(relaxing intro music) >> Welcome back to theCUBE's Coverage here live at reinvent 2022. We're here at the Executive Summit upstairs with the Accenture Set three sets broadcasting live four days with theCUBE. I'm John Furrier your host, with two great guests, cube alumnis, back Tanuja Randery, managing director Amazon web service for Europe middle East and Africa, known as EMEA. Welcome back to the Cube. >> Thank you. >> Great to see you. And Karthik Narain, who's the Accenture first cloud lead. Great to see you back again. >> Thank you. >> Thanks for coming back on. All right, so business transformation is all about digital transformation taken to its conclusion. When companies transform, they are now a digital business. Technologies powering value proposition, data security all in the keynotes higher level service at industry specific solutions. The dynamics of the industry are changing radically in front of our eyes for for the better. Karthik, what's your position on this as Accenture looks at this, we've covered all your successes during the pandemic with AWS. What, what do you guys see out there now as this next layer of power dynamics in the industry take place? >> I think cloud is getting interesting and I think there's a general trend towards specialization that's happening in the world of cloud. And cloud is also moving from a general purpose technology backbone to providing specific industry capabilities for every customer within various industries. But the industry cloud is not a new term. It has been used in the past and it's been used in the past in various degrees, whether that's building horizontal solutions, certain specialized SaaS software or providing capabilities that are horizontal for certain industries. But we see the evolution of industry cloud a little differently and a lot more dynamic, which is we see this as a marketplace where ecosystem of capabilities are going to come together to interact with a common data platform data backbone, data model with workflows that'll come together and integrate all of this stuff and help clients reinvent their industry with newer capabilities, but at the same time use the power of democratized innovation that's already there within that industry. So that's the kind of change we are seeing where customers in their strategy are going to implement industry cloud as one of the tenants as they go through their strategy. >> Yeah, and I see in my notes, fit for purposes is a buzzword people are talking about right size in the cloud and then just building on that. And what's interesting, Tanuja I want to get your thoughts because in the US we're one country, so yeah, integrating is kind of within services. You have purview over countries and these regions it's global impact. This is now a global environment. So it's not just the US North America, it's Latin America it's EMEA, this is another variable in the cross connecting of these fit for purpose. What's your view of the these industry specific solutions? >> Yeah, no and thanks Karthik 'cause I'm a hundred percent aligned. You know, I mean, you know this better than me, John, but 90% of workloads have not yet moved to the cloud. And the only way that we think that's going to happen is by bringing together business and IT. So what does that mean? It means starting with business use cases whether that's digital banking or smart connected factories or frankly if it's predictive maintenance or connected beds. But how do we take those use cases leverage them to really drive outcomes with the technology behind them? I think that's the key unlock that we have to get to. And very specifically, and Adam talked about this a lot today, but data, data is the single unifier for all of business and IT coming together to drive value, right? However, the issue is there's a ton of it, (John Furrier chuckling) right? In fact, fun fact if you put all the data that's going to be created over the next five years, which is more than the last 30 years, on a one terabyte little floppy, disk drive, remember those? Well that's going to be 15 round trips to the moon (John Furrier chuckling) and back. That's how much data it is. So our perspective is you got to unify, single data lake, you got to modernize with AI and ML, and then you're going to have to drive innovation on that. Now, I'll give you one tiny example if I may which I love Ryanair, big airline, 150 million passengers. They are also the largest supplier of ham and cheese sandwiches in the air. And catering at that scale is really difficult, right? If you have too much food wastage, sustainability issues, too little customers are really unhappy. So we work with them leveraging AWS cloud and AI ML to build a panini predictor. And in essence, it's taking the data they've got, data we've got, and actually giving them the opportunity to have just the right number of paninis. >> I love the lock and and the key is data to unlock the value. We heard that in the keynote. Karthik, you guys have been working together with AWS and a lot of successes. We've covered some of those on the cube. As you look at these industry solutions they're not the obvious big problems. They're like businesses, you know it could be the pizza shop it could be the dentist office, it could be any business any industry specific carries over. What is the key to unlock it? Is it the data? Is it the solution? What's that key? >> I think, you know the easier answer is all of the about, but like Tanuja said it all starts by bringing the data together and this is a funny thing. It's not creating new data. This data is there within enterprises. Our clients have these data the industries have the data, but for ages these data has been trapped in functional silos and organizations have been doing analytics within those functions. It's about bringing the data together whether that's a single data warehouse or a data mesh. Those are architectural considerations. But it's about bringing cross-functional data together as step one. Step two, is about utilizing the power of cloud for democratized innovation. It's no longer about one company trying to reinvent the wheel, or create a a new wheel within their enterprise. It's about looking around through the power of cloud marketplace to see if there's a solution that is already existing can we use that? Or if I've created something within my company can I use that as a service for others to use? So, the number one thing is using the power of democratized innovation. Second thing is how do you standardize and digitize functions that does not need to be reinvented every single time so that, you know, your organization can do it or you could use that or take that from elsewhere. And the third element is using the power of the platform economy or platforms to find new avenues of revenue opportunity, customer engagement and experiences. So these are all the things that differentiates organization, but all of this is underpinned by a unified data model that helps, you know, use all the (indistinct) there. >> Tanuja, you have mentioned earlier that not everyone has their journey of the cloud looks the same and certainly in the US and EMEA you have different countries and different areas. >> Yep. >> Their journeys are different. Some want speed and fees, some will roll their own. I mean data brick CEO, when I interviewed them that last week, they started database on a credit card swiped it and they didn't want any support. Amazon's knocking on their door saying, "you want support?" "No, we got it covered." Obviously they're from Berkeley and they're nerds, and they're cool. They can roll their own, but not everyone can. >> Yeah. >> And so you have a mix of customer profiles. How do you view that and what's your strategy? How do you get them over productive seeing that business value? What's that transformation look like? >> Yeah, John, you're absolutely right. So you've got those who are born in cloud, they're very savvy, they know exactly what they need. However, what I do find increasingly, even with these digital native customers, is they're also starting to talk business use cases. So they're talking about, "okay how do I take my platform and build a whole bunch of new services on top of that platform?" So, we still have to work with them on this business use case dimension for the next curve of growth that they want to drive. Currently with the global macroeconomic factors obviously they're also very concerned about profitability and costs. So that's one model. In the enterprise space, you have differences. >> Yeah. >> Right, You have the sort of very, very, very savvy enterprises, right? Who know exactly what they're looking for. But for them then it's about how do I lean into sustainability? In fact, we did a survey, and 77% of users that we surveyed said that they could accelerate their sustainably goals by using cloud. So in many cases they haven't cracked that and we can help them do that. So it's really about horses for courses there. And then, then with some other companies, they've done a lot of the basic infrastructure modernization. However, what they haven't been able to yet do is figure out how they're going to actually become a tech company. So I keep getting asked, can I become a tech company? How do I do that? Right? And then finally there are companies which don't have the skills. So if I go to the SMB segment, they don't always have the skills or the resources. And there using scalable market platforms like AWS marketplace, >> Yeah. >> Allows them to get access to solutions without having to have all the capabilities. So it really is- >> This is where partner network really kind of comes in. >> Absolutely. >> Huge value. Having that channel of solution providers I use that term specifically 'cause you're providing the solution for those folks. >> Yeah. Exact- >> And then the folks at the enterprise, we had a quote on the analyst segment earlier on our Cube, "spend more, save more." >> Yeah. >> That's the cloud equations, >> Yeah. because you're going to get it on sustainability you're going to save it on, you're going to save on cost recovery for revenue, time to revenue. So the cloud is the answer for a lot of enterprises out of the recession. >> Absolutely, and in fact, we need to lean in now you heard Adam say this, right? I mean the cost savings potential alone from on-prem to cloud is between 40 and 60 percent. Just that. But I don't think that's it John. >> The bell tightening he said is reigning some right size. Okay, but then also do more, he didn't say that, but analysts are generally saying, if you spend right on the cloud, you'll save more. That's a general thesis. >> Yeah. >> Do you agree with that? >> I absolutely think so. And by the way, usage is, people use it differently as they get smarter. We're constantly working with our customers by the way though, to continuously cost optimize. So you heard about our Graviton3 instances for example. We're using that to constantly optimize, but at the same time, what are the workloads that you haven't yet brought over to the cloud? (John Furrier chuckling) And so supply chain is a great idea. Our health cloud initiative. So we worked with Accenture on the Accenture Health Insights platform, which runs on AWS as an example or the Goldman Sachs one last year, if you remember. >> I do >> The financial cloud. So those, those are some of the things that I think make it easier for people to consume cloud and reimagine their businesses. >> It's funny, I was talking with Adam and we had a little debate about what an ISV is and I talked to the CEO of Mongo. They don't see themselves on the ISV. As they grew up on the cloud, they become platforms, they have their own ISVs and data bricks and Snowflake and others are developing that dynamic. But there's still ISVs out there. So there's a dynamic of growth going on and the need for partners and our belief is that the ecosystem is going to start doubling in size we believe, because of the demand for purpose built or so out of the box. I hate to use that word "out of the box", but you know turnkey solutions that you can buy another one if it breaks. But use the building blocks if you want to build the foundation. That is more durable, more customizable. Do that if you can. >> Well, >> but- >> we've got a phenomenal, >> shall we talk about this? >> Yeah, go get into- >> So, we've built a five year vision together, Accenture and us. which is called Velocity and you'll be much better in describing it, but I'll give you the simple version of Velocity which is taking AWS powered industry solutions and bringing it to market faster, more repeatable and at lower cost. And so think about vertical solutions sitting on a horizontal accelerator platform able to be deployed making transformation less complex. >> Yeah. >> Karthik, weight in on this, because I've talked to you about this before. We've said years ago the horizontal scalability of the cloud's a beautiful thing but verticals where the ML works great too. Now you got ML in all aspects of it. Horizontal verticals here now. >> Yeah, Yeah, absolutely. Again, the power of this kind of platform that we are launching, by the way we're launching tomorrow we are very excited about it, is, create a platform- >> What are you launching tomorrow? Hold on, I got news out there. What's launching? >> We are going to launch a giant platform, which will help clients accelerate their journey to industry cloud. So that's going to happen tomorrow. So what this platform would provide is that this is going to provide the horizontal capabilities that will help clients bootstrap their launch into cloud. And once they get into cloud, they would be able to build industry solutions on this. The way I imagine this is create the chassis that you need for your industry and then add the cartridges, industry cartridges, which are going to be solutions that are going to be built on top of it. And we are going to do this across various industries starting from, you know, healthcare, life sciences to energy to, you know, public services and so on and so forth >> You're going to create a channel machine. A channel creation machine, you're going to allow people to build their own solutions on top of that platform. And that's launching tomorrow. Make sure we get the news on that. >> Exactly. And- >> Ah, No, >> Sorry, and we genuinely believe the power of industry cloud, if you think about it in the past to create a solution one had to be an ISV to create a solution. What cloud is providing for industry today in the concept of industry clouds, this, industry companies are creating industry solution. The best example is, along with, you know, AWS and Accenture, Ecopetrol, which is a leader in the energy industry, has created a platform, you know called Water Intelligence and Management platform. And through this platform, they are attacking the audacious goal of water sustainability, which is going to be a huge problem for humanity that everybody needs to solve. As part of this platform, the goal is to reduce, you know, fresh water usage by 66% or zero, you know, you know, impact to, you know, groundwater is going to be the goal or ambition of Ecopetrol. So all of this is possible because industry players want to jump to the bandwagon because they have all the toolkit of of the cloud that's available with which they could build a software platform with which they can power their entire industry. >> And make money and have a good business. You guys are doing great. Final word, partnership. Where's it go next? You're doing great. Put a plugin for the Accenture AWS partnership. >> Well, I mean we have a phenomenal relationship and partnership, which is amazing. We really believe in the power of three which is the GSI, the ISV, and us together. And I have to go back to the thing I keep focused on 90% of workloads not in cloud. I think together we can enable those companies to come into the cloud. Very importantly, start to innovate launch new products and refuel the economy. So I think- >> We'll have to check on that >> Very, very optimistic. >> We'll have to check on that number. >> That seems a little- >> You got to check on that number. >> 90 seems a little bit amazing. >> 90% of workloads. >> That sounds, maybe, I'd be surprised. Maybe a little bit lower than that. Maybe. We'll see. >> We got to start turning it. >> It's still a lot. >> (laughs) It's still a lot. >> A lot more. Still first, still early days. Thanks so much for the conversation Karthik great to see you again Tanuja, thanks for your time. >> Thank you, John. >> Congratulations, on your success. Okay, this is theCube up here in the executive summit. You're watching theCube, the leader in high tech coverage, we'll be right back with more coverage here, and the Accenture set after the short break. (calm outro music)

(upbeat music) >> Hello, everyone. Welcome back to theCUBE's coverage of AWS re:Invent! 2021. I'm John Furrier, your host for the theCUBE, a lot of great action here. A lot of great solutions. Great keynote. The future of cloud's going to be all about purpose-built software platforms, enabling more and more SaaS, faster performance with custom chips, all enabling great stuff. I have two great guests here. Who are going to talk about it from Accenture. We've got Karthik Narain, global lead of Accenture's Cloud First. Welcome to the program. Good to see you and Chris Wegmann, AABG Accenture Amazon Business Group. Technology leads senior manager. Thanks for coming on. >> Great to be here. >> I was commenting before we came on about Accenture's work you guys been doing with the clouds in my article, I posted before re:Invent!. Dave Vellante coined the term superclouds, which we kind of just put out there, but the idea that people can build really strong platforms that enable a new kind of Saas has been the big wave. Connect has been a great example. We heard on stage from Adam, the CEO. Chris, this has been something that's been a real change where it's not just lift and shift and refactor, it's build value in a platform and new SaaS capabilities. What's your reaction to that? >> Yeah, I would absolutely agree. We've seen this change over time. We've seen the lift and shift and modernize and it's definitely moved into the Superclouds. I like the term, but you know, we call them cloud continuums, which we'll talk a little bit about, it's about building these purpose-built solutions. I think if you look at the keynote today, you look at, everybody that was on stage. United and everyone talking about what they're building, their technology companies now, they're not just the business. >> You guys did some new research, coining new terms and Cloud First. What is this all about? What is this new wave you guys are talking about? >> Yeah, so John, you know, few years ago, when people talked about cloud, they generally meant public cloud. I think the definition of cloud is changing and expanding. And from now on, whenever people talk about cloud, it's actually a cloud continuum. It's a continuum of capability from public to Edge and everything in between all seamlessly connected by Cloud First networks, which means all the capabilities that customers used to get from one public cloud destination. They can actually access that across the continuum, whether that be in their own private data center, using the capability of cloud with AWS's Outpost and other capabilities. Or they could use the capability in their Edge location, whether it's their retail centers, their warehouse locations, manufacturing and so on and so forth. So organizations are using the power of cloud beyond one purpose and one destination, but more as an operating system going forward. >> Chris, what's your take on this redefinition of cloud what's your take on it? >> I think it's much needed. I think Andy kicked it off last year when he recognized the term hybrid. We all, who've has been around a while kind of chuckled because they finally said the word. But if you look at the keynote today, they just continued it. Adam picked it up and ran with it. If you look at all the services, Wavelength and all the different services, there's not a single customer that I have, that's just using EC2 or S3 right. They're using all these different services you saw today. You saw all the different services that United put up on the screen. That DISH put up on the screen. So yeah, it's how people and companies, if they're truly going to transform and truly use cloud to transform, you have to use the whole continuum. >> Yeah. And I think the continuum message is a good one because if you look at what the evolution is, that was interesting to. Adam went on and did kind of a history lesson in the beginning, it felt like I was in the Star Wars movie, like back in the old days. And then you kind of progressed. You had to be really elite to roll your own cloud. And the hyperscalers did that, you saw that. Now you still have elite technical people, but now it's general purpose, or purpose built. It's like having prefabricated platforms and open source. We've learned that why do you want to reinvent the wheel if you don't have to? So if I want a call center I get Connect, if I want to have a big plugin platform, I can still build on top of and have that SaaS unique application. This seems logical. This is new. (laughter) This is the continuum. I mean, it seems obvious now looking at it, but how far along in are people getting this. Karthik, what's your take on this? >> I think customers are getting it. They are looking at cloud more as an operating system for their future innovation. They liked the concept that they got from the public cloud, which is easy configurability, consumability and automatability of their infrastructure assets. And when you can get that capability as an operating system for your entire enterprise, and you could innovate across the spectrum, that's extremely powerful. We see companies accelerating their adoption to cloud, but we are also seeing over the last three years, a lot of that adoption was using cloud as a migration destination. But now with the power of the cloud continuum, where innovation is available, that so many new services that Adam launched today, you could use truly cloud as an innovation engine. And we're actually seeing that the clients who are using the cloud continuum for innovation are doing much better than the ones that are using cloud as a migration destination. In fact, they're doing two X to three X use of cloud for innovation and uplifting knowlEdge where they are actually using three X more cloud for sustainability purposes. So huge, huge value. >> Yeah, I mean, this is a great point. Great insight, because what you're saying is essentially you can't hide anymore. The projects are either going to be successful or not. You can see whether it's useful or not, and now you're tying cloud adoption and outcomes together. Where you can look it and saying, we need to make this outcome work. Not for building, for building sake. Those projects were discovered during the pandemic. Why are we doing that? So you can't hide that ball anymore. >> Right and everybody's got to do it now, right? I mean, you don't have a choice. The pandemic is now forcing companies to change. They've changed. And that the research shows that the companies that have truly adopted the whole continuum are doing much better than the companies that didn't. >> What's pattern in this continuum research you guys, what's the big takeaway that you guys have found in that study, in that customer experience that you're having. What's the big, Aha moment. >> I think there are a few things. Number one surprising aspect is that the companies that use cloud for a broader innovation objective, actually, were saving more than the ones that use cloud just as a cost saving initiative. That was a big, Aha moment. Number two, when you talk about all of this innovation that AWS provides, sometimes it's easy for organizations to shrug it off saying, this looks like this is only for the elite companies, or this is only for the digitally native companies to follow. But our research showed that the companies that were successful adopting cloud continuum, the ones that we call less continuum competitors, 60% of them are pre-digitally born organizations. And they were reaping the benefits and they were growing faster, saving more, being more innovative than all others. So this is truly usable across the spectrum of the G 2000 enterprise. >> Yeah, and I think it's a no brainer, but now that you have, customers are transforming, they have multiple clouds. You have AWS, Azure, Google cloud, people were trying to find their swim lane. We heard about skill gap shortage. We did some reporting on that, that this idea of multi-cloud maybe not, I can't hire enough people. I'm going to bet on this cloud, maybe use that cloud. How are people looking at that? How do you guys see that the cloud competitive continuum, or how is the cloud competition affecting the cloud continuum from a customer standpoint? >> Yeah. I mean, you got to look at it, do you use the whole continuum? You've got a lot of cases, you got to be on the same cloud, right. You can use the whole, you got to use all the different components, all the different services. So I think we are seeing customers that are picking one and starting with one and then adding others. I see a lot of my customers who are using multiple clouds, but they're using them in different business units, right? So they may pick one business unit to go deep with AWS on, they may go use another business unit to go deep on another cloud, right? So yeah, I mean, everyone is getting multiple, but a lot of they're starting with one and then adding a second one or a third along the way. >> Karthik, this is what I was trying to get out of my story. It's a hard, very nuanced point. But if you look at the success of say Snowflake and Databricks, all bet on Amazon and their superclouds, they are on Amazon, but they're now working with Azure as well, because why wouldn't you want to open up your market? >> Exactly. And even the industry companies that want to monetize their capabilities using the digital ecosystems are doing that. For example, Siemens wanted to bring all their capabilities in manufacturing and machine operating system into a platform called MindSphere. And they knew that their end goal was going to be multi-cloud, but they want to practice, leveraging the power of cloud with one platform. And when they created MindSphere, they started with AWS and they created that solution in the public cloud and private cloud also at the Edge by leveraging the power of cloud from public to Edge and proved it out. And once it started working and they were able to roll it out for customers. Now they are giving customers the choice to be able to use it in other clouds as well. >> Yeah Karthik, you mentioned earlier at the top of our interview about the platform of the cloud and Dave and I were talking on our keynote review. We did a little history lesson of when Microsoft owned the monopoly of windows, the system software, and they had the application suite with office, but they still wanted developers to build on top of windows. Okay. But now with cloud that's one big windows platform like thing. So the developers ecosystem is evolving. And so one of the things we're watching, I want to get your reaction to this. Is in every major inflection point in the computer industry, when new ways to build and write code rolled out, the application owners always wanted their software to run on the fastest platform. Speeds and feeds matter in these shifts, because why would I want to have my software run slower? >> Yeah. >> What is your reaction to that? >> Yeah, absolutely. And again, there's a lot of things that the industry is going through and we are pushing the envelope on digitization. And today's keynote. When you saw the CEO of NASDAQ talking about the technology bottlenecks that were preventing the matching algorithm to be finally taken to cloud. Now that capability that's