(upbeat music) >> Hello, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're here in the studios in Palo Alto, California. Two great guests Linda Tong, general manager of Cisco AppDynamics and Garrick Linn, architect of operations at Match.com. Thanks for joining us. We're talking about AppDynamics, Match.com and customer experience. Mainly around cloud migration. So Linda, great to see you and Garrick, thanks for coming on theCUBE. >> Great to see you again. Thank you for having us. >> Same here. >> Linda, you're a CUBE alumni. we've talked about cloud migration application performance, modern application development, all powered by the Cloud, right? So this is really key and people are relying on the cloud and cloud scale and data to drive the digital transformation, the digital services and applications right now. How has the pandemic affected your customers and their expectations for digital experiences? >> Oh boy, I mean the pandemic has been, it has been rough for our customers, you know, and part of that is what Garrick's going to tell you a little bit more about today, but folks are seeing this increase in expectancy of accelerated speed and delivering innovation, building great applications and iterating on them quickly. And frankly, their customers' demands we're engaging with them through digital services. And that has led to this massive increase in, one, the types of technologies that they're consuming to build and deliver these applications. And two the complexity upon how they actually wrap their arms around it and understand what's going on and deliver these great experiences. And so it's been a rough road for our customers and what we find with AppDynamics and Cisco is our ability to partner with our customers to help them wrap their arms around that complexity. >> John: Garrick, I'd love to get your commentary on this because I'll say, Match.com has been at large-scale for many, many years, and now the pandemic comes in now a new user experience, more accelerated, more action, more things are happening, right? So this is truly the hybrid world coming together. I mean, it is kind of the same game, but kind of new patterns are emerging. What have you seen in the pandemic around the expectations and the services and you guys are providing in the digital experiences? >> Yeah, sure. So as you mentioned, Match has been around for quite some time. We've been here for over 25 years. We have an interesting mix, heterogeneous, technology, some old stuff, some new stuff. A lot of the mentality that we try to bring is to innovate. The pandemic was, it brought a lot of uncertainty. We weren't really sure how people were going to react. Was it going to be everybody kind of hunkers down on dating definitely is something that requires human interaction in multiple levels. And it turned out that people were still very much interested in getting to a place where they can find human connections and you know Match as a premium product tries to make that delightful. And so we had our hands full, especially at the beginning, things like, by checking the video features, how does that work? What are the expectations? Is that going to creep people out? If we try to offer that, are they going to use it? How are they going to date? How are they going to talk? How can we make sure that they're safe? All these kinds of things went into it. And so when we have been using AppDynamics for you know, years now, well before the pandemic, and we use that in order to get a gauge, not just on the type of traffic and load, but also, "Hey, you've got these new features, "how do they fit into this huge complex environment?" And so some of those timelines that maybe were a little bit more relaxed were very much accelerated, And like a lot of companies, we had to figure out how to deliver on that. >> John: Yeah, Linda, I want to get your thoughts. We've talked about in the past, AppDynamics has been a leader in really accelerating the value for customers. Now with the pandemic, you mentioned these new experiences are being pulled in from the physical world, right? So you have things that were happening on digital in the application space. Now you have more experiences coming in because there's no places to meet face to face. Now it's coming together, but people have been seeing the value. Well, if I can't meet in person Match.com are going to do some things, new things, online chat, whatever. This dynamic of old way, new way is changing and cloud is powering that. What are you seeing in terms of your customers' journeys around what was once pre-pandemic and now post-pandemic? >> Well, a big part of that is more and more of these experiences rely on digital services and these amazing sort of ways to connect with each other and in a very digital space, expectations of customers have changed. So not only do you experience applications and you want it to be simple, easy to use, delightful, and it delivers on the needs that you want. But on top of that, you expect it to be performant. You expect it to be secure. You expect there to be frankly, no hiccups whatsoever, because now this is your way to connect with others. This is your way to find dates or go on dates. And the last thing you want, is watching your screen pixelate, as you're trying to have an important conversation. And these kinds of experiences and these challenges as people build more and more of these digital services to build these connections, frankly, require a lot more of folks like Garrick and his team. They now have to deliver amazing experiences with perfect performance, no security risks, no bumps in the night. And that's really tough, right? Expectations have gone through the roof. >> John: Yeah, the whole story on that one point, just to kind of add live in this was that that whole concept of moving fast used to take months, right? I mean, weeks, months, now it's days and hours. So months to weeks, days and hours but Garrick, this is the challenge. This is the opportunity with the cloud. Can you just take us through your cloud journey and your goals and some of the impacts that has had on your transition to the cloud? What does that look like? >> Yeah, so we've had our on-prem data centers for quite some time, and we started putting our toe in, I guess, although it was a kind of intense at the beginning, just trying to get people on board and to say, "Hey, this is possible." We started out with a fairly small SWAT team then managed within a couple of months, working closely with our developers. We have a lot of smart people, you know, with background or overall, just security folks over devs to just demonstrate that we could do it. So we managed to take something like 80% of our front end traffic for most of the day, just kind of spinning that up, learning lessons from that, knowing what we didn't know. AppDynamics, if we didn't have that would have been almost impossible to get a read if for no other reason, then just one little tidbit. We used to have a data center in Virginia. And so physics being what it is, you know, there's just been a flight that we have to contend with. And for a couple, few years, we hadn't had the 30 millisecond or so round trip latency on there. So all of a sudden we're going back to the cloud that reintroduced this latency. So what does that mean? Will you be asked to sort of glide by and absorb it? How do we track it? How can we figure out what the Delta is between, you know, here's how we've done things on-prem. Here's how it looks out here. If you are the cross, you know, calls and, you know, AppDynamics was what we used to be able to get a read and say, "Hey, look, it isn't as good as we know we can make it, but it's something, it's a starting point. Here's why, we can show you the graphs. We can show you the data. Let's do this thing." So we then pulled back and we have focused this year on actually our affinity apps, which is a collection of applications that are also going to be okay just in, and so we've been asked to get those completely migrated over. We're going to be running in hybrid mode for a while. We're going to need to be able to compare apples to apples, apples to orangutans, all that. And this is one of the main things for you, we describe. >> {John] If I can just follow up on that just real quick, because I think this is a good point. You got the data points, you double down on that. You're looking at real data, and then you look at success and you double down, that's the playbook. So, and the other thing is that you guys actually have a real operation that's running full throttled, right? (John laughs) So, yeah, so I can see that nice balance. What does the future look like beyond that? Because when you got a business that's scaling, it's running, it's like changing the airplane engine out at 30,000 feet. You got to continue to push the envelope. >> Yup, so, and no, exactly right. Again, we're a premium product. And so we've got to back that up. And that means, maintaining high availability. And so over the next few years, we're going to be looking at what have we already do? What can we move in piecemeal kind of way where it makes sense? What are the things that we can rethink? We're also using AppDynamics as part of our containerization initiative. You know, we've got lots of virtual infrastructure, but what is it, again, what does it look like on-prem, in a container, go down the list of different things that might be different. And then to be able to compare that to what it looks like, in the cloud. So it's going to be a while yet, but like a lot of companies, when we got into this, we didn't think it was going to be done in six months. Even if we have to deliver those features at a much faster rate, we know that the long haul, we got to make smart decisions and plan the capacity, and, you know, get there. (chuckles) >> John: That's a real pragmatic approach. Linda, you and I both are sports fans. We've talked in the past about sports, and the old adage, what inning are we in growth? It's to use that baseball metaphor. I would say it's a double header, game one won by the cloud, game two is happening now. And the trend is this end-to-end mature, operationally focused customer base. And IT, where IT has shifted to the cloud right now. And they're having this new view of what modern is. End-to-end, understanding different stacks relative to applications. It's not as simple as it was before, but it's relevant. Can you share your views on how that's playing out because, or do you agree with that? And do you see that as an important part of the customer? >> Yeah, I mean, I think it's, that complexity that the IT organizations are seeing now, as they fully adopt the cloud for all their new applications and start to migrate some of their existing applications over. That world is only increasing in complexity. The way that you can virtualize your applications, break them out into millions of services, the dependencies you have on third party applications or SaaS services. These things only add that many more data points that you now have to cover and think about and make sure that those things deliver upon their SLAs, right? And wrapping your arms around that requires a partner to help you separate signal from noise. Because now you're going into a world without simplicity that you just mentioned has gotten to some point where it's beyond what you can actually sort of keep in your mind. Beyond what you can just look at data and sift through and understand, you really need tools and systems that come together, and understand that data for you and start to represent your business to you in a new way and abstract away those layers of complexity. While you do that, because I think, as you talk about those innings, that first inning, second inning, or rather first game, second game in the series, it's not a full migration to the cloud, right? There are going to be some applications that stay on-prem that stay in their traditional environments and may never move. And then some of them are going to go hybrid. Some will keep parts of the applications on-prem, and they're going to start to modularize components of it. And so it's not going to be sort of a mass scale migration. And then we're all in the promised land. And we deal with the cloud complexity. It's going to be ever increasing complexity. As we now introduce so many variants of applications, so many variants of technology, and what people are going to need is someone who can help them cover that entire estate and understand it at scale. >> John: Yeah, I mean, I think it's the enterprise conversion, if you will of cloud operations on-premises because of the reasons. And now you've got the edge. Garrick, this is the whole kind of end-to-end stack conversation view. And by the way, there isn't one tech stack to rule them all because you have different use cases. You might have an application that needs a financial gateway or have other capabilities. So integration's