(energetic music) >> Welcome to the Silicon Angle News update Mobile World Congress theCUBE coverage live on the floor for four days. I'm John Furrier, in the studio here. Dave Vellante, Lisa Martin onsite. Intel in the news, Intel accelerates 5G network virtualization with radio access network boost for Xeon processors. Intel, well known for power and computing, they today announced their integrated virtual radio access network into its latest fourth gen Intel Xeon system on a chip. This move will help network operators gear up their efforts to deliver Cloud native features for next generation 5G core and edge networks. This announcement came today at MWC, formerly knows Mobile World Congress. In Barcelona, Intel is taking the latest step in its mission to virtualize the world's networks, including Core, Open RAN and Edge. Network virtualization is the key capability for communication service providers as they migrate from fixed function hardware to programmable software defined platforms. This provides greater agility and greater cost efficiency. According to Intel, this is the demand for agile, high performance, scalable networks requiring adoption. Fully virtualized software based platforms run on general purpose processors. Intel believes that network operators need to accelerate network virtualization to get the most out of these new architectures, and that's where it can be made its mark. With Intel vRAN Boost, it delivers twice the capability and capacity gains over its previous generation of silicon with the same power envelope with 20% in power savings that results from an integrated acceleration. In addition, Intel announced new infrastructure power manager for 5G core reference software that's designed to work with vRAN Boost. Intel also showcased its new Intel Converged Edge media platform designed to deliver multiple video services from a shared multi-tenant architecture. The platform leverages Cloud native scalability to respond to the shifting demands. Lastly, Intel announced a range of Agilex 7 Field Programmable Gate Arrays and eASIC N5X structured applications specific integrated circuits designed for individual cloud communications and embedded applications. Intel is targeting the power consumption which is energy and more horsepower for chips, which is going to power the industrial internet edge. That's going to be Cloud native. Big news happening at Mobile World Congress. theCUBE is there. Go to siliconangle.com for all the news and special report and live feed on theCUBE.net. (energetic music)

>>Hey, welcome back to the cubes. Continuing coverage of AWS reinvent 2021. I'm your host, Lisa Martin. This is day two, our first full day of coverage. But day two, we have two life sets here with AWS and its ecosystem partners to remote sets over a hundred guests on the program. We're going to be talking about the next decade of cloud innovation, and I'm pleased to welcome back to cube alumni to the program. Justin Borkman is here, the co-founder and CEO of Starburst and Teresa Tung, the cloud first chief technologist at Accenture guys. Welcome back to the queue. Thank you. Thank you for having me. Good to have you back. So, so Teresa, I was doing some research on you and I see you are the most prolific prolific inventor at Accenture with over 220 patents and patent applications. That's huge. Congratulations. Thank you. Thank you. And I love your title. I think it's intriguing. I'd like to learn a little bit more about your role cloud-first chief technologist. Tell me about, >>Well, I get to think about the future of cloud and if you think about clouded powers, everything experiences in our everyday lives and our homes and our car in our stores. So pretty much I get to be cute, right? The rest of Accenture's James Bond >>And your queue. I like that. Wow. What a great analogy. Just to talk to me a little bit, I know service has been on the program before, but give me a little bit of an overview of the company, what you guys do. What were some of the gaps in the markets that you saw a few years ago and said, we have an idea to solve this? Sure. >>So Starburst offers a distributed query engine, which essentially means we're able to run SQL queries on data anywhere, uh, could be in traditional relational databases, data lakes in the cloud on-prem. And I think that was the gap that we saw was basically that people had data everywhere and really had a challenge with how they analyze that data. And, uh, my co-founders are the creators of an open source project originally called Presto now called Trino. And it's how Facebook and Netflix and Airbnb and, and a number of the internet companies run their analytics. And so our idea was basically to take that, commercialize that and make it enterprise grade for the thousands of other companies that are struggling with data management, data analytics problems. >>And that's one of the things we've seen explode during the last 22 months, among many other things is data, right? In every company. These days has to be a data company. If they're not, there's a competitor in the rear view rear view mirror, ready to come and take that place. We're going to talk about the data mesh Teresa, we're going to start with you. This is not a new car. This is a new concept. Talk to us about what a data mesh is and why organizations need to embrace this >>Approach. So there's a canonical definition about data mesh with four attributes and any data geek or data architect really resonates with them. So number one, it's really routed decentralized domain ownership. So data is not within a single line of business within a single entity within a single partner has to be across different domains. Second is publishing data as products. And so instead of these really, you know, technology solutions, data sets, data tables, really thinking about the product and who's going to use it. The third one is really around self-service infrastructure. So you want everybody to be able to use those products. And finally, number four, it's really about federated and global governance. So even though their products, you really need to make sure that you're doing the right things, but what's data money. >>We're not talking about a single tool here, right? This is more of a, an approach, a solution. >>It is a data strategy first and foremost, right? So companies, they are multi-cloud, they have many projects going on, they are on premise. So what do you do about it? And so that's the reality of the situation today, and it's first and foremost, a business strategy and framework to think about the data. And then there's a new architecture that underlines and supports that >>Just didn't talk to me about when you're having customer conversations. Obviously organizations need to have a core data strategy that runs the business. They need to be able to, to democratize really truly democratized data access across all business units. What are some of the, what are some of your customer conversations like are customers really embracing the data strategy, vision and approach? >>Yeah, well, I think as you alluded to, you know, every business is data-driven today and the pandemic, if anything has accelerated digital transformation in that move to become data-driven. So it's imperative that every business of every shape and size really put the power of data in the hands of everyone within their organization. And I think part of what's making data mesh resonates so well, is that decentralization concept that Teresa spoke about? Like, I think companies acknowledge that data is inherently decentralized. They have a lot of different database systems, different teams and data mesh is a framework for thinking about that. Then not only acknowledges that reality, but also braces it and basically says there's actually advantages to this decentralized approach. And so I think that's, what's driving the interest level in the data mesh, uh, paradigm. And it's been exciting to work with customers as they think about that strategy. And I think that, you know, essentially every company in the space is, is in transition, whether they're moving from on cloud to the prem, uh, to, uh, sorry, from on-prem to the cloud or from one cloud to another cloud or undergoing that digital transformation, they have left behind data everywhere. And so they're, they're trying to wrestle with how to grasp that. >>And there's, we know that there's so much value in data. The, the need is to be able to get it, to be able to analyze it quickly in real time. I think another thing we learned in the pandemic is it real-time is no longer a nice to have. It is essential for businesses in every organization. So Theresa let's talk about how Accenture and servers are working together to take the data mesh from a concept of framework and put this into production into execution. >>Yeah. I mean, many clients are already doing some aspect of the data mesh as I listed those four attributes. I'm sure everybody thought like I'm already doing some of this. And so a lot of that is reviewing your existing data projects and looking at it from a data product landscape we're at Amazon, right? Amazon famous for being customer obsessed. So in data, we're not always customer obsessed. We put up tables, we put up data sets, feature stores. Who's actually going to use this data. What's the value from it. And I think that's a big change. And so a lot of what we're doing is helping apply that product lens, a literal product lens and thinking about the customer. >>So what are some w you know, we often talk about outcomes, everything being outcomes focused and customers, vendors wanting to help customers deliver big outcomes, you know, cost reduction, et cetera, things like that. How, what are some of the key outcomes Theresa that the data mesh framework unlocks for organizations in any industry to be able to leverage? >>Yeah. I mean, it really depends on the product. Some of it is organizational efficiency and data-driven decisions. So just by the able to see the data, see what's happening now, that's great. But then you have so beyond the, now what the, so what the analytics, right. Both predictive prescriptive analytics. So what, so now I have all this data I can analyze and drive and predict. And then finally, the, what if, if I have this data and my partners have this data in this mesh, and I can use it, I can ask a lot of what if and, and kind of game out scenarios about what if I did things differently, all of this in a very virtualized data-driven fashion, >>Right? Well, we've been talking about being data-driven for years and years and years, but it's one thing to say that it's a whole other thing to actually be able to put that into practice and to use it, to develop new products and services, delight customers, right. And, and really achieve the competitive advantage that businesses want to have. Just so talk to me about how your customer conversations have changed in the last 22 months, as we've seen this massive acceleration of digital transformation companies initially, really trying to survive and figure out how to pivot, not once, but multiple times. How are those customer conversations changing now is as that data strategy becomes core to the survival of every business and its ability to thrive. >>Yeah. I mean, I think it's accelerated everything and, and that's been obviously good for companies like us and like Accenture, cause there's a lot of work to be done out there. Um, but I think it's a transition from a storage centric mindset to more of an analytics centric mindset. You know, I think traditionally data warehousing has been all about moving data into one central place. And, and once you get it there, then you can analyze it. But I think companies don't have the time to wait for that anymore. Right there, there's no time to build all the ETL pipelines and maintain them and get all of that data together. We need to shorten that time to insight. And that's really what we, what we've been focusing on with our, with our customers, >>Shorten that time to insight to get that value out of the data faster. Exactly. Like I said, you know, the time is no longer a nice to have. It's an absolute differentiator for folks in every business. And as, as in our consumer lives, we have this expectation that we can get whatever we want on our phone, on any device, 24 by seven. And of course now in our business lives, we're having the same expectation, but you have to be able to unlock that access to that data, to be able to do the analytics, to make the decisions based on what the data say. Are you, are you finding our total? Let's talk about a little bit about the go to market strategy. You guys go in together. Talk to me about how you're working with AWS, Theresa, we'll start with you. And then Justin we'll head over to you. Okay. >>Well, a lot of this is powered by the cloud, right? So being able to imagine a new data business to run the analytics on it and then push it out, all of that is often cloud-based. But then the great thing about data mesh it's it gives you a framework to look at and tap into multi-cloud on-prem edge data, right? Data that can't be moved because it is a private and secure has to be at the edge and on-prem so you need to have that's their data reality. And the cloud really makes this easier to do. And then with data virtualization, especially coming from the digital natives, we know it scales >>Just to talk to me about it from your perspective that the GTL. >>Yeah. So, I mean, I think, uh, data mesh is really about people process and technology. I think Theresa alluded to it as a strategy. It's, it's more than just technology. Obviously we bring some of that technology to bear by allowing customers to query the data where it lives. But the people in process side is just as important training people to kind of think about how they do data management, data analytics differently is essential thinking about how to create data as a product. That's one of the core principles that Theresa mentioned, you know, that's where I think, um, you know, folks like Accenture can be really instrumental in helping people drive that transformational change within their organization. And that's >>Hard. Transformational change is hard with, you know, the last 22 months. I've been hard on everyone for every reason. How are you facilitating? I'm curious, like to get Theresa, we'll start with you, your perspectives on how our together as servers and Accenture, with the power of AWS, helping to drive that cultural change within organizations. Because like we talked about Justin there, nobody has extra time to waste on anything these days. >>The good news is there's that imperative, right? Every business is a digital business. We found that our technology leaders, right, the top 10% investors in digital, they are outperforming are the laggards. So before pandemic, it's times to post pep devek times five, so there's a need to change. And so data is really the heart of the company. That's how you unlock your technical debt into technical wealth. And so really using cloud and technologies like Starburst and data virtualization is how we can actually do that. >>And so how do you, Justin, how does Starburst help organizations transfer that technical debt or reduce it? How does the D how does the data much help facilitate that? Because we talk about technical debt and it can, it can really add up. >>Yeah, well, a lot of people use us, uh, or think about us as an abstraction layer above the different data sources that they have. So they may have legacy data sources today. Um, then maybe they want to move off of over time, um, could be classical data, warehouses, other classical, uh, relational databases, perhaps they're moving to the cloud. And by leveraging Starburst as this abstraction, they can query the data that they have today, while in the background, moving data into the cloud or moving it into the new data stores that they want to utilize. And it sort of hides that complexity. It decouples the end user experience, the business analyst, the data scientists from where the data lives. And I think that gives people a lot of freedom and a lot of optionality. And I think, you know, the only constant is change. Um, and so creating an architecture that can stand the test of time, I think is really, really important. >>Absolutely. Speaking of change, I just saw the announcement about Starburst galaxy fully managed SAS platform now available in all three major clouds. Of course, here we are at AWS. This is a, is this a big directional shift for servers? >>It is, you know, uh, I think there's great precedent within open source enterprise software companies like Mongo DB or confluent who started with a self managed product, much the way that we did, and then moved in the direction of creating a SAS product, a cloud hosted, fully managed product that really I think, expands the market. And that's really essentially what we're doing with galaxy galaxy is designed to be as easy as possible. Um, you know, Starburst was already powerful. This makes it powerful and easy. And, uh, and, and in our view, can, can hopefully expand the market to thousands of potential customers that can now leverage this technology in a, in a faster, easier way, >>Just in sticking with you for a minute. Talk to me about kind of where you're going in, where services heading in terms of support for the data mesh architecture across industries. >>Yeah. So a couple of things that we've, we've done recently, and whether we're doing, uh, as we speak, one is, uh, we introduced a new capability. We call star gate. Now star gate is a connector between Starburst clusters. So you're going to have a Starbucks cluster, and let's say Azure service cluster in AWS, a Starbucks cluster, maybe an AWS west and AWS east. And this basically pushes the processing to where the data lives. So again, living within this construct of, uh, of decentralized data that a data mesh is all about, this allows you to do that at an even greater level of abstraction. So it doesn't even matter what cloud region the data lives in or what cloud entirely it lives in. And there are a lot of important applications for this, not only latency in terms of giving you fast, uh, ability to join across those different clouds, but also, uh, data sovereignty constraints, right? >>Um, increasingly important, especially in Europe, but increasingly everywhere. And, you know, if your data isn't Switzerland, it needs to stay in Switzerland. So starting date as a way of pushing the processing to Switzerland. So you're minimizing the data that you need to pull back to complete your analysis. And, uh, and so we think that's a big deal about, you know, kind of enabling a data mash on a, on a global scale. Um, another thing we're working on back to the point of data products is how do customers curate and create these data products and share them within their organization. And so we're investing heavily in our product to make that easier as well, because I think back to one of the things, uh, Theresa said, it's, it's really all about, uh, making this practical and finding quick wins that customers can deploy, deploy in their data mess journey, right? >>This quick wins are key. So Theresa, last question to you, where should companies go to get started today? Obviously everybody has gotten, we're still in this work from anywhere environment. Companies have tons of data, tons of sources of data, did it, infrastructure's already in place. How did they go and get started with data? >>I think they should start looking at their data projects and thinking about the best data products. I think just that mindset shift about thinking about who's this for what's the business value. And then underneath that architecture and support comes to bear. And then thinking about who are the products that your product could work better with just like any other practice partnerships, like what we have with AWS, right? Like that's a stronger together sort of thing, >>Right? So there's that kind of that cultural component that really strategic shift in thinking and on the architecture. Awesome guys, thank you so much for joining me on the program, coming back on the cube at re-invent talking about data mesh really help. You can help organizations and industry put that together and what's going on at service. We appreciate your time. Thanks again. All right. For my guests, I'm Lisa Martin, you're watching the cubes coverage of AWS reinvent 2021. The cube is the leader in global live tech coverage. We'll be right back.

>>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.