available at with AWS is what is enabling that matching algorithm to be taken to cloud through the power of Edge. So there's so much technology innovation, that's happening. That's constantly expanding the boundaries of posibilities. >> I mean, that's exactly the point. And I wrote this in my story and it came out on the keynote today, which was Adam saying, the clouds expanding that's the continuum. If it's running cloud operations, does it matter what it is? I mean, it's, if you're at the Edge and you're running cloud, maybe cause you want latency, of course you want to have low latency. Why wouldn't you want outposts. Again, this is all cloud operations. DevSecOps data is now kind of cloud operationalized. That seems to be what's happening. >> Yeah, I think the developers love the fact that they can write for one and put it anywhere, right? And whether it's a EKS on Inside, I don't even know what you call anymore, the public cloud, right? Or all the way out at the Edge, right? You write it once, you can deploy it there and it makes their lives a lot easier. And you know, as you said, it's all about performance. So they get the best option. >> Well, We love having you guys on the theCUBE, Accenture. You guys have really smart, talented people, always great commentary. Dave and I were looking at reviewing the tape so to speak. It's not really tape anymore. It's it's digitally stored on a S3, but we were looking back at 2016 when we first started talking about horizontally scalable cloud and vertically specialized applications. If you look at the keynote today and squint through the announcements, Amazon's going to offer full horizontal scalability and vertical specialization at the app level with machine learning capabilities. This means that you need data to be horizontally addressable, which is kind of counterintuitive, but you're seeing all the success on data lakes and lakes. This is the new architecture. It's kind of proven now, what do you guys think? >> Yeah, again, the aspect of cloud is about democratised innovation. The first element is, even though there's so much infrastructure build-out and infrastructural elements where there's continuous innovation going on, the enterprises and developers are moving from Bivives built decisions to assembling and consuming options. And when they assemble and consume, they want newer and newer services to be available. That is very specific to their industry and specific to functions, whether it is supply chain function or manufacturing function or so on and so forth. For this, there are going to be specific data that is going to be required, or operational for that particular use-case. But the whole idea of predictive analytics and AI and machine learning and data science is about how do you find correlations between operational data for a particular capability, with things that in the previous world was unrelated. For that you need to bring all of this data together. Time will tell whether all the data is going to move to one location or is there going to be distributed computing of that data with more technology, but that's the role that data is going to play in these verticalized solutions. >> Yeah, I mean, that's awesome. I want to get you guys while I got one, a couple of minutes left. Advice to people that look into go this next level. They know the continuum is coming, you guys been providing great solutions and advice to your customers. For the folks watching, what advice can you give where they're just putting their toe in the water or want to go full in? >> Yeah, so, we found in that research that there were some common patterns that were followed by these continuum competitors, the ones that were succeeding or winning in the cloud. And there was namely four of them, the first one, and these four can be adopted by others for them to also win in the continuum. The first one was looking at the power of the continuum, how the technology is evolving and creating a strategy to take advantage of the evolution of the continuum. That's number one. Number two, this is about organizational change. So don't go about this change in a soft manner. There are elements that you need to change within your organization to imbibe this wholeheartedly. That's the second thing. Third thing is one common aspect that all the continuum competitors followed was they put experience at the forefront for everything. For their end customers. Last but not the least. This is a holistic journey and an enterprise wide journey. And this would require CSO level, CEO level commitment on a longer term to achieve this. So with these four things, most companies can achieve the successes that the continuum competitors are seeing. >> Awesome insight, Chris, real quick, 30 seconds. What's your advice. >> Chris: Don't be afraid. (laughter) It's pretty simple. >> The water's warm, come on in >> Yeah, come on in. A lot of gone before you, right? It can be scary. It can be daunting, right? A lot of services. Don't be scared to get in and go at it. >> Yeah, one of the jobs I love about being theCUBE host is, you talk to people many years earlier, you guys got it right at Accenture. Congratulations. You were deploying, you saw this wave of purpose-built before anyone else and congratulations. Great success. >> Thanks, thanks for having us on theCUBE. >> Okay, I'm John Furrier. You're watching us here live in Las Vegas, for AWS re:Invent 2021 coverage. TheCUBE, the leader in tech coverage. (upbeat music)

>> Announcer: From around the globe, it's theCUBE, with digital coverage of AWS re:Invent Executive Summit 2020. Sponsored by Accenture and AWS. >> Welcome to CUBE 365's coverage of the Accenture Executive Summit, part of AWS re:Invent. I'm your host, Rebecca Knight. Today we are joined by a CUBE alum, Karthik Narain. He is Accenture's senior managing director and lead Accenture Cloud First, welcome back to the show Karthik. >> Thank you. Thanks for having me here. >> Always a pleasure. So I want to talk to you. You are an industry veteran, you've been in Silicon Valley for decades. I want to hear from your perspective what the impact of the COVID-19 pandemic has been, what are you hearing from clients? What are they struggling with? What are their challenges that they're facing day to day? >> I think COVID-19 has been a eye-opener from various facets, first and foremost, it's a health situation that everybody's facing, which not just has economic bearings to it. It has enterprise and organizational bearing to it, and most importantly, it's very personal to people because they themselves and their friends, family, near and dear ones are going through this challenge from various different dimension. But putting that aside, when you come to it from an organizational enterprise standpoint, it has changed everything, the behavior of organizations coming together, working in their campuses, working with each other as friends, family, and near and dear colleagues, all of them are operating differently. So that's one big change to get things done in a completely different way from how they used to get things done. Number two, a lot of things that were planned for normal scenarios, like their global supply chain, how they interact with their client, customers, how they co-innovate with their partners, and how their employees contribute to the success of an organization, they're all changed. And there are no data models that give them a hint of something like this for them to be prepared for this. So we are seeing organizations that have adapted to this reasonably okay, and are launching to innovate faster in this, and there are organizations that have started with struggling, but are continuing to struggle. And the gap between the leaders and laggards are widening. So this is creating opportunities in a different way for the leaders with a lot of pivot in their business, but it's also creating significant challenge for the laggards, as we defined in our future systems research that we did a year ago, and those organizations are struggling further. So the gap is actually widening. >> So you just talked about the widening gap. You've talked about the tremendous uncertainty that so many companies, even the ones who have adapted reasonably well in this time. Talk a little bit about Accenture Cloud First and why now? >> I think it's a great question. We believe that for many of our clients COVID-19 has turned cloud from an experimentation aspiration to an urgent mandate. What I mean by that is everybody has been doing something on the other end cloud. There's no company that says "We don't believe in cloud," or "We don't want to do cloud." It was how much they did in cloud. And they were experimenting, they were doing the new things in cloud, but they were operating a lot of their core business outside the cloud or not in the cloud. Those organizations have struggled to operate in this new normal, in a remote fashion, as well as their ability to pivot to all the changes the pandemic has brought to them. But on the other hand, the organizations that had a solid foundation in cloud were able to pivot faster and are actually gone into the stage of innovating faster and driving a new behavior in the market, new behavior within their organization. So we are seeing that this pandemic has actually fast forwarded something that we always believed was going to happen, this movement to cloud over the next decade, it has fast forwarded it to happen in the next three to five years. And it's created this moment where it's a once in an era, really replatforming of businesses in the cloud that we are going to see. And we see this moment as a cloud-first moment where organizations will use cloud as the canvas, as the foundation with which they're going to reimagine their business after they were born in the cloud. And this requires a whole new strategy. And at Accenture, we are doing a lot in cloud, but we thought that this is the moment where we bring all of that capabilities together because we need a strategy for addressing movement to cloud or embracing cloud in a holistic fashion. And that's what Accenture Cloud First brings together, a holistic strategy, a team that's 70,000 plus people that's coming together with rich cloud skills, but investing to tie in all the various capabilities of cloud to deliver that holistic strategy to our clients. >> So I want you to delve into a little bit more about what this strategy actually entails. I mean, it's clearly about embracing change and being willing to experiment, and having capabilities to innovate. Can you tell us a little bit more about what this strategy entails? >> Yeah. The reason why we say there is a need for strategy is like I said, cloud is not new. There's almost every customer client is doing something with the cloud, but all of them have taken different approaches to cloud and different boundaries to cloud. Some organizations say, "I just need to consolidate my multiple data centers to a small data center footprint and move the rest to cloud." Certain other organizations say that "Oh, I'm going to move certain workloads to cloud." Certain other organizations said, "Oh, I'm going to build this greenfield application or workload in cloud." Certain others said, "I'm going to use the power of AI/ML in the cloud to analyze my data and derive insights." But a cloud-first strategy is all of this tied with the corporate strategy of the organization with an industry specific cloud journey. To say, if in this current industry, if I were to be reborn in the cloud, would I do it in the exact same fashion that I did it in the past, which means that the products and services that they offer need to be reimagined, how they interact with their customers and partners need to be revisited, how they build and operate their IT systems need to be reimagined, how they unearth the data from all the systems under which they are trapped need to be liberated so that you could derive insights. A cloud-first strategy hence is a corporate-wide strategy, and it's a C-suite responsibility. It doesn't take the ownership away from the CIO or CDIO, but the CIOs and CDIOs felt that it was just their problem and they were to solve it, and everyone else being a customer. Now the center of gravity is elevated to it becoming a C-suite agenda on everybody's agenda, where probably the CDIO is the instrument to execute that. That's a holistic cloud-first strategy. >> And it's a strategy, but the way you're describing it, it sounds like it's also a mindset and an approach, as you were saying, this idea of being reborn in the cloud. So now how do I think about things? How do I communicate? How do I collaborate? How do I get done what I need to get done? Talk a little bit about how this has changed the way you support your clients and how Accenture Cloud First is changing your approach to cloud services. >> Wonderful. You know, I did not cover one very important aspect in my previous question, but that's exactly what you just asked me now, which is, to do all of this, I talked about all the variables an organization or an enterprise is going to go through, but the good part is they have one constant. And what is that? That is their employees, because if those employees are able to embrace this change, if they are able to change themselves, pivot themselves, retool and train themselves, to be able to operate in this new cloud-first world, the ability to reimagine every function of the business would be happening at speed. And cloud-first approach is to do all of this at speed, because innovation is directly proportional to the rate of probability on experimentation. You need to experiment a lot, for any kind of experimentation, there's a probability of success, and organizations need to have an ability and a mechanism for them to be able to innovate faster, for which they need to experiment a lot. The more they experiment and the lower cost at which they experiment is going to help them experiment a lot, and experiment them at speed, fail fast, succeed more. And hence, they're going to be able to operate this at speed. So the cloud-first mindset is all about speed. I'm helping the clients fast track their innovation journey, and this is going to happen, like I said, across the enterprise in every function, across every department, and the agent of this change is going to be the employees who have to embrace this change through new skills and new tooling, and new mindset that they need to adapt to. >> So Karthik, what you're describing, it sounds so exciting. And yet for a pandemic-weary workforce that's been working remotely, that may be dealing with uncertainty for their kid's school and for so many other aspects of their life, it sounds hard. So how are you helping your clients, employees get onboard with this? And because the change management is often the hardest part. >> Yeah, I think it's, again, a great question. A bottle has only so much capacity. Something got to come out for something else to go in. That's what you're saying, it's absolutely right. And that is again, the power of cloud. The reason why cloud is such a fundamental breakthrough technology and capability for us to succeed in this era, because it helps in various forms. What we talked so far is the power of innovation that we could create, but cloud can also simplify the life of the employees in an enterprise. There are several activities and tasks that people do in managing their complex infrastructure, complex IT landscape. They used to do certain jobs and activities in a very difficult and a roundabout way, cloud has simplified and democratized a lot of these activities, so that things which had to be done in the past, like managing the complexity of the infrastructure, keeping them up all the time, managing the obsolescence of the capabilities and technologies and infrastructure, all of that could be offloaded to the cloud, so that the time that is available for all of these employees can be used to further innovate. Every organization is going to spend almost the same amount of money, but rather than spending activities, by looking at the rear view mirror, on keeping the lights on, they're going to spend more money, more time, more energy, and spend their skills on things that are going to add value to their organization. Because every innovation that an enterprise can give to their end customer need not come from that enterprise. The world of platform economy is about democratizing innovation. And the power of cloud is to get all of these capabilities from outside the four walls of the enterprise. >> It will add value to the organization, but I would imagine also add value to that employee's life because the employee will be more engaged in his or her job and therefore bring more excitement and energy into his or her day-to-day activities too. >> Absolutely. Absolutely. And this is a normal evolution we would have seen, everybody would have seen in their lives, that they keep moving up the value chain of what activities that gets performed by those individuals. And this is, you know, no more true than how the United States, as an economy has operated where this is a powerhouse of innovation, where the work that's done inside the country keeps moving up the value chain and US leverages the global economy for a lot of things that is required to power the United States. And that global economic phenomenon is very true for an enterprise as well. There are things that an enterprise needs to do themselves, there are things an employee needs to do themselves, but there are things that they could leverage from the external innovation and the power of innovation that is coming from technologies like cloud. >> So at Accenture, you have long, deep stand, sorry, you have deep and long standing relationships with many cloud service providers, including AWS. How does the Accenture Cloud First strategy, how does it affect your relationships with those providers? >> Yeah. We have great relationships with cloud providers like AWS. And in fact, in the cloud world, it was one of the first capability that we started about 13 years ago, when we started developing these capabilities. But five years ago, we hit a very important milestone where the two organizations came together and said that we are forging a formal partnership with joint investments to build this partnership, and we named that as Accenture AWS Business Group, AABG, where we co-invested, brought skills together and developed solutions. And we will continue to do that, and through that investment, we've also made several acquisitions that you would have seen in the recent times, like Enimbos and Gekko that we made acquisitions in Europe. But now we're taking this to the next level. What we are saying is through cloud-first and the $3 billion investment that we are bringing in through cloud-first, we are going to make specific investment to create unique joint solution and landing zones, foundation cloud packs, with which clients can accelerate their innovation or their journey to cloud-first. And one great example is what we are doing with Takeda, a global pharmaceutical giant, with whom we've signed a five-year partnership. And it was out in the media just a month ago or so, where the two organizations are coming together, we have created a partnership as a power of three partnership where the three organizations are jointly holding hands and taking responsibility for the innovation and the leadership position that Takeda wants to get to. With this, we are going to simplify their operating model and organization by providing it flexibility. We're going to provide a lot more insights. Takeda is a 230 year old organization. Imagine the amount of trapped data and intelligence that is there. How about bringing all of that together with the power of AWS and Accenture and Takeda to drive more customer insights, come up with breakthrough R and D, accelerate clinical trials, and improve the patient experience using AI, ML, and edge technologies. So all of these things that we will do through this partnership with joint investment from Accenture Cloud First, as well as partner like AWS, so that Takeda can realize their gain. And their CEO actually made a statement that five years from now, every Takeda employee will have an AI assistant that's going to make that Takeda employee move up the value chain on how they contribute and add value to the future of Takeda, with the AI assistant making them even more equipped and smarter than what they could be otherwise. >> So, one last question to close this out here. What is your future vision for Accenture Cloud First? What are we going to be talking about at next year's Accenture Executive Summit? >> Yeah, the future is going to be evolving, but the part that is exciting to me, and this is a fundamental belief that we are entering a new era of industrial revolution, from industrial first, second, and third industrial, the third happened probably 20 years ago with the advent of silicon and computers and all of that stuff that happened in the Silicon Valley. I think the fourth industrial revolution is going to be in the cross section of physical, digital, and biological boundaries. And there's a great article in World Economic Forum that your audience can Google and read about it. But the reason why this is very, very important is we are seeing a disturbing phenomenon that over the last 10 years, we are seeing a plateauing of the labor productivity and innovation, which has dropped to about 2.1%. And when you see that kind of phenomenon over that long a period of time, there has to be breakthrough innovation that needs to happen to come out of this barrier and get to the next base camp, as I would call it, to further this productivity lag that we are seeing, and that is going to happen in the intersection of the physical, digital, and biological boundaries. And I think cloud is going to be the connective tissue between all of these three, to be able to provide that, where it's the edge, especially is going to come closer to the human lives. It's going to come from cloud. Pictorally in your mind, you can think about cloud as central, either in a private cloud, in a data center, or in a public cloud, everywhere. But when you think about edge, it's going to be far-reaching and coming close to where we live and where we work and where we get entertained and so on and so forth. And there's going to be intervention in a positive way in the field of medicine, in the field of entertainment, in the field of manufacturing, in the field of mobility, when I say mobility, human mobility, people, transportation, and so on and so forth, with all of this stuff, cloud is going to be the connective tissue and the vision of cloud-first is going to be plowing through this big change that is going to happen. And the evolution that is going to happen where, you know, the human race of mankind, or personkind, being very gender neutral in today's world, cloud-first needs to be that beacon of creating the next generation vision for enterprises to take advantage of that kind of an exciting future. And that's why in Accenture we say "Let there be change" as our purpose. And I genuinely believe that cloud-first is going to be in the forefront of that change agenda, both for Accenture as well as for the rest of the world. >> Excellent. Let there be change indeed. Thank you so much for joining us Karthik. A pleasure talking to you. >> Thank you so much, Rebecca. >> I'm Rebecca Knight, stay tuned for more of CUBE 365's coverage of the Accenture Executive Summit.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Splunk .conf19. Brought to you by Splunk. >> Okay, welcome back, everyone. It's theCUBE's live coverage here in Las Vegas for Splunk's .conf 2019. It's the 10th year of .conf and we have two great guests, Rick Fitz, senior vice president, general manager of groups at Splunk, and Karthik Rau, vice president, area GM of SignalFx. The big story is SignalFx acquired by Splunk. Rick, you sponsored that. Guys, welcome to theCUBE, great to see you guys again. >> Yeah, great to be here, Jeff. >> Great to be here. >> They just broke a world record for the bike on intro there. >> Rick: They did. >> Pretty exciting what's going on here, a lot of records being broken. Splunk just continues to move the needle on capabilities, product, platform, brand messaging. SignalFx coming, we've been reporting on it since their founding, really in your wheelhouse, you guys bought them for a good number, a big number? >> Rick: Yup. >> Why? What's going on? Why the interest in SignalFx? >> You know, for a long time, we've been watching, I would say, perhaps, patiently, watching the market and the trends, and we were really waiting for a time where the new application architecture was really going to kind of start to take hold, where this cloud native trend that we've been seeing where people are building applications, where people are actually delivering applications to market in quite a different way, would finally get some escape velocity, and we've been watching patiently for that to occur. And as we saw that last year start to accelerate, really, we went out and surveyed the entire market and, of course, at the end of that survey, resulted in the acquisition of SignalFX, and also of Omnition. And so we bought those two companies, and have combined them to deliver on our vision of what we've trying to do for DevOps. >> Rick, you and I had a conversation in 2015 here in theCUBE at the .Conf at that time, you were on the IoT, you saw this wave, again, you've been patient. What about IT operations that's happening now that makes this so critical for Splunk? 