huge. This only increases the point Linda was making about complexity behind the scenes. How does AppDynamics help you with this for Match.com? >> So we have quite a bit of infrastructure, you know, a lot of it is shared, well, most of all, maintaining, sandboxes for user data and that sort of thing. And so now the navigating that space is always interesting. So for instance, one of the new things that we have coming out is Star.com It's out there right now. It's a dating site that's geared towards single parents. It does share some of the infrastructure, but we're realizing what that means, how is that different, how our registration flow is different, how our subscription flow is different. Where are the things that DevOps are actively trying to improve on and rethink? That's one of the things that we try to focus on when we're trying to kind of pick out, like, is this a good candidate to move over to the cloud sooner or later? Is this a good candidate for something that needs to be maybe bake a little bit more? And having established those baselines with the shared infrastructure, and having a pretty good understanding of how they react, how they work really helps us, you know, tee up these new initiatives and in front of those needs in a more efficient way. So yeah, absolutely. >> John: What's some of the activity you guys seen? And what's the peak activity on Match.com these days? >> Yeah, so dating apps in general, but not so particular we use a nested or breast fractal peak, and it's a pattern that, from what they told me back in the old days, took a little while to realize was a thing. And not just like, oh we changed something and then did this and produced that. So every evening is our peak basically. So with taking time zones into account, obviously, in the United States from about five to 10 o'clock at night or so, we get this, growing, burst of traffic. So that can be anywhere from 23% sometimes. It kind of varies. Then we have a weekly peak where every, you know, Sunday and Monday we expect a higher amount of traffic than we would other days. And it kind of makes sense from an Archer psychology kind of standpoint where, you know, you're coming off of dates, you're trying to set dates up. That's where a lot of that activity is. And then we have a yearly peak, which goes from around Christmas to President's day. Believe it or not, it's President's day, it's not Valentine's day. And so the sort of thing where when we're trying to plan for capacity and we do a lot of, what cost squeeze tests, were not quite as I guess, engineering, but hey, what does it look like if we go down in capacity by 50%, what happens? where are the weak points? A January, Monday night is very different from a May, Thursday in June (chuckles). So we have to predict, we can anticipate some of that, but we don't know for sure, a lot can change in a year. So when we're preparing for a yearly peak, we really have to pay attention. We have to prep. We have to plan for that and work with that to figure out how we can get through it and maintain that level of service. >> That's awesome, and AppDynamics to help you to do that. I'd love to get a bot to give me the optimal dating times, to share with my single friends. Great stuff. Linda, thank you for coming. Great to see you. Congratulations on a great case study. Great story. How large-scale applications and are working in the modern cloud. So congratulations on your success. Thanks for coming on theCUBE. Appreciate it. >> Awesome, thank you, so good to be here. >> Okay, CUBE coverage of re:Invent 2021. I'm John Furrier with theCUBE. Thanks for watching. (upbeat music)

>>mhm. What the special cube conversation here in Palo alto, I'm john Kerry host of the cube. We're here talking about kubernetes Cloud native and all things Cloud, cloud enterprise amir Sure VP of product and morgan Stanley is with me and we are great to have you on the cube. Thanks for coming on. I appreciate you taking the time, >>appreciate it, john good to be here. You >>know, cloud Native obviously super hot right now as the edges around the corner, you're seeing people looking at five G looking at amazon's wavelength outposts you've got as you got a lot of cloud companies really pushing distributed computing and I think one of the things that people really are getting into is okay, how do I take the cloud and re factor my business and then that's one business side then, the technical side. Okay, How do I do it? Like it's not that easy. Right. So it sounds, it sounds really easy to just go to move to the cloud. This is something that's been a big problem. So I know you guys in the center of all this uh and you've got, you know, microservices, kubernetes at the core of this, take a minute to introduce the company, what you guys do then I want to get into some specific questions. >>Mhm, of course. Well, bob Sani is a startup? Silicon Valley startup and what we do is automate system configuration that's typically worked at an engineer does and take lengthy and if done incorrectly at least to a lot of errors and cost overruns and the user experience problems. We completely automate that using an Ai and ml back end so that the engineering can focus on writing code and not worry about having to tune the little pieces working together. >>You know, I love the, I was talking to a V. C on our last uh startup showcase, cloud startup showcase and uh really prominent VC and he was talking about down stack up stack benefits and he says if you're going to be a down stack um, provider, you got to solve a problem. It has to be a big problem that people don't want to deal with. So, and you start getting into some of the systems configuration when you have automation at the center of this as a table stakes item problems are cropping up as new use cases are emerging. Can you talk about some of the problems that you guys see that you solve for developers and companies, >>of course. So they're basically, they're, the problem expresses itself in a number of domains. The first one is that he who pays the bills is separate from he who consumes the resources. It's the engineers that consume the resources and the incentives are to deliver code rapidly and deliver code that works well, but they don't really care about paying the bills. And then the CFO office sees the bills and there's a disparity between the two. The reason that creates a problem, a business problem is that the developers uh, will over provision stuff, uh to make sure that everything works and uh, they don't want to get caught in the middle of the night. You know, the bill comes due at the end of the month or into the quarter and then the CFO has smoke coming out of his ears because there's been clawed overruns. Then the reaction happens to all right, let's cut costs. And then, you know, there's an edict that comes down that says everything, reduce everything by 30%. So people go across and give a haircut to everything. So what happens next to systems out of balance? There's allocation resource misallocation and uh, systems start uh, suffering. So the customers become unhappy. And ironically, if you're not provisioned correctly, Not ironically, but maybe understandably, customers start suffering and that leads to a revenue problem down the line if you have too many problems unhappy. So you have to be very careful about how you cut costs and how you apportion resources. So both the revenue side is happy and it costs are happy because it all comes down to product experience and what the customers consume. You >>know, that's something that everyone who's done. Cloud development knows, you know, whose fault is it? You know, it's this fall. But now you can actually see the services you leave a switch open or, you know, I'm oversimplifying it. But, you know, you experiment services, you can the bills can just have massive, you know, overruns and then, and then you got to call the cloud company and you gotta call the engineers and say why did you do this? You got to get a refund or or the bad one. Bad apple could ruin it for everyone as you, as you highlighted over the bigger companies. So I have to ask you mean everyone lives this. How do companies have cost overruns? Is their patterns that you see that you guys wrote software 4-1, automate the obvious ones. Is there is there are certain things that you know always happen. Are there areas that have some indications? So why do, first of all, why do companies have cloud cost overruns? >>That's a great question. And let's start with a bit of history where we came from a pre cloud world, you built your own data centers, which means that you have an upfront Capex cost and you spend the money and you were forced to live within the needs that your data center provided. You really couldn't spend anymore. That provided kind of a predictable expenditure bottle it came in big chunks. But you know what, your budget was going to be four years from now, three years from now. And you built for that with the cloud computing, Your consumption is now on on demand basis and it's api enabled. So the developer can just ask for more resources. So without any kind of tools that tell the developer here is x amount of CPU or X amount of memory that you need for this particular service, that for it to deliver the right uh, performance that for the customer. The developers incentivized to basically give it a lot more than the application needs. Why? Because the developer doesn't want to pick up service tickets. He's incentivizing delivering functionality quickly and moving on to next project, not in optimizing costs. So that creates kind of uh an agency problem that the guy that actually controls how research are consumed is not incentivized to control the consumption of these resources. And we see that across the board in every company, engineers, engineering organization is a separate organization than the financial organization. So the control place is different. The consumption place and it breaks down the patterns are over provisions. And what we want to do is give engineers the tools to consume precisely the right amount of resources for the service level objectives that they have, given that you want a transaction rate of X and the literacy rate of Why here's how you configure your cloud infrastructure. So the application delivers according to the sls with the least possible resources consumed. >>So on this tool you guys have in the software you guys have, how how do you guys go to mark with that, you target the business buyer or the developer themselves and and how do you handle the developers say, I don't want anyone looking over my shoulder. I'm gonna go, I'm gonna have a blank check to do whatever it takes, um how do you guys roll that out because actually the business benefits are significant controlling the budget, I get that. Um how do you guys rolling this out? How do people engage with you? What's your strategy? >>Right. Are there, is the application owner, is the guy that owns the PML for the application? It tends to be a VP level or a senior director person that owns a SAAS platform and he or she is responsible for delivering good products to the market and delivering good financial results to the CFO So in that person of everything is rolled up, but that person will always favor the revenue site, which means consume more resources than you need in order to maximize customer happiness, therefore faster growth and uh they do that while sacrificing the cost side. So by giving the product owner the optimization tools autonomous of optimization tools that Sandy has, we allow him or her to deliver the right experience to the customer, with the right sufficient resources and address both the performance and the cost side of equation simultaneously, >>awesome. Can you talk about the impact c I C D s having in the cloud native computing on the optimization cycle? Um Obviously, you know, shifting left for security, we hear a lot of that, you're hearing a lot of more microservices being spun up, spun down automatically. Uh I'll see kubernetes clusters are going mainstream, you start to see a lot more dynamic uh activity if you if you in these new workflows, what is the impact of these new CSC D cloud? Native computing on the optimization cycle? >>C i c D is there to enable a fast delivery of software features basically. Uh So, you know, we have a combination of get get ups where you can just pull down repositories, libraries, open source projects from left and right. And using glue code, developers can deliver functionality really quick. In fact, microservices are there in service of that capability, deliver functionality quickly by being able to build functional blocks and then through a piece you put everything together. So ci cd is just accelerates the software delivery code. Between the time the boss says, give me an application until the application team plus the devops team plus SRE team puts it out in production. Now we can do this