(tranquil music) >> Alation was an early pioneer in solving some of the most challenging problems in so-called big data. Founded early last decade, the company's metadata management and data catalog have always been considered leading examples of modern tooling by customers and analysts alike. Governance is one area that customers identified as a requirement to extend their use of Alation's platform. And it became an opportunity for the company to expand its scope and total available market. Alation is doing just that today, announcing new data governance capabilities, and partner integrations that align with the market's direction of supporting federated governance. In other words, a centralized view of policy to accommodate distributed data in this world of an ever expanding data cloud, which we talk about all the time in theCUBE. And with me to discuss these trends and this announcement is Satyen Sangani, who's the CEO and co-founder of Alation. Satyen, welcome back to the CUBE. Good to see you. >> Thank you Dave, It's great to be back. >> Okay, so you heard my open, please tell us about the patterns that you were seeing in the market and what you were hearing from customers that led you in this direction and then we'll get into the announcement. >> Yeah, so I think there are really two patterns, right? I mean, when we started building this notion of a data catalog, as you said a decade ago, there was this idea that metadata management broadly classified was something that belonged in IT, lived in IT and was essentially managed by IT, right? I always liken it to kind of an inventory management system within a warehouse relative to Amazon.com, which has obviously broadly published for the business. And so, with the idea of bringing all of this data directly to the business and allowing people arbitrarily, depending on their role to use the data. You know, you saw one trend, which was just this massive, shift in how much data was available at any given time. I think the other thing that happened was that at the same time, data governance went through a real transitionary phase where there was a lot of demand often spurred by regulations. Whether that's GDPR, CCPA or more recently than that, certainly the Basel accord. And if you think about all of those regulations, people had to get something in a place. Now what we ended up finding out was when we were selling in to add accounts, people would say, well guess what? I've got this data governance thing going on, but nobody's really using it. I built this business glossary, it's been three years. Nothing's been really very effective. And we were never able to get the value and we need to get value because there are so many more people now accessing and using and leveraging the data. And so with that, we started really considering whether or not we needed to build a formal capability in the market. And that's what we're today that we're doing. >> I liked the way you framed that. And if you think back, we were there as you were in the early big day-to-days. And all the talk was about volume, variety and velocity. And those are sort of IT concepts. How do you deal with all these technical challenges? And then the fourth V which you just mentioned was value. And that's where the line of business really comes in. So let's get into the news. What are you announcing today? >> So we're announcing a new application on top of Alation's Catalog platform, which is an Alations data governance application. That application will be released with our 2021.3 release on September 14th. And what's exciting about that is that we are going to now allow customers to discreetly and elegantly and quickly consume a new application to get data governance regimes off the ground and initiatives off the ground, much more quickly than they've ever been able to do. This app is really all about time to value. It's about allowing customers to be able to consume what they need when they need it in order to be able to get successful governance initiatives going. And so that's what we're trying to deliver. >> So maybe you could talk a little bit about how you think about data governance and specifically your data governance approach. And maybe what's different about Alation's solution. >> Yeah, I think there's a couple of things that are different. I think the first thing that's most critically different is that we move beyond this notion of sort of policy declaration into the world of policy application and policy enforcement, right? I think a lot of data governance regimes basically stand up and say, look you know, it's all about people and then process and then technology. And what we need to do is declare who all the governors are and who all the stewards are. And then we're going to get all our policies in the same place and then the business will follow them. And the reality is people don't change their workflows to go off and arbitrarily follow some data governance policy that they don't know exists, or they don't want to actually have to follow up. And so really what you've got to do is make sure that the policy and the knowledge exists as in where the data exists. And that's why it's so critical to build governance into the catalog. And so what we're doing here is we're basically saying, look, you could declare policies with a new policy center inside of Alation. Those policies will get automatically created in some cases by integrating with technologies like Snowflake. But beyond that, what we're also doing is we're saying, look, we're going to move into the world of taking those policies and applying them to the data on an automated basis using ML and AI and basically saying that now it doesn't have to be some massive boil the ocean three-year regime to get very little value in a very limited business loss rate. Rather all of your data sets, all of your terms can be put into a single place on an automated basis. That's constantly being used by people and constantly being updated by the new systems that are coming online. And that's what's exciting about it. >> So I just want to follow up on that. So if I'm hearing you correctly, it's the humans are in the loop, but it's not the only source of policy, right? The machines are assisting. And in some cases managing end-to-end that policy. Is that right? >> You've got it. I think the the biggest challenge with data governance today is that it basically relies a little bit like the Golden Gate Bridge. You know, you start painting it and by the time you're done painting it, you've got to go back and start painting it again, because it relies upon people. And there's just too much change in the weather and there's too much traffic and there's just too much going on in the world of data. And frankly in today's world, that's not even an apt analogy because often what happens is midway through. You've got to restart painting from the very beginning because everything's changed. And so there's so much change in the IT landscape that the traditional way of doing data governance just doesn't work. >> Got it, so in winning through the press release, three things kind of stood out. I wonder if we could unpack them, there were multi-cloud, governance and security. And then of course the AI or what I like to call machine intelligence in there. And what you call the people centric approach. So I wonder if we could dig in into these and help us understand how they fit together. So thinking about multi-cloud governance, how do you think about that? Why is that so challenging and how are you solving that problem? >> Yeah, well every cloud technology provider has its own set of capabilities and platforms. And often those slight differences are causing differences in how those technologies are adopted. And so some teams optimize for certain capabilities and certain infrastructure over others. And that's true even within businesses. And of course, IT teams are also trying to diversify their IT portfolios. And that's another reason to go multi-cloud. So being able to have a governance capability that spans, certainly all of the good grade called megascalers, but also these new, huge emerging platforms like Snowflake of course and others. Those are really critical capabilities that are important for our customers to be able to get a handle on top of. And so this idea of being cloud agnostic and being able to sort of have a single control plane for all of your policies, for all of your data sets, that's a critical must have in a governance regime today. So that's point number one. >> Okay and then the machine learning piece, the AI, you're obviously injecting that into the application, but maybe tell us what that means both maybe technically and from a business stand point. >> Yeah, so this idea of a data policy, right? Can be sometimes by operational teams, but basically it's a set of rules around how one should and should not be able to use data, right? And so those are great rules. It could be that people who are in one country shouldn't be able to access the data of another country, very simple role, right? But how do you actually enforce that? Like you can declare it, but if there is a end point on a server that allows you to access the data, the policy is effectively moot. And so what you got to go do is make sure that at the point of leverage or at the point of usage, people know what the policy happens to be. And that's where AI come in. You can say let's document all the data sets that happened to be domiciled in Korea or in China. And therefore make sure that those are arbitrarily segregated so that when people want to use that as datasets, they know that the policy exists and they know that it's been applied to that particular dataset. That's somewhere where AI and ML can be super valuable rather than a human being trying to document thousands of databases or tens of thousands of data sets, which is really kind of a (mumbles) exercise. And so, that application of automation is really critical and being able to do governance at the scale that most enterprises have to do it. >> You got it 'cause humans just can't do that at scale. Now what do you mean by people-centric approach? Can you explain that? >> Yeah, often what I find with governance is that there's this notion of kind of there's this heavy notion of how one should deal with the data, right? So often what I find is that there are certain folks who think, oh well, we're going to declare the rules and people are just going to follow them. And if you've ever been well, a parent or in some cases seeing government operate, you realize that that actually isn't how things work. And involve them in how things are run. And if you do that, right? You're going to get a lot more success in how you apply rules and procedures because people will understand that and people know why they exist. And so what we do within this governance regime is we basically say, look, we want to make sure that the people who are using the data, leveraging the data are also the people who are stewarding the data. There shouldn't be a separate role of data steward that is arbitrarily defined off, just because you've been assigned to a job that you never wanted to do. Rather it should be a part of your day job. And it should be something that you do because you really want to do it. And it's a part of your workflow. And so this idea of being people centric is all about how do you engage the analyst, the product managers, the sales operation managers, to document those sales data sets and those product data sets. So that in fact, those people can be the ones who are answering the questions, not somebody off to the side who knows nothing about the data. >> Yeah, I think you've talked in previous CUBE interviews about context and that really fits to this discussion. So these capabilities are part of an application, which is what? it's a module onto your existing platform. And it's sort of it's a single platform, right? I mean, we're not bespoke products. Maybe you can talk about that. >> Yeah, that's exactly right. I mean, it's funny because we've evolved and built a relation with a lot of capability. I mean, interestingly we're launching this data governance application but I would say 60% of our almost 300 customers would say they do a form or a significant part of data governance, leveraging relations. So it's not like we're new to this market. We've been selling in this market for years. What's different though, is that we've talked a lot about the catalog as a platform over the last year. And we think that that's a really important concept because what is a platform? It's a capability that has multiple applications built on top of it, definitionally. And it's also a capability where third party developers can leverage APIs and SDKs to build applications. And thirdly, it has all of the requisite capabilities and content. So that those application developers, whether it's first party from Alation or third party can really build those applications efficiently, elegantly and economically well. And the catalog is a natural platform because it contains all of the knowledge of the datasets. And it has all of the people who might be leveraging data in some fundamental way. And so this idea of building this data governance module allows a very specialized audience of people in governance to be able to leverage the full capabilities of the platform, to be able to do their work faster, easier, much more simply and easily than they ever could have. And that's why we're so excited about this launch, because we think it's one example of many applications, whether it's ourselves building it or third parties that could be done so much more elegantly than it previously could have been. Because we have so much knowledge of the data and so much knowledge of how the company operates. >> Irrespective of the underlying cloud platform is what I heard before. >> irrespective of the underlying cloud platform, because the data as you know, lives everywhere. It's going to live in AWS, it's going to live in Snowflake. It's going to live on-premise inside of an Oracle database. That's not going to be changed. It's going to live in Teradata. It's going to live all over the place. And as a consequence of that, we've got to be able to connect to everything and we've got to be able to know everything. >> Okay, so that leads me to another big part of the announcement, which is the partnership and integration with Snowflake. Talk about how that came about. I mean, why snowflake? How should customers think about the future of data management. In the context of this relationship, obviously Snowflake talks about the data cloud. I want to understand that better and where you fit. >> Yeah, so interestingly, this partnership like most great partnerships was born in the field. We at the late part of last year had observed with Snowflake that we were in scores of their biggest accounts. And we found that when you found a really, really large Snowflake engagement, often you were going to be complementing that with a reasonable engagement with Alation. And so seeing that pattern as we were going out and raising our funding route at the beginning of this year, we basically found that Snowflake obviously with their Snowflake Ventures Investment arm realized how strategic having a great answer in the governance market happened to be. Now there are other use cases that we do with Snowflake. We can certainly get into those. But what we realized was that if you had a huge scale, Snowflake engagement, governance was a rate limiter to customers' ability to grow faster. And therefore also Snowflake's ability to grow faster within that account. And so we worked with them to not only develop a partnership but much more critically a roadmap that was really robust. And so we're now starting to deliver on that roadmap and are super excited to share a lot of those capabilities in this release. And so that means that we're automatically ingesting policies and controls from Snowflake into Alation, giving full transparency into both setting and also modifying and understanding those policies for anybody. And so that gives you another control plane through which to be able to manage all of the data inside of your enterprise, irrespective of how many instances of Snowflake you have and irrespective of how many controls you have available to you. >> And again, on which cloud runs on. So I want to follow up with that really interesting because Snowflake's promise of the data cloud, is it essentially abstracts the underlying complexity of the cloud. And I'm trying to understand, okay, how much of this is vision, how much is is real? And it's fine to have a Northstar, but sometimes you get lost in the marketing. And then the other part of the promise, and of course, big value proposition is data sharing. I mean, I think they've nailed that use case, but the challenge when you start sharing data is federated governance. And as well, I think you mentioned Oracle, Teradata that stuff's not all in the cloud, a lot of that stuff on-prem and you guys can deal with that as well. So help us sort of to those circles, if you can. Where do you fit into that equation? >> I think, so look, Snowflake is a magical technology and in the sense that if you look at the data, I mean, it reveals a very, very clear story of the ability to adopt Snowflake very quickly. So any data team with an organization can get up and running with the Snowflake instance with extraordinary speed and capability. Now that means that you could have scores, hundreds of instances of Snowflake within a single institution. And to the extent that you want to be able to govern that data to your point, you've got to have a single control plane through which you can manage all of those various instances. Whether they're combined or merged or completely federated and distinct from each other. Now, the other problem that comes up on governance is also discoverability. If you have all these instances, how do you know what the right hand is doing if the left hand is working independently of it? You need some way to be able to coordinate that effort. And so that idea of discoverability and governance is really the value proposition that Alation brings to the table. And the idea there is that people can then can get up and running much more quickly because, hey, what if I want to spin up a Snowflake instance, but there's somebody else, two teams over those already solved the problem or has the data that I need? Well, then maybe I don't even need to do that anymore. Or maybe I can build on top of that work to be able to get to even better outcome even faster. And so that's the sort of kind of one plus one equals three equation that we're trying to build with them. >> So that makes sense and that leads me to one of my favorite topics with the notion is this burgeoning movement around the concept of a data mesh in it. In other words, the notion that increasingly organizations are going to push to decentralize their data architectures and at the same time support a centralized policy. What do you think about this trend? How do you see Alation fitting in? >> Yeah, maybe in a different CUBE conversation. We can talk a little bit about my sort of stylized history of data, but I've got this basic theory that like everybody started out what sort of this idea of a single source of truth. That was a great term back in the 90s where people were like, look, we just need to build a single source of truth and we can take all of our data and physically land it up in a single place. And when we do that, it's going to all be clean, available and perfect. And we'll get back to the garden of Eden, right? And I think that idea has always been sort of this elusive thing that nobody's ever been able to really accomplish, right? Because in any data environment, what you're going to find is that if people use data, they create more data, right? And so in that world, you know, like that notion of centralization is always going to be fighting this idea of data sprawl. And so this concept of data mesh I think is, you know, there's formal technical definitions. But I'll stick with maybe a very informal one, which is the one that you offered. Which is just sort of this decentralized mode of architecture. You can't have decentralization if nobody knows how to access those different data points, 'cause otherwise they'll just have copies and sprawl and rework. And so you need a catalog and you need centralized policies so that people know what's available to them. And people have some way of being able to get conformed data. Like if you've got data spread out all over the place, how do you know which is the right master? How do you know what's the right customer record? How do you know what's your right chart of accounts? You've got to have services that exist in order to be able to find that stuff and to be able to leverage them consistently. And so, to me the data mesh is really a continuation of this idea, which the catalog really enabled. Which is if you can build a single source of reference, not a single source of truth, but a single place where people can find and discover the data, then you can govern a single plane and you can build consistent architectural rules so that different services can exist in a decentralized way without having to sort of bear all the costs of centralization. And I think that's a super exciting trend 'cause it gives power back to people who want to use the data more quickly and efficiently. >> And I think as we were talking about before, it's not about just the IT technical aspects, hey, it works. It's about putting power in the hands of the lines of business. And a big part of the data mesh conversation is around building data products and putting context or putting data in the hands of the people who have the context. And so it seems to me that Alation, okay, so you could have a catalog that is of the line of businesses catalog, but then there's an Uber catalog that sort of rolls up. So you've got full visibility. It seems that you've fit perfectly into that data mesh. And whether it's a data hub, a data warehouse, data lake, I mean, you don't care. I mean, that's just another node that you can help manage. >> That's exactly right. I mean, it's funny because we all look at these market scapes where people see these vendor landscapes of 500 or 800 different data and AI and ML and data architecture vendors. And often I get asked, well, why doesn't somebody come along and like consolidate all this stuff? And the reality is that tools are a reflection of how people think. And when people have different problems and different sets of experiences, they're going to want to use the best tool in order to be able to solve their problem. And so the nice thing about having a mesh architecture is you can use whatever tool you want. You just have to expose your data in a consistent way. And if you have a catalog, you can be able to have different teams using different infrastructure, different tools, different fundamental methods of building the software. But as long as they're exposing it in a consistent way, it doesn't matter. You don't necessarily need to care how it's built. You just need to know that you've got good data available to you. And that's exactly what a catalog does. >> Well, at least your catalog. I think the data mesh, it should be tools that are agnostic. And I think there are certain tools that are, I think you guys started with that principle. Not every data catalog is going to enable that, but I think that is the trend Satyen. And I think you guys have always fit into that. It's just that I think you were ahead of the time. Hey, we'll give you the last word. Give us the closing thoughts and bring us home. >> Well, I mean that's exactly right. Like, not all the catalogs are created equal and certainly not all governance is created equal. And I think most people say these words and think that are simple to get into. And then it's a death by a thousand cuts. I was literally on the phone with a chief data officer yesterday of a major distributor. And they basically said, look, like we've got sprawl everywhere. We've got data everywhere. We've got it in every type of system. And so having that sophistication turned into something that's actually easy to use is a super hard problem. And it's the one that we're focused on every single day that we wake up and every single night when we go to sleep. And so, that's kind of what we do. And we're here to make governance easy, to make data discovery easy. Those are the things that we hold our hats on. And we're super excited to put this release out 'cause we think it's going to make customers so much more capable of building on top of the problems that they've already solved. And that's what we're here to do. >> Good stuff, Satyen. Thanks so much, congratulations on the announcement and great to see you again. >> You too, Dave. Great talking. >> All right, thanks for watching this CUBE conversation. This is Dave Vellante, we'll see you next time. (tranquil music)

