>> Welcome back everyone to the closing remarks here before we kick off our ecosystem portion of the program. We're live in Palo Alto for theCUBE special presentation of Supercloud 2. It's the second edition, the first one was in August. I'm John Furrier with Dave Vellante. Here to wrap up with our special guest analyst George Gilbert, investor and industry legend former colleague of ours, analyst at Wikibon. George great to see you. Dave, you know, wrapping up this day what in a phenomenal program. We had a contribution from industry vendors, industry experts, practitioners and customers building and redefining their company's business model. Rolling out technology for Supercloud and multicloud and ultimately changing how they do data. And data was the theme today. So very, very great program. Before we jump into our favorite parts let's give a shout out to the folks who make this possible. Free contents our mission. We'll always stay true to that mission. We want to thank VMware, alkira, ChaosSearch, prosimo for being sponsors of this great program. We will have Supercloud 3 coming up in a month or so, or two months. We'll see. Or sooner, we don't know. But it'll be more about security, but a lot more momentum. Okay, so that's... >> And don't forget too that this program not going to end now. We've got a whole ecosystem speaks track so stay tuned for that. >> John: Yeah, we got another 20 interviews. Feels like it. >> Well, you're going to hear from Saks, Veronika Durgin. You're going to hear from Western Union, Harveer Singh. You're going to hear from Ionis Pharmaceuticals, Nick Taylor. Brian Gracely chimes in on Supecloud. So he's the man behind the cloud cast. >> Yeah, and you know, the practitioners again, pay attention to also to the cloud networking interviews. Lot of change going on there that's going to be disruptive and actually change the landscape as well. Again, as Supercloud progresses to be the next big thing. If you're not on this next wave, you'll drift what, as Pat Gelsinger says. >> Yep. >> To kick off the closing segments, George, Dave, this is a wave that's been identified. Again, people debate the word all you want Supercloud. It is a gateway to multicloud eventually it is the standard for new applications, new ways to do data. There's new computer science being generated and customer requirements being addressed. So it's the confluence of, you know, tectonic plates shifting in the industry, new computer science seeing things like AI and machine learning and data at the center of it and new infrastructure all kind of coming together. So, to me, that's my takeaway so far. That is the big story and it's going to change society and ultimately the business models of these companies. >> Well, we've had 10, you know, you think about it we came out of the financial crisis. We've had 10, 12 years despite the Covid of tech success, right? And just now CIOs are starting to hit the brakes. And so my point is you've had all this innovation building up for a decade and you've got this massive ecosystem that is running on the cloud and the ecosystem is saying, hey, we can have even more value by tapping best of of breed across clouds. And you've got customers saying, hey, we need help. We want to do more and we want to point our business and our intellectual property, our software tooling at our customers and monetize our data. So you have all these forces coming together and it's sort of entering a new era. >> George, I want to go to you for a second because you are big contributor to this event. Your interview with Bob Moglia with Dave was I thought a watershed moment for me to hear that the data apps, how databases are being rethought because we've been seeing a diversity of databases with Amazon Web services, you know, promoting no one database rules of the world. Now it's not one database kind of architecture that's puling these new apps. What's your takeaway from this event? >> So if you keep your eye on this North Star where instead of building apps that are based on code you're building apps that are defined by data coming off of things that are linked to the real world like people, places, things and activities. Then the idea is, and the example we use is, you know, Uber but it could be, you know, amazon.com is defined by stuff coming off data in the Amazon ecosystem or marketplace. And then the question is, and everyone was talking at different angles on this, which was, where's the data live? How much do you hide from the developer? You know, and when can you offer that? You know, and you started with Walmart which was describing apps, traditional apps that are just code. And frankly that's easier to make that cross cloud and you know, essentially location independent. As soon as you have data you need data management technology that a customer does not have the sophistication to build. And then the argument was like, so how much can you hide from the developer who's building data apps? Tristan's version was you take the modern data stack and you start adding these APIs that define business concepts like bookings, billings and revenue, you know, or in the Uber example like drivers and riders, you know, and ETA's and prices. But those things execute still on the data warehouse or data lakehouse. Then Bob Muglia was saying you're not really hiding enough from the developer because you still got to say how to do all that. And his vision is not only do you hide where the data is but you hide how to sort of get at all that code by just saying what you want. You define how a car and how a driver and how a rider works. And then those things automatically figure out underneath the cover. >> So huge challenges, right? There's governance, there's security, they could be big blockers to, you know, the Supercloud but the industry's going to be attacking that problem. >> Well, what's your take? What's your favorite segment? Zhamak Dehghani came on, she's starting in that company, exclusive news. That was big notable moment for theCUBE. She launched her company. She pioneered the data mesh concept. And I think what George is saying and what data mesh points to is something that we've been saying for a long time. That data is now going to