs and find cool ways to make improvements that aren't necessarily coming from the top down. It sounds like you guys have addressed that philosophy wholeheartedly. >> So, we absolutely have addressed it wholeheartedly, I think there was a lot of luck involved, and I wanted to sort of describe it, is that one of the things that worked well for us is people were excited to use Sumo more and more. They were more excited to see what they could do with the tool, what insights they could get, and so, you'd see your neighbor looking at it, and they'd look at a dashboard and they'd say, hey, can I do a little bit of that? And so much so, in the last year, we've seen a lot of unplanned value come out. So, a third of the value we got out of the Sumo in the past year was unplanned. It was things people didn't, processes they didn't know they would improve that really just came from this groundswell, from what I would call the community. And I think that's where you get, that unlocks a lot of the potential, because you really can't do things from sort of the planned high level. You really need people actively engaged and doing stuff you wouldn't expect. >> That's great. So, I want to talk a little bit about security. Security's a big topic here, it's a topic everywhere we go. And now, with connected devices, and connected keys, and connected doorbells, it seems like, oh, here we go again, and there's this constant talk that security's got to be baked in throughout the entire process. How are you guys dealing with security? It's obviously got to be right at the top of mind in terms of priorities while you're still connecting the sprinklers-- >> No, absolutely. >> And the thermostat and everything else. >> Security and privacy are both critical. I link in privacy even though you didn't ask about it, because, as you think about devices like cameras and things like this, privacy is top of mind. Also, in terms of regulation like GDPR. And so, because of that, we're really looking at both cases, the challenge for both security and privacy is, it really cuts through your whole organization and every process, and by the way, every process that every partner at the organization has, because we can have something that could be exploited from an attack through a customer service representative, that could be a person in the customer service organization, it could be how someone social engineered that. And so, what we've really needed is this kind of continuous intelligence that can span all of these processes, because in something like security, you're as good as your weakest process. And that doesn't mean that we don't focus on all the things that you talked about. We're industry-leading from a device perspective to have hardware baked-in keys and do things in the manufacturing process that lead to something that could be as secure as anything, but that's really the secret of using a lot of the continuous intelligence tools like Sumo, is that all of these could-bes aren't enough. You have to bring it together by having the intelligence that spans those processes to make sure that all of them are elevated, because at the end of the day, a security attack is going to attack your weakest thing, not your strongest thing. >> Right, so one of the other topics here that's talked about is this exponential growth of data, and you guys are part of the problem, 'cause now we got sensors, and light switches, and all these other things that are kickin' off data that, before, we weren't monitoring. And so, from an execution point of view at the company, when you've got so much data that you need to turn into information, and then actionable insight, you said Sumo's got some unique characteristics that allow you guys to get more leverage out of that platform. I wonder if you could dig into that a little bit more. >> And I'd like to reframe the data discussion a little bit, because a lot of people look at it as a problem, and I want to really talk about the opportunity side. So, part of that goes to our story, where we started off at Kickstarter with a few thousand users. We have over 50 million active users now. >> Jeff: 50 million? >> 50 million, our Android application in the Google Play Store had been been downloaded around 200 million times, so it gives you some idea of that size and scope. So, the data is an opportunity. There's an opportunity to build a customer base, to excite people, and to manage the processes that do that. And what's great now is that the availability of this data means that you can do it in more ways than you ever could before. The problem is, you need a tool that brings this together to be able to do that, and doing that well is difficult. Difficult both on the teams, and difficult because of the size, scope, and complexity of the systems because of the data that you mentioned. But the reason you want to do it is so that you can cross the chasm in terms of this opportunity. And more and more companies have this opportunity out in front of them. One of the things that's been really exciting about the cloud is it sort of democratized the entry point, but that wasn't good enough. Just because you could get in the game with three people, it's like making a, you can make a application in a mobile application store, either on Google's or on Apple's, really easily, that gets you in there. What you really need to do is manage the intelligence that goes from that, and for us, it's been really exciting to be able to take our decisions and make them data-driven. And we can do that by this explosion of data because it is there. >> Right, and the data is good, and I think we see data as an asset, it hasn't really hit balance sheets officially yet, but I think you see it in the valuations of companies like Google, and Facebook, and Amazon, right, who obviously have these crazy, giant multiples of their revenue, one, because they're growing, but two, because they have so much data. So, the market's kind of valuing that data without explicitly calling it out as a line item on the balance sheet. That said, not all data has the same value, not all data needs to be treated the same. And so, it really opens up an opportunity to say how do you tier it? So, you don't want to get, y'know, spend a ton of money on a piece of data and a big, fat stream that somebody leaves open on Amazon accidentally, suddenly have a big bill, and that maybe wasn't the most valuable, so. >> I'd actually double down on what you said, because for a typical company, one of the things that's also been true of the mega-scale companies that you pointed out with, is there's a lot of uniformity in their data. So, a company like Amazon, they have customer orders and they've got orders at this massive scale. A typical company doesn't look like that. Their data spread is more fragmented, smaller scale, and so, because of that, they want to make different decisions. And this is the same thing that has already happened in the storage area. People are really comfortable with storage that they're going to have in either disaster recovery, or long-term storage, and they want a very low-cost footprint around that. They've got their hot data, and they're much more willing to have that data managed differently, and at a higher cost rate, because it's much more valuable. We're looking for tools that span that, not just in storage, but in the ingestion, and the management, and the querying of that data, because, like you said, for most businesses, a lot of data is infrequently looked at, or looked at in response to a situation, so I'll never know which 10% of the data will be looked at. It'll be based on, oh, I got audited, or some other business event that happens. And so, this is one of the keys things that businesses are now struggling with. One of them is that, hey, they want to adopt these practices to become modern, or more modernized, but the second one is, to really be able to tier the data because they couldn't treat all the data as if it's hot data, just like they already figured that out for storage. >> Right, it's pretty interesting, 'cause it's been going on for storage forever, and we really saw it, I think, with the rise of Flash, which was super-high quality but super-expensive in the early days, that's coming down. And then, at the other end, we have the Glacier Storage and the cold storage just put it away. I want to get your last thoughts, last answer, Robert. As you look forward, I can't believe we're already in middle of September of 2019, it's fascinating to me that time flies so fast, but as you look forward, what are some of your priorities over the next year or so? How are you guys moving the ball down the field? >> One of the things that we're looking at was the data problem that you were talking about, if, really looking at our infrequent data, and being able to manage that effectively, both from the types of insights that we can get from that, so a lot of this starts to be better usage of machine learning, pattern recognition, AI, and so that we can, the ideal situation for us in that type of data is it got touched once, it got looked at once, and then we could understand how to action it later, that deferred action. And then, how to trigger that deferred action, as well as the tiering that we sort of talked about, that all data's not-- >> Created equal. >> Created equally, and so both those things are happening. Just to put some numbers on this, as why, is that we have 150 terabytes or so of data that is somewhat interesting to our business generated on a daily basis. >> 150 terabytes a day? >> 150 terabytes a day. >> That's interesting, that's the good stuff. >> And out of that, I'd say 10 terabytes is really actionable. And so, that gives you an idea. The other part is how that's growing, where a year ago, we would've been at maybe 60 terabytes of what I would've called this interesting data, and maybe five terabytes of immediately actionable. And so, this is following that, where that's exponentially growing, and it's a big number, so that's what we really think about. >> So, you scared? Because those curves, those curves get steep. >> It's the same way, we look at it as a huge opportunity, so what will happen is, either people will create value out of that for customers, in which case, actually, the opportunity, because it's at such a scale, it will be great for everyone, or, number two, it just becomes noise. And so, it isn't really something to get scared of, because worst case is, it became noise to you. We really want to be one of those people who are getting value out of it, and see the business growth and the consumer value growth out of that. I'm pretty optimistic that we'll be able to do it, because we really, if I look back three, four, years, we've just been able to figure out a way, and I think it will continue to do that. >> All right, well, Robert, thanks for taking a few minutes of your time and sharing the story, it's a great story. >> Thank you, appreciate being here. >> All right, he's Robert, I'm Jeff, you're watching theCUBE. We're at Sumo Logic Illuminate 2019. Thanks for watching, we'll see you next time.

