>>Earlier this week, Oracle announced the new X nine M generation of exit data platforms for its cloud at customer and legacy on prem deployments. And the company made some enhancements to its zero data loss, recovery appliance. CLRA something we've covered quite often since its announcement. We had a video exclusive with one Louisa who was the executive vice president of mission critical database technologies. At Oracle. We did that on the day of the announcement who got his take on it. And I asked Oracle, Hey, can we get some subject matter experts, some technical gurus to dig deeper and get more details on the architecture because we want to better understand some of the performance claims that Oracle is making. And with me today is Susan. Who's the vice president of product management for exit data database machine. Bob tome is the vice president of product management for exit data cloud at customer. And Tim chin is the senior director of product management for DRA folks. Welcome to this power panel and welcome to the cube. >>Thank you, Dave. >>Can we start with you? Um, Juan and I, we talked about the X nine M a that Oracle just launched a couple of days ago. Maybe you could give us a recap, some of the, what do we need to know? The, especially I'm interested in the big numbers once more so we can just understand the claims you're making around this announcement. We can dig into that. >>Absolutely. They've very excited to do that. In a nutshell, we have the world's fastest database machine for both LTP and analytics, and we made that even faster, not just simply faster, but for all LPP we made it 70% faster and we took the oil PPV ops all the way up to 27.6 million read IOPS and mind you, this is being measured at the sequel layer for analytics. We did pretty much the same thing, an 87% increase in analytics. And we broke through that one terabyte per second barrier, absolutely phenomenal stuff. Now, while all those numbers by themselves are fascinating, here's something that's even more fascinating in my mind, 80% of the product development work for extra data, X nine M was done during COVID, which means all of us were remote. And what that meant was extreme levels of teamwork between the development teams, manufacturing teams, procurement teams, software teams, the works. I mean, everybody coming together as one to deliver this product, I think it's kudos to everybody who touched this product in one way or the other extremely proud of it. >>Thank you for making that point. And I'm laughing because it's like you the same bolt of a mission-critical OLT T O LTP performance. You had the world record, and now you're saying, adding on top of that. Um, but, okay. But, so there are customers that still, you know, build the builder and they're trying to build their own exit data. What they do is they buy their own servers and storage and networking components. And I do that when I talk to them, they'll say, look, they want to maintain their independence. They don't want to get locked in Oracle, or maybe they believe it's cheaper. You know, maybe they're sort of focused on the, the, the CapEx the CFO has him in the headlock, or they might, sometimes they talk about, they want a platform that can support, you know, horizontal, uh, apps, maybe not Oracle stuff, or, or maybe they're just trying to preserve their job. I don't know, but why shouldn't these customers roll their own and why can't they get similar results just using standard off the shelf technologies? >>Great question. It's going to require a little involved answer, but let's just look at the statistics to begin with. Oracle's exit data was first productized in Delaware to the market in 2008. And at that point in time itself, we had industry leadership across a number of metrics. Today, we are at the 11th generation of exit data, and we are way far ahead than the competition, like 50 X, faster hundred X faster, right? I mean, we are talking orders of magnitude faster. How did we achieve this? And I think the answer to your question is going to lie in what are we doing at the engineering level to make these magical numbers come to, uh, for right first, it starts with the hardware. Oracle has its own hardware server design team, where we are embedding in capabilities towards increasing performance, reliability, security, and scalability down at the hardware level, the database, which is a user level process talks to the hardware directly. >>The only reason we can do this is because we own the source code for pretty much everything in between, starting with the database, going into the operating system, the hypervisor. And as I, as I just mentioned the hardware, and then we also worked with the former elements on this entire thing, the key to making extra data, the best Oracle database machine lies in that engineering, where we take the operating system, make it fit like tongue and groove into, uh, a bit with the opera, with the hardware, and then do the same with the database. And because we have got this deep insight into what are the workloads that are, that are running at any given point in time on the compute side of extra data, we can then do micromanagement at the software layers of how traffic flows are flowing through the entire system and do things like, you know, prioritize all PP transactions on a very specific, uh, you know, queue on the RDMA. >>We'll converse Ethan at be able to do smart scan, use the compute elements in the storage tier to be able to offload SQL processing. They call them the longer I used formats of data, extend them into flash, just a whole bunch of things that we've been doing over the last 12 years, because we have this deep engineering, you can try to cobble a system together, which sort of looks like an extra data. It's got a network and it's got storage, tiering compute here, but you're not going to be able to achieve anything close to what we are doing. The biggest deal in my mind, apart from the performance and the high availability is the security, because we are testing the stack top to bottom. When you're trying to build your own best of breed kind of stuff. You're not going to be able to do that because it depended on the server that had to do something and HP to do something else or Dell to do something else and a Brocade switch to do something it's not possible. We can do this, we've done it. We've proven it. We've delivered it for over a decade. End of story. For as far as I'm concerned, >>I mean, you know, at this fine, remember when Oracle purchased Sohn and I know a big part of that purchase was to get Java, but I remember saying at the time it was a brilliant acquisition. I was looking at it from a financial standpoint. I think you paid seven and a half billion for it. And it automatically, when you're, when Safra was able to get back to sort of pre acquisition margins, you got the Oracle uplift in terms of revenue multiples. So then that standpoint, it was a no brainer, but the other thing is back in the Unix days, it was like HP. Oracle was the standard. And, and in terms of all the benchmarks and performance, but even then, I'm sure you work closely with HP, but it was like to get the stuff to work together, you know, make sure that it was going to be able to recover according to your standards, but you couldn't actually do that deep engineering that you just described now earlier, Subin you, you, you, you stated that the X sign now in M you get, oh, LTP IO, IOP reads at 27 million IOPS. Uh, you got 19 microseconds latency, so pretty impressive stuff, impressive numbers. And you kind of just went there. Um, but how are you measuring these numbers versus other performance claims from your competitors? What what's, you know, are you, are you stacking the deck? Can you give you share with us there? >>Sure. So Shada incidents, we are mentioning it at the sequel layer. This is not some kind of an ion meter or a micro benchmark. That's looking at just a flash subsystem or just a persistent memory subsystem. This is measured at the compute, not doing an entire set of transactions. And how many times can you finish that? Right? So that's how it's being measured. Now. Most people cannot measure it like that because of the disparity and the number of vendors that are involved in that particular solution, right? You've got servers from vendor a and storage from vendor B, the storage network from vendor C, the operating system from vendor D. How do you tune all of these things on your own? You cannot write. I mean, there's only certain bells and whistles and knobs that are available for you to tune, but so that's how we are measuring the 19 microseconds is at the sequel layer. >>What that means is this a real world customer running a real world. Workload is guaranteed to get that kind of a latency. None of the other suppliers can make that claim. This is the real world capability. Now let's take a look at that 19 microseconds we boast and we say, Hey, we had an order of magnitude two orders of magnitude faster than everybody else. When it comes down to latency. And one things that this is we'll do our magic while it is magical. The magic is really grounded in deep engineering and deep physics and science. The way we implement this is we, first of all, put the persistent memory tier in the storage. And that way it's shared across all of the database instances that are running on the compute tier. Then we have this ultra fast hundred gigabit ethernet RDMA over converged ethernet fabric. >>With this, what we have been able to do is at the hardware level between two network interface guides that are resident on that fabric, we create paths that enable high priority low-latency communication between any two end points on that fabric. And then given the fact that we implemented persistent memory in the storage tier, what that means is with that persistent memory, sitting on the memory bus of the processor in the storage tier, we can perform it remote direct memory access operation from the compute tier to memory address spaces in the persistent memory of the storage tier, without the involvement of the operating system on either end, no context, switches, knowing processing latencies and all of that. So it's hardware to hardware, communication with security built in, which is immutable, right? So all of this is built into the hardware itself. So there's no software involved. You perform a read, the data comes back 19 microseconds, boom. End of story. >>Yeah. So that's key to my next topic, which is security because if you're not getting the OSTP involved and that's, you know, very oftentimes if I can get access to the OSTP, I get privileged. Like I can really take advantage of that as a hacker. But so, but, but before I go there, like Oracle talks about, it's got a huge percentage of the Gayety 7% of the fortune 100 companies run their mission, critical workloads on exit data. But so that's not only important to the companies, but they're serving consumer me, right. I'm going to my ATM or I'm swiping my credit card. And Juan mentioned that you use a layered security model. I just sort of inferred anyway, that, that having this stuff in hardware and not have to involve access to the OS actually contributes to better security. But can you describe this in a bit more detail? >>So yeah, what Brian was talking about was this layered security set differently. It is defense in depth, and that's been our mantra and philosophy for several years now. So what does that entail? As I mentioned earlier, we designed our own servers. We do this for performance. We also do it for security. We've got a number of features that are built into the hardware that make sure that we've got immutable areas of form where we, for instance, let me give you this example. If you take an article x86 server, just a standard x86 server, not even express in the form of an extra data system, even if you had super user privileges sitting on top of an operating system, you cannot modify the bias as a user, as a super user that has to be done through the system management network. So we put gates and protection modes, et cetera, right in the hardware itself. >>Now, of course the security of that hardware goes all the way back to the fact that we own the design. We've got a global supply chain, but we are making sure that our supply chain is protected monitored. And, uh, we also protect the last mile of the supply chain, which is we can detect if there's been any tampering of form where that's been, uh, that's occurred in the hardware while the hardware shipped from our factory to the customers, uh, docks. Right? So we, we know that something's been tampered with the moment it comes back up on the customer. So that's on the hardware. Let's take a look at the operating system, Oracle Linux, we own article the next, the entire source code. And what shipping on exit data is the unbreakable enterprise Connell, the carnal and the operating system itself have been reduced in terms of eliminating all unnecessary packages from that operating system bundle. >>When we deliver it in the form of the data, let's put some real numbers on that. A standard Oracle Linux or a standard Linux distribution has got about 5,000 plus packages. These things include like print servers, web servers, a whole bunch of stuff that you're not absolutely going to use at all on exit data. Why ship those? Because the moment you ship more stuff than you need, you are increasing the, uh, the target, uh, that attackers can get to. So on AXA data, there are only 701 packages. So compare this 5,413 packages on a standard Linux, 701 and exit data. So we reduced the attack surface another aspect on this, when we, we do our own STIG, uh, ASCAP benchmarking. If you take a standard Linux and you run that ASCAP benchmark, you'll get about a 30% pass score on exit data. It's 90 plus percent. >>So which means we are doing the heavy lifting of doing the security checks on the operating system before it even goes out to the factory. And then you layer on Oracle database, transparent data encryption. We've got all kinds of protection capabilities, data reduction, being able to do an authentication on a user ID basis, being able to log it, being able to track it, being able to determine who access the system when and log back. So it's basically defend at every single layer. And then of course the customer's responsibility. It doesn't just stop by getting this high secure, uh, environment. They have to do their own job of them securing their network perimeters, securing who has physical access to the system and everything else. So it's a giant responsibility. And as you mentioned, you know, you as a consumer going to an ATM machine and withdrawing money, you would do 200. You don't want to see 5,000 deducted from your account. And so all of this is made possible with exited and the amount of security focus that we have on the system >>And the bank doesn't want to see it the other way. So I'm geeking out here in the cube, but I got one more question for you. Juan talked about X nine M best system for database consolidation. So I, I kinda, you know, it was built to handle all LTP analytics, et cetera. So I want to push you a little bit on this because I can make an argument that, that this is kind of a Swiss army knife versus the best screwdriver or the best knife. How do you respond to that concern and how, how do you respond to the concern that you're putting too many eggs in one basket? Like, what do you tell people to fear you're consolidating workloads to save money, but you're also narrowing the blast radius. Isn't that a problem? >>Very good question there. So, yes. So this is an interesting problem, and it is a balancing act. As you correctly pointed out, you want to have the economies of scale that you get when you consolidate more and more databases, but at the same time, when something happens when hardware fails or there's an attack, you want to make sure that you have business continuity. So what we are doing on exit data, first of all, as I mentioned, we are designing our own hardware and a building in reliability into the system and at the hardware layer, that means having redundancy, redundancy for fans, power supplies. We even have the ability to isolate faulty cores on the processor. And we've got this a tremendous amount of sweeping that's going on by the system management stack, looking for problem areas and trying to contain them as much as possible within the hardware itself. >>Then you take it up to the software layer. We used our reliability to then build high availability. What that implies is, and that's fundamental to the exited architecture is this entire scale out model, our based system, you cannot go smaller than having two database nodes and three storage cells. Why is that? That's because you want to have high availability of your database instances. So if something happens to one server hardware, software, whatever you got another server that's ready to take on that load. And then with real application clusters, you can then switch over between these two, why three storage cells. We want to make sure that when you have got duplicate copies of data, because you at least want to have one additional copy of your data in case something happens to the disc that has got that only that one copy, right? So the reason we have got three is because then you can Stripe data across these three different servers and deliver high availability. >>Now you take that up to the rack level. A lot of things happen. Now, when you're really talking about the blast radius, you want to make sure that if something physically happens to this data center, that you have infrastructure that's available for it to function for business continuity, we maintain, which is why we have the maximum availability architecture. So with components like golden gate and active data guard, and other ways by which we can keep to this distant systems in sync is extremely critical for us to deliver these high availability paths that make, uh, the whole equation about how many eggs in one basket versus containing the containment of the blast radius. A lot easier to grapple with because business continuity is something which is paramount to us. I mean, Oracle, the enterprise is running on Xcel data. Our high value cloud customers are running on extra data. And I'm sure Bob's going to talk a lot more about the cloud piece of it. So I think we have all the tools in place to, to go after that optimization on how many eggs in one basket was his blast radius. It's a question of working through the solution and the criticalities of that particular instance. >>Okay, great. Thank you for that detailed soup. We're going to give you a break. You go take a breath, get a, get a drink of water. Maybe we'll come back to you. If we have time, let's go to Bob, Bob, Bob tome, X data cloud at customer X nine M earlier this week, Juan said kinda, kinda cocky. What we're bothering, comparing exit data against your cloud, a customer against outpost or Azure stack. Can you elaborate on, on why that is? >>Sure. Or you, you know, first of all, I want to say, I love, I love baby. We go south posts. You know why it affirms everything that we've been doing for the past four and a half years with clouded customer. It affirms that cloud is running that running cloud services in customers' data center is a large and important market, large and important enough that AWS felt that the need provide these, um, you know, these customers with an AWS option, even if it only supports a sliver of the functionality that they provide in the public cloud. And that's what they're doing. They're giving it a sliver and they're not exactly leading with the best they could offer. So for that reason, you know, that reason alone, there's really nothing to compare. And so we, we give them the benefit of the doubt and we actually are using their public cloud solutions. >>Another point most customers are looking to deploy to Oracle cloud, a customer they're looking for a per performance, scalable, secure, and highly available platform to deploy. What's offered their most critical databases. Most often they are Oracle databases does outposts for an Oracle database. No. Does outpost run a comparable database? Not really does outposts run Amazon's top OTP and analytics database services, the ones that are top in their cloud public cloud. No, that we couldn't find anything that runs outposts that's worth comparing against X data clouded customer, which is why the comparisons are against their public cloud products. And even with that still we're looking at numbers like 50 times a hundred times slower, right? So then there's the Azure stack. One of the key benefits to, um, you know, that customers love about the cloud that I think is really under, appreciated it under