really quickly. The problem is though, nobody optimizes in the process. So when we deliver 1.0 in six months or less, we've done zero in terms of optimization and at one point, oh, becomes a way that we go through QA in many cases, unfortunately. And it also becomes a way that we go through the optimization. The customer screams that you eyes Laghi, you know, the throughput is really slow and we tinker and tinker and tinker and by the time it typically goes through a 12 month cycle of maturation, we get that system stability in the right performance with a I and machine learning that a person has enabled. We can deliver that, we can shrink that time out considerably. In fact, uh you know what we're going to announce in q khan is something that be called Kite storm is the ability to uh install our product and kubernetes environment in roughly 20 minutes and within two days you get the results. So before you have this optimization cycle that was going on for a very long time now that it's frank down and because of Ci Cd, you know, you don't have the luxury of waiting and the system itself can become part of the way of contributing system. The system being the uh ai ml service, that the presiding deliveries can be uh part and parcel of the Ci cd pipeline, that optimizes the code and gives you the right configuration and you get to go. So >>you guys are really getting down and injecting in some uh instrumentation for metadata around key areas. That right. Is that kind of how it's working? Are you getting in there with codes going to watch? Um how was it working under the hood? Can you just give me a quick example of, you know, how this would play out and what people might expect, how it would handle, >>of course. So what the way we optimize application performance is we have to have a metric against which we measure performance. That metric is an S L O service level, objective and in a kubernetes environment, we typically tap into Prometheus, which is the metrics gathering place metrics database for kubernetes workloads and we really focus on red metrics, the rate of transactions, the error rate and the for delay or latency. So we focus on these three metrics and what we have to do is inject a small container, it's an open source container into the application work space that we call that a container. Servo. Servo interacts with Prometheus to get the metrics and then it talks to our back end to tell the M L engine what's happening and then L engine and does this analysis and comes back with a new configuration which then servo implements in a canary instance. So the Canary instances where we run our experiments and we compare it against the main line, Which the application is doing after roughly 20 generations or so. The Bellingen Learns what part of the problem space to focus on in order to optimize to deliver optimal results. And then it very quickly comes to the right set of solutions to try and it tries those inside uh inside the canary instance and when it finds the optimal solution, it gives the recommendation back to the application team or alternatively, when you have enough trust in the tiny you can ought to promote it into mainline that >>gets the learning in there is a great example of some cloud native action. I want to get into some examples with your customer, but before we get there, I want to ask you, since I have you here, if you don't mind, what is cloud native mean these days, because you know, cloud native become kind of much cloud computing, um which essentially go move to the cloud, but as people start developing in the cloud where there's real new benefits, people talk about the word cloud native, could you take a quick minute to define? What is cloud Native, Does that even mean? What does cloud native mean? >>I'll try to give you my understanding government, we could get into a bit of philosophy. Uh Yeah, that's good. But basically cloud Native means it's, your application is built for the cloud and it takes advantages of the inherent benefits that a cloud environment can give you, which means that you can grow and shrink resources on the fly, if you built your application correctly, that you can scale up and scale down, you're a number of instances very quickly and uh, everything has taken advantage of a P I S so initially that was kind of done inside of the environment. Uh AWS Ec two is a perfect example of that. Kubernetes shifted cloud native to container its workload because it allows for rapid, more, rapid deployment and even enables or it takes advantage of a more rapid development cycle as we look forward. Cloud Native is more likely to be a surplus environment where you write functions and the backend systems of the cloud service provider, just give you that capability and you don't have to worry about maintaining and managing a fleet of any sort, whether it's VMS or containers, that's where it's gonna go. Currently we are to contain our space >>so as you start getting into the service molly good land, which we've been playing with, loves that as you get into that, that's going to accelerate more data. So I gotta ask you as you get into more of this this month, I will say monitoring or observe ability, how we want to look at it. You gotta get at the data. This becomes a critical part of solving a lot of problems and also making sure the machine learning is learning the right thing. How do you view that you guys over there? Because I think everyone is like getting that cloud native and it's not hard sell to say that's all good, but we can go back, you know, the expression ships created ships and then you have shipwrecks, you know, there's always a double edged sword here. So what's the downside? If you don't get the data right? >>Uh well, so the for us, the problem is not too much data, it's lack of data. So if you don't get data right is you don't have enough data. And the places where optimization cannot be automated is where the transaction rates are slow, where you don't have enough fruit. But coming into the application and it really becomes difficult to optimize that application with any kind of speed. You have to be able to profile the application long enough to know what moves its needle and in order for you to hit the S. L. O. Targets. So it's not too much data, it's not enough data. That seems to be the problem. And there are a lot of applications that are expensive to run but have a low throughput. And I would uh in all cases actually in every customer environment that have been in, where that's been the case if the application is just over provision, if you have a low throughput environment and it's costing too much, don't use ml to solve it. That's a wrong application of the technology. Just take a sledgehammer and back your resources by 50%, see what happens. And if that thing breaks back it again, until you find the baggage point. >>Exactly for you over prison, you bang it back down again. It's like the old school now with the cloud. Take me through some examples when you guys had some success, obviously you guys are in the right area right now, you're seeing a lot of people looking at this area to do that in some cases like changing the whole data center and respect of their business. But as you get it with customers with the app side, what some successes can you share some of the use cases, what you guys are being successful, your customers can get some examples. >>Yeah. So well known financial software for midsize businesses that that does accounting. It's uh there are customer during a large fleet and this product has been around for a while. It's not a container ice product. This product runs on VMS. Angela is a large component of that. So the problem for this particular vendor has been that they run on heterogeneous fleet that the application has been a along around for a very long time. And as new instance types on AWS have come in, developers have used those. So the fleet itself is quite heterogeneous and depending on the time of the day and what kind of reports are being run by organisations, they, the mix of resources that the applications need are different. So uh when we started analyzing the stack, we started we started looking at three different tiers, we looked at the database level, we looked at the job of mid tier and we looked at the web front end. And uh one of the things that became counterproductive is that m L. Discovered that using for the mid tier using larger instances but fear of a lot for better performance and lower cost and uh typically your gut feel is to go with smaller instances and more of a larger fleet if you would. But in this case, what the ML produced was completely counter intuitive And the net result for the customer was 78% cost reduction while agency went down by 10%. So think about it that you're, the response time is less, uh 10% less but your costs are down almost 80% 78% in this case. And the other are the fact that happened in the job of mitt here is that we improve garbage collection significantly and because whenever garbage collection happens on a JV M it takes a pause and that from a customer perspective it reflects as downtime because the machines are not responding so by tuning garbage collection Andrzej VMS across this very large fleet we were able to recover over 5000 minutes and month across the entire fleet. So uh, these are some substantial savings and this is what the right application of machine learning on a large fleet can do for assess business. >>And so talk about this fleet dynamic, You mentioned several lists. How do you see the future evolving for you guys? Where are you skating to where the puck is? As the expression goes? Um obviously with server list is going to have essentially unlimited fleets potentially That's gonna put a lot of power in the hands of developers. Okay. And people building experiences, What's the next five years look like for you guys? >>So I'm looking at the product from a product perspective, the service market depends on the mercy of the cloud service provider and typically the algorithms that they use. Uh basically they keep very few instances warm for you until you're the rate of api calls goes up and they start they start uh start turning on VMS are containers for you and then the system becomes more responsive over time. One place that we can optimize the service environment is give predictability of what the cyclicality of load is. So we can pre provision those instances and warm up the engine before the loads come into the system always stays responsive. You may have noticed that some of your apps on your phone that when you start them up, they may have a start up like a minute or two. Especially if it's a it's a terror gap. What's happening in those cases that you're starting an api calls goes in containers being started up for you to start up that instance, not enough of our warm to give you that rapid response. And that can lead to customer churn. So by by analyzing what the load on the overall load of the system is and pre provision the system. We can prevent the downtime uh prevent the lag to start up black on the downside. Which when you know when the usage goes down, it doesn't make sense to keep that many instances up. So we can talk to the back in infrastructure and the commission of those VMS in order to make to prevent cost creeps basically. So that's one place that we're thinking about extending our technology. >>So it's like, it's like the classic example where people say, oh during black monday everyone searches to do e commerce. You guys are thinking about it on A level that's a user centric kind of use case where you look at the application and be smart about what the expectation is on any given situation and then flex the resources on that. Is that right? That by getting right? So if it's your example, the app is a good one. If I wanted to load fast, that's the expectation. It better load fast. >>Yes, that's exactly but more romantic. So I use valentine's day and flowers my example. But you know, it doesn't have to be annual cycles. It can be daily cycles or hourly cycles. And all those patterns are learning about by an Ml back in. >>Alright, so I gotta ask you love the, this, this this new concept because most people think auto scaling right? Because that's a server concept. Can auto scale or database. Okay. On a scale up, you're getting down to the point where, okay, we'll keep the engines warm, getting more detailed. How do you explain this versus a concept like auto scaling. Is it the same as a cousins? >>They're they're basically the way they're expressed, it's the same technology but their way there expressed is different. So uh in a cooper native environment, the H. B A is your auto scaler basically in response to the need, response more instances and you get more containers going on. What happens as services? Less environment is you're unaware of the underpinnings that do that scale up for you. But there is an auto Scaler in place that does that