flip the script on how apps behave. And the Uber example I think is illustrated 'cause people can relate to Uber. But imagine that for every business whether it's a manufacturing business or retail or oil and gas or FinTech, they can look at their business like a game almost gamify it with data, riders, cars you know, moving data around the value of data. This is something that Adam Selipsky teased out at AWS, Dave. So what's your takeaway from this Supercloud? Where are we in your mind? Well big thing is data products and decentralizing your data architecture, but putting data in the hands of domain experts who can actually monetize the data. And I think that's, to me that's really exciting. Because look, data products financial industry has always been doing building data products. Mortgage backed securities is a data product. But why should the financial industry have all the fun? I mean virtually every organization can tap its ecosystem build data products, take its internal IP and processes and software and point it to the world and actually begin to make money out of it. >> Okay, so let's go around the horn. I'll start, I'll get you guys some time to think. Next question, what did you learn today? I learned that I think it's an infrastructure game and talking to Kit Colbert at VMware, I think it's all about infrastructure refactoring and I think the data's going to be an ingredient that's going to be operating system like. I think you're going to see the infrastructure influencing operations that will enable Superclouds to be real. And developers won't even know what a Supercloud is because they'll be using it. It's the operations focus is going to be very critical. Just like DevOps movements started Cloud native I think you're going to see a data native movement and I think infrastructure is critical as people go to the next level. That's my big takeaway today. And I'll say the data conversation is at the center. I think security, data are going to be always active horizontally scalable concepts, but every company's going to reset their infrastructure, how it looks and if it's not set up for data and or things that there need to be agile on, it's going to be a non-starter. So I think that's the cloud NextGen, distributed computing. >> I mean, what came into focus for me was I think the hyperscaler is going to continue to do their thing, you know, and be very, very successful and they're each coming at it from different approaches. We talk about this all the time in theCUBE. Amazon the best infrastructure, you know, Google's got its you know, data and AI thing and it's playing catch up and Microsoft's got this massive estate. Okay, cool. Check. The next wave of innovation which is coming from data, I've always said follow the data. That's where the where the money's going to be is going to come from other places. People want to be able to, organizations want to be able to share data across clouds across their organization, outside of their ecosystem and make money with that data sharing. They don't want to FTP it anymore. I got it. You take it. They want to work with live data in real time and I think the edge, we didn't talk much about the edge today is going to even take that to a new level real time inferencing at the edge, AI and and being able to do new things with data that we haven't even seen. But playing around with ChatGPT, it's blowing our mind. And I think you're right, it's like when we first saw the browser, holy crap, this is going to change the world. >> Yeah. And the ChatGPT by the way is going to create a wave of machine learning and data refactoring for sure. But also Howie Liu had an interesting comment, he was asked by a VC how much to replicate that and he said it's in the hundreds of millions, not billions. Now if you asked that same question how much does it cost to replicate AWS? The CapEx alone is unstoppable, they're already done. So, you know, the hyperscalers are going to continue to boom. I think they're going to drive the infrastructure. I think Amazon's going to be really strong at silicon and physics and squeeze every ounce atom out of every physical thing and then get latency as your bottleneck and the rest is all going to be... >> That never blew me away, a hundred million to create kind of an open AI, you know, competitor. Look at companies like Lacework. >> John: Some people have that much cash on the balance sheet. >> These are security companies that have raised a billion dollars, right? To compete. You know, so... >> If you're not shifting left what do you do with data, shift up? >> But, you know. >> What did you learn, George? >> I'm listening to you and I think you're helping me crystallize something which is the software infrastructure to enable the data apps is wide open. The way Zhamak described it is like if you want a data product like a sales and operation plan, that is built on other data products, like a sales plan which has a forecast in it, it has a production plan, it has a procurement plan and then a sales and operation plan is actually a composition of all those and they call each other. Now in her current platform, you need to expose to the developer a certain amount of mechanics on how to move all that data, when to move it. Like what happens if something fails. Now Muglia is saying I can hide that completely. So all you have to say is what you want and the underlying machinery takes care of everything. The problem is Muglia stuff is still a few years off. And Tristan is saying, I can give you much of that today but it's got to run in the data warehouse. So this trade offs all different ways. But again, I agree with you that the Cloud platform vendors or the ecosystem participants who can run across Cloud platforms and private infrastructure will be the next platform. And then the cloud platform is sort of where you run the big honking centralized stuff where someone else manages the operations. >> Sounds like middleware to me, Dave >> And key is, I'll just end with this. The key is being able to get to the data, whether it's in a data warehouse or a data lake or a S3 bucket or an object store, Oracle database, whatever. It's got to be inclusive that is critical to execute on the