>> Narrator: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We're coming to you today from our Palo Alto studios with theCUBE conversation, talking about data, and we're excited to have our next guest. He's been on a number of times, many times, CUBE alum, really at the forefront of helping companies and customers be more data centric in their activities. So we'd like to welcome onto the show Satyen Sangani. He is the co founder and CEO of Alation. Satyen, great to see you. >> Great to see you, Jeff. It's good to see you again in this new world, a new format. >> It is a new world, a new format, and what's crazy is, in March and April we were talking about this light switch moment, and now we've just turned the calendar to October and it seems like we're going to be doing this thing for a little bit longer. So, it is kind of the new normal, and even I think when it's over, I don't think everything's going to go back to the way it was, so here we are, but you guys have some exciting news to announce, so let's just jump to the news and then we'll get into a little bit more of the nitty gritty. So what do you got coming out today, right? >> Yeah its so. >> What we are announcing today is basically Alation 2020, which is probably one of the biggest releases that I've been with, that we've had since I've been with the company. We with it are releasing three things. So in some sense, there's a lot of simplicity to the release. The first thing that we're releasing is a new experience around what we call the business user experience, which will bring in a whole new set of users into the catalog. The second thing that we're announcing is basically around Alation analytics and the third is around what we would describe as a cloud-native architecture. In total, it brings a fully transformative experience, basically lowering the total cost of getting to a data management experience, lower and data intelligent experience, much lower than previously had been the case. >> And you guys have a really simple mission, right? You're just trying to help your customers be more data, what's the right word? Data centric, use data more often and to help people actually make that decision. And you had an interesting quote in another interview, you talked about trying to be the Yelp for information which is such a nice kind of humanizing way to think about it because data isn't necessarily that way and I think, you mentioned before we turned on the cameras, that for a lot of people, maybe it's just easier to ignore the data. If I can just get the decision through, on a gut and intuition and get onto my next decision. >> Yeah, you know it's funny. I mean, we live in a time where people talk a lot about fake news and alternative facts and our vision is to empower a curious and rational world and I always smile when I say that a little bit, because it's such a crazy vision, right? Like how you get people to be curious and how do you get people to think rationally? But you know, to us, it's about one making the data really accessible, just allowing people to find the data they need when and as they want it. And the second is for people to be able to think scientifically, teaching people to take the facts at their disposal and interpret them correctly. And we think that if those two skills existed, just the ability to find information and interpret it correctly, people can make a lot better decisions. And so the Yelp analogy is a perfect one, because if you think about it, Yelp did that for local businesses, just like Amazon did it for really complicated products on the web and what we're trying to do at Alation is, in some sense very simple, which is to just take information and make it super usable for people who want to use it. >> Great, but I'm sure there's the critics out there, right? Who say, yeah, we've heard this before the promise of BI has been around forever and I think a lot of peoples think it just didn't work whether the data was too hard to get access to, whether it was too hard to manipulate, whether it was too hard to pull insights out, whether there's just too much scrubbing and manipulating. So, what is some of the secret sauce to take? What is a very complex world? And again and you got some very large customers with some giant data sets and to, I don't want to say humanize it, but kind of humanize it and make it easier, more accessible for that business analyst not just generally, but more specifically when I need it to make a decision. >> Yeah I mean, it's so funny because, making something, data is like a lot of software death by 1000 cuts. I mean you look at something from the outside and it looks really, really, really simple, but then you kind of dwell into any problem and that can be CRM something like Salesforce, or it can be something like service now with ITSM, but these are all really, really complicated spaces and getting into the depths and the detail of it is really hard. And data is really no different, like data is just the sort of exhaust from all of those different systems that exist inside of your company. So the detail around the data in your company is exhaustingly minute. And so, how do you make something like that simple? I think really the biggest challenge there is progressively revealing complexity, right? Giving people the right amount of information at the right amount of time. So, one of the really clever things that we do in this business user experience is we allow people to search for and receive the information that's most relevant to them. And we determined that relevance based upon the other people in the enterprise that happen to be using that data. And we know what other people are using in that company, because we look at the logs to understand which data sources are used most often, and which reports are used most often. So right after that, when you get something, you just see the name of the report and it could be around the revenues of a certain product line. But the first thing that you see is who else uses it. And that's something that people can identify with, you may not necessarily know what the algorithm was or what the formula might be, how the business glossary term relates to some data model or data artifact, but you know the person and if you know the person, then you can trust the information. And so, a lot of what we do is spend time on design to think about what is it that a person expects to see and how do they verify what's true. And that's what helps us really understand what to serve up to somebody so that they can navigate this really complicated, relevant data. >> That's awesome, cause there's really a signal to noise problem, right? And I think I've heard you speak before. >> Yeah >> And of course this is not new information, right? There's just so much data, right? The increasing proliferation of data. And it's not that there's that much more data, we're just capturing a lot more of it. So your signal to noise problem just gets worse and worse and worse. And so what you're talking about is really kind of helping filter that down to get through a lot of that, a lot of that noise, so that you can find the piece of information within the giant haystack. That is what you're looking for at this particular time in this particular moment. >> Yeah and it's a really tough problem. I mean, one of the things that, it's true that we've been talking about this problem for such a long time. And in some instance, if we're lucky, we're going to be talking about it for a lot longer because it used to be that the problem was, back when I was growing up, you were doing research on a topic and you'd go to the card catalog and you'd go to the Dewey decimal system. And in your elementary school or high school library, you might be lucky if you were to find, one, two or three books that map to the topic that you were looking for. Now, you go to Google and you find 10,000 books. Now you go inside of an enterprise and you find 4,000 relational database tables and 200 reports about an artifact that you happened to be looking for. And so really the problem is what do I trust? And what's correct and getting to that level of accuracy around information, if there's so much information out there is really the big problem of our time and I think, for me it's a real privilege to be able to work on it because I think if we can teach people to use information better and better then they can make better decisions and that can help the world in so many different. >> Right, right, my other favorite example that everybody knows is photographs, right? Back when you only got 24 and a roll and cost you six bucks to develop it. Those were pretty special and now you go buy a fancy camera. You can shoot 11, 11 frames a second. You go out and shoot the kids at the soccer game. You come home with 5,000 photos. How do you find the good photo? It's a real, >> Yeah. >> It's a real problem. If you've ever faced something like that, it's kind of a splash of water in the face. Like where do I even begin? But the other piece that you talk about a lot, which is slightly different but related is context, and in favorite concept, it's like 55, right? That's a number, but if you don't have any context for that number, is it a temperature? Is it cold inside the building? Is it a speed? Is it too slow on i5? Or is it fast because I'm on a bicycle going down a Hill and without context data is just, it's just a number. It doesn't mean anything. So you guys really by adding this metadata around the data are adding a lot more contextual information to help figure out kind of what that signal is from the noise. >> Yap, you'll get facts from anywhere, right? Like, you're going to have a Hitchcock, you've got a 55 or 42, and you can figure out like what the meaning of the universe is and apparently the answer is 42 and what does that mean? It might mean a million different things and that, to me, that context is the difference between, suspecting and knowing. And there's the difference between having confidence and basically guessing. And I think to the extent that we can provide more of that over time, that's, what's going to make us, an ever more valuable partner to the customers that we satisfy today. >> Right, well, I do know why 42 is always the answer 'cause that's Ronnie Lot and that's always the answer. So, that one I know that's an easy one. (both chuckles) But it is really interesting and then you guys just came out. I heard Aaron Kalb on, one of your co-founders the other day and we talked about this new report that you guys have sponsored the Data Culture Report and really, putting some granularity on a Data Culture Index and I thought it was pretty interesting and I'm excited that you guys are going to be doing this, longitudinally because whether you do or do not necessarily agree with the method, it does give you a number, It does give you a score, It's a relatively simple formula. And at least you can compare yourself over time to see how you're tracking. I wonder if you could share, I mean, the thing that jumps out right off the top of that report is something we were talking about before we turned the cameras on that, people's perception of where they are on this path doesn't necessarily map out when you go bottoms up and add the score versus top down when I'm just making an assessment. >> Yeah, it's funny, it's kind of the equivalent of everybody thinks they're an above average driver or everybody thinks they're above average in terms of obviously intelligence. And obviously that mathematically is not possible or true, but I think in the world of data management, we all talk about data, we all talk about how important it is to use data. And if you're a data management professional, you want people in your company to use more data. But ironically, the discipline of data management doesn't actually use a lot of data itself. It tends to be a very slow methodical process driven gut oriented process to develop things like, what data models exist and how do I use my infrastructure and where do I put my data and which data quality is best? Like all of those things tend to be, somewhat heuristic driven or gut driven and they don't have to be and a big part of our release actually is around this product called Alation Analytics. And what we do with that product is really quite interesting. We start measuring elements of how your organization uses data by team, by data source, by use case. And then we give you transparency into what's going on with the data inside of your landscape and eco-system. So you can start to actually score yourself both internally, but also as we reveal in our customer success methodology against other customers, to understand what it is that you're doing well and what it is that you're doing badly. And so you don't need necessarily to have a ton of guts instinct anymore. You can look at the data of yourselves and others to figure out where you need to improve. And so that's a pretty exciting thing and I think this notion that says, look, you think you're good, but are you really good? I mean, that's fundamental to improvement in business process and improvement in data management, improvement in data culture fundamentally for every company that we work with. >> Right, right and if you don't know, there's a problem, and if you're not measuring it, then there's no way to improve on it, right? Cause you can't, you don't know, what you're measuring is. >> Right. >> But I'm curious of the three buckets that you guys measured. So you measured data search and discovery was bucket number one, data literacy, you know what you do once you find it and then data governance in terms of managing. It feels like that the search and discovery, which is, it sounds like what you're primarily focused on is the biggest gap because you can't get to those other two buckets unless you can find and understand what you're looking for. So is that JIve or is that really not problem, is it more than manipulation of the data once you get it? >> Yeah, I mean we focus really. We focus on all three and I think that, certainly it's the case that it's a virtuous cycle. So if you think about kind of search and discovery of data, if you have very little context, then it's really hard to guide people to the right bit of information. But if I know for example that a certain data is used by a certain team and then a new member of that team comes on board. Then I can go ahead and serve them with exactly that bit of data, because I know that the human relationships are quite tight in the context graph on the back end. And so that comes from basically building more context over time. Now that context can come from a stewardship process implemented by a data governance framework. It can come from, building better data literacy through having more analytics. But however, that context is built and revealed, there tends to be a virtuous cycle, which is you get more, people searching for data. Then once they've searched for the data, you know how to necessarily build up the right context. And that's generally done through data governance and data stewardship. And then once that happens, you're building literacy in the organization. So people then know what data to search for. So that tends to be a cycle. Now, often people don't recognize that cycle. And so they focus on one thing thinking that you can do one to the exclusion of the others, but of course that's not the case. You have to do all three. >> Great and I would presume you're using some good machine, Machine Learning and Artificial Intelligence in that process to continue to improve it over time as you get more data, the metadata around the data in terms of the usage and I think, again I saw in another interview there talking about, where should people invest? What is the good data? What's the crap data? what's the stuff we shouldn't use 'cause nobody ever uses it or what's the stuff, maybe we need to look and decide whether we want to keep it or not versus, the stuff that's guiding a lot of decisions with Bob, Mary and Joe, that seems to be a good investment. So, it's a great application of applied AI Machine Learning to a very specific process to again get you in this virtuous cycle. That sounds awesome. >> Yeah, I know it is and it's really helpful to, I mean, it's really helpful to think about this, I mean the problem, one of the biggest problems with data is that it's so abstract, but it's really helpful to think about it in just terms of use cases. Like if I'm using a customer dataset and I want to join that with a transaction dataset, just knowing which other transaction datasets people joined with that customer dataset can be super helpful. If I'm an analyst coming in to try to answer a question or ask a question, and so context can come in different ways, just in the same way that Amazon, their people who bought this product also bought this product. You can have all of the same analogies exist. People who use this product also use that product. And so being able to generate all that intelligence from the back end to serve up simple seeming experience on the front end is the fun part of the problem. >> Well I'm just curious, cause there's so many pieces of this thing going on. What's kind of the, aha moment when you're in with a new customer and you finish the install and you've done all the crawling and where all the datasets are, and you've got some baseline information about who's using what I mean, what is kind of the, Oh, my goodness. When they see this thing suddenly delivering results that they've never had at their fingertips before. >> Yeah, it's so funny 'cause you can show Alation as a demo and you can show it to people with data sets that are fake. And so we have this like medical provider data set that, we've got in there and we've got a whole bunch of other data sets that are in there and people look at it and interestingly enough, a lot of time, they're like, Oh yeah, I can kind of see it work and I can kind of like understand that. And then you turn it on against their own data. The data they have been using every single day and literally their faces change. They look at the data and they say, Oh my God, like, this is a dataset that Steven uses, I didn't even know that Steven thought that this data existed and, Oh my God, like people are using this data in this particular way. They shouldn't be using that data at all, Like I thought I deprecated that dataset two years ago. And so people have all of these interesting insights and it's interesting how much more real it gets when you turn it on against the company's systems themselves. And so that's been a really fun thing that I've just seen over and over again, over the course of multiple years where people just turn on the cup, they turn on the product and all of a sudden it just changes their view of how they've been doing it all along. And that's been really fun and exciting. >> That's great yeah, cause it means something to them, right? It's not numbers on a page, It's actually, it's people, it's customers, it's relationships, It's a lot of things. That's a great story and I'm curious too, in that process, is it more often that they just didn't know that there were these other buckets of reports and other buckets of data or was it more that they just didn't have access to it? Or if they did, they didn't really know how to manipulate it or to integrate it into their own workflow. >> Yeah, It's kind of funny and it's somewhat role dependent, but it's kind of all of the above. So, if you think about it, if you're a data management professional, often you kind of know what data sources might exist in the enterprise, but you don't necessarily know how people are using the data. And so you look at data and you're like, Oh my God, I can't believe this team is using this data for this particular purpose. They shouldn't be doing that. They should be using this other data set. I deprecated that data set like two years ago. And then sometimes if you're a data scientist, you're you find, Oh my gosh, there's this new database that I otherwise didn't realize existed. And so now I can use that data and I can process that for building some new machine learning algorithms. In one case we've had a customer where they had the same data set procured five different times. So it was a pure, it was a data set that cost multiple hundreds of thousands of dollars. They were spending $2 million overall on a data set where they could have been spending literally one fifth of that amount. And then you had a sort of another case finally, where you're basically just looking at it and saying, Hey, I remember that data set. I knew I had that dataset, but I just don't remember exactly where it was. Where did I put that report? And so it's exactly the same way that you would use Google. Sometimes you use it for knowledge discovery, but sometimes you also use it for just remembering the thing you forgot. >> Right but, but the thing, like I remember when people were trying to put Google search in that companies just to find records not necessarily to support data efforts and the knock was always, you didn't have enough traffic to drive the algorithm to really have effective search say across a large enterprise that has a lot of records, but not necessarily a lot of activity. So, that's a similar type of problem that you must have. So is it really extracting that extra context of other people's usage that helps you get around kind of that you just don't have a big numbers? >> Yeah, I mean that kind of is fundamentally the special sauce. I mean, I think a lot of data management has been this sort of manual brute force effort where I get a whole bunch of consultants or a whole bunch of people in the room and we do this big documentation session. And all of a sudden we hope that we've kind of, painted the golden gate bridge is at work. But, knowing that three to six months later, you're going to have to go back and repaint the golden gate bridge overall all over again, if not immediately, depending on the size and scale of your company. The one thing that Google did to sort of crawl the web was to really understand, Oh, if a certain webpage was linked to super often, then that web page is probably a really useful webpage. And when we crawled the logs, we basically do the exact same thing. And that's really informed getting a really, really specific day one view of your data without having to have a whole bunch of manual effort. And that's been really just dramatical. I mean, it's been, it's allowed people to really see their data very quickly and new different ways and I think a big part of this is just friction reduction, right? We'd all love to have an organized data world. We'd love to organize all the information in a company, but for anybody has an email inbox, organizing your own inbox, let alone organizing every database in your company just seems like a specificity in effort. And so being able to focus people on what's the most important thing has been the most important thing. And that's kind of why we've been so successful. >> I love it and I love just kind of the human factors kind of overlay, that you've done to add the metadata with the knowledge of who is accessing these things and how are they accessing it. And the other thing I think is so important Satyen is, we talk about innovation all the time. Everybody wants more innovation and they've got DevOps so they can get software out faster, et cetera, et cetera. But, I fundamentally believe in my heart of hearts that it's much more foundational than that, right? That if you just get more people, access to more information and then the ability to manipulate and clean knowledge out of that information and then actually take action and have the power and the authority to take action. And you have that across, everyone in the company or an increasing number of people in the company. Now suddenly you're leveraging all those brains, right? You're leveraging all that insight. You're leveraging all that kind of First Line experience to drive kind of a DevOps type of innovation with each individual person, as opposed to, kind of classic waterfall with the Chief Innovation Officer, Doing PowerPoints in his office, on his own time. And then coming down from the mountain and handing it out to everybody to go build. So it's a really a kind of paradox that by adding more human factors to the data, you're actually making it so much more usable and so much more accessible and ultimately more valuable. >> Yeah, it's funny we, there's this new term of art called data intelligence. And it's interesting because there's lots of people who are trying to define it and there's this idea and I think IDC, IDC has got a definition and you can go look it up, but if you think about the core word of intelligence, it basically DevOps down to the ability to acquire information or skills, right? And so if you then apply that to companies and data, data intelligence then stands to reason. It's sort of the ability for an organization to acquire, information or skills leveraging their data. And that's not just for the company, but it's for every individual inside of that company. And we talk a lot about how much change is going on in the world with COVID and with wildfires here in California. And then obviously with the elections and then with new regulations and with preferences, cause now that COVID happened everybody's at home. So what products and what services do you have to deliver to them? And all of this change is, basically what every company has to keep up with to survive, right? If capitalism is creative destruction, the world's getting destroyed, like, unfortunately more often than we'd like it to be,. >> Right. >> And so then you're say there going, Oh my God, how do I deal with all of this? And it used to be the case that you could just build a company off of being really good at one thing. Like you could just be the best like logistics delivery company, but that was great yesterday when you were delivering to restaurants. But since there are no restaurants in business, you would just have to change your entire business model and be really good at delivering to homes. And how do you go do that? Well, the only way to really go do that, is to be really, really intelligent throughout your entire company. And that's a function of data. That's a function of your ability to adapt to a world around you. And that's not just some CEO cause literally by the time it gets to the CEO, it's probably too late. Innovations got to be occurring on the ground floor. And people have got to repackage things really quickly. >> I love it, I love it. And I love the other human factor that we talked about earlier. It's just, people are curious, right? So if you can make it easy for them to fulfill their curiosity, they're going to naturally seek out the information and use it versus if you make it painful, like a no fun lesson, then people's eyes roll in and they don't pay attention. So I think that it's such an insightful way to address the problem and really the opportunity and the other piece I think that's so different when you're going down the card catalog analogy earlier, right? Is there was a day when all the information was in that library. And if you went to the UCLA psych library, every single reference that you could ever find is in that library, I know I've been there, It was awesome, but that's not the way anymore, right? You can't have all the information and it's pulling your own information along with public information and as much information as you can. where you start to build that competitive advantage. So I think it's a really great way to kind of frame this thing where information in and of itself is really not that valuable. It's about the context, the usability, the speed of these ability and that democratization is where you really start to get these force multipliers and using data as opposed to just talking about data. >> Yeah and I think that that's the big insight, right? Like if you're a CEO and you're kind of looking at your Chief Data Officer or Chief Data and Analytics Officer. The real question that you're trying to ask yourself is, how often do my people use data? How measurable is it? Like how much do people, what is the level at which people are making decisions leveraging data and that's something that, you can talk about in a board room and you can talk about in a management meeting, but that's not where the question gets answered. The question gets really answered in the actual behaviors of individuals. And the only way to answer that question, if you're a Chief Analytics Officer or somebody who's responsible for data usage within the company is by measuring it and managing it and training it and making sure it's a part of every process and every decision by building habit and building those habits are just super hard. And that's, I think the thing that we've chosen to be sort of the best in the world at, and it's really hard. I mean, we're still learning about how to do it, but, from our customers and then taking that knowledge and kind of learning about it over time. >> Right, well, that's fantastic. And if it wasn't hard, it wouldn't be valuable. So those are always the best problems to solve. So Satyen, really enjoyed the conversation. Congratulations to you and the team on the new release. I'm sure there's lots of sweat, blood and tears that went into that effort. So congrats on getting that out and really great to catch up. Look forward to our next catch up. >> You too Jeff, It's been great to talk. Thank you so much. >> All right, take care. All righty Satyen and I'm Jeff, you're watching theCUBE. We'll see you next time. Thanks for watching. (ethereal music)