appreciated is really that it's a single vendor solution, right? You have a problem with cloud service could be I as pass SAS doesn't matter. And there's a single vendor responsible for fixing your issue as your stack is missing big here, because they're a multi-vendor cloud solution like AWS outposts. Also, they don't exactly offer the same services in the cloud that they offer on prem. And from what I hear, it can be a management nightmare requiring specialized administrators to keep that beast running. >>Okay. So, well, thanks for that. I'll I'll grant you that, first of all, granted that Oracle was the first with that same, same vision. I always tell people that, you know, if they say, well, we were first I'm like, well, actually, no, Oracle's first having said that, Bob and I hear you that, that right now, outpost is a one Datto version. It doesn't have all the bells and whistles, but neither did your cloud when you first launched your cloud. So let's, let's let it bake for a while and we'll come back in a couple of years and see how things compare. So if you're up for it. Yeah. >>Just remember that we're still in the oven too. Right. >>Okay. All right. Good. I love it. I love the, the chutzpah. One also talked about Deutsche bank. Um, and that, I, I mean, I saw that Deutsche bank announcement, how they're working with Oracle, they're modernizing their infrastructure around database. They're building other services around that and kind of building their own sort of version of a cloud for their customers. How does exit data cloud a customer fit in to that whole Deutsche bank deal? Is, is this solution unique to Deutsche bank? Do you see other organizations adopting clouded customer for similar reasons and use cases? >>Yeah, I'll start with that. First. I want to say that I don't think Georgia bank is unique. They want what all customers want. They want to be able to run their most important workloads. The ones today running their data center on exit eight as a non other high-end systems in a cloud environment where they can benefit from things like cloud economics, cloud operations, cloud automations, but they can't move to public cloud. They need to maintain the service levels, the performance, the scalability of the security and the availability that their business has. It has come to depend on most clouds can't provide that. Although actually Oracle's cloud can our public cloud Ken, because our public cloud does run exit data, but still even with that, they can't do it because as a bank, they're subject to lots of rules and regulations, they cannot move their 40 petabytes of data to a point outside the control of their data center. >>They have thousands of interconnected databases, right? And applications. It's like a rat's nest, right? And this is similar many large customers have this problem. How do you move that to the cloud? You can move it piecemeal. Uh, I'm going to move these apps and, you know, not move those apps. Um, but suddenly ended up with these things where some pieces are up here. Some pieces are down here. The thing just dies because of the long latency over a land connection, it just doesn't work. Right. So you can also shut it down. Let's shut it down on, on Friday and move everything all at once. Unfortunately, when you're looking at it, a state decides that most customers have, you're not going to be able to, you're going to be down for a month, right? Who can, who can tolerate that? So it's a big challenge and exited cloud a customer let's then move to the cloud without losing control of their data. >>And without unhappy having to untangle that thousands of interconnected databases. So, you know, that's why these customers are choosing X data, clouded customer. More importantly, it sets them up for the future with exited cloud at customer, they can run not just in their data center, but they could also run in public cloud, adjacent sites, giving them a path to moving some work out of the data center and ultimately into the public cloud. You know, as I said, they're not unique. Other banks are watching and some are acting and it's not just banks. Just last week. Telefonica telco in Spain announced their intent to migrate the bulk of their Oracle databases to excavate a cloud at customer. This will be the key cloud platform running. They're running in their data center to support both new services, as well as mission critical and operational systems. And one last important point exited cloud a customer can also run autonomous database. Even if customers aren't today ready to adopt this. A lot of them are interested in it. They see it as a key piece of the puzzle moving forward in the future and customers know that they can easily start to migrate to autonomous in the future as they're ready. And this of course is going to drive additional efficiencies and additional cost savings. >>So, Bob, I got a question for you because you know, Oracle's playing both sides, right? You've got a cloud, you know, you've got a true public cloud now. And, and obviously you have a huge on-premise state. When I talk to companies that don't own a cloud, uh, whether it's Dell or HPE, Cisco, et cetera, they have made, they make the point. And I agree with them by the way that the world is hybrid, not everything's going into the, to the cloud. However, I had a lot of respect for folks at Amazon as well. And they believed long-term, they'll say this, they've got them on record of saying this, that they believe long-term ultimately all workloads are going to be running in the cloud. Now, I guess it depends on how you define the cloud. The cloud is expanding and all that other stuff. But my question to you, because again, you kind of on both sides, here are our hybrid solutions like cloud at customer. Do you see them as a stepping stone to the cloud, or is cloud in your data center, sort of a continuous sort of permanent, you know, essential play >>That. That's a great question. As I recall, people debated this a few years back when we first introduced clouded customer. And at that point, some people I'm talking about even internal Oracle, right? Some people saw this as a stop gap measure to let people leverage cloud benefits until they're really ready for the public cloud. But I think over the past four and a half years, the changing the thinking has changed a little bit on this. And everyone kind of agrees that clouded customer may be a stepping stone for some customers, but others see that as the end game, right? Not every workload can run in the public cloud, not at least not given the, um, you know, today's regulations and the issues that are faced by many of these regulated industries. These industries move very, very slowly and customers are content to, and in many cases required to retain complete control of their data and they will be running under their control. They'll be running with that data under their control and the data center for the foreseeable future. >>Oh, I got another question for kind of just, if I could take a little tangent, cause the other thing I hear from the, on the, the, the on-prem don't own, the cloud folks is it's actually cheaper to run in on-prem, uh, because they're getting better at automation, et cetera. When you get the exact opposite from the cloud guys, they roll their eyes. Are you kidding me? It's way cheaper to run it in the cloud, which is more cost-effective is it one of those? It depends, Bob. >>Um, you know, the great thing about numbers is you can make, you can, you can kind of twist them to show anything that you want, right? That's a have spreadsheet. Can I, can, I can sell you on anything? Um, I think that there's, there's customers who look at it and they say, oh, on-premise sheet is cheaper. And there's customers who look at it and say, the cloud is cheaper. If you, um, you know, there's a lot of ways that you may incur savings in the cloud. A lot of it has to do with the cloud economics, the ability to pay for what you're using and only what you're using. If you were to kind of, you know, if you, if you size something for your peak workload and then, you know, on prem, you probably put a little bit of a buffer in it, right? >>If you size everything for that, you're gonna find that you're paying, you know, this much, right? All the time you're paying for peak workload all the time with the cloud, of course, we support scaling up, scaling down. We supply, we support you're paying for what you use and you can scale up and scale down. That's where the big savings is now. There's also additional savings associated with you. Don't have the cloud vendors like work. Well, we manage that infrastructure for you. You no longer have to worry about it. Um, we have a lot of automation, things that you use to either, you know, probably what used to happen is you used to have to spend hours and hours or years or whatever, scripting these things yourselves. We now have this automation to do it. We have, um, you eyes that make things ad hoc things, as simple as point and click and, uh, you know, that eliminates errors. And, and it's often difficult to put a cost on those things. And I think the more enlightened customers can put a cost on all of those. So the people that were saying it's cheaper to run on prem, uh, they, they either, you know, have a very stable workload that never changes and their environment never changes, um, or more likely. They just really haven't thought through the, all the hidden costs out there. >>All right, you got some new features. Thank you for that. By the way, you got some new features in, in cloud, a customer, a what are those? Do I have to upgrade to X nine M to, to get >>All right. So, you know, we're always introducing new features for clouded customer, but two significant things that we've rolled out recently are operator access control and elastic storage expansion. As we discussed, many organizations are using Axeda cloud a customer they're attracting the cloud economics, the operational benefits, but they're required by regulations to retain control and visibility of their data, as well as any infrastructure that sits inside their data center with operator access control, enabled cloud operations, staff members must request access to a customer system, a customer, it team grants, a designated person, specific access to a specific component for a specific period of time with specific privileges, they can then kind of view audit controls in real time. And if they see something they don't like, you know, Hey, what's this guy doing? It looks like he's, he's stealing my data or doing something I don't like, boom. >>They can kill that operators, access the session, the connections, everything right away. And this gives everyone, especially customers that need to, you know, regulate remote access to their infrastructure. It gives them the confidence that they need to use exit data cloud, uh, conduct, customer service. And, and the other thing that's new is, um, elastic storage expansion. Customers could out add additional service to their system either at initial deployment or after the fact. And this really provides two important benefits. The first is that they can right size their configuration if they need only the minimum compute capacity, but they don't need the maximum number of storage servers to get that capacity. They don't need to subscribe to kind of a fixed shape. We used to have fixed shapes, I guess, with hundreds of unnecessary database cores, just to get the storage capacity, they can select a smaller system. >>And then incrementally add on that storage. The second benefit is the, is kind of key for many customers. You are at a storage, guess what you can add more. And that way, when you're out of storage, that's really important. Now they'll get to your last part of that question. Do you need a deck, a new, uh, exit aquatic customer XIM system to get these features? No they're available for all gen two exited clouded customer systems. That's really one of the best things about cloud. The service you subscribed to today just keeps getting better and better. And unless there's some technical limitation that, you know, we, and it, which is rare, most new features are available even for the oldest cloud customer systems. >>Cool. And you can bring that in on from my, my last question for you, Bob is a, another one on security. Obviously, again, we talked to Susan about this. It's a big deal. How can customer data be secure if it's in the cloud, if somebody, other than the, their own vetted employees are managing the underlying infrastructure, is is that a concern you hear a lot and how do you handle that? >>You know, it's, it's only something because a lot of these customers, they have big, you know, security people and it's their job to be concerned about that kind of stuff. And security. However, is one of the biggest, but least appreciate appreciated benefits of cloud cloud vendors, such as Oracle hire the best and brightest security experts to ensure that their clouds are secure. Something that only the largest customers can afford to do. You're a small, small shop. You're not going to be able to, you know, hire some of this expertise. So you're better off being in the cloud. Customers who are running in the Oracle cloud can also use articles, data, safe tool, which we provide, which basically lets you inspect your databases, insurance. Sure that everything is locked down and secure and your data is secure. But your question is actually a little bit different. >>It was about potential internal threats to company's data. Given the cloud vendor, not the customer's employees have access to the infrastructure that sits beneath the databases and really the first and most important thing we do to protect customers' data is we encrypt that database by default. Actually Subin listed a whole laundry list of things, but that's the one thing I want to point out. We encrypt your database. It's, you know, it's, it's encrypted. Yes. It sits on our infrastructure. Yes. Our operations persons can actually see those data files sitting on the infrastructure, but guess what? They can't see the data. The data is encrypted. All they see as kind of a big encrypted blob. Um, so they can't access the data themselves. And you know, as you'd expect, we have very tight controls over operations access to the infrastructure. They need to securely log in using mechanisms by stuff to present, prevent unauthorized access. >>And then all access is logged and suspicious. Activities are investigated, but that still may not be enough for some customers, especially the ones I mentioned earlier, the regulated industries. And that's why we offer app operator access control. As I mentioned, that gives customers complete control over the access to the infrastructure. The, when the, what ops can do, how long can they do it? Customers can monitor in real time. And if they see something they don't like they stop it immediately. Lastly, I just want to mention Oracle's data ball feature. This prevents administrators from accessing data, protecting data from road operators, robot, world operations, whether they be from Oracle or from the customer's own it staff, this database option. A lot of ball is sorry. Database ball data vault is included when running a license included service on exited clouded customer. So basically to get it with the service. Got it. >>Hi Tom. Thank you so much. It's unbelievable, Bob. I mean, we've got a lot to unpack there, but uh, we're going to give you a break now and go to Tim, Tim chin, zero data loss, recovery appliance. We always love that name. The big guy we think named it, but nobody will tell us, but we've been talking about security. There's been a lot of news around ransomware attacks. Every industry around the globe, any knucklehead with, uh, with a high school diploma could become a ransomware attack or go in the dark web, get, get ransomware as a service stick, a, put a stick in and take a piece of the VIG and hopefully get arrested. Um, with, when you think about database, how do you deal with the ransomware challenge? >>Yeah, Dave, um, that's an extremely important and timely question. Um, we are hearing this from our customers. We just talk about ha and backup strategies and ransomware, um, has been coming up more and more. Um, and the unfortunate thing that these ransoms are actually paid, um, uh, in the hope of the re you know, the, uh, the ability to access the data again. So what that means it tells me is that today's recovery solutions and processes are not sufficient to get these systems back in a reliable and timely manner. Um, and so you have to pay the ransom, right, to get, uh, to get the, even a hope of getting the data back now for databases. This can have a huge impact because we're talking about transactional workloads. And so even a compromise of just a few minutes, a blip, um, can affect hundreds or even thousands of transactions. This can literally represent hundreds of lost orders, right? If you're a big manufacturing company or even like millions of dollars worth of, uh, financial transactions in a bank. Right. Um, and that's why protecting databases at a transaction level is especially critical, um, for ransomware. And that's a huge contrast to traditional backup approaches. Okay. >>So how do you approach that? What do you, what do you do specifically for ransomware protection for the database? >>Yeah, so we have the zero data loss recovery appliance, which we announced the X nine M generation. Um, it is really the only solution in the market, which offers that transaction level of protection, which allows all transactions to be recovered with zero RPO, zero again, and this is only possible because Oracle has very innovative and unique technology called real-time redo, which captures all the transactional changes from the databases by the appliance, and then stored as well by the appliance, moreover, the appliance validates all these backups and reading. So you want to make sure that you can recover them after you've sent them, right? So it's not just a file level integrity check on a file system. That's actual database level of validation that the Oracle blocks and the redo that I mentioned can be restored and recovered as a usable database, any kind of, um, malicious attack or modification of that backup data and transmit that, or if it's even stored on the appliance and it was compromised would be immediately detected and reported by that validation. >>So this allows administrators to take action. This is removing that system from the network. And so it's a huge leap in terms of what customers can get today. The last thing I just want to point out is we call our cyber vault deployment, right? Um, a lot of customers in the industry are creating what we call air gapped environments, where they have a separate location where their backup copies are stored physically network separated from the production systems. And so this prevents ransomware for possibly infiltrating that last good copy of backups. So you can deploy recovery appliance in a cyber vault and have it synchronized at random times when the network's available, uh, to, to keep it in sync. Right. Um, so that combined with our transaction level zero data loss validation, it's a nice package and really a game changer in protecting and recovering your databases from modern day cyber threats. >>Okay, great. Thank you for clarifying that air gap piece. Cause I, there was some confusion about that. Every data protection and backup company that I know as a ransomware solution, it's like the hottest topic going, you got newer players in, in, in recovery and backup like rubric Cohesity. They raised a ton of dough. Dell has got solutions, HPE just acquired Zerto to deal with this problem. And other things IBM has got stuff. Veem seems to be doing pretty well. Veritas got a range of, of recovery solutions. They're sort of all out there. What's your take on these and their strategy and how do you differentiate? >>Yeah, it's a pretty crowded market, like you said. Um, I think the first thing you really have to keep in mind and understand that these vendors, these new and up and coming, um, uh, uh, vendors start in the copy data management, we call CDN space and they're not traditional backup recovery designed are purpose built for the purpose of CDM products is to provide these fast point in time copies for test dev non-production use, and that's a viable problem and it needs a solution. So you create these one time copy and then you create snapshots. Um, after you apply these incremental changes to that copy, and then the snapshot can be quickly restored and presented as like it's a fully populated, uh, file. And this is all done through the underlying storage of block pointers. So all of this kind of sounds really cool and modern, right? It's