>> Narrator: From Burlingame, California, it's theCUBE. Covering Sumo Logic Illuminate 2019. Brought to you by Sumo Logic. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE. We're at Sumo Logic Illuminate 2019. It's at the Hyatt Regency San Francisco Airport. We're excited to be back. It's our second year, so third year of the show, and really, one of the key tenants of this whole event is the report. It's the fourth year of the report. It's The Continuous Intelligence Report, and here to tell us all about it is the VP of Product Marketing, Kalyan Ramanathan. He's, like I said, VP, Product Management of Sumo Logic. Great to see you again. >> All right, thank you, Jeff. >> What a beautiful report. >> Absolutely, I love the cover and I love the data in the report even more. >> Yeah, but you cheat, you cheat. >> How come? >> 'Cause it's not a survey. You guys actually take real data. >> Ah, that's exactly right, exactly right. >> No, I love them, let's jump into it. No, it's a pretty interesting fact, though, and it came out in the keynote that this is not a survey. Tell us how you get the data. >> Yeah, I mean, so as you already know, Sumo Logic is a continuous intelligence platform. And what we do is to help our customers manage the operations and security of the mission critical application. And the way we do that is by collecting machine data from our customers, and many of our customers, we have two thousand, our customers, they're all running modern applications in the cloud, and when we collect this machine data, we can grade insights into how are these customers building their applications, how are these customers running and securing their application, and that insight is what is reflected in this report. And so, you're exactly right, this is not a survey. This is data from our customers that we bring into our system and then what we do is really treat things once we get this data into our system. First and foremost, we completely anonymize this data. So, we don't-- >> I was going to say Let's make sure we have to get that out. >> Yes, absolutely, so we don't have any customer references in this data. Two, we genericize this data. So, we're not looking for anomalies. We are looking for broad patterns, broad trends that we can apply across all of our customers and all of these enterprises that are running modern mission critical applications in the cloud. And then three, we analyze ten weeks to Sunday. We look at these datas, we look at what stands out in terms of good sample sizes, and that's what we reflect in this report. >> Okay, and just to close a loop on that, are there some applications that you don't include? 'Cause they're just legacy applications that're running on the cloud that doesn't give you good information, or you're basically taking them all in? >> Yeah, it's a good point, I mean we collect all data and we collect all applications, so we don't opt-in applications or out applications for that matter because we don't care about it. But what we do look for is significant sample size because we want to make sure that we're not talking about onesie-twosie applications here or there. We're looking for applications that have significant eruption in the cloud and that's what gets reflected in this report. >> Okay, well, let's jump into it. We don't have time to go through the whole thing here now, but people can get it online. They can download their own version and go through it at their leisure. Biggest change from last year as the fourth year of the report. >> Yeah, I mean, look, there are three big insights that we see in this report. The first one is, while we continue to see AWS rule in the cloud and that's not surprising at all, we're starting to see pretty dramatic adoption of multi-cloud technologies. So, two years ago, we saw a smidgen of multi-cloud in this report. Now, we have seen almost a 50% growth year over year in terms of multi-cloud adoption amongst enterprises who are in the cloud, and that's a substantial jump albeit from a smaller baseline. >> Do you have visibility if those are new applications or are those existing ones that are migrating to different platforms? Are they splitting? Do you have any kind of visibility into that? >> Yeah, I mean, it's an interesting point, and part of this is very related to the growth of Kubernetes that we also see in this report. What ypu've seen is that, in AWS itself, Kubernetes adoption has gone up significantly, what's even more interesting is that, as you think about multi-cloud adoption, we see a lot of Kubernetes, Kubernetes as the platform that is driving this multi-cloud adoption. There is a very interesting chart in this report on page nine. Obviously, I think you guys can see this if they want to download the report. If you're looking at AWS only, we see one in five customers are adopting Kubernetes. If you're looking at AWS and GCP, Google Cloud Platform, we see almost 60% of our customers are adopting Kubernetes. Now, when you put in AWS-- >> One in five at AWS, 60% we got Google, so that means four out of five at GCP are using Kubernetes and bring that average up. >> And then, if you look at AWS, Azure, and GCP, now you're talking about the creme de la creme customers who want to adopt all three clouds, it's almost 80% adoption of Kubernetes, so what it tells you is that Kubernetes has almost become this new Linux in the cloud world. If I want to deploy my application across multiple clouds, guess what, Kubernetes is that platform that enables me to deploy my application and then port it and re-target it to any other cloud or, for that matter, even an on-prem environment. >> Now, I mean, you don't see motivation behind action, but I'm just curious how much of it is now that I have Kubernetes. I can do multi-cloud or I've been wanting to do multi-cloud, and now that I have Kubernetes, I have an avenue. >> Yeah, it started another question. What's the chicken and what's the egg right here? My general sense, and we've debated this endlessly in our company, our general sense has been that the initiative to go multi-cloud typically comes top down in an organization. It's usually the CIO or the CSO who says, you know what, we need to go multi-cloud. And there are various reasons to go multi-cloud, some of which you heard in our keynote today. It could be for more reliability, it could be for more choice that you may want, it could be because you don't want to get logged into any one cloud render, so that decision usually comes top down. But then, now, the engineering teams, the ops teams have to support that decision, and what these engineering teams and these ops teams have realized is that, if they deploy Kubernetes, they have a very good option available now to port their applications very easily across these various cloud platforms. So, Kubernetes, in some sense, is supporting the top down decision to go multi-cloud which is something that is shown in spades as a result of this report. >> So, another thing that jumped out at me, or is there another top trend you want to make sure we cover before we get in some of those specifics? >> I mean we can talk to-- >> Yeah, one of them, one of them that jumped out at me was Docker. The Docker adoption. So, Docker was the hottest thing since sliced bread about four years ago, and is the shade of Kubernetes, not that they're replacements for one another specifically, but it definitely put a little bit of appall in the buzz that was the Docker, yet here, the Docker utilization, Docker use is growing year over year. 30%! >> I'll be the first one to tell you that Docker adoption has not stalled at all. This is shown in the report. It's shown in customers that we talk to. I mean, everyone is down the path of containerizing their application. The value of Docker is indisputable. That I get better agility, that I get better portability with Docker cannot be questioned. Now, what is indeed happening is that everyone who is deploying Docker today is choosing a orchestration technology and that orchestration technology happens to be Kubernetes. Again, Kubernetes is the king of the hill. If I'm deploying Docker, I'm deploying Kubernetes along with it. >> Okay, another one that jumped out at me, which shouldn't be a big surprise, but I'm a huge fan of Andy Jassy, we do all the AWS shows, and one of always the shining moments is he throws up the slide, he's got the Customer slide. >> There you go. >> It's the Services slide which is, in like, 2.6 font across a 100-foot screen that fills Las Vegas, and yet, your guys' findings is that it's really: the top ten applications are the vast majority of the AWS offerings that are being consumed. >> Yep, not just that. It's that the top services in AWS are the infrastructure-as-a-service services. These are the core services that you need if you have to build an application in AWS. You need ECDO, I need Esri, I need identity access management. Otherwise, I can't even log into AWS. So, this again goes back to that first point that I was making was that multi-cloud adoption is top of mind for many, many customers right now. It's something that many enterprises think of, and so, if I want to indeed be able to port my application from AWS to any other environment, guess what I should be doing? I shouldn't be adopting every AWS service out there because if I frankly adopted all these AWS services, the tentacles of the cloud render are just so that I will not be able to port away from my cloud render to any other cloud service out there. So, to a certain extent, many of the data points that we have in this report support the story that enterprises are becoming more conscious of the cloud platform choices that they are making. They want to at least keep an option of adopting the second or the third cloud out there, and they're consciously, therefore choosing the services that they are building their applications with. >> So, another hot topic, right? Computer 101 is databases. We're just up the road from Oracle. Oracle OpenWorld's next week. A lot of verbal jabs between Oracle and some of the cloud providers on the databases, et cetera. So, what do the database findings come back as? >> I mean, look at the top four databases: Redis, MySQL, Postgres, Mongo. You know what's common across them? They're all open-source. They're all open-source database, so if you're building your application, find standard components that you can then build your application on, whether it's a community that you can then take and move to any other cloud that you want to. That's takeaway number one. Takeaway number two, look at where Oracle is in this report. I think they're the eighth database in the cloud. I actually talked to a few customers of ours today. >> Now, are you sampling from Oracle's cloud? Is that a dataset? >> No, this is-- >> Yes, right, okay. So, I thought I want to make sure. >> And, if AWS is almost the universe of cloud today, we can debate at some bids, but it is close enough, I'd say, it tells you where Oracle is in this cloud universe, so our friends at Redwood City may talk about cloud day in and day out, but it's very clear that they're not making much of intent in the cloud at this point. >> And then, is this the first year the rollup of the type of database that NoSQL exceeded relational database? >> No, I mean, we've been doing this for the last two years, and it's very clear that NoSQL is ahead of SQL in the cloud, and I think the way we think about it is primarily because, when you are re-architecting your applications in the cloud, the cloud gives you a timeline, it gives you an opportunity to reconsider how you build out your data layer, and many of our customers are saying NoSQL is the way to go. The scalability demands, the reliability demands, so if my application was such that I now have the opportunity to rethink and redo my data layer, and frankly, NoSQL is winning the game. >> Right, it's winning big time. Another big one: serverless, Lambda. Actually, I'm kind of surprised it took so long to get to Lambda 'cause we've been going to smaller atomic units of compute, store, and networking for so, so long, but it sounds like, looks like we're starting to hit some critical mass here. >> Yeah, I mean, look, Lambda's ready for primetime. I mean we have seen that tipping point out here. Almost one in three customers of ours are using Lambda in production environments. And then, if you cast a wider net, go beyond production and even look at dev tests, what we see is that almost 60% of Sumo Logic's customers, and if you look at 2,000 customers, that's a pretty big sample size. Almost 60% of enterprises are using Lambda in some way, shape, or form. So, I think it's not surprising that Lambda is getting used quite well in the enterprise. The question really is: what are these people doing with Lambda? What's the intent behind the use of Lambda? And that's where I think we have to do some more research. My general sense, and I think it's shared widely within Sumo Logic, is that Lambda's still at the edges of the application. It's not at the core of the application. People are not building your mission critical application on Lambda yet because I think that that paradigm of thinking about event-driven application is still a little foreign to many organizations, so I think it'll take a few more years for an entire application to be built on Lambda. >> But you would think, if it's variable demand applications, whether that's a marketing promotion around the Super Bowl or running the books at the end of the month, I guess it's easy enough to just fire up the servers versus doing a pure Lambda at this point in time, but it seems like a natural fit. >> If you're doing the utility type application and you want to start it and you want to kill it and not use it after an event has come and gone, absolutely, Lambda's the way to go. The economics of Lambda. Lambda absolutely makes sense. Having said that, I mean, if you're to build a true mission critical application that you're going to be keeping on for a while to come, I'm not seeing a lot of that in Lambda yet, but it's definitely getting there. I mean we have lots of customers who are building some serious stuff on Lambda. >> Well, a lot of great information. It's nice to have the longitudinal aspect as you do this year over year, and again, we're glad you're cheating 'cause you're getting good data. >> (chuckles) >> (laughs) You're not asking people questions. >> Yeah, I mean, I'd like to finish out by saying this is a report that Sumo Logic builds every year, not because we want to sell Sumo Logic. It's because we want to give back to our community. We want our community to build great apps. We want them to understand how their peers are building some amazing mission critical apps in the cloud and so, please download this report, learn from how your peers are doing things, and that's our only intent and goal from this report. >> Great, well, thanks for sharing the information and a great catch-up, nice event. >> All right, thank you very much, Jeff. >> All right, he's Kalyan, I'm Jeff. You're watching theCUBE. We're at Sumo Logic Illuminate 2019. Thanks for watching, we'll see you next time. (upbeat electronic music)