scale up for you. So the question becomes that we're in a stack from a customer's perspective, are you talking about if you imagine your instances we're dealing with the H. B. A. If you're managing at the functional level we have to have api calls on the service provider's infrastructure to pre warm up the engine before the load comes. >>I love I love this under the hood is kind of love new dynamics kind of the same wine, new bottle but still computer science, still coding, still cool and relevant to make these experiences great. Thanks for coming on this cube conversation. I really appreciate it. Take a minute to put a plug in for the company. What are you guys doing in terms of status funding scale employees, what are you looking for? And if someone's watching this and there should be a customer of you guys, what what's, what's, what's going on in their world? What tells them that they need to be calling you? >>Yeah, so we're serious. Dave we've had the privilege of uh, our we've been privileged by having a very good success with large enterprises. Uh, if you go to our website, you'll see the logos of who we have, we will be at Q khan and there were going to be actively targeting the mid market or smaller kubernetes instances, as I mentioned, it's gonna take about 20 minutes to get started and we'll show the results in two hours. And our goal is for our customers to deliver the best user experience in terms of performance, reliability. Uh, so that they, they delight their customers in return and they do so without breaking the bank. So deliver excellent products, do it at the most efficient way possible, deliver a good financial results for your stakeholders. This is what we do. So we encourage anybody who is running a SAS company to come and take a look at us because we think we can help them and we can accelerate there. The growth at the lower cost >>and the last thing people need is have someone coming breathing down their necks saying, hey, we're getting overcharged. Why are you guys screwing up when they're not? They're trying to make a great experience. And I think this is kind of where people really want to do push the envelope and not have to go back and revisit the cost overruns, which if it's actually a good sign if you get some cost overruns here and there because you're experimenting. But again, you don't want to get out of control. >>You don't want to be a visual like the U. S. Debt. >>Exactly. I'm here. Thank you for coming on. Great. We'll see a coupe con. The key will be there in person is a hybrid event. So uh, coupon is gonna be awesome and thanks for coming on the key. Appreciate it. >>John is a pleasure. Thank you for having me on. >>Okay. I'm john fryer with acute here in Palo alto California remote interview with upsetting hot startup series. I'm sure they're gonna do well in the right spot in the market. Really well poisoned cloud Native. Thanks for watching. Yeah.

>> Live from San Francisco, its theCUBE. Covering RSA Conference 2019. Brought to you by Forescout. >> Hey welcome back everybody Jeff Frick here with theCUBE. We're at RSA Conference North America 2019. 40,000 plus people in the brand newly refinished. Moscone, they finally got it done and it looks great, we're excited to be here and the guy, one of the many people responsible for this whole event is joining us for a return visit. He's Rohit Ghai, the president of RSA. Rohit, congratulations on another incredible event. >> Thank you, it is incredible indeed and the scope of the conversation, the breadth of the conversation, amazing. >> Right, I was looking a couple of years ago I think it was Valentine's Day, thankfully you didn't do Valentine's Day this year 'cause I don't think Moscone was ready for you. >> That's right, I don't think that would have played out well, yes (laughs). >> So lets jump into it a little bit, kind of general impressions you know security is not getting any less in demand. We're seeing increased threats, we're getting dumbed down to breaches. Give me the facts, how many vendors are here displaying today, how many sponsors? What are are some of the basics? >> Yeah, so look 40,000 plus attendees you know we have 800 plus folks on the show floor. There is a total of 1,700 plus vendors in this industry so its a very fragmented industry and everybody whose anybody in cyber-security is actually here. The other stat that is interesting is in terms of shared voice and the media coverage that actually happens at the RSA conference, if you just put that together that's more than any of the social conversations throughout the year. So this one week will generate more shared voice around cyber-security than the entire year. >> It's the place to be. So let's jump into it, so one of the big issues that you've always talked about is using a really kind of business approach to assessing risk and some of the math behind making a good business decision on how much you invest and what do you protect. You've expanded that vision a little bit this year. Tell us a little more about that. >> We see our role as RSA to provide a safe passage of the world to its digital future state. As you know digital transformation is a buzz-word. Every company is trying to go digital but they don't know what they don't know. Technology is premiering things where its never been before. It's inside baby monitors, inside pace makers, inside cars. Companies that are adapting this technology don't have the competency to actually mitigate risk. The stat I use is one-trillion lines of code will be shipped over the next decade by companies that have shipped exactly zero lines of code. >> One trillion new marginal lines of code. >> So, the meta point is we face unprecedented digital risk, because of adoption of digital technology. So technology is a force for the good but you have to embrace it mindfully and pay attention to digital risk management and that's our role. The role of RSA is to help companies manage digital risk. >> Right, and how do they sort through it all? I just feel for all this between the number of threats, the number of solutions, the IOT is coming on board, 'Internet of Things'. The OT is now being connected to the IT, your head's got to be just spinning. >> It feels overwhelming doesn't it. What I say is anytime you feel overwhelmed you could do three things. You have to reduce the amount of work, you do that by designing security in, resilient infrastructure. Second is that you have to automate work. Which is basically using technology like artificial intelligence and machine learning. But as you know the bad guys have all the AI and ML we the good guys do. So the third recipe for success is business driven security. Which means you have to apply business contacts to your security posture, so you focus on the right problems. The right cyber incidents right here right now. And that's our unique advantage the good guys, the only advantage we the good guys have is our understanding of our business contacts. We call that business driven security. >> So an interesting piece of that is how the value proposition is changing. It used to be the young kid hacking the school site giving himself an A. Then it got to people getting into bank accounts and personal information. But now we're seeing with the nation's states, we're seeing political motivation. >> Exactly. >> There's a lot of different motivations so it gets into this whole evaluation of data, what is the data that they want and is it valuable? Because what they want or is valuable tomorrow might be different than what it was today. >> You're right, the clock speed of digital business is markedly enhanced. So you need solutions that can move at the pace of business. So its no longer about efficacy, its about speed, both on the risk side and security you need solutions that can process this vast ocean of data, make sense of it, to prioritize your response. To focus on the things that are most important right now. >> Yeah, its crazy. Then we have this other trend that's happening now, which is kind of Big-Tech like from Big-Oil meaning not a positive connotation in a blowback. Where people are kind of waking up to the fact that my data is important and people are using it for ways that I didn't necessarily want them to. So this trust issue is really really significant. >> It is significant because in fact the topic of my keynote yesterday. We call it the trust landscape in which we painted a story that we are at the beginning of an era which is a trust crisis. Where people are losing faith in technology as a force for good and unless we act now we will put humanity in harms way and get in the way of human progress. And I think there is some things we need to do, if you think about trust, trust is based on reputation. Trust is not perfection, I don't trust you because you're perfect. I trust you because I can count on how you're going to behave in certain circumstances. Its based on your reputation. >> Right. >> If you think about today we are inviting complete strangers into our cars and homes with platforms like Airbnb and Uber Lyft. Because there is a technology trust platform. We need that on the enterprise side and what we're doing in the cyber security world is, we are actually making withdrawals from our trust or reputation bank account because breaches and bad news is the only thing that's reported. We are not reporting good cyber incidents. So that's the place where we need to work toward, where we are able to not just take withdrawals from our reputation bank account but make deposits by reporting not just bad cyber news but good cyber news. >> Right. >> When we prevent breaches or when we mitigate business impact or cyber incidents. All of those things we need to be more transparent about that. >> But its kind of tricky right now because its the old spy dilemma, you don't want to tell them that you caught them because then you are not in a position to catch them the next time. >> Yes, I think there is solutions there though. I think the reason we have been guarded in cyber security to share good news is because again we don't want to reveal details of our security posture. And we don't want to taunt the bad guy and attract attention towards ourselves. Having said that I think there is a way to do that anonymously without compromising your security posture and having this quantified way to measure your reputation or your cyber capability. >> Right, its really interesting that you go down this trust angle because the whole fake news thing. Is protecting your reputation really of more significant value than necessarily, I don't know, make up some other kind of silly data breach but your reputation and the trust that comes from that or the relationship you have with your customer is really really important. >> Absolutely, your reputation ascertains how your company will live through any crisis incident, right? And in the past corporate reputations were based on things like corporate social responsibility. Your conduct in the physical world, environment, sustainability, corporate ethics, in terms of how you are treating your employees on a fair basis. In the digital world, just like you have corporate social responsibility, you have corporate digital responsibility. You need to demonstrate conduct in terms of how you deal with data, how you take care of consumer data and are a good custodian for it. How you participate in the ecosystem. The Facebook Cambridge analytica example, when you share data with partners you have to feel accountability to that. So in this hyper-connected economy, third-party risk is actually probably higher than first party risk. So you no longer just need to worry about your own data landscape and your own infrastructure landscape. You need to worry about your ecosystem as well. >> Right, and that's before you count in if its an API based economy and you've got stuff in the cloud, you've got stuff in your data center, you've got stuff at remote locations. So the complexity is significantly changed. >> Absolutely. The good news is there's a great recipe which is digital risk management. Risk and trust have to coexist right? If you don't take risks you can't make progress or innovate but in order to have trust you need to have predictability. And that comes through a risk management approach and that's why RSA is so excited about this idea of digital risk management. Its a great responsibility to chart the course to the digital future of the world. >> Well you've certainly got everybody's ear as you said everybody whose anybody is here and this is the place to be this week so congratulations again on a very big and successful show and we're excited that we got to sit down this time not standing in the hallway. >> Thank you, thank you. >> Alright thanks again. >> I enjoyed the conversation. >> Alrighty, he's Rohit, I'm Jeff, you're watching theCUBE. We're at RSA North American conference in Moscone. Thanks for watching we'll see you next time.