'Cause IT operations, we know what automation's doing, machine learning toolkit, getting a lot of rave reviews. People love to automate things, but more apps are coming. What's the motivation now? What was the critical linchpin for you to make this happen? >> Yeah, exactly. What we're seeing is, in traditional IT operations is this world where developers build these monolithic applications, hand 'em off to operations, and they operate it. And then in the same conversation, you'll get handed over to somebody running, if you will, developer engineering or cloud engineering or they have various different levels for it but you're really dealing with an engineering organization and they're being tasked with digitization of their enterprise and very strategic investments are being made there, but they're also being asked to build things at high availability, high scalability, and highly reliable with lots of change. So it's kind of the competitive advantage of the enterprise. And as I was seeing that occur more and more I just saw the distance between IT operations and development, kind of, separate, and I said, wow, that's interesting 'cause it's being driven by this new application architecture, or cloud native architecture. And I didn't want to be left behind. I wanted to actually be able to build a bridge for IT operations into this future. And I think this future trend is something that's going to be lasting for the next 10, 15 to 20 years. So I think this is very strategic to Splunk and very important for us to get right for the long-term, but I also see my role as part of Splunk, is to make sure that we take IT operations into this new world, because these new worlds, and if you will, the existing worlds, those operating models are quite different. >> John: Yeah. >> They operate differently. They think differently. They, in one they own their code, they're on call. In another one they're waiting for something to fix so then they try to, you know, we're waiting for something to break and then they fix it. So we're trying to actually help enterprises across that entire gambit with some pattern. >> And certainly with security the theme here, at this event, this is a security event too, on top of everything right? So, this is what it's turned into. >> Rick: That's right. >> Data is driving a lot of security polemetry and data's important for security, so. >> Yeah. >> I mean, that's operations. >> That's right. And your apps have to be secured, in both worlds. >> Yeah. >> So, I think Splunk has a role to play in helping in this transformation for all of IT as it becomes much more developer centric. And, of course, as I said, that is really one of the strategic reasons why we led the acquisition Citadel FX in Omni. >> Well, we're looking forward to seeing how you handle the acquisition, of course, we were fans of the deal. Karthik, I got to ask you, every single company in observability space is going public. So, why, you could have gone public, why Splunk? Why sell to these guys? What made it a fit for you? >> Well, ultimately, we look at a number of things, or we looked at a number of things in making the decision and we wouldn't have done this with anyone other than Splunk. Just a strategic fit was just so great on so many levels. You know, when we started the company our goal was to solve the modern dream observability challenges for anyone building a cloud native application, and we knew that was going to be a long road. They're going to be a lot of things we needed to invest in and develop. And so we started on the metric side. We layered on distributive tracing and we took a philosophy that we wanted to build an enterprise great, scalable, robust, feature-rich set of technologies. We weren't in the market to build, you know, SMB, kind of very simple, limited type of a product. We're really focused on the larger, more sophisticated customers. And so, as we looked at continuing to extend our portfolio, one of the things that we needed to invest in was in the logging space because, when you think about the trifecta of monitoring data types that you need, you know, logging is a big part of it. And we knew that we wouldn't be able to go and build a logging system from the ground up that would be robust enough to support enterprise use cases, and so we started a partnership conversation with Rick and team, and it just became very clear through that process that there was a tremendous amount of product fit, vision fit, culture fit, values fit. Just everything was so aligned that we realized that we could do so much more together as one company. So, we rounded out the solution portfolio, or the technology portfolio quite substantially over night by becoming a part of Splunk and then the other part of it too is, you know, we saw as we were dealing with customers, we were dealing mostly with native cloud native, cloud first customers. But a lot of the customers that we were, that were prospects, that we were talking too were more traditional enterprises who were not 100% of the way there yet. Some of them weren't even 10% of the way there yet. And it was difficult for us to really engage in conversations early with them, to help them understand what does it mean to shift from traditional IT ops to DevOps because we didn't have a relationship with them on the IT ops side of things, and so, the other thing that we were really excited about being a part of Splunk is we can be a part of that conversation from the very beginning when the customer, you know, maybe they're just beginning to think about it and they don't have the urgency of doing it today but we can be there with them from the very beginning and help them get there on their timelines. >> This is an interesting discussion point because what you're highlighting and we've had conversations about your company about being a platform, not just a tool. So, you're getting at is that as you guys started getting more market share, you're platform needs, you needed logging. And meet the market leader, right here right? >> Yeah. >> That's right. >> So, you guys need them, so, partnering's hard when you're trying to build a platform. Now, you can have a platform that enables partners to build on top of it, but components of a full baked platform, it's hard to partner. Rick, what's your thoughts and reaction to that, because that's my statement, but do you agree with it? It's hard to partner in the platform, it's core competency. Look it, he struggled with logging 'cause he'd have to build out a boat load of new investment and you guys are already, just to catch up. >> Yeah, that's right. And I think the thing that needs to be stated here is in your large scale enterprises, they are truly looking for the best to breed, highly scalable environments, right, that we're talking about here. And, they want, they encouraged us to take a step in this direction. It was an obvious choice and I think that has been the reaction that we've kind of heard universally. Like, this is a great idea. This is a really strategic thing that you've Splunk folks have actually done. And so that's really encouraging and so I would agree with you. Partnering, and we were talking through it, but as we were talking, it's like, this is better not to partner in this case. >> John: Better together. >> One of the things that's really important is that logs, you know, that's what were all about. We've actually spent a lot of time in trying to invest into this streaming world of dealing with things in stream. And these guys have perfected it for Metrix, which is, that's the strategic aspect of this. And then combining what they had already done with Tracing, with Omnition, it just doubles down on this future of this application architecture that I mentioned. >> Some MMAs have a couple flavors to them. You buy a company, you throw them under a general manager, an executive, they kind of live there. Founders lead, you get the core tech, some team. The other scenario is full team comes in, hits the ground running. They're building out. They're going to own the build-out. It's seems to me based upon the Omnition acquisition, you're giving Karthik and team, kind of some reign here. >> Rick: Yeah. >> To go build this out. Is that how you guys see it? >> Yeah, that's exactly right. And so, both Speros and Karthik report to me. I'm their onboarding czar, as it were. But were really what we're going to focus on is customer success and achieving our business case. And really capitalizing on the opportunity. These guys were running a hundred miles an hour and we got to get them to got a thousand miles and we're only going to make adjustments to the business case in order to achieve that. And that's what we're here to do is to shepherd this organization in its entirety to the greatness that I think we all see out there. We're going to do that in a very careful, cautious way. >> Karthik, Omnition is a acquisition stealth company. Kind of a commitment saying hey, here's some more horsepower. Talk about how that happened and what's the purpose behind that acquisition. >> Well, I can let Rick talk to how it happened. And I'll talk about the other plans, so. >> When we surveyed the market we actually found that people have certain strengths. These guys that actually started their journey into tracing. I guess their first release was last December and so they've made some strides. And we kind of found Omnition through this discussion and we went like, oh my gosh. And we were in the process of doing the acquisition, doing due diligence. And we set everything on their roadmap is what these guys have done and vice versa. This is another combination that we can't pass up. This is, and what I told him the day we closed, I said, if you had the capital you would have done this, and he's like, yeah I would've. (chuckles) >> One of the things that Rick had asked me during our process was, what are the top three things that you would invest in if you had Slunk resources behind you. And I said Microservices APM, Microservices APM, Microservices APM, and so. >> And I got a big grin 'cause I obviously couldn't disclose what we doing but.. >> You know, the Omnition team, they're still in stealth so there's not a whole lot out there on the web about them. It's a phenomenal team. They've got people who are committers on some major open source projects, deeply technical, very, very shared philosophy to what we had a SignalFx in terms of open instrumentation, not having any proprietary lock in how you collect an instrument data. Very similar philosophies around leveraging the power of analytics and monitoring. And we just actually focused on different parts of the problem because we're both relatively early in this effort. So, we effectively doubled up the teams capacity over night and accelerated our roadmap by several quarters, so, I'm really excited about what we can do together with them. >> Well, are they the Bay area or they from.. >> They are Bay area base, yes. >> Okay cool. Well, I want to get your guys' thoughts on the keynote today. Feedback was authentic, kind of very cool keynote. As you guys bring this together, Rick, Karthik team, the optics, the messaging, what's the core positioning? What's, as you guys look at wholistic view now that you've invested in and are building out for customers, what's the posture? Take us through the keynote positioning. What's the marketplace, customer message around the future here? >> Yeah, I think it's really clear that what we're trying to do for IT organizations and application development organizations is build solutions that are modern and helpful to their core mission. And, by the way as I mentioned, in the world of new development, it's different, it's a different solution set. It's a different approach, a different operating model than it is in current IT operations. And so, one of the key messages we wanted to resonate is that we have the right solutions in both these worlds for you and that we're trying to develop an operating model of reactive response, a quick response, or engaging the right person in the problem, through our use of VictorOps for example, and using that as a way to be very intelligent about how we educate the people that are engaging in resolution process. So, we are trying to create a bridge to both worlds so that they can both be successful. And then under pit that, of course, with automation that can be leveraged in both worlds as well. So, that's what we're trying to convey. We know it's early days, by the way, these guys have been with the company for three weeks, so, it's kind of like, wow. >> Culture shock. >> Culture shock. >> Throw into deep water. Yeah, let's throw you out on stage in front of 11,000 people and see if you can swim and they did phenomenal, by the way. But that was kind of the key message and we're so excited because we just, we feel like were just in the first inning of perhaps a 19 or 20 inning game, 'cause I think it's going to be a lot of fun. >> Karthik: Yeah it is. >> And it's going to be close out here but we're really excited to be able to bring this to market. >> I mean, it's amazing coming in now three weeks in to see the breath of technology that's available and that's going platform. And, you know, what struck me today watching the keynote was just, you know it's such a feature rich and such a broad platform from everything in the, with the core, indexing capabilities that everyone's known about a long time. All of the ML, the additional capabilities we're going to bring in on the metric side. >> Yeah. >> And then the use cases just across every persona, there's just so much that we can do. >> What do you think of the culture? Are they run hard? They a playful company? They like to work hard, play hard? >> Yup. >> But they also are focused on real customer value. They got great engaged communities. What's your take of the culture so far? >> Yeah, absolutely. I mean culture fit was a really important part for us if we're going to be acquired by a company and be a part of a larger organization. Their kindred spirits I feel to the way we ran SignalFx. It's a very customer focused organization, great technology and engineering culture. And it's hard to find both, right? It feels like every organization is very important and very well respected. It's not like heavily skewed to it's just all about engineers, it's all about sales, it's very balanced culture and it's very customer focused. >> Guys, congratulations. Big deal. They don't see these kind of mega deals, they come along once in a while. It's a big bet. Good luck with everything, Rick. Thanks for coming on. Final question for both of you, what's the big take-a-way to take back to the office as you leave .Conf this week? What's going to resinate the most with you guys that you're going to take back as feedback? >> For me its, you know, I get my energies from customer conversations. We all do here at Splunk. If you're having a bad day, go talk to a customer and then they walk you and stop you in the hall and say, you know we really thank you again doing what you do. And so it just, I take back from this always that what we do matters and is important and just keep chugging along at it because we're doing some really good work out there that's really helping lives. And that's really important. >> John: That's good therapy. >> Yeah. >> When a bad day, talk to a customer. >> Go talk to a customer. >> I love you guys. (laughs) What's your take-a-way? >> I'm just, I'm thrilled at the number of customers who are coming up to me and saying how excited they are about the acquisition and working with us. You know, that's really re-affirming for me and it's just super exciting to see what we have ahead of us. >> You guys have a great tech following. A lot of tech leaders who knew you guys, knew you had good stuff so congratulations. Great Validation. >> Yup. Thank you. >> John: Good job >> Thank you John. >> Thanks you guys for coming on theCUBE. Great insight. Thanks for sharing all that data. (laughs) Data to everywhere here on theCUBE. I'm John Furrier, more coverage after this short break. (upbeat music)

>>Live from Las Vegas. That's the Q covering splunk.com 19 brought to you by Splunk. >>You know, kind of leaning on that heavily. Automation, certainly very important. But what does enterprise and what does enterprise security 6.0 bring to the table. So can you take us through the evolution of where you guys are at with, with Splunk, if you want to handle that enterprise security? So yeah, generally enterprise security has traditionally had really, really good use cases for like the external threats that we're talking about. But like you said, it's very difficult to crack the insider threat part. And so we leveraging machine learning toolkit has started to build that into Splunk to make sure that you know, you can protect your data. And, uh, you know, Tyler and I specifically did this because we saw that there was immaturity in the cybersecurity market for insider threat. And so one of the things that we're actually doing in this top, in addition to talking about what we've done, we're actually giving examples of actionable use cases that people can take home and do themselves. >>Like we're giving them an exact sample code of how to find some outliers. They give me an example of what, so the use case that we go over in the talk is a user logs in at a weird time of day outside of their baseline and they exfiltrate a large amount of data in a low and slow fashion. Um, but they're doing this obviously outside of the scope of their normal behavior. So we give some good searches that you can take home and look at how could I make a baseline, how could I establish that there's deviations from that baseline from a statistical standpoint, and identify this in the future and find the needle in the haystack using the machine learning toolkit. And then if I have a sock that I want to send notables to or some sort of some notification to how do we make that happen, how do we make the transition from machine learning toolkit over to enterprise security or however your SOC operates? >>How do you do that? Do you guys write your own code for that? Or you guys use Splunk? So Splunk has a lot of internal tools and there's a couple of things that need to be pointed out of how to make this happen because we're aggregating large amounts of data. We go through a lot of those finer points in the talk, but sending those through to make sure that they're high confidence is the, is the channel you guys are codifying the cross connect from the machine, learning to the other systems. All right, so I've got to ask, this is basically pattern recognition. You want to look at baselining, how do people, can people hide in that baseline data? So like I'll give you, if I'm saying I'm an evil genius, I say, Hey, I knew these guys looking for Romans anomalies in my baseline, so I'm going to go low and slow in my baseline. >>Can you look for that too? Yeah, there are. There absolutely are ways of, fortunately, uh, there's a lot of different people who are doing research in that space on the defensive side. And so there's a ton of use cases to look at and if you aggregate over a long enough period of time, it becomes incredibly hard to hide. And so the baselines that we recommend building generally look at your 90 day or 120 day out. Um, I guess viewpoint. So you really want to be able to measure that. And most insider threat that happen occur within that 30 to 90 day window. And so the research seems to indicate that those timelines will actually work. Now if you were in there and you read all the code and you did all of the work to see how all of the things come through and you really understood the machine learning minded, I'm sure there's absolutely a way to get in if you're that sophisticated. >>But most of the times they just trying to steal stuff and get out or compromise a system. Um, so is there other patterns that you guys have seen in terms of the that are kind of low hanging fruit priorities that people aren't paying attention to and what's the levels of importance to I guess get ahold of or have some sort of mechanism for managing insider threats? I passwords I've seen one but I mean like there's been a lot of recent papers that have come out in lateral movement and privilege escalation. I think it's an area where a lot of people haven't spent enough time doing research. We've looked into models around PowerShell, um, so that we can identify when a user's maliciously executing PowerShell scripts. I think there's stuff that's getting attention now that when it really needs to, but it is a little bit too late. >>Uh, the community is a bit behind the curve on it and see sharks becoming more of a pattern to seeing a lot more C sharp power shells kind of in hunted down kind of crippled or like identified. You can't operate that way, what we're seeing but, but is that an insider and do that. And do insiders come in with the knowledge of doing C sharp? Those are gonna come from the outside. So I mean, what's the sophistic I guess my question is what's the sophistication levels of an insider threat? Depends on the level a, so the cert inside of dread Institute has aggregated about 15,000 different events. And it could be something as simple as a user who goes in with the intent to do something bad. It could be a person who converted from the inside at any level of the enterprise for some reason. >>Or it could be someone who gets, you know, really upset after a bad review. That might be the one person who has access and he's being socially engineered as well as all kinds of different vectors coming in there. And so, you know, in addition to somebody malicious like that, that you know, there's the accidental, you're phishing campaigns here, somebody's important clicks on an email that they think is from somebody else important or something like that. And you know, we're looking fair for that as well. And that's definitely spear fishing's been very successful. That's a hard one to crack. It is. They have that malware and they're looking at, you can say HR data's out of this guy, just got a bad review, good tennis cinema, a resume or a job opening for, and that's got the hidden code built in. We've seen that move many times. >>Yeah, and natural language processing and more importantly, natural language understanding can be used to get a lot of those cases out. If you're ingesting the text of the email data, well you guys are at a very professional high end from Sai C I mean the history of storied history goes way back and a lot of government contracts do. They do a lot of heavy lifting from anywhere from development to running full big time OSS networks. So there's a lot of history there. What does sustain of the yard? What do you guys look at as state of the art right now in security? Given the fact that you have some visibility into some of the bigger contracts relative to endpoint protection or general cyber, what's the current state of the art? What's, what should people be thinking about or what are you guys excited about? What are some of the areas that is state of the art relative to cyber, cyber security around data usage. >>So, I mean, one of the things, and I saw that there were some talks about it, but not natural language processing and sentiment analysis has gotten, has come a long way. It is much easier to understand, you know, or to have machines understand what, what people are trying to say or what they're doing. And especially, for example, if somebody's like web searching history, you know, and you might think of somebody might do a search for how do I hide downloading a file or something like that. And, and that's something that, well, we know immediately as people, but you know, we have, our customer for example, has 1000000001.2 billion events a day. So you know, if the billion, a billion seconds, that's 30 years. Yeah. So like that's, it's, it's a big number. You know, we, we, we hear those numbers thrown around a lot, but it's a big number to put it in perspective. >>So we're getting that a day and so how do we pick out, it's hard to step of that problem. The eight staff, you can't put stamp on that. Most cutting edge papers that have come out recently have been trying to understand the logs. They're having them machine learning to understand the actual logs that are coming in to identify those anomalies. But that's a massive computation problem. It's a huge undertaking to kind of set that up. Uh, so I really have seen a lot of stuff actually at concierge, some of the innovations that they're doing to optimize that because finding the needle in the haystack is obviously difficult. That's the whole challenge. But there's a lot of work that's being done in Splunk to make that happen a lot faster. And there's some work that's being done at the edge. It's not a lot, but the cutting edge is actually logging and looking at every single log that comes in and understanding it and having a robot say, boom, check that one out. >>Yeah. And also the sentiment, it gets better with the data because we all crushed those billions of events. And you can get a, you know, smiley face or that'd be face depending upon what's happening. It could be, Oh this is bad. But this, this comes back down to the data points you mentioned logs is now beyond logs. I've got tracing other, other signals coming in across the networks. So that's not, that's a massive problem. You need automation, you've got to feed the beast by the machines and you got to do it within whatever computation capabilities you have. And I always say it's a moving train hard. The Target's moving all the time. You guys are standing on top of it. Um, what do you guys think of the event? What's the, what's the most important thing happening here@splunk.com this year? I'd love to have both of you guys take away in on that. >>There's a ton of innovation in the machine learning space. All of the pipelines really that I've, I've been working on in the last year are being augmented and improved by the staff. That's developing content in the machine learning and deep learning space that's belongs. So to me that's by far the most important thing. Your, your take on this, um, between the automation. I know in the last year or so, Splunk has just bought a lot of different companies that do a lot of things that now we can, instead of having to build it ourselves or having to go to three or four different people on top to build a complete solution for the federal government or for whoever your customer is, you can, you know, Splunk is becoming more of a one stop shop. And I think just upgrading all of these things to have all the capabilities working together so that, for example, Phantom, Phantom, you know, giving you that orchestration and automation after. >>For example, if we have an EMS notable events saying, Hey, possible insider threat, maybe they automate the first thing of checking, you know, pull immediately pulling those logs and emailing them or putting them in front of the SOC analyst immediately. So that in, in addition to, Hey, you need to check this person out, it's, you need to check this person out here is the first five pages of what you need to look at. Oh, talking about the impact of that because without that soar feature. Okay. The automation orchestration piece of it, security, orchestration and automation piece of it without where are you know, speed. What's the impact? What's the alternative? Yes. So when we're, right now, when we're giving information to our EES or analysts through yes, they look at it and then they have to click five, six, seven times to get up the tabs that they need to make it done. >>And if we can have those tabs pre populated or just have them, you know, either one click or just come up on their screen for once they open it up. I mean their time is important. Especially when we're talking about an insider threat whom might turn to, yeah, the alternative is five X increase in timespan by the SOC analyst and no one wants that. They want to be called vented with the data ready to go. Ready, alert on it. All right, so final few guys are awesome insights. Walking data upsets right here. Love the inside. Love the love the insights. So final question for the folks watching that are Splunk customers who are not as on the cutting edge, as you guys pioneering this field, what advice would you give them? Like if you had to, you know, shake your friend egg, you know, get off your button, do this, do that. What is the, what do people need to pay attention to that's super urgent that you would implore on them? What would you, what would your advice be once you start that one? >>One of the things that I would actually say is, you know, we can code really cool things. We can do really cool things, but one of the most important things that he and I do as part of our processes before we go to the machine and code, the really cool things. We sometimes just step back and talk for a half an hour talk for an hour of, Hey, what are you thinking about? Hey, what is a thing that you know or what are we reading? What and what are we? And you know, formulating a plan because instead of just jumping into it, if you formulate a plan, then you can come up with you know, better things and augmented and implemented versus a smash and grab on the other side of just, all right, here's the thing, let's let's dump it in there. So you're saying is just for you jump in the data pool and start swimming around, take a step back, collaborate with your peers or get some kind of a game thinking plan. >>We spent a lot of hours, white boarding, but I would to to add to that, it's augment that we spent a lot of time reading the scientific research that's being done by a lot of the teams that are out solving these types of problems. And sometimes they come back and say, Hey, we tried this solution and it didn't work. But you can learn from those failures just like you can learn from the successes. So I recommend getting out and reading. There's a ton of literature in that space around cyber. So always be moving. Always be learning. Always be collaborating. Yeah, it's moving training guys, thanks for the insights Epic session here. Thanks for coming on and sharing your knowledge on the cube, the cube. We're already one big data source here for you. All the knowledge here at.com our seventh year, their 10th year is the cubes coverage. I'm John furry with back after this short break.