vision that you just talked about 'cause that data's in different systems and you're not going to put it all into some new system. >> So creating middleware in the cloud that sounds what it sounds like to me. >> It's like, you discovered PaaS >> It's a super PaaS. >> But it's platform services 'cause PaaS connotes like a tightly integrated platform. >> Well this is the real thing that's going on. We're going to see how this evolves. George, great to have you on, Dave. Thanks for the summary. I enjoyed this segment a lot today. This ends our stage performance live here in Palo Alto. As you know, we're live stage performance and syndicate out virtually. Our afternoon program's going to kick in now you're going to hear some great interviews. We got ChaosSearch. Defining the network Supercloud from prosimo. Future of Cloud Network, alkira. We got Saks, a retail company here, Veronika Durgin. We got Dave with Western Union. So a lot of customers, a pharmaceutical company Warner Brothers, Discovery, media company. And then you know, what is really needed for Supercloud, good panels. So stay with us for the afternoon program. That's part two of Supercloud 2. This is a wrap up for our stage live performance. I'm John Furrier with Dave Vellante and George Gilbert here wrapping up. Thanks for watching and enjoy the program. (bright music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Welcome back to Vegas, guys and girls, it's great to have you with us. The Cube Live. Si finishing our second day of coverage of Palo Alto Ignite. 22 from MGM Grand in Las Vegas. Lisa Martin here with Dave Valante. Dave Cybersecurity is one of my favorite topics to talk about because it is so interesting. It is so dynamic. My other favorite thing is to hear the voice of our vendors' customers. And we could to >>Do that. I always love to have the customer on you get you get right to the heart of the matter. Yeah. Really understand. You know, what I like to do is sort of when I listen to the keynotes, try to see how well it aligns with what the customers are actually doing. Yeah. So let's >>Do it. We're gonna unpack that now. Michael Fagan joins us, the Chief Transformation Officer at Village Roadshow. Welcome Michael. It's great to have you >>And thank you. It's a pleasure to be here. >>So this is a really interesting entertainment company. I find the name interesting, but talk to us a little bit about Village Roadshow so the audience gets an understanding of all of the things that you guys do cuz theme parks is part of >>This. Yeah, so Village Road show's Australia's largest cinema exhibitor in conjunction with our partners at event. We also own and operate Australia's largest theme parks. We have Warner Brothers movie World, wet and Wild. SeaWorld Top Golf in Australia is, is operated by us plus more. We also do studio, we also own movie studios, so Aquaman, parts of the Caribbean. We're, we're filming our movie studios Elvis last year. And we also distribute and produce movies and TV shows. Quite diverse group. >>Yeah, you guys have won a lot of awards. I mean, I don't know, academy Awards, golden Globe, all that stuff, you know, and so it's good. Congratulations. Yeah. >>Thank you. >>Cool stuff. I wanna also, before we dig into the use case here, talk to us about the role of a chief transformation officer. How long have you been in that role? What does it encompass and what do you get to drive from a transformation perspective? Yeah, >>So the, the, the nature and pace of disruption is accelerating and on, on one side. And then on the other side, the running business as usual is becoming increasingly complex and, and more difficult to do. So running both simultaneously and at pace can put organizations at risk, both financially and and other ways. So in my role as Chief Transformation officer, I support the rest of the executive team by giving them additional capacity and also bring capability to the team that wasn't there before. So I do a lot of strategic and thought leadership. There's some executive coaching in there, a lot of financial modeling and analysis. And I believe that when a transformation role in particularly a chief transformation role is done correctly, it's a very hands-on role. So there's certain things where I, I dive right down and I'm actually hands in, hands-on leading teams or leading pieces of work. So I might be leading particular projects. I tried to drive profit revenue and profitability across the divisions and does any multi or cross-divisional opportunities or initiative, then I will, I will lead those. >>The transformation, you know, a while ago was cloud, right? Okay, hey, cloud and transformation officers, whether or not they had that title, we'll tell you, look, you gotta change the operating model. You can't just, you know, lift and shift in the cloud. That's, you know, that's pennies. We want, you know, big bucks. That's the operating. Now it's, I'm my question is, is did the pandemic just accelerate your transformation or, or was it, you know, deeper than that? >>Yeah, so what in my role have both digital and business transformation, some of it has been organizational. I think the pandemic has had a, a significant and long lasting effect on society, not just on, on business. So I think if you think about how work work used to be a, a place you went to and how it was done beforehand, before the, before COVID versus now where, you know, previously, you know, within the enterprise you had all of the users, you had all of the applications, you had all of the data, you had all of the people. And then since March, 2020, just overnight, that kind of inverted and, you know, you had people working from home and a person working from home as a branch office of one. So, so we ended up with another thousand branches literally overnight. A lot of the applications that we use are now SASS or cloud-based, whether that's timekeeping with Kronos or communica employee communication or work Jam. So they're not sitting within our data center, they're not sitting within, within our enterprise. It's all external. >>So from a security perspective, you obviously had to respond to that and we heard a lot about endpoint and cloud security and refactoring the