>> Narrator: From Burlingame, California, it's theCUBE, covering Sumo Logic Illuminate 2019! Brought to you by Sumo Logic. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Sumo Logic Illuminate 2019 event. It's at the Hyatt Regency San Francisco Airport, about eight hundred, nine hundred people, our second year. It's the third year of the event. Excited to be here and watch it grow. We've seen a bunch of these things grow from little to big over a number of years and it's always fun to kind of be here for the zenith. We're excited to be joined by our next guest, she's an analyst. It's Nancy Gohring, Senior Analyst for 451 Research. Nancy, great to see you. >> Thank you, thanks for having me. >> Absolutely. So first off, just kind of impressions of the event here. >> Yeah, good stuff, you know? Definitely trying to, you know, get on top of some of the big trends, you know, the big news here was their new Kubernetes monitoring tool. So obviously kind of staying on the leading edge of the cloud-native technologies. >> It's amazing how fast it's growing, you know. Doing some research for this event, I found some of your stuff out on the internet, and just one quote, I think it's from years ago, but just for people to kind of understand the scale, I think you said Google was launching four billion containers a week, Twitter had twelve thousand services, Uber four thousand microservices, Yelp ingesting twenty-five million data points per minute, and I think this is a two or three year old presentation, I mean, the scale in which the data is moving is astronomical. >> Yeah, well if you think of Google launching four billion containers every week, they're collecting a number of different data points about a container spinning up, about the operation of that container while it's alive, about the container spinning down. So it's not even just four billion pieces of data, it's, you know, multiply that by ten or twenty or many more. So yeah, so the volume of operations data that people are faced with, is just, you know out of this world. And some of that is beginning to get abstracted away in terms of what you need to look at so you know Kubernetes is an orchestration engine so that's helping move thing around. You still need to collect that data to inform automation tools, right, so even humans aren't really looking at it, it's being used to drive automation. >> Right. >> It still has to be collected. >> Right. And there's still configurations and settings and dials and it seems like a lot of the breaches that we hear about today are just people misconfiguring something on AWS >> Yeah, it's human error. >> It's human error. And so how do we kind of square the circle cause the data's only growing the quantity, the sources, the complexity, the lack of structure and that's before we add IOT and now we have edge devices and they're all reporting in from home. >> Yeah >> Crazy problems. >> It's really, I think, driving a lot of the investments and the focus in more sophisticated analytics, right, so that's why you're hearing a lot more about machine learning and AI in this space. It's because humans can't just look at that huge volume of data and figure out what it means. So, the development of machine learning tools, for instance, is going to pull out a piece of data that's important. Like, here's the anomaly, this is the thing you should be paying attention to. And then obviously getting increasingly sophisticated, right, in terms of correlating data from different parts of your infrastructure in order to make sense of it. >> Right. And then, oh, by the way, they're all made up of microservices that are all interconnected and API is the third party providers >> Yeah. >> I mean the complexity is ridiculous. >> Yeah, and then, you know, and I've been actually thinking and talking a lot recently about organizational issues within companies that exacerbates some of these challenges. So you mentioned microservices. So, a lot of times, you know, you've got DevOps groups and an individual DevOps group is responsible for a, or multiple, microservices, right. They're all running sort of autonomous. They're doing their own thing, right, so that they can move quickly. But is there anybody overseeing the application that's made up of maybe a thousand microservices? And in some cases the answer is "no". And so it may look like all the microservices are operating well, but the user experience actually is not good. And no one really notices until the user starts complaining. So, it's like things start, you know you have to think about organizational things. Who's responsible for that, right? If you're on a DevOps team and your job as been to support these certain services and not the whole, like, who's responsible for the whole application? >> Right. >> And that's, it's a challenge. It's something, actually, in our surveys, we're hearing from people that they're looking for people, that skill set, someone who understands how to look at microservices as they work together to deliver a service, right, it's a pain point. >> Shouldn't the project, or the product manager for that application would hopefully have some visibilities to kind of what they're trying to optimize for. >> In some cases they're not technical enough, right, a product manager doesn't necessarily have the depth to know that. Or they're not used to using tools that the DevOps team or the operations team would use to track the performance of an application. >> Right. >> So sometimes it's just a matter of having the right tooling in front of them >> And then even the performance. It's like, what are you optimizing for? Are you optimizing for security? Are you optimizing for speed? Are you optimizing for... >> Experience... >> You can't optimize for everything. You've got to stack rank order at some point in time, so that would also then drive in a different prioritization or the way that you look at those microservices' performance. >> Yeah, yeah. >> Interesting. So another big topic that comes up often is the vision of a single pane of glass. And, you know, I can't help but think as in my work day how often I'm tabbing between you know, sales force, and email, and slack, and Asana, and a couple of browsers are open. I mean, it's bananas, you know. It's no longer just that email is the only thing that's open on my desk all day. >> Yeah. >> And then you can only imagine the DevOps world that we saw just crazy complexity around, again, managing all the microservices, the APIs, so what kinds of, sort of, what are you seeing in kind of the development of that? And there's so many vendors now, and so many services. >> Yeah. >> It's not just, we're just going to put in HP open view and that's the standard and that's what we're all on. >> So if you're looking at it from the lens of monitoring or observability or performance, traditionally you had different tools that looked at, say, different layers of a service. So you had a tool that was looking at infrastructure - it was your infrastructure monitoring tool. You had an application performance monitoring tool. You might have a network performance monitoring tool. You might have point tools that are looking just at the data base layer. But as things get more complicated, as applications are getting much more complex, looking at that data in a silo tool tends to obscure the bigger picture. You don't understand when you're looking at the separate tools how some piece of infrastructure might be impacting the application, for instance. And so, the idea is to bring all of that operations data about the performance of an application into one spot where you can run, again, these more sophisticated analytics so that you can understand the relationship between the different layers of the application stack, also horizontally, right, so, how microservices that are dependent on eachother how one microservice might be impacting the performance of another, so that's conceptually the idea behind having a single pane of glass. Now the execution can happen in a bunch of different ways. So you can have one vendor, there are vendors that are growing horizontally, so they're collecting data across the stack. There's other vendors that are positioning themselves as that sort of central data repository. So they may not directly collect all of that data, but they might ingest some data that another monitoring vendor has collected. So, there's, and, you know, there's always going to be good arguments for best of breed tools right, so, you know, in most cases, businesses are not going to settle on just one monitoring tool that does it all. But that's conceptually the reason, right, is you want to bring all of this data together however you get it, however it's being collected, so that you can analyze it and understand that "big picture" performance of a complicated application. >> Right. But then, even then, as you said, you don't even want to, you're not really monitoring the application performance per se, you're just waiting for the, you're waiting for some of those needles to fall out of the haystack, cause you just, you just can. There's so much stuff. And you know, it's where do you focus your priority. You know, what's most critical, what needs attention now. >> (Nancy) Yeah. >> And if, without a machine to help kind of, point you in the right direction, you're going to have a hard time finding that needle. >> Yeah, and there's a lot of different approaches that are beginning to develop. And one is this idea of SLO's, or Service Level Objectives. And so, for instance a really common Service Level Objective that teams are looking at is latency. So, the latency of the service should never drop under whatever- a hundred milliseconds, and if it does, I want to be alerted. And also, if it drops below that objective for a certain amount of time that can actually help you as a team allocate resources. So, if you're not living up to that Service Level Objective, maybe you should shift some people's time to working on improving the application instead of developing a new feature. Right? >> (Jeff) Right. >> So it can really help you prioritize your time because you know what? There was a time, people in operations teams, or DevOps teams, had a really hard time, and they still do, figuring out which problems are important. 'Cause you've always, people always have a lot of performance problems going on. So which do you focus your time on? And it's been pretty opaque. It's hard to see, is this performance impacting the bottom line in my business? Is this impacting, you know, my customers? Are we losing business over this? Like, that's, that's a really common question that people can't answer. >> Right. >> So, yeah, people are beginning to develop these approaches to try to figure out how to prioritize work on performance problems. >> It's interesting 'cause the other one that you've mentioned before, kind of this post incident review instead of a post mortem and you know, you talked about culture, and "words matter" >> (Nancy) Yeah. >> And I think that's a really interesting take because it's, it implies, we're going to learn, and we're going to go forward as opposed to "it's dead". >> (Linda) Yeah. >> And, you know, we're going to yell at eachother, and someone's going to get blamed... >> (Linda) That's exactly it... >> And we're going to move on. So, you know, how has that kind of evolved and how does that really help organizations do a better job? >> There's, I mean, there's much more of a focus on setting aside time to do that kind of analysis, right? So look at how we're performing as a team. Look at how we responded to an incident so that you can find ways that you can do better next time. And some of that is real tactical, right, it's tweaking alerts. Did we not get an alert? You know, did we not even know this problem was happening? So maybe you build new alerts or get rid of a bunch of alerts that did nothing. You know, there's a lot you can learn and again, to your point, I think part of the reason people have started calling it a post incident review instead of a post mortem is because, yeah, you don't want that to be as session where people are feeling like blame, you know, this is my fault, I screwed up, I spent way too long on this, or I hadn't set things up properly. It's meant to be productive. >> Right. >> Let's find the weak points and fill them. Right? Fill those gaps. >> It's funny you had another, there was another thing I found, you were talking about not, not necessarily the post mortem but, you know, people being much more pro-active, much more, you know, thoughtful as to how they are going to take care of these things. And it is really more of a social, cultural change than necessarily the technical piece. That culture piece is so, so important. >> It is, and especially, you know, right now there's a lot of focus on tooling and that can cause some, you know, interesting issues. So you know, especially in an organization that has really adopted DevOps practices like, the idea of a DevOps team is that it's very autonomous. They do what they need to do, right, to move fast and to get the job done and that often includes choosing your own tools. But that has created a number of problems especially in monitoring. So if you have a hundred DevOps teams and they all have chosen their own monitoring tools, like, this is not efficient. So it's not a good idea because those tools aren't talking to each other, even though they're microservices that are dependent on each other. It's inefficient from a business perspective. You've got all these relationships with vendors and in some cases with a single vendor. You might have fifty instances of the same monitoring tool that, you know, you have fifty accounts with them. Like that's just totally inefficient. And then you've got people on a DevOps and individual, all the individual DevOps teams have a person who's supposed to be the resident expert in these tools, like, maybe you should share that knowledge across... But my point is you get into this situation where you have hundreds of monitoring tools. Sometimes forty, fifty monitoring tools. You realize that's a problem. How do you address that problem? 'Cause you're going to have to go out and tell people you can't use this tool that you love, that helps you do your job, that you chose. So again this whole cultural question comes up. Like, how do you manage that transition in a way that's going to be productive? >> The other one that you brought up that was interesting was where the support team basically tells the business team you only have X-number of incidents, we're going to give you a budget. (laughs) >> Yeah. >> If you exceed the budget we're not going to help you. It's a really different way to think about prioritization... >> Yeah, I don't necessarily think that's a great approach. I mean there was somebody who did that but like... >> But I think its kind of, it's kind of an interesting thing. And you talked about it in that I think it was one of your presentations or speeches where, you know, it makes you kind of re-think, you know, why do we have so many incidents? >> Yeah. >> And there shouldn't be that many incidents. And maybe some of the responsibility should be shifted to think about why, and the how, and is it more of a systemic problem than a feature problem, or a bug, or... >> Right >> A piece of broken code, so again I think there's so many, kind of, cultural opportunities to re-think this, in this world of continuous development, continuous publishing, continuous pushing out of new code. >> Yeah, yeah. For sure. (laughs) >> Alright Nancy, well thanks for taking a few minutes and it was really great to talk to you. >> Thanks for having me. >> Alright, she's Nancy, I'm Jeff. You're watching theCUBE, where it's Sumo Logic Illuminate 2019. Thanks for watching We'll see you next time (electonic music)