>> Narrator: From theCUBE's studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, this is Dave Vellante. Welcome to this CUBE conversation. We got a really cool company that we're going to introduce you to, and Anthony Brooks Williams is here. He's the CEO of that company, HVR. Anthony, good to see you. Thanks for coming on. >> Hey Dave, good to see you again, appreciate it. >> Yeah cheers, so tell us a little bit about HVR. Give us the background of the company, we'll get into a little bit of the history. >> Yeah sure, so at HVR we are changing the way companies routes and access their data. And as we know, data really is the lifeblood of organizations today, and if that stops moving, or stop circulating, well, there's a problem. And people want to make decisions on the freshest data. And so what we do is we move critical business data around these organizations, the most predominant place today is to the cloud, into platforms such as Snowflake, where we've seen massive traction. >> Yeah boy, have we ever. I mean, of course, last week, we saw the Snowflake IPO. The industry is abuzz with that, but so tell us a little bit more about the history of the company. What's the background of you guys? Where did you all come from? >> Sure, the company originated out of the Netherlands, at Amsterdam, founded in 2012, helping solve the issue that customer's was having moving data efficiently at scale across all across a wide area network. And obviously, the cloud is one of those endpoint. And therefore a company, such as the Dutch Postal Service personnel, where today we now move the data to Azure and AWS. But it was really around how you can efficiently move data at scale across these networks. And I have a bit of a background in this, dating back from early 2000s, when I founded a company that did auditing recovery, or SQL Server databases. And we did that through reading the logs. And so then sold that company to Golden Gate, and had that sort of foundation there, in those early days. So, I mean again, Azure haven't been moving data efficiently as we can across these organizations with it, with the key aim of allowing customers to make decisions on the freshest data. Which today's really, table stakes. >> Yeah, so, okay, so we should think about you, as I want to invoke Einstein here, move as much data as you need to, but no more, right? 'Cause it's hard to move data. So your high speeds kind of data mover, efficiency at scale. Is that how we should think about you? >> Absolutely, I mean, at our core, we are CDC trades that capture moving incremental workloads of data, moving the updates across the network, you mean, combined with the distributed architecture that's highly flexible and extensible. And these days, just that one point, customers want to make decisions on us as much as they can get. We have companies that we're doing this for, a large apparel company that's taking some of their not only their core sales data, but some of that IoT data that they get, and sort of blending that together. And given the ability to have a full view of the organization, so they can make better decisions. So it's moving as much data as they can, but also, you need to do that in a very efficient way. >> Yeah, I mean, you mentioned Snowflake, so what I'd like to do is take my old data warehouse, and whatever, let it do what it does, reporting and compliance, stuff like that, but then bring as much data as I need into my Snowflake, or whatever modern cloud database I'm using, and then apply whatever machine intelligence, and really analyze it. So really that is kind of the problem that you're solving, is getting all that data to a place where it actually can be acted on, and turned into insights, is that right? >> Absolutely, I mean, part of what we need to do is there's a whole story around multi-cloud, and that's obviously where Snowflake fit in as well. But from our point of views of supporting over 30 different platforms. I mean data is generated, data is created in a number of different source systems. And so our ability to support each of those in this very efficient way, using these techniques such as CDCs, is going to capture the data at source, and then weaving it together into some consolidated platform where they can do the type of analysis they need to do on that. And obviously, the cloud is the predominant target system of choice with something like a Snowflake there in either these clouds. I mean, we support a number of different technologies in there. But yeah, it's about getting all that data together so they can make decisions on all areas of the business. So I'd love to get into the secret sauce a little bit. I mean we've heard luminaries like Andy Jassie stand up at last year at Reinvent, he talked about Nitro, and the big pipes, and how hard it is to move data at scale. So what's the secret sauce that you guys have that allow you to be so effective at this? >> Absolutely, I mean, it starts with  how you going to acquire data? And you want to do that in the least obtrusive way to the database. So we'll actually go in, and we read the transaction logs of each of these databases. They all generate logs. And we go read the logs systems, all these different source systems, and then put it through our webs and secret sauce, and how we how we move the data, and how we compress that data as well. So, I mean, if you want to move data across a wide area network, I mean, the technique that a few companies use, such as ourselves, is change data capture. And you're moving incremental updates, incremental workloads, the change data across a network. But then combine that with the ability that we have around some of the compression techniques that we use, and, and then just into very distributed architecture, that was one of the things that made me join HVR after my previous experiences, and seeing that how that really fits in today's world of real time and cloud. I mean, those are table stakes things. >> Okay, so it's that change data capture? >> Yeah. >> Now, of course, you've got to initially seed the target. And so you do that, if I understand you use data reduction techniques, so that you're minimizing the amount of data. And then what? Do you use asynchronous methodologies, dial it down, dial it up, off hours, how does that work? >> Absolutely, exactly what you've said they mean. So we're going to we're, initially, there's an initial association, or an initial concept, where you take a copy of all of that data that sits in that source system, and replicating that over to the target system, you turn on that CDC mechanism, which is then weaving that change data. At the same time, you're compressing it, you're encrypting it, you're making sure it's highly secure, and loading that in the most efficient way into their target systems. And so we either do a lot of that, or we also work with, if there's a ETL vendor involved, that's doing some level of transformations, and they take over the transformation capabilities, or loading. We obviously do a fair amount of that ourselves as well. But it depends on what is the architecture that's in there for the customer as well. The key thing is that what we also have is, we have this compare and repair ability that's built into the product. So we will move data across, and we make sure that data that gets moved from A to B is absolutely accurate. I mean people want to know that their data can move faster, they want it to be efficient, but they also want it to be secure. They want to know that they have a peace of mind to make decisions on accurate data. And that's some stuff that we have built into the products as well, supported across all the different platforms as well. So something else that just sets us apart in that as well. >> So I want to understand the business case, if you will. I mean, is it as simple as, "Hey, we can move way more data faster. "We can do it at a lower cost." What's the business case for you guys, and the business impact? >> Absolutely, so I mean, the key thing is the business case is moving that data as efficiently as we can across this, so they can make these decisions. So our biggest online retailer in the US uses us, on the biggest busiest system. They have some standard vendors in there, but they use us, because of the scalability that we can achieve there, of making decisions on their financial data, and all the transactions that happen between the main E-commerce site, and all the third party vendors. That's us moving that data across there as efficiently as they can. And first we look at it as pretty much it's subscription based, and it's all connection based type pricing as well. >> Okay, I want to ask you about pricing. >> Yeah. >> Pricing transparency is a big topic in the industry today, but how do you how do you price? Let's start there. >> Yeah, we charge a simple per connection price. So what are the number of source systems, a connection is a source system or a target system. And we try to very simply, we try and keep it as simple as possible, and charge them on the connections. So they will buy a packet of five connections, they have source systems, two target systems. And it's pretty much as simple as that. >> You mentioned security before. So you're encrypting the data. So your data in motion's encrypted. What else do we need to know about security? >> Yeah, you mean, that we have this concept and how we handle, and we have this wallet concept, and how we integrate with the standard security systems that those customers have already, in the in this architecture. So it's something that we're constantly doing. I mean, there's there's a data encryption at rest. And initially, the whole aim is to make sure that the customer feels safe, that the data that is moving is highly secure. >> Let's talk a little bit about cloud, and maybe the architecture. Are you running in the cloud, are you running on prem, both, across clouds. How does that work? >> Yeah, all of the above. So I mean, what we see today is majority of the data is still generated on prem. And then the majority of the talks we see are in the cloud, and this is not a one time thing, this is continuous. I mean, they've moved their analytical workload into the cloud. You mean they have these large events a few times a year, and they want the ability to scale up and scale down. So we typically see you mean, right now, you need analytics, data warehouses, that type of workload is sitting in the cloud, because of the elasticity, and the scalability, and the reasons the cloud was brought on. So absolutely, we can support the cloud to cloud, we can support on prem to cloud, I think you mean, a lot of companies adopting this hybrid strategy that we've seen certainly for the foreseeable next five years. But yeah, absolutely. The source of target systems considered on prem or in the cloud. >> And where's the point of control? Is it wherever I want it to be? >> Absolutely. >> Is it in one of the clouds on prem? >> Yeah absolutely, you can put that point of control where you want it to be. We have a concept of agents, these agents search on the source and target systems. And then we have the, it's at the edge of your brain, the hub that is controlling what is happening. This data movement that can be sitting with a source system, separately, or on target system. So it's highly extensible and flexible architecture there as well. >> So if something goes wrong, it's the HVR brain that helps me recover, right? And make sure that I don't have all kinds of data corruption. Maybe you could explain that a little bit, what happens when something goes wrong? >> Yeah absolutely, I mean, we have things that are built into the product that help us highlight what has gone wrong, and how we can correct those. And then there's alerts that get sent back to us to the to the end customer. And there's been a whole bunch of training, and stuff that's taken place for then what actions they can take, but there's a lot of it is controlled through HVR core system that handles that. So we are working next step. So as we move as a service into more of an autonomous data integration model ourselves, whichever, a bunch of exciting things coming up, that just takes that off to the next levels. >> Right, well Golden Gate Heritage just sold that to Oracle, they're pretty hardcore about things like recovery. Anthony, how do you think about the market? The total available market? Can you take us through your opportunity broadly? >> Yeah absolutely, you mean, there's the core opportunity in the space that we play, as where customers want to move data, they don't want to do data integration, they want to move data from A to B. There's those that are then branching out more to moving a lot of their business workloads to the cloud on a continuous basis. And then where we're seeing a lot of traction around this particular data that resides in these critical business systems such as SAP, that is something you're asking earlier about, what are some core things on our product. We have the ability to unpack, to unlock that data that sits in some of these SAP environments. So we can go, and then decode this data that sits between these cluster pool tables, combine that with our CDC techniques, and move their data across a network. And so particularly, sort of bringing it back a little bit, what we're seeing today, people are adopting the cloud, the massive adoption of Snowflake. I mean, as we see their growth, a lot of that is driven through consumption, why? It's these big, large enterprises that are now ready to consume more. We've seen that tail wind from our perspective, as well as taking these workloads such as SAP, and moving that into something like these cloud platforms, such as a Snowflake. And so that's where we see the immediate opportunity for us. And then and then branching out from there further, but I mean, that is the core immediate area of focus right now. >> Okay, so we've talked about Snowflake a couple of times, and other platforms, they're not the only one, but they're the hot one right now. When you think about what organizations are doing, they're trying to really streamline their data pipeline to get to turn raw data into insights. So you're seeing that emerging organizations, that data pipeline, we've been talking about it for quite some time. I mean, Snowflake, obviously, is one piece of that. Where's your value in that pipeline? Is it all about getting the data into that stream? >> Yeah, you just mentioned something there that we have an issue internally that's called raw data to ready data. And that's about capturing this data, moving that across. And that's where we building value on that data as well, particularly around some of our SAP type initiatives, and solutions related to that, that we're bringing out as well. So one it's absolutely going in acquiring that data. It's then moving it as efficiently as we can at scale, which a lot of people talk about, we truly operate at scale, the biggest companies in the world use us to do that, across there and giving them that ability to make decisions on the freshest data. Therein lies the value of them being able to make decisions on data that is a few seconds, few minutes old, versus some other technology they may be using that takes hours days. You mean that is it, keeping large companies that we work with today. I mean keeping toner paper on shelves, I mean one thing that happened after COVID. I mean one of our big customers was making them out their former process, and making the shelves are full. Another healthcare provider being able to do analysis on what was happening on supplies from the hospital, and the other providers during this COVID crisis. So that's where it's a lot of that value, helping them reinvent their businesses, drive down that digital transformation strategy, is the key areas there. No data, they can't make those type of decisions. >> Yeah, so I mean, your vision really, I mean, you're betting on data. I always say don't bet against the data. But really, that's kind of the premise here. Is the data is going to continue to grow. And data, I often say data is plentiful insights aren't. And we use the Broma you said before. So really, maybe, good to summarize the vision for us, where you want to take this thing? Yeah, absolutely so we're going to continue building on what we have, making it easier to use. Certainly, as we move, as more customers move into the cloud. And then from there, I mean, we have some strategic initiatives of looking at some acquisitions as well, just to build on around offering, and some of the other core areas. But ultimately, it's getting closer to the business user. In today's world, there is many IT tech-savvy people sitting in the business side of organization, as they are in IT, if not more. And so as we go down that flow with our product, it's getting closer to those end users, because they're at the forefront of wanting this data. As we said that the data is the lifeblood of an organization. And so given an ability to drive the actual power that they need to run the data, is a core part of that vision. So we have some some strategic initiatives around some acquisitions, as well, but also continue to build on the product. I mean, there's, as I say, I mean sources and targets come and go, there's new ones that are created each week, and new adoptions, and so we've got to support those. That's our table stakes, and then continue to make it easier to use, scale even quicker, more autonomous, those type of things. >> And you're working with a lot of big companies, the company's well funded if Crunchbase is up to date, over $50 million in funding. Give us up right there. >> Yeah absolutely, I mean a company is well funded, we're on a good footing. Obviously, it's a very hot space to be in. With COVID this year, like everybody, we sat down and looked in sort of everyone said, "Okay well, let's have a look how "this whole thing's going to shake out, "and get good plan A, B and C in action." And we've sort of ended up with Plan A plus, we've done an annual budget for the year. We had our best quarter ever, and Q2, 193% year over year growth. And it's just, the momentum is just there, I think at large. I mean obviously, it sounds cliche, a lot of people say it around digital transformation and COVID. Absolutely, we've been building this engine for a few years now. And it's really clicked into gear. And I think projects due to COVID and things that would have taken nine, 12 months to happen, they're sort of taking a month or two now. It's been getting driven down from the top. So all of that's come together for us very fortunately, the timing has been ideal. And then tie in something like a Snowflake traction, as you said, we support many other platforms. But all of that together, it just set up really nicely for us, fortunately. >> That's amazing, I mean, with all the turmoil that's going on in the world right now. And all the pain in many businesses. I tell you, I interview people all day every day, and the technology business is really humming. So that's awesome to hear that you guys. I mean, especially if you're in the right place, and data is the place to be. Anthony, thanks so much for coming on theCUBE and summarizing your thoughts, and give us the update on HVR, really interesting. >> Absolutely, I appreciate the time and opportunity. >> Alright, and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (upbeat music)

>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.

>> From around the globe, it's theCUBE with coverage of KubeCon and CloudNativeCon Europe 2020 Virtual brought to you by Red Hat, the CloudNative Computing Foundation, and Ecosystem Partners. >> Hi and welcome back to theCUBE's coverage of KubeCon, CloudNativeCon 2020 in Europe, of course the virtual edition. I'm Stu Miniman and happy to welcome back to the program one of the keynote speakers, he's also a board member of the CNCF, Vijoy Pandey who is the vice president and chief technology officer for Cloud at Cisco. Vijoy, nice to see you and thanks so much for joining us. >> Thank you Stu, and nice to see you again. It's a strange setting to be in but as long as we are both health, everything is good. >> Yeah, it's still a, we still get to be together a little bit even though while we're apart, we love the engagement and interaction that we normally get through the community but we just have to do it a little bit differently this year. So we're going to get to your keynote. We've had you on the program to talk about "Network, Please Evolve", been watching that journey. But why don't we start it first, you know, you've had a little bit of change in roles and responsibility. I know there's been some restructuring at Cisco since the last time we got together. So give us the update on your role. >> Yeah, so that, yeah let's start there. So I've taken on a new responsibility. It's VP of Engineering and Research for a new group that's been formed at Cisco. It's called Emerging Tech and Incubation. Liz Centoni leads that and she reports into Chuck. The role, the charter for this team, this new team, is to incubate the next bets for Cisco. And, if you can imagine, it's natural for Cisco to start with bets which are closer to its core business, but the charter for this group is to mover further and further out from Cisco's core business and takes this core into newer markets, into newer products, and newer businesses. I am running the engineering and research for that group. And, again, the whole deal behind this is to be a little bit nimble, to be a little startupy in nature, where you bring ideas, you incubate them, you iterate pretty fast and you throw out 80% of those and concentrate on the 20% that make sense to take forward as a venture. >> Interesting. So it reminds me a little bit, but different, I remember John Chambers a number of years back talking about various adjacencies, trying to grow those next, you know, multi-billion dollar businesses inside Cisco. In some ways, Vijoy, it reminds me a little bit of your previous company, very well known for, you know, driving innovation, giving engineering 20% of their time to work on things. Give us a little bit of insight. What's kind of an example of a bet that you might be looking at in the space? Bring us inside a little bit. >> Well that's actually a good question and I think a little bit of that comparison is, are those conversations that taking place within Cisco as well as to how far out from Cisco's core business do we want to get when we're incubating these bets. And, yes, my previous employer, I mean Google X actually goes pretty far out when it comes to incubations. The core business being primarily around ads, now Google Cloud as well, but you have things like Verily and Calico and others which are pretty far out from where Google started. And the way we are looking at these things within Cisco is, it's a new muscle for Cisco so we want to prove ourselves first. So the first few bets that we are betting upon are pretty close to Cisco's core but still not fitting into Cisco's BU when it comes to go-to-market alignment or business alignment. So while the first bets that we are taking into account is around API being the queen when it comes to the future of infrastructure, so to speak. So it's not just making our infrastructure consumable as infrastructure's code, but also talking about developer relevance, talking about how developers are actually influencing infrastructure deployments. So if you think about the problem statement in that sense, then networking needs to evolve. And I talked a lot about this in the past couple of keynotes where Cisco's core business has been around connecting and securing physical endpoints, physical I/O endpoints, whatever they happen to be, of whatever type they happen to be. And one of the bets that we are, actually two of the bets that we are going after is around connecting and securing API endpoints wherever they happen to be of whatever type they happen to be. And so API networking, or app networking, is one big bet that we're going after. Our other big bet is around API security and that has a bunch of other connotations to it where we think about security moving from runtime security where traditionally Cisco has played in that space, especially on the infrastructure side, but moving into API security which is only under the developer pipeline and higher up in the stack. So those are two big bets that we're going after and as you can see, they're pretty close to Cisco's core business but also very differentiated from where Cisco is today. And once when you prove some of these bets out, you can walk further and further away or a few degrees away from Cisco's core as it exists today. >> All right, well Vijoy, I mentioned you're also on the board for the CNCF, maybe let's talk a little bit about open source. How does that play into what you're looking at for emerging technologies and these bets, you know, so many companies, that's an integral piece, and we've watched, you know really, the maturation of Cisco's journey, participating in these open source environments. So help us tie in where Cisco is when it comes to open source. >> So, yeah, so I think we've been pretty deeply involved in open source in our past. We've been deeply involved in Linux foundational networking. We've actually chartered FD.io as a project there and we still are. We've been involved in OpenStack. We are big supporters of OpenStack. We have a couple of products that are on the OpenStack offering. And as you all know, we've been involved in CNCF right from the get go as a foundational member. We brought NSM as a project. It's sandbox currently. We're hoping to move it forward. But even beyond that, I mean we are big users of open source. You know a lot of us has offerings that we have from Cisco and you would not know this if you're not inside of Cisco, but Webex, for example, is a big, big user of linger D right from the get go from version 1.0. But we don't talk about it, which is sad. I think for example, we use Kubernetes pretty deeply in our DNAC platform on the enterprise site. We use Kubernetes very deeply in our security platforms. So we are pretty deep users internally in all our SAS products. But we want to press the accelerator and accelerate this whole journey towards open source quite a bit moving forward as part of ET&I, Emerging Tech and Incubation as well. So you will see more of us in open source forums, not just the NCF but very recently we joined the Linux Foundation for Public Health as a premier foundational member. Dan Kohn, our old friend, is actually chartering that initiative and we actually are big believers in handling data in ethical and privacy preserving ways. So that's actually something that enticed us to join Linux Foundation for Public Health and we will be working very closely with Dan and the foundational companies there to, not just bring open source, but also evangelize and use what comes out of that forum. >> All right. Well, Vijoy, I think it's time for us to dig into your keynote. We've spoken with you in previous KubeCons about the "Network, Please