>> Live from San Francisco, it's theCUBE! Covering IBM Think 2019. Brought to you by IBM. >> Welcome back, live here in San Francisco, it's theCUBE's coverage of IBM Think 2019. I'm John Furrier--Stu Miniman. Stu, four days, we're on our fourth day, the sun's shining, they've shut down Howard Street here at IBM. Big event for IBM, in San Francisco, not Las Vegas. Lot of great cloud action, lot of great AI data developers. Great story, good to see you again. Our next two guests, Julie Lockner, Director, Offering Management, Portfolio Operations at IBM, Data+AI, great to see you. >> Thank you, it's great to see you too, thank you. >> And Jay Limburn, Director of Offering Management, IBM Data+AI, thanks for coming on. >> Hey guys, great to be here. >> So, we've chatted many times at events, the role of data. So, we're religious about data, data flows through our blood, but IBM has put it all together now. All the reorgs are over, everyone's kind of, the table is set for IBM. The data path is clear, it's part of applications. It's feeding the apps. AI's the key workload inside the application. This is now a fully set-up group, give us the update, what's the focus? >> Yeah, it's really exciting because, if you think about it, before, we were called IBM Analytics, and that really is only a part of what we do. Now that we're Data+AI, that means that not only are we responsible for delivering data assets, and technology that supports those data assets to our customers, but infusing AI, not only in the technologies that we have, but also helping them build applications so they can fuse AI into their business processes. >> It's pretty broad, I mean, data's very much a broad swath of things. Analytics, you know, wrangling data, setting things up, cataloging them. Take me through how you guys set this up. How do you present it to the marketplace? How are clients engaged with it? Because it's pretty broad. But it could be, it needs to be specific. Take us through the methodology. >> So, you probably heard a lot of people today talk about the ladder to AI, right? This is IBM's view of how we explain our client's journey towards AI. It really starts at the bottom rung of the ladder, where we've got the collection of information. Collect your data. Once you've collected your data, you move up to the next rung, which is the Organize. And this is really where all the governance stuff comes in. This is how we can provide a view across that data, understand that data, provide trust to that data, and then serve that up to the consumers of that information, so they can actually use that in AI. That's where all the data science capabilities come in, allowing people to actually be able to consume that information. >> So, the bottom set is just really all the hard and heavy lifting that data scientists actually don't want to do. >> And writing algorithms, the collecting, the ingesting of data from any source, that's the bottom? And then, tell me about that next layer up, from the collection-- >> So, Collect is the physical assets or the collection of the data that you're going to be using for AI. If you don't get that foundation right, it doesn't really make sense. You have to have the data first. The piece in the middle that Jay was referring to, that's called Organize, our whole divisions are actually organized around these ladders to AI, so, Collect, Organize, Analyze, Infuse. On the Organize side, as Jay was mentioning, it's all about inventorying the data assets, knowing what data you have, then providing data quality rules, governance, compliance-type offerings, that allow organizations to not just know your data, trust your data, but then make it available so you can use your data, and the users are those data scientists, they're the analytics teams, they're the operation organizations that need to be able to build their solutions on top of trusted data. >> So, where does the Catalog fit in? Which level does that come into? >> Yeah, so, think of the Data Catalog as the DNS for data, all right? It's the way in which you can provide a full view of all of your information. Whether it's structured information, unstructured information, data you've got on PRAM and data you've got in a cloud somewhere. >> That's in the Organize layer, right? >> That's all in the Organize layer. So, if you can collect that information, you can then provide capabilities that allow you to understand the quality of that data, know where that data's come from, and then, finally, if you serve that up inside a compelling, business-friendly experience, so that a data scientist can go to one place, quickly make a decision on if that's the right data for them, and allow them to go and be productive by building a data science model, then we're really able to move the needle on making those data science organizations efficient, allowing us to build better models to transform their business. >> Yeah, and a big part of that is, if you think about what makes Amazon successful, it's because they know where all their products are, from the vendor, to when it shows up on the doorstep. What the Catalog provides is really the similar capability of, I would call it inventory management of your data assets, where we know where the data came from, its source--in that Collect layer-- who's transformed it, who's accessed it, if they're even allowed to see it, so, data privacy policies are part of that, and then being able to just serve up that data to those users. Being able to see that whole end-to-end lineage is a key point, critical point of the ladder to AI. Especially when you start to think about things like bias detection, which is a big part of the Analyze layer. >> But one of the things we've been digging into on theCUBE is, is data the next flywheel of innovation? You know, it used to be I just had my information, many years ago we started talking about, "Okay, I need to be able to access all that other information." We hear things like 80% of the data out there isn't really searchable today. So, how do you see data, data gravity, all those pieces, as the next flywheel of innovation? >> Yeah, I think it's key. I mean, we've talked a lot about how, you can't do AI without information architecture. And it's absolutely true. And getting that view of that data in a single location, so it is like the DNS of the internet. So you know exactly where to search, you can get hold of that data, and then you've got tools that give you self-service access to actually get hold of the data without any need of support from IT to get access to it. It's really a key-- >> Yeah, but to the point you were just asking about, data gravity? I mean, being able to do this where the data resides. So, for example, we have a lot of our customers that are mergers and acquisitions. Some teams have a lot of data assets that are on-premises, others have large data lakes in AWS or Azure. How do you inventory those assets and really have a view of what you have available across that landscape? Part of what we've been focusing on this year is making our technology work across all of those clouds. And having a single view of your assets but knowing where it resides. >> So, Julie, this environment is a bit more complicated than the old data warehousing, or even what we were looking at with big data and Hadoop and all those pieces. >> Isn't that the truth? >> Help explain why we're actually going to be able to get the information, leverage and drive new business value out of data today, when we've struggled so many times in the past. >> Well, I think the biggest thing that's changed is the adoption of DevOps, and when I say adoption of DevOps and things like containerization and Docker containers, Kubernetes, the ability to provision data assets very quickly, no matter where they are, build these very quick value-producing applications based on AI, Artificial Intelligence APIs, is what's allowing us to take advantage of this multi-cloud landscape. If you didn't have that DevOps foundation, you'd still be building ETL jobs in data warehouses, and that was 20 years ago. Today, it's much more about these microservices-based architecture, building up these AI-- >> Well, that's the key point, and the "Fuse" part of the stack, I think, or ladder. Stack? Ladder? >> Ladder. (laughs) >> Ladder to success! Is key, because you're seeing the applications that have data native into the app, where it has to have certain characteristics, whether it's a realtime healthcare app, or retail app, and we had the retail folks on earlier, it's like, oh my god, this now has to be addressable very fast, so, the old fenced-off data warehouse-- "Hey, give me that data!"--pull it over. You need a sub-second latency, or milliseconds. So, this is now a requirement. >> That's right. >> So, how are people getting there? What are some use cases? >> Sure. I'll start with the healthcare 'cause you brought that up. One of the big use cases for technology that we provide is really around taking information that might be realtime, or batch data, and providing the ability to analyze that data very quickly in realtime to the point where you can predict when someone might potentially have a cardiac arrest. And yesterday's keynote that Rob Thomas presented, a demonstration that showed the ability to take data from a wearable device, combine it with data that's sitting in an Amazon... MySQL database, be able to predict who is the most at-risk of having a potential cardiac arrest! >> That's me! >> And then present that to a call center of cardiologists. So, this company that we work with, iCure, really took that entire stack, Organize, Collect, Organize, Analyze, Infuse, and built an application in a matter of six weeks. Now, that's the most compelling part. We were able to build the solution, inventory their data assets, tie it to the industry model, healthcare industry model, and predict when someone might potentially-- >> Do you have that demo on you? The device? >> Of course I do. I know, I know. So, here is, this is called a BraveHeart Life Sensor. And essentially, it's a Bluetooth device. I know! If you put it on! (laughs) >> If I put it on, it'll track... Biometric? It'll start capturing information about your heart, ECG, and on Valentine's Day, right? My heart to yours, happy Valentine's Day to my husband, of course. The ability to be able to capture all this data here on the device, stream it to an AI engine that can then immediately classify whether or not someone has an anomaly in their ECG signal. You couldn't do that without having a complete ladder to AI capability. >> So, realtime telemetry from the heart. So, I see timing's important if you're about to have a heart attack. >> Yeah. >> Pretty important. >> And that's a great example of, you mentioned the speed. It's all about being able to capture that data in whatever form it's coming in, understand what that data is, know if you can trust that data, and then put it in the hands of the individuals that can do something valuable with the analysis from that data. >> Yeah, you have to able to trust it. Especially-- >> So, you brought up earlier bias in data. So, I want to bring that up in context of this. This