>> live from San Francisco. It's the Cube covering Google Club Next nineteen Rodeo by Google Cloud and its ecosystem partners. >> Hey, welcome back. Everyone's the live Cube covers here in San Francisco for Google Cloud. Next nineteen. I'm Javert Day Volante here on the ground floor, day two of three days of wall to wall coverage to great guests. We got Kartik lost. Meena Ryan, product management director of Cloud Identity for Google and Kim parent chief security officer for Doctor on Demand. Guys, welcome to the Cube. Appreciated Coming on. >> Great to be here. >> Thank you so honestly Way covering Google Cloud and Google for many, many years. And one of the things that jumps out at me, besides allows the transformation for the enterprise is Google's always had great technology, and last year I did an interview, and we learned a lot about what's going on the chip level with the devices you got. Chrome browser. Always extension. All these security features built into a lot of the edge devices that Google has, so there's definitely a security DNA in there and Google the world. But now, when you start getting into cloud access and permissions yesterday and the Kino, Thomas Kurian and Jennifer Lin said, Hey, let's focus on agility. Not all his access stuff. This is kind of really were identity matters. Kartik talk about what's going on with cloud identity. Where are we? What's the big news? >> Yeah, thank you. So clouded. Entities are solution to manage identity devices and the whole axis management for the clouds. And you must have heard of beyond Corp and the whole zero trust model and access. One thing we know about the cloud if you don't make the access simple and easy and at the same time you don't provide security. You can get it right. So you need security and you need that consumer level simplicity. >> Think it meant explain beyond core. This is important. Just take a minute to refresh for the folks that might not know some of the innovations. They're just start >> awesome. Yeah. So traditional on premises world, the security model was your corporate network. Your trust smaller. Lose The corporate network invested a lot to get to keep the bad people out. You get the right people on and that made ten T applications on premises. Your data was on premises now the Internet being a new network, you work from anywhere. Work is no longer a thing. You work from anywhere. What gets done right? So what is the new access? More look like? That's what people have been struggling with. What Google came up with in two thousand eleven is this model called Beyond Core versus Security Access Model will rely on three things. Who you are is a user authentication the device identity and security question and last but not least, the context off. What are you trying to access in very trying to access from So these things together from how you security and access model And this is all about identity. And this is Bianca. >> And anyone who has a mobile device knows what two factor authentication is. That's when you get a text messages. That's just two factor M. F. A multi factor. Authentication really is where the action is, and you mentioned three of them. There's also other dimensions. This is where you guys are really taking to the next level. Yeah, where are we with FAA and some of the advances around multi factor >> s O. So I think keeping you on the highlight is wear always about customer choice. We meet customers where they are. So customers today have invested in things like one time use passwords and things like that. So we support all of that here in cloud identity. But a technology that we are super excited about the security, Keith. And it's built on the fighter standard. And it's inserted this into your USB slot of that make sense. And we just announced here at next you can now use your android phone as a security key. So this basically means you don't have to enter any codes because all those codes you enter can be fished on way. Have this thing at Google and we talked about it last time. Since we roll our security keys. No Google account, it's >> harder for the hackers. Really Good job, Kim. Let's get the reality. You run a business. You've been involved in a lot of start ups. You've been cloud nated with your company. Now talk about your environment does at the end of the year, the chief security officer, the buck stops with you. You've got to figure this out. How are you dealing with all this? These threats at the same time trying to be innovative with your company. >> So for clarity. So I've been there six years since the very beginning of the company. And we started the company with zero hardware, all cloud and before there was beaten beyond Corp. Where there was it was called de-perimeterization. And that's effectively the posture we took from the very beginning so our users could go anywhere. And our I always say, our corporate network is like your local coffee shop. You know, WiFi like that's the way we view it. We wanted to be just a secure there at the coffee shop, you know, we don't care. Like we always have people assessing us and they're looking at a corporate network saying, You know, where your switches that you're, you know, like where your hardware like, we want to come in and look at all like we don't have anything like, >> there's no force. The scan >> is like way. Just all go to the Starbucks will be the same thing. So that's part of it. And now you know, when we started like way wanted to wrap a lot of our services in the Google, but we had the problem with hip a compliance. So in the early days, Google didn't have six years ago. In our early days, Google didn't have a lot of hip, a compliant services. Now they do. Now we're moving. We're trying to move everything we do almost in the Google. That's not because we just love everything about Google. It's for me. I have assessed Google security are team has assessed their security. We have contracts with them and in health care. It's very hard to take on new vendors and say Hey, is there security? Okay, are their contracts okay? It's like a months long process and then even at the end of the day, you still have another vendor out there that sharing your day, that you're sharing your data with them and it's precarious for me. It just it doubles my threat landscape. When I go from Google toe one more, it's like if I put my data there, >> so you're saying multi vendor the old way. This is actually a problematic situation for you. Both technically and what operate timewise or both are super >> problematic for me in terms of like where we spread our data to like It just means that company every hack against that company is brutal for us, like And you know, the other side of the equation is Google has really good pricing. Comparatively, yes, Today we're talking about Big Query, for example, and they wanted to compare Big Query to some other systems and be crazy. G, c p. And And we looked at the other systems and we couldn't find the pricing online. And, like Google's pricing was right there was completely transparent. Easy to understand. The >> security's been vetted. The security's >> exactly Kim. Can you explain when you said the multi vendor of creates problems for you? Why is this? Is it not so much that one vendor is better? The other assistant? It's different. It's different processes or their discernible differences in the quality of the security. >> There are definitely discernible differences in quality, for sure. Yeah, >> and then add to that different processes. Skill sets. Is that writer? Yes, Double click on that E >> everybody away. There's always some I mean almost every vendor. You know, there's always something that you're not perfectly okay with. On the part of the security is something you don't totally like about it. And the more vendors you add, you have. Okay. This person, they're not too good on their physical security at their data center or they're not too good on their policies. They're not too good on their disaster recovery. Like there's you always give a little bit somewhere. I hate to say it, but it's true. It's like nobody's super >> perfect like it's It's so it's a multiplication effects on the trade offs that you have to make. Yeah, it's necessarily bad, but it's just not the way you want to do it. All right? Okay. >> All the time. So you got to get in an S L A u have meetings. You gotta do something vetting. It's learning curves like on the airport taking your shoes off. Yeah. Yeah. And then there's the >> other part. Beyond the security is also downtime. Like if they suffer downtime. How much is that going to impact our company? >> Karthik, you talked about this This new access mall, this three layer who authentication that is the device trusted in the context. I don't understand how you balance the ratio between sort of false positives versus blocking. I think for authentication and devices pretty clear I can authenticate. You are. I don't trust this device. You're not getting in, but the context is interesting. Is that like a tap on the shoulder with with looking at mail? Hey, be careful. Or how are you balancing that? The context realm? >> Yeah, I think it's all about customer choice. Again, customers have, but they look at their application footprint there, making clear decisions on Hey, this is a parole application is a super sensitive as an example, maybe about based meeting application. Brotherly, not a sensitive. So when they're making decisions about hey, you have a manage device. I will need a manage device in order for you to access the payroll application. But if you have you bring your own device. I'm off perfectly fine if you launch a meeting from that. So those are the levels that people are making decisions on today, and it's super easy to segment and classify your application. >> Talk about the the people that are out there watching might say, You know what? I've been really struggling with identity. I've had, you know, l'd app servers at all this stuff out there, you name it. They've all kinds of access medals over the years, the perimeters now gone. So I got a deal to coffee shop, kind of working experience and multiple devices. All these things are reality. I gotta put a plan together. So the folks that are trying to figure this out, what's that? You guys have both weigh in on on approach to take or certain framework. What's what's? How does someone get the first few steps off to go out towards good cloud identity? >> Sure, I only go first, so I think many ways. That's what we try to simplify it. One solution that we call cloud identity because what people want is I want that model. Seems like a huge mountain in front of me, like how do I figure these things out? I'm getting a lot of these terminologies, so I think the key is to just get started on. We've given them lots of ways. You can take the whole of cloud identity solution back to Kim's point. It can be one license from us, that's it and you're done. It's one unified. You I thinks like that. You can also, if you just want to run state three applications on DCP we have something called identity ofher Proxy. It's very fast. Just load yaps random on disability and experience this beyond >> work Classic enterprise Khun >> Yeah, you run all the applications and dcpd and you can And now they're announcing some things that help you connect back with John Thomas application. That's a great way to get started. >> Karthik painted this picture of Okay, it's no perimeter. You can't just dig a moat. The queen wants to leave the castle. All the security, you know, metaphors that we use. I'm interested in how you're approaching response to these days because you have to make trade us because there are discernible differences with different vendors. Make the assumption that people are going to get in so response becomes increasingly important. What have you changed to respond more quickly? What is Google doing to help? >> Well, yeah, So in a model where we are using, a lot of different vendors were having to like they're not necessarily giving us response and detection. Google. Every service we'd wrap into them automatically gets effectively gets wrapped into our security dashboard. There's a couple of different passwords we can use and weaken. Do reporting. We do it. A tremendous amount of compliance content, compliance controls on our DLP, out of e mail out of Dr and there's detection. There's like it's like we don't have to buy an extra tool for detection for every different type of service we have, it's just built into the Google platform, which is it's It's phenomenal from >> detection baked in, It's just >> baked in. We're not to pay extra for it. In fact, I mean way by the enterprise license because it's completely worth it for us. Um, you know, assumes that came out, the enterprise part of it and all the extra tools. We were just immediately on that because the vault is a big thing for us as well. It's like not only response, but how you dig through your assets toe. Look for evidence of things like, if you have some sort of legal case, you need vault, Tio, you know, make the proper ah, data store for that stuff >> is prioritization to Is it not like, figure it out? Okay, which, which threats to actually go after and step out? And I guess other automation. I mean, I don't know if you're automating your run book and things of that nature. But automation is our friends. Ah, big friend of starting >> on the product measures I What's the roadmap looks like and you share any insight into what your priorities are to go the next level. Aussie Enterprise Focus. For Google Cloud is clear Customs on stage. You guys have got a lot of integration points from Chromebooks G Sweep all the way down through Big Query with Auto ML All the stuff's happening. What's on your plate for road map? What things are you innovating around? >> I mean, it's beyond car vision that we're continuing to roll out. We've just ruled out this bit of a sweet access, for example, but all these conditions come in. Do you want to take that to G et? You're gonna look. We're looking at extending that context framework with all the third party applications that we have even answers Thing called beyond our devices FBI and beyond Corp Alliance, because we know it's not just Google security posture. Customers are made investments and other security companies and you want to make sure all of that interoperate really nicely. So you see a lot more of that coming out >> immigration with other security platform. Certainly, enterprises require that I buy everything on the planet these days to protect themselves >> Like there's another company. Let's say that you're using for securing your devices. That sends a signal thing. I trust this device. It security, passing my checks. You want to make sure that that comes through and >> now we're gonna go. But what's your boss's title? Kim Theo, you report to the CEO. Yeah, Awesome guys. >> Creation. Thank you >> way. We've seen a lot of shifts in where security is usually now pretty much right. Strategic is core for the operations with their own practices. So, guys, thanks for coming on. Thanks for the thing you think of the show so far. What's the What's The takeaway came I'll go to you first. What's your What's the vibe of the >> show? It's a little tough for me because I have one of my senior security engineers here, and he's been going to a lot of the events and he comes to me and just >> look at all >> this stuff that they have like, way were just going over before this. I was like, Oh my God, we want to go back to our r R R office and take it all in right today. You know, if we could So yeah, it's a little tough because >> in the candy store way >> love it because again, it's like it's already paying for it. It's like they're just adding on services that we wanted, that we're gonna pay for it now. It's >> and carted quickly. Just get the last word I know was commenting on our opening this morning around how Google's got all five been falling Google since really the beginning of the company and I know for a fact is a tana big day that secures all spread for the company matter. Just kind of getting it. Yeah, share some inside quickly about what's inside Google. From a security asset standpoint, I p software. >> Absolutely. I mean, security's built from the ground up. We've been seeing that and going back to the candy store analogy. It feels like you've always had this amazing candy, but now there's like a stampede to get it, and it's just built in from the ground up. I love the solution. Focus that you found the keynotes and all the sessions that's happening. >> That's handsome connective tissue like Antos. Maybe the kind of people together. >> Yeah. I don't like >> guys. Thanks for coming on. We appreciate Kartik, Kim. Thanks for coming on. It's accused. Live coverage here on the ground floor were on the floor here. Day two of Google Cloud next here in San Francisco on Jeffrey David Lantz Stevens for more coverage after this short break.

>> Narrator: Live from San Francisco, it's theCUBE, covering Google Cloud Next 19. Brought to you by Google Cloud and it's ecosystem partners. >> Hello and welcome back to theCUBE's live coverage, here in San Francisco, the Moscone Center. This is theCUBE's live coverage of Google Next 19, Google Cloud computing conference. I'm John Furrier, Dave Vellante my cohost. Stu Miniman's here as well, he'll be coming on doing interviews. Our next guests are the founder and CEO of SignalFx, Karthik Rau, and Leonid Ingolnik, EVP of engineering. SignalFx has been a great company, we've been following for many, many years. Pioneer in a lot of the monitoring and serviceability of applications, now prime time, the world has spun to their doorstep. Karthik, congratulations on your success. It's prime time for your business. >> Ya, thank you, John. >> John: Welcome back. >> Great to be on, we're on again. >> I'm glad that you're on because we talked six years ago about some of the trends, we saw early. We saw the containers, Docker movement, and also Kubernetes got massive growth. You had the visibility of what these services are going to look like, cloud web services, kind of the next level. It's kind of here right now. >> Yeah, absolutely, there are two things that we predicted would happen. One was that architectures would get a lot more distributed, elastic, and it would require a more low-latency monitoring system that could do realtime analytics. That was one of the key changes. And then the other thing that we predicted was that developers would get more involved in operations. Which is the whole DevOps movement. And now both of those are very much in the mainstream, so we're really excited to see these trends. >> And looking at the Google keynotes today, obviously we're starting to see the realization of true infrastructure as code, you're starting to see the beginning signals of, look at, we can actually program the infrastructure, and not even have to deal with it. This is key, and you guys have some hardcore news, so let's get that out of the way. You guys got some updates, let's get into the news, and then we can get into the conversation around what you guys are doing in the industry. >> So, today we're bringing three things to the conference, to boost customers and prospects, starting with announcing our support for cloud functions. Cloud functions are great technology that we're seeing adopted by retail. For spiky workloads, things where you have a flash sale and you need to understand what's happening, it may be lasting minutes, where our platform really shows off the best, which is the one second resolution data. Some of our flash sales we see from existing customers don't last a minute, right, so looking at this in a minute resolution of being able to react to this in a machine time rather than human time, is something that our customers now expect. The second thing we are focusing on is Istio, and Istio on GKE specifically. We're seeing service mesh adoption continuing to go both in new, modern application, as well as taking legacy workloads and unlocking the potential of taking those legacy workloads to the cloud. And with Istio, and specifically on Microservices APM, it's not just applicable to Microservices, we see a lot of our customers realizing a lot of value from tracing abilities that a service mesh like Istio provides, an ability to understand you topology and service interactions for free, out of the box, whether it's on-premise with Istio or on the Google environment. And then lastly, so we see customers and prospects adopt Kubernetes, we're also starting to see the next layer above Kubernetes coming in. And, with Knative, getting the support out of the box, whether it's the dashboard, the tracing of the metrics, and that, that's the third announcement we have today. We're fully integrated with Google's offerings, and we're able to monitor and provide you with some actionable content, just in a flick of a switch. >> So support of Knative out of the box. >> Leonid: Out of the box. >> Full SignalFx, with Knative on Google Cloud. >> That is correct. So those three things. >> Karthik, I wonder if you could give us some insight as to what's going on in the marketplace. A multicloud is obviously a tailwind to you, but multicloud, to date, hasn't really been a strategy, it's sort of been an outcome of multi vendor. So, is multicloud increasingly becoming a strategy for your customers, and what specific role are you playing there to facilitate that? >> Yeah, absolutely. I think particularly most of the larger enterprise accounts tend to have a multi vendor strategy, for almost every category, right? Including cloud, which typically is one of their largest spends. Typically what we see is people looking at certain classes or workloads, running on particular clouds, so it may be transactional systems running on AWS. A lot of their more traditional enterprise workloads that were running on Windows servers, potentially running on Azure, we see a lot of interest in data intensive sorts of analytics workloads, potentially running on GCP. And so I think larger companies tend to kind of look at it in terms of, what's the best platform for the use case that they have in mind. But in general, they are looking at multiple cloud vendors. >> So we heard some customers onstage today, talking about their strategy, I think Thomas asked one retail customer, how'd you decide what to put where? And essentially he said, well, it's either going to go into the cloud, lift and shift, we're going to refactor it, reprogram it essentially, or we're going to sunset it. What he didn't say is, we're going to leave some stuff on-prem. Which somewhat surprised me, 'cause of course, especially into financial services you're going to get a lot of stuff left on-prem. So what's your play, with regard to those various strategies, and for the legacy stuff, I know you're cloud native, that's your claim to fame, but can you help those legacy customers as well? Talk about that. >> Yes, absolutely. >> So I think, what we've seen is it's a given now, that organizations are going to move to cloud. It's a question of when, not if. And the cloud form factors are just, are fundamentally different, they're software-defined. Right, a traditional data center, you're monitoring network equipment, storage devices, you're monitoring disks and fan failures on individual servers. When you're running in a cloud, it's a software-defined infrastructure, and it's far more elastic. And so even if you're just lifting and shifting, how you think about monitoring and observing this new cloud infrastructure's fundamentally different. So we're there for the very first step of the journey for an organization, to get the visibility they need into the new architecture, and many times we're also helping them understand the before and after, so how do I compare my performance in my on-premise data center to what it looks like in the cloud? That's step one. Step two is, they start chipping away at those monoliths, or they have new initiatives, that are digital initiatives, that are running in Kubernetes, or container based architectures, microservices based architectures, and that is a fundamentally different world. How you observe and monitor, deploy, not just monitor, the entire supply chain of how you manage these systems is different. So there, they have to look at different solutions, and we're obviously one of the key players, helping them there. >> Leonid, we've been doing theCUBE now for a decade, and I think John, it was a decade ago we said, we made the statement that sampling is dead. So I love your approach, you're not just taking small samples to do your performance monitoring. What's the architecture that enables you to do that, could you talk about that a little bit? >> So I think the most interesting thing with more modern architectures, especially with microservices adoption, is the complexity of how the transaction flows through the system. And then, basically tossing the coin, like we used to be able to do, in previous generations, to capture some traces and get the data you need. Doesn't work anymore, because it's very tough to predict at the beginning of the trace where the transaction's going to go. We're taking a completely different approach on the market. We look at every single transaction, at scales, we have prospects that are talking at us about volumes of giga span in minutes, so one billion spans observed a minute, and with some of the interesting tech we've built, we are able to pick the interesting things. And the interesting things have a couple categories, transactions that occur infrequently, transactions that are maybe above P90, right, the slow ones, because when look about performance and the understanding of how the application performs, you really want to know what's slow, not what's normal. But you also have to capture enough of what's normal. So with some of our tech, we're still able to keep about 1% of transactions, but the right ones, and that's the biggest differentiator with what we put together for the APM product. >> One of the things I want to talk about with you guys is how you relate to some of Google's announcements. The key things, I'm oversimplifying now, but they got a server list kind of announcement, got Cloud Run environment things, the regions, which is global, and then obviously open source commitment. You mentioned functions, you mentioned Knative, obviously open source. You're seeing open source being much more of a production IT capability, so you guys obviously hit that with these solutions, so the question I have for you guys is, how hard is it for you guys to provide that real time monitoring, because Google needs to build an ecosystem, that's what they're not talking about, they didn't really talk about on stage, their ecosystem. So you guys are a natural fit into service mesh, which they showed onstage, Jennifer Lin showed a great demo. So Google has to build an ecosystem, you guys are clearly positioned, through your announcements, that you're deeply integrated with Google. Cisco announced and integration, obviously they have an integration, so integration seems to be the secret sauce, (laughs) with cloud, to play in this ecosystem. Could you guys elaborate on that dynamic, because it kind of changes the old formula for ecosystems? >> Yeah, it's very different, right? In the old days, you had proprietary systems, so the only way you could actually build an integration is, you had to get your product managers in a conference room with the vendor and get visibility in the roadmap, access to everything, and that's why there were, it just took a lot longer to get things done. I think what you're seeing with Google is, they've taken a very standards based approach to everything, right? So, whatever technologies that they're releasing, they're trying to build it as a standard, you can run it on any cloud. Instrumentation is a core part of their philosophy of any technologies that they're releasing, such that, you have a new platform, it has a metrics library, other standards based mechanisms to collect metrics, traces, events. What that does is it makes it easy for the ecosystem to just pick it up, right? Our belief has been, you know, in the old days monitoring was all about proprietary instrumentation and collection. Today it's all about analysis. So the fact that all of this is openly available, in open source or standards based mechanisms, is great for us, it's great for the customers, it's great for the ecosystem. >> That's their one-to-many way of building integration systems. >> And that's why you guys are supporting Knative, as an example. >> Yep. >> That's really kind of supporting the open source ecosystem, ties it to Google cloud. >> Yeah, I mean, we generally support, our customers are running in every single configuration (John laughing) and type of technology you can imagine, so it's our work philosophy to just be everywhere they are, and to support all of the tech that they might be running. But in general we're big supporters of open source, in that, you know, developers are now running most software. That's the world of web services and SaaS. And developers have a preference for understanding the stacks that they're running on, and being able to control it and so that is obviously why open source has just taken off the way it has. >> I think the other dynamic of embracing open-source and standards is it allows us to focus, not on the meetings with product managers and getting an insight into the roadmap, but on getting the standards based integrations deeply configured with some of, for example, content we provide out of the box for use to your own Google versus for use to your own premise or use to anywhere else. And that's where the differentiation and the value for the customer is, not in kind of getting together on the roadmap and figuring out what to build next. >> You guys should move fast to take advantage of the lift that they get. I'd love it if you guys could just take a minute each to explain SignalFx value proposition 'cause you guys I think are perfectly positioned now as this becomes infrastructure as code with cloud. When should a customer call you guys? When are guys needed? When do guys get called in? Where are you winning? Take a minute to explain when and where you guys fit into the customer environment. >> I would say as soon as a customer starts to leverage a cloud infrastructure, whether that's public cloud, private cloud, open shift, to open stack, pivotal cloud foundry, or a public cloud, how you monitor your infrastructure will be fundamentally different, and we can help you with that. And then along your journey, once you've moved to cloud and you start thinking about how do I build modern application architectures, modern web services, devops, then we are necessary. You cannot get to the cloud native stage where you're releasing software every week unless you have a monitoring system like SignalFx. >> Great, just great. I want to also get your pick your brain on some dynamic that I saw in the keynote, it might not be obvious to the folks that are in the mainstream, but Jennifer Lin gave a demo of taking a workload, and porting it over with a small script, no code modifications, running it on a container. >> Dave: The cloud vMotion >> Anthos migrate was the product but basically migrating workload into containers in the Kubernetes engine automatically with no re-writes, she said what you, where you want. So that kind of, I can see what she did there and that's very cool and that's a game changer that's infrastructureless code, but then she moves to a conversation around services meshes. 