Evolve" theme that you've been driving on, and big focus you talked about was SD-WAN. Of course anybody that been watching the industry has watched the real ascension of SD-WAN. We've called it one of those just critical foundational pieces of companies enabling Multicloud, so help us, you know, help explain to our audience a little bit, you know, what do you mean when you talk about things like CloudNative, SD-WAN, and how that helps people really enable their applications in the modern environment? >> Yeah, so, well we we've been talking about SD-WAN for a while. I mean, it's one of the transformational technologies of our time where prior to SD-WAN existing, you had to stitch all of these MPLS labels and actual data connectivity across to your enterprise or branch and SD-WAN came in and changed the game there. But I think SD-WAN as it exists today is application-alaware. And that's one of the big things that I talk about in my keynote. Also, we've talked about how NSM, the other side of the spectrum, is how NSM, or network service mesh, has actually helped us simplify operational complexities, simplify the ticketing and process hell that any developer needs to go through just to get a multicloud, multicluster app up and running. So the keynote actually talked about bringing those two things together where we've talked about using NSM in the past, in chapter one and chapter two, ah chapter two, no this is chapter three and at some point I would like to stop the chapters. I don't want this to be like, like an encyclopedia of networking (mumbling) But we are at chapter three and we are talking about how you can take the same consumption models that I talked about in chapter two which is just adding a simple annotation in your CRD and extending that notion of multicloud, multicluster wires within the components of our application but extending it all the way down to the user in an enterprise. And as you saw an example, Gavin Russom is trying to give a keynote holographically and he's suffering from SD-WAN being application alaware. And using this construct of a simple annotation, we can actually make SD-WAN CloudNative. We can make it application-aware, and we can guarantee the SLOs that Gavin is looking for in terms of 3D video, in terms of file access or audio just to make sure that he's successful and Ross doesn't come in and take his place. >> Well I expect Gavin will do something to mess things up on his own even if the technology works flawly. You know, Vijoy the modernization journey that customers are on is a neverending story. I understand the chapters need to end on the current volume that you're working on. But, you know, we'd love to get your view point. You talk about things like service mesh. It's definitely been a hot topic of conversation for the last couple of years. What are you hearing from your customers? What are some of the the kind of real challenges but opportunities that they see in today's CloudNative space? >> In general, service meshes are here to stay. In fact, they're here to proliferate to some degree and we are seeing a lot of that happening where not only are we seeing different service meshes coming into the picture through various open source mechanisms. You've got Istio there, you've got linger D, you've got various proprietary notions around control planes like App Mesh from Amazon. There's Console which is an open source project But not part of (mumbles) today. So there's a whole bunch of service meshes in terms of control planes coming in on volumes becoming a de facto side car data plane, whatever you would like to call it, de facto standard there which is good for the community I would say. But this proliferation of control planes is actually a problem. And I see customers actually deploying a multitude of service meshes in their environment. And that's here to stay. In fact, we are seeing a whole bunch of things that we would use different tools for. Like API Gate was in the past. And those functions are actually rolling into service meshes. And so I think service meshes are here to stay. I think the diversity of some service meshes is here to stay. And so some work has to be done in bringing these things together and that's something that we are trying to focus in on all as well because that's something that our customers are asking for. >> Yeah, actually you connected for me something I wanted to get your viewpoint on. Dial back you know 10, 15 years ago and everybody would say, "Ah, you know, I really want to have single pane of glass "to be able to manage everything." Cisco's partnering with all of the major cloud providers. I saw, you know, not that long before this event, Google had their Google Cloud show talking about the partnership that you have with Cisco with Google. They have Anthos. You look at Azure has Arc. You know, VMware has Tanzu. Everybody's talking about, really, kind of this multicluster management type of solution out there. And just want to get your viewpoint on this Vijoy is to, you know, how are we doing on the management plane and what do you think we need to do as a industry as a whole to make things better for customers? >> Yeah, but I think this is where I think we need to be careful as an industry, as a community and make things simpler for our customers because, like I said, the proliferation of all of these control planes begs the question, do we need to build something else to bring all of these things together. And I think the SMI apropos from Microsoft is bang on on that front where you're trying to unify at least the consumption model around how you consume these service meshes. But it's not just a question of service meshes. As you saw in the SD-WAN and also going back in the Google discussion that you just, or Google conference that we just offered It's also how SD-WANs are going to interoperate with the services that exist within these cloud silos to some degree. And how does that happen? And there was a teaser there that you saw earlier in the keynote where we are taking those constructs that we talked about in the Google conference and bringing it all the way to a CloudNative environment in the keynote. But I think the bigger problem here is how do we manage this complexity of disparate stacks, whether it's service meshes, whether it's development stacks, or whether it's SD-WAN deployments, how do we manage that complexity? And, single pane of glass is over loaded as a term because it brings in these notions of big, monolithic panes of glass. And I think that's not the way we should be solving it. We should be solving it towards using API simplicity and API interoperability. I think that's where we as a community need to go. >> Absolutely. Well, Vijoy, as you said, you know, the API economy should be able to help on these, you know, multi, the service architecture should allow things to be more flexible and give me the visibility I need without trying to have to build something that's completely monolithic. Vijoy, thanks so much for joining. Looking forward to hearing more about the big bets coming out of Cisco and congratulations on the new role. >> Thank you Stu. It was a pleasure to be here. >> All right, and stay tuned for much more coverage of theCUBE at KubeCon, CloudNativeCon. I'm Stu Miniman and thanks for watching. (light digital music)

>>LA Las Vegas. It's the cube hovering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Hey, welcome back to the cube. Our day one coverage of AWS reinvent 19 continues. Lisa Martin with Dave Volante. Dave and I have a couple of guests we'd like you to walk up. We've got Anthony Brooks billions, the CEO of HBR back on the cube. You're alumni. We should get you a pin and snowflake alumni. But Chris, your new Chris Dagon, chief revenue officer from snowflake. Chris, welcome to the program. Excited to be here. All right guys. So even though both companies have been on before, Anthony, let's start with you. Give our audience a refresher about HVR, who you guys are at, what you do. >>Sure. So we're in the data integration space, particularly a real time data integration. So we move data to the cloud in the in the most efficient way and we make sure it's secure and it's accurate and you're moving into environments such as snowflake. Um, and that's where we've got some really good customers that we happy to talk about joint custody that we're doing together. But Chris can tell us a little bit about snowflake. >>Sure. And snowflake is a cloud data warehousing company. We are cloud native, we are on AWS or on GCP and we're on Azure. And if you look at the competitive landscape, we compete with our friends at Amazon. We compete with our friends at Microsoft and our friends at Google. So it's super interesting place to be, but it very exciting at the same time and super excited to partner with Anthony and some others who aren't really a friends. That's correct. So I wonder if we could start by just talking about the data warehouse sort of trends that you guys see. When I talk to practitioners in the old days, they used to say to me things like, Oh, infrastructure management, it's such a nightmare. It's like a snake swallowing a basketball every time until it comes out with a new chips. We chase it because we just need more performance and we can't get our jobs done fast enough. And there's only three. There's three guys that we got to go through to get any answers and it was just never really lived up to the promise of 360 degree view of your business and realtime analytics. How has that changed? >>Well, there's that too. I mean obviously the cloud has had a big difference on that illustrious city. Um, what you would find is in, in, in yesterday, customers have these, a retail customer has these big events twice a year. And so to do an analysis on what's being sold and Casper's transactions, they bought this big data warehouse environment for two events a year typically. And so what's happening that's highly cost, highly costly as we know to maintain and then cause the advances in technology and trips and stuff. And then you move into this cloud world which gives you that Lester city of scale up, scale down as you need to. And then particular where we've got Tonies snowflake that is built for that environment and that elicited city. And so you get someone like us that can move this data at today's scale and volume through these techniques we have into an environment that then bleeds into helping them solve the challenge that you talk about of Yesi of >>these big clunky environments. That side, I think you, I think you kind of nailed it. I think like early days. So our founders are from Oracle and they were building Oracle AI nine nine, 10 G. and when I interviewed them I was the first sales rep showing up and day one I'm like, what the heck am I selling? And when I met them I said, tell me what the benefit of snowflake is. And they're like, well at Oracle, and we'd go talk to customers and they'd say, Oracles, you know, I have this problem with Oracle. They'd say, Hey, that's, you know, seven generations ago were Oracle. Do you have an upgraded to the latest code? So one of the things they talked about as being a service, Hey, we want to make it really easy. You never have to upgrade the service. And then to your point around, you have a fixed amount of resources on premise, so you can't all of a sudden if you have a new project, do you want to bring on the first question I asked when I started snowflake to customers was how long does it take you to kick off a net new workload onto your data, onto your Vertica and it take them nine to 12 months because they'd have to go procure the new hardware, install it, and guess what? >>With snowflake, you can make an instantaneous decision and because of our last test city, because the benefits of our partner from Amazon, you can really grow with your demand of your business. >>Many don't have the luxury of nine to 12 months anymore, Chris, because we all know if, if an enterprise legacy business isn't thinking, there's somebody not far behind me who has the elasticity, who has the appetite, who's who understands the opportunity that cloud provides. If you're not thinking that, as auntie Jessie will say, you're going to be on the wrong end of that equation. But for large enterprises, that's hard. The whole change culture is very hard to do. I'd love to get your perspective, Chris, what you're seeing in terms of industries shifting their mindsets to understand the value that they could unlock with this data, but how are big industries legacy industries changing? >>I'd say that, look, we were chasing Amad, we were chasing the cloud providers early days, so five years ago, we're selling to ad tech and online gaming companies today. What's happened in the industry is, and I'll give you a perfect example, is Ben wa and I, one of our founders went out to one of the largest investment banks on wall street five years ago, and they said, and they have more money than God, and they say, Hey, we love what you've built. We love, when are you going to run on premise? And Ben, Ben wa uttered this phrase of, Hey, you will run on the public cloud before we ever run in the private cloud. And guess what? He was a truth teller because five years later, they are one of our largest customers today. And they made the decision to move to the cloud and we're seeing financial services at a blistering face moved to the cloud. >>And that's where, you know, partnering with folks from HR is super important for us because we don't have the ability to just magically have this data appear in the cloud. And that's where we rely quite heavily on on instance. So Anthony, in the financial services world in particular, it used to be a cloud. Never that was an evil word. Automation. No, we have to have full control and in migration, never digital transformation to start to change those things. It's really become an imperative, but it's by in particular is really challenging. So I wonder if we could dig into that a little bit and help us understand how you solve that problem. >>Yes. A customer say they want to adopt some of these technologies. So there's the migration route. They may want to go adopt some of these, these cloud databases, the cloud data warehouses. And so we have some areas where we, you know, we can do that and keep the business up and running at the same time. So the techniques we use are we reading the transactional logs, other databases or something called CDC. And so there'll be an initial transfer of the bulk of the data initiative stantiating or refresh. At that same time we capturing data out of the transaction logs, wildlife systems live and doing a migration to the new environment or into snowflakes world, capturing data where it's happening, where the data is generated and moving that real time securely, accurately into this environment for somewhere like 1-800-FLOWERS where they can do this, make better decisions to say the cost is better at point of sale. >>So have all their business divisions pulling it in. So there's the migration aspects and then there's the, the use case around the realtime reporting as well. So you're essentially refueling the plane. Well while you're in mid air. Um, yeah, that's a good one. So what does the customer see? How disruptive is it? How do you minimize that disruption? Well, the good thing is, well we've all got these experienced teams like Chris said that have been around the block and a lot of us have done this. What we do, what ed days fail for the last 15 years, that companies like golden gate that we sold to Oracle and those things. And so there's a whole consultative approach to them versus just here's some software, good luck with it. So there's that aspect where there's a lot of planning that goes into that and then through that using our technologies that are well suited to this Appleton shows some good success and that's a key focus for us. And in our world, in this subscription by SAS top world, customer success is key. And so we have to build a lot of that into how we make this successful as well. >>I think it's a barrier to entry, like going, going from on premise to the cloud. That's the number one pushback that we get when we go out and say, Hey, we have a cloud native data warehouse. Like how the heck are we going to get the data to the cloud? And that's where, you know, a partnership with HR. Super important. Yeah. >>What are some of the things that you guys encountered? Because we many businesses live in the multi-cloud world most of the time, not by strategy, right? A lot of the CIO say, well we sort of inherited this, or it's M and a or it's developers that have preference. How do you help customers move data appropriately based on the value that the perceived value that it can give in what is really a multi world today? Chris, we'll start with you. >>Yeah, I think so. So as we go into customers, I think the biggest hurdle for them to move to the cloud is security because they think the cloud is not secure. So if we, if you look at our engagement with customers, we go in and we actually have to sell the value snowflake and then they say, well, okay great, go talk to the security team. And then we talked to security team and say, Hey, let me show you how we secure data. And then then they have to get comfortable around how they're going to actually move, get the data from on premise to the cloud. And that's again, when we engage with partners like her. So yeah, >>and then we go through a whole process with a customer. There's a taking some of that data in a, in a POC type environment and proving that after, as before it gets rolled out. And a lot of, you know, references and case studies around it as well. >>Depends on the customer that you have some customers who are bold and it doesn't matter the size. We have a fortune 100 customer who literally had an on premise Teradata system that they moved from on prem, from on premise 30 to choose snowflake in 111 days because they were all in. You have other customers that say, Hey, I'm going to take it easy. I'm going to workload by workload. And it just depends. And the mileage may vary is what can it give us an example of maybe a customer example or in what workloads they moved? Was it reporting? What other kinds? Yeah. >>Oh yeah. We got a couple of, you mean we could talk a little bit about 1-800-FLOWERS. We can talk about someone like Pitney Bowes where they were moving from Oracle to secret server. It's a bunch of SAP data sitting in SAP ECC. So there's some complexity around how you acquire, how you decode that data, which we ever built a unique ability to do where we can decode the cluster and pool tables coupled with our CDC technique and they had some stringent performance loads, um, that a bunch of the vendors couldn't meet the needs between both our companies. And so we were able to solve their challenge for them jointly and move this data at scale in the performance that they needed out with these articles, secret server enrollments into, into snowflake. >>I almost feel like when you have an SAP environment, it's almost stuck in SAP. So to get it out is like, it's scary, right? And this is where it's super awesome for us to do work like this. >>On that front, I wanted to understand your thoughts on transformation. It's a word, it's a theme of reinvent 2019. It's a word that we hear at every event, whether we're talking about digital transformation, workforce, it, et cetera. But one of the things that Andy Jassy said this morning was that got us start. It's this is more than technology, right? This, the next gen cloud is more than technology. It's about getting those senior leaders on board. Chris, your perspective, looking at financial services first, we were really surprised at how quickly they've been able to move. Understanding presumably that if they don't, there's going to be other businesses. But are you seeing that as the chief revenue officer or your conversations starting at that CEO level? >>It kinda has to like in the reason why if you do in bottoms up approach and say, Hey, I've got a great technology and you sell this great technology to, you know, a tech person. The reality is unless the C E O CIO or CTO has an initiative to do digital transformation and move to the cloud, you'll die. You'll die in security, you'll die in legal lawyers love to kill deals. And so those are the two areas that I see D deals, you know, slow down significantly. And that's where, you know, we, it's, it's getting through those processes and finding the champion at the CEO level, CIO level, CTO level. If you're, if you're a modern day CIO and you do not have a a cloud strategy, you're probably going to get replaced >>in 18 months. So you know, you better get on board and you'd better take, you know, taking advantage of what's happening in the industry. >>And I think that coupled with the fact that in today's world, you mean, you said there's a, it gets thrown around as a, as a theme and particularly the last couple of years, I think it's, it's now it is actually a strategy and, and reality because what Josephine is that there's as many it tech savvy people sit in the business side of organizations today that used to sit in legacy it. And I think it's that coupled with the leadership driving it that's, that's demanding it, that demanding to be able to access that certain type of data in a geo to make decisions that affect the business. Right now. >>I wonder if we could talk a little bit more about some of the innovations that are coming up. I mean I've been really hard on data. The data warehouse industry, you can tell I'm jaded. I've been around a long time. I mean I've always said that that Sarbanes Oxley saved the old school BI and data warehousing and because all the reporting requirements, and again that business never lived up to its promises, but it seems like there's this whole new set of workloads emerging in the cloud where you take a data warehouse like a snowflake, you may be bringing in some ML tools, maybe it's Databricks or whatever. You HVR helping you sort of virtualize the data and people are driving new workloads that are, that are bringing insights that they couldn't get before in near real time. What are you seeing in terms of some of those gestalt trends and how are companies taking advantage of these innovations? >>I think one is just the general proliferation of data. There's just more data and like you're saying from many different sources, so they're capturing data from CNC machines in factories, you know like like we do for someone like GE, that type of data is to data financial data that's sitting in a BU taking all of that and going there's just as boss some of data, how can we get a total view of our business and at a board level make better decisions and that's where they got put it in I snowflake in this an elastic environment that allows them to do this consolidated view of that whole organization, but I think it's largely been driven by things that digitize their sensors on everything and there's just a sheer volume of data. I think all of that coming together is what's, what's driven it >>is is data access. We talked about security a little bit, but who has rights to access the data? Is that a challenge? How are you guys solving that or is it, I mean I think it's like anything like once people start to understand how a date where we're an acid compliant date sequel database, so we whatever your security you use on your on premise, you can use the same on snowflake. It's just a misperception that the industry has that being on, on in a data center is more secure than being in the cloud and it's actually wrong. I guess my question is not so much security in the cloud, it's more what you were saying about the disparate data sources that coming in hard and fast now. And how do you keep track of who has access to the data? I mean is it another security tool or is it a partnership within owes? >>Yeah, absolutely man. So there's also, there's in financial data, there's certain geos, data leaves, certain geos, whether it be in the EU or certain companies, particularly this end, there's big banks now California, there's stuff that we can do from a security perspective in the data that we move that's secure, it's encrypted. If we capturing data from multiple different sources, items we have that we have the ability to take it all through one, one proxy in the firewall, which does, it helps him a lot in that aspect. Something unique in our technology. But then there's other tools that they have and largely you sit down with them and it's their sort of governance that they have in the, in the organization to go, how do they tackle that and the rules they set around it, you know? >>Well, last question I have is, so we're seeing, you know, I look at the spending data and my breaking analysis, go on my LinkedIn, you'll see it snowflakes off the charts. It's up there with, with robotic process automation and obviously Redshift. Very strong. Do you see those two? I think you addressed it before, but I'd love to get you on record sort of coexisting and thriving. Really, that's not the enemy, right? It's the, it's the Terra data's and the IBM's and the Oracles. The, >>I think, look, uh, you know, Amazon, our relationship with Amazon is like a, you know, a 20 year marriage, right? Sometimes there's good days, sometimes there's bad days. And I think, uh, you know, every year about this time, you know, we get a bat phone call from someone at Amazon saying, Hey, you know, the Redshift team's coming out with a snowflake killer. And I've heard that literally for six years now. Um, it turns out that there's an opportunity for us to coexist. Turns out there's an opportunity for us to compete. Um, and it's all about how they handle themselves as a business. Amazon has been tremendous in separation of that, of, okay, are going to partner here, we're going to compete here, and we're okay if you guys beat us. And, and so that's how they operate. But yes, it is complex and it's, it's, there are challenges. >>Well, the marketplace guys must love you though because you're selling a lot of computers. >>Well, yeah, yeah. This is three guys. They, when they left, we have a summer thing. You mean NWS have a technological DMS, their data migration service, they work with us. They refer opportunities to us when it's these big enterprises that are use cases, scale complexity, volume of data. That's what we do. We're not necessary into the the smaller mom and pop type shops that just want to adopt it, and I think that's where we all both able to go coexist together. There's more than enough. >>All right. You're right. It's like, it's like, Hey, we have champions in the Esri group, the EEC tuna group, that private link group, you know, across all the Amazon products. So there's a lot of friends of ours. Yeah, the red shift team doesn't like us, but that's okay. I can live in >>healthy coopertition, but it just goes to show that not only do customers and partners have toys, but they're exercising it. Gentlemen, thank you for joining David knee on the key of this afternoon. We appreciate your time. Thank you for having us. Pleasure our pleasure for Dave Volante. I'm Lisa Martin. You're watching the queue from day one of our coverage of AWS reinvent 19 thanks for watching.