network and identity. These guys aren't really an identity. They partner for that, but still a lot of change in focus that the CISO had to deal with. How, how did you guys respond to that? And, and you had a rush to do it. Yeah. And so as you sit back now, where do you go from here? >>Well we had, we had two major triggers for our, our network and security transformation. The first being COVID itself, and then the second beam, we had a, a major MPLS telco renewal that came up. So that gave you an opportunity to look at what we were doing and essentially our network was designed for a near, that no longer exists for when, for when p like I said, when people, when people were from home, all the applications were inside. So, and we had aging infrastructure, our firewalls were end of life. So initially we started off with an SD WAN at the SD WAN layer and an SD WAN implementation. But when we investigated and saw the security capabilities that are available now, we that to a full sassy WAN implementation. >>Why Palo Alto Networks? Because you, you had, you said you had an aging infrastructure designed for an era that doesn't exist anymore, but you also had a number of tools. We've been talking about a consolidation a lot the last couple days. Yeah. How did, what did you consolidate and why with Palo Alto? >>So we had a great partner in Australia, incidentally also called Cube. Cube Networks. Yeah. That we worked with great >>Names. Yeah, right. >>So we, so we, we worked for Cube. We ran a, a form of tender process. And Palo Alto with, you know, Prisma access and Global Global Protect was the only, the only solution that gave us everything that we needed in terms of network modernization, the agility that we required. So for example, in our theme part, we want to send out a hotdog cart or an ice cream cart, and that becomes, all of a sudden you got a new branch that I want to spin up this branch in 10 minutes and then I wanna spin it back down again. So from agility perspective, from a flexibility perspective, the security that, that we wanted, you know, from a zero trust perspective, and they were the only, certainly from a zero trust perspective, they're probably the only vendor that, that exists that, that actually provided the, the, all those capabilities. >>And did you consolidate tools or you were in the process of consolidating tools now? >>Yeah, so we actually, we actually consolidated down to, to, to a, to a single vendor. And in my previous role I had, I had implemented SD WAN before and you know, interoperability is a, is a major issue in the IT industry. I think there's, it's probably the only industry in the, the only industry I can think of certainly that where we, we ship products that aren't ready. They're not of all the features, they, they don't have all the features that they should have. They're their plans. They were releasing patches, releasing additional features every, every couple of months. So, you know, if you, if if Ford sold the card, I said, Hey, you're gonna give you backseats in a couple of months, they'd be uproar. But, but we do that all the time in, in it. So I had, when I previously implemented an Sdwan transformation, I had products from two tier one vendors that just didn't talk to one another. And so when I went and spoke to those vendors, they just went, well, it's not me. It's clearly, clearly those guys. So, so there's a lot to be said for having a, you know, a champion team rather than a team of champions. And Palo Alto have got that full stack fully integrated that was, you know, exactly meant what we were looking for. >>They've been talking a lot the last couple days about integration and it, and I've talked with some of their executives and some analysts as well, including Dave about that seems to be a differentiator for them because they really focus on that. Their m and a strategy is very, it seems to be very clear and there's purpose on that backend integration instead of leaving it to the customer, like Village Road show to do it. They also talked a lot about the consolidation. I'm just curious, Michael, in terms of like what you've heard at the show in the last couple of days. >>Yeah, I mean I've been hearing to same mess, but actually we've, we've lived in a >>You're living it. That's what I wanted to >>Know. So, so, you know, we had a choice of, you know, do you try and purchase so-called best of breed products and then put a lot of effort into integrating them and trying to get them to work, which is not really what we want to spend time doing. I don't, I don't wanna be famous for, you know, integration and, you know, great infrastructure. I want to be, I want Village to be famous for delivering great experiences to our customers. Memories that last a lifetime. And you know, when kids grow up in Australia, they, everybody remembers going to the theme parks. That's what, that's what I want our team to be doing and to be delivering those great experiences, not to be trying to plug together bits of software and it may or may not work and have vendors pointing at one another and then we are left carrying the cannon and holding the >>Baby. So what was the before and after, can you give us a sense as to how life changed, you know, pre that consolidation versus post? >>Yeah, so our, our, our infrastructure, say our infrastructure was designed for, you know, the, you know, old ways of working where we had you knowm routers that were, you know, not designed for cloud, for modern traffic, including cloud Destin traffic, an old MPLS network. We used to back haul all the traffic from, from our branches back to central location run where we've got, you know, firewall walls, we've got a dmz, we could run advanced inspection services on that. So if you had a branch that wanted to access a website that was housed next door, even if it was across the country, then it would, we would pull that all the way back to Melbourne. We would apply advanced inspection services to it, send it up to the cloud out back across the country. Traffic would come back, come down to us, back out to our branch. >>So you talk about crossing the country four times, even at the website is, is situated next door now with, with our sasi sdwan