'Cause once you get these things on a containerized, inside the Kubernetes engine, you're kind of enabled for using service meshes. This is like the Holy Grail of microservices. This is a big growth area. Can you guys explain what this means, what does this service mesh mean, 'cause once these workloads start to be containerized you're going to see much more migration to this new model. Where does service mesh kick in and why is it important and what should people pay attention to? >> Well I would say one of the fundamental challenges of microservices is what people are calling more and more, observability, right. Because you have so many systems, like a single application or a single transaction, what is an application anymore? A single transaction can flow through dozens, hundreds, of individual microservices. So, and you're changing your applications all the time. So figuring out when you've introduced a problem very quickly is a big challenge. And so one of the big benefits service mesh brings is it provides automatic instrumentation of your applications and requests in a way that makes it very out of the box to get visibility across your entire environment. So that is step one, getting that visibility. The next step is then you obviously need to analyze this corpus of data and its massive, and that's where a solution like SignalFx comes in we can collect all this data and help you really T-signal for noise. Then the last step really is how do you take action on that data, how do you automate responses? Whether it's rolling back a canary release, or shifting a load balancing strategy so that if there's a bad node you stop sending traffic to that. All of that can be automated. And so what service mesh is doing is it's providing the sub street to allow you to really provide that closed loop automation, that infrastructure is code, you know that's the movement that everyone is really focusing on right now. It's a key technology to enable that. >> Tell me about the observability trends, because this has been a hot venture funded area. We hear trace, dynamic tracing, these are techniques, there's a variety of different mechanisms for observability. How does Kubernetes, and now service mesh's impact observability, where is the puck going to be, if you're going to skate to where the puck is, what's the state of the situation? >> Well I think what it does is it makes instrumentation a lot easier. So typically a challenge when you're running a old Java application from 10 years ago, getting visibility into the app, it's a monolith. You to get the full visibility and the full call stack, that's harder to collect. When you're in a microservices world with service mesh, you're getting that visibility automatically. And what becomes more important is understanding the east/west latencies across all these different microservices. So because instrumentation is so much easier with all these new technologies, what it means for monitoring is it really shifts the focus to who can make the most sense of this data, who can provide assistance to the operators to really help them pinpoint when there is a problem, what is the potential cause, and to triage it very quickly. So again, the whole value proposition is shifted to the analysis. >> So Leonid given that, what are your engineering priorities, maybe share a little road map if you could? >> Sure, so if you think about what we just talked about, adoption of Kubernetes, or service meshes, the challenges that those environments bring both the femorality of the environments on which you now deploy compared to what most of the operators and application developers are used to, as well as the constant motion in the system, right. Kupernetes will move the workload several times an hour and the amount of data those systems tend to generate becomes fairly difficult to cope not just to a monitoring system, but to a human, right? So how can you take about what Karthik talked about all this noise and get it into an actionable intelligence across tens of millions times series an hour possibly in the middle of the night, how do you get the operator to the root cause very quickly? And what kind of technologies do we need to have as a vendor, and that's where we spend a lot of time thinking about, how do we provide actionable insight for those highly femoral environments that are getting even more femoral? >> One of the themes that's here, and already we're seeing it pop out of Google Next, and we've seen it in the other cloud shows we've gone to is, complexity is increasing, and the business model that seems to work well is taking complexity and making things simple. >> Mhm >> Right >> Whether it's extraction layers or other techniques, how does a customer, who's got all these new suppliers, new dynamics, new shift in the marketplace, new business models, how does a customer deploy IT, deploy cloud, and move the complexity to a simplicity model? This is a hard challenge. >> Well, I think that's one of the fundamental mental model shifts that an organization needs to make. Complexity was your enemy in the old days. Right, because you were releasing software once a year, twice a year and so you don't want it to be complex. But if your goal is speed and innovation, you're going to have to accept some complexity to get that speed and innovation. You just have to decide where is that complexity acceptable and how do you change your processes and your tooling to minimize the impact of that complexity. So I think I would disagree with that sentiment because I think organizations have to start thinking about things differently if they really want to move quickly. >> So embrace complexity. >> You have to embrace complexity and you have to think about what are the mitigating factors I need to take in my organization structure, my processes, my tooling, to compensate for the additional complexity I'm creating, but still release software as quickly as I used to. >> I would add, I think in a lot of ways you're shifting the complexity from infrastructure management more up the stack. >> That's, ya. >> In many ways IT is getting more complex, to your point Karthik. >> Ya, I mean all of these extractions make perhaps the underlying infrastructure less complex to manage but you're absolutely right Dave, the applications will become more complex when you move to microservices and you've got 50 pizza box teams working on a bunch of microservices, there's an organizational dynamic as much as there's a tech dynamic, right. How do you get these 50 teams to communicate with one another if there's a issue, an incident. >> And the data pathways, the data pipelines, the journey of that data, is much, much more complex. >> Ya absolutely. >> Final question, as the developers and operators come together, that seems to be a big trend. Developers want frictionless environment, programmable internet, they're going to be spitting up these services and then the operators have to run it. Those worlds are coming together. What's your thoughts on the operations side and developers coming together? >> I think they're two peas in a pod. They're two parts, they're two necessary parts. I think you will see more and more automation move up the stack. I think the place to start is really in the infrastructure layer and it will make the lives of operators of these cloud environments simpler. And then I think that automation will move up the stack as well over time. >> What's the most important story coming out of Google Next, if you can just kind of read the tea leaves, get a sense of what's going on here? 2019, whole new year, whole new game changing. What are your guys' thoughts on what's kind of going on in the cloud business this year? What' going on at Google Next? What's the big story? >> Well I think from my perspective it's very clear they're focused a lot on multi cloud, cloud agnostic and where the right ones run anywhere and run on Google. That seems to be a big push. And then the other is they're just behind on go to market and they seem to be focusing quite a bit on investing in all of the other elements, non-technology elements, to make organizations successful. >> Leonid, on the tech side, what do you see as the big in story here? >> I think Google was always found on the tech and they're continuing to deepen it. I think more interesting for me the story is about the go to market and embracing the complexity of the enterprise. >> Right >> And recognizing that not every application that will come to Google Cloud will be architected in a modern way. The thousands upon thousands of applications that have to lift and shift still and surviving some of the announcements around the service mesh are great enablers for those customers to start embracing the cloud technology. >> Tech geeks love service mesh, I'm a big fan. Guys, thanks for sharing the insight. Give a quick plug for what's going on for SignalFx. What's going on in the company? What are you guys looking to do? Are you hiring, are you expanding, what's going on? >> Ya we're in rapid growth here as a company. We're really excited about microservices APM product that we introduced late last year and what that does is it brings distributed trace analytics to our core monitoring platform. So what that allows you to do is get bottoms up visibility into each individual component through our metrics system, but also a transaction oriented view through our micro services APM product. Bringing the two together, super excited about the level of sophistication and analytics that it's going to bring our customers. >> What's the head count? What's the head count now, roughly? >> We're about 250 people right now. >> 250 okay, and you've raised over nine figures, I think? >> Over a hundred million dollars yeah. >> That's great, congratulations. >> So Karthik as a founder, what's it like to have the vision early and seeing it, and staying the course? And you've stayed on the right wave. >> Yeah. >> And now the wave's gotten bigger, what's it like to be the founder and be where you are now? >> It's terrifying at first because you don't know if the markets are going to move in the direction you need them to, but it's very gratifying when that actually happens and we're very fortunate that the world is moving very squarely into cloud based architectures, and not just cloud but all of these modern run times that are exactly what we predicted the world would look like for the last six years now. >> And you had a great team, engineering team was solid, you've got great chops. Any advice for entrepreneurs out there who are now getting into this world, maybe younger entrepreneurs coming out, building some applications? What's your advice to other founders that are... >> I could spend hours on that topic (laughter) >> I think >> Dave: Ship early and often >> You just have to continue to have faith and conviction in your beliefs and stick it out because there are lots of twists and turns, especially in the early days if you're betting ahead of the curve, you need to be patient and continue to have belief in yourself and your ideas. >> Well congratulations the world has right spun to your doorstep, congratulations with SignalFx. Thanks for coming on theCube. We're in San Francisco for theCube's coverage. Day one of three days. I'm John with Dave Vellante. Stay with us for more live coverage after this short break. (light electronic music)

>> Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Welcome back here at the Sands, as we conclude our coverage here of day one of AWS re:Invent, we've been live on theCUBE, we'll be back with you again on Wednesday and Thursday, but glad you're here with us on Tuesday for our coverage, along with Justin Warren, I'm John Walls, and we're joined now from two executives from SignalFx, Karthik Rau, who's a CEO, and Arijit Mukherji who's the CTO >> Hi. >> At SignalFx, gentlemen thank you for being with us. >> Oh, it's a pleasure being on. >> Alright, so just tell us a little bit about what you do, and why you're here, and then we'll dive in from there. If you would. >> Sure, SignalFx is a cloud monitoring service, designed for operators of applications and infrastructure that might be running in the cloud. Our origins came out of Facebook, so Arijit and much of our technical team are responsible for building the monitoring systems in Facebook, back in the mid 2000's when they had their famous move fast and break things culture. >> Right. >> Which today everyone calls devops, and so what we've really focused on is building a far more analytics centric monitoring approach, that focuses a lot on identifying the patterns that are really meaningful and we believe that's a far more important problem to solve in today's distributive environments. >> And you made some news not too long ago, you've unleashed a new product into the marketplace, Arijit, if you would. >> Yes, yes, we are very excited to launch our, what we call a SignalFx microservices APM product. And it's really aimed at giving customers visibility into their transaction flow that's happening in their microservices environment. As you know, we're moving to microservices, the individual pieces are becoming smaller, and they're growing in number, and so the complexity of those interactions becoming harder and harder to manage, and this product is aimed, basically to help our customers make sense of those, and monitor them effectively. >> A theme that's come up a couple of times today on theCUBE, is that the complexity of the modern way of doing things, in cloud native services, microservices, it's beyond human comprehension >> That is exactly right. >> You need to have the assistance of tools like IPM, and I think we were talking just before we went live, that this is a distributive tracing type approach to microservices, is that correct? >> That is correct. So the goal is to have a lightweight approach, where you can very easily generate the spans, and traces from all your microservices, the whole environment, and the value that we provide is to sort of take them, baseline them to give you a sense of how performance is happening overall in the environment, but more importantly to your point earlier, is how can we help the customer using data signs to help them guide them towards the problem when it is happening, where it is happening, so that you know you can reduce the MTTR, which is sort of the key part of all of this, so that's been much of the focus of the product, yes. >> Okay, so for customers who are looking to re-platform onto microservices, or some of these newer ways of doing things, what is it about SignalFx that helps them to understand how to change an application from one way of doing things, you know monolithic type application, into something more microservices driven? How does SignalFx actually help them with that journey? >> Well our customers who are early in the cloud journey, are doing a number of things. One, they are able to get complete visibility into the old, right, so you typically want to look at a side by side, so you're able to leverage in our smart agent, collect information about your monolithic stack, get full visibility into what the performance looks like in that particular environment, but then what we do better than anyone else, is give you comprehensive visibility into the new stack, and give you the analytics that will allow you to really compare one versus the other, so one of the things that's very different about SignalFx, is we have a very rich analytics capability in the backend, so, collecting metric data across your environment, whether it's your old stack or your new stack, we're able to provide very sophisticated analytics to identify meaningful patterns, outliers, anomalies, and to look across all of your metadata to be able to identify whether those patterns are specific to a subset of machines or a particular version of code, and that's typically very helpful to customers as they're moving from the old to the new. >> Yeah, can you give me an example then, I mean, in terms of specificity that you've provided, you talk about sophisticated measurements, or stats, just something that would tell us, oh I see, that was kind of an aha moment, maybe for one of your customers. >> Yeah, so the thing that is unique about us is, that because we have a strong metrics product that's backing this, because we have a strong analytics capability that's backing this, when we do distributive tracing, we are tracing and providing you insight not only into your application, like what it is doing, we are actually able to correlate that with your infrastructure, so let's say your application is running in a container, if there is a problem we can actually let you correlate that application in the performance of that to the container, to the host, to the infrastructure, top down as well as sort of left right, so to speak, and that has been sort of key, because what we find is having that capability, really helps our short circuit, the resolution time, because a lot of times the problem may be vertical, other times it may be broad, like horizontal, right? So our goal is to catch both of them. >> Okay then. >> So you're able to identify the root cause of an issue much quicker, so your teams can go and find, that server's failing, I need to go and replace a piece of hardware, or there's a storage issue, and you can just dial it straight in really quickly, is that, is that-- >> Yes well. >> In modern environments, you're far more likely to see performance issues in a small subset of your transactions than you are to see just a massive outage, right? A lot of modern distributed systems are designed to be resilient to individual node failures, for example, in a future that we just launched along with our microservices APM, is something called outlier analyzer, so let's say all your metrics indicate the service is performing fine, but you have point five percent of your users complaining that the performance is terrible. That's where tracing really helps, 'cause now you can look at every transaction, you can understand exactly where, you know, things might be slow, but it's typically a trial and error process, you have to go through every single trace you have to sort of figure out is it a particular version of code, or particular server. Our outlier analyzer feature will automatically look through all of the outliers, identify the over represented dimensions, and guide you to those specific problematic areas, right? So you run our outlier analyzer, it'll tell you, you know, this particular machine is overrepresented in your long tail traces. >> Yeah. >> Or this particular version of code is overrepresented. So, it short circuits the entire troubleshooting process by orders of magnitude. >> Yeah, that kind of intermittent error is always really really hard to find, something which just explodes and catches on fire, that's easy to find. >> And it's extremely difficult for a human to find it by trial and error across a distributed system, that can involve thousands of components right, so you really really have to leverage analytics and that's really what SignalFx is incredibly strong at doing. >> Yeah, so we're basically replacing luck with tools. >> Trial and error and luck, with a more prescriptive trouble shooting. >> Yeah, so for customers who have gone through this journey, and they've actually re-platformed an application, they've converted it into microservices, and they're doing cloud native things, and you've helped some of these customers. What's an example of a customer who's living in that new world, or what's the view like from where they're sitting, where they have all these lovely tools, and they're not relying on luck anymore, what's their sort of daily life like? >> Well I think the biggest difference is, they're now able to automate a lot of remediation, if you can be more intelligent in the signals that you're capturing, apply more intelligent analytics, then you, especially in today's environments, you can automate a lot of remediation, today's frameworks are highly automatable. And so one example of this, we have one of our larger Fortune 500 accounts, they do a number of launches, product launches, where they get massive amounts of load during a product launch and this is not atypical in today's environments. >> Yeah. >> And prior to having the real time data collection analysis with SignalFx, they would have two rooms full of people, supporting every single launch, and very reactively, you know, and something would go wrong they would have to go and figure out what was happening. With SignalFx they are now able to build very sophisticated analyses on the data as they're spinning up containers and instances to support a shoe launch, and they've now actually automated a lot of their remediation, whether it's auto-scaling, or rolling back of you know canary releases and such, and they've gone from having two rooms full of people to having just one on call engineer every time they do a launch, and it's also enabled them to be a lot more aggressive in doing these launches because they just have a lot more confidence in their ability to execute them. You know, that's one example. >> You know Justin was talking about some of the trends, we've heard a lot about today, the one I guess, or one of the constants has been about the pace, the rapidity of innovation, the rapidity of change, and so in your world, what do you think is the next hate to say big thing, but what mountain are you trying to climb now, that you haven't already conquered? >> So in my view, there's some very very encouraging trends that are coming our way actually, there was a talk that I presented earlier today about the concept of service measures and how I feel that they are going to be the next big thing because I think they attack a lot of the core operational challenges that we face in our microservices environment including how well you can instrument your environment, how well do the different types of instrumentation, your metrics, your APM, your logs, how well are they relatable, how tightly coupled are they? Right, how quickly can you make configuration changes within the environment in a more foolproof manner, that's more automated, that is more consistent, and so I feel like technology like that is going to transform how we do software in a few years from now, I see that advancing very very quickly, and something that's very related to that, and something I eluded to, to the talk earlier too was, this concept of feedback driven automation, where now I am no longer just going and just configuring my infrastructure to behave the way I want it to. In fact I'm also observing it as it is running, using high quality monitoring tools like SignalFx, and then using that to create new feedback, because if things sort of diverge from my intent, that I should be able to get it back to where I want to be, and all of this must happen without human interaction, because we work in the order of minutes, while you know automation can do this in seconds. This is absolutely fascinating, I think this is one of those big trends, that are coming down the pipe. >> Karthik anything to add to that? >> No I think Arijit nailed it. (laughing) >> Excellent, alright. Gentlemen thanks for being with us. >> Thank you. >> Good luck with the rest of the show, I'm sure it's been very good for you so far, and for the next two days, have a great time. >> Okay. >> Thank you very much. >> Excellent, thanks for being with us. >> We are concluding our coverage, day one, here of AWS reinvent for Justin Warren, I'm John Walls, we thank you for watching theCUBE. (upbeat music)

>> From Union Square in downtown San Francisco, it's theCUBE covering PagerDuty Summit '18. Now here's Jeff Frick. >> Hey welcome back everybody. Jeff Frick here with theCUBe. We're at PagerDuty Summit at the Westin St. Francis in Union Square, historic venue. Our second time to this show, there's about 900 people here talking about kind of the future of dev ops, but going a lot further than dev ops. And we're excited to have a couple of CUBE alumni here at the conference from SignalFX. We've got Arjit Mukarji. >> Mukarji, yeah. >> Thank you. And Karthik Rao, co-founder and CEO of Signal FX. Gentlemen, welcome. >> Thank you very much. >> So what do you do at PagerDuty Summit? >> Well we've been partners with PagerDuty for a long time now, we've known them since the very early days, we share a common investor. But we both operate very squarely in the same space, which is companies moving towards dev ops development and deployment methodologies, leveraging cloud and native architectures. We solve a different part of the problem around monitoring and observation and we partner with them very closely around incident management Once a problem is detected, we typically integrate in with PagerDuty and trigger whatever incident management paths that our customers are orchestrating by PagerDuty. So, it's been really an integral part of our entire work flow since we started the company. So we're very close partners with them. >> Yeah, it's interesting 'cause Jen announced they have 300 integrations or 300+ integrations, whatever the number is, and to the outside looking in, it might look like a lot of those are competitive, like there's a lot of work flow and notification types of partners in that ecosystem, but in fact, lots of different people with lots of different slices of the pie. >> That is good. >> Yeah, absolutely. It's a really big problem space that everyone is trying to solve in this day and age. Some of our competitors are in that list, but you know we partner very closely with PagerDuty. As I mentioned earlier, our focus really is around problem detection and leveraging the most intelligent algorithms, statistical models in real time to detect patterns that are occurring in a production environment and triggering an alert, and typically we're integrating in with PagerDuty and PagerDuty deals with the human elements of once something has been detected, how do you manage that incident? How do you router to the appropriate people? One of the things that's really interesting as this world is changing towards these dev ops models is the number of people that have to get involved is substantially greater than it was before. In the old days, you would have an alert go into a knock and you have a specialist group of people with very specific runbooks because your software wasn't changing very often. In today's world, your software is changing sometimes on a daily basis, and it could be changing across dozens of teams, hundreds of teams in larger organizations. And so, there's a problem on the detection side because companies like SignalFX have to do a really great job of detecting problems as they emerge across these disparate teams, across a much, much, much, larger environment with much larger volumes of data and then companies like PagerDuty really have to deal with a far more complex set of requirements around making sure the right people get notified at the right time. And so they're two very different problems and we're very happy to- and have been partnering with them for a number of years now. >> And again, the complexity around the APIs where the app is running, there's so many levels now of new complexity compared to when it was just one app, running on one system, probably in your own data center, probably that you wrote, compared to this kind of API centric multi-cloud world that we live in today. >> That is exactly right because what's happening is our application architectures are changing 'cause we used to have these monoliths, we used to have three tiers and whatnot, and we're replacing that with the micro-services, loosely cabled systems, and whatnot. At the same time, the substrate on which we are running those services, those are also changing. Right, so instead of servers, now we have virtual machines, we have cloud distances and containers and pods and what-have-you. So in a way, we are sort of growing below too in some sense and so that's why sort of monitoring this kind of complex, more numerous environment is becoming a harder challenge. We're doing this for a good cause, because we want to move faster, we want to innovate faster, but at the same time, it's also making the established problems harder, which is sort of what requires newer tools, which sort of brings companies like us into the picture. >> Right, yep. And then just the shear scale, volume, number of data that's flowing through the pipes now on all these different applications is growing exponentially, right? We see time and time again, so it really begs for a smarter approach. >> Absolutely, I mean on two levels right? The number of minutes of software consumption is up exponentially, right? Since the smartphone came out in 2007, you've got billions of people connected to software now, connected all the time, so the load is up order sum magnitude which is driving, even if you didn't change the architectures, you would have to build out substantially more back-end systems, but now the architectures are changing as well, where every physical server is now parceled up into VMs which are parceled up into containers. And so the number of systems are also up by order sum magnitude. And so there's no possible way for a human to respond to individual alerts happening on individual systems, you're just going to drown in noise. So the requirements of this new world really are, you have to have an analytic spaced approach to monitoring and more automation, more intelligence around detecting the patterns that really matter. >> Right. Which is such a great opportunity for artificial intelligence, right, a machine learning. And we talk about it all the time, everyone wants to talk about those, kind of as a vendor-led something that you buy. Yeah, that's kind of okay, but really where the huge benefit is, companies like you guys and PagerDuty using that technology, integrated in with what you deliver on your core to do a much better job in this crazy increasing scale of volume that's run with these machines. >> Yes, because the systems are becoming so complex that even if you asked a human to go and set up the perfect monitoring or perfect alerting, et cetera, it might be quite a hard challenge, right? So, as a result sort of automation, computer intelligence, et cetera needs to be brought in to bear, because again, it's a more complex system, we need higher order systems that have dealed with them. >> Right. >> You are very, very right, yes. And that's a trend we are starting to see within the product, we are actually focusing a lot on sort of data science capabilities which too are sort of making them more and more sort of machine running and automation. In the future, we have capabilities in the product that can look at populations and identify outliers, look at cyclical problems and identify outliers again. So the idea is to make it easy for users to monitor a complex system without having to get into the guts, so to speak. >> Right. >> And to do it on various sorts of data, right? I think you have an interesting use case that we've been experimenting with recently. >> That's right. >> If you want to talk about that. >> Yeah, so I actually have a talk tomorrow, it's called "Interesting One." It's about monitoring social signals, monitoring humans. So we have these systems, we have these metrics platforms and they are quite generic, the tools that we have nowadays and are sort of available to us are quite powerful, and the set of inputs need not be isolated to what the computers are telling me. Why not look at other things, why not look at business signals? In my case, I'm going to talk about monitoring what the humans are doing on Slack as a way for me to know whether there's something of interest that's going on in my infrastructure, in my service that I need to be aware of, right? And you'll be shocked how surprisingly accurate it tends to be. It's just an interesting thing, and it makes one wonder what else is out there for us to sort of look at? Why confine ourselves, right? >> Right. It's funny because we hear about sentiment analysis in social media all the time, but more in the context of Pepsi or a big consumer brand that's trying to figure out how people feel. But to do it inside your own company on your own internal tool, like a Slack, that's a whole different level of insight. >> You'd be surprised at the number of companies that monitor Twitter to understand whether they have an adage. >> That's right. >> Yeah, because in this day and age, users are on Twitter within seconds if something is perceived to be slow, or something is perceived to be down, they're on Twitter. So there are all sorts of other interesting signals to potentially pull from. >> Right, right. Well and guess what, we were just at AT&T Spark yesterday and the 5G's coming and it's 100x more data'll be flowing through the mobiles, so the problem's not going to get any smaller any time soon. >> No. >> Absolutely. >> So what else have you guys been up to since we last spoke? Continuing to grow, making some interesting moves. >> Absolutely- >> Crossing oceans. >> We've been very, very busy, one of the big areas of investment for us has been international growth, so we've been investing quite a bit in Europe. We have just introduced an instance of our service that's based in a European data center. For a lot of our European-based clients, they prefer to have data locality, data residency within the European Union, so that's something new that we just introduced last month, continue to have a ton of momentum, outed AMIA, they're very much on the cloud journey, and embracing cloud and embracing dev ops, so it's really great to see that momentum out there. >> Right, and clearly with GDPR and those types of things, you have to have a presence for certain types of customers, certain types of data. Anything surprising in that move that you didn't expect or? >> No, I don't know, I'll let you. >> Not in that move, but it's just interesting to see how quickly some of these modern technologies are getting adopted and how- one of the things sort of we talk about a lot in our trade is ephemeral, right? So how things are short-lived nowadays, and you used to lease these servers that used to stay in your data center for three years, then you went to Amazon and you leased your instances, which probably lived for a few months or a few days, then they became containers, and the containers sometimes only for a few hours or for- you know. And then, if you think about serverless and whatnot, it's in a whole different level, and the amount of ephemeral that's going on, especially in the more cloud native companies, was a little bit of a surprise in the sense that, it actually poses a very interesting challenge in how do you monitor something that's changing so fast? And we had to have a lot of engineering put in to sort of make that problem more tractable for us. And it continues to be an area of investment. That to me, was something that was a little bit of a surprise when we started off. Much of this doctorization and coordinating was not yet in place, and so that was an interesting technical challenge as well as a surprise. >> Well I'm curious too as instances, right so there's the core instances that are running core businesses that don't change that much, but it's a promotion, it's a this or that, right? It's a spin up app and a spin down app. Are those even going up on the same infrastructure from the first time they do it to the second time they do it. I mean, how much are you learning that you can leverage as people are doing things differently over and over again as their objectives change, their applications change, they're going to go to market around that specific application. That's changing all the time as well. >> Yeah, so I think the challenge there is to sort of build, at least from a technical point of view, from SignalFX point of view, is build something that is versatile enough to handle these different use cases. We've got new use cases, new ways of doing things are going to continue to happen, probably going to keep on accelerating. So the challenge for us is good and bad, is how do we make a platform that is generic, that can be used for anything that may come down the pike, not only just now. At the second time, how do we innovate to continue to be up to speed with the latest of that's what's going on in terms of infrastructure trends, software delivery trends, and whatnot. Because if we're not able to do that, then that puts us sort of behind. >> Right, right. >> So it's a sort of lot of phonetic innovation, but it's also exciting at the same time. >> Right, right, right. And just the whole concept too, where I think what's best practice quickly becomes expected baseline really, really fast. I mean, what's cutting edge, innovative now unfortunately or fortunately, that become the benchmark by which everything else is measured overnight. That's the thing that just amazes me, what was magical yesterday is just expected, boring behavior today. Alright good, so as we get to the end of the year a lot of exciting stuff, you guys said you're going to be at Reinvent, we will see you there. Anything else that you're looking forward to over the next couple months? >> Just, we're really excited about Reinvent's big show for us, and we'll have some good announcements around the show. And yeah, looking forward to just continuing to do what we've been doing and deliver more rally to our customers. >> Love it, just keep working hard. >> Yep. >> Alright. Arjit, hope your throat gets better before your big talk tomorrow. >> Yeah, that's right. >> Alright, thanks for stopping by Karthik, it was great to see you. >> Great to see you. >> I'm Jeff, you're watching theCUBE, we're at PagerDuty Summit at the Westin St. Francis in San Francisco. Thanks for watching, see you next time.