>> from Burlingame, California It's the Cube covering Suma logic Illuminate 2019. Brought to You by Sumer Logic >> Hey, welcome back, everybody. Jeffrey here with the Cube were at the higher Regency San Francisco Airport at Suma Logic, Illuminate, 2019 were here last year for our first time. It's a 30 year the show. It's probably 809 100 people around. 1000 packed house just had the finish. The keynote. And we're really excited to have our first guest of the day. Who's been here since the very beginning is Bruno Critic, the founding VP of product and strategy for Suma Logic, you know, great to see you. Likewise. Thank you. So I did a little homework and you're actually on the cube aws reinvent, I think 2013. Wow. How far has the cloud journey progressed? Since efforts? I think it was our first year at reinvented as well. >> That's the second year agreement, >> right? So what? What an adventure. You guys made a good bet six years ago. Seems to be paying off pretty well. >> It really has been re kind of slipped out that the cloud is gonna be a real thing. Put all of our bats into it and have been executing ever since. And I think we were right. They think it is no longer a question. Is this cloud thing gonna be re alarm enterprise gonna adopt it? It's just how quickly and how much. >> Right? Right. But we've seen kind of this continual evolution, right? Was this jump into public cloud? Everybody jumped in with both feet, and now they're pulling back a little bit. But now really seen this growth of the hybrid cloud Big announcement here with Antos and Google Cloud Platform and in containers. And, you know, the rise of doctor and the rise of kubernetes. So I don't know, a CZ. You look a kind of the evolution. A lot of positive things kind of being added to the ecosystem that have helped you guys in your core mission. >> That's right. Look, you know, five years ago, which is such a short time, But yet instead of the speed of the technology adoption and change, you know it's in It's in millennia. What's happened over the last few years is technology stocks have changed dramatically. We've gone from okay, we can host some v ems in the cloud and put some databases in the cloud. So we're now building micro service's architecture, leveraging new technologies like Kubernetes like Serverless Technologies and all the stuff And, you know, some one of the fastest growing technologies that's being adopted by some village custom base, actually the fastest kubernetes and also the fastest customer segment growing customer segments. ImmuLogic is multi clog customers, basically that sort of desire by enterprise to build choice into their offerings. Being able to have leverage over the providers is really coming to fruition right now, >> right? But the multi cloud almost it makes a lot of sense, right, because we're over and over. You want to put your workload in the environment that supposed appropriate for the workload. It kind of. It kind of flipped the bid. It was no longer. Here's your infrastructure. What kind of APs can you build on it? Now here's my app. Where should it run that maybe on Prem it may be in a public cloud. It may be in a data center, so it's kind of logical that we've come into this this hybrid cloud world that said, Now you've got a whole another layer of complexity that that's been added on. And that's really been a big part of the rise of kubernetes. >> That's right. And so, as you're adopting service's that are not equal, right, you have to create a layer that insulate you from those. Service is if you look a tw r continues intelligence report that we just announced today. You will also see that how customers and enterprise are adopting cloud service is is they're essentially adopting the basic and core compute storage network, and database service is there's a long, long tail of service that are very infrequently adopted. And that is because enterprise they're looking for a way to not get to lock Tintin into anyone. Service provider kubernetes Give them Give them that layer of insulation with in thoughts and other technologies like that, you are now able to seamlessly manage all those workloads rather there on your on premise in AWS in G C. P. In azure or anywhere else, >> right? So there's so much we can unpack. You're one of the things I want to touch on which you talked about six years ago, but it's even more thing appropriate. Today is kind of this scale this exponential growth of data on this exponential scale of complexity. And we, as people, has been written about by a lot of smart people, and I, we have a real hard time. Is humans with exponential growth. Everything's linear. Tow us. So as you look at this exponential growth and now we're trying to get insights. Now we've got a I ot and this machine a machine data, which is a whole another multiple orders of magnitude. You can't work in that world with a single painted glass with somebody looking at a dashboard that's trying to find a yellow light that's earned it. I'm going to go read. You don't have analytics. Your hose. >> That's right. This is no longer world of Ding dong lights, right? You can just like to say, Okay, red, green, yellow. The as sort of companies go digital right? Which is driving this growth in data, you know? Ultimately, that data is governed by Moore's law. Moore's law says machines are gonna be able to do twice as much every 18 to 24 months. Well, that guess what? They're gonna tell you what they're doing twice as much. Every 18 to 24 months, and that is an exponential growth rate, right? The challenge that is, budgets don't grow at that rate, either, right? So budgets are not exponentially growing. So how do you cope with the onslaught of this data? And if you're running a digital service, right, if you're serving your customers digital generating revenue through digital means, which is just about every industry. At this point in time, you must get that data because if you don't get the data, you can't run your business. This data is useful not just in operations and security. It's useful for general business abuse, useful in marketing and product management in sales and their complexity. And the analytics required to actually make sense of that data and serve it to the right constituency in the business is really hard. And that has been whatever we have been trying to solve, including this economics of machine. Dad and me talked about it today. Keynote. We're trying t bend the cost curve >> Moore's law >> yet delivered analytics that the enterprise can leverage to really not just operate an application but run their business >> right. So let's talk about this concept of observe ability. You've written box about it. When you talk to people about observe ability, what should they be thinking about? How are you defining it? Why is it important? >> It's great question, So observe ability right now is being defined as a technique right. The simplest way to think about it is people think, observe a witty I need to have these three data sets and I have observed ability. And then you have to ask yourself a question. First of all, what is Observe ability and why does it matter? I think there's a a big misconception in the market how people adopt this is that they think, observe abilities the end. But it isn't observe. Ability is the means of achieving a goal. And what we like to talk about is what is the goal? Observe, observe ability right now. Observe abilities talked about strictly in the devil up space, right? Basically, how am I going to get obs Erv City into an application? And it's maybe runtime how it's running, whether it's up and performance. The challenge with that is that is a pigeon pigeon hole view off, observe ability, observe ability. If you think about it, we talk about objectives during observe ability. Operability tau sa two ns Sorry could be up time in performance. Well, guess what a different group like security observe. Ability is not getting breached. Understanding your compliance posture. Making sure that you are compliant with with regular to re rules and things like that observe ability to a business person to a product manager who's who owns a P N. L. On some product is how are my users using this product powers my application being adopted where users having trouble. What are they and where's the user experience? Poor right? So all of this data is multifaceted and multi useful as multi uses and observing Tow us. Is his objectives driven? If you don't know what your object it is, observe. Ability is just a tool. >> I love that, you know, because it falls under this thing We talked about off the two, which is, you know, there's data, right, and then there's information in the data and then, but it is a useful information because it has to be applied to something that's right in and of itself. It has no value, and what you're talking about really is getting the right data to the right person at the right time, which kind of stumbled into another area, which is how do you drive innovation in an organization? In one of the simple concepts is democratization. Get more people more than data more than tools to manipulate the data. Then piano manager is gonna make a different decision based on different visibility than Security Person or the Dev Ops person. So how is how is that evolving? Where do you see it going? Where was it in the past? And you know, I think he made it interesting or remain made. Interesting thing in the keynote where you guys let your software be available to everyone. And there was a lot of people talking about giving Maur. People Maur access to the tools and more of the data so that they can start to drive this innovation >> abuse of an example of one of the one of the sort of aspects of when we talk about continued continues intelligence. What do we mean? So this concept of agile development didn't evolve because people somehow thought, Hey, why don't we just try to push court production all the time? Break stuff all the time. What's the What's the reason why that came about? It did not come about because somehow somebody decided so better. Software development model It's because cos try to innovate faster, so they they wanted Toa accelerate. How they deliver digital product and service is to their customers. And what's facilitates that delivery cycle is the feedback loop. They get out of their data. They push code early. They observed the data. They understand what it's telling them about how their customers are using their products, and service is what products are working with or not. And they're quickly baking that feedback back into their development cycles into the business business cycles. To make better Prada effectively, it evolved as a as a tool to differentiate and out innovate the competition. And that's to a large degree one of the ways that you deliver the right inside to the right group to improve your business right. And so this is applicable across all use cases in order pot. All departments are on the company, but that's just one example of how you think of this continuous innovation, continuous data from to use analytics and don't >> spend two years doing an M r d and another two years doing a P R d and then another to your shift >> When you when you actually ship it. Half of the assumptions that you made two years ago already all the main along, right? So now you've gotta go. You've wasted half of your development time, and you've only released half of the value that you could have other, >> right? Right. And your assumptions are not gonna be correct, right? You just don't know until you get that >> you think over time, like two years of kubernetes with a single digits percentage adoption technology and soon was customer base. Now it's 1/3 right? Right? Which means no things have changed. If I had made an assumption as of two years ago on communities, I would have no way wouldn't have done this announcement, >> right? Right. >> But we did it in an interactive mode and re benefit from that continuous information continues intelligence that we do in our own >> right, right? We fed Joe and the boys on lots of times so that it's a pretty interesting how fast that came and how it really kind of over took. Doctor has informed they contain it. Even the doctor, according to reporters. Still getting a Tana Tana traction >> and it's >> working in conjunction with communities. Communities allows you to manage those containers right, And Dr Containers are always part of the ecosystem. And so it's, you know, you know, it's like the management layer and the actual container layer, >> right? So as you look forward to give you the last word, you know, as we're really kind of getting into the SIA Teague World and five G's coming just around around the corner, which is gonna have a giant impact on an industrial I ity and this machine a machine communications, what are some of your priorities? What are you looking, you know, kind of a little bit down the road and keeping an eye on >> interesting question. You know, we used to think about I ot as is the new domain. We should think about I or tea. And maybe we need to build a solution for right. It turns out our biggest customers, customers and the way that I have personally reframed my thinking about Iris is the following Computational capacity is ubiquitous. Now, what used to be a modern application 345 years ago was something that your access to your laptop or three or mobile app, and maybe you're a smart watch Now the computation that you interface with runs in your doorbell, you know, in a light switch in your light bulbs and how's it runs everywhere runs in your shoe because when you're around, it talks to your phone to tell you how many steps you've taken, all the stuff right? Essentially, enterprises building application to serve their customers are simply pushing computation farther and farther into our being, like everywhere. There's now I, P Networks, CP use memory and all of those distributed computers are now running the applications that are serving us in our lives, right? And to me, that's what I ot is. It's just an extension off what the digital service is our and we interface with does, and it so happens that when you push computation farther and farther into our lives, you get more and more computers participating. You get more data, and many of our largest customers are essentially ingesting their full stack of iron devices to serve their customers >> right crazy future and you know, it just kind of this continual Adam ization to of computer store and memory. Well, Bruno, hopefully it will not be six years before we see you again. Congrats on the conference. And thanks for taking a few minutes. Absolutely. All right. He's Bruno. I'm Jeff. You're watching the Cube where? It's suma logic illuminate at the Hyatt Regency seven square port. Thanks for watching. We'll see you next time.