like new and upcoming and lots of people in the market doing this. Well, it's really not that modern because we've, we know storage, snapshot technologies has been around for years. Right. Um, what these new vendors have been doing is essentially repackaging the old technology for backup and recovery use cases and having sort of an easier to use automation interface wrapped around it. >>Yeah. So you mentioned a copy data management, uh, last year, active FIO. Uh, they started that whole space from what I recall at one point there, they value more than a billion dollars. They were acquired by Google. Uh, and as I say, they kind of created that, that category. So fast forward a little bit, nine months a year, whatever it's been, do you see that Google active FIO offer in, in, in customer engagements? Is that something that you run into? >>We really don't. Um, yeah, it was really popular and known some years ago, but we really don't hear about it anymore. Um, after the acquisition, you look at all the collateral and the marketing, they are really a CDM and backup solution exclusively for Google cloud use cases. And they're not being positioned as for on premises or any other use cases outside of Google cloud. That's what, 90, 90 plus percent of your market there that isn't addressable now by Activia. So really we don't see them in any of our engagements at this time. >>I want to come back and push it a little bit, uh, on some of the tech that you said, it's kind of really not that modern. Uh, I mean it's, if they certainly position it as modern, a lot of the engineers who are building there's new sort of backup and recovery capabilities came from the hyperscalers, whether it's copy data management, you know, the bot mock quote, unquote modern backup recovery, it's kind of a data management, sort of this nice all in one solution seems pretty compelling. How does recovery clients specifically stack up? You know, a lot of people think it's a niche product for, for really high end use cases. Is that fair? How do you see a town? >>Yeah. Yeah. So it's, I think it's so important to just, you know, understand, again, the fundamental use of this technology is to create data copies for test W's right. Um, and that's really different than operational backup recovery in which you must have this ability to do full and point in time recoverability in any production outage or Dr. Situation. Um, and then more importantly, after you recover and your applications are back in business, that performance must continue to meet servers levels as before. And when you look at a CDM product, um, and you restore a snapshot and you say with that product and the application is brought up on that restored snapshot, what happens or your production application is now running on actual read rideable snapshots on backup storage. Remember they don't restore all the data back to the production, uh, level stores. They're restoring it as a snapshot okay. >>Onto their storage. And so you have a huge difference in performance. Now running these applications where they instantly recovered, if you will database. So to meet these true operational requirements, you have to fully restore the files to production storage period. And so recovery appliance was first and foremost designed to accomplish this. It's an operational recovery solution, right? We accomplish that. Like I mentioned, with this real-time transaction protection, we have incremental forever backup strategies. So that you're just taking just the changes every day. And you, you can create these virtual full backups that are quickly restored, fully restored, if you will, at 24 terabytes an hour. And we validate and document that performance very clearly in our website. And of course we provide that continuous recovery validation for all the backups that are stored on the system. So it's, um, it's a very nice, complete solution. >>It scales to meet your demands, hundreds of thousands of databases, you know, it's, um, you know, these CDM products might seem great and they work well for a few databases, but then you put a real enterprise load and these hundreds of databases, and we've seen a lot of times where it just buckles, you know, it can't handle that kind of load in that, uh, in that scale. Uh, and, and this is important because customers read their marketing and read the collateral like, Hey, instant recovery. Why wouldn't I want that? Well, it's, you know, nicer than it looks, you know, it always sounds better. Right. Um, and so we have to educate them and about exactly what that means for the database, especially backup recovery use cases. And they're not really handled well, um, with their products. >>I know I'm like way over. I had a lot of questions on this announcement and I was gonna, I was gonna let you go, Tim, but you just mentioned something that, that gave me one more question if I may. So you talked about, uh, supporting hundreds of thousands of databases. You petabytes, you have real world use cases that, that actually leverage the, the appliance in these types of environments. Where does it really shine? >>Yeah. Let me just give you just two real quick ones. You know, we have a company energy transfer, the major natural gas and pipeline operator in the U S so they are a big part of our country's critical infrastructure services. We know ransomware, and these kinds of threats are, you know, are very much viable. We saw the colonial pipeline incident that happened, right? And so the attack, right, critical services while energy transfer was running, lots of databases and their legacy backup environments just couldn't keep up with their enterprise needs. They had backups taking like, well, over a day, they had restores taking several hours. Um, and so they had problems and they couldn't meet their SLS. They moved to the recovery appliance and now they're seeing backwards complete with that incremental forever in just 15 minutes. So that's like a 48 times improvement in backup time. >>And they're also seeing restores completing in about 30 minutes, right. Versus several hours. So it's a, it's a huge difference for them. And they also get that nice recovery validation and monitoring by the system. They know the health of their enterprise at their fingertips. The second quick one is just a global financial services customer. Um, and they have like over 10,000 databases globally and they, they really couldn't find a solution other than throw more hardware kind of approach to, uh, to fix their backups. Well, this, uh, not that the failures and not as the issues. So they moved to recovery appliance and they saw their failed backup rates go down for Matta plea. They saw four times better backup and restore performance. Um, and they have also a very nice centralized way to monitor and manage the system. Uh, real-time view if you will, that data protection health for their entire environment. Uh, and they can show this to the executive management and auditing teams. This is great for compliance reporting. Um, and so they finally done that. They have north of 50 plus, um, recovery appliances a day across that on global enterprise. >>Love it. Thank you for that. Um, uh, guys, great power panel. We have a lot of Oracle customers in our community and the best way to, to help them is to, I get to ask you a bunch of questions and get the experts to answer. So I wonder if you could bring us home, maybe you could just sort of give us the, the top takeaways that you want to your customers to remember in our audience to remember from this announcement. >>Sure, sorry. Uh, I want to actually pick up from where Tim left off and talk about a real customer use case. This is hot off the press. One of the largest banks in the United States, they decided to, that they needed to update. So performance software update on 3000 of their database instances, which are spanning 68, exited a clusters, massive undertaking, correct. They finished the entire task in three hours, three hours to update 3000 databases and 68 exited a clusters. Talk about availability, try doing this on any other infrastructure, no way anyone's going to be able to achieve this. So that's on terms of the availability, right? We are engineering in all of the aspects of database management, performance, security availability, being able to provide redundancy at every single level is all part of the design philosophy and how we are engineering this product. And as far as we are concerned, the, the goal is for forever. >>We are just going to continue to go down this path of increasing performance, increasing the security aspect of the, uh, of the infrastructure, as well as our Oracle database and keep going on this. You know, this, while these have been great results that we've delivered with extra data X nine M the, the journey is on and to our customers. The biggest advantage that you're going to get from the kind of performance metrics that we are driving with extra data is consolidation consolidate more, move, more database instances onto the extended platform, gain the benefits from that consolidation, reduce your operational expenses, reduce your capital expenses. They use your management expenses, all of those, bring it down to accelerator. Your total cost of ownership is guaranteed to go down. Those are my key takeaways, Dave >>Guys, you've been really generous with your time. Uh Subin uh, uh, uh, Bob, Tim, I appreciate you taking my questions and we'll willingness to go toe to toe, really? Thanks for your time. >>You're welcome, David. Thank you. Thank you. >>And thank you for watching this video exclusive from the cube. This is Dave Volante, and we'll see you next time. Be well.

>>from around the globe. It's the Cube with digital coverage of data automated and event. Siri's Brought to You by Iot Tahoe Welcome, everyone to the second episode in our data automated Siri's made possible with support from Iot Tahoe. Today we're gonna drill into the data lifecycle, meaning the sequence of stages that data travels through from creation to consumption to archive. The problem, as we discussed in our last episode, is that data pipelines, they're complicated, They're cumbersome, that disjointed, and they involve highly manual processes. Ah, smart data lifecycle uses automation and metadata to approve agility, performance, data quality and governance and ultimately reduce costs and time to outcomes. Now, in today's session will define the data lifecycle in detail and provide perspectives on what makes a data lifecycle smart and importantly, how to build smarts into your processes. In a moment, we'll be back with Adam Worthington from ethos to kick things off, and then we'll go into an export power panel to dig into the tech behind smart data life cycles, and it will hop into the crowdchat and give you a chance to ask questions. So stay right there. You're watching the cube innovation impact influence. Welcome >>to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. >>High tech digital coverage. Okay, we're back with Adam Worthington. Adam, good to see you. How are things across the pond? >>Thank you, I'm sure. >>Okay, so let's let's set it up. Tell us about yourself. What? Your role is a CTO and >>automatically. As you said, we found a way to have a pretty in company ourselves that we're in our third year on. Do we specialize in emerging disruptive technologies within the infrastructure? That's the kind of cloud space on my phone is the technical lead. So I kind of my job to be an expert in all of the technologies that we work with, which can be a bit of a challenge if you have a huge for phone is one of the reasons, like deliberately focusing on on also kind of pieces a successful validation and evaluation of new technologies. >>So you guys really technology experts, data experts and probably also expert in process and delivering customer outcomes. Right? >>That's a great word there, Dave Outcomes. That's a lot of what I like to speak to customers about. >>Let's talk about smart data, you know, when you when you throw in terms like this is it kind of can feel buzz, wordy. But what are the critical aspects of so called smart data? >>Help to step back a little bit, seen a little bit more in terms of kind of where I can see the types of problems I saw. I'm really an infrastructure solution architect trace on and what I kind of benefit we organically. But over time my personal framework, I focused on three core design principal simplicity, flexibility, inefficient, whatever it was designing. And obviously they need different things, depending on what the technology area is working with. But that's a pretty good. So they're the kind of areas that a smart approach to data will directly address. Reducing silos that comes from simplifying, so moving away from conflict of infrastructure, reducing the amount of copies of data that we have across the infrastructure and reducing the amount of application environments that need different areas so smarter get with data in my eyes anyway, the further we moved away from this. >>But how does it work? I mean, how do you know what's what's involved in injecting smarts into your data lifecycle? >>I think one of my I actually did not ready, but generally one of my favorite quotes from the French lost a mathematician, Blaise Pascal. He said, If I get this right, I have written a short letter, but I didn't have time. But Israel, I love that quite for lots of reasons >>why >>direct application in terms of what we're talking about, it is actually really complicated. These developers technology capabilities to make things simple, more directly meet the needs of the business. So you provide self service capabilities that they just need to stop driving. I mean, making data on infrastructure makes the business users using >>your job. Correct me. If I'm wrong is to kind of put that all together in a solution and then help the customer realize that we talked about earlier that business out. >>Yeah, enough if they said in understanding both sides so that it keeps us on our ability to deliver on exactly what you just said is big experts in the capabilities and new a better way to do things but also having the kind of the business understanding to be able to ask the right questions. That's how new a better price is. Positions another area that I really like his stuff with their platforms. You can do more with less. And that's not just about using data redundancy. That's about creating application environments, that conservative and then the infrastructure to service different requirements that are able to use the random Io thing without getting too kind of low level as well as the sequential. So what that means is you don't necessarily have to move data from application environment a do one thing related, and then move it to the application environment. Be that environment free terms of an analytics on the left Right works. Both keep the data where it is, use it or different different requirements within the infrastructure and again do more with less. And what that does is not just about simplicity and efficiency. It significantly reduces the time to value of that as well. >>Do you have examples that you can share with us even if they're anonymous customers that you work with that are maybe a little further down on the journey. Or maybe not >>looking at the you mentioned data protection earlier. So another organization This is a project which is just kind of hearing confessions moment, huge organization. They're literally petabytes of data that was servicing their back up in archive. And what they have is not just this realization they have combined. I think I different that they have dependent on the what area of infrastructure they were backing up, whether it was virtualization, that was different because they were backing up PC's June 6th. They're backing up another database environment, using something else in the cloud knowledge bases approach that we recommended to work with them on. They were able to significantly reduce complexity and reduce the amount of time that it systems of what they were able to achieve and what this is again. One of the clients have They've gone above the threshold of being able to back up for that. >>Adam, give us the final thoughts, bring us home. In this segment, >>the family built something we didn't particularly such on, that I think it is really barely hidden. It is spoken about as much as I think it is, that agile approaches to infrastructure we're going to be touched on there could be complicated on the lack of it efficient, the impact, a user's ability to be agile. But what you find with traditional approaches and you already touched on some of the kind of benefits new approaches there. It's often very prescriptive, designed for a particular as the infrastructure environment, the way that it served up the users in kind of a packaged. Either way, it means that they need to use it in that whatever wave in data bases, that kind of service of as it comes in from a flexibility standpoint. But for this platform approach, which is the right way to address technology in my eyes enables, it's the infrastructure to be used. Flexible piece of it, the business users of the data users what we find this capability into their innovating in the way they use that on the White House. I bring benefits. This is a platform to prescriptive, and they are able to do that. What you're doing with these new approaches is all of the metrics that we touched on and pass it from a cost standpoint from a visibility standpoint, but what it means is that the innovators in the business want really, is to really understand what they're looking to achieve and now have to to innovate with us. Now, I think I've started to see that with projects season places. If you do it in the right way, you articulate the capability and empower the business users in the right ways. Very significantly. Better position. The advantages on really matching significantly bigger than their competition. Yeah, >>Super Adam in a really exciting space. And we spent the last 10 years gathering all this data, you know, trying to slog through it and figure it out. And now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insights. So, Adam or they didn't thanks so much for coming on the Cube and participating in this program. >>Exciting times with that. Thank you very much Today. >>Now we're going to go into the power panel and go deeper into the technologies that enable smart data life cycles. Stay right there. You're watching the cube. Are >>you interested in test driving? The i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up a service and support provided by Iot. Top. Click on the Link and connect with the data engineer to learn more and see Iot Tahoe in action. >>Welcome back, everybody to the power panel driving business performance with smart data life cycles. Leicester Waters is here. He's the chief technology officer from Iot Tahoe. He's joined by Patrick Smith, who was field CTO from pure storage. And is that data? Who's a system engineering manager at KohI City? Gentlemen, good to see you. Thanks so much for coming on this panel. >>Thank you. >>Let's start with Lester. I wonder if each of you could just give us a quick overview of your role. And what's the number one problem that you're focused on solving for your customers? Let's start with Lester Fleet. >>Yes, I'm Lost Waters, chief technology officer for Iot Tahoe and really the number one problem that we're trying to solve for our customers is to understand, help them understand what they have, because if they don't understand what they have in terms of their data. They can't manage it. They can't control it. The cap monitor. They can't ensure compliance. So really, that's finding all you can about your data that you have. And building a catalog that could be readily consumed by the entire business is what we do. >>Patrick Field, CTO in your title That says to me, You're talking to customers all the time, so you got a good perspective on it. Give us your take on things here. >>Yeah, absolutely. So my patches in here on day talkto customers and prospects in lots of different verticals across the region. And as they look at their environments and their data landscape, they're faced with massive growth in the data that they're trying to analyze and demands to be able to get insight our stuff and to deliver better business value faster than they've ever had to do in the past. So >>got it. And is that of course, Kohi City. You're like the new kid on the block. You guys were really growing rapidly created this whole notion of data management, backup and and beyond. But I'm assistant system engineering manager. What are you seeing from from from customers your role and the number one problem that you're solving. >>Yeah, sure. So the number one problem I see time and