transformation just pops out to the cloud now straight away. And the, the difference in performance for our, for our team and for our customers, it, it's phenomenal. So you'll talk about saving minutes, you know, on a log on and, and seconds then and on, on an average transaction and second zone sound like a lot. But when you, it's every click up, they're saving a second and add up. You're talking about thousands of man hours every month that we've saved. >>If near Zuke were sitting right here and said, what could we do better? You know, what do you need from us that we're not delivering today that you want to, you want us to deliver that would change your life. Yeah, >>There's two things. One, one of which I think they're all, they're already doing, but I actually haven't experienced myself. It's around the autonomous digital experience management. So I've now got a thousand users who are sitting at home and they've got, when they've got a problem, I don't know, is it, is it my problem or is it their problem? So I know that p were working on a, an A solution that digital experience solution, which can actually tell, well actually know you're sitting in your kitchen and your routes in your front room, maybe you should move closer to the route. So there, there they, that's one thing. And the second thing is using AI to tell me things that I wouldn't be able to figure out with a human training. A lot of time sifting through data. So things like where I've potentially overcompensated and, you know, overdelivered on the network and security side or of potentially underdelivered on a security side. So having AI to, you know, assess all of those millions and probably billions of, you know, transactions and packets that are moving around our network and say, Hey, you could optimize it more if you, if you dial this down or dial this up. >>So you said earlier we, this industry has a habit of shipping products before, you know they're ready. So based on your experience, seems like, first of all, it sounds like you got a at least decent technical background as well. When do you expect to have that capability? Realistically? When can we expect that as an industry? >>I think I, I think, like I said, the the rate and nature of change is, is, I think it's accelerating. The halflife of degree is short. I think when I left university, what I, what I learned in first year was, was obsolete within five years, I'd say now it's probably obsolete of you. What'd you learn in first year? It's probably obsolete by the time you finish your degree. >>Six months. Yeah, >>It's true. So I think the, the, the rate of change and the, the partnership that I see Palo building with the likes of AWS and Google and that and how they're coming together to, to solve, to jointly solve these problems is I think we will see this within 12 months. >>Who, who are your clouds? You got multiple clouds >>Or We got multiple clouds. Mostly aws, but there are certain things that we run that run in run in Azure as well. We, we don't really have much in GCP or, or, or some of the other >>Azure for collaboration and teams, stuff like that. >>Ah, we, we run, we run SAP that's we hosted in, in Azure and our cinema ticketing system is, is was run in Azure. It's, it was only available in, in in Azure the time we're mo we are mostly an AWS >>Shop. And what do you do with aws? I mean, pretty much everything else is >>Much every, everything else, anything that's customer facing our websites, they give us great stability. Great, great availability, great performance, you know, we've had and, and, and, and a very variable as well. So, we'll, you know, our, our pattern of selling movie tickets is typically, you know, fairly flat except when, you know, there's a launch of a, of a new movie. So all of a sudden we might say you might sell, you know, at 9:00 AM when, you know, spider-Man went on sale last year, I think we sold 100 times the amount of tickets in the forest, 10 minutes. So our website didn't just scale look beautifully, just took in all of that extra traffic scale up. We're at only any intervention and then scale back down >>Taylor Swift needs that she does need that. So yeah. And so is your vision to have Palo Alto networks security infrastructure have be a common sort of layer across those clouds and maybe even some on-prem? Is it, are you, are you working toward that? Yeah, >>We, yeah, we, yeah, we, we'd love to have, you know, our end, our end customers don't really care about the infrastructure that we run. They won't be >>Able to unless it breaks. >>Unless it breaks. Yeah. They wanna be able to go to see a movie. Do you wanna be able to get on a rollercoaster? They wanna be able to go, you know, play around around a top golf. So having that convergence and that seamless integration of working across cloud network security now for most of our team, they, they don't know and they don't need to know. In fact, I, I frankly don't want them to know and be, be thinking about networks and clouds. I kind of want them thinking about how do we sell more cinema tickets? How do we give a great experience to our guests? How do we give long lasting lifetime memories to, to the people who come visit our parks? >>That's what they want. They want that experience. Right. I'd love to get your final thoughts on, we, we had you give a great overview of the ch the role that you play as Chief transformation officer. You own digital transformation, you want business transformation. What advice would you give to either other treat chief transformation officers, CISOs, CSOs, CEOs about partnering, what's the right partner to really improve your security posture? >>I think there's, there's two things. One is if you haven't looked at this in the last two years and made some changes, you're outta date. Yeah. Because the world has changed. We've seen, I mean, I've heard somebody say it was two decades worth of, I actually think it's probably five 50 years worth of change in, in Australia in terms of working habits. So one, you need to do something. Yeah. Need to, you need to have a look at this. The second thing I think is to try and partner with someone that has similar values to your organization. So Village is a, it's