>> from Burlingame, California It's the Cube covering Suma logic illuminate 2019. Brought to you by Sumer Logic >> Hey, welcome back there. Ready Geoffrey here with the Cube where it's suma logic illuminate 2019. We're here >> at the Hyatt Regency San Francisco Airport is about 809 100 people packed house in the keynote earlier this afternoon. Really excited tohave. The guy that was >> running the whole show was running the whole show here for this company. He's remain Sayer, >> the president and CEO of To Malachi Remain great to see you again. You too. Thanks. Absolutely. So 30 year. The show Second year of us being here. Wonder if you could just kind of reflect on how this thing is growing. >> Yeah. I mean, I think it's really a testament to the community more so than sumo, and we've seen a lot of growth naturally, because of where customers are with their own adoption of technologies such as cloud, but also transformations that they're going through like digital transformation, cloud transformation so naturally that allows for more audience of people to attend conference like this. Because this is not a sales marketing conference. This is a user conference. And as evidenced by the fact that 60 plus percent of the content is users themselves in the community present. >> Right? And you talked about the theme is really this intelligence gap, which, which was really a key piece of the key note. And it's interesting because talking about data in huge amounts of data flow, exponential growth and types of data, flow of data, sources of data and your data is just data until it turns into information. And then if it turns into good information, that actually could maybe turn into some intelligence and some action that you can do something with. But there's no person that has the ability to manage the data flows now that we're starting to see. So you guys are really coming at that at the core? You've been at it for a long time. You made some great early on bets being cloud native and now really starting to see the benefits as this exponential growth of data just hits everybody >> you're spot on, I think, um, you know, maybe to add to that, I think the challenge that we see despite the tsunami of data growth, is that a lot of organizations still struggle because the lacked ability to be able to share the insights and intelligence they glean from this data. So a lot of things we spoke about the key note today was the whole notion of the intelligence gap that exists. And that's predicated on the fact that you know, we're all going through some sort of transformation or migration or business model change. And with that comes five challenges that we talked about with respect to continues intelligence we internally has actually referred to as a challenge of minding the gap of intelligence trap because we need to help our customers become intelligent and collaborate, communicate much more effectively by virtue of what we've become that what we've become is that trusted partner, that data steward that is sitting on all this valuable insights that we need to be able provide continuously to our community of users. >> Right, if you talked about it really out along three different metrics, right, the operations metrics, which is probably what people think of top of mine security metric on then, as well as the business metric. And, you know, we had a Robert Parker on earlier from smart thing Samsung Smart Things, and he made an interesting comment that they are pervasive users of Suma logic within the company, which I thought was really interesting because everyone's chasing innovation. How do you get innovative? I think one of the core ways, as you give more people more access to more data and the tools to actually do something with it. That seems to be a big piece of the of the smart thing story. And that's really a big part of your guys. Messaging. >> Yeah, I mean, I think unlike other vendors who have restrictions on adoption and usage on or charging by user model, you know, we're trying to make sure we tear those silos down on one of the nature's by nature. One of things you have to do is provide ubiquitous access, and second thing you have to do is built to dress all different types of data so you can get value for all those users and ubiquitous access. And so you hear about that through not just smart things, but a lot of other customers and partners that are here today because that's unlike the old models, >> right? Right. It's interesting, right? Minds, we backed you know, 97 97 98 99 when first started seeing people build Web applications. And they had all these pricing models based on, you know, cores and CP use because it was based on how many employees were inside the inside the walls and would have access to the applications. And they try to apply this to to a public Web page. It doesn't work. Still see some of that nasty legacy stuff, though, >> right? And would now it was 20 years later. So you made >> a big announcement today about really changing your pricing model. Two more fit the realities of the world in which we live. >> Yeah, look on the surface. Why it seems revolutionary. It's not. It's evolutionary for sumo. It's something we've been doing since we first started. For example, we always provided a service that charges an average for the month, not for the penalty. You're going over a day we didn't charge for user's because that's antiquated model. More importantly, we actually provide in an economic model all along the mere the business model of all these companies. So the more you ingest and use the lower your cost become not more right. And so the things we announced today is a further commitment that we have been making to the community and effectively taken the headache away from them because he looked at these other tools, for example, that provide observe ability for monitoring or for security. You have to go calculate, count the licenses. You have to go look at the number data point for a minute. You have to look at the number nodes and who wants to manage software you want to manage Service's. And so what we've done has really taken the next license taken existing licensing mall that we have to the next level and providing a credit based system so that you can flex and choose what you want to use in a given day and give a month and given Pierre recycle across a new suite of packing ages or a suite of products that we brought to market >> right or whatever, whatever you are optimizing for that particular day. That particular moment, that particular business >> but also ties or something you mentioned earlier it it actually helps tear down those silos that other vendors air creating because it provides ubiquitous access to all users for all different types of data, right? And instead of trying to keep those silos and separation that exists, that further challenges intelligence gap that we're seeing in intelligence. Economy, >> right, Right. What? Another great slide. I thought earlier in the keynote was given by Anheuser Busch, and he talked about his security infrastructure and all different layers of security in the solution that he has for, you know, front kind of front door and fishing, etcetera, etcetera. But the great thing is, you basically crossed all those applications stack and and it's a pretty interesting position for you guys to be in to be able to integrate with all these other kind of point solutions that make up parts of the puzzle and to bring it all back and to still have kind of this one ubiquitous Data Analytics platform to go on and do stuff with that. >> Yeah, I mean, I think it's truth be told, something we've been doing for a long time. I think the visual that you saw there is the challenged a lot of our customers have, and specifically they have these silos >> of >> endpoint or firewall or email or whatever else, and it could only make sense of it by leveraging the monitoring of those silos to an intelligence platform like sumo. And so the same thing that you saw in security with Anheuser Busch being able to leverage the silos into intelligence platform for security. We see in the monitoring space for developers and operations team so they'll have silo tools. But observe ability, is not it. You need continuous reliability, and therefore you need to be able to take all those different types of dead and signals, just like you saw on security for the different types of infrastructure and applications that your manager aging and provide an intelligence based system and service, not a monitoring based on the system of service. >> Right? Another big trend that's happening. You guys were riding this wave and you're Jennifer up from from Google Cloud and she she had the same presentation on Antos. I think at the Google Cloud platform, someone earlier today, you know the Mo Mentum behind Hybrid Cloud as kind of the whipsaw. You know, it's all jump into public and then let's not jump in and its hybrid and its multi. The fact of the matter is, everything's going to work supposed to be, which is its workloads Pacific and the works load should run where the workload should run Really a great moment, um, for you guys to be ableto leverage because regardless of where the work flows running based on where it should run, I need to see it in a unified front. Back at the back of the ranch. >> Have a Jeff. I think this is what we saw even last year when we put the continuous intelligence report out, then let alone the changes we saw this year. For example, we saw Container Technologies moved from development to production last year in north of two ex growth. Now we're seeing orchestration technologies like Kubernetes more than two Ex Growth. And what's driving the multi cloud common that you made is because the customers want flexibility and choice of where those work clothes run. Historically, they have been able to do that until now. Leveraging contain orchestration, technology that builds an abstraction layer from the eye. Astor infrastructure is a service later, and obviously a testament to what Google's been doing with an throws in the partnership we have with them. Tow develop and integrate things for anthros. Ston service mesh. >> Yeah. So what's next? What do you looking for? I can't believe we're almost done with 2019. It still shocks me every time I flipped a calendar. >> What? Your priorities going forward? >> Another great event. 2020 year of insight and all knowledge. What we're saying we're gonna be, >> uh >> you >> know, we started down this journey before the market was there, and I think the unique position and fortunate position that we're in right now is Maura, Maura, that market opportunities to us and the community's getting more powerful and stronger day by day and year by year. So we're very early innings of this honestly. And so what do we see? Going forward to your question is a lot of the execution of our strategy that we set out a while ago to build the only continuous intelligence platform. And more importantly, the new category of software called into continues intelligence. That's really mirroring the OMB operating model and economic model of every single digital business that needs to thrive, not just survive >> right in an era of exponential growth data, complexity, sources, types, which is ah, good place to be all right. Well, we're mean. I know you're super busy. Thanks for taking a few minutes. And congratulations on a great show for sure. All right, >> ease remain. I'm Jeff. You're watching the Q word. Suma logic illuminate 2019. >> Thanks for watching. We'll see you next time.

>> Narrator: From Burlingame, California, it's the Cube, covering Sumo Logic Illuminate 2019. Brought to you by Sumo Logic. >> Hey welcome back everybody! Jeff Frick here with the Cube, we're at the Sumo Logic Illuminate conference, it's at the Hyatt San Francisco Airport. About 700, 800 people, full house in the keynote earlier today, all about operational process monitoring, all this crazy data is being kicked out of the Cloud and IoT and all these crazy next-gen applications. We're excited to have a very close friend of mine, CEO of a very hot company, Kamal Shah, the CEO of StackRox. Kamal, great to see you! >> Thank you, and great to be here, Jeff! >> Absolutely! So for folks that aren't familiar with StackRox, give us the overview. >> Sure, so in a nutshell, we do Kubernetes Security, and so as we've heard all day today, enterprises are deploying microservices, containers, Kubernetes, and we do security for your cloud data infrastructure. >> So how does security work for Kubernetes versus security for other things? >> Yeah, so the use cases for security, or the mission for the security team is the same, right? You got to harden your environment to prevent the bad guys from getting in. >> And, you have to make sure, despite your best efforts, if somebody does break in, then you catch them before they do any damage, right? But the how you do security has to evolve for the cloud data stack, right? It has to understand the containers are immutable affirm all infrastructure, you have to understand that it's not just about the container, but it's also about the orchestrator, and specifically Kubernetes, and it's also about making sure that you seamlessly integrate with dev ops processes, automation and workflow. So it requires a fundamentally different approach to security than traditional security tools. >> So you know, we talk a lot about the increasing attack area that's offered by IoT, right? And increasing attack area that's offered by all those API's and all these interconnected applications, but I've never heard anyone really talk about containers or orchestration as kind of a new attack surface. Did we just stop paying attention? Is that something you're seeing happen? >> Yeah it's something that is starting to emerge, and we've seen some high-profile breachers at a large next generation electric car company, and a large shopping site where misconfigurations led to security breaches in the Kubernetes' environment, and Kubernetes' ecosystem also did a Cube security audit, and so I think we're going to start to hear a lot more, because there's more and more applications are being deployed in production. It's creating a new attack area, and as the old saying goes, the predators go where there's food in the system. >> And so if you're not proactive about it, I think it's going to really hurt as you deploy containers in Kubernetes. >> Right, so we hear over and over and over again about breaches because people misconfigure stuff. That just seems to happen, whether it's a database or this, that, and the other. And I think we can pretty much safely assume everyone's going to get breached if they haven't got breached already, 'cause we hear about it all the time. How do you catch them fast, limit the damage and try not to have too much vulnerabilities? >> Exactly, so the use cases for what we do at Kubernetes are the same. Right? Its vulnerability management, it's configuration management, and we just did a study around the state of container in Kubernetes security and misconfigeration was the number one concern. Because the reality is that Kubernetes, there are a lot of knobs. And each knob has multiple options, so if you're not careful you can really misconfigure your environment and make it so much easier for attackers. >> Right, right. >> And it's precisely what happened at