>> Live from San Francisco, it's theCUBE covering Google Cloud Next 2018, brought to you by Google Cloud and its ecosystem partners. (techy music) >> Hello everyone, welcome back to theCUBE's live coverage here. We're in San Francisco for Google Cloud's major conference, Next 2018. I'm John Furrier, here for three days. Wall to wall coverage on day one. We've got two great guests from SignalFX, Karthik Rau, founder and CEO, and Rajesh Raman, who's the chief architect. Signal's a hot startup in the area. Way ahead of its time, but now as the world gets more advanced, the solution is front and center as the value proposition if cloud moves into the mainstream, devops going to a world at large scale. Not just networking, monitoring, applications, you've got service meshes booming, great topic. Karthik, great to see you, Rajesh, thanks for joining us. >> Thank you. >> John, great to be on. >> So, first of all let's just get it out of the way, you guys have some fresh funding in May, so just quickly give an update on the company. You guys raised-- >> Yeah >> A series... >> A series D. >> Series D, give us, but how much? >> Yeah, so we raised $45 million from General Catalyst leading the round back in May, been building a ton of momentum as a company, close to a couple hundred people today. We're using a lot of that to expand internationally. We've got a team in Europe now, just opened up a team in Australia. So, things have been going great. >> Congratulations, we've had chats before, always been impressed. You guys have a great stable of awesome engineers and talent in the company doing some great work, but it begs the question, I always like to get into the what ifs. What if I could have large scale application development environments with programmable infrastructure, how does that change things? So, Karthik, what's... How as that what if changes, now that is what's happening you're starting to see the cloud at scale for the common masses of enterprises, where old ways of doing things are kind of moving away. It's like horse and buggy versus having a car for the first time-- >> Yeah. >> Jobs are changing, but the value doesn't necessarily change. You still go from point A to point B, you still got an engine, people who care about fixing cars, so people just want to drive the cloud, some people want to get under the hood, whole new architecture. >> Yeah. >> What's the what if of if I could have all these resources, what's the challenges and what do you guys solve. >> Well, I think there are a couple of challenges in this new environment. One is the number of components are just orders of magnitude more than they used to be in a cloud environment, right? We went from having physical machines that live for three years in a data center, divide it up into VMs 10 years ago, now divided up into containers for every process. Not only that, but these containers get spun up and spun down every few minutes or every few hours, and so it's just the number of components in the churn is just massive. So, that in and of itself requires a far more analytics-based approach to understand patterns rather than what's happening on an individual component. The second thing that's changed is the operating model's fundamentally different, because now you're building and running web services, and when you're running web services the people who build the software are the ones who technically are responsible for operating it. And so, you know, you have more updates, you've got more people involved, you've got lots of different components that all need to interact with one another, and so having a communication framework across all of these disparate teams become really, really, really critical. So, those are the two fundamental changes as you move from, you know, for operating these modern, massively distributed-- >> Yes. >> Applications. >> And I'll just add just some observation data that we've seeing in theCUBE is those same folks building aren't necessarily operators, so they want to be in and out fast, right? (laughs) >> They don't want to be running and operating all the time, they want to push some code. Melody Meckfessel here at Google ran a survey with developers and said, you know, "What makes you happy," and it was two things that bothered developers: technical debt and speed for deployments, commits, and the commit number was around minutes. If you can't get something done in minutes then they're onto something else, so the mind share attention of developers and technicos. So, this is a challenge at scale when you have technical debt, which we've seen companies come out of the woodwork, "Oh, yeah, "I'm going to automate something, "I'm going to throw some compute at it with the cloud "with the best monitoring package on the planet "and look how great it is," but all they did was just code some instrumentation and that's it. >> Mm-hmm. >> They weren't dealing with a lot of moving parts. Now as more things come in this is a challenge that a lot of companies face. You guys kind of solved this problem... >> Yeah, absolutely, so maybe Rajesh was a part of the team at Facebook that built the Facebook monitoring system, and that's actually what gave us a lot of the vision to start SignalFX five-and-a-half years ago, so maybe-- >> Tell about the protection, the vision-- >> Yeah. >> And what you guys are doing. >> Yeah, so CICD, you know, it kind of, like, underlies a lot of this vision of, like, moving fast. You mentioned that people wanted, like, you know, push their code in a few minutes... The thing that makes that possible is for you to have observability into what's happening while that push happens, because it's one thing to push very fast, it's another thing to recognize that you might have pushed something bad and to be able to revert it very quickly, too. And so, you'd only need, like, you know, good observability into all the things that matter that characterize the health of your system to be able to quickly recognize patterns, to be able to quickly recognize anomalies, and to be able to maybe push forward or even roll back very quickly. So, I think, like, observability is like a very key aspect of this entire CICD story. >> That's great, and that's great to know that you were over at Facebook because obviously Facebook built, at scale from the ground up, a lot of opensource. Obviously they contributed a lot to opensource, but it's interesting, as they matured and you start to see their philosophy change. It used to be move fast, break stuff. >> Yeah. >> To move fast, be reliable. >> Yeah. >> This is now the norm that's the table stakes in cloud. You have to move fast, you got to push code, but you got to maintain an operational integrity. This is, like, not like an option. This is, like, standard. >> Absolutely. >> How do you guys help solve that problem? >> So, I think there are a few different aspects to it. So, the first is to, you know, people need to ensure that they have observability into their application, so this is ensuring that you have the right kind of instrumentation in place. Thankfully this is kind of becoming commoditized right now and getting metrics from your system. The second part, and the more key part, is then being able to process this data in a real time way. You know, have high resolution, very low latency, and then to be able to do real time streaming analytics on this data. In highly elastic environments when things come and go very quickly, the identity of any individual, like, component is less important than the aggregate system behavior, and so you really need the analytics capability to kind of, like, go across this data, do various kinds of aggregations, compare it against past data, do predictive analytics, that sort of thing. So, analytics becomes the very key concept of, you know, how you operate these environments. >> It sounds so easy. >> Yeah, well one thing I'll add to that, so you know, to your point a lot of big companies sometimes are scared by this. You know, "How do we," you know... "We can't move quickly and break things," and everything that they've designed is around having process and structure to check and make sure everything is clean before they push changes out, and now we're in this world where, you know, an intern or a developer can push directly on a production, how do you manage that? The key thing in this modern world when you're trying to release software quickly, Rajesh hit on this earlier, you need the magic undo button. >> Yeah. >> That is the key to this entire process. You need to design your software, you need to design your process, and you need to design your tools so that if you introduce something bad you catch it immediately and you can roll it back. So, lots of devops practices are oriented around this, right? The idea of a canary release, I'm going to roll out an update to one percent of my systems and users, test it out, observe all the metrics, make sure everything is clean before I roll it out to everyone else, and the ability to roll back quickly is also important. But in order to do all of this you need the visibility, you need the metrics, and you need to be able to do analytics on it quickly to identify the patterns as they emerge. >> That's a great point and I'd love to just double down on that and get your thoughts because some of the Google Cloud people who are operating at this scale, I put them on this whole service-centric architecture, because they're services. We're talking about services, managing sets of services, having analytics, observation space, the reverting back and the undo button, the magic button do-over, whatever you want to call it, but the interesting thing is clean. Having a clean service whether it's an API, message queue, or an event, this stuff's happening all over the place in the new services world. How do you guys help there, is that where you guys get involved? Do you see up in that layer, how far up are you guys looking at some of the instrumentation and the insights? >> Yeah, you want to take that? >> Yeah, sure, so you know, the one thing that we really like about SignalFX and we were very keen on when we built the platform is that we are very agnostic about metrics. We're happy to accept metrics from anywhere, we'll take instrumentation-- >> (chuckles) You don't discriminate against metrics. >> We'll take instrumentation from cloud environment, we'll take, you know, metrics from opensource systems and premier applications, so you know, some of these systems are already kind of built in to get metrics from. You know, we talk to the Kafkas and Cassandras of the world, for example. We can also talk to GCP and AWS and grab metrics from their system. I think the interesting question is like when people really are taking the devops philosophy of, like, so how do you instrument your own application, what questions do you want to ask from your environment that answer the critical questions that you kind of have, and so you know, that's the one, that's the next step in the hierarchy of needs is for people to ask the right kinds of questions, and you know, instrument their applications properly. But like having done that, we can go up and down the stack in terms of, like, insight into whether all the way from your cloud environment through opensource systems, all the way up-- >> So, you guys'll take data from anyone, just stream it in-- >> Yeah. >> Normal mechanisms there, what's the value added, where's the secret sauce on SignalFX? >> So, I think value, it's all about analytics. We are all about analytics, so we are able to look at patterns of the data, we can go up and down the stack and correlate across different layers of software, look at interactions across components in your microservice, for example. You know, one really interesting thing that's happening, as you might be aware, like the whole service mesh aspect of it, which lets us, gives us insight into interactions between components-- >> Yeah. >> In a microservices architecture, so you know, we are able to get all that data and give you insight into how your whole system is working. >> So, you guys, you can see in the microservices layer? >> Absolutely. >> Yeah. >> That's powerful. >> And the key point is monitoring really has become an analytics problem, that's what we keep saying, right, because what's happening on an individual component is no longer as interesting as what's happening across the entire service, so you have to aggregate the information and look at the trend across the entire service, but the second thing that's really important is you need to be able to do it quickly, and this is where our streaming real time system really mattes. And people might ask, "Why does it "matter to do something real time." Like, "Seconds versus minutes, can a human actually "process something in seconds versus minutes?" Perhaps not, but everyone's moving towards automation, right? >> Yeah. >> So, if you want to move to a system where you have a closed loop, you have automation, and guess what, all of these modern systems, all the stuff that Google's talking here is all about automation. >> Yeah. >> And in that world seconds versus minutes, it means a tremendous amount of difference, right, where if you can find signals that will tell you there's an emerging problem within seconds and then you can revert a bad code push or you can auto-scale a cluster or you can, you know, change your load balancing algorithms all within seconds, that is what enables you to deliver, you know, 4.9s, 5.9s type of availability. >> And the consequences of not having that is outages-- >> Yeah, outages. >> Performance. >> Performance degradations, unhappy customers. I mean the cost to a brand now of having any kind of a performance issue is enormous, right? People are on Twitter before your team knows about it. (chuckles) >> Actually, you guys have a lot of the things you're solving, what is the core problem that you solve, what's the value proposition if you narrow it down that's high order bit for SignalFX? What's the corporate problem you solve? >> Well, we're solving the monitoring and observation problem for people operating cloud applications, so what happens is when you use SignalFX you have the confidence to move quickly, right? It gives you the safety net to be able to deploy changes on a daily basis, to have the shared context across a distributed team, so if you've got hundreds of two pizza box teams working together we give you that framework, the communication framework and the proactive intelligence to find issues as they emerge and proactively address them. And bottom line what that means is you can move as quickly as a Google or a Facebook or a Netflix even if you're a traditional Fortune 500 company that's regulated, and you know, you think you may not be able to do it but you really can. >> You give them the turbo charge, basically, for the analytics. All right, here's a question for you, what are the core guiding principles for the company? You guys obviously have a lot going on so you've got a core tech team, I mentioned it earlier. >> Mm-hmm. >> What are some of the guiding principles as you guys hire, build product, talk to customers, what's the key DNA of SignalFX? >> Yeah, I would say we are a very impact-driven company, so I'm, you know, very, very proud of all the people that we have on the team. We've got a lot of entrepreneurs who are focused on solving big problems, solving problems that customers may not necessarily know they need at the time, but as the market evolves we're there to solve it for them. So, we're a very customer-centric company. We have fantastic, we invest aggressively in technology, so it's not just about wrapping a pretty UI around, you know, Bolton Tech. We have real differentiated technology that solves real problems for people, and you know, I think we've in general just tried to skate to where the puck is and understand where the market's headed as a company. >> What are some of the customer feedback that you're getting? For folks that don't know SignalFX, what are some of the things that you're hearing from customers, why are you winning, what are some of the examples, can you share some color commentary? >> Yeah, I'll give one example, a Fortune 500 company that has been very aggressively investing in cloud the past, you know, four or five years, built an entire digital team, and their entire initiative is, like a lot of people in the Fortune 500 now, is to have a direct-to-consumer type of a relationship, and one of the things that they struggled with early was how do they move quickly, support product launches that might have massive load, and have the visibility to know that they can do that and catch issues as they emerge, and they didn't have a solution that could give that visibility to them until they leveraged SignalFX. And so now, if you talk to people there they'll say that they've essentially gone from defense every time they did one of these product launches to being on offense and really understanding what it takes to successfully launch a product and they're doing way more of these, so-- >> Moving the needle on time to market. >> Moving their business forward, you know, and digital transformation just by-- >> Yeah. >> Having SignalFX as a core enabler. >> It's the cloud version of putting out fires, so to speak, when you do product launches, right? >> Yeah. >> I got to ask you guys a question. You guys are both industry veterans, obviously Facebook has a storied history. We know all the great things that happen on the infrastructure side. Karthik, you've been in VM where you've seen the movie before where VM, where it made the market, changed IT for the better, still talk about the VMwares now. Now as we see cloud taking that next transformational push, describe the wave we're on right now, because it's kind of an interesting time in tech history where the talent that's coming in is pretty amazing. The young guns coming in with opensource the way it's flourishing is pretty phenomenal. Some of the smartest computer science and/or engineering talent is really solving what was old school B2B problems that really no one really wanted to solve. I mean, it was people were buying IT. Now you're talking about building operating systems, so the computer science kind of mojo in the enterprise has upped a bit. >> Mm-hmm. >> What's this wave about, how would you describe the wave of this time in history of the tech industry? >> Do you want to... (laughs) I'll add my take but why don't you go first. >> I think the thing that I find striking is just like, you know, when people used to talk about big data, you know, a few years ago, and now that is like, that's just normal. >> Yeah. >> And like, the amount of compute and the amount of storage that people are able to, you know, bring to command at-- >> Yeah. >> On any problem, it's just incredible, and that's just going to, I think, like continue to grow, right? That's going to be an amazing thing to watch. I think, you know, what this means... It also has interesting implications for, you know, companies like SignalFX who are trying to be in the monitoring space because the mojo used to be you had to have all this complicated software to do the instrumentation. Well, the instrumentations part is easy, but now all the value that's going to come about monitoring is in what you do with all that data, how you analyze it and look for, like, you know, so the whole AI ops and all that is going to be the key of the whole monitoring problem going forward, you know, five, 10 years from now, but we already see that analytics is such a key aspect of the whole thing, so... >> Yeah, I'm very, I think we're at the beginning, still at the beginning of a massive 30 to 40 year cycle, and this hasn't happened since the PC revolution in the 1970s, right, so the smartphone comes out 2007, massively opens up the market for software-based services to several billion people who are connected all the time now, drives a massive refresh of the backend infrastructure, drives the adoption of opensource, and so we're at this magical point now where the market for software-based services is just exploding, and every enterprise, you know, is becoming a software company, and you know, I think the volume of data that we're accumulating is just growing exponentially and what you can do with AI at this point, it's just... We're just beginning to see the benefit of it, so I think this is a really, really exciting time and I think we're just at the beginning. Most of the enterprises, and even the tech companies, are just beginning to capitalize on what is in store for us in the industry. >> I find it to be intoxicating, fun, and just great people coming in. To your point about the beginning of a 40 year run, also the nature of software development is being modernized at an extremely accelerated pace, so as people in the enterprise start re-imagining how they do software, because if they're a software company they've never had product managers. I mean, so the notion of what is a product, how do you launch a product, is all kind of first generation problems and opportunities, so I think to me it's really the enablement... And this is really what I think people are looking for is who can take the burden off my shoulders, help me move faster, more gas, less brake. >> Mm-hmm. >> Go faster, drive value, and then ultimately compete, because competitive advantage with technology... What does that mean to you guys, because how do you react to that because what you essentially are doing is creating instrumentation for enabling companies to create new value faster with technology and software, in some cases at a level that they've never seen before. What do you, how do you react to that? >> Well, I think that's exactly what we do, right, I mean, every company, I think most companies realized that they had to invest in software and focus on all these new opportunities at the early part of this decade. First thing they had to do was figure out who's going to build all this software, so most of them had to go hire engineers or build digital teams. They had to decide where are they going to run, the cloud wars of, you know, the early part of this decade. Do we build a private cloud, do we use a public cloud, I think both of those things have happened and people are now comfortable with those decisions. The third leg, which is squarely in the space that we're in, which is how do you operationalize this new model, and I think people are working through that now. As they get through that in the next few years, the companies like SignalFX helping every company, operationalize it very quickly, I think that's when the true promise of this new digital era will be realized where you'll start to see all of these fantastic applications, mobile apps, web service apps, direct-to-consumer streamlined supply chains. We're just beginning to see the benefit of that, and we'll see when that happens then the volume of data that they're collecting will increase exponentially and then the promise of machine learning and AI will take an altogether nother step. >> You got to know how to automate it before you can automate it, basically. What's next, final question for you guys, what's going on with SignalFX, what are you guys going to conquer, what's the next major milestones for you guys, what are you looking to do? >> Yeah, well we're continuing to focus on driving value for our customers, so we're expanding our geographic presence, so we're doing a lot of international expansion at this point. We're hiring a lot of engineers, so if anyone is interested in a development job, reach out to us. >> What kind of engineers are you looking to hire? >> Rajesh, you want to take that, sorry. (chuckles) What kind of engineers... >> What kind of engineers you looking to hire? >> Everything. (chuckles) >> I mean, all kinds of engineers, especially distributed systems engineers, front end engineers, full stack engineers, like real tech, all the good engineers we can get. >> (chuckles) Awesome. >> A lot of product development, there's a lot of interesting things happening in this space, and so we're, you know, continuing to invest very aggressively. >> Large scale distributed systems. >> Yep. >> You've got decentralized right around the corner, so you've got a lot of stuff happening. >> Yeah. >> Yeah. >> Great job to have you coming on, thanks for coming on, Karthik. >> Great, great to be on. >> Rajesh, thank you so much. >> My pleasure. >> SignalFX here in the cloud of Google here at Next, it's theCUBE, theCUBE cloud, CUBE data, we're bringing it all to you. I'm John Furrier, thanks for watching. More coverage, stay with us, we'll be back after this short break. (techy music)

>> Live from San Francisco. It's theCUBE covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hey welcome back everyone. It's theCUBE live here in San Francisco for Google Next 2018. I'm John Furrier with Dave Vellante. This is day one of wall to wall three days of live coach here on the floor. Our next guest is Karthik Lakshminarayanan who is the director of product manage for cloud identity, one of the core products at the edge authenticating users, people, and applications and devices. Karthik thanks for coming on. >> Yeah thank you, it's great to be here. >> So take a minute to explain because obviously cloud identity, we've seen identity systems in the enterprise, anyone who's dealt in the enterprise who have been buying I.T., who have been buying I.T. stuff. >> Yes. >> That's around identity and then something new comes out and I got to refresh that, I got to buy this, rip this out, replace this. So identity has been super important but it's been kind of stovepiped within applications. The cloud is horizontally scaled but the benefit of the cloud is that you kind of do it once, if you do it right, architecturally you can scale it. >> Absolutely. >> Take a minute to explain how cloud identity works, and how does it fit into the future of what people expect from the cloud. >> Yeah, absolutely, thank you. And cloud identity, our solution is to help organizations securely manage people, applications, and devices in the cloud. So it's exactly like what you're talking about. User identity is evolving because organizations are now coming in and saying "What is this mobile cloud thing? "How do I adjust?" Because users are getting increasingly trained on continual like behavior they just want to turn on, connect to their cloud services, use their mobile devices and be up and running. Organizations have been trained for years to think about the corporate network as their security parameter, so how does that happen in the cloud when the data is no longer on premises? So that's what we do with cloud identity where we look at signals from your users, from your devices, and other things that we're trying to do and give you a different way of accessing the cloud. >> For the folks watching who might have missed the keynote it's going to be on demand, go to YouTube, but I'm sure it's on the Google Cloud channel. Now one of the things Diane Green said, and then also we saw in the demos, we were talking before we came on camera was, you showed a demo of basically cloud and on-prem solution, looked just like one dashboard just the note and the network, and everything's kind of clean. Diane Green then mentioned that when she came to Google Cloud 20 years ago, was to just share what was already built over 25 years or 20 years to the masses. So okay, that's cool. But the question I want to ask you is, people don't want to be like Google or buy Google stuff to implement it in their non Google environment. They want to use the Google services. So they want the benefits of what you guys have experienced, so this is kind of a cultural nuance within Google Cloud where it's like you don't have to tell them be like Google, just use the services. Identity is super important. You have all this institutional knowledge, and low latency signals, from whether it's Android, Chrome, search, user experience. How are you guys putting that into.. Does that help your product? Is that a benefit of the cusp? Or is that more of a future thing? Because when you're at a service I can almost see identity as a service scaling to a point where all these things are kind of taken care of. What's your vision? >> Yeah, absolutely. A couple things. One is something called BeyondCorp. I think a lot of folks are familiar with, it stands for beyond the corporate network. And I want to touch on a couple things. One, is that today we make the access decisions based on who you are as a user, the state of your device, and then context. And context is really king now in a cloud based world. Where we look at signals, signals around the data that we can get even from our consumer services, but carefully curated and making sure we meet all of the compliance policies. Where we can now look at these signals and we do what we call context server access. So the idea that, what are you trying to access? Where are you accessing from? And who are you as a user and what kind of device are you at? That's the perfect combination of what you just said and we call that context server access and that is absolutely central to how we offer cloud identity. >> That's the classic example I've seen that we are Gmail customers, with Gsuite So when I log in from Paris, "Hey wait a minute, you're not in Paris." So you guys, is this an example of that? >> Yeah, it's funny, I feel like you're part of our team because we call this the superman scenario. Because if you just logged in from say California, then a moment later we see an access request coming in from Paris, we know it's not just because you have the valid username or password, we know that's not right. That's just a trivial example. Like Google does a great job of crawling the web. So we don't just know what the good sides are, we know what the bad sides are. So you even try to access a bad site we can stop you. There's all kinds of things we do with this. >> So I wonder if I can ask you about enterprise I.T. John at our kick off this morning said Google's 10 or maybe even 15 years ahead. And as he was just saying, people can't go that fast to be like Google. So how do you.. I think of a caravan with the fastest truck in the military caravan, has to slow down so the whole caravan can keep up. How do you manage the fact that you're going so fast but enterprises move, we sometimes joke, they move at the speed of the CIO. What's your perspective on that and how do you deal with that challenge? >> No, absolutely. So I think our core philosophy and design philosophy is how we built the product is meeting customers from where they are that's