a wonderful, innovative company. Very agile. So the, like the, the concept of gold class cinema, so, you know, big proceeds, recliners, waiter service, elevated foods concept that, that was invented by village in 1997. Thank you. And we had thanks finally came to the states so decade later, I mean we would've had the CEO of every major cinema chain in the world come to come to Melbourne and have a look at what Village is doing and go, yeah, we're gonna export that back around around the world. It's probably one of, one of Australia's unknown exports. Yeah. So it's, yeah, so, so partnering. So we've got a great innovation history and we'd like to think of ourselves as pretty agile. So working with partners who are, have a similar thought process and, and managed to an outcome and not to a contract Yeah. Is, is important for us. >>It's all about outcomes. And you've had some great outcomes, Michael, thank you for joining us on the program, walking us through Village Roadshow, the challenges that you had, how you tackled them, and, and next time I think I'm in a movie theater and I'm in reclining chair, I'm gonna think about you and village. So thank you. We appreciate your insights, your time. Thank you. Thanks Michael. For Michael Fagan and Dave Valante. I'm Lisa Martin. You've been watching The Cube. Our live coverage of Palo Alto Networks. Ignite comes to an end. We thank you so much for watching. We appreciate you. You're watching the Cube, the leader in live enterprise and emerging emerging tech coverage next year. >>Yeah.

>>Good afternoon and welcome back to the Classiest Show in Technology. This is the Cube we are at AWS Reinvent 2022 in Fabulous Sin City. That's why I've got my sequence on. We love a little Vegas, don't we? I'm joined by John Farer, another, another Vegas >>Fan. I don't have my sequence, I left it in my room. We're >>Gonna have to figure out how to get us 20 as soon as possible. What's been your biggest shock for you at the show so far? >>Well, I think the data story and security is so awesome. I love how that's front and center. If you look at the minutes of the keynote of Adamski, the CEO on day one, it's all bulked into data and security. All worked hand in hand. That's on top of already the innovation of their infrastructure. So I think you're gonna see a lot of interplay going on in this next segment. It's gonna tell a lot of that innovation story that's coming next. It's pretty awesome. >>It is pretty awesome, and I'm super excited. It's not only what we do here on the Cube, it's also in my show notes. We are gonna be geeking out for the next segment. Please welcome Paul and Puja. Wonderful to have you both here. Paul from Amazon, s3, glacier, and Pujan, CEO of kuo. I wanna turn to you Pujan, to start us off, just in case the audience isn't familiar, give us the Kuo pitch. >>Yeah, so basically Kuo is a, a backup as a service offering, right? Built in AWS four aws, right? And effectively going after, you know, any service that a customer uses on top of aws, right? And so a lot of the data sitting on s3, right? So that's been like our, our big use case going and basically building backup and air gap protection for, for s3. But we basically go to every other service, e c two, ebs, dynamo, you know, you name it, right? So basically do the whole thing >>And the relationship with aws. Can you guys share, I mean, you got you here together. You guys are a great partnership. Born in the cloud, operation in the cloud. Absolutely. I think talk about the partnership with aws. >>Absolutely. I think the last five years of building on AWS has been phenomenal, right? And I love the platform. It's, it's a very pure platform for us. You know, the APIs and, and the access you get and access you get to the service teams like Paul sitting here and the other teams you have gotten access to, I think has been phenomenal. But we also have, I would say, pushed the envelope in terms of how innovative we have been and how aggressive we have been in utilizing all the innovation that AWS has built in over the last few years. But it would not have happened without the fantastic partnership with the service teams. >>Paul, talk about the, AM the S3 part of this. What's the story there? >>Well, it's been great working with the CUO team over the course of the last few years. We were just upstairs diving deep into the, to the features that they're taking advantage of. They really push us hard on behalf of customers, and it's been a, it's just been a great relationship over the last years. >>That's awesome. And the ecosystem at such a, we're gonna hear tomorrow, the keynote on the, from Aruba who's gonna tend over the ecosystem. You guys are working together. There's a lot of strategic partnerships, so much collaboration between you guys that makes it very, this is the next gen cloud of cloud environment we're seeing. And you heard the, the economies around the corner. It's still gonna be challenging, but still there's more growth in the cloud. This is not stopping. This is impacts the customers. What are the customers saying to you guys when you work backwards from their needs? They want it faster, easier, cheaper. They want it more integrated. What are some of the things, all those you guys hearing from customers? >>So for us, you know, if you think about it, like, you know, as people are moving to the cloud, especially like take a use case like s3, right? So much of critical data sitting on top of S3 today. And so what folks have realized that as they're, you know, putting all of those, you know, what, over two 50 trillion objects, you know, sitting on s3, a lot of them need backup and data protection because there could be accidental deletions, there could be software bugs, there could be a ransomware type event due to which you need a second copy of the data that is outside of your security domain, right? But again, that needs to get be done at the, at the right price point, right? And that's where like a technology like Columbia comes in because since we've been built on the cloud, we've optimized it correctly. So especially for folks who are very cost conscious, given the macroeconomic conditions, we are heading into a technology that's built