again speaking with customers. It's around data fragmentation. So do two things like organic growth, even maybe budgetary limitations. Infrastructure has grown over time very piecemeal, and it's highly distributed internally. And just to be clear, you know, when I say internally, that >>could be >>that it's on multiple platforms or silos within an on Prem infrastructure that it also does extend to the cloud as well. >>Right Cloud is cool. Everybody wants to be in the cloud, right? So you're right, It creates, Ah, maybe unintended consequences. So let's start with the business outcome and kind of try to work backwards to people you know. They want to get more insights from data they want to have. Ah, Mawr efficient data lifecycle. But so let's let me start with you were thinking about like the North Star for creating data driven cultures. You know, what is the North Star or customers >>here? I think the North Star, in a nutshell, is driving value from your data. Without question, I mean way, differentiate ourselves these days by even nuances in our data now, underpinning that, there's a lot of things that have to happen to make that work out. Well, you know, for example, making sure you adequately protect your data, you know? Do you have a good You have a good storage sub system? Do you have a good backup and recovery point objectives? Recovery time objective. How do you Ah, are you fully compliant? Are you ensuring that you're taking all the boxes? There's a lot of regulations these days in terms with respect to compliance, data retention, data, privacy and so forth. Are you taking those boxes? Are you being efficient with your, uh, your your your data? You know, In other words, I think there's a statistic that someone mentioned me the other day that 53% of all businesses have between three and 15 copies of the same data. So you know, finding and eliminating does is it is part of the part of the problem is when you do a chase, >>um, I I like to think of you're right, no doubt, business value and and a lot of that comes from reducing the end in cycle times. But anything that you guys would would add to that. Patrick, Maybe start with Patrick. >>Yeah, I think I think in value from your data really hits on tips on what everyone wants to achieve. But I think there are a couple of key steps in doing that. First of all, is getting access to the data and asked that, Really, it's three big problems, firstly, working out what you've got. Secondly, looking at what? After working on what you've got, how to get access to it? Because it's all very well knowing that you've got some data. But if you can't get access to it either because of privacy reasons, security reasons, then that's a big challenge. And then finally, once you've got access to the data making sure that you can process that data in a timely manner >>for me, you know it would be that an organization has got a really good global view of all of its data. It understands the data flow and dependencies within their infrastructure, understands that precise legal and compliance requirements, and you had the ability to action changes or initiatives within their environment to give the fun. But with a cloud like agility. Um, you know, and that's no easy feat, right? That is hard work. >>Okay, so we've we've talked about. The challenge is in some of the objectives, but there's a lot of blockers out there, and I want to understand how you guys are helping remove them. So So, Lester. But what do you see as some of the big blockers in terms of people really leaning in? So this smart data lifecycle >>yeah, Silos is is probably one of the biggest one I see in business is yes, it's it's my data, not your data. Lots of lots of compartmentalization. Breaking that down is one of the one of the challenges. And having the right tools to help you do that is only part of the solution. There's obviously a lot of cultural things that need to take place Teoh to break down those silos and work together. If you can identify where you have redundant data across your enterprise, you might be able to consolidate those. >>So, Patrick, so one of the blockers that I see is legacy infrastructure, technical debt, sucking all the budget you got. You know, too many people have having to look after, >>as you look at the infrastructure that supports people's data landscapes today for primarily legacy reasons. The infrastructure itself is siloed. So you have different technologies with different underlying hardware and different management methodologies that they're there for good reason, because historically you have to have specific fitness, the purpose for different data requirements. And that's one of the challenges that we tackled head on a pure with with the flash blade technology and the concept of the data, a platform that can deliver in different characteristics for the different workloads. But from a consistent data platform >>now is that I want to go to you because, you know, in the world in your world, which to me goes beyond backup. And one of the challenges is, you know, they say backup is one thing. Recovery is everything, but as well. The the CFO doesn't want to pay for just protection, and one of things that I like about what you guys have done is you. You broadened the perspective to get more value out of your what was once seen as an insurance policy. >>I do see one of the one of the biggest blockers as the fact that the task at hand can, you know, can be overwhelming for customers. But the key here is to remember that it's not an overnight change. It's not, you know, a flick of a switch. It's something that can be tackled in a very piecemeal manner on. Absolutely. Like you said, You know, reduction in TCO and being able to leverage the data for other purposes is a key driver for this. So, you know, this can be this can be resolved. It would be very, you know, pretty straightforward. It can be quite painless as well. Same goes for unstructured data, which is very complex to manage. And, you know, we've all heard the stats from the the analysts. You know, data obviously is growing at an extremely rapid rate, but actually, when you look at that, you know how is actually growing. 80% of that growth is actually in unstructured data, and only 20% of that growth is in unstructured data. S o. You know, these are quick win areas that customers can realize immediate tco improvement and increased agility as well >>paint a picture of this guy that you could bring up the life cycle. You know what you can see here is you've got this this cycle, the data lifecycle and what we're wanting to do is inject intelligence or smarts into this, like like life cycles. You see, you start with ingestion or creation of data. You're you're storing it. You got to put it somewhere, right? You gotta classify it. You got to protect it. And then, of course, you want to reduce the copies, make it, you know, efficient on. And then you want to prepare it so that businesses can actually sumit. And then you've got clients and governance and privacy issues, and I wonder if we could start with you. Lester, this is, you know, the picture of the life cycle. What role does automation play in terms of injecting smarts into the lifecycle? >>Automation is key here, especially from the discover it catalog and classify perspective. I've seen companies where they geo and will take and dump their all of their database scheme is into a spreadsheet so that they can sit down and manually figure out what attributes 37 means for a column names, Uh, and that's that's only the tip of the iceberg. So being able to do automatically detect what you have automatically deduced where what's consuming the data, you know, upstream and downstream. Being able to understand all of the things related to the lifecycle of your data. Back up archive deletion. It is key. And so we're having having good tool. IShares is very >>important. So, Patrick, obviously you participate in the store piece of this picture s I wonder if you could talk more specifically about that. But I'm also interested in how you effect the whole system view the the end end cycle time. >>Yeah, I think Leicester kind of hit the nail on the head in terms of the importance of automation because the data volumes are just just so massive. Now that you can, you can you can effectively manage or understand or catalog your data without automation. Once you understand the data and the value of the data, then that's where you can work out where the data needs to be at any point in >>time, right? So pure and kohi city obviously partner to do that and of course, is that you guys were part of the protect you certainly part of the retain. But Also, you provide data management capabilities and analytics. I wonder if you could add some color there. >>Yeah, absolutely. So, like you said, you know, we focused pretty heavily on data protection. Is just one of our one of our areas on that infrastructure. It is just sitting there, really? Can, you know, with the legacy infrastructure, It's just sitting there, you know, consuming power, space cooling and pretty inefficient. And what, if anything, that protest is a key part of that. If I If I have a modern data platform such as, you know, the cohesive data platform, I can actually do a lot of analytics on that through application. So we have a marketplace for APS. >>I wonder if we could talk about metadata. It's It's increasingly important. Metadata is data about the data, but Leicester maybe explain why it's so important and what role it plays in terms of creating smart data lifecycle. A >>lot of people think it's just about the data itself, but there's a lot of extended characteristics about your data. So so imagine if or my data life cycle I can communicate with the backup system from Kohi City and find out when the last time that data was backed up or where is backed up to. I can communicate exchange data with pure storage and find out what two years? And is the data at the right tier commensurate with its use level pointed out and being able to share that metadata across systems? I think that's the direction that we're going in right now. We're at the stage where just identifying the metadata and trying to bring it together and catalog the next stage will be OK using the AP eyes it that that we have between our systems can't communicate and share that data and build good solutions for customers to use. >>It's a huge point that you just made. I mean, you know, 10 years ago, automating classification was the big problem, and it was machine intelligence, you know, obviously attacking that, But your point about as machines start communicating to each other and you start, it's cloud to cloud. There's all kinds of metadata, uh, kind of new meta data that's being created. I often joke that someday there's gonna be more metadata than data, so that brings us to cloud and that I'd like to start with you. >>You know, I do think, you know, having the cloud is a great thing. And it has got its role to play, and you can have many different permutations and iterations of how you use it on. Um, you know, I may have sort of mentioned previously. You know, I've seen customers go into the cloud very, very quickly, and actually recently, they're starting to remove workloads from the cloud. And the reason why this happens is that, you know, Cloud has got its role to play, but it's not right for absolutely everything, especially in their current form as well. A good analogy I like to use on this may sound a little bit cliche, but you know, when you compare clouds versus on premises data centers, you can use the analogy of houses and hotels. So to give you an idea so you know, when we look at hotels, that's like the equivalent of a cloud, right? I can get everything I need from there. I can get my food, my water, my outdoor facilities. If I need to accommodate more people, I can rent some more rooms. I don't have to maintain the hotel. It's all done for me. When you look at houses the equivalent to on premises infrastructure, I pretty much have to do everything myself, right. So I have to purchase the house. I have to maintain it. I have to buy my own food and water. Eat it. You have to make improvements myself. But then why do we all live in houses? No, in hotels. And the simple answer that I can I can only think of is, is that it's cheaper, right. It's cheaper to do it myself. But that's not to say that hotels haven't got their role to play. Um, you know? So, for example, if I've got loads of visitors coming over for the weekend, I'm not going to go build an extension to my house just for them. I will burst into my hotel into the cloud, um, and use it for, you know, for for things like that. So what I'm really saying is the cloud is great for many things, but it can work out costlier for certain applications, while others are a perfect >>It's an interesting analogy. I hadn't thought of that before, but you're right because I was going to say Well, part of it is you want the cloud experience everywhere, but you don't always want the cloud experience especially, you know, when you're with your family, you want certain privacy that I've not heard that before. He's out. So that's the new perspective s Oh, thank you, but but But Patrick, I do want to come back to that cloud experience because, in fact, that's what's happening. In a lot of cases, organizations are extending the cloud properties of automation on Prem. >>Yeah, I thought, as I thought, a really interesting point and a great analogy for the use of the public cloud. And it really reinforces the importance of the hybrid and multi cloud environment because it gives you the flexibility to choose where is the optimal environment to run your business workloads? And that's what it's all about and the flexibility to change which environment you're running in, either for more months to the next or from one year to the next. Because workloads change and the characteristics that are available in the cloud change, the hybrid cloud is something that we've we've lived with ourselves of pure, So our pure one management technology actually sits in hybrid cloud and what we we started off entirely cloud native. But now we use public cloud for compute. We use our own technology at the end of a high performance network link to support our data platform. So we get the best of both worlds and I think that's where a lot of our customers are trying to get to. >>Alright, I want to come back in a moment there. But before we do, let's see, I wonder if we could talk a little bit about compliance, governance and privacy. I think the Brits hung on. This panel is still in the EU for now, but the you are looking at new rules. New regulations going beyond GDP are where does sort of privacy governance, compliance fit in the data lifecycle, then, is that I want your thoughts on this as well. >>Yeah, this is this is a very important point because the landscape for for compliance, around data privacy and data retention is changing very rapidly. And being able to keep up with those changing regulations in an automated fashion is the only way you're gonna be able to do it. Even I think there's a some sort of Ah, maybe ruling coming out today or tomorrow with the changed in the r. So this is things are all very key points and being able to codify those rules into some software. Whether you know, Iot Tahoe or or your storage system or kohi city, it will help you be compliant is crucial. >>Yeah. Is that anything you can add there? I mean, it's really is your wheelhouse. >>Yeah, absolutely. So, you know, I think anybody who's watching this probably has gotten the message that, you know, less silos is better. And it absolutely it also applies to data in the cloud is where as well. So you know, my aiming Teoh consolidate into fewer platforms, customers can realize a lot better control over their data. And the natural effect of this is that it makes meeting compliance and governance a lot easier. So when it's consolidated, you can start to confidently understand who's accessing your data. How frequently are they accessing the data? You can also do things like, you know, detecting anomalous file access activities and quickly identify potential threats. >>Okay, Patrick, we were talking. You talked earlier about storage optimization. We talked to Adam Worthington about the business case, the numerator, which is the business value, and then the denominator, which is the cost and what's unique about pure in this regard. >>Yeah, and I think there are. There are multiple time dimensions to that. Firstly, if you look at the difference between legacy storage platforms that used to take up racks or aisles of space in the data center, the flash technology that underpins flash blade way effectively switch out racks rack units on. It has a big play in terms of data center footprint, and the environmental is associated with the data center. If you look at extending out storage efficiencies and the benefits it brings, just the performance has a direct effect on start we whether that's, you know, the start from the simplicity that platform so that it's easy and efficient to manage, whether it's the efficiency you get from your data. Scientists who are using the outcomes from the platform, making them more efficient to new. If you look at some of our customers in the financial space there, their time to results are improved by 10 or 20 x by switching to our technology from legacy technologies for their analytics, platforms. >>The guys we've been running, you know, Cube interviews in our studios remotely for the last 120 days is probably the first interview I've done where haven't started off talking about Cove it, Lester. I wonder if you could talk about smart data lifecycle and how it fits into this isolation economy. And hopefully, what will soon be a post isolation economy? >>Yeah, Come. It has dramatically accelerated the data economy. I think. You know, first and foremost, we've all learned to work at home. You know, we've all had that experience where, you know, people would have been all about being able to work at home just a couple days a week. And here we are working five days. That's how to knock on impact to infrastructure, to be able to support that. But going further than that, you know, the data economy is all about how a business can leverage their data to compete in this New World order that we are now in code has really been a forcing function to, you know, it's probably one of the few good things that have come out of government is that we've been forced to adapt and It's a zoo. Been an interesting journey and it continues to be so >>like Lester said, you know, we've We're seeing huge impact here. Working from home has pretty much become the norm. Now, you know, companies have been forced into basically making it work. If you look online retail, that's accelerated dramatically as well. Unified communications and videoconferencing. So really, you know the point here, is that Yes, absolutely. We're you know, we've compressed, you know, in the past, maybe four months. What already would have taken maybe even five years, maybe 10 years or so >>We got to wrap. But Celester Louis, let me ask you to sort of get paint. A picture of the sort of journey the maturity model that people have to take. You know, if they want to get into it, where did they start? And where are they going to give us that view, >>I think, versus knowing what you have. You don't know what you have. You can't manage it. You can't control that. You can't secure what you can't ensure. It's a compliant s so that that's first and foremost. Uh, the second is really, you know, ensuring that your compliance once, once you know what you have. Are you securing it? Are you following the regulatory? The applicable regulations? Are you able to evidence that, uh, how are you storing your data? Are you archiving it? Are you storing it effectively and efficiently? Um, you know, have you Nirvana from my perspective, is really getting to a point where you you've consolidated your data, you've broken down the silos and you have a virtually self service environment by which the business can consume and build upon their data. And really, at the end of the day, as we said at the beginning, it's all about driving value out of your data. And ah, the automation is is key to this, sir. This journey >>that's awesome and you just described is sort of a winning data culture. Lester, Patrick, thanks so much for participating in this power panel. >>Thank you, David. >>Alright, So great overview of the steps in the data lifecycle and how to inject smarts into the process is really to drive business outcomes. Now it's your turn. Hop into the crowd chat, please log in with Twitter or linked in or Facebook. Ask questions, answer questions and engage with the community. Let's crowdchat, right. Yeah, yeah, yeah.