(upbeat music) >> Hi, and welcome to another Cube Conversation where we speak with thought leaders in depth about the topics that are most important to the overall technology community. I'm Peter Burris, your host. Every business inspires to be a digital business, which is every business, faces a significant challenge. They need to use their data in new and value creating ways. But some of that data is not lending itself to new applications, new uses because it's locked up in formats, in technologies and applications that don't lend themselves to change. That's one of the big challenges that every business faces. What can they do to help unlock, to help liberate their data from older formats and older approaches so they can create new sources of value with it. To have that conversation, we're joined by a great guest. Greg Tinker is the CTO and Founder of SereneIT. Greg, welcome back to theCube. >> Thank you Peter, very appreciate it buddy. >> It's been a long time. This is your first time here with SereneIT so why don't you tell us a little bit about SereneIT. >> Sure, so at a high level we are a technology partner. SereneIT focuses on the next generation model structures of engineering first. There's a lot of VARS, in simplest terms, I would say we're a value at a reseller, sure. But we capitalize and focus just on the VA. Anybody can bring VR. The legacy approach of just being a reseller is no longer valid in our industry. Complexities and trying to have a situation where you can liberate data, try to take it from a legacy entrenched model, process, procedure and go into a new modern IT software defined ecosystem is very complex. And our objective is to make the, enablement of IT serene or simple and that's where SereneIT comes from. >> You know, I love the name but if you go back 20 years as you said, the asset that IT was focused on and took care of was the hardware. >> That's right. >> And we bought the hardware from a reseller, they just made the installations, configurations and what not. But as you said, today we're focused on the data. That's the asset. >> That's correct. >> And just as we used to have challenges uplifting and all the things we had to do with hardware, we're having similar types of challenges when you think about how to apply data to new uses, sustain that asset feature of it but apply it in new ways to create new value. As you talk to customers, what is the problem that you find they're encountering as they try to think what to do with their high value traditional data? So there's actually, I'll call it three strategic problems. Becoming to where it to be a workload optimized model structures or your data driven intelligence, trying to pull something out of the data model, trying to pull something out of the data, make it tangible to the business. And then trying to figure out a way to make it easy to enable the users, that is the employees to do something with the data the have. Making it more of a cloud-centric approach. Everybody wants that easy button now. So at a high level, trying to make that a possibility is where we spend our time today. And give you a quick example of that would be legacy block storage. We do a lot in the storage world. And we focus on software defined storage apparatus or solutions. So a lot of our clients are kind of mired down with legacy block, via Fibre Channel basics that were great for their era. But today with cost being a big factor in trying to be able to leverage an ecosystem where I can take my data, wherever my data sits and leverage it on multiple different apparatuses, be it BlueData, be in Kubernetes, be it name your favorite Docker solution. Trying to be able to use that in an ecosystem in a software defined hyper cloud, doing that on a legacy block is very problematic. And that's where we help customers transition from that legacy mindset, legacy IT infrastructure into a more of a modern software defined data program. >> So what's talk about that. Because there's a more modern technology, but really what they're doing is they're saying, look I've got this data, using these protocols like Fibre Channel with these applications and it's doing its job. >> That's right. >> But I want to create options on how I might use that data in the future, options that aren't available to me or aren't available to my business if it stays locked inside Fibre Channel for example. >> That's correct. >> So what you're really doing, is you're giving them paths to new options with their data that can be sustained whatever the technology is. Have I got that right? >> In a nutshell, Frank I would agree with your sentiment on that, your comment is spot on. We take customers data, we look at the business as a whole. And we focus on, what is the core of the business? Be it, maybe it's a High-Performance Computing Cluster Maybe it's a Oracle, Cyrus, Informant name your favorite data base structure. Maybe it's MapR, maybe it's a Dupe. We look at the business and determine, how are we using that data? How much data do we need? What's my data working set size? Understanding that and then we actually would design a solution that will be a software defined ecosystem that we can move that data in. And nine times out of 10 we can do it on the fly. Rarely, rarely ever do we have an outage to do it. Or that might be a small few minute outage window when we do a cut over, where we keep everything in mirroring Lockstep . >> Well that's one of the beauties of software defined is that you have those kinds of flexibilities. >> That's right. >> But think of, so talk to me a little bit about the you are, the customer realizes they have a problem. They find you guys. >> Sure. >> So how do they find you? >> So we do a lot with large scale Fortune 50, Fortune 100, the large scale enterprise businesses. And we do that with our, we're known in the engineering world, big accounts, because of our backdrop in HP engineering. And so HP brings us a lot into these accounts to help them solve a big business problem. So that's how a lot of our customers are finding us today. We are reaching out with media, like theCube here to talk to clients about the fact that we do exist and that we exist to help them consume a more modern IT in footprint. To help them go from that legacy model into that more modern model. >> Okay, so the customer realizes they have a problem, HPE and others, help identify you guys, matches you together. You show up, how do you work with the customer? Is it your big brains and the customer passive? Or you're working side by side to help them accelerate their journey? >> We find it best that we do it in a cohesive manner. We sit down and have a long discussion with their, usually their Chief Executive officer, their CTO, Chief Technology Officer, we'll sit down and talk about the business constraints. And then we'll go down to the directors the guys on the front lines that see the problems on a day to day basis. And we look at where their constraints are. Is it performance, IOP driven. Nine times out of 10, those problems are no longer there. They were solved years ago. Today it's more about the legacy model of, let me log a ticket to stand up a new virtual machine to a SQL database to do this application. So I've logged the ticket, a week two later I finally get a virtual machine. And now I got to get five more teams engaged, I get it online. Total business takes about a month to get some new apparatus up. Where if we go into a software defined ecosystem where we have these playbooks and this model written for the business, we can do that in 10 minutes. Be it on Nutanix do it with SimpliVity, VMware models, we don't' differentiate that. We let the customer tell us which one they use. 'Cause everybody has their liking. Be it some are VMware shop, some are Hyper-V, some are KBM. We do all of them. >> But the point is you want to help them move form an old world that was focused on executing the tasks associate with bringing the system up to a new world that's focused on the resources being able to configure themselves, being able to bring to bring themselves up test themselves in a software defined manner introducing some of those DevOps processes. Whatever the technology is, they have the people and the process to execute the technology. >> That's exactly right, because the technology in a nutshell. If you look at just technology itself that's not the hard part. Not for us anyway, 'cause we're an engineering team that's what we do well. The data driven intelligence stuff and helping customers bring more value out of their data. We can help them with that and show them exactly how we would do it. Be it a different technologies and stuff and we'll get into that discussion later. But the biggest problem we see is the people and processes which you just mentioned. Pushing the button, achieve an objective. That is where the old way of being very ticket driven Siloed approach, really slow down the economics of business. Was a huge driving force of not achieving the ROI that you actually set out to do years ago. Where we have one client that has a little over 4,000 servers and how my team and I explain it to the clients. Come out to the Golden Gate bridge. January 1 you start painting. December 30th you're done painting and January 1 you start painting again. You never get done. It's always getting painted. Patching of these large scale enterprises is the exact same way. You can't patch all the servers on a Saturday. You can't patch three thousand machines, BIOS, firmware, the list goes on. What we do for them is we actually put in an apparatus engine, basically an automation engine and instead of an army of 10 people doing firmware or BIOS and all the stuff updates, we automated 100% of that entire process. That's what SereneIT does. Help a customer take a, could be a legacy model, bare metal machine and show them how we can automate the bare metal machine. We can do the exact same thing in any hypervisor on the planet today. >> So that it's done faster, simpler. The outcome is more predictable. The result is more measurable. >> Yes. >> That's really great stuff. Let's go back to this notion of data because we kind of started with this idea of data and having to evolve the formats increasing the flexibility of it's utilization. We talked about hypervisors and all that technology is kind of sucking it forward, bringing that data forward making it possible to do things with it, but still the data itself is a major challenge. How are you working with customers to get them to envision the new data world independent of some of these other technologies? >> Sure, okay. So yeah, we have clients right now, we have (mumbled) systems these are global file systems that have enormous amount of data in it, some of it is compiled code logics for drivers and firmware and Kernel code structure that are forthcoming technologies that aren't even released yet. We have clients that have data based structure with ascii text is very common road driven. We have customers that have flat ascii files that are just flat text files. So we help the customers grab data from that existing data footprint for new lines of business. Determine what are we touching, how are we touching and how often are we touching it and why are we touching it? Case in point, when you have a large manufacturer doing chip design and your looking at a global file system you're trying to give assertation data as to what drivers are our developers working on most frequently. In the medical community, we have a client we're working on at global scale, we're doing real time data analytics to figure out if we're doing SQL injection from a hacker. So we show them exactly how we can do this in an inline driver stack and show them how to do it with the technology reducing their actual CapEx expend. There's legacy tools out there that work great. You know one of these is like, I won't give names of product and stuff, but there's a lot of cool technologies that's been around for a long time. >> That works. >> That works. >> And it just needs a smart person, or a smart team to put it together so it can be applied. >> That's what we've been doing with our clients is trying to show them that we can take the data that you have, be it flat ascii files or binary data structures. And we can show them that we can give you data analytics and pull that back. We have another client in law industry that we manage worldwide and we do e-discovery. On trying to figure out phrases and things that are maybe concerning to them in a financial world that is the global market. And we're able to give them that data structure on their own intellectual property and we give that to them in real time. We give them a dashboard so they can log in to the dashboard and they can see real time data transparency at a moments notice, so they can tell what the market is doing in Britain or they can tell what the market is doing in Singapore or U.S. by just looking at a dashboard and we're pulling data back. And we're pulling it from outside of world data points, this could be Facebook. Real time feeds, news, media and we pull it from internal data feeds. Email transactions that are going from their financial, they have like CIO's the Chief Investment Officers. Most people think of that as an information officer, right? So we're able to pull data from that and show them that they have a great deal of intellectual property at their fingertips that honestly they've never used before and that's what we're helping customers do today. >> Greg Tinker, Founder, CTO SereneIT. Thanks so much for being on theCube. >> Thank you very much Peter. >> And once again want to thank you for listening to this Cube Conversation. Until next time. (upbeat music)

>>live from Washington D. C. It's the Cube covering Bumi World 19 to Bide Movie. >>Hey, welcome back to the Cubes Coverage of Bumi World 19 from Washington D. C. I'm Lisa Martin with John Ferrier and John and I have a Cube alumni sitting with us. We have the chief product officer off. Del blew me. Steve would Steve, Welcome back. >>Thank you. It's great to be back. I could see again. John. Great must meet you >>back. Wise Enjoyed your keynote this morning, Man. There were so many nuggets and there I couldn't type faster. But one of my favorite things that you said is that no one is asking for less data. Slower? >>Yes, OK did I like kind of like saying because it frames things very clearly. It's just because it's clearly a prole. Every relates to him in the audience, but it was kind of amusing, so they've really got it immediately as I get that, that's a fair statement, so >>so like, and then you kind of took us the audience back. Thio 11 months ago at Bumi World 18. Some of the things that you guys said this is what we're going to be really focused on redefining the eye and I pass to be intelligent. Give her audience who wasn't able to see your keynote A little bit of that historical from 11 months ago. So what you guys are delivering today what the Bumi platform looks like today? >>Yeah, sure. So I mean, a lot of showed last Army, we kind of owe. Then we feel like we is like craters. The industry have to kind of try lead it. Where? Where is it going next? That's our big kind of duty, I guess. And so it's been taken over when we had the founder of booming attend, which was nice, but yes, so the big thing we should Last year was kind of the next generation, which is really a unified look and feel super easy to build applications that spend all of the portfolio and art in our that we offer our customers. We wanted to make it very collaborative, so users of business or business analysts or quick technical people can work together and use. Our platform is a collaboration space of the right controls in place. Eso stuff like that was really good to show that our new solutions. Overview. We've been definitely encouraging partners to put Maur intellectual property into our platform to excel, help accelerate their customers. Helping our customers just get people on board as quickly as possible. In fact, actually owned boarding employees on boarding was the solution we showed last year. >>That was fantastic. I couldn't believe how complex that was at Bumi. And when you guys said, We've got to change this huge improvements. >>Yeah, well, it was sort of a discovery that came up from one of our cells. Engineers got Andy Tiller did a fantastic job. He didn't enjoy his, um, his own boarding experience abuse me and then sort of building a solution. And we're like, we like we can actually do this way better on the platform. But what was amazing was that even for a company the size of Bumi, which is about 1000 people, we have, like, nearly 100 integration points and systems had to be coordinated to on board a single employee 100. Yeah, it's a lot, you know. So it became a really connectivity problem, actually, on >>boarding >>bits relatively easy. It's just, like connected all these systems. That's the hard bit. So yeah, we're excited to show that I think we got a kick out of seeing you together than we give progress on how we're moving that forward with various demos >>you don't want to ask you. Last year we asked the chief operating officer and the CEO Bumi what their investment priorities were going into the next year. And they said Number one was product. So that was a key thing. First and foremost go to market and then customer equation. But a product has been a big focus. That continues to be. What >>is >>the problem? Does it mean product when your chief product officer, what do you overseeing? Talk about What is the product? What is the platform And is there a difference? >>Yeah, I mean, so we we talk about the problem because we're in the product group, but we definitely see it as a platform. The investment in product is great. It means I get to spend lots of money like about my new converse. I won't try to show them, but way, but yeah, I mean, the investment partners being that we know that as we get Maur is this is this economy keeps building of integration and connective iti wanna continue to hold our leadership. We need to invest in product to make it easier. The expectations of our users is that they get a really premium experience when they're on board it onto the platform. We have to make sure we keep up to date with all of that effort. So a lot of what we talked about, it's how one is that we break our product up into discreet service is to allow us to move faster from an engineering perspective. And there's a lot of stuff that goes on there to think about ourselves as a platform to make sure we're fully extensible on. Then providing Maura Maura service is that people can build on our platform. So a lot of that investment just driving those >>activity. Rick was on yesterday talking about the big bets they made early on that are paying off. One of them was Aussie Cloud. On seeing that as you look at the architecture of this kind of new era of clobbering cloud to point, are we calling it? There's new requirements. It's the glue layers being built out. You need data to be accessible on addressable and available in real time, and you have multiple systems to talk to hence the integration you guys are doing. But this new mega trends happening is event driven architectures, which you guys talk about. There's a P I's just going from rest ful to state. And so you have micro service is here. So these air new dynamics Can >>you take >>a minute displaying like what all this means And what is event driven infrastructure? >>Yeah, a venture of architecture. But yeah, that's well, that's what we've been calling it. But yeah, I mean, it's basically that we're going to models where we're responding in real time to things that are happening out there on that revolt that involves a whole new level of scale. But, you know, we're also getting to things like streaming soas. Data come comes in, it's coming in, not in these packets, but it's constantly being fed to you, sir, constantly having to process it. You know, before in the integration space, it was like what? You'd set up a schedule you'd say, move that data at midnight from there to there and then it got faster and booming, provided real time, which was a request response that you send it personally, require a response back. But now it's like we're not going to just send it to you as a discreet thing. We're going to send it to you constantly, so event driven architectures. But how do you handle this continuous influx of data? And it's not getting any less. So how do you kind of manage this? We're being pulled in. Both ends were being pulled. There's never been more data that you never wanted to have faster. So it's like, How do you manage that? So for Bhumi, you know, that's why we're investing so heavily. >>Used to be in the old days when things were slower, events were like a trigger in a network management software alarm notification. Now they're happening. All the time is more and more events and paying attention to what events becomes a non human thing. Yeah, it's a software thing. Is that kind of where this is going? >>Yeah, well, I >>mean, we've been thinking >>a lot about that, like we sort of feel it. One is that we're gonna grow up from being on iPods to more of a data management vendor. We think that, like where the data manager in the future will come from an I pass, that we will be managing your data across like all of these systems from the catalogue and preparation to the, you know, actually integration and surfacing it up in real time and all that kind of streaming side. So I know it's Ah yeah, it's an evolving field for sure. >>One final point on this topic of product AP eyes have been great. They really made the market. Going back to the original Web service is in early two thousands to cloud. Where does a P I go? A A p I to dot or whatever you call it. What's the next Gen Place for AP? Eyes? >>Well, so it's interesting course. So we >>have >>a slightly different view of a pie management. That may be the typical AP management space, which is one thing to declare openly. But I think I >>want to >>go with that. Were right in the sense that cause I would think that because I'm a product, >>it's a good thing for a product. I don't think so Go >>and we're more than a little opinionated. So >>it is here, >>but yeah. Is that like sure. I mean, with a p I You need a gateway you need for the proxy ap eyes. Wherever they may be, wherever they may be developed. Other you build him and Bumi or you code them yourself when you told him, Manage those and throttle and scale and add policies and, you know, have developers registered to use them and monitor their usage and cut them off and have quotas. All that kind of that is old, fantastically good stuff. You know, there's lots of understeer doing a lot of that. We're adding Maur Mork capabilities there. But for us, a p I is really about AP enabling absolutely everything like we're in this world where you got refrigerators, two autonomous vehicles to cloud infrastructure to pivotal to all these different environments. And you have to have a tool that how do you How do you manage a P I across this incredibly disparate landscape of tools, technologies, things, infrastructure and it's one thing to say. OK, we could manage a P eyes and you install our software. Well, that's not good enough because, you know, with our customer like Jack in the box. They have 2200 plus retail locations. Nice have joked in my keynote that it's like painting Golden Gate Bridge. If you had to upgrade your gateway every time there was enough grade needed. It's like pain the Golden Gate Bridge to get to the end and you start all over again. That's 2200 plus retail locations. You know, I work for Dow. Ultimately is the holy owner of our business. He put five billion P seas on the planet. What if you had a gateway on five billion peces like, How do you manage that from a single control plane in the cloud? And that's what we're after. How do you do that huge scale AP enabling literally everything. >>And this was kind of under the concept of run anywhere that waas Yes, >>yes, yeah, and that was because we wanted to emphasize that it was about running Ap eyes and a pen, enabling things wherever they may be. That's why we put it under the run anywhere Banner. >>What's the biggest thing that you guys have done this year from last movie world that you're proud of? In terms of product or technology or something that could be of some obscure something prominent. What do you do? You proud of? What's the big thing? >>Yeah, well, for a point of perspective, it would be the AP I side for sure, because that was that was a big lift. There was a lot of work involved. We kind of moved ourselves forward very, very quickly in our capabilities on a p I with Gateway portal proxy, you know, literally within the span of just over a year. So that was Ah, big left. But I would, you know, because I also run engineering. So I feel like I need to, like, geek out a little bit. I mean, one of my proud things is, actually, we started wrestling and wrangling that 30 terabytes plus of metadata and starting to see what's in there. And like, anything in data science, you know, you're kind of like looking at weaken start. We started seeing all sorts of cool new things. Now I'm not gonna talk about it the