correctly so that, you know, you get the right architecture and the right solution at the right price point and the scale, right? Talking about trillions of objects, billions of objects within a single customer, within a single bucket sometimes. And that's where Columbia comes in. Cause we basically do that at scale without, again, impacting the, the customer's wallet more than it needs to. >>The porridge has to be the right temperature and the right size bowl. With the right spoon. You've got a lot of complexity when it comes to solving those customer challenges. You have a couple customer story examples you're allowed to share with us. Correct? Paul, do you want to kick one off? Go ahead. Oh, puja. All right. >>No, absolutely. I think there's a ton of them. I, I'll talk about, you know, want to begin with like Cox Automotive, right? A phenomenal customer that we, all of us have worked together with them. And again, looking for a solution to backup S3 to essentially go air gap protection outside of their account, right? They looked at doing it themselves, right? They thought they'll go and basically do it themselves. And then they fortunately bumped into Columbia, they looked at our architecture, looked at what it would really go and take to build it. And guess what, sitting in 2022, getting 23 right now, nobody wants to go and build this themselves. They actually want a turnkey solution that just does it, right? And so, again, we are a phenomenal joint customer of ours doing this at a pretty massive scale, right? And there are many more like that. There's Warner Brothers that are essentially going into the cloud from on premises, right? And they're going really fast accelerating the usage on aws again, looking at, you know, backup and data protection and using clum because of our extreme simplicity that we provide. >>Yeah, I think it's, you've got a, a lot of different people solving different problems that you're working with all the time. Millions of customers. Well, how do you prioritize? >>Well, for us, it really all comes down to fundamentals, right? So Amazon, s3 s unique distributed architecture delivers industry leading durability, availability, performance and security at virtually unlimited scale, right? And it's really been delivering on the fundamentals that has earned the trust of so many customers of all sizes and industries over the course of over 16 years. Now, in terms of how we prioritize on behalf of those customers, we always say that 90% of our roadmap comes directly from what customers are telling us is important. And a large number of our customers now are using S3 through lumino, which is why the relationship is so important. We're here talking about customer use cases here at the show, and we do that regularly throughout the year as well. And that's, that's how we land on a road. >>And what are the, what are the top stories from customers? What, what are they telling you? What's the number one top three things you're hearing? >>I tell you, like, again, it just comes down to the fundamentals, right? Of security, availability, durability and performance at virtually unlimited scale. Like that is the first customer first discussions that we have with customers talking about durable storage, for >>Sure. What I find interesting in, you mentioned scale, right? That comes up a lot scale with data. Yeah. That we heard data. The big theme here, security, what's in my S3 bucket? Can you find out what's in there? Is it backed up properly? How do I get it back? Where's the ransomware? Why not just target the ransomware? So how do you navigate the, the security challenges, the, the need to store all that scale data? What's the secret sauce? >>Yeah, so I think the, the big thing is we'll start with the, you know, how we have architected the product, right? If you think about it, this, you're dealing with a lot of scale, right? You get to a hundred million, a billion and billions very fast on S3 few, especially on a cloud native application. So it starts with the visibility, right? It's basically about, like we have things where you do, where you create a subset of your buckets called protection groups that you can essentially, you know, do it based on prefixes. So now you can essentially figure out what prefix you want to back up and what you don't want to back up. Maybe there's log data that you don't care about, so you don't back that up, right? And it all starts with that visibility that you give. And the prefix level data protection then comes the scale, which is where I was telling you, right? We have basically built an orchestration engine, right? It's like we call the ES for Lambdas, right? So we have a internal orchestration engine and essentially what what we have done is we have our own language internally that spawns off these lambdas, right? And they go after these S3 partitions do the right things and then you basically reel them back. So things like that that we do that are not possible if you're not built on the >>Clock. Well also, I mean, just mind blowing and go back 10 years. Yeah. I mean you got Lambda. What you're talking about here is the gift of the cloud innovation. Yeah. So the benefit of S3 is now accelerated. This is the story this year. Yeah. I mean they're highlighting it at scale, not just in the data, but like what we knew when Lambda came out and what S3 could do. But now mainstream solutions are coming in. Does that change your backup plans? Because we're gonna see a lot more end to end, lot more solutions. We heard that on the keynote. Some are saying it's more complexity. Of course it might, but you can abstract another way with the cloud that's the best part of the cloud. So these abstraction leads. So what's your view on that? But I wanna get your thoughts because you guys are perfectly positioned for this scale, but there's more coming. Yes. Yes. Exactly. What, how are you looking at that? >>So again, I think the, you know, obviously the, the S3 teams and every team in AWS is basically pushing the envelope in terms of innovation. But the key for a partner like us is to go and take that innovation. A lot of complex architectures behind the scene. But what you deliver to the customer is simple. I'll