live from Las Vegas it's the cube covering AWS reinvent 2018 brought to you by Amazon Web Services Intel and their ecosystem partners welcome back everyone live here the cube coverage at Amazon Web service AWS reinvent 2018 our sixth year covering Amazon now 52,000 lost people here packed house this is where the industry gathers to really kind of check out the future where the state of the cloud business is what it means to enterprise I'm John Fourier the post of the cube with Lauren Cooney co-host me.we this week on set one of two sets here our next two guests Jeff monk resulting systems engineer stealthWatch cloud that's now part of Cisco Systems and Earth has been Product Marketing Manager jetbrains welcome to the cube guys thanks for coming on thanks launched six years now we've been covering Amazon we were here when kind of people didn't really understand what it was we saw here so Jerry Chen just gave him a venture capitalist and Braille app and we're like this is gonna be big it's big but the big news here this week is on premises okay you guys cisco you own premises with routing networking developers of programming applications in the cloud needs to run on premise it's a big theme it's all kind of coming together it's kind of first validation this year that on-premises is not going away and cloud is becoming more prevalent for data and analytics for coding for DevOps but now working seamlessly together you guys agree with this recently announced the deal with AWS right you have networking which the critical part of the holy trinity of infrastructure network storage compute powering a new class of software development and tools what's your view on this I mean give us a take yeah so from a Cisco stealthWatch standpoint like you said we see that customers are not necessarily going away from on-premise deployments a lot of organizations have got large data centers and Colo facilities they still run all right and they've also got workloads in the public cloud so what we see is you know any some kind of mixture of organizations that have still got bare metal servers and virtual machines on premise that they need visibility into and one protect then they've also got public cloud workloads that are virtual machines but then they've gone beyond virtual machines and there are things like micro services and server lists and containers and they need a solution that can protect all those different environments and that's what stealthWatch comes into play and i want to get you guys saw it on on this because i'll see now security used to be a blocker for cloud it can't put seven the cloud skids not secure now security is their baseline at least needs more work you've got to have that visibility and you guys have a programmable strategy for the network is now coding be pcs is becoming more important than ever before right how is security evolving as compute start to get more powerful storage of storage data it's not going away it's only growing with IOT and IOT edge with connectivity networking now has to up its game write an application of elves don't want anything to do with all that anymore they want to just program so what's this mean for people what are security right for security yeah so what we're seeing and I mentioned a second ago was the expansion into micro services serverless cloud native if you will and organizations are continuing to go that route but what they don't realize is as they expand into those different technologies they're actually increased creating an increasing attack surface if you will right they're not really thinking about that and what they're doing is opening up multiple new points out to the internet that are vulnerable and it to exposure and risk right so they're not thinking about securing those new environments that are deploying and that's where we come into play also awesome let's talk about jet Breen what do you guys do what's the relationship with Cisco how do you fit in what's the story so let me start with introducing jetbrains little but you're just talking about all these various spaces where people have to run their code nowadays yeah if you want to develop for all these environments you need tools that allow you to develop for all these environments at JetBrains that were tooling professionals what we do we are software developers we make tools for software developers we really want to give the developer all this power in their hands to be able to develop insight for example containers and step through their code as they go inside these environments of course our own products and our own services they are all a lot of them are hosts on AWS and Cisco comes in there and healthy let's make sure that all of our servants that we have online remains secure and the relationship with Cisco is part of the go-to-market you guys share products together what's the relationship as jetbrains is actually a stealthWatch customer they've been a customer for a few years now and we actually protect all of their Amazon workloads they've got deployed in the Amazon infrastructure anything from ec2 instances to RDS redshift lambdas pretty much any sort of service that they're using from a compute standpoint in Amazon stealthWatch cause in protecting for a few years now so with kubernetes and now lambda the old days was was still grade you spit up an instance ten seconds lambda you can do this in really really high velocity how does that change the tooling how does it impact your world it's a customer so for us as the customer self watch it impacts us that we have to of course make sure that whenever these lambdas fire we know what's going on and we can see what's happening and one of the things we really want to do within Jefferson we want to give our developers we want to empower they want to make sure that they can experiment that they can make new things and it's all Excel what really helps us make sure that when our developers are out there doing things we can still maintain that we're following the best practices and everything stay secure how does automation guys weave in because kubernetes is a big battleground right now we're seeing important one as orchestrating and managing cluster certainly the state of application data unstated applications also with AP is obviously growing visibility is critical but automations may be right around the horizon ku Bernays at some point gonna be automated away and if so what's that looked like from software standpoint because yeah it's dynamic now so what we see from a kubernetes and a container orchestration perspective is that the kubernetes itself is designed to do the automation all right it's elastic expand and contract right but what you may be looking at today is a small kubernetes cluster with a couple of nodes and a couple dozen pods then all sudden tomorrow based on load you could be looking at hundreds of nodes and thousands of pots a massively increased attack surface if you will it right there's a building into and trying to figure out what's going on there right stealth watts cloud luckily we're there we're in kubernetes today and what we do is we deploy automatically in the kubernetes environment and in a way that allows us to expand with you automatically so as your cluster expands we will give you complete visibility into everything that's moving east west in kubernetes as well as north south so it's a very simple deployment doesn't matter where kubernetes lives we've got you covered if people are going to download stealthWatch from the catalog right what is it how would you describe right so stealthWatch cloud it is a SAS offering all right so we get asked that a lot just today over in the booth you know we've got a lot of questions about where do we put our sensors where do you put the collectors people if they're having a hard time wrapping their heads around the fact that it's straight API calls okay we're bringing in cloud trail we're bringing in I am and cloud watch BPC flow logs right and we're bringing it all in all automated over the API AWS - AWS where we live and it is a SAS billing offering writes if there's nothing that you have to go deploy it's a 5-minute integration you can buy it right there on the AWS marketplace like you said for public or private network monitoring and it's a subscription billing so it's a true SAS you're looking to kind of expand you know your footprint in this space with kubernetes is there any thought of you know some sort of code donation to kubernetes to actually increase your footprint among users and get them more engaged or is that something that you you know talked about thought about things like that donating code donating some code yeah I don't honestly don't think there's anything that we've ever discussed about donating commenting like that what about you guys are donating code to the kubernetes project well just to increase your footprint right so you would have available as a component of kubernetes and people would put into there great idea yeah yeah it's not something that I know that we discussed but yeah I mean if we could deploy something that would be open source that we actually part of that project that would be a huge visibility for us and I think that's big sensitive you look at what's going on in Cisco whether things like to give you guys a prop here is that the def net developer community has really taken - cloud native and with definite create dev net at Cisco live and Cisco Barcelona we've been this past year what a sea change I mean you got command line interface dudes going hey I need to be dashboard oriented meaning I gotta automate stuff so the notion of programming the network it's not a foreign concept to network engineers they're pretty smart right they get things so how is this world of all I mean how is the persona of a Cisco customer that needs to get more software development shops going what's it like I mean is there future dashboards as their future gonna be scripts event alerts let me manage it so how do you guys see that persona evolving I think what we see and you can probably relate to this also erst is that more and more organizations it doesn't matter how averse they are to cloud and new development technologies more organizations are going towards a DevOps oh yeah framework with C ICD constant continuous integration and continuous delivery right so it's hard to avoid the fact that that's where the paradigm is shifting and in doing so as we move into more cloud native and serverless capabilities you're looking at things that don't get necessarily involved operating systems and IP addresses and traditional endpoints and that's where most organizations are going so and so from a security perspective we've got to go there also know about your relationship with just as a customer are you happy what's it like how's the product so if I were very happy we've had some great experiences with the onboarding of stealthWatch cloud yeah we had some of course you know as you're starting to get started we needed a little bit of assistance getting used to the tool and getting started and getting anything configured the support was very helpful and they really helped us get started and then at some point we actually did some of this cloud automation and we set up terraform scripts so we could actually automatically configure stealthWatch cloud into many of our AWS accounts great great stuff final question for Cisco what's next for you guys on the product side anything going on give a quick plug of what's happened yeah I'd say what's next for us from a stealth watch cloud standpoint is you're going to see more integration with the Cisco portfolio we're integrating with the Cisco identity services engine integrating with the next-gen firewall integrating with the new encrypted traffic analytics that you've probably discussed here on the cube before so it's a tiger portfolio integration because that really sets us apart awesome guys thanks for coming on the key appreciate the insight good to see a customer here thanks for coming I appreciate very good job kubernetes at the head start as at the center of all the action with developers cluster man has been scaling up lamda server list this is the really the fasting programming gold networks is key the queue bringing all the coverage here live in Las Vegas for 80 bus reinvent 2018 I'm Shepard Lauren Cooney stay with us for more coverage after this short break [Music]

>> Hey, welcome back, everybody. Jeff Frick here with theCUBE. It's International Women's Day 2018, there's stuff going on all around the world. We're up at the Accenture event at downtown San Fancisco. 400 people at the Hotel Nikko, lot of great panels, a lot of interesting conversations, a lot of good energy. Really about diversity and inclusion and not just cause it's the right thing to do, but it actually drives better business outcomes. Hm, how about that? So we're really excited to have our next guest, it's Vivienne Ming. She's a founder and chair of Socos Labs, Vivienne, welcome. >> It's a pleasure to be here. >> Yeah, so what is Socos Labs? >> So, Socos Labs is a think tank, it's my fifth company, because apparently, I can't seem to take a hint. And we are using artificial intelligence and neuroscience and economic theory to explore the future of what it means to be human. >> So who do you work with? Who are some of your clients? >> So we partner with enormous and wonderful groups around the world, for example, we're helping the Make A Wish Foundation help kids make better wishes, so we preserve what's meaningful to the child, but try and make it even more resonant with the community and the family that's around them. We've done wonderful work here with Accenture to look at what actually predicts the best career and life outcomes, and use that to actually help their employees. Not for Accenture's sake, but for the 425,000 people get to live better, richer lives. >> Right, right. That's interesting, cause that's really in line with that research that they released today, you know, what are these factors, I think they identified 40 that have a significant impact, and then a sub set of 14 within three buckets, it's very analytical, it's very center, it's great. >> I love numbers. I'm you know, by training, I'm a theoretical neuroscientist, which is a field where we study machine learning to better understand the brain, and we study the brain to come up with better machine learning. And then I started my first company in education, and to me, it's always about, not even just generating a bunch of numbers, but figuring out what actually makes a difference. What can you do? In education, in mental health, in inclusion, or just on the job, that will actually drive someone to a better life outcome. And one of those outcomes is they're more productive. >> Right, right. >> And they're more engaged on the job, more creative. You know, a big driver behind what I do is the incredible research on how many, it's called the Lost Einsteins Research. >> The Lost Einsteins. >> Lost Einsteins. >> So a famous economist, Raj Chetty at Stanford just released a new paper on this, showing that kids from high wealth backgrounds, are 10 times as likely as middle class peers to, for example, have patents or to have that big impact in people's lives. In our research, we find the same thing, but on the scales of orders of magnitude difference. What if every little kid in Oakland, or in Johannesburg, or in a rural village in India, had the same chances I had to invent and contribute. That's the world I want to live in. It's wonderful working with a group like Accenture, the Lego Foundation, the World Bank, that agree that that really matters. >> Right, it's just interesting, the democratization theme comes up over and over and over, and it's really not that complicated of a thing, right? If you give more people access to the data, more people access to the tools, it'd make it easier for them to manipulate the data, you're just going to get more innovation, right? It's not brain surgery. >> You get more people contributing to what we sometimes call the creative class, which you know, right now, probably is about 1.5% of the world population. Maybe 150, 200 million people, it sounds like a big number, but we're pushing eight billion. What would the world be like not if all of them, just imagine instead of 200 million people, it was 400. Or it was a billion people, what would the world be like if a billion people had the chance to really drive the good in our lives. So on my panel, I had the chance to throw out this line that I was quoted as saying once. "Ambitious men have been promising us rocket ships and AI, "and self-driving cars, "but if every little girl had been given the reins "to her own potential, we'd already have them". And we don't talk not just about every little girl, but every little kid. >> Right, right. >> That doesn't have the chance. You know, if even one percent of them had that chance, it would change the world. >> So you must be a happy camper in the world though, rendering today with all the massive compute, cloud delivery and compute and store it to anyone, I mean, all those resources asymptotically approaching zero cost and availability via cloud anywhere in this whole big data revolution, AI and machine learning. >> I love it. I mean, I wouldn't build AI, which that's, I'm a one trick pony in some sense. I do a lot of different work, but there's always machine learning under the hood for my companies. And my philanthropic work. But I think there is something as important as amazing a tool as it is, the connectivity, the automation, the artificial intelligence as a perhaps dominant tool of the future, is still just a tool. >> Jeff: Right. >> These are messy human problems, they will only ever have messy human solutions. But now, me as a scientist can say, "Here's a possible solution". And then me as an entrepreneur, or a philanthropist, can say, "Great. "Now with something like AI, we can actually share that "solution with everybody". >> Right. So give us a little bit of some surprise insights that came out of your panel, for which I was not able to attend, I was out here doing interviews. >> So you know, I would say the theme of our panel was about role modeling. >> So I was the weirdo outlier on the panel, so we had Oakland mayor Libby Schaaf, we had the CFO of the Warriors, Jennifer was great, and we talked about simply being visible, and doing the work that we do in AI, in sports, in politics. That alone changes people's lives, which is a well studied phenomenon. The number one predictor of a kid from an underrepresented population, taking a scholarship, you know, believing they can be successful in politics is someone from their neighborhood went before them and showed them that it was possible. >> And seeing somebody that looks like