inside side, But you start to see new things. We start to see ways that that meditated can be applied. So we built the infrastructure It's huge scale, massive scale they might have meditated, were ingesting and then analyzing eyes helping us, you know, improve productivity across the platforms. We talk a lot about being more efficient, more effective, so you'll see more of that in the pub. >>Can you clear up the just the commentary around the definition around single tenant instance? And when customers do multi tenant, because the benefit of the single tenants what the main core value proposition with the data, the unification of data? That's awesome. But there's also potential opportunities with customs. Might want have a roll run through things. So you have flexibility. Is that true? Is that the definite Take us through what the difference when, when multi tenant kicks in and what's >>well, so on our platform multi tendencies s. So if you think about the build experience when you're your dragon dropping, pointing, clicking, building your work flows or your processes for managing your data, you do that in the cloud, and then you can decide where you wanna put that. So where is that actually gonna be executed? And you can put it in our cloud, which is our multi tenant cloud, and then you. Could we manage it all for you? And that's fantastic. You can point or manage. Cloud service is if you have very specific requirements, usually around security, Sometimes around hyper scale. Well may put you in a manage cloud service environment. But then, if you have very sensitive data, you may want to run that workload and then stole our little run time. Adam, you know behind your firewall so we never see the data. So it's super sensitive. We don't see it. We >>see how >>it's running and we manage it. We have grade that that infrastructure for you, but we never see your data, so it kind of gives you the best of both worlds. You could be a cloud first, cloud only vendor, and you can be a traditional on perimeter. You could be a hybrid of both >>is not a requirement. The product. It's a customer choice. >>It's a total customer choice. I think that's pretty cool. Yeah, and I think actually we're one of the few that does it the way we've been doing for a long time. And it's hard, by the way, because it's like maintaining that compatibility For 10 plus years, is quite difficult to make sure everything works every time. We have, like 9000 >>customers and 80 plus countries. But on the the 30 plus terabytes of anonymous metadata, you are very clear this morning and saying that it's just the metadata that's not the actual have any any, you know, private information from any of our customers. But in terms of leveraging that data for those insights where some of the things that from last spoon me world to this one, that that access to all that data has what some are. Some of the announcements, maybe that came out today that you guys looked at saying, It's these are some of the nuggets that were able to pull out because we have the access to this musing. Maybe it's a I or what not gonna give you >>some examples in one was the the suggested filters. And it was a simple thing. I did sort of like that joke of It's one small step for Bhumi customers, but a giant leap for booming engineering. But because we rebuild a whole bunch of infrastructure to dio but suggested filters just making it easier to query information of various systems. And it is cool because it literally is looking your system, comparing it with other customers systems based on how you've configured in this case Attilio environment and then working out actually, based on what people are doing. This is kind of what the filter might look like for you, which is very, very personalized to the user. Based on intelligence. We have more That's on the bill tight. We have more on the deployment side because you can show you, actually hey, few of built in a p. I do want to deploy it out, too. A raspberry pie will. Actually, you probably want to configure the AP. I like this where you may find you see some issues here, and that's not static information that's evolving from the metadata. We can see the performance of your systems against the Oxy. All right, In that environment, I do it a bit like this. Or if you deploy to say, I Jules, we might make recommendations based on that process of that, a p I or that data quality hub that you wantto excess just make your systems run like this. So it's kind of predicting how you deployed >>I was about to say, Are you helping customers get predicted with us? >>Yes. And there's lots we can do there. I mean, like, so we'll do Maura. Maura. But we can automatically optimize your deployment. So if it's in our cloud, that that'll happens automatically. So helps us, too. But for customers, it's also making just go. Okay, we'll deploy it. And then the leverage that community to so see what works best. The most successful deployment, the most successful architecture and the way you've deployed it is was what you'll be matched with. And then the same with the run time. With monitoring, we can start to look at things and see will. Well, not slowing down a little bit. Actually, it's Linden the string error. A little bit, actually, based on what we've seen before, that system may be about to fall over, so you might want to get all not before completely does what it's gonna do. >>Well, we got you here. I want to get your definition of cloud two point. Oh, on We've been riffing on this. Been more of a takeoff on Web two point. Oh, because cloud one daughter was anything Amazon you know storage. Compute some networking, but it's Amazon that working. But you scale up start ups will go there. It's beautiful thing, but now it's enterprise. Start to embrace cloud with hybrid on premises and deal with all these hard problems and challenges. Crazy opportunity. An operating model for on premises Cloud Club one Dato Amazon. Really easy to work with. Scales are beautiful. Cloud to point is different. I got things to deal with. Observe, abilities, a hot thing you got kubernetes containers you got. How would you define what cloud? Two pointers for Enterprise? >>We'll think because we're all about the data cloud 2.0, is really like for us. Ah, data problem. I mean, it's just like E think before I mean, I was part of cells force for a while. Is this whole idea of like earlier data in the cloud will manageable for you. But when you're getting into the kind of environments were seeing, say, there's just too much data like you, it's not feasible. I mean, give you an example. Bumi itself. We moved our infrastructure customers was transplanted customers from Rackspace to eight of us Last year it was a big engineering lift to do. You can imagine moving 9000 plus customers over on our cloud Ah, design surface that but so we did that, but actually to move the data, it was so much it was actually faster to put the disk drives in the back of a van. No mobile moving over snowball using the wheel network, you know, the engine motor e one and then put the hard drives in. And then we did our sink to bring them back up so that we have the same data in both locations. And that's just an example of the kind of customer data that customers are routinely struggling with. And cloud wasn't set up for that. But that's becoming day to day now, so you need a highly distributed architecture. It was probably why we announced the Adam Fabric, which is really a fabric of connectivity, as much as is a fabric of data, so we don't need to move your data around. You can leave it where it is. We can do some analysis on it as part of an end to end >>Program Cube alumni that I was on the cube a couple weeks ago, he said. Data is the new software, data and software. What's your reaction to that when you hear that? >>To some extent, >>I think that's a CZ, A bit of a business process geek. I think you know this process around data for sure. But But I do think I've heard similar things with, like, actually, applications come and go. Business processes come and go, but the data remains so I think maybe in some respects, your date is the new software Could be a term I I could buy into a Well, >>Steve, it's been great having you on the Cube with John and me sharing all of the things that you guys have done in the last 11 months. I can't wait to see how everything becomes a P. I enabled. Still, next Bumi World, you gotta come back. Yeah, All right. Our pleasure for John Ferrier. I'm Lisa Martin. You're watching the Cube from Bhumi World 19. Thanks for watching

(upbeat music) >> Hey, welcome back everybody. Jeff Frick with The Cube. If you can't tell over my shoulder, we are at Oracle Park. It's a glorious day. The marine layer is burning off and it is really spectacular. We're happy to be here. Haven't been here since, I think 2014. It's an interesting event called Sports Tech Tokyo World Demo Day. About 25 technology companies in the sports area are giving demos all day today. It's a huge program, and we're excited to have our next guest coming from the analytics side. She's Sezin Aksoy, Global Data Strategy and Analytics for AXS. >> Correct. >> Welcome. >> Thank you. >> Absolutely. >> Glad to be here. >> So Global Data Strategy. Everything's all about data. >> Correct. >> So, somebody's really happy to have you on board. What are so... What do you, what are you working on, what was top of line. >> Sure, so it's going to sound cheesy but data is the power of the world. >> Yes. >> It's going to empower people making better decisions, so that's kind of my role is at AXS. So AXS is the ticketing platform for live entertainment events. We operate in the US, Europe, as well as in Japan. And, if you think about it, when a consumer comes to your website, that's the first touchpoint that you have. Whether they buy the ticket or don't. Whether they buy or sell, and transfer the ticket, or they attend the event, all those are various touchpoints that we are collecting. So that we can inform our clients to make better decisions with data. >> Right. >> Whether it's pricing decisions, or marketing decisions, or scanning an event, which gates will be more busier than others. So, that's kind of what my team works on. >> Excellent. So, let's jump into a little bit on the dynamic pricing. >> Sizen: Hm mm. >> Because we saw, we've seen dynamic pricing. And you said you were in the airline industry. >> Correct. >> We've seen it in the hotel industry. >> Yup. >> My father in law talks about when he was doing dynamic pricing as a young kid. >> Sizen: Okay. Just making a call when somebody came through the door, at eleven o'clock. >> Sizen: Yeah. (laughs) >> Jeffrey: What's my marginal cost... >> Okay, yep. >> Jeffrey: with somebody in that room or not. There's really slow to get beyond, kind of the entertain, oh excuse me, the travel industry for other people... >> Hm mm. Yep. >> To kind of get on board the dynamic pricing. >> Yeah. We saw the Giants here... >> Yep. >> Actually a couple of years ago. We came by, they were starting to do dynamic pricing. >> Sizen: Hm mm. >> A Friday night Dodger game, compared to a Tuesday day... >> Sizen: Yep. >> Milwaukee game, very, very different. >> Sizen: Hm mm. >> So, what are some of the factors going in, what are some of the resistance, >> Sizen: Yeah. >> that had to be overcome for people to actually accept that it's okay to charge more for a Friday night Dodger game, than a Tuesday afternoon Milwaukee game. >> Yep, so yeah, so my background start with the airlines, which is where dynamic pricing, revenue management started at, specifically the American Airlines. If you think about there are a lot of similarities between airlines and live entertainments. Fixed costs, you have to, flight has to go, or the game has to be played no matter how many people are there. So, you really have a limited time to really maximize your revenue. And you kind of have a product that the demand level is different by day, whether it's a Tuesday game or Friday game. It really something you have to study the sort of the behavior from the consumers when they buy their tickets. What are the factors they put into play to make that decision? And in that mix, San Francisco Giants was one of the first teams that actually incorporated dynamic pricing about ten years ago, that slowly. The challenges with it is we are not as the consumer, not as trained to know that the price may change. Hotels, airlines been doing it for years and years. >> Right. >> And for them, also it didn't start from like doing all the flights in day one. So it's really needs to be a phased approach. It needs to be a lot of education for the public, and to think about the right way to think about it is, you want incentivize people to buy early. And you want to make sure they are the ones that getting the best price, and not necessarily the people that are buying last minute. >> Right. >> If you're buying last minute, then you must accept that it maybe the available today you're not looking for or the price not you looking for. But I will say though that plans change, people decide to not attend the game. The reason is that, potential for finding other seats for that similar game. But, really for you, have your plans. It's better to buy early, and that's kind of what the industries needs to be trained on, more and more. >> Right. >> Was there more opportunity in getting additional value out of that high demand game? Or was the bigger opportunity in getting, kind of lowering the prices on the less desirable games, and getting kind of marginal revenue on that side. Where was the easy money made, >> Yeah. >> Jeffrey: On dynamic pricing? I mean the immediate impact is from the high value seats for the high value games, cause that's really is your premium product at that point. But in the meantime, there's always a low number of seats that you have in your premium area. And if you find the right price, and if you start earlier. And really the goal is to sell all the seats, and to fill all the seats. >> Right. >> Also, just selling the seats is not, doesn't get you far enough. You want to make sure people actually come to the game, and they're the people that are going to attend the game. Right? >> Right. >> So, if you kind of, the lower level has many more seats, so it's really has to be both ways. It can't be in one area, either dynamic pricing and you don't do it. It's just all about training the public and consumers. >> Right. Now, the other interesting you said in your kind of intro, was keeping track of... What are the busiest turnstiles? And where people coming? And the flow within the game. >> Sizen: Yep. >> What are some of the analytics that you do there, >> Sizen: Yep. >> And how are teams using those... >> Sizen: Yep. >> that information to provide a better fan experience? >> Yeah, so we have scanned data, and we actually have it real time. So, we are able to provide the teams. We have kineses streams, not to go too technical, to kind of empower them to do their game operations in a certain way. So example would be, you could study the past games and understand where people came from. Typically for a Friday game verse a Tuesday game, your crowd will look different, right. The Friday game, maybe the more the families or Saturday or Sunday. But Tuesday may be more corporate world, right. So understanding they're patterns, but also than having that data accessible to you to real time. So, that way you're able to see how many people are coming in from this one gate to other. You can man the gates differently that way. And the real time data is not something that comes just easily. There's a lot of infrastructure built for it. >> Right. >> But we've done it at AXS, and we've been able to provide to the teams so they can manage their getting in better. >> Right. >> So real time's interesting cause you know a lot of these conversations about real time, and I would say, "How do you define real time?" And in my mind, it's in time to do something about it. >> Exactly. >> So, using real time, I mean are there things they can do in real time to either lighten the load at an overdone gate, or... >> Sizen: Yeah. >> What are some of the real time impacts that people are using this data to do? >> Yeah, so exactly the example you provided. Like making sure there are more people at this one gate as opposed to others. But also, like knowing who's coming into the arena. So AXS's I-D ticketing, I-D based ticketing platform, so we actually know who's coming in. It's a rotating barcode, so if you just copy-paste the ticket, and text your friend. That doesn't work, that eliminates fraud as well. But because we know who's coming in, you can actually empower your sales reps as a team to make sure you are, you know, if they are coming to a suite or a premium area. So in so actually just scanned in, so you kind of come up with ideas for sales reps. As well as some of the marketing activations, like... It could be that you have people that typically come in late. You want to incentivize them. You could actually come up with promotions on merch and food and beverage to incentivize them early, right? Or at the same time you can actually, there are some platforms that do marketing activation. You may have had a lot of hotdogs left that you couldn't sell. Towards the late quarter, you could send a message to everyone saying, "Okay, ya know, hot dogs are 20 percent off." >> Right, right. >> So that, you need real time for it, for data for that. Cause you again need to know how many people scanned in. You may want to know how many people scanned out. So for some conferences and other type events, you want to make sure there's a Fire Marshall rules, so you want to make sure. So all the real time data is helpful for that if you just look at the purchaser data, you're not going to get that specifically there. >> That's really interesting cause I was going to say, What are some of the next things that we can expect to see dynamic pricing applied to, and you just went through them which are really situational specific. >> Yep. >> Opportunities to clear inventory, to do whatever. >> Exactly, it's not just a ticket purchase. It could be applied to other things as well. >> Right, Right. >> Yeah. >> How cool. So what other kind of data sets are you looking at to help teams that maybe we're not thinking about. >> Sure, just when people buy their tickets. What marketing may have they done, so that we can understand the web traffic, and did they buy the ticket when you send out that email. Or did they buy it three days later. So that's one area. As well as sort of, the inventory that you have available for that game. Does it sell faster for that Friday game versus a Tuesday game? We also, we're a comprehensive marketplace where we have both primary and secondary in the same map. To give the convenience back to the consumers, so you kind of have a chance to see all the inventory available in front of you. So, a bit of understanding how tickets transact in the secondary marketplace is helpful for the teams to really price their product better. Cause sometimes we have... I work for a team, so I have that background where you may have just 20 price points, and you've done it for 20 years but it's been certainly changing then. But now that you have all these different data points on the second, you also you kind of maybe is like, 'Okay I need 40 price points really because there's that much differentiation demand. >> Wow, really sophisticated analysis... >> Yeah, it's a passion area for me, so... >> And doing the real time, real time data flow and everything. >> Yeah, yeah. A really interesting, interesting conversation. >> Yeah. >> To go so far beyond just dynamic pricing. >> Exactly. >> It uses more sophisticated methods to get more value, provide better experience for the fans. >> And actually in Japan, they do more about dynamic pricing. So they utilize our platform to actually able to price every seat differently if they wanted to. We've just went out with on sales for Big League teams, and that's how they apply that. So it's been used elsewhere, maybe in the U-S in sports. It's definitely catching up, and it's much much big difference from the 10 years ago. But, I think Japan has already been kind of doing that. >> Excellent. >> Mm hm. >> Well Sizen, thanks for taking a few minutes, and sharing those stories. There's a lot going on behind the scenes that may not be conscious of, but hopefully we're getting the benefit of. >> Yeah, thank you. >> All right. Sizen, and I'm Jeff. Yes, we're live. They're banging on something down there. I'm not sure what, but keep watching. We'lls be here at Oracle Park in San Francisco. Thanks for watching, and see ya next time. (upbeat music)

>> from Santa Clara, California It's the Cube covering techno ovation World Pitch Summit 2019 Brought to You by Silicon Angle Media Now here's Sonia to Gari >> Hi and welcome to the Cube. I'm your host, >> Sonia to Gari, and we're here at Oracle's >> Agnew's campus in Santa Clara, California covering techno vacations. World Pitch Summit 2019 a pitch competition in which girls from around the world developed mobile lapse in order to create positive change >> in the world with us. Today we have team >> powerful daisies from Brazil. Um, and their acts called safe tears. So their members are on a Toronado. Uh, Clara Patan. Um, Anna Julia Uh, Giacomelli um Emmanuel Amara Skin and Julie Carr Bio. Welcome to the Cuban. Congratulations on your being finalists. Thank you. So your app safe tears tell us more about that. >> So our APP is a suicide prevention app in which its user gets his own glass of blue feelings, where to use their ads or remove tears accordingly with his feelings. So if the user said they had tears any, they're happy they take theirs out. >> Wow, that's amazing. So can you tell us how someone would use Thea >> So let's say I'm set. So I go to the app and I at use. So add those as my 2% rise is the absolute send motivational messages to me like saying go talk to somebody over find help and also encouraging me to be to know, to get better. And if I'm happy, I take tourists out and I get messages like congratulating me too because I'm doing better. >> So is there like a graph of your improvement of how you feel some days you feel the other days >> we would like to implement dead in your future. But right now, in this version of the app that is not available >> OK, well, yeah, that would be a great thing, Thio. So how did you come up with this idea? >> So in our community, there was a lot of suicide cases and off course with friends and family, and it was something that really needed more help. So we went Thio lecture about suicide, and the woman said that we are like a glass of water. We we feel that up and then one day all the water gets out and then somebody you know tries to suicide themselves. So we wanted this person to thio like realize that she's getting wars so she can find help before anything bad happens. >> And I know that sometimes giving advice to someone who's depressed can be very tricky. And you have to make sure saying the right thing. So how did you find out what kind of advice to give in your app? >> Yeah, we had help over school psychologist. So she was there with those the whole time we were developing and she helped us do Every single message is that the absense to the person is, you know, viewed by >> her And have you seen has anyone used the app and has felt better? Any success stories >> they're hesitant to launch, But we did tested it and people really liked it and thought that they would use it. >> That's amazing. So how >> did you all meet and why did >> you decide to join techno vacation? >> So we were from the same school from different classes where we're from the same school. So we met there and our teacher showed us the documentary code girl and their inspired us to join techno vacation because we thought it would be a cool experience. >> And so how detective ation help you achieve your goals and make your act better. >> So without techno vacation, of course, we couldn't be here and get all this experience in learning's to improve our app. So it's helping a lot. >> And, um, can you tell us more specifically like, what skills have you learned from Tekken? Ovation. >> Like programming, big public speaking and about business. We learn a lot like doing the business plan about marketing and publicity and all that. And I heard you >> guys had an amazing week this week. You went to whoever you saw Golden Gate Bridge. Can you tell us more? About what? The highlights of the wiki pad? >> Yeah, we went to Webber, of course. And we talked to people there. He was amazing. Talk to employees and see how is life there. And also we went to the Golden Bridge and we crossed the bridge. It was a Bahar, you know, we're not used to exercising. Right? And last night we had a dance party. What? She was really fun and we got to interact with people from all over the world and it was amazing. >> That's so great. Well, thank you so much for coming on. I'm so looking forward to seeing your app in the APP store one day. And congratulations. And good luck for the pitch tonight. >> Thank you so much. This has been team >> powerful daisies from Brazil. This'd the Cube. We'll see you next time.