is just one example of wearables, Fitbits, all kinds of things happening. >> New sources of tech, yeah. >> In healthcare, retail, all kinds of edge, realtime, is bias of data. And the other one's privacy because now you have a new kind of data source going into the cloud. And then, so, this fits into what part of the ladder? So, the ladder needs a secure piece. >> Tell me about that. >> Yeah, it does. So, that really falls into that Organize piece of that ladder, the governance aspects around it. If you're going to make data available for self-service, you've got to still make sure that that data's protected, and that you're not going to go and break any kind of regulatory law around that data. So, we actually can use technology now to understand what that data is, whether it contains sensitive information, credit card numbers, and expose that information out to those consumers, yet still masking the key elements that should be protected. And that's really important, because data science is a hugely inefficient business. Data scientists are spending too much time looking for information. And worse than that, they actually don't have all the information available that they need, because certain information needs to be protected. But what we can do now is expose information that wasn't previously available, but protect just the key parts of that information, so we're still ensuring it's safe. >> That's a really key point. It's the classic iceberg, right? What you see: "Oh, data science is going to "change the game of our business!" And then when they realize what's underneath the water, it's like, all this set-up, incompatible data, dirty data, data cleaning, and then all of a sudden it just doesn't work, right? This is the reality. Are you guys seeing this? Do you see that? >> Yeah, absolutely. I think we're only just really at the beginning of a crest of a wave, here. I think organizations know they want to get to AI, the ladder to AI really helps explain and it helps to understand how they can get there. And we're able then to solve that through our technology, and help them get there and drive those efficiencies that they need. >> And just to add to that, I mean, now that there's more data assets available, you can't manually classify, tag and inventory all that data, determine whether or not it contains sensitive data. And that's where infusing machine learning into our products has really allowed our customers to automate the process. I mentioned, the only way that we were able to deploy this application in six weeks, is because we used a lot of the embedded machine learning to identify the patient data that was considered sensitive, tag it as patient data, and then, when the data scientists were actually building the models in that same environment, it was masked. So, they knew that they had access to the data, but they weren't allowed to see it. It's perfectly--especially with HIMSS' conference this week as well! You were talking about this there. >> Great use case with healthcare. >> Love to hear you speak about the ecosystem being built around this. Everything, open APIs, I'm guessing? >> Oh, yeah. What kind of partners are-- >> Jay, talk a little bit-- >> Yeah, so, one of the key things we're doing is ensuring that we're able to keep this stuff open. We don't want to curate a proprietary system. We're already big supporters of open source, as you know, in IBM. One of the things that we're heavily-invested in is our open metadata strategy. Open metadata is part of the open source ODPi Foundation. Project Egeria defines a standard for common metadata interchange. And what that means is that, any of these metadata systems that adopt this standard can freely share and exchange metadata across that landscape, so that wherever your data is, whichever systems it's stored in, wherever that metadata is harvested, it can play part of that network and share that metadata across those systems. >> I'd like to get your thoughts on something, Julie. You've been on the analyst side, you're now at IBM. Jay, if you can weigh in on this too, that'd be great. We, here, we see all the trends and go to all the events and one of the things that's popping up that's clear within the IBM ecosystem because you guys have a lot of business customers, is that a new kind of business app developer's coming in. And we've seen data science highlight the citizen data scientist, so if data is code, part of the application, and all the ladder stuff kind of falls into place, that means we're going to see new kinds of applications. So, how are you guys looking at, this is kind of a, not like the cloud-native, hardcore DevOps developer. It's the person that says, "Hey, I can innovate "a business model." I see a business model innovation that's not so much about building technology, it's about using insight and a unique... Formula or algorithm, to tweak something. That's not a lot of programming involved. 'Cause with Cloud and Cloud Private, all these back end systems, that's an ecosystem partner opportunity for you guys, but it's not your classic ISV. So, there's a new breed of business apps that we see coming, your thoughts on this? >> Yeah, it's almost like taking business process optimization as a discipline, and turning it into micro-applications. You want to be able to leverage data that's available and accessible, be able to insert that particular Artificial Intelligence machine learning algorithm to optimize that business process, and then get out of the way. Because if you try to reinvent your entire business process, culture typically gets in the way of some of these things. >> I thought, as an application value, 'cause there's value creation here, right? >> Absolutely. >> You were talking about, so, is this a new kind of genre of developer, or-- >> It really is, I mean... If you take the citizen data scientist, an example that you mentioned earlier. It's really about lowering the entry point to that technology. How can you allow individuals with lower levels of skills to actually get in and be productive and create something valuable? It shouldn't be just a practice that's held away for the hardcore developer anymore. It's about lowering the entry point with the set of tools. One of the things we have in Watson Studio, for example, our data science platform, is just that. It's about providing wizards and walkthroughs to allow people to develop productive use models very easily, without needing hardcore coding skills. >> Yeah, I also think, though, that, in order for these value-added applications to be built, the data has to be business-ready. That's how you accelerate these application development life cycles. That's how you get the new class of application developers productive, is making sure that they start with a business-ready foundation. >> So, how are you guys going to go after this new market? What's the marketing strategy? Again, this is like, forward-pioneering kind of things happening. What's the strategy, how are you going to enable this, what's the plan? >> Well, there's two parts of it. One is, when Jay was mentioning the Open Metadata Repository Services, our key strategy is embedding Catalog everywhere and anywhere we can. We believe that having that open metadata exchange allows us to open up access to metadata across these applications. So, really, that's first and foremost, is making sure that we can catalog and inventory data assets that might not necessarily be in the IBM Cloud, or in IBM products. That's really the first step. >> Absolutely. The second step, I would say, is really taking all of our capabilities, making them, from the ground up, microservices-enabled, delivering them through Docker containers and making sure that they can port across whatever cloud deployment model our customers want to be able to execute on. And being able to optimize the runtime engines, whether it's data integration, data movement, data virtualization, based on data gravity, that you had mentioned-- >> So, something like a whole new developer program opportunity to bring to the market. >> Absolutely. I mean, there is, I think there is a huge opportunity for, from an education perspective, to help our customers build these applications. But it starts with understanding the data assets, understanding what they can do with it, and using self-service-type tools that Jay was referring to. >> And all of that underpinned with the trust. If you don't trust your data, the data scientist is not going to know whether or not they're using the right thing. >> So, the ladder's great. Great way for people to figure out where they are, it's like looking in the mirror, on the organization. How early is this? What inning are we in? How do you guys see the progression? How far along are we? Obviously, you have some data, examples, some people are doing it end-to-end. What's the maturity look like? What's the uptake? >> Go ahead, Jay. >> So, I think we're at the beginning of a crest of a wave. As I say, there's been a lot of discussion so far, even if you compare this year's conference to last year's. A lot of the discussion last year was, "What's possible with AI?" This year's conference is much more about, "What are we doing with AI?" And I think we're now getting to the point where people can actually start to be productive and really start to change their business through that. >> Yeah and, just to add to that, I mean, the ladder to AI was introduced last year, and it has gained so much adoption in the marketplace and our customers, they're actually organizing their business that way. So, the Collect divisions are the database teams, are now expanding to Hadoop and Cloudera, and Hortonworks and Mongo. They're organizing their data governance teams around the Organize pillar, where they're doing things like data integration, data replication. So, I feel like the maturity of this ladder to AI is really enabling our customers to achieve it much faster than-- >> I was talking to Dave Vellante about this, and we're seeing that, you know, we've been covering IBM since, it's the 10th year of theCUBE, all ten years. It's been, watching the progression. The past couple of years has been setting the table, everyone seems to be pumping, it makes sense, everything's hanging together, it's in one group. Data's not one, "This group, that group," it's all, Data, AI, all Analytics, all Watson. Smart, and the ladder just allows you to understand where a customer is, and then-- >> Well, and also, we mentioned the emphasis on open source. It allows our customers to take an inventory of, what do they have, internally, with IBM assets, externally, open source, so that they can actually start to architect their information architecture, using the same kind of analogy. >> And an opportunity for developers too, great. Julie, thanks for coming on. Jay, appreciate it. >> Thank you so much for the opportunity, happy Valentine's Day! Happy Valentine's Day, we're theCUBE. I'm John Furrier, Stu Miniman here, live in San Francisco at the Moscone Center, and the whole street's shut down, Howard Street. Huge event, 30,000 people, we'll be back with more Day Four coverage after this short break.