them in that role. >> And so seeing a CFO of the Warriors, one of the great sports teams in the world today... >> Right. >> Is you know, this little Filipino woman, to put it in the way I think other people would perceive her and realize no, she does the numbers, she drives the company, and it's not despite who she is, it's because she brought something unique to the table that no one else had, plus the smarts. >> Jeff: Right. >> And made a difference to see Libby Schaaf get up there, with a lot of controversy right now, in the bigger political context. >> Jeff: Yes, yes. >> And show that you can make a difference. When people marginalize you, when I went out and raised money for my first company, I had venture capitalists literally pat me on the head and treat me like a little girl, and what I learned very quickly is there are always going to be some one that's going to see the truth in what I can bring. Go find those people, work with them, and then show the rest of the world what's possible. >> Right. It's pretty interesting, Robin Matlock is a CMO at VMware, we do a lot of stuff with VMware, and they put in a women in tech lunch thing a couple years ago, and we were talking, and I was interviewing her, she said, you know, I'd never really took the time to think about it. I was just working my tail off, and doing my thing, and you know, suddenly here I am, I'm CMO of this great company, and then it kind of took her a minute, and somebody kind of said, wait, you need to either take advantage of that opportunity in that platform to help others that maybe weren't quite so driven or are looking for those role models to say, "She looks kind of like me, "maybe I want to be the CMO of a big tech company". >> Well part of what's amazing you know, I get to work in education and work force, and part of what's amazing, whether you're talking about parents or the C Suite, or politicians is... A lot of that role modeling comes just from you being you. Go out, do good work in the world. But for some people, you know, there's an opportunity that doesn't exist for a lot of others. I'm a real outlier. I was not born a woman. I went through gender transition, it was a long time ago, and so for most people like me, being open about who you are means losing your job, it means not being taken seriously in any way, I mean, the change over the last couple of years has been astonishing. >> Jeff: It's been crazy, right? >> But part of my life is being able to be that person. I can take it. You know, my companies have made money, my inventions I've come up with have literally saved lives. >> Right. >> No one cares, in a sense, who I am anymore. That allows me to be visible. It allows me to just be very open about who I am and what I've experienced and been through, and then say to other people, it's not about me, it's not about whether I'm happy. It's about whether I'm serving my purpose. And I believe that I am, and does anything else about me really matter in this world? >> Right. It really seems, it's interesting, kind of sub text of diversity inclusion, not so much about your skin color or things that are easy to classify on your tax form, but it's really more just being your whole you. And no longer being suppressed to fit in a mold, not necessarily that's good or bad, but this is the way we did it, and thank you, we like you, we hired you, here you go, you know? Here's your big stack of rags, here's your desk, and we expect you to wear this to work. But that to me seems like the bigger story here that it's the whole person because there's so much value in the whole versus just concentrating on a slice. >> You know, it's really interesting, again, this is another area where I get to do hard numbers research, and when I do research, I'm talking looking at 122 million people. And building models to explain their career outcomes, and their life outcomes. And what we find here is one, everybody's biased. Everybody. I can't make an unbiased AI. There are no unbiased rats. The problem is when you refuse to acknowledge it. And you refuse to do something about it. And on the other side, to quote a friend of mine, "Everybody is covering for something. "Everybody has something in their life that they feel like "compromises them a little bit". So you know, even if we're talking about you know, the rich white straight guy, everyone's favorite punching bag. And I used to be one of them, so I try and take it easy. It is, the truth is, every one of them is covering for something, also. And if we can say again, it's not about me, which amazingly, actually allows you to be you. It's not about what other people think of me, it's not about whether they always agree with everything I say, or that I agree with what my boss says. It is about whether I'm making a difference in the world. And I've used that as my business strategy for the last 10 years of my life, and even when it seems like the worst strategy ever, you know, saying no to being chief scientist after you know, Fortune 50 company, one after another. Every time, my life got better. And my success grew. And it's not just an anecdote. Again, we see it in the data. So you build companies around principles like that. Who are you? Bring that person to work, and then you own the leadership challenge up, and I'm going to let that person flourish. And I'm going to let them tell me that I'm wrong. They got to prove it to me. But I'm going to let 'em tell it me, and give them the chance. You build a company like that, you know, what's clear to me is over the next 10 years, the defining market for global competition will be talent. Creative talent. And if you can't figure out how to tap the entire global work force, you cannot compete in that space. >> Right. The whole work force, and the whole person within that work force. It's really interesting, Jackie from Intel was on the panel that I got to talk, to see if she talked about you know, four really simple things, you know? Have impact. Undeniable, measurable impact, be visible, have data to back it up, and just of course, be tenacious, which is good career advice all the time, but you know. >> It's always good. >> Now when you know, cause before, a lot of people didn't have that option. Or they didn't feel they had the option to necessarily be purpose driven or be their old self, because then they get thrown out on the street and companies weren't as... Still, not that inclusive, right? >> Vivienne: I get it, believe me. >> You get it. So it is this new opportunity, but they have to because they can't get enough people. They can't get enough talent. It's really about ROI, this is not just to do the right thing. >> If even if you look at it from a selfish standpoint, there is the entire rest of the professional world competing for that traditional pipeline to get into the company. So being different, being you, it's a-- I mean, forgive me for putting it this way, but it's a marketing strategy, right? This is how you stand out from everyone else. One of my companies, we built this giant database of people all over the world, to predict how good people were at their job. And our goal was to take bias out of the hiring process. And when I was a chief scientist of that company, every time I gave a talk in public, 50 people would come up afterwards and say, "What should I do to get a better job?" And what they really meant was, what should I write on my resume, you know, how should I position myself, what's the next hot skill? >> Right. >> And my advice, which I meant genuinely, even though I don't think they always took it as such, was do good work and share it with the world. Not just my personal experience. We see it again and again in these massive data sets. The people that have the exceptional careers are the ones that just went out there and did something because it needed to get done. Maybe they did it inside their last job, maybe they did it personally as a side project, or they did a start up, or philanthropy. Whatever it was they did it, and they did it with passion. And that got noticed. So you know, again, just sort of selfishly, why compete with the other 150 million people looking for that same desirable job when the person that you are, I know it's terrifying, it is terrifying to put yourself out there. But the person you are is what you are better at than everyone else in the world. Be that person. That is your route to the best job you can possibly get. >> By rule, right? You're the best you you can be, but by rule, you're not as good at being somebody else. >> It sounds like a corny line, but the science backs it up. >> That's great. All right Vivienne, I could go on for a very long time, but unfortunately, we're going to have to leave it there. I really enjoyed the conversation. >> It was a lot of fun. >> And thanks for spending a few minutes with us. All right, she's Vivienne, I'm Jeff, you're watching theCUBE from the Accenture Women in Tech event in downtown San Francisco. Thanks for watching. (upbeat electronic music)

(hopeful futuristic music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are really excited to have this cube conversation here in the Palo Alto studio with a real close friend of theCUBE, and repeat alumni, Leslie Berlin. I want to get her official title; she's the historian for the Silicon Valley archive at Stanford. Last time we talked to Leslie, she had just come out with a book about Robert Noyce, and the man behind the microchip. If you haven't seen that, go check it out. But now she's got a new book, it's called "Troublemakers," which is a really appropriate title. And it's really about kind of the next phase of Silicon Valley growth, and it's hitting bookstores. I'm sure you can buy it wherever you can buy any other book, and we're excited to have you on Leslie, great to see you again. >> So good to see you Jeff. >> Absolutely, so the last book you wrote was really just about Noyce, and obviously, Intel, very specific in, you know, the silicon in Silicon Valley obviously. >> Right yeah. >> This is a much, kind of broader history with again just great characters. I mean, it's a tech history book, but it's really a character novel; I love it. >> Well thanks, yeah; I mean, I really wanted to find people. They had to meet a few criteria. They had to be interesting, they had to be important, they had to be, in my book, a little unknown; and most important, they had to be super-duper interesting. >> Jeff Frick: Yeah. >> And what I love about this generation is I look at Noyce's generation of innovators, who sort of working in the... Are getting their start in the 60s. And they really kind of set the tone for the valley in a lot of ways, but the valley at that point was still just all about chips. And then you have this new generation show up in the 70s, and they come up with the personal computer, they come up with video games. They sort of launch the venture capital industry in the way we know it now. Biotech, the internet gets started via the ARPANET, and they kind of set the tone for where we are today around the world in this modern, sort of tech infused, life that we live. >> Right, right, and it's interesting to me, because there's so many things that kind of define what Silicon Valley is. And of course, people are trying to replicate it all over the place, all over the world. But really, a lot of those kind of attributes were started by this class of entrepreneurs. Like just venture capital, the whole concept of having kind of a high risk, high return, small carve out from an institution, to put in a tech venture with basically a PowerPoint and some faith was a brand new concept back in the day. >> Leslie Berlin: Yeah, and no PowerPoint even. >> Well that's right, no PowerPoint, which is probably a good thing. >> You're right, because we're talking about the 1970s. I mean, what's so, really was very surprising to me about this book, and really important for understanding early venture capital, is that now a lot of venture capitalists are professional investors. But these venture capitalists pretty much to a man, and they were all men at that point, they were all operating guys, all of them. They worked at Fairchild, they worked at Intel, they worked at HP; and that was really part of the value that they brought to these propositions was they had money, yes, but they also had done this before. >> Jeff Frick: Right. >> And that was really, really important. >> Right, another concept that kind of comes out, and I think we've seen it time and time again is kind of this partnership of kind of the crazy super enthusiastic visionary that maybe is hard to work with and drives everybody nuts, and then always kind of has the other person, again, generally a guy in this time still a lot, who's kind of the doer. And it was really the Bushnell-Alcorn story around Atari that really brought that home where you had this guy way out front of the curve but you have to have the person behind who's actually building the vision in real material. >> Yeah, I mean I think something that's really important to understand, and this is something that I was really trying to bring out in the book, is that we usually only have room in our stories for one person in the spotlight when innovation is a team sport. And so, the kind of relationship that you're talking about with Nolan Bushnell, who started Atari, and Al Alcorn who was the first engineer there, it's a great example of that. And Nolan is exactly this very out there person, big curly hair, talkative, outgoing guy. After Atari he starts Chuck E. Cheese, which kind of tells you everything you need to know about someone who's dreaming up Chuck E. Cheese, super creative, super out there, super fun oriented. And you have working with him, Al Alcorn, who's a very straight laced for the time, by which I mean, he tried LSD but only once. (cumulative laughing) Engineer, and I think that what's important to understand is how much they needed each other, because the stories are so often only about the exuberant out front guy. To understand that those are just dreams, they are not reality without these other people. And how important, I mean, Al Alcorn told me look, "I couldn't have done this without Nolan, "kind of constantly pushing me." >> Right, right. >> And then in the Apple example, you actually see a third really important person, which to me was possibly the most exciting part of everything I discovered, which was the importance of the guy named Mike Markkula. Because in Jobs you had the visionary, and in Woz you had the engineer, but the two of them together, they had an idea, they had a great product, the Apple II, but they didn't have a company. And when Mike Markkula shows up at the garage, you know, Steve Jobs is 21 years old. >> Jeff Frick: Right. >> He has had 17 months of business experience in his life, and it's all his attack for Atari, actually. And so how that company became a business is due to Mike Markkula, this very quiet guy, very, very ambitious guy. He talked them up from a thousand stock options at Intel to 20,000 stock options at Intel when he got there, just before the IPO, which is how he could then turn around and help finance >> Jeff Frick: Right. >> The birth of Apple. And he pulled into Apple all of the chip people that he had worked with, and that is really what turned Apple into a company. So you had the visionary, you had the tech guy, you also needed a business person. >> But it's funny though because in that story of his visit to the garage he's specifically taken by the engineering elegance of the board >> Leslie Berlin: Right. >> That Woz put together, which I thought was really neat. So yeah, he's a successful business man. Yes he was bringing a lot of kind of business acumen value to the opportunity, but what struck him, and he specifically talks about what chips he used, how he planned for the power supply. Just very elegant engineering stuff that touched him, and he could recognize that they were so far ahead of the curve. And I think that's such another interesting point is that things that we so take for granted like mice, and UI, and UX. I mean the Atari example, for them to even think of actually building it that would operate with a television was just, I mean you might as well go to Venus, forget Mars, I mean that was such a crazy idea. >> Yeah, I mean I think Al ran to Walgreens or something like that and just sort of picked out the closest t.v. to figure out how he could build what turned out to be Pong, the first super successful video game. And I mean, if you look also at another story I tell is about Xerox Park; and specifically about a guy named Bob Taylor, who, I know I keep saying, "Oh this might be my favorite part." But Bob Taylor is another incredible story. This is the guy who convinced DARPA to start, it was then called ARPA, to start the ARPANET, which became the internet in a lot of ways. And then he goes on and he starts the computer sciences lab at Xerox Park. And that is the lab that Steve Jobs comes to in 1979, and for the first time sees a GUI, sees a mouse, sees Windows. And this is... The history behind that, and these people all working together, these very sophisticated Ph.D. engineers were all working together under the guidance of Bob Taylor, a Texan with a drawl and a Master's Degree in Psychology. So what it takes to lead, I think, is a really interesting question that gets raised in this book. >> So another great personality, Sandra Kurtzig. >> Yeah. >> I had to look to see if she's still alive. She's still alive. >> Leslie Berlin: Yeah. >> I'd love to get her in some time, we'll have to arrange for that next time, but her story is pretty fascinating, because she's a woman, and we still have big women issues in the tech industry, and this is years ago, but she was aggressive, she was a fantastic sales person, and she could code. And what was really interesting is she started her own software company. The whole concept of software kind of separated from hardware was completely alien. She couldn't even convince the HP guys to let her have access to a machine to write basically an NRP system that would add a ton of value to these big, expensive machines that they were selling. >> Yeah, you know what's interesting, she was able to get access to the machine. And HP, this is not a well known part of HP's history, is how important it was in helping launch little bitty companies in the valley. It was a wonderful sort of... Benefited all these small companies. But she had to go and read to them the definition of what an OEM was to make an argument that I am adding value to your machines by putting software on it. And software was such an unknown concept. A, people who heard she was selling software thought she was selling lingerie. And B, Larry Ellison tells a hilarious story of going to talk to