the two examples I sighted earlier. So a misconfigerations is important, runtime security is important, and also compliance. Let's not forget about compliance, right. You have to make sure that you meet your PCI, HIPAA, NIST, and CIS benchmark standards for this cloud native stock. >> So what we're seeing is that these are all becoming very, very important and as a result, it's increasing awareness as Kubernetes gets more prominent. >> Right, and then they are creating and tearing down hundreds, thousands, millions of these things at a nidicolous pace. >> I mean exactly. Kubernetes came out of Google, they open sourced it, and it's really what allows you to deploy, manage, containers at scale. Apparently, they manage hundreds of millions of container a day using Kubernetes, it's incredible. >> Jeff: Oh yeah, I saw a statistic that Google launches 4 billion containers per week. That was from a presentation, actually from a 451 analyst from like 2 years ago. So one can only imagine the scale. >> We are also seeing not quite 4 billion containers per week, but we are seeing thousands, and tens of thousands of containers at scale at companies everywhere. They are all deployed in production, and now they are waking up to security. The good news here is that they are waiting for breaches to happen before they solve the problem. There's still a lack of awareness, and what Sumo Logic has done today with the announcement around continued intelligence for Kubernetes just increases the awareness around, hey we have to solve observability, which is logs, metrics, and tracing, which is what Sumo does, and security for your cloud native infrastructures. >> Yeah, I mean the automation is so important, right? You can't do any of this stuff with this exponential growth of data, exponential growth of pushes, of new code releases. There's so many pieces in this, so automation is a huge piece of the puzzle. >> Automation is paramount and with this new infrastructure there aren't enough security people to solve this. So security has to become everybody's responsibility. And the only way we are going to solve this is to automate it. It also has to integrate with your DebOps processes and automation and work flows. If you don't, then the DebOps body is going to reject the security organ, right? So it has to be seamless in the way you deploy it. >> It's interesting you say that because we go to RSA, forty thousand people, more vendor than you can count, it bulges Moscone to the absolute edges. Everyone says over and over that security has to be baked in the entire process from beginning to end, it's not a bolt on and can never be successful as a bolt on. So it surprises me to hear you say that still a lot of people are kind of behind the curve. >> Well I mean if you think about I, even though they say that, right? In a traditional model of the application you go to spend 6 months building it and then you can go spend a couple of weeks or month hardening and putting security around it. But when you are launching applications every 6 hours, you can spend 6 days addressing security, so it has to be built in. And speaking of RSA, if you recall, last year the big talk at RSA was around AI, right. Everything was AI driven security. My prediction, my bold prediction for this RSA is it's going to be all around Kubernetes security. >> Yeah, well it's applied AI. Applied AI for Kubernetes. >> Exactly. >> And that's what you need. I always feel for the SISO just walking the floor at RSA going, "Where do I begin? I mean where do I spend my money, how do I prioritize?" It's kind of like an insurance problem. You can't insure to the nth degree. You got to have a budget, but how do you deploy your assets? It's got to be super, super confusing. >> It really is. I think what your seeing is that SISO's are relying on their DEV and IT ops teams, right? They are partnering with the VP of platform, the VP of infrastructure, the VP engineering, because when you think about this new world security is really, the ownership of security is now shifting from the information's security teams to DevOps teams. So security teams still drive policy, and they still want to make sure they do the trust and verify, but the implementation of the security is now being owned by DevOps teams. So its a big cultural shift that's going on in organizations today. SISO's have to realize that it's no longer just them, but they have to partner with their DevOps counterparts to effectively address security for this cloud native stock. >> Right, so tell us a little bit about the relationship with Sumo. How do the applications work together? What's the solution look like when the 2 solutions are brought together. >> So Sumo has been a great partner. We have several joint customers. The simplest way to think about this is that Sumo does observability for Kubernetes, so that's logs, metrics, and tracing, and we do security from Kubernetes. We are the yin to their yang. What we do is we have taken all the intelligence we get from security and we feed it into the Sumo dashboard. Sumo customers get a single pane of glass, not just for the observability data, but also for their security violations, weather its for vulnerability, weathers it's for configuration or if it's for runtime threats, right? You get it all in one single place. >> Right. So I just want to get your take on kind of this rise of the momentum behind Hybrid Cloud that we've seen recently. Big announcement at Google Cloud show, with Anthos. Big announcement between VMware and Amazon. It always kind of swings back and forth. It was all in to public cloud and now there's a little bit of a pullback in Hybrid, but that's terrific for you. The fact of the matter is workload should run where they should run, they don't really care it's what's appropriate. Horses for courses, right? >> Precisely so, we see the shift from public cloud to Multi-cloud, and then from Multi-cloud to Hybrid cloud. The underlying infrastructure that makes that a reality are containers and Kubernetes, right? And that's why we've seen this tremendous momentum on Kubernetes. What we are seeing is customers that want to give their Dev teams that flexibility to pick their favorite cloud, or to do it on premises, their private clouds. But they want to make it in a single security solution that gets integrated no matter where you run your infrastructure and that's integrated back to your Sumo dashboard. So you have visibility across all Dev teams, all your application infrastructure, regardless of where they are running. There is one security standard that gets implemented. That is really, that's the future. You don't want to be beholden to a one claw provider, you want flexibility, you want choice. Kubernetes allows you to do that. >> Well and the whole thing becomes more autotomized, right, with autonomic memory, autonomic compute, autonomic store, throw that on an IoT and Edges and now you're starting to distribute all those pieces all over the place, which is going to happen. >> Kamal: It is going to happen for sure. >> All right, looking forward I can't believe we're almost through 2019, it still shocks me everyday I look at the calendar, but what are some of your priorities looking forward? What are you guys working on? What do you see coming down the pipe? >> Yes, so you touches on a couple of these. So today, is a lot of talk around Kubernete. We are seeing Kubernetes also get deployed in IoT and edge devices, we are also seeing they are being used to manage serve-less infrastructure. So we are going to continue to evolve as Kubernetes evolves. The other big trend that we are seeing in the market today is around service mesh. People talk a lot about Istio and Linkerd and using service mesh as your policy framework to drive consistent policies across applications, so that's another area where we are innovating very rapidly and that will become, I think, more and more real in enterprise deployments over 2020. >> Well, congratulations Kamal to you and the team. I think you picked a good horse to ride on, I should say ship, right, with Kubernetes. Thanks for taking a few minutes. >> No, thank you for having me. I can officially say now that I've checked off one of my professional bucket-list items, which is to be on the Cube with an old friend. So thank you for having me. >> Check that box man. All right, he's Kamal, I'm Jeff, you're watching the Cube. Were at Sumo Logic Illuminate from the Hyatt San Francisco Airport. Thanks for watching, see you next time.

>> Narrator: From Burlingame, California, it's theCUBE, covering Sumo Logic Illuminate 2019! Brought to you by Sumo Logic. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at Sumo Logic Illuminate at the Hyatt Regency, San Francisco Airport. Our second year here, about a thousand people, third year of the conference, a really good vibe. You know I think some of these cases where the marketers really come to Sumo Logic in terms of data and data monitoring, and there's so many applications that are business and security, and operations. We're excited to have our next guest. He is Lior Mechlovich. He's an architect at Informatica. Lior, great to see you. >> Great to see you, too. >> Absolutely, so you said you've been coming to this for a couple years, just kind of general impressions as it's grown. >> Sure, it's my third year, it's grown very nicely. Always exciting. I think there's a very nice vibe to this conference. I always learn new things so we've been with Sumo for more than four years now for Informatica. And excited as always. >> Yeah, and we've been covering the Informatica show. I think we've looked it up, since 2015 so, we've been doing a lot of work and you guys are right in the heart of this whole data thing, >> Right. >> And you been part of the kind of migration from pretty much pure on-prem, to Cloud. There's rush to public Cloud, and then now kind of this Hybrid model. So you kind of look at the data perspective you know, what's kind of your take as this thing has evolved over the last several years? >> Sure, so we have been around for 26 years. I think building a lot of on-pram, data platforms for being the enterprise Cloud data management that Informatica sells with basically getting your data inside our outside the organization from Clouds, on-pram, or whatever integration pattern you have and we decided four or five years ago to be a Cloud-first company and migrated most of our products to be on Cloud to provide them as a service. And for us, it was a huge journey, we needed to take some offering that we had in the Cloud, some products, and really revamp and building a new microscopic architecture and then slowly migrate all the customers. It took us over a year to make that. We currently run on all three Cloud providers. And really using Sumo mentoring tools to really understand the impact that we have on our customers during this migration. It was a very successful. They hardly noticed that-- >> Oh good. >> Only the nice UI, but they hardly noticed the problems. I mean we really changed a lot of things. >> What is some of the things you learned in that process that you can apply now with just some of your customers in terms of data migration and operating in a Cloud situation versus a traditional data center? >> Sure, so I would definitely highlight the need to be able to roll back and the need to always keep really good money to working it and understanding how the end user getting impacted. And so we really kept that in mind. Everything we do try do always do it side by side, and then when we migrate we're really sure that it is successful and there's no impact on the customer. So I think that's definitely too, harshly monitored everything and be able to roll back when you need to, because you will need to at some point. >> But the rollback is funny, because it use to be you had you know the release cadence was significantly slower than now. And now you've got all these kind of micro pushes that are going on multiple times a day. >> Yeah. >> So how does that impact kind of keeping that safety net? That rollback safety net? >> So it's interesting. So we actually don't deploy that many times a day. Where we can really impact the customer so we deploy the things that are not customer urgent impacting production more, but still the really heavy productions of the thrilling part of the customer; we try to minimize that and make it very customer aware okay. >> So basically they choose their own windows of maintenance and all that. But our customers again hospitals, all kinds of very important, then we are in charge of the data byte in those places. So we don't want to just push whatever we can. We really cannot take that, even a rollback of 1%, it can be very bad so we're a bit more conservative models of deployments, but actually that means we put a lot of effort in our monitoring. What is going on doing those deployments. >> Right. All right, so what are the big trends that happen? I mean containers have been around for awhile, but we really saw kind of the rise of containers in terms of the popular consciousness with Docker couple three or four years ago, and then a couple years ago the Cooper 90's coming in for the orchestration. From your point of view how had those things impacted your world, and how you do your job, and take care of your customers? >> Sure, so for us Cubernetics is really a great opportunity to standardize the way that we deploy across different products. So we have our platform, but we have also different products; different people across the globe. We're a very multi-globe organization, and to get a standard like Cubernetics to help us standardize to get more releases, more stable environments that really solves a lot of problems, because we had this migration that I talked about really left us with a lot of clusters across the globe, different time zones. It was really hard to standardize on the pipelines, and to deploy to really minimize the problems that we give to the end user at the end. >> So we really took that opportunity to use Cubernetics, to use containers to minimize the difference it has from the developer machine all the way to production. To automate the most we can so when it's really is excelling in this. Yeah, so that's where we really... took those containers apart. Today we are in migrating, so not all of that, but we truly see the benefits of standardization, of immutable infrastructure as that key component for us. >> This is just so great, because you have such a longitudinal point of view having been in. The company's been at it for awhile, and you've been at the company for awhile. So another topic I'd love to get your thought is just kind of this exponential explosion of data. I mean it would be curious not to know the numbers, but kind of the scale of data in which you guys are dealing with for you customers, and how that has changed over the last several years before you even really factor in IOT, and this next kind of machine to machine explosion? >> So we definitely see that explosion of data. It's not just the explosion. It's also the different types, and where data has been on-prem, now moving to Cloud. Where do people want to run off all those work loads? As of course a lot of feedback for us as well need to support all the Cloud providers when we use to do a lot of Hadoop on-prem, right? It's all changed now too. >> All