key. So meeting customers where they are, so we recognize, take some of our advanced technology. And we recognize that organizations are still building a lot of applications on premises, so we took the power and made that available on premises. You just saw that today. Another example, we connect to systems of record. We know Microsoft Active directly is largely the identity record of choice in large organizations. So we connect very seamlessly with them, we sync with them, and we use a federated identity story so you don't have to move to all in Google Cloud, you connect Google Cloud, you augment your existing infrastructure and that's how we make it all work. So, really making sure that we are inclusive, and meeting customers where they are is how we've designed everything including cloud identity. >> And I follow up with, is architecturally, how do you future proof it? Now part of it is you have a lead on the rest of the world. You have visibility on things that others aren't going to see for years. But at the same time, you don't know, you can't predict the future, right? So how do you future proof your system architecturally? Maybe talk about that. >> Yeah, I think that a couple things for us, we are big on open systems, so we make sure that the cloud as we all know is built on standards. So as an example, the security keys that we talked about was largely invented at Google but we made sure we contributed that back into the standards community. That's an example. We are big on APIs, making sure all our APIs are out there and we support federated standards like Skim and those others things. So we make sure that an organization can use not just us, but whatever identity system of choice, and we interconnect to standards and APIs and I think that's the way forward. >> So I asked you since you do product management which is you're building products, I mean, I used to run a product group at a big company and products are built differently now, than they are with the cloud. So how has the role in building a product change? Product management, you got to have the right features, you got to have customers. We're living in a services world, where you have a service as the product or the platform is the product in a cloud centric world. How do you guys do that product and share some insights for the folks watching, customers get an insight into how you guys work because it's not your classic product management, or is it? How are you guys doing things differently because business models are being built as a service. Things are changing so fast that a new service like Istio can literally change someone's business overnight, leveraging some of these core services that you guys have. >> So let me share a couple things. I think some things are always going to be the same if we do our jobs right. Which is that customers, customer needs, and making sure the solutions we provide, not features, but solutions, meet customer needs. I think in that regard, whether you deliver it as a service, or as a on-prem, does not matter, that's a delivery model. But we want to make sure we take care of our customers. I think one of the challenges we find on the cloud side is the piece of which we are delivering features and a lot of times the I.T. person or the decision maker in an organization want to make sure they stay in the loop on this, they are getting ahead of planning. You don't want to change that vent out so rapidly that the users are confused, they're getting help desk calls and things like that. So we are have a very structured communications mechanism that we work with, we share roadmaps and timelines so it helps organizations really think about what's coming. I think the service delivery and service consumption is more of a partnership now, even though on the consumer side you might think it's just as a service we push a change. I think its really a partnership. >> And it's faster too, I imagine. >> Absolutely faster. >> Your acceleration of service is faster. >> I think we can meet needs exactly, we can meet needs a lot faster. I wanted to call out that Google consciously takes into account the fact that we don't want our changes to be so fast and so disruptive, we want them to be well received so we really partner with our partners in the custom organizations. >> Its interesting Dave mentioned the caravan example, I would say that enterprises move at a glacial pace. >> Any users feel that way. >> But they're buying I.T. in the past, now they're essentially leveraging scaled services that are prebuilt so they can get things going faster. This is the new normal where they'll be buying services not I.T. products. >> Correct. >> You mentioned solutions, solutions and services. Is that kind of what you're getting at? >> Yeah, I think absolutely. If you think about what's happened as mentioned earlier today, I.T. was a cost center, now they're moving into like, hey how do we get ahead and build a competitive advantage? So I think absolutely, you said it well so plus one. >> Karthik you talked about some of the standards that built up the internet, and now you're seeing with blockchain a spate of new protocols being developed, all this innovation, a lot of talk about K.Y.C. know your customer, and antimoney laundering, AML. Perspectives on what's happening in that blockchain world. Obviously it's relevant to identity, what are you thoughts on what's happening there? >> Yeah, a couple things. One is that we think blockchain is very interesting, it's something that we continue to look at. I personally look at blockchain as amazing technology but we go back to what are the use cases and needs that we need to solve. So let me throw something out there, it's not very well thought out, it's just an idea. But we think about one of the things we've tossed around is bring your own identity. There's a time when identity was think about your cell phone number, if you remember was once tied to your provider, you change your provider, you had to get a new number. And now you have portability you don't think about it. So if you think about you as a user you are who you are, and then there is an identity or a profile that exists on a personal side. There's identity that happens so there is protection in this context that is accessed things like that that blockchain can now enable 'cause you now take your identity and you go with you whether you are in the consumer context, you are in the work context, or even switching from one job to another or one role to another within the organization. So I think blockchain could be technology that is very foundational and fundamental to decentralize notions where I as an organization manage your policies and lots of other things but who you are as a person stays with you. >> The old model was bring your device to work. >> Yes. >> Your base was bring your identity to the world under one immutable own your own data, trustful way. Enabling, identity as a service on a whole 'nother level. >> Very different level. I think were not dead today because right now I think organizations are shifting mainly from wrap their arms around the user and the identity and they're super paranoid about moving to the cloud. I think the first step is making them fundamentally comfortable with everything they need. But once we build I think your trust point is key once you have that governance and that secure platform we can start shifting towards bring your own identity and how can that all coexist. >> And why do you think the consternation about moving to the cloud. Is it because it's still unknown? It's still somewhat new? Because I mean by all accounts when you talk to the experts, they'll admit the cloud is more secure than what I can do on prem. Why the consternation? >> Absolutely, I think the key part is the simplicity that comes and I think it's a new model that has not yet been mastered, so cloud is secure, yes, but when my users start doing things that I don't really want them to do, what we call is shadow I.T., they're very worried about it. And then on the flip side they've been trained for years, decades on this whole old model of corporate network and now were saying the cloud is open and the internet is your new network. So that I think scares a lot of people but customers when they come to Google and they see our BeyondCorp story and our cloud identity story, then they know that they can achieve both. Higher access for employees and advanced security for organizations. >> I think the Beyond Corporate is very relevant. We've been tracking that we find that super fascinating. On the shadow I.T., we've been reporting on shadow I.T., it's our ninth year today. But shadow I.T. though, is just an early adopter form of DevOps, so I think shadow I.T. has kind of regulated itself to as a stepping stone for cloud. SAP used to do shadow I.T. as presales and then customers moved everything to the cloud so I think shadow I.T. is much more of a kind of kindergarten or first step to DevOps. >> I think DevOps is where a lot of organizations are moving. I think depending on where the organization is going back they like the I.T. admin led model, they're experimenting with DevOps, there's a lot of experimentation going on. I think what I like about shadow I.T. and not from a security risk perspective but it's signal that clear intent from the user to the organization saying I want access to these services fast and make it simple. >> It's like an R and D sand box the way I look at it. Final question for you I know you got to go. Thanks for coming on, I appreciate your time. How are you guys going to roll out this identity as a service, who's your competition, how do you guys compare, what's the story, what's the vision? Share some of the competitive strengths and weakness. What's going on? >> Yeah, I think three things for us. It's already available today, you can go to cloud.google.com/identity. Sign up for a free trial and we give you everything from identity as a service to device management and all of that. The things that we focus on is like smart, secure, and simple. The idea that we can use ML based security to automatically protect, no longer can an I.T. admin go in and set reactive policies. We just have to use data and set proactive policies and protect them. To your points earlier about end points and other data coming into that's the smart piece. We also have a unified single pane of glass, unified administration, one admin controlled to manage everything because people are complaining about the complexity of these solutions that they got to put together. So you get cloud identity you get one thing everything from not just the administration but also the licensing. One price and you're done. You never have to worry about it. And the last but not the least, it has to be secure. The things we talked about from security keys, I've never changed my password for the two years I've been at Google. I use security keys and never typed an RSA key or anything like that. It's fascinating how simple we can make it so that's really what we like smart, secure, and simple. >> Awesome, well congratulations. Looking forward to see how this scales out certainly foundationally identity is super important. Identity is one of the bedrock of cloud. It's part of that system that scales theCUBE. Bringing you all the best content scaling here at Moscone with all the great content from Google Next. I'm John Furrier and Dave Vellante. Stay with us from day one coverage of three days of live coverage here in San Francisco. We'll be right back.

>> Announcer: Live from the Moscone Center. It's theCUBE! Covering AWS Summit San Francisco 2018. Brought to you by Amazon Web Services. (upbeat techno music) >> Hey, welcome back, everyone. We're live here in San Francisco. This is theCUBE's exclusive coverage of AWS Amazon Web Services Summit 2018 with my co-host Stu Miniman. We have two great guests. Hot startup from SingleFx, the CEO, Karthik Rau, and the CTO, Arijit Mukherji. Welcome to theCUBE. Good to see you again. >> Karthik: Yeah, great to see you again. Thanks for having us. >> So, we've been following you guys. You've been out five years. Two years in stealth, three years ago you launched on theCUBE. >> Karthik: Right here on theCUBE. >> We see you at AWS and VMware. Cloud's changed a lot. So, let's get an update. Karthik, take a minute to explain where you guys are at now company-wise, employees, traction momentum, product. Where are you guys at now? >> Karthik: Yeah, absolutely. So, SignalFx, first of all, let me tell you what we do. SignalFx is a realtime streaming operational intelligence solution. Basically, what that means is we collect monitoring data, operational data across the entire cloud environment, from the infrastructure all the way up to the applications. And we apply realtime analytics on that data to help people be a lot more proactive in their monitoring of these distributed environments. We launched the company in 2015. We come ... I'll let Arijit talk about our origins. We came out of Facebook. And we had a lot of experience building this to Facebook. In the past three years, we've been building up our company aggressively. We've now got hundreds of customers including several large Fortune 500 accounts, large web scale accounts like Acquia and HubSpot and Yelp and KAYAK. And we're over 100 employees now, about 120 employees. And yeah, doing great. >> So, Werner Vogels, the CTO, laid out on stage plus a great Matt Wood conversation about machine learning but the real thing that Werner laid out was the old way, the web server, multi-tier architecture stack kind of thing going on there to a more cloud DevOps horizontally scalable where sets of servers that could be spawned in parallel creates a new kind of operating model but also creates challenges around what to instrument. You know, as we would joke, someone left the lights on, implying EC2s been running. And all these kinds of things are going on. And you mentioned some of the Facebook kind of challenges. People were building their own scale. What have you guys learned and how does that apply today's modern infrastructure? What are some of the threshold challenges that companies are facing when they say, one, already there or I want to get there? How do you guys look at the main issues? >> Karthik: Do you want to take that? >> Yeah, so monitoring modern environments and infrastructure is actually quite a challenge. There's obviously a few things going around. One, as you mentioned, is the variety, the sheer variety of things. No longer just the three-tier architecture I have cloud services. I have containers. I have lambdas. I have my own applications. I have the cloud infrastructure itself that all needs to be monitored. And things are also becoming far more numerous. So, there's just many more of everything, right? And so, making sense of that space is becoming a big challenge. And our company was founded on the idea that monitoring is becoming an analytics problem. So, it's no longer about looking at individual servers or applications instances. It's more about making sense holistically over what's going on and being able to combine different types of data from different systems together to provide you with that high level view and that's the kind of functionality that we at SignalFx have been trying to provide. >> What are some of the data flows volumes look like. Cause I've heard multiple people talk about either Facebook or in open compute environments where there's just so much data coming in from the instrumentation that no human could actually get their arms around it. And you need to supplement it with machine learning and intelligence. I mean, is that something that you're seeing? What are some of the -- >> Yes, so actually what we see is different prospects or customers will be in different stages of a spectrum where maybe they were in a stage one where they're sort of using traditional architectures and then moving to these more modern systems. And as they get more modernized themselves, their use cases or the ways they wanted to do monitoring also gets more advanced. And so, we see the whole spectrum of it, as you mentioned. And so, understanding analytically how what we're is doing is great. But then you also want to take the human out of things as much as possible, right? >> Yeah. >> And make things more automated. And you want to look at the data and how things are behaving to learn from existing patterns to find outlines. So, that's really a very interesting challenge. And what I look at what we can do as a company going forward, like all the technological stuff that we can invest in, it's quite interesting. >> Yeah, Karthik, take us inside your customers. How does this modern monitoring, how does it change their business? How does it impact things like feedback loops and DevOps and everything that customers are having to deal in this kind of ever changing environment? >> Yeah, well I'll give you an example. There's a Fortune 500 company. They do product launches. And this is one of our customers and their product launches drive so much traffic that they do 80% of their business in the first two minutes of a product launch. And this is not at all uncommon in today's economy. And they're leveraging a lot of modern technologies, container architectures, serverless function architectures to spin up a bunch of capacity during these launches. And they were effectively flying blind most of the time. Because most of the traditional systems management monitoring solutions are not designed, A, to handle that volume. But, B, to handle the instant discovery requirements of if you're going to do 80% of your business in the first two minutes. So, the challenge is you're always playing defense. You're reacting to issues. And you're mostly flying blind. By leveraging SignalFx, they're getting realtime visibility, realtime discovery of these components as they're coming up. We're the only solution that can do that. So, literally within seconds of spinning up all of these containers, they're getting live streams into their dashboards, and live analytics, and live alerts. And what that's enabled them to do is be a lot more aggressive and effectively doing a lot more of these launches. So, that's driving their business and it's helping them drive their digital strategy forward. >> And microservices is really enabling you guys to be more relevant. Because truly the signal from the noise is where all these services reporting to? >> Karthik: Yeah. >> You talk about container madness. >> Karthik: There are two fundamental problems. So, one there's an architecture shift. And that's driving massive amounts of volume. You have physical machines that will live for three years in a data center. Divide it up into VMs, 10, 20 VMs per server. That'll maybe live for a few months. To now every process running in it's own container that might live for a few minutes. So, you have a massive exponential explosion in the number of components. But that's not the only problem. I was part of an architectural shift at VMware for a number of years. We weren't just affecting an architecture change. What's happening now is there's a cultural change and a process change that's happening as well. Because with containers, your development team can push changes directly out into a production environment. And what you're finding is you're going from sequential product development to parallel product development and a massive exponential increase in the number of code pushes. The only way you can operationalize that is you have to have realtime visibility in everything that's happening. Otherwise, the left arm doesn't know what the right arm is doing. >> John: And you need prescriptive and predictive analytics. >> Exactly. And you need predictive analytics to identify there's something unusual here. It's not a problem yet. But this is highly unusual and maybe it's your canary release. We need to do a code push. So, you want to roll it back. So, having that level of predictiveness becomes absolutely critical. >> Yeah, you mentioned realtime. We used to argue what really is realtime. And it was usually well in time to react to what the customer needs. What does realtime mean to your customers? Architecturally, is there something you do different to kind of understand what that means? >> Arijit: Yeah, so we actually fundamentally took a very different approach when we build a product. Where, typically, monitoring our metrics, monitoring was done with what we call a store and create or a batch-like architecture where you store all the data points that are coming in, then you create from it to any other use cases. While what we build at SignalFx is a fully end-to-end streaming architecture which is realtime. And what we mean by realtime is like two to three seconds between a data point coming through us and it's firing an alert or showing up in your chart. So, that's the kind of realtime. And it requires us to do lots of innovations up and down the stack. And we've built a lot of IP. We've got now patterns. And more are coming because the approach we took was quite novel. Different from-- >> John: You guys got a great management team. And looking at what you guys have done. I've been impressed with you guys. I want to just ask, Karthik, you mentioned about all these parallel processes that are going on. Totally agree. The process change, operationalizing an all new cultural way to create software manage the data. I mean, it really is the perfect storm for innovation. But also, it could literally screw people up. So, I got to ask you, who are you targeting for your customer? Who is the person that you talk to? Assuming it's kind of DevOps, so it's more like a cloud architect. Who do you target? Who do you sell to? Who's the buyer? Who uses your service? >> Karthik: Well, we see ... Every enterprise we see following a very similar journey. So, the first stage is, typically, you're just getting familiar with cloud. And you're probably just lifting and shifting enterprise workloads into the cloud. Probably experimenting with big data on the cloud. You're not yet doing microservices or containers or DevOps. And for them, we're still selling largely to classic IT. There just trying to get better visibility into their digital environment, you know, they're cloud environment. But then, what ends up happening is they very quickly get to what we call basically chaos. It's stage two. And it has a lot of parallels to shadow IT. What happened with SAS, where you have hundreds of different SAS tools is happening all over again with cloud but you've got hundreds or thousands of different operational tools. Different ways of doing monitoring, logging, security. And every team is doing it's own thing. And so, that's a big problem for enterprises who are trying to build best practices across their broader team. In that place, we're typically selling to departments because they don't have a centralize strategy yet. But what we find is the organizations at maturity have figured out that it's important to have certain centralized core services. And that doesn't mean they're forced on the end users. But they provide best practices around monitoring, logging, and such. And just make it easy for them to use those solutions. So, that's almost a new IT organization. It's platform engineering -- >> John: Is that a cloud architect? >> Platform engineering team, infrastructure engineering team, and they are effectively building best practices around the new stack not the traditional stack. >> So, you are or aren't targeting department level? Are you are? >> We sell to departments. But we also sell to the teams that are standardizing across the entire organization. >> So, cloud architects, for instance? >> Depends on the stage of the cloud journey. >> Or company. >> And the company, exactly. >> From an architectural standpoint, you talked that there's virtualization, there's containers, now serverless. How do you even figure out what to monitor in serverless? How fast is that changing? And how is that impacting your road map? >> So, serverless brings a very interesting challenge because they are very, very ephemeral. Like they're ephemeral in some sense. So, we realize there are two things. One is serverless, there's a reason why things are moving faster. It's because you want to be able to move faster. But then you also need to be able to monitor faster. It's no good monitoring serverless at five minutes later, for example. So, one of the things we invested in was how to get metrics, etc. and telemetry from these serverless environments in a very fast fashion. And that's something that we've done. The second thing we are doing that really works for this environment is afterall it's not about how many times a serverless function ran, it's about the value that it's providing the application that's running on it. And by focusing on a platform that let's you send these application metrics in great detail and then be able to monitor and analyze them, I think really amplifies the value in some sense. So, those are the two ... >> John: And talk about the ecosystems. One of the things I want to ask you guys because we've been seeing a collision between a lot of the different clouds. Clients want multicloud. Well, obviously, we're here at Amazon. They believe they should be the only cloud. But I think most customers would look at either legacy systems with some instrumentation and operational data to edge of the network, for instance. I mean, look at the edge of the network. That's just an extension of the data center depending on how you look at it. So, how do you guys view that kind of direction where customer says, "Hey, you know, I got a cloud architect. We're on Amazon. Of course, we have some old Microsoft stuff. So, we've got Azure going up there. We're kicking the tires on Google. And I got this whole IoT Edge project. SignalFx, instrument that for me. (laughs) Is that what you do? Or how do you deal with that? How would you deal with that kind of conversation? >> Well, I think most enterprises, the larger companies we see looking at multiple clouds. And they have different workloads running in different clouds, depending on the needs and what they're looking to do. So, the nice thing about a solution like SignalFx is we span all of these different architectures. And what we find is that most of the larger companies want to separate their business process solutions from their runtime architectures. Because they want to have a solution like SignalFx that it doesn't matter who you're using. If you choose to have your analytics intensive workloads in Google Cloud and your eCommerce workloads in Amazon, but you only want one system that will page someone in the middle of the night if there's a problem, then you have SignalFx to do that. And then you have your choice of runtime environments depending on what your developers need or what the business demands. We provide a lot of that glue across the different environments. >> Do you see that as the preferred architecture with most customers? Cause that makes a lot of sense. I mean, whether you're doing other data services, it kind of makes sense to separate out. Is that consistent? >> To have different applications >> Yeah. >> In different clouds? It depends. I mean, I think we see some people who are more comfortable running on a single cloud vendor and they make the decision based on what a portfolio of platforms and service features that are available. And they really like those, and they say it's easy to just go with one. But more often, we find people wanting to at least have some percentage running in a different cloud vendor. >> John: All right, final question. What's the secret sauce for the company? Tell us about the secret sauce. >> Arijit: I think-- >> We got the patents. I heard patents. You don't have to show all this exactly. But what is the secret DNA of the tech? What's the magic? >> I think it's our very unique architecture. It's entirely different from what you have. It's streaming and it focuses on scale, on timeliness, as well as on analytics capability. I think that unique combination is very special for us. And that, in a way, sort of allows us to address very, very different use cases, including this hybrid environments and what not, in a very effective way. So, it's a very, very powerful platform that can be used for many use cases. >> All right, so that was John's final question. Karthik, I've got one last one for you. What's it like being a CEO of a software company in the cloud era today compared to what it's been earlier in our career? >> Well, it's moving very, very quickly, right? I mean, technology always move very quickly. But I think compared to when I was at VMware in the mid 2000s, it just feels like every 18 months there's a new technology wave. You know, when we started our company five years ago, that was the first year that AWS eclipsed a billion dollars in sales and Dagra hadn't even launched. It launched a month after we started the company. And then serverless came. And now function architecture is all there. So, there's just so much change happening, and it's happening so quickly, it forces vendors like us to really be on the cutting edge and forward looking and making sure that you're keeping an eye out for what's coming cause the markets are moving way faster, I think, then they were 15 years ago. >> John: Well, Karthik, thanks so much. We appreciate you guys coming on, SignalFx. I'll give you the final word on the interview. Take a minute to share something with the audience that they might not know about SignalFx that they should know about. >> Well, I think what people may not realize is how realtime we can actually get. I think most people are used to doing all their monitoring and observation, and they think of realtime in the order of minutes, or if you can get stuff every 30 seconds. We really are the only realtime solution. That's why we say real realtime. We're on the order of seconds. You can build really, really sophisticated analytics and get visibility like you can't anywhere else. So, it's real, realtime. >> And that's soon to be table stakes. TheCUBE is realtime. We're live right here, on theCUBE here, in San Francisco at Amazon Web Services, AWS Summit 2018. We've been covering all the Amazon re:Invents since it started, of course. I'm John Furrier with Stu Miniman. Back with more live coverage after this short break. (upbeat techno music) (gentle instrumental music)