>> from Santa Clara, California It's the Cube covering techno ovation World Pitch Summit 2019 Brought to you by Silicon Angle Media Now here's Sonia to Gari >> Hi and welcome to the Cube. I'm your host, Sonia to Gari. And we're here at Oracle's Agnew's campus in Santa Clara, California covering techno vacations World Pitch Summit 2019. Ah, pitch competition in which girls from around the world developed mobile lapse in order to create positive change in the world with us. Today we have Team Coco from Kazakhstan. Welcome. The members are, um Dilma as camel Over and Mallika Bree by Ava Uh, Donna Ulanova and Lube of do Chen Kuo Welcome. And congratulations on being finalists. Thank you. So your app is called tech Go. Can you tell us more about it? >> Yes. Uh so so techo in three d mobile application, which has a minute reality and as connected to the hardware which has dedicated for the behavioral change of people for so that they can become more conscious and like a friendly. >> And can you tell us more about how it works? Yes, >> of course there is. Luba, who can explain this? Okay. S >> o r application is about an astronaut who needs to save the planet. S O Firstly is there is a game in which a person needs to save your hair. Virtual airs by selling some ecological problems in it so that he or she wrote, be educated to both real life scenarios. And I also have a step counter which tracks your carbon footprint and encourages people to trust Morgan Friend the transportation options And that's a rare make really impact is that we connect our application with a special trash boxes in our city. All those locations are shown on the map, and coming to this place is user received trash box. And since Rosa Garbage and then because he has restaurants carriage here, she will get some points and your impact will be realized in the eventuality. Yeah, >> So what impact in society do you hope that this app will help change >> Rapids three t mobile application and it's a game. That is why Gamification and theater magic reality, which is a r which is inside this game a cz more visually in psychological attractive to people and those challenges that we provide a game are intensified so that most of the people. When they accomplish their goals, they might get, like, have a certain profit out of it so that they can become worker friendly and gain benefits. This is how we want to make sure that people might gain my changed a behavior for the sake of ecology. >> That's awesome. So you're using essentially a game incentivize people to make better choices in their everyday lives. That's great. And so how >> did you >> come up with this idea? >> So look, I will explain >> this. Actually, there were before some eco trash boxes in our school because like the thing off, ecological problems and recycling is one of the most talked about topics in Kazakhstan nowadays. And like in our school, the students try. Thio make this echo charge boxes, but they were always empty because students wasn't incent ified to recycle the garbage. And we tested our up in our school and we already launched it in our school and this ups incentivize our students. And now this I could trash boxes with our hard way always full. So >> that's awesome. See, you already found some success with your app. Thank you. Do you think that that this is a problem in the bigger community. >> Oh, maybe Donna Comptel. >> So we're saying that we started locally, but we got to go globally within that, uh, a pollution, like a pollution global problem and we trying to solve all over the world. So in our game, we have the whole world that you become an astronaut. So you should be aware for hold the problem that was happening in the earth. So we are trying to engage and educate people to be more global on to be more responsible for our final for our home. >> It sounds like everyone in the world should download that app. Yes, I do hope Thio uh, expand if you get the funding. >> Yes, um, we plan to expand not only in our country, Kazakhstan on only locally, but also globally. And we would like to create the eco friendly community across Central Asia since we want to make sure that consciousness is global in our area. >> And what struggles have you faced trying to create this app? >> Um, probably there were some struggles and off course in the realization and, uh, the realization of technical part of this project and creating a business model, since we are not very experienced in this kind of things. But since we have participated in techno vacation and we were immersed in this protest and were modified Thio motivated. Yeah, and we're motivated to learn all this things and acquire those skills. And this is why we became more experienced in this stuff. So right now, uh, those struggles that we face before not longer problem for us. So yeah, this what we faced? >> So techno vacation has definitely helped. Do you improve your app and yes, right houses. Tech innovation Helped you? >> Yeah, Um, probably someone else wants to ask you this question. >> How is SECNAV ation help? You were What skills have you learned from this journey? For >> example, one of the most important skills, I guess iss a teamwork. Like after we started to work on the one project, we started to listen each other excavation actually helped us too. Um, I understand the opinions off other people and like to understand the problems in our society. We start to dream bigger to think bigger, wider kind of that >> That's amazing. And also take Novation helping us >> to explore new companies to be more like open a person to come to The company's asked about the help on not like B just like see the problems and trying to solve trying to find a solution and be the people of the world and be responsible for our planet for what's happening in our local community on be aware of everything. >> And, um So I heard you guys had an amazing week. Um, you you went to whoever You went some other places. So can you tell us more about your week >> you want? So we went to amazing places in a Silicon Valley in a San Francisco San Jose and we so, like it'd, for example, Golden Gate Bridge. And also the Alcatraz so were so impressed by their architecture by the people by the nature on DDE. We just expected a lot of Onda. We just got this old expectations come to the reality on dhe. We hope that that kind of dream will come true in our future, and we gonna to work in a one of the big companies that were located here. I know all the universities. So >> how is it like going to the different tech companies and seeing it in real life. >> So we >> visited Uber Company and Google Ventures, and both we I have seen people who work is there, and we're really impressive on. And we really like it. It? Yeah. And, uh, I think so. Before, like in my childhood, I dreaming to be to be in Silicon Valley, to goes there and, like, meet people who are work already working you And now, like my dream came through. >> That's awesome. And you get to see California And you you might be able to win today. So thank you so much for being on. I wish you all the best. And I hope you haven't amazing pitch tonight. Thank you. This has been Team Coco from Kazakhstan. I'm your host, Sonia to Garey. This is the Cube. Stay tuned for more

>> from our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Hi! And welcome to the Cube studios from the Cube conversation, where we go in depth with thought leaders driving innovation across the tech industry on hosted a Peter Burress. What are we talking about today? One of the key indicators of success and additional business is how fast you can translate your data into new value streams. That means sharing it better, accelerating the rate at which you're running those models, making it dramatically easier to administrate large volumes of data at scale with a lot of different uses. That's a significant challenge. Is going to require a rethinking of how we manage many of those data assets and how we utilize him. Notto have that conversation. We're here with Le'Ron v. Bell, who was the CEO of work a Iot leering. Welcome back to the Cube. >> Thank you very much for having >> me. So before we get to the kind of a big problem, give us an update. What's going on at work a Iot these days? >> So very recently we announced around CIA financing for the company. Another 31.7 a $1,000,000 we've actually had a very unorthodox way of raising thiss round. Instead of going to the traditional VC lead round, we actually went to our business partners and joined forces with them into building a stronger where Collier for customers we started with and video that has seen a lot of success going with us to their customers. Because when Abel and Video to deploy more G pews so they're customers can either solve bigger problems or solve their problems faster. The second pillar off the data center is networking. So we've had melon ox investing in the company because there are the leader ofthe fast NETWORKINGS. So between and Vidia, melon, ox and work are yo u have very strong pillars. Iran compute network and storage performance is crucial, but it's not the only thing customers care about, so customers need extremely fast access to their data. But they're also accumulating and keeping and storing tremendous amount of it. So we've actually had the whole hard drive industry investing in us, with Sigi and Western Digital both investing in the company and finally one off a very successful go to market partner, Hewlett Pocket enterprise invested in us throw their Pathfinder program. So we're showing tremendous back from the industry, supporting our vision off, enabling next generation performance, two applications and the ability to scale to any workload >> graduations. And it's good money. But it's also smart money that has a lot of operational elements and just repeat it. It's a melon ox, our video video, H P E C Gate and Western Digital eso. It's It's an interesting group, but it's a group that will absolutely sustain and further your drive to try to solve some of these key data Orient problems. But let's talk about what some of those key day or data oriented problems where I set up front that one of the challenges that any business that has that generates a lot of it's value out of digital assets is how fast and how easily and with what kind of fidelity can I reuse and process and move those data assets? How are how is the industry attending? How's that working in the industry today, and where do you think we're going? >> So that's part on So businesses today, through different kind of workloads, need toe access, tremendous amount of data extremely quickly, and the question of how they're going to compare to their cohort is actually based on how quickly and how well they can go through the data and process it. And that's what we're solving for our customers. And we're now looking into several applications where speed and performance. On the one hand, I have to go hand in hand with extreme scale. So we see great success in machine learning, where in videos in we're going after Life Sciences, where the genomic models, the cryo here microscopy the computational chemistry all are now accelerated. And for the pharmacy, because for the research interested to actually get to conclusion, they serve to sift through a lot of data. We are working extremely well at financial analytics, either for the banks, for the hedge funds for the quantitative trading Cos. Because we allow them to go through data much, much quicker. Actually, only last week I had the grades to rate the customer where we were able to change the amount of time they go through one analytic cycle from almost two hours, four minutes. >> This is in a financial analytics >> Exactly. And I think last time I was here was telling you about one of their turn was driving companies using us taking, uh, time to I poke another their single up from two weeks to four hours. So we see consistent 122 orders of monk to speed time in wall clock. So we're not just showing we're faster for a benchmark. We're showing our customer that by leveraging our technology, they get results significantly faster. We're also successful in engineering around chip designed soft rebuild fluid dynamics. We've announced Melon ox as an idiot customer. The chip designed customers, so they're not only a partner, they have brought our technology in house, and they're leveraging us for the next chips. And recently we've also discovered that we are great help for running Noah scale databases in the clouds running ah sparkles plank or Cassandra over work. A Iot is more than twice faster than running over the Standard MPs elected elastic clock services. >> All right, so let's talk about this because your solving problems that really only recently have been within range of some of the technology, but we still see some struggling. The way I described it is that storage for a long time was focused on persisting data transactions executed. Make sure you persisted Now is moved to these life life sciences, machine learning, genomics, those types of outpatients of five workloads we're talking about. How can I share data? How can I deploy and use data faster? But the historian of the storage industry still predicated on this designs were mainly focused on persistent. You think about block storage and filers and whatnot. How is Wecker Io advancing that knowledge that technology space of, you know, reorganizing are rethinking storage for the types, performance and scale that some of these use cases require. >> This is actually a great question. We actually started the company. We We had a long legacy at IBM. We now have no Andy from, uh, metta, uh, kind of prints from the emcee. We see what happens. Page be current storage portfolio for the large Players are very big and very convoluted, and we've decided when we're starting to come see that we're solving it. So our aim is to solve all the little issues storage has had for the last four decades. So if you look at what customers used today, if they need the out most performance they go to direct attached. This's what fusion I awards a violin memory today, these air Envy me devices. The downside is that data is cannot be sure, but it cannot even be backed up. If a server goes away, you're done. Then if customers had to have some way of managing the data they bought Block san, and then they deployed the volume to a server and run still a local file system over that it wasn't as performance as the Daz. But at least you could back it up. You can manage it some. What has happened over the last 15 years, customers realized more. Moore's law has ended, so upscaling stopped working and people have to go out scaling. And now it means that they have to share data to stop to solve their problems. >> More perils more >> probably them out ofthe Mohr servers. More computers have to share data to actually being able to solve the problem, and for a while customers were able to use the traditional filers like Aneta. For this, kill a pilot like an eyes alone or the traditional parlor file system like the GP affair spectrum scale or luster, but these were significantly slower than sand and block or direct attached. Also, they could never scale matter data. You were limited about how many files that can put in a single, uh, directory, and you were limited by hot spots into that meta data. And to solve that, some customers moved to an object storage. It was a lot harder to work with. Performance was unimpressive. You had to rewrite our application, but at least he could scale what were doing at work a Iot. We're reconfiguring the storage market. We're creating a storage solution that's actually not part of any of these for categories that the industry has, uh, become used to. So we are fasted and direct attached, they say is some people hear it that their mind blows off were faster, the direct attached, whereas resilient and durable as San, we provide the semantics off shirt file, so it's perfect your ability and where as Kayla Bill for capacity and matter data as an object storage >> so performance and scale, plus administrative control and simplicity exactly alright. So because that's kind of what you just went through is those four things now now is we think about this. So the solution needs to be borrow from the best of these, but in a way that allows to be applied to work clothes that feature very, very large amounts of data but typically organized as smaller files requiring an enormous amount of parallelism on a lot of change. Because that's a big part of their hot spot with metadata is that you're constantly re shuffling things. So going forward, how does this how does the work I owe solution generally hit that hot spot And specifically, how are you going to apply these partnerships that you just put together on the investment toe actually come to market even faster and more successfully? >> All right, so these are actually two questions. True, the technology that we have eyes the only one that paralyzed Io in a perfect way and also meditate on the perfect way >> to strangers >> and sustains it parla Liz, um, buy load balancing. So for a CZ, we talked about the hot sport some customers have, or we also run natively in the cloud. You may get a noisy neighbor, so if you aren't employing constant load balancing alongside the extreme parallelism, you're going to be bound to a bottleneck, and we're the only solution that actually couples the ability to break each operation to a lot of small ones and make sure it distributed work to the re sources that are available. Doing that allows us to provide the tremendous performance at tremendous scale, so that answers the technology question >> without breaking or without without introducing unbelievable complexity in the administration. >> It's actually makes everything simpler because looking, for example, in the ER our town was driving example. Um, the reason they were able to break down from two weeks to four hours is that before us they had to copy data from their objects, George to a filer. But the father wasn't fast enough, so they also had to copy the data from the filer to a local file system. And these copies are what has added so much complexity into the workflow and made it so slow because when you copy, you don't compute >> and loss of fidelity along the way right? OK, so how is this money and these partnerships going to translate into accelerated ionization? >> So we are leveraging some off the funds for Mohr Engineering coming up with more features supporting Mohr enterprise applications were gonna leverage some of the funds for doing marketing. And we're actually spending on marketing programs with thes five good partners within video with melon ox with sick it with Western Digital and with Hewlett Packard Enterprise. But we're also deploying joint sales motion. So we're now plugged into in video and plugged, anted to melon ox and plugging booked the Western Digital and to Hillary Pocket Enterprise so we can leverage their internal resource now that they have realized through their business units and the investment arm that we make sense that we can actually go and serve their customers more effectively and better. >> Well, well, Kaio is introduced A road through the unique on new technology into makes perfect sense. But it is unique and it's relatively new, and sometimes enterprises might go well. That's a little bit too immature for me, but if the problem than it solves is that valuable will bite the bullet. But even more importantly, a partnership line up like this has got to be ameliorating some of the concerns that your fearing from the marketplace >> definitely so when and video tells the customers Hey, we have tested it in our laps. Where in Hewlett Packard Enterprise? Till the customer, not only we have tested it in our lab, but the support is going to come out of point. Next. Thes customers now have the ability to keep buying from their trusted partners. But get the intellectual property off a nor company with better, uh, intellectual property abilities another great benefit that comes to us. We are 100% channel lead company. We are not doing direct sales and working with these partners, we actually have their channel plans open to us so we can go together and we can implement Go to Market Strategy is together with they're partners that already know howto work with them. And we're just enabling and answering the technical of technical questions, talking about the roadmap, talking about how to deploy. But the whole ecosystem keeps running in the fishing way it already runs, so we don't have to go and reinvent the whales on how how we interact with these partners. Obviously, we also interact with them directly. >> You could focus on solving the problem exactly great. Alright, so once again, thanks for joining us for another cube conversation. Le'Ron zero ofwork I Oh, it's been great talking to you again in the Cube. >> Thank you very much. I always enjoy coming over here >> on Peter Burress until next time.