the Cloud providers, the data it's theirs. So the data move, data locality is a big thing, Now we need to run all those things on the Cloud. So, I don't remember the exact numbers. I guess we're doing something of 2.5 billion transactions a month for like number of records that we serve. That being we usually just see more work loads, more people, more use cases for onboarding more data from Cloud applications. The data became more dispersed not just more data, but the sources has become like everybody needs to integrate Salesforce or Workday with their on-prem that gives unique opportunities for this kind of data. >> Well, it's funny when you talk about the workloads, because it always use to be, do you bring the workload to the data or the data to the workload, and a knock on the Cloud is that you got to get all the data into the Cloud, and pay for the transport of the data. And there's data gravity that said once you have it in a central location like that the opportunity to put applications against that data is much much higher than if you're bringing the data to the application. You see and how are customers taking advantage of that opportunity? >> So for sure we saw they did that move to the Cloud. When we started from on naked Cloud 10 years ago our entire model was hybrid, so we can stick around on-prem, because the data was on-prem, and since then our hybrid model that you still run both on-prem and on Cloud, you can see the change right? You can see more of our agents. We have an agent based architecture to really being deployed much more on easy 2's, on AKS or whatever to run those workloads in the Cloud. >> Right, but I would imagine the number of workloads applied to each data set now have increased significantly, because now it's in that central repository. >> Yes, and definitely you can see those data legs being built, and mostly in the Cloud. That gives unique opportunity. >> So just get your perspective after a couple days here. I know you haven't been here for a couple of days. We're just getting started at this show. What does Sumo Logic bring to you and your team? What does it enable you to do that you couldn't otherwise do? Why are you happy to be a customer of Sumo? >> Sure, so four and foremost it's the democratization of data. I really like to say that internally. In an organization that's spread across the globe, really sharing insides based on data, it's very important. When you have many R&D centers that can just send this summary; send the data and show people what they mean saves so much time, and so we use it across. We use the customer success, product management to understand feature being used, SRE's, developers. All of those really can communicate based on data. In this Microsoft Office tool you cannot do it without that. You cannot do it without linking, because the different products that we onboard on the platform will not be able to communicate effectively without that. So that's very important, and giving that landing pages dashboard templates for onboarded services to have this kind of standard to follow to monitor how to operate that's very important for us. >> That's great. Go ahead. I'm sorry I interrupted you. >> Sorry, and the key place that we brought Sumo in is basically for instant management. So how to understand when something doesn't work just to try to understand the blast radius, which products are impacted. We have a variety of products, so just in minutes we minimize that in four hours to minutes trying to understand what exactly is going on. Who's impacted to update the customer  and all that. >> I love the part you talked about the democratization, because again I talk about it all the time, and I'll talk about it again, but to drive innovation in a company I think such a key piece of it is to enable more people to have more information, and the tools to manipulate that information, so they see opportunities to make improvements here, there, and otherwise and it sounds like you guys are really using it for that. >> Definitely. Definitely. >> In this case. >> You know when you get some people that you never knew that even though we have a customer support guys that did some crazy dash wars that we had no idea it's possible even, and they really getting chance to work with customers better to really tell the customer, "Oh, you just did that and that. "Maybe you'll try this option." And we found that even communicating, and really minimize the time it takes for them to figure out what's going on that it's been really impactful. >> With no call to It to help (laughs). >> And it was never the intent, so we wanted to allow dev's and off's to operate, and all of a sudden you're getting customer support without even telling them. >> Good, well Lior thanks for sharing your story and really appreciate you taking the time. >> Thank you. >> All right, he's Lior. I'm Jeff. You're watching theCube. We are at Sumo Logic Illuminate 2019. Thanks for watching. We'll see you next time. (upbeat techno music)

>> from Burlingame, California It's the Cube covering Suma logic Illuminate 2019. Brought to You by Sumer Logic >> Hey, welcome back, everybody. Jefe Rick here with the cue, We're at the higher Regency San Francisco airport. Here. It's Suma logic. Illuminate 2019. It's our second year here, the third year of the show. I think it's about 1000 people that Kino was packed. A lot of great energy, a lot of good community as we see a lot of these smaller show, especially when they're getting started. It's all about community is a lot of sharing of information. It's a really cool time in the life of these companies. Worksite have our next guest slightly irreverent cool culture will dig into it a little bit with Justin bike. Oh, he is the co founder of Expelled Justin Creek to see >> likewise. Thanks for Thanks for having me. >> Absolutely. So give us the give us a rundown expel what you guys all about. >> So in a nutshell, were a 24 73 65 transparent, manage security provider. What that really means is on a 24 hours a day, seven days a week basis. We're looking for you know, signs. They're bad guys inside your environment. If they're there, we're gonna tell you they're they're gonna tell you what they're up to and help you get >> rid of them. Now, the transparent word comes up time and time again, Looking at some of your guys materials that transparent in terms of we can see inside the black box and how you're operating is that transparent. Like we're just going to tell you and show you what transparency is Obviously a really important piece of your messaging. >> Yeah, kind of all of the above. We try to take it to heart and try to really mean it. I think the easiest way to think about it is we want our customers to feel like we're just another part of their team. Right. And the easiest way for us to do that is to let them be a part of what we do on a day in and day out basis. That means if they want a ride shotgun with us when we're working an incident, they can They can watch everything that we do. Watch the investigation unfold in real time if they want to get in there and work with us. They certainly have the ability to do that. And then, you know, we take transparency pretty far. We put our real actual prices on our website, which is not something you see a lot of security companies do. So we just try to be as up front as we can be in the way that we approach dealing with our customers and working with him over time. >> Right? So we we cover our say, we've been covering our safe forever. It's it's 40,000 people at Mosconi. Few is more vendors than you can count, all talking about security. So, you know, you're in the year in the business and then remain in his keynote, put up a security section, had a whole bunch of companies. How did people sort this? I always think of the poor CEO on the other side is being told, you know, you got a big security in every place. You've gotta have a B A B Y o d was using their own iPhones and now we got I t With all these connected devices, the threat surfaces expanding geometrically. How should people think about security? How do you guys play in this kind of morass of complexity. >> You know, it's an interesting question how people deal with it. I think that's why you're starting to see a lot of these really strong in a lot of cases, sort of regional and local see. So groups start to form right because they want to get together and actually talk about a. I'm dealing with this challenge. What do you do and how are you handling this problem? And the only way to do that is to learn from appears, right. Everybody's in this fight together. As for how we fit in, one of the things that we try to do is help customers who have made a lot of investments in a lot of different security technology make sense of it. All right, so you've got 56789 10 20 pieces of security technology. They're deployed there, all blinking red lights of like, Hey, this might be a threat. This might be an intruder inside your infrastructure, and you've got a handful of people that work Monday through Friday, 8 to 5 ish. Somebody's gotta look at that all day long, and that's what we're there for. So our job is to make space for our customers, to do the things they actually love about security instead of just sitting there trying to keep up with a constant, you know, basically overflow of alert. >> Right? And you guys are socking the service. Is that Is that a hard sell? Is that easy sell to people? Understand? They need kind of the augmentation. How does that how does that go over with the customer? It has >> been, I think it's over the years it's become, I think, an easier thing for people to wrap their head around. Because at the end of the day, everybody is infrastructure to grow and write. Their footprint of what they need to protect is growing. They can't. Still, nobody can hire enough people that they need. That's a pervasive problem. It's a top five c. So problem has been for you will never go for a long time. So you've got all this security technology. You get the whole network instrument and then suddenly the business moves to the cloud. You have two instrument that too, and you have to do it using the team that you've gotten. That's not enough people And so what choice do you really have? You need somebody to come in and help provide that 24 7 coverage. There's certain things that your security at the institute, that on Lee be done from inside the business. Right things. They remove your program forward, let your team focus on that and all the stuff around monitoring technology to look for signs. They're bad guys in the environment. Let a provider like expel, help you out, >> right? So let me get your take on kind of the explosion of data. But both the quantity of the date as well as the sources of the data, as well as the structure of the date or the lack of structure in a lot of this data, it's growing exponentially right in people. And we all have our time kind of wrapping our heads around exponential growth, one of the kind of the fundamental problems that we have from your point of view, as you see this and you see your customer struggling with it and interests. Other kind of dichotomy is, Is it? Is it is it Is it asset? Of course, there's a lot of good stuff in there hopefully, But it's also liability because it's expensive. It's expensive. The hold. It's expensive to move. It's expensive to store. How do you help people deal with Keep it secure in this explosion of data environment that we are. >> You know, if there were a silver bullet, answer that question, we'd probably be the only security company in existence. >> They would be on a boat >> in >> the Caribbean or >> something like that, you know, being able to apply the technology that we're, you know, that we can bring to bear, which helps our analysts take all of these different disparate data sources. So we can take your I d. S. We can take your e t r. We can take your cloud control platform, your cloud control plane like an aws cloudwatch cloudtrail all that sort of stuff, bring it in one place, makes sense of it, put it together in a way that contextualized is it against what we know about your business. That's ah, whole lot of the battle right there is just being able to help somebody sort of understand what's going on. What does it mean to my business? What do I do about it? What do I do next on? If you can free up that chunk of time, you let the customer focus on those sort of more tactful things that they need to do inside their business, which is, you know, what's the next big evolution of security inside? You know our company, >> right? The word assume a logic fit in for you guys were here, obviously at the sumo logic event. And you know, the scale of the complexity of this stuff is getting beyond the ability of a human keep track of quite frankly. So there's got to be some automation. There's got to be tools. Even though you guys were transparent, there's just there's just some things I can't look into. How are using similar logic? How's it helping you do your business? You >> know, our partnership with Suma Logic came about actually from our 1st 2 customers who are actually symbolize your customers. And so they're sending all of their infrastructure data. They're sending all their security data and assume a logic, and they came to us and they said all that it is there. If you want to monitor our infrastructure, start there right on our ability because it lives in the cloud. There's no sort of management for the customer to do our villages plug right into that and immediately, like within an hour, to start getting security value out of what the customer has inside Suma Logic is, it's pretty substantial to be able to just start immediately telling him, giving him visibility into what's going on. So that's kind of how the relationship came about and how we worked with him today is we find that again. A lot of our customers have just a ton of data, security or otherwise that they need to store. They need to do it in a place that's going to scale with, um, so your traditional on Crim. You know, it's like a more old school Sim. We just gotta keep buying drives and buying drives and buying more and more places to store things. It's a tough life, right? So Cloud hosted platform, like Suma Logic lets you continue to scale, lets you quickly and easily search that they didn't do it in a reasonably cost effective way. It's a great way for us to work with customers. You don't have any visibility today. We know the folks over assume a logic really well, it's super easy to get up and running and get it up really quickly. It's easy for us to plug into. We can get you visibility in your environment really, really fast. If you don't have any today, >> so is that enabled you to bring a different scale of data to bear on your analytics is too. We know there's bad guys in there. You know how fast you can find him and shut him down to take action? >> I think so. Because of the way the technology with Suma logic scales that lets customers send more data, then they may have otherwise ordinarily sent toe like a more traditional Sam or something like that on what that does is that gives us more data to look at when we have more today to look at. We have more visibility and what's going on the customer environment. We can start delivering more value to them. Tell you hey, did you know this is going on over