venture capitalists about... When he's trying to start Oracle, he had co-founders, which I'm not sure everybody knows. And he and his co-founders were going to try to start Oracle, and these venture capitalists would, he said, not only throw him out of the office for such a crazy idea, but their secretaries would double check that he hadn't stolen the copy of Business Week off the table because what kind of nut job are we talking to here? >> Software. >> Yeah, where as now, I mean when you think about it, this is software valley. >> Right, right, it's software, even, world. There's so many great stories, again, "Troublemakers" just go out and get it wherever you buy a book. The whole recombinant DNA story and the birth of Genentech, A, is interesting, but I think the more kind of unique twist was the guy at Stanford, who really took it upon himself to take the commercialization of academic, generated, basic research to a whole 'nother level that had never been done. I guess it was like a sleepy little something in Manhattan they would send some paper to, but this guy took it to a whole 'nother level. >> Oh yeah, I mean before Niels showed up, Niels Reimers, he I believe that Stanford had made something like $3,000 off of the IP from its professors and students in the previous decades, and Niels said "There had to be a better way to do this." And he's the person who decided, we ought to be able to patent recombinant DNA. And one of the stories that's very, very interesting is what a cultural shift that required, whereas engineers had always thought in terms of, "How can this be practical?" For biologists this was seen as really an unpleasant thing to be doing, don't think about that we're about basic research. So in addition to having to convince all sorts of government agencies and the University of California system, which co-patented this, to make it possible, just almost on a paperwork level... >> Right. >> He had to convince the scientists themselves. And it was not a foregone conclusion, and a lot of people think that what kept the two named co-inventors of recombinant DNA, Stan Cohen and Herb Boyer, from winning the Nobel Prize is that they were seen as having benefited from the work of others, but having claimed all the credit, which is not, A, isn't fair, and B, both of those men had worried about that from the very beginning and kept saying, "We need to make sure that this includes everyone." >> Right. >> But that's not just the origins of the biotech industry in the valley, the entire landscape of how universities get their ideas to the public was transformed, and that whole story, there are these ideas that used to be in university labs, used to be locked up in the DOD, like you know, the ARPANET. And this is the time when those ideas start making their way out in a significant way. >> But it's this elegant dance, because it's basic research, and they want it to benefit all, but then you commercialize it, right? And then it's benefiting the few. But if you don't commercialize it and it doesn't get out, you really don't benefit very many. So they really had to walk this fine line to kind of serve both masters. >> Absolutely, and I mean it was even more complicated than that, because researchers didn't have to pay for it, it was... The thing that's amazing to me is that we look back at these people and say, "Oh these are trailblazers." And when I talked to them, because something that was really exciting about this book was that I got to talk to every one of the primary characters, you talk to them, and they say, "I was just putting one foot in front of the other." It's only when you sort of look behind them years later that you see, "Oh my God, they forged a completely new trail." But here it was just, "No I need to get to here, "and now I need to get to here." And that's what helped them get through. That's why I start the book with the quote from Raiders of the Lost Ark where Sallah asks Indy, you know basically, how are you going to stop, "Stop that car." And he says, "How are you going to do it Indy?" And Indy says, "I don't know "I'm making it up as I go along." And that really could almost be a theme in a lot of cases here that they knew where they needed to get to, and they just had to make it up to get there. >> Yeah, and there's a whole 'nother tranche on the Genentech story; they couldn't get all of the financing, so they actually used outsourcing, you know, so that whole kind of approach to business, which was really new and innovative. But we're running out of time, and I wanted to follow up on the last comment that you made. As a historian, you know, you are so fortunate or smart to pick your field that you can talk to the individual. So, I think you said, you've been doing interviews for five or six years for this book, it's 100 pages of notes in the back, don't miss the notes. >> But also don't think the book's too long. >> No, it's a good book, it's an easy read. But as you reflect on these individuals and these personalities, so there's obviously the stories you spent a lot of time writing about, but I'm wondering if there's some things that you see over and over again that just impress you. Is there a pattern, or is it just, as you said, just people working hard, putting one step in front of the other, and taking those risks that in hindsight are so big? >> I would say, I would point to a few things. I'd point to audacity; there really is a certain kind of adventurousness, at an almost unimaginable level, and persistence. I would also point to a third feature at that time that I think was really important, which was for a purpose that was creative. You know, I mean there was the notion, I think the metaphor of pioneering is much more what they were doing then what we would necessarily... Today we would call it disruption, and I think there's a difference there. And their vision was creative, I think of them as rebels with a cause. >> Right, right; is disruption the right... Is disruption, is that the right way that we should be thinking about it today or are just kind of backfilling the disruption after the fact that it happens do you think? >> I don't know, I mean I've given this a lot of thought, because I actually think, well, you know, the valley at this point, two-thirds of the people who are working in the tech industry in the valley were born outside of this country right now, actually 76 percent of the women. >> Jeff Frick: 76 percent? Wow. >> 76 percent of the women, I think it's age 25 to 44 working in tech were born outside of the United States. Okay, so the pioneering metaphor, that's just not the right metaphor anymore. The disruptive metaphor has a lot of the same concepts, but it has, it sounds to me more like blowing things up, and doesn't really thing so far as to, "Okay, what comes next?" >> Jeff Frick: Right, right. >> And I think we have to be sure that we continue to do that. >> Right, well because clearly, I mean, the Facebooks are the classic example where, you know, when he built that thing at Harvard, it was not to build a new platform that was going to have the power to disrupt global elections. You're trying to get dates, right? I mean, it was pretty simple. >> Right. >> Simple concept and yet, as you said, by putting one foot in front of the other as things roll out, he gets smart people, they see opportunities and take advantage of it, it becomes a much different thing, as has Google, as has Amazon. >> That's the way it goes, that's exactly... I mean, and you look back at the chip industry. These guys just didn't want to work for a boss they didn't like, and they wanted to build a transistor. And 20 years later a huge portion of the U.S. economy rests on the decisions they're making and the choices. And so I think this has been a continuous story in Silicon Valley. People start with a cool, small idea and it just grows so fast among them and around them with other people contributing, some people they wish didn't contribute, okay then what comes next? >> Jeff Frick: Right, right. >> That's what we figure out now. >> All right, audacity, creativity and persistence. Did I get it? >> And a goal. >> And a goal, and a goal. Pong, I mean was a great goal. (cumulative laughing) All right, so Leslie, thanks for taking a few minutes. Congratulations on the book; go out, get the book, you will not be disappointed. And of course, the Bob Noyce book is awesome as well, so... >> Thanks. >> Thanks for taking a few minutes and congratulations. >> Thank you so much Jeff. >> All right this is Leslie Berlin, I'm Jeff Frick, you're watching theCUBE. See you next time, thanks for watching. (electronic music)

>> Announcer: Live, from Boston, Massachusetts, it's the Cube. Covering Red Hat Summit 2017. Brought to you by Red Hat. (futuristic tone) >> Welcome back to the Cube's coverage of the Red Hat Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Stu Miniman. We are welcoming right now Sam Ramji. He is the Vice President of Product Management Google Cloud Platforms. Thanks so much for joining us. >> Thank you, Rebecca, really appreciate it. And Stu good to see you again. >> So in your keynote, you talked about how this is the age of the developer. You said this is the best time in history to be a developer. We have more veneration, more cred in the industry. People get us, people respect us. And yet you also talked about how it is also the most challenging time to be a developer. Can you unpack that a little bit for our viewers? >> Yeah, absolutely. So I think there's two parts that make it really difficult. One is just the velocity of all the different pieces, how fast they're moving, right? How do you stay on top of all the different latest technology, right? How do you unpack all of the new buzzwords? How do you say this is a cloud, that's not a cloud? So you're constantly racing to keep up, but you're also maintaining all of your old systems, which is the other part that makes it so complex. Many old systems weren't built for modernization. They were just kind of like hey, this is a really cool thing, and they were built without any sense of the history, or the future that they'd be used in. So imagine the modern enterprise developer who's got a ship software at high rates of speed, support new business initiatives, they've got to deliver innovation, and they have to bridge the very new with the very old. Because if your mobile app doesn't talk to your mainframe, you are not going to move money. It's that simple. There's layers of technology architecture. In fact, you could think of it as technology archeology, as I mentioned in the keynote, right, this we don't want to create a new genre of people called programmer archeologists, who have to go-- >> I'm picturing them just chipping away. >> Sam: I don't think it'll be as exciting as Indiana Jones. >> No. >> Digging through layers of the stack is not really what people want to be doing with their time. >> Sam: Temple of the lost kernel. >> I love it. >> So Sam, it's interesting to kind of see, I was at the Google Cloud event a couple months ago, and here you bring up the term open cloud, which part of me wants to poke a hole in that and be like, come on, everybody has their cloud. Come on, you want to lock everybody in, you've got the best technology, therefore why isn't it just being open because it's great to say open and maybe people will trust you. Help explain that. >> Puppies, freedom, apple pie, motherhood, right. >> Stu: Yeah, yeah. (laughs) >> So there's a couple sides to that. One, we think the cloud is just a spectacular opportunity. We think about 1.2 trillion dollars in current spend will end up in cloud. And the cloud market depending on how you measure it is in the mid 20 billions today. So there's just unbounded upside. So we don't have to be a aspirational monopolist in order to be a successful business. And in fact, if you wind the clock forward, you will see that every market ends up breaking down into a closed system and a closed company, and an open platform. And the open platforms tend to grow more slowly, sort of exponential versus logarithmic, is how we think about it. So it's a pragmatic business strategy. Think about Linux in '97. Think about Linux in 2002. Think about Linux in 2007. Think about Linux in 2012. Think about Linux today. Look at that rate. It's the only thing that you're going to use. So open is very pragmatic that way. It's pragmatic in another direction which is customer choice. Customers are going to come for things that give them more options. Because your job is to future proof your business, to create what in the financial community call optionality. So how do you get that? In 2011, about eight other people and I created a nonprofit called the Open Cloud Initiative. And the Initiative is long since dead, we didn't fund it right, we kind of got these ideas baked, and then moved on. >> Stu: There's another OCI now. >> That's right, it's the Open Container Initiative. But we had three really crisp concepts there. We said number one, an open cloud will be based on open source. There won't be stuff that you can't get, can't replicate, can't build yourself. Second, we said, it'll have open access. There'll be no barriers to entry or exit. There won't be any discrimination on which users can or can't come in, and there won't be any blockers to being able to take your stuff out. 'Cause we felt that without open access, the cloud would be unsafe at any speed, to borrow a quote from Ralph Nader. And then third, built on an open ecosystem. So if you are assuming that you have to be able to be open to tens of thousands of different ideas, tens of thousands of different software applications, which are maybe database infrastructure, things that as a cloud provider, you might want to be a first party provider of. Well those things have to compete, or trade off or enrich each other in a consistent way, in a way that's fair, which is kind of what we mean when we say open ecosystem, but being able to be pulled through is going to give you that rate of change that you need to be exponential rather than logarithmic. So it's based on some fairly durable concepts, but I welcome you to poke holes in it. >> So we did an event with MIT a little while back. We had Marshall Van Alstyne, professor at BU who I know you know. He's an advisor at Cloud Foundry, and he talked about those platforms and it was interesting, you know, with the phone system you had Apple who got lots of the money, smaller market share as opposed to Android, which of course comes out of Google, has all of the adoption but less revenue. So, not sure it's this, yeah. >> Interestingly, we've run those curves, and you kind of see that same logarithmic versus exponential shift happening in Android. So we've seen, I don't have the latest numbers on the top of my head, but that is generating billions of dollars of third party revenue now. So share does shift over time in favor of openness and faster innovation. >> So let's bring it back to Red Hat here, because if I talk to all the big public cloud guys, Microsoft has embraced open source. >> And they're not just guys, actually, there's lots of women. >> Rebecca: Yes, thank you. >> Stu: I apologize. >> Sorry, I'm in a little bit of a jam here, where I'm trying to tell people the collective noun for technologists is not guys. >> Stu: Okay. >> It could be people, it could be folks, internally we use squirrels from time to time, just to invite people in. >> So, when I talk to the cloud squirrels, Microsoft has embraced open source. Amazon has an interesting relationship. >> I was there when that happened. >> You and I both know the people that they've brought in who have very good credibility in the open source community that are helping out Amazon there. Is it Kubernetes that makes you open because I look at what Red Hat's doing, we say okay, if I want to be able to live across many clouds or in my own data centers, Kubernetes is a layer to do that. It comes back to some of the things like Cloud Foundry. Is that what makes it open because I have choice, or is there more to it that you want to cover from an open cloud standpoint, from a Google standpoint? >> Open and choice effectively is a spectrum of effort. If it's incredibly difficult, it's the same as not having a choice. If it's incredibly easy, then you're saying actually, you really are free to come and go. So Kubernetes is kind of the brightest star in the solar system of open cloud. There's a lot of other technologies, new things that are coming out, like istio and pluri. I don't want to lose you in word soup. Linker D, container D, a lot of other things, because this is a whole new field, a whole fabric that has to come to bear, that just like the internet, can layer on top of your existing data centers or your existing clouds, that you can have other applications or other capabilities layered on top of it. So this permission-less innovation idea is getting reborn in the cloud era, not on top of TCP/IP, we take that for granted, but on top of Kubernetes and all of the linked projects. So yeah, that's a big part of it. >> I want to continue on with that idea of permission-less innovation and talk about the culture of open source, particularly because of what you were saying in the keynote about how it's not about the code, it's about the community. And you were using words like empathy and trust, and things that we don't necessarily think of as synonymous with engineers. >> Sam: Isn't it? >> So, can you just talk a little bit about how you've seen the culture change, particularly since your days at Microsoft, and now being at Google, in terms of how people are working together? >> Absolutely, so the first thing is why did it change? It became an economic imperative. Let's look at software industry competition back in the 90s. In general, the biggest got the mostest. If you could assemble the largest number of very intelligent engineers, and put them all on the same project, you would overwhelm your competition. So we saw that play out again and again. Then this new form of collaboration came around, not just birthed by Linux, but also Apache and a number of other things, where it's like oh, we don't have to work for the same company in order to collaborate. And all of a sudden we started seeing those masses grow as big as the number of engineers who went a single company. Ten thousand people, ten thousand engineers, share the copyright to the Linux kernel. At no point have they worked at the same company. At no point could a company have afforded to get all of them together. So this economic imperative that marks what I think of as the first half of the thirty years of open source that we've been in. The second half has been more us all waking up, and realizing open source has got to be inclusive. A diverse world needs diverse solutions built by diverse people. How do we increase our empathy? How do we increase our understanding so that we can collaborate? Because if we think each other is a jerk, if we get turned off of building our great ideas into software because some community member has said something that's just fundamentally not cool, or deeply hurtful, we are human beings and we do take our toys away, and say I'm not going to be there. >> That's the crux of it too. >> It's absolutely a cutthroat industry, but I think one of the things I'm seeing, I've been in Silicon Valley for 22 years, less three years for a stint at Microsoft, I've actually started to see the community become more self-reflective and like, if we can have cutthroat competition in corporations, we don't have to make that personal. 