(click) >> Hey, Lisa Martin on the ground with theCUBE at Google for the Sixth Annual CloudNOW Top Women in Cloud Awards Event, our second year covering this, very excited to be joined by tonight's emcee, Becky Wanta, the founder of RSW1C. Welcome to theCUBE. >> Thank you. >> It's great to have you here. So tell us a little bit about what you do and your background as a technology leader. >> So, I've been in technology for close to 40 years. I started out as a software. >> Sorry, I don't even, what? (laughing) >> Ha, ha, ha, it's a long time ago, yeah. So I started out as a developer back in the Department of Defense. So it wasn't rocket science in the early days when I began because it was back when computers took up whole rooms and I realized I had an affinity for that. So, I leveraged that, but then I got into, at that time, and I'm from northern California, if you remember right, the Department of Defense was drawing down. And so I decided I was going to leverage my experience in IT to get into either integrative financial services or healthcare, right. So I took over running all of tech for the Money Store at the time which you would have no idea who that is. And then that got acquired by Wells Fargo First Union, so I took over as their Global CTO for Wells Fargo. And what you'll see is, so let me just tell you about RSW1C because what it is is it's a technology consulting firm that's me. And the reason I have it is because tech changes so much that it's easy to stay current. And when I get brought into companies, and you'll look at me, so I've been the executive officer for tiny little companies like PepsiCo, Wells Fargo, Southwest Airlines. >> The small ones. >> Yeah, tiny, not really, MGM Resorts International, the largest worker's comp company in California, a company that, unborn midsize SMB in southern California that just wrapped up last year. And when I get brought into these companies, I get brought in to transform them. It's at a time in the maturation of these companies, these tiny little brands we've mentioned, where they're ready to jettison IT. So I take that very seriously because I know technology is that gateway to keep that competitive advantage. And the beauty is of that the companies I've mentioned, they're all number one in their markets. And when you're number one, there's only one direction to go, so they take that very seriously. >> How do you come in there and help an MGM Grand Resorts transform? >> So what happened in MGM's case and probably in the last five CIO positions that I've taken, they've met me as a consultant, again, from RSW1C. And then when I look into what needs to happen and I have the conversation, because everybody thinks they want to do digital transformation, and it's not an easy journey and if you don't have the executive sponsorship, don't even try it at home, right? And so, in MGM's case, they had been talking. MGM's the largest taxpayer in Nevada. People think about it as MGM Grand. It's 19 brands on The Strip. >> Is that right? >> It's Bellagio, MGM, so it's the largest taxpayer in Nevada. So it owns 44,860 rooms on The Strip. So if I just counted now, you have Circa Circa, Slots of Fun, Mirage, Bellagio, Monte Carlo, New York, New York, um, MGM Grand Las Vegas, MGM Grand Detroit. They're in the countries and so forth. So it's huge. And that includes Mandalay, ARIA, and all those, so it's huge, right? And so in MGM's case, they knew they wanted to do M life, so M life game changes their industry. And I put that in. This will be our nine year anniversary coming up on Valentine's Day. Thirty years they talked about it, and I put in with a great team And that was part of the transformation into a new way of running their business. >> Wow, we have a couple of minutes left. I'd love to get your perspective on being a female leader in tech. Who were your mentors back in the day? And who are your mentors now? >> So, I don't have any mentors. I never did. Because when I started in the industry, there wasn't a lot of women. And obviously, technology was fairly new which is why one of my passions is around helping the next generation be hugely successful. And one of the things that's important is in the space of tech, I like this mantra, this mantra that says, "How about brains "and beauty that gets you in the door? "How about having the confidence in yourself?" So I want to help a lot of the next generation be hugely successful. And that's what Jocelyn has built with CloudNow, her and Susan. And I'm a big proponent of this because I think it's a chance for us to give back and help the next generation of leaders in a non-traditional way be hugely successful in brands, in companies that are going to unleash their passion and show them how to do that. Because, the good news is that I'm a total bum, Lisa. I've never had a job. I love what I do, and I do it around the clock, so. >> Oh, if only more people could say that. That's so cool. But what we've seen with CloudNow, this is our second year covering it, I love talking to the winners and even the folks that are keynoting or helping to sponsor scholarships. There's so much opportunity. >> There really is. >> And it's so exciting when you can see someone whose life is changing as a result of finding a mentor or having enough conviction to say, "You know what? "I am interested in a STEM field. "I'm going to pursue that." >> Right. >> So, we thank you so much Becky for stopping by theCUBE. And your career is amazing. >> Thanks. >> And I'm sure you probably are mentors to countless, countless men and women out there. >> Absolutely. >> Well, thanks again for stopping by. >> Thank you, Lisa. >> Thank you for watching theCUBE. I'm Lisa Martin on the ground at Google with the CloudNow Sixth Annual Top Women in Cloud Awards Event. Stick around, we'll be right back.

(instrumental electronic music) (crowd) >> Hey welcome back everybody, Jeff Frick, here with The Cube. We are live in Moscone Center, with 40,000 security experts at the RSA Conference, the biggest conference of its size, and one of the biggest tech conferences in the industry, second maybe only to Salesforce and Oracle's. So, there's a lot people here, a lot of action-- >> Absolutely. >> We're excited to be joined by the president of RSA, Rohit Ghai. Welcome. >> Thank you. Thank you. >> So first thing, kind of impressions of the show, we were here briefly last year, this thing was 34,000. This year, they're saying it's 40. >> Forty thousand, yeah. Look, RSA has the great burden and privilege of bringing the cyber security community together, and it's a true testimonial to the caliber of the people that this year we are able to attract 40,000 people. We have almost 500 plus, 550-something, I believe vendors and exhibitors. And the level of the conversation, in terms of the CEOs from different countries, the CEOs from all the mega corporations, public sector participants, the entire gamut of cyber security stakeholders are here today. >> That's an interesting kind of take because on one hand, you think there's so many people, but as a few people had mentioned earlier, really they're all here so, and on the grand scheme of things, it's not that many people. It's really this group of people-- >> Exactly. >> And they all know each other. People are all giving each other hugs, as they're walking up and down the booth, so this really is it. >> This is a community, and it's a tight-knit community. It's all the good guys and some linked together (laughing), and figured out what to do about the bad guys (laughing). >> I know, I just hope they all don't go to the bad side at the same time, we'd be in trouble. >> Absolutely. >> One of the things that comes up over and over at tech conferences specifically, and at here, too, is the ecosystem. >> Rohit: Yeah. >> Right? Nobody can do it alone-- >> Rohit: Yep. >> You've got to have an ecosystem-- >> Rohit: Yep. >> And there's a lot of conversations about sharing information-- >> Yep. >> More broadly-- >> Yep. Yep. >> More automated, faster-- >> Rohit: Yep. >> Really an important part of the strategy to fight the bad guys. >> Absolutely. In fact, that was a recurring theme from all the keynote speakers this morning, the notion of working together. The only shot we have of beating the bad guys is if we collaborate and share the information that we have, and go at it together. So, the ecosystem is super important to your point. >> Yep. So, what are some that are accounted for the people that aren't here-- >> Rohit: Yep. Kind of the key themes, some of the big announcement that RSA's make-- >> Rohit: Yeah. >> And I know the press release feed is full (laughing) this morning-- >> Rohit: Yeah. >> But what are you guys excited about for this year? >> Look, what I'm most excited about is a new approach. And here's the way I tee it up, the bad guys are getting really good, right? Every company is going digital, and digital companies are really juicy targets. We don't have enough good guys to fight on our behalf, enough trained good guys, which means we ought to bring technology to assist use, all the things like advanced, artificial intelligence, machine learning, data science, all those things have great capabilities, but the reality is we have to realize the bad guys have all the same technology that we do. So, it's not a technology problem anymore-- >> Right, right. >> We have to play to our strengths, play to our advantage, so this new approach, we call it business-driven security, which means take the security incidents and apply business context to it, enabling customers to take command of their cyber risk, and secure and protect what matters most. >> Right, right. >> So, it's a sense of prioritization, and if we do that successfully, then we are able to keep the bad guys, they're only inside the door, but we can curtail the damage and we can detect the breaches, and respond in a much more expedient manner. >> Right, always the problems within arm's race, right? Both people have the same amount of weapons, so it's how to use those weapons-- >> Rohit: It's how to use the weapons. >> More effectively. >> Absolutely. And therein the context is super important if you're going to apply business context to the way you apply that information-- >> Right. >> With those tools, that's how you win. >> Now, another theme that keeps coming up is kind of state-sponsored threats-- >> Rohit: Yep, yep. >> Which are different than, maybe, kind of commercially, or just-- >> Rohit: Yep, Yep. >> Kind of