here something you weren't previously looking at because it scales reasonably well. That's something that we can start doing for >> her just because you have a good kind of macro point of view on multiple customers and the market. I just love to get your take on now. We used to hear all the time that, you know, the time between the breach and the knowledge of a breach was like 260 days. Whatever. And we keep hearing whatever show you out. It's coming down. It's coming down, coming down, but at the same time, pretty much every day. You know, you hear about a new breech and it's it's it's, I think everybody is going to be breached, it seems like, and it's really more of a function of How fast can you find out? You know, how quickly can you can you cut down on the damage and take the action? And I wonder if you could share your thoughts of kind of, You know, I still think some people think there's a moat strategy that you can just keep people out, and it's just it's just not, >> you know, somebody who is an attacker that's determined enough there. Well, fuck. You know, they've got enough funding. We're gonna get in and think about it like your house, right? Like, but all the doors. Put all the locks on his many doors. You want as many bars on as many windows as you want. If somebody wants it badly enough and they have enough time, they have enough planning. They have enough money, they're getting in your house, right? And so what you want to do? You want to know when they get in there so that you can react pretty quickly and so sure that like you dwell time of how long before, you know, from the time the intruder got in the environment till the time they were actually discovered. You obviously don't want that to be hundreds and hundreds of days, So it is important to figure out when they're they're what have they accessed when they're in there? So you understand what risk your date is that? Where are they In your environment? And that's the kind of thing you want to make sure that you have instrumentation to be able to see quick, because you can't. There is no silver bullet. You can't just keep the attackers out. You can't say I've prevent all these prevention mechanisms in. Nobody's gonna get in, so I don't need to worry about trying to find them once they're inside. It's just not the case. It's not really. >> And have you guys built a technological answer to social manipulation for penetration? I mean, my favorite examples are the, you know, somebody calling. You know I can't get into the company softball game. Can you please click on this or another one? I heard a Vegas. A Vegas casino was breached via that. The bomb it, er in the fish tank in the lobby. That was that was a connected thermometer so the fish wouldn't die. I mean, are we are you thinking about, you know, kind of social engineering is still a really effective way to get into these places and tools to break those kind of that access. >> Yes. Oh, social engineering. Absolutely. If you look across our customer base, the incidents and our customers deal with the number one by a long shot vector for how these companies are actually getting compromised in the first place. It's fishing, right? It's ominous. Intuit e mail. And I'm gonna convince you to click on this link, or I'm gonna convince you to open this file or I'm gonna convince you to give me a password or something like that, because at the end of the day, some of these things are pretty good and it's hard to spot a fake. It's just really difficult spot a fake if it's well tailored. A lot of the security companies and I'll give credit to a lot of the infrastructure. Providers like Google have done a really good job at trying to flash warning signs. You've never received an E mail like this from this person before. You've never received an e mail from this person with an attachment. You've never received an e mail from this person with, you know, from this domain or anything like that. They're starting to get more and more sophisticated around some of those mechanisms. But at the end of the day, social engineering fishing, that is the number one vector. It's a really hard problems. All then the security industry hasn't solved yet. >> Yeah, All right, well, let's get his job security for you. Well, Justin, thanks for taking a few minutes and really enjoyed the conversation. Thanks for having me. Alright. He's Justin. I'm Jeff. You're watching the Cube. Were Touma logic illuminate in San Francisco Hyatt Regency? Thanks for watching

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> Hi everybody, welcome back to theCUBE's coverage of the IBM Chief Data Officer Event. We're here at Fisherman's Wharf in San Francisco at the Centric Hyatt Hotel. This is the 10th anniversary of IBM's Chief Data Officer Summits. In the recent years, anyway, they do one in San Francisco and one in Boston each year, and theCUBE has covered a number of them. I think this is our eighth CDO conference. I'm Dave Vellante, and theCUBE, we like to go out, especially to events like this that are intimate, there's about 140 chief data officers here. We've had the chief data officer from AstraZeneca on, even though he doesn't take that title. We've got a panel coming up later on in the day. And I want to talk about the evolution of that role. The chief data officer emerged out of kind of a wonky, back-office role. It was all about 10, 12 years ago, data quality, master data management, governance, compliance. And as the whole big data meme came into focus and people were realizing that data is the new source of competitive advantage, that data was going to be a source of innovation, what happened was that role emerged, that CDO, chief data officer role, emerged out of the back office and came right to the front and center. And the chief data officer really started to better understand and help companies understand how to monetize the data. Now monetization of data could mean more revenue. It could mean cutting costs. It could mean lowering risk. It could mean, in a hospital situation, saving lives, sort of broad definition of monetization. But it was really understanding how data contributed to value, and then finding ways to operationalize that to speed up time to value, to lower cost, to lower risk. And that required a lot of things. It required new skill sets, new training. It required a partnership with the lines of business. It required new technologies like artificial intelligence, which have just only recently come into a point where it's gone mainstream. Of course, when I started in the business several years ago, AI was the hot topic, but you didn't have the compute power. You didn't have the data, you didn't have the cloud. So we see the new innovation engine, not as Moore's Law, the doubling of transistors every 18 months, doubling of performance. Really no, we see the new innovation cocktail as data as the substrate, applying machine intelligence to that data, and then scaling it with the cloud. And through that cloud model, being able to attract startups and innovation. I come back to the chief data officer here, and IBM Chief Data Officer Summit, that's really where the chief data officer comes in. Now, the role in the organization is fuzzy. If you ask people what's a chief data officer, you'll get 20 different answers. Many answers are focused on compliance, particularly in what emerged, again, in those regulated industries: financial service, healthcare, and government. Those are the first to have chief data officers. But now CDOs have gone mainstream. So what we're seeing here from IBM is the broadening of that role and that definition and those responsibilities. Confusing things is the chief digital officer or the chief analytics officer. Those are roles that have also emerged, so there's a lot of overlap and a lot of fuzziness. To whom should the chief data officer report? Many say it should not be the CIO. Many say they should be peers. Many say the CIO's responsibility is similar to the chief data officer, getting value out of data, although I would argue that's never really been the case. The role of the CIO has largely been to make sure that the technology infrastructure works and that applications are delivered with high availability, with great performance, and are able to be developed in an agile manner. That's sort of a more recent sort of phenomenon that's come forth. And the chief digital officer is really around the company's face. What does that company's brand look like? What does that company's go-to-market look like? What does the customer see? Whereas the chief data officer's really been around the data strategy, what the sort of framework should be around compliance and governance, and, again, monetization. Not that they're responsible for the monetization, but they responsible for setting that framework and then communicating it across the company, accelerating the skill sets and the training of existing staff and complementing with new staff and really driving that framework throughout the organization in partnership with the chief digital officer, the chief analytics officer, and the chief information officer. That's how I see it anyway. Martin Schroeder, the senior vice president of IBM, came on today with Inderpal Bhandari, who is the chief data officer of IBM, the global chief data officer. Martin Schroeder used to be the CFO at IBM. He talked a lot, kind of borrowing from Ginni Rometty's themes in previous conferences, chapter one of digital which he called random acts of digital, and chapter two is how to take this mainstream. IBM makes a big deal out of the fact that it doesn't appropriate your data, particularly your personal data, to sell ads. IBM's obviously in the B2B business, so that's IBM's little back-ended shot at Google and Facebook and Amazon who obviously appropriate our data to sell ads or sell goods. IBM doesn't do that. I'm interested in IBM's opinion on big tech. There's a lot of conversations now. Elizabeth Warren wants to break up big tech. IBM was under the watchful eye of the DOJ 25 years ago, 30 years ago. IBM essentially had a monopoly in the business, and the DOJ wanted to make sure that IBM wasn't using that monopoly to hurt consumers and competitors. Now what IBM did, the DOJ ruled that IBM had to separate its applications business, actually couldn't be in the applications business. Another ruling was that they had to publish the interfaces to IBM mainframes so that competitors could actually build plug-compatible products. That was the world back then. It was all about peripherals plugging into mainframes and sort of applications being developed. So the DOJ took away IBM's power. Fast forward 30 years, now we're hearing Google, Amazon, and Facebook coming under fire from politicians. Should they break up those companies? Now those companies are probably the three leaders in AI. IBM might debate that. I think generally, at theCUBE and SiliconANGLE, we believe that those three companies are leading the charge in AI, along with China Inc: Alibaba, Tencent, Baidu, et cetera, and the Chinese government. So here's the question. What would happen if you broke up big tech? I would surmise that if you break up big tech, those little techs that you break up, Amazon Web Services, WhatsApp, Instagram, those little techs would get bigger. Now, however, the government is implying that it wants to break those up because those entities have access to our data. Google's got access to all the search data. If you start splitting them up, that'll make it harder for them to leverage that data. I would argue those small techs would get bigger, number one. Number two, I would argue if you're worried about China, which clearly you're seeing President Trump is worried about China, placing tariffs on China, playing hardball with China, which is not necessarily a bad thing. In fact, I think it's a good thing because China has been accused, and we all know, of taking IP, stealing IP essentially, and really not putting in those IP protections. So, okay, playing hardball to try to get a quid pro quo on IP protections is a good thing. Not good for trade long term. I'd like to see those trade barriers go away, but if it's a negotiation tactic, okay. I can live with it. However, going after the three AI leaders, Amazon, Facebook, and Google, and trying to take them down or break them up, actually, if you're a nationalist, could be a bad thing. Why would you want to handcuff the AI leaders? Third point is unless they're breaking the law. So I think that should be the decision point. Are those three companies, and others, using monopoly power to thwart competition? I would argue that Microsoft actually did use its monopoly power back in the '80s and '90s, in particular in the '90s, when it put Netscape out of business, it put Lotus out of business, it put WordPerfect out of business, it put Novell out of the business. Now, maybe those are strong words, but in fact, Microsoft's bundling, its pricing practices, caught those companies off guard. Remember, Jim Barksdale, the CEO of Netscape, said we don't need the browser. He was wrong. Microsoft killed Netscape by bundling Internet Explorer into its operating system. So the DOJ stepped in, some would argue too late, and put handcuffs on Microsoft so they couldn't use that monopoly power. And I would argue that you saw from that two things. One, granted, Microsoft was overly focused on Windows. That was kind of their raison d'etre, and they missed a lot of other opportunities. But the DOJ definitely slowed them down, and I think appropriately. And if out of that myopic focus on Windows, and to a certain extent, the Department of Justice and the government, the FTC as well, you saw the emergence of internet companies. Now, Microsoft did a major pivot to the internet. They didn't do a major pivot to the cloud until Satya Nadella came in, and now Microsoft is one of those other big tech companies that is under the watchful eye. But I think Microsoft went through that and perhaps learned its lesson. We'll see what happens with Facebook, Google, and Amazon. Facebook, in particular, seems to be conflicted right now. Should we take down a video that has somewhat fake news implications or is a deep hack? Or should we just dial down? We saw this recently with Facebook. They dialed down the promotion. So you almost see Facebook trying to have its cake and eat it too, which personally, I don't think that's the right approach. I think Facebook either has to say damn the torpedoes. It's open content, we're going to promote it. Or do the right thing and take those videos down, those fake news videos. It can't have it both ways. So Facebook seems to be somewhat conflicted. They are probably under the most scrutiny now, as well as Google, who's being accused, anyway, certainly we've seen this in the EU, of promoting its own ads over its competitors' ads. So people are going to be watching that. And, of course, Amazon just having too much power. Having too much power is not necessarily an indication of abusing monopoly power, but you know the government is watching. So that bears watching. theCUBE is going to be covering that. We'll be here all day, covering the IBM CDO event. I'm Dave Vallente, you're watching theCUBE. #IBMCDO, DM us or Tweet us @theCUBE. I'm @Dvallente, keep it right there. We'll be right back right after this short break. (upbeat music)