(upbeat techno music) >> Narrator: Live, from San Jose, it's theCUBE! Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to Big Data SV, everybody. My name is Dave Vellante and this is theCUBE, the leader in live tech coverage. You know, this is our 10th big data event. When we first started covering big data, back in 2010, it was Hadoop, and everything was a batch job. About four or five years ago, everybody started talking about real time and the ability to affect outcomes before you lose the customer. Lewis Kaneshiro was here. He's the CEO of Streamlio and he's joined by Karthik Ramasamy who's the chief product officer. They're both co-founders. Gentlemen, welcome to theCUBE. My first question is, why did you start this company? >> Sure, we came together around a vision that enterprises need to access the value around fast data. And so as you mentioned, enterprises are moving out of the slow data era, and looking for a fast data value to their data, to really deliver that back to their users or their use cases. And so, coming together around that idea of real time action what we did was we realized that enterprises can't all access this data with projects right now that are not meant to work together, that are very difficult, perhaps, to stitch together. So what we did was create an intelligent platform for fast data that's really accessible to enterprises of all sizes. What we do is we unify the core components to access fast data, which is messaging, compute and stream storage, accessing the best of breed open-source technology that's really open-source out of Twitter and Yahoo! >> It's a good thing I was going to ask why does the world need to know there are, you know, streaming platforms, but Lewis kind of touched on it, 'cause it's too hard. It's too complicated, so you guys are trying to simplify all that. >> Yep, the reason mainly we wanted to simplify it because, based on all our experiences at Twitter and Yahoo! one of the key aspects was to to simplify it so that it's conceivable by regular enterprise because Twitter and Yahoo! kind of our position can afford the talent and the expertise in order to do this real time platforms. But when it goes to normal enterprises, they don't have access to the expertise and the cost benefits that they might have to reincur. So, because of that we wanted to use these open-source projects, the Twitter and the Yahoo!'s provider, combine them, and make sure that you have a simple, easy, drag and drop kind of interface, so that it's easily conceivable for any enterprise. Essentially, what we are trying to do is reduce the (mumbles) for enterprises for real time, for all enterprises. >> Dave: Yeah, enterprises will pay up... >> Yes. >> For a solution. The companies that you used to work for, they all gladly throw engineering at the problem. >> Yeah. >> Sure. >> To save time, but most organizations, they don't have the resources and so. Okay, so how does it, would it work prior to Streamlio? Maybe take us through sort of how a company would attack this problem, the complexities of what they have to deal with, and what life is like with you guys. >> So, current state of the world is it's fragmented solution, today. So the state of the world is where you take multiple pieces of different projects and you'd assemble them together in formats so that you can do (mumbles) right? So the reason why people end up doing is each of these big data projects that people use was the same for completely different purpose. Like messaging is one, and compute is another one, and third one is storage one. So, essentially what we have done as company is to simplify this aspect by integrating this well-known, best-of-the-breed projects called, for messaging we use something called Apache Poser, for compute we use something called Apache Krem, from Twitter, and similarly for storage, for real time storage, we use something called Apache Bookkeeper, so and to unify them, so that, under the hoods, it may be three systems, but, as a user, when you are using it, it serves or functions as a single system. So you install the system, and ingest your data, express your computation, and get the results out, in one single system. >> So you've unified or converged these functions. If I understand it correctly, we talking off camera a little bit, the team, Lewis, that you've assembled actually developed a lot of these, or hugely committed to these open-source projects, right? >> Absolutely, co-creators of each of the projects and what that allows us to do is to really integrate, at a deep level, each project. For example, Pulsar is actually a pub/sub system that is built on Bookkeeper, and Bookkeeper, in our minds, is a pure list best-of-breed stream storage solution. So, fast and durable storage. That storage is also used in Apache Heron to store State. So, as you can see, enterprises, rather than stitching together multiple different solutions for queuing, streaming, compute, and storage, now have one option that they can install in a very small cluster, and operationally it's very simple to scale up. We simply add nodes if you get data spikes. And what this allows is enterprises to access new and exciting use cases that really weren't possible before. For example, machine learning model deployment to real time. So I'm a data scientist and what I found is in data science, you spend a lot of time training models in batch mode. It's a legacy type of approach, but once the model is trained, you want to put that model into production in real time so that you can deliver that value back to a user in real time. Let's call it under two second SLA. So, that has been a great use case for Streamlio because we are a ready made intelligent platform for fast data, for MLai deployment. >> And the use cases are typically stateful and your persisting data, is that right? >> Yes, use cases, it can be used for stateless use cases also, but the key advantage that we bring to a table is stateful storage. And since we ship along with the storage (mumbles) stateful storage becomes much easier because of the fact that it can be used to store a real intermediate state of the computation or it can be used for the staging (mumbles) data when it spills over from what the memory is it's automatically stored to disk or you can even in the data for as long as you want so that you can unlock the value later after the data has been processed for the fast data. You can access the lazy data later, in time. >> So give us the run-down on the company, funding, you know, VCs, head count. Give us the basics. >> Sure, we raise Series A from Lightspeed Venture Partners, lead by John Vrionis and Sudip Chakrabarti. We've raised seven and a half million and emerged from stealth back in August. That allowed us to ramp up our team to 17, now, mainly engineers, in order to really have a very solid product, but we launched post rev, prelaunch and some of our customers are really looking at geo replication across multiple data centers and so active, active geo replication is an open-source feature in Apache Pulsar, and that's been a huge draw, compared to some other solutions that are out there. As you can see, this theme of simplifying architecture is where Streamlio sits, so unifying, queuing and streaming allows us to replace a number of different legacy systems. So that's been one avenue to help growth. The other, obviously is on the compute piece. As enterprises are finding new and exciting use cases to deliver back to their users, the compute piece needs to scale up and down. We also announce Pulsar Functions, which is stream-native compute that allows very simple function computation in native Python and Java, so you spin out the Apache Python cluster or Streamlio platform, and you simply have compute functionality. That allows us to access edge use cases, so IOT is a huge, kind of exciting POC's for us right now where we have connected car examples that don't need heavyweight schedule or deployment at the edge. It's Pulsar Pulsar functions. What that allows us to do are things like fraud detection, anomaly detection at the edge, model deployment at the edge, interpolation, observability, and alerts. >> And, so how do you charge for this? Is it usage based. >> Sure. What we found is enterprise are more comfortable on a per node basis, simply because we have the ambition to really scale up and help enterprises really use Streamlio as their fast data platform across the entire enterprise. We found that having a per data charge rate actually would limit that growth, and so per node and shared architecture. So, we took an early investment in optimizing around Kubernetes. And so, as enterprises are adopting Kubernetes, we are the most simple installation on Kubernetes, so on-prem, multicloud, at the edge. >> I love it, so I mean for years we've just been talking about the complexity headwinds in this big data space. We certainly saw that with Hadoop. You know, Spark was designed to certainly solve some of those problems, but. Sounds like you're doing some really good work to take that further. Lewis and Karthik, thank you so much for coming on theCUBE. I really appreciate it. >> Thanks for having us, Dave. >> All right, thank you for watching. We're here at Big Data SV, live from San Jose. We'll be right back. (techno music)

>> Narrator: Hi from the Wigwam in Phoenix, Arizona, it is theCUBE, covering Data Platforms 2017. Brought to you by Qubole. >> Hey welcome back everybody. Jeff Frick with theCUBE. We are down at the historic Wigwam 99 years young just outside of Phoenix, Arizona, Data Platforms 2017. It is really talking about a new approach to big data in cloud put on by Qubole about 200 people, very interesting conversation this morning and we're really interested to have Karthik Ramasamy. He is the co-founder of Streamlio which is still in stealth mode according to his LinkedIn profile so we won't talk about that but long time Twitter guy and really shared some great lessons this morning about things that you guys learned while growing Twitter. So welcome. >> Thank you, thanks for having me. >> Absolutely. One of the key parts of your whole talk was this concept of real time. I always joke with people real time is in time to do something about it. You went through a bunch of examples of real time is really a variable depending on what the right application is but at Twitter real time was super, super important. >> Yes it is indeed important because the nature of the streaming data, the nature of the Twitter data is streaming data because the tweets are coming at a high velocity. And Twitter positioned itself as more of a real time delivery company because that way what happens is whatever the information that we get within Twitter we need to have a strong time budget before we can deliver it to people so that people when they consume the information the information is live or real time. >> But the real time too is becoming obviously for Twitter but for lot of big enterprises it is more and more important and the great analogy I referred before is you used a sample data, is the sample historic data to make decisions. Now you want to keep all the data in real time to make decisions, so its a very different way you drive your decision-making process. >> Very different way of thinking. Especially considering the fact as you said the enterprises are getting into understanding what real time means for them and but if you look at some of the traditional enterprise like financial, they understand the value of real time. Similarly the upcoming new used cases like IoT they understand the value of real time like autonomous vehicles where they have to make quick decisions. Healthcare you have to make quick decisions because the preventive and the predictive maintenance is very important in those kind of segments. So because of those segments, its getting really popular and traditional enterprises like retail and all they're also valuing real time because it allows to blend in into the user behavior so that they can recommend products and other things in real time so that people can react to that so that its becoming more and more important. That's what I would say. >> So Hadoop started out as mostly batch infrastructure and Twitter was pioneer in the design pattern to accommodate both batch and in real time. How has that big data infrastructure evolved so that one, you don't have to split batch in real time and what should we expect going forward to make that platform stronger in terms of in your real time analytics and potentially so that it can inform decisions in systems of record. >> I think like today as of now there are two different infrastructures. One is in general is the Hadoop infrastructure. Other one is more of a real time infrastructure at this point. And the Hadoop is kind of considered as this monolithic, not monolithic, its kind of a mega store where every data like similar to all the rivers kind of reach the sea, it kind of becomes a storage sea where all the data comes and stores there. But before the data comes and stores there, lot of analytics and lot of visibility about the data from the point of its creation before it ends up there it setting done on those rive, whatever you call the data river so you could get lot of analytics done during the time before it ends up so that its more live than the other analytics. Hadoop had its own kind of limitations in terms of how much data it can handle, how real time the data can be. For example, you can kind of dump the data in real time into Hadoop but until you close the file you cannot see the data at all. There is a time budget gets into play there. And you could do smaller files like small, small files writing but the namenode will blob because like within a day you write million files, the namenode is not going to sustain that. So those are the trade-off. That's one of the reason we have to end up doing new real time infrastructure like the distributor log that allows you to the moment the data comes in data is immediately visible within the three to five millisecond timeframe. >> The distributed log you're talking about would be Kafka. The output of that would be to train the model or just score a model and then would that model essentially be carved off from this big data platform and be integrated within a system of record where would informed decisions. >> There are multiple things you could do. First of all, the distributed log essentially the data is kind of, you can think about as a data staging environment where the data kind of lands up there and once it lands up there when there's a lot of sharing of that same data going on in real time, when several jobs are taking they're using some popular data source, it provides a high fan out in the sense like 100 jobs can consume the same data they can be at different parts of the data itself. So that provides a nice sharing environment. Now once the data is around there, now the data is being used for different kind of analytics and one of them could be a model enhancement because typically in the back segment you build the model because you're looking at lot of data and other things, then once the model is built that model is pre-loaded into the real time computer environment like HERON then you look up this model and serve data based on that model whatever it tells you. For example when you do a ad serving to look up that model and what is our relevant ad for you to click. Then the next aspect is model enhancement. Because users behavior is going to change, over a period of time. Now can you capture and incrementally update the model so that those things are also partly done on the real time aspects rather than recomputing the batch and again and again and again. >> Okay so its sort of like a what's the delta? >> Karthik: Yes. >> Let's train on the delta and lets score on the delta. >> Yes and once the delta gets updated then when the new user behavior comes in they can look at that new model what that's being continuously being enhanced and once that enhancement is kind of captured you know that user behavior is changing. And ads are served accordingly. >> Okay so now that our customers are getting serious about moving to the cloud with their big data platforms and the applications on them, have you seen a change in the patterns of the apps they're looking to build or a change in the makeup of the platform that they want to use. >> SO that depends on, typically like, one disclosure is I've worked with Amazon and all, the AWS but within the companies that I worked for its everything is an on frame but thing is having said that cloud is nice because it gives you machines on the fly whenever you need to and it gives a bunch of tools around it where you can bootstrap it and all the various stuff. This works ideal for a smaller company and medium companies but the big companies one of the this things that we calculate in terms of the costwise how much is the cost that we have to pay versus doing it inhouse so there's still a huge gap unless cloud provider is going to provide a huge discount or whatever for the big companies to move in. So that is always a challenge that we get into because think about I have 10 or 20,000 notes of Hadoop can I move all of them into Amazon AWS, how much I am going to pay? Versus the cost of maintaining my own data centers and everything. I would say like I don't know the latest pricing and other things but approximately it comes to three x in terms of cost wise. >> If you're using... >> Our own on-prem and the data center and all of the staffing and everything. There's a difference of I would say three x. >> For on-prem being higher. >> On-prem being lower. >> Lower? >> Yes. >> But that assumes then that you've got flat utilization. >> Flat utilization but, I mean cloud of course I have the expands out of scale and all the various thing that you can, it gives an illusion of unlimited resources but in our case if you're provisioning so much machines in most of the at least 50 or 60% of the machines are used for production but the rest of them are used for staging, development, and all the various other environments so which means like the total cost of those machines even though like only is 50% utilized still you end up saving so much shit like operate out one-third of the cost that might be in the cloud. >> Alright Karthik, that opens up a whole can of interesting conversations. Again we just don't have time to jump into. So I'll give you the last word. When can we expect you to come out of stealth or is that stealthy too? >> It is kind of, that is stealthy too. >> Okay fair enough, I don't want to put you on the spot but thanks for stopping by and sharing your story. >> Karthik: Thanks, thanks for everything. >> Alright, he is Karthik, he is George, I'm Jeff. You're watching theCUBE. We are in the Wigwam resort just outside of phoenix at Data Platforms 2017. We will be back after this short break. Thanks for watching.

hi Jeff Rick here with the cube welcome were excited to to get out and talk to startups people that are founding companies when they come out of stealth mode we're in a great position that we get a chance to talk to him early and we're really excited to have a cute conversation with karthik rao the founder and CEO of signal effects just coming out of stealth congratulations thank you Jeff so how long you've been working behind the scenes trying to get this thing going yeah we've been at it for two years now so two years a founder and I started the company in February of 2013 so excited to finally launch and make our product available to the world all right excellent congratulations that's always a great thing we've launched a few companies on the cube so hopefully this will be another great success so talk a little bit about first off you and your journey we have a lot of entrepreneurs that watch a show and I think it's it's an interesting topic as to how do you get to the place where you basically found in launched a company yeah absolutely I started my career at a company at a cloud company before cloud really exists this is a market there's a company called loud cloud oh yeah Marc Andreessen right recent horse or two of the company and we were trying to do what the public cloud vendors are doing today before the market was really all that big and before the technologies really existed to do it well but that was my first introduction to cloud o came out of college and that's where I met my co-founder Phillip Lou as well Phil and I were both working on the monitoring products at loud cloud from there I ended up at VMware for a good run of about seven years where I ran product had always wanted to start a company and then a couple of years ago Phil and I thought the timing was right and we had a great idea and decided to go build signal effects together okay so what was kind of the genesis of the idea you know a lot of times it's a cool technology looking for a problem to solve a lot of times it's a problem that you know and if I only had one of these they would solve my problems so how did the how did that whole process work yeah it was rooted in personal experience my co-founder phil was at Facebook for several years and was responsible for building the monitoring systems at Facebook and through our personal experience and what we'd seen in the marketplace we had a fundamental belief and a vision that monitoring for modern applications is now an analytics problem modern applications are distributed they're not you know a single database running on is system you know even small companies now have hundreds of VMs running on public cloud infrastructure and so the only way to really understand what's happening across all of these distributed applications is to collect the data centrally and use analytics and so that was our fundamental insight when we started signal effects what we saw in the marketplace was that most of the monitoring technologies haven't really evolved in the past 15 or 20 years and they're still largely designed for traditional static enterprise applications where if you get an alert when an individual node is down or a static thresholds been passed that's enough but that doesn't really work for modern apps because they're so distributed right if one node out of your twenty nodes is having a problem it doesn't necessarily mean that your application is having a trough having a problem and so the only way to really draw that insight is to collect the data and do analytics on it and that's what signal okay really because that distributed nature of modern of modern apps and modern architecture yes there are three things that are fundamentally different number one modern applications are distributed in nature and so you really have to look at patterns across many systems number two they're changing for more frequently than traditional enterprise apps because they're hosted for the most part route applications so you can push changes out every day if you want to and then third they're typically operated by product organizations and not IT organizations so you have developers or DevOps organizations that are actually operating the software and those three changes are quite substantial and require a new set of products right and so the other guys are just they're still kind of in the you know fire off the pager alert something is going down it's very noisy yes when you're firing off alerts every time an individual alert goes off when you've got thousands of a DM and we all know that the trend these days is towards micro services architectures you know small componentized you know containers or VMs and so you don't have to have a very sophisticated large application to have a lot of systems it's so do you fit into other existing kind of infrastructure monitoring systems or kind of infrastructure management systems so I'm sure you know it's another tool right guys got to manage a lot of stuff how does that work yeah we are focused on the analytics part of the problem okay so we collect data from any sources so our customers are typically sending us data you know infrastructure data that they're collecting using their own agents we have agents that we can provide to collect it a lot of the developers are instrumenting their own metrics that they care about so for example they might care about latency metrics and knowing Layton sees by customer by region so they'll send us all that data and then we provide a very rich analytics solution and platform for them to monitor all of this and and in real time detect patterns and anomalies so you just said you have customers but you coming out stealth so you have some beta customers already yes we have great customers already now just beta customers right are great console customers awesome yes congratulation thank you very much they're very excited about our product and we you know they range from small startups to fairly large web companies that are sending in tens of billions of data points every day into signal effects right right and again in the interest of sharing the knowledge with all of our entrepreneurs out there you know when did they get involved in the process how much of the kind of product development definition did they did they participate in you said you've been at it for a couple years yeah we've had a lot of conviction about this space from the very beginning because we our team had solved this problem for themselves and in previous experiences but we did include we've been in beta for about six months but better to launch and so over the course of those six months we recalibrated based on feedback we got from customers but on the whole we you know are we philosophy and the approach that we took was was pretty much validated by the early customers that we engaged with okay excellent and so um I assume your venture funded we are can you can you talk about who your who your backers are yes we raised twenty eight and a half million dollars eight million dollars yeah twenty-eight point five million dollars from andreessen horowitz okay with Ben Horowitz on our board okay and Charles River ventures with a lurker on our board and how big are you now time in terms of the company well we're just getting started now right at this is 1 million all that money - well we we've got a great group of engineers or our company is you know and still in the few dozen people stage at this point ok we're planning to invest aggressively in building out our team both on R&D and on the go-to-market side this excellent once you detect patterns and anomalies what's kind of the action steps you work with with other systems to swap stuff out together because now I hear like it's these huge data centers they don't swap out this they don't swap out machines they swap out racks it's soon they'll be swapping out data centers so what are some of the prescriptive things that people are using they couldn't do before by using your yeah I'll give you a great example of that one of our early beta customers they do code pushes very aggressively you know once a week they'll push out changes into their environment and they had a signal effects console open which and we're a real-time solution so every second they're seeing updates of what was happening in their infrastructure they pushed out their code and they immediately detected a memory leak and they saw their memory usage just growing immediately after they did their code Bush and they were able to roll it back before any of their users noticed any issues and so that's an example of these days a lot of problems introduced into environments are human driven problems it's a code push it's a new user gets onboard it or a new customer gets onboard and all of a sudden there's 10x the load onto your systems and so when you have a product like signal effects where you can in real time understand everything that's happening in your environment you can quickly detect these changes and determine what the appropriate next step is and that appropriate next step will depend on your application and who you are and what you're building right so our key philosophies we get out of your way but we give you all of the insights and the tools to figure out what's happening in your arm right it's interesting that really kind of two comes from from your partners you know kind of Facebook experience right because they're pushing out new code all the time when there's no fast and break things right right exactly and then you're at VMware so you know kind of the enterprise site so what if you could speak a little bit about kind of this consumerization of IT on the enterprise side and not so much the way that the look and feel of the thing works but really taking best practices from a consumer IT companies like Facebook like Amazon that really changed the game because it used to be the big enterprise software guys had the best apps now it's it's really flipped for people like Google and Netflix and those guys have the best apps and even more importantly they drive the expectation of the behavior of an application every Enterprise is finally getting it and then are they really embracing it we're definitely seeing a growth in new application development I think you know when I spend a lot of time talking to CIOs at enterprises as well and they all understand that in order to be competitive you have to invest in applications it's not enough to just view IT as a cost center and they're all beginning to invest in application development and in some cases these are digital media teams that are separate from traditional IT and other places it's you know they're they're more closely tied together but we absolutely see a kind of growth in application development in many of these end up looking a lot like the development teams that we see here in the Bay Area you know and companies that are building staffs and consumer cloud apps yeah exciting time so you should coming out of stealth what's kind of your your next kind of milestone that you're looking forward to you have a big some announcements you got show you're gonna kind of watch out we're we're we're gonna see you make a big splash well for us it's it's steadily building our business and so we hope to you know we're launching now and we've got a lot of great customers already and hope to sign on several more and help our customers build great applications about that's our focus again congratulations two years that's a big development project Karthik thank growl the founder and CEO of signal effects just launching their company coming out of stealth we'd love to get them on the cube share the knowledge with you guys both the people that are trying to start your own company take a little inspiration as well as as the people that need the service tomorrow with the cloud with a modern application thanks a lot thank you Jeff thank you you're watching Jeff Rick cube conversation see you next time