activists. >> Rohit: Yep. >> That's really changing the game because-- >> Rohit: It is. >> The resources behind those folks significantly bigger. >> Indeed. So, there's new kind of bad guys, like the nation state threat actors, and their objectives are totally different, right? Their objective is not just to steal data, but to tamper with data, and change the conversations as we saw in the case of the election-- >> Right, right. this year, the presidential elections. By tampering data you can actually shift conversations and influence outcomes, so it's a whole new ball game, in terms of the new types of threats and new types of threat actors like nation states, who are getting into the game. >> Yeah, I thought one of the interesting points that came up earlier in the keynote today-- >> Rohit: Yeah. >> I think they called it salting or spiking the algorithm-- >> Rohit: Yep. >> With intentional bad data to send the algorithm on a path, in which it really shouldn't go. >> Exactly, exactly. And the way you respond to that is, again, to back to my point around business-driven security. If you have data, and if you understand the business context around how that data ought to be used, then you're able to protect it and secure it, and make sure it doesn't get weaponized, or used against you. >> Right, right. And another theme that came up at another session I attended is kind of the unique role that companies are in versus-- >> Rohit: Yep. >> The government-- >> Rohit: Yep. >> Because even if there is state-sponsored-- >> Rohit: Yep. >> Issues going on-- >> Rohit: Yep. >> Because many of the companies, RSA included-- >> Rohit: Yeah. >> Operate globally across the number of geos. >> Yep. >> They potentially have even more data, different data, to fight the threat than any one government does on its own. >> Indeed, and this is where sharing of information is vital, and along those lines, RSA is excited to announce this year that we've joined the Cyber Threat Alliance, which is a consortium of private companies who have decided that it's not the threat intel data, it's how you use it that's going to be the differentiating factor. >> Right. >> So, in the spirit and vein of working together, we are sharing threat data with each other, so that we can respond to the bad guys. >> Right. So, give you the last word-- >> Rohit: Yeah. >> It's February 14th, Happy Valentine's Day. Start of the new year, what are some of your priorities as you look down the other road, what are we going to be talking about a year from now? >> Yeah. >> What's things that are on your plate that you're really thinking about? >> Yeah, yeah. Look, so, in the vein of Valentine's Day, I totally love cyber security (laughing). Let me say that, and in terms of what we're looking forward to. Look, RSA is in the game to innovate and set the table, and set the agenda for the cyber security market. We play the role of bringing the cyber security community together, but it's our innovation along the axis of business-driven security. We want to take that conversation, drive that into the industry because we believe that without that, we don't have a shot of beating the bad guys. >> Right. Alright, well, we're all rooting for you (laughing)-- >> Thank you. I appreciate that. >> And everybody else in this building, alright. >> I appreciate that. Thanks. >> He's Rohit. I'm Jeff. You're watching The Cube, live from RSA 2017, in downtown San Francisco. Thanks for watching. >> Thank you. (instrumental electronic music) (upbeat instrumental music)

(upbeat instrumental music) >> Hey welcome back everybody. Jeff Frick here with the Cube. We're at the RSA Conference in downtown San Francisco. 40,000 security professionals here talking about how to keep us all safe, especially when we're in autonomous vehicles, especially when we have connected nest devices. It's a crazy wild world. We're excited to be joined by Derek Manky, the global security strategist for Fortinet. Welcome. >> Hey thanks, pleasure to be here. >> Absolutely. >> We'll talk security right? >> Well I hope so. So for folks that aren't familiar with Fortinet, give us kind of the overview of what you guys are doing. >> Sure I mean tons of different things. So, you know, my department, I work directly with our global threat intelligence team and our labs. So for over 15 years now, we've been building up our labs. We have over 200 threat analysts and researchers worldwide combing through data at any given minute. But the problem is, the data. We live in a big data world now. There's so much, it's very easy to become overwhelmed with data. So we've taken an approach where we have a very intelligent human expertise team, but we've invested a lot into automation, machine learning, artificial intelligence, that you're going to find that's a very important thing moving forward because we need to be able to stay on par with the bad guys. >> Right right. >> The bad guys are very good at automation. They don't have anything holding them down. They're flying full-force, so we're trying to keep up to them. And, you know there's a lot of great initiatives like cyber threat alliance, of course, so we made a big announcement this week on that too. >> Right. So really as things have evolved over those 10 years, I mean the bad news is the amount of data that you guys have to keep track of is growing exponentially. The good news is the tools like machine learning and AI and Spark and Hadoop and, you know the tools that you have to use are much more sophisticated as well. It kind of works both sides of the coin at the same time. >> Yeah but you know what? One thing that we found is that there is a lot of information here, there is a lot of data being thrown out there. You have to make sense of the data. So a big theme and a big focus of ours is making data actionable. So threat intelligence actionable. How do you cross what we call the last mile? How do you take data and information and put it into transparent security controls so the end users, like all of our customers, don't have to do that manually. The manual work is what's killing a lot of people out there. There's a huge gap in cyber security professionals out there. People like network administrators, by the time they receive, say, a PDF document or something manual that they have to plug in an IP address or an update, it's often too late. A lot of this information is very perishable, very fluid. So, we're trying to automate that into the security controls. That comes from a lot of that big data, analytics on the back end. We call it a security fabric. So this is where we can weave in that information into all of our different products. End point, from end point all the way up to the cloud. And the cyber threat alliance is a very big initiative. So we're a founding member of that along with the other founding members I mentioned this week. We're working together to share information. And the goal of that is to share information on a platform and then as a member of the CTA founding member take that information in and push that out into those controls in near real time. That's the big thing. >> That was the big thing right? Because people have shared data before. But it's really kind of this real time emphasis to get it in real time. You know using things like Spark and streaming data. So that you're not reacting after the fact. In the old stat they used to quote us, you know people didn't even know for like 250 days. >> Derek: Yeah. >> Or whatever it was. >> We're bringing a lot of illumination to intelligence as well. Visibility's a big thing. Speed is a very big thing right? How can we get that information out very quickly because like I said the bad guys are moving a million miles a minute. So it's a really important initiative what we're doing with that. The other thing is the quality of information. A lot of information is too hastily shared and I think humans we're at that tipping point right now. Where humans can't fully trust automation. It's like autonomous vehicles. >> Right right. >> You're not going to put it fully in control right? You have to start getting a trust exercise with it and that's what we're trying to do, a lot of this intelligence. >> What was interesting in the keynote this morning one of the new threads they highlighted is people actually feeding the algorithms bad information. >> Poisoning yeah, yeah. Absolutely, yeah, yeah. >> Salting the algorithm is what they call it. To send it down a different path than it should be going. >> I mean the bad guys will put all this thought throughout and evasion techniques. But that's another really nice thing about the cyber threat alliance. Is that we're all collaborating. So we're giving confidence ratings to this. So it's also a quality of sharing system which the industry very badly needs in my opinion too. >> So what's next? Looking at 2017, we're getting started this February. Oh it's Valentine's Day February 14. >> Happy Valentine's Day. >> Happy Valentine's. So a year from now and we talk, what's the top of my priorities? What are you working on for the next little while? >> Yeah absolutely. Again we're going down the CMO automation. You're going to see a lot on the security fabric that we have. So this is how we can have machines automatically learning about environments. Automatically adapting to environments. You look at a lot of security problems out there a lot of the times it's security 101. It's people misconfiguring firewalls, misconfiguring policies and devices. Not having a proper security device in front of their crown jewels or their asset, their digital asset. So that is a big theme that we're doing, it's taking that intelligence and starting to empower our products and solutions to make intelligence decisions on their own. >> Right. >> That's a very big leap forward and we've made significant progress with that. >> It's interesting that you mention that. There's still a lot of 101 work that people aren't doing to the degree that they should. There was a great line in the keynote this morning that every company has at least one person that will click on anything. >> Weakest link in the chain right? Yeah. >> Absolutely. Alright well Derek thanks for stopping by. And congrats on a great show. And really some exciting stuff with that cyber threat alliance. >> Great yeah thanks, a pleasure. >> Alright he's Derek Manky I'm Jeff Frick. You're watching the Cube from RSA in downtown San Francisco. Thanks for watching. (instrumental music)