'Cause every likelihood of open source projects is you're employed as a professional engineer at a company, and that employment agreement might change. Especially in containers, right? Great container developers you'll see they move from one company to another, whether it's a giant company like Google, or whether it's a big startup like Docker, or any range of companies. Or Red Hat. So, this sort of general sense that there is a community is starting to help us make better open source, and you can't be effective in a community if you don't have empathy and you don't start focusing on understanding code of conduct community norms. >> Sam, I'm curious how you look at this spectrum of with this complexity out there, how much will your average customer, and you can segment it anywhere you want, but they say, okay I'm going to engage with this, do open source, get involved, and what spectrum of customers are going to be like, well, let me just run it on Google because you've got a great platform, I'm not going to have Google engineers and you guys have lots of smart people that can do that in any of the platform. How do you see that spectrum of customer, is it by what their business IT needs are, is it the size of the customer, is there a decision tree that you guys have worked out yet to try to help end users with what do they own, what do they outsource? It's in clouds more than outsourcing these days. The deal of outsourcing was your mess for less, and this should be somewhat more transformational and hopefully more business value, right? >> Yeah, Urs Hölzle, who's our SVP of Technical Infrastructure, says, the cloud is not a co-location facility. It is different, it is not your server that you shipped up and you know, ran. It's an integrated set of services that should make it incredibly easy to do computing. And we have tons of very intelligent women and men operating our cloud. We think about things like how do you balance velocity and reliability? We have a discipline called site reliability engineering. We've published a book on it, a community is growing up around that, it's sort of the mainstream version of dev ops. So there are a bunch of components that any company at any size can adopt, as long as you need both velocity and reliability. This has always been the tyranny of the or. If I can move fast I can break things, but even Mark Zuckerberg recently said you know, move fast and break fewer things. Kind of a shift, 'cause you don't want to break a lot of people's experience. How do you do that, while making sure that you have high reliability? It really defies simple classification. We have seen companies from startups to mom and pop shops, all the way to giant enterprises adopting cloud, adopting Google cloud platform. One of the big draws is of course, data analytics. Google is a deeply data intensive business, and we've taken that to eleven basically with machine learning, which is why it was so important to explain tense or flow, offer that as open source, and be able to move AI forward. Any company, at any size that wants to do high speed, high scale data analytics, is coming to GCP. We've seen it basically break down into, what's the business value, how close is it to the decision maker, and how motivated is an engineer to learn something different and give cloud a try. >> Because the engineer has to get better at working with the data, understanding the data, and deriving the right insights from the data. >> You're exactly right. Engineers are people, and people need to learn, and they need to be motivated to change. >> Sam, last question I have for you is, you've been involved in many different projects. We look at from the outside and say, okay, how much should be company driven, how much does a foundation get involved? We've seen certain foundations that have done very well, and others that have struggled. It's very interesting to watch Google. We'd give you good as we've talked on the Cube so far. Kubernetes seems to be going well. Great adoption. Google participates, but not too much, and Red Hat I think would agree with that. So congratulations on that piece. >> Sam: Thank you. >> What's your learnings that you've had as you've been involved in some of these various initiatives, couple foundations. We interviewed you when you were back at the Cloud Foundry, and things like that, so, what have you learned that you might want to say, hey, here's some guidelines. >> Yeah, so I think the first guideline is the core of a foundation is, the core purpose of a foundation is bootstrapping trust. So where trust is missing, then you will need that in order to create better contribution and higher velocity in the project. If there's trust there, if there's a benevolent dictator and everyone says that person's fine or that company's fine, then you won't necessarily need a foundation. You've seen a lot of changes in open source startups, dot coms that are also a dot org, shifting to models where you say well, this thing is actually so big it needs to not be owned by any one company. And therefore, to get the next level of contribution, we need to be able to bring in giant companies, then we create trust at that next level. So foundations are really there for trust. It's really important to be strong enough to get something off the ground, and this is the challenge we had at Cloud Foundry, it was a VMware project and then a Pivotal project, and many people believe this is great open source, but it's not an open community, but the technology had to keep working really well. So we how do we have a majority contributor, and start opening up, in a thoughtful process and bringing people in, until you can say what our target is to have the main contributor be less than 50% of the code commits. 'Cause then the majority is really coming from the community. Other projects that have been around for longer, maybe they started out with no majority. Those organizations, those projects tend to be self-organizing, and what they need is just a foundation to build a place that people can contribute money to, so the community can have events. So there's two very different types of organizations. One's almost like a charity, to say I really care about this popular open source project, and I want to be able to give something back, and others are more like a trade association, which is like, we need to enable very complex coordination between big companies that have a lot at stake, in which case you'll create a different class of foundation. >> Great, well Sam Ramji, thank you so much for being with us here on the Cube. I'm Rebecca Knight, and for your host Stu Miniman, please join us back in a bit. (futuristic tone)

>> Announcer: Live, from Las Vegas, it's theCube! Covering NAB 2017. Brought to you by HGST. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at NAB 2017, a hundred thousand people. The Las Vegas Convention Center is packed. And it's everything you could ever want to get involved in video and media, and it' pretty crazy exciting. I hope, trying and get the guys from spending all of our budget money next year on new cameras. But, we're excited to have Brian Raleigh on. He's a VP post-production and production business intelligence at ABC Studios. Welcome. >> Thank you, I'm excited to be here, as well. >> Absolutely, so first impressions of the show. You said you haven't been that many times. As you walk around, what strikes you? >> Yeah, this is only my second time here. I will say I've seen plenty of booths that have the words Ingestion, Transcode, Archival, Distribution, there certainly is a lot of distribution out here, the broadcasting convention. >> Jeff: Right. >> Which makes sense. >> But you're involved in that pesky little process between what comes off the camera and what goes out to distribution. >> Yeah, exactly. We're prior to broadcast, right. So my world is really production and post-production, and the production management systems we use within them. >> Right. So love to hear, kind of, how is that world evolved? It used to be you had an artist on a machine, with local files doing the editing and all this stuff, and clearly that world is long, long gone. >> Yeah, most of our production and post-production workflow is in the cloud. >> Jeff: Right. >> Or however you want to call it. And very recently, what we've done, is we've tried to move on from the kind of, email-based world and saving everything on your desktop-based world, a lot of it revolves around the push to move off of that revolves around security. >> Jeff: Right. >> Efficiencies, better distribution, better control over who has access to what. So my role is really to introduce digital production management systems. Digital daily systems, digital purchase order systems. Digital scheduling systems. >> Jeff: Right. >> Kind of take us into more of like a wholistic, one-way world that covers both the production side as well as the studio side. >> And where would you say you are on that journey? >> Year one, is what I would say. >> Year one. Early in year one, early days. >> So our department is called, the Production Business Intelligence Department, but that's really, I would say we have more enthusiasm for business intelligence than we do have knowledge of business intelligence. >> Jeff: Right, right. >> So phase one is really getting our systems rolled out. To get these digital systems in use, with 100% adoption, on all of our shows, and all of our studio and network users. Once we have that piece done, we can actually start to collect the data and make some use out of it. >> Right, right And how has kind of the efficiency in the workflow, I know you're still early days, how do you anticipate it really being impacted by moving to more cloud-based systems versus local, on your hard drive controlled? >> Yeah, so security is a hundred times better than it was before, right? Just because everything is hidden behind a password now. Access is much more controlled. Efficiency has increased many times over, as well. I'll say that we project, over the course of our first year with these systems, we will have reduced our email count, just within the studio, by 650,000. >> Well, who doesn't love that. >> Right, exactly. I keep telling them it's good. >> Jeff: Golly. >> Everything is more searchable now. >> Jeff: Right. >> Higher quality. We're getting things faster. Our PAs are no longer burning thousands of DVDs and distributing them all across town, so it's improved our world in many ways. >> Right, and how do you kind of boil that ocean. Is it kind of by department, is it by show, is it one little slice kind of spread really, really wide? I mean, that's a big roll out. You guys are a huge studio. >> When the department, that used to be called the Production Technology Department, and when it started eight, nine years ago, the approach was really like, let's build everything in house, and try to piece it out one by one. What we have learned is, that doesn't really work. It's really difficult to get adoption and it was going to take a huge workforce in order to build what we needed. >> Jeff: Right. >> So we started to go with the Best in Breed approach, with these applications. And what came with them was 24/7 support and kind of white glove training and admin services. >> Jeff: Right. >> So because I have a really small centralized team, they can focus on just the training administration. And we have really this third-party service team that comes with each one of these production management systems that we use. >> Right. >> So we've been able boil the ocean because we have a lot of help. >> Right. And the other nice thing is just because of the nature of the studios, teams kind of form around shows, right? So now you can onboard a new team around your infrastructure piece. They do the show for one season, two seasons, however many season. >> Brian: Yeah. >> Then they go away. >> Yeah, what's been really good is even though it's a huge training endeavor, for sure, with our production teams, because we have something like, 8,000 people on our freelance production teams at any time. And they're a transient workforce. They go from studio to studio and show to show. >> Jeff: Right. >> But I think something like 60 to 70% of the people that we hire, we've hired before. >> Jeff: Okay. >> So the good news is once we've trained them once, there's a good likelihood that we won't need to train them again. >> Right. And, so there's kind of the application centric piece of it, and then there's kind of the infrastructure piece behind the application. I mean, good news is, you didn't have it eight years ago, but I mean the development's on the infrastructure side around storage and bandwidth and CPU. Huge change from where it was before. I mean, could you even have done what you were hoping to do eight years, kind of compared to where you are today? No, I don't think, the companies just didn't exist at that point. That's right. So the companies weren't there because the technology wasn't there. >> Jeff: Right. >> Now they've both kind of aligned, and aligned at a good time, right? When I think people are ready to hear that we need to modernize the studio. There's so much competition out there, that we need to make sure that we're doing things as good or better than everyone else. >> Right. And you said security a bunch of times. >> Brian: Yeah. >> So was the security, was it a security hole? Was it people forgetting their laptop at the coffee shop? >> Brian: Yeah. >> I mean what were some of your main security concerns that you've now been able to address? >> It's interesting. So we're ABC Studios, but we do a lot of co-productions with Marvel Studios. And Marvel Studios culture is very security centric. And because we worked so hand-in-hand with them, we've been very cognizant of the security abilities of these applications as we bring them in. So I will say, we didn't have any big outbreaks, right? We didn't have, we had shows like Lost, that people were really concerned about. >> Right. >> Scripts getting out, but more recently, we haven't had these huge high security titles, but now that Marvel is onboard, it's made us very security conscious. >> Okay. And it's more early leaks that people getting access to the assets-- >> Yeah, mostly we're worried about scripts. >> Right, right. Really, mostly scripts, as opposed to images, or-- >> Well, you're right. Scripts and rough cuts, I would say. >> Right, right, right. Okay, so that's kind of the bat, the stick. In terms of a carrot, what were some of the benefits that you hoped to achieve or you are really starting to achieve on the carrot side of the equation? >> Well, so we're still in phase one, as I said, in kind of rolling out these applications. >> Right, we'll let you talk about this in private. We will not hold you to whatever you say that's being, actually in production. >> The carrot, is so we're now called production business intelligence, but we don't have much intelligence, at this point, so, now that we're seeing some light at the end of the tunnel, in terms of rolling out these systems, the hope is, the carrot is, we're going to be able to find some really great business insights from the data we collect. The kinds of questions we want to be able to answer are things like, which of our directors that are hire are costing us the most in production staff overtime. When an editor's cut delivers, and it delivers 11 minutes long, how does that correlate with the length and complexity of the script? You start to learn these things, and the hope will be that what was going to be a nine-day production schedule, we really can do it in eight. >> Jeff: Right. >> We'll have the data, not just anecdotally, but like real data to back that up. >> Right. Now I wonder, and don't tell me if you can't, but within kind of the whole budget of a movie, production, post-production, distribution, promotion, what piece is post-production? I mean, I just think of the complexity of it. It can be just a sinkhole, if it's not managed well. >> Yeah, as a part of the production, well it depends on the show, right? >> Right, right, you know, kind of a general-- >> The variance is in visual effects, right, but I would say 10 to 20% of the budget is post-production. >> Jeff: Okay. >> And the systems piece of it is much, much less. >> Jeff: Right, right. >> One, maybe one percent. >> Right. So you could make a pretty significant impact >> Yeah. >> On the budget by being more efficient. >> For sure. >> And leveraging that intelligence. >> Well, below the line, which is what these systems really do impact, so not just post-production but production, as well as two-thirds of the budget. So absolutely, I mean that's many millions of dollars. >> Right, right. Okay, so as you look forward, have you got any insights that are kind of helping you drive to the next place, or are you just kind of working down a road map as you look at 2017, I know we're a third of the way through, which I find really hard to believe. What's kind of on your agenda, what's next, where are you going next? >> I'd say we're still working down the roadmap. We have, like I said, we have documents figured out, we have digital dailies figured out, we have production purchase orders figured out, now we're going to start looking at asset management. And we're going to start looking at scheduling. In hopes that ultimately we can really, I guess the real vision here is that we can have kind of a production ratio, right? We can start to rate our productions against each other based on all of this information that we have, but it requires some additional systems first. >> All right, Brian. Well, I wish you, at least you've got 650,000 less emails. >> I know it's a good start. >> I mean that should free up a ton of time. >> Brian: Yes. >> That's a great start. All right, he's Brian Raleigh from ABC, I'm Jeff Frick. Again, thanks for stopping by. >> Brian: Thank you. >> All right, you're watching theCUBE, from NAB 2017. Thanks for watching. (techno music)