give you one more example. One of the things we launched that, you know, Paul and others are very excited about, is this ability to do instant access on the backup, right? So you could have billions of objects that you backed up. Maybe you need just 10,000 of them for a DR test. And we can basically create like an instant virtual bucket on top of that backup that you can instantly restore >>Spinning up a sandbox of temporary data to go check it >>Out. Exactly. Offer an inte application. >>Think we're geeking out right now. >>Yeah, I know. Brought that part of the segment, John. Don't worry, we're safely there. But, >>But that's the thing, right? That all that is possible because of all the, the scale and innovation and all the APIs and everything that, you know, Paul and the team gives us that we go and build on top of >>Paul, geek out on with us on this. We >>Are super excited for instant restore >>For store. I mean, automation programmability. >>It is, I mean it's the logical next step for backup in the cloud. Exactly. Yeah. But it's a super hard engineering problem to go solve for customers. I mean, the RTO benefits alone are super compelling, but then there's a cost element as well of not having to bring back all that stuff for a test restore, for example. And so it's, it's been really great to, to work with the team on that. We have some ideas on how we may help solve it from our side, and we're looking forward to collaborating on it. >>This is a great illustration of what I was writing about this week around the classic cloud, which is great. And as Adam said, and used like to use the word and, and you got this new functionality we're seeing emerge from the growth. Yes. From the companies that are built on Amazon web services that are growing. You're a partner, they have a lot of other partners and people are taking over restaurant here off action. I mean, there's real growth and new functionality on top of aws. You guys are no different. What's, are you prepared for that? Are you ready to go? >>Yeah, no, absolutely. And I think if you think about, if you think about it, right, I think it's also about doing this without impacting the primary application. Like if the customer is running a primary application at scale on s3, a backup application like ours can't come in and really mess with that. So I think being able to do things where, and this is where you solve really hard computer science problems, right? Where you're bottling yourself. If you are essentially seeing any kind of, you know, interfering with the primary, you're going to cut yourself down. You're gonna go after a different partition. So there are a lot of things you need to do behind the scenes, which is again, all the complexity, all of that, but deliver the, to the customer a very, very simple thing. >>You know, Paul, I wanna get your thoughts and I want you to chime in. Yeah. In 2014, I interviewed Steven Schmidt, my first interview with the, he was the CISO then, and now he's a CSO and, and former ciso, he's back at that time, the word was the cloud's not secure. Now we're talking about security. Just in the complexity of how you're partitioning and managing your sub portions, how you explained it, it's harder for the attackers. The cloud in its in its architecture has become a more secure environment. Yeah. Well, and getting more secure as you have laying out this, this is a new dynamic. This is good. Can you explain the, >>I mean, I, I can just tell you that at AWS security is job zero and that it will always be our number one priority, right? We have a, an infrastructure with under AWS that is vetted and approved to run even top secret workloads, which benefits all customers in all regions. >>And your, your security posture is embedded on top of that. And you got your own stuff. >>Yeah. And if you think of it as a shared responsibility model, so security of the cloud is the responsibility of the cloud provider, but then security of the data on top of it. Like you, you go and delete stuff, your software goes and does something that resiliency, the integrity of the data is your responsibility as a customer. And that's where, you know, we come in. Who >>Shared responsibility has been such a hot topic all week. Yeah. >>I gotta ask him one more question. Cause this is fascinating. And we are talking about on the cube all day today after we saw the announcement and Adam's comment on the cube, Adams LE's comment on the keynote. I mean, he said, if you're gonna tighten your belt, meaning economic cost recovery, re right sizing. If you want to tighten your belt, come to the cloud. So I have to ask you guys, Puja, if you can comment, that'd be great. There's a lot of other competitors out there that aren't born on aws. What is the customer gonna do when they tighten the build? What does that mean? They're gonna go to, to the individual contracts. They're gonna work in the marketplace. I mean this, there's a new dynamic in town. It's called AWS 2022. They weren't really around much in the recession of 2008. They were just starting to grow. Now they're an economic force. People like yourselves have embedded in there. There's a lot of competition. What's gonna happen? >>I think people are gonna just go to a place like, you know, AWS marketplace. You're going to essentially look for solutions and essentially like, and, and the right solutions built in are going to be self-service like aws. It's a very self-service thing. A hundred percent. So you go and do self-service, you figure out what's working, what's not working. Also, the model has to be consumption oriented. No longer can you expect the customer to go and pay a bunch of money for shelfware, right? It's like, like how we charge how AWS charges, which is you pay for what you consume. That and all has to be front and center, >>Right? I think that's a really, I think that's a really important >>Point. It's time >>And I think it's time. So we have a new challenge on the cube. We give you 30 seconds roughly to give us your extraordinarily hot take your shining thought leadership moment and, and highlight what you think is the most important takeaway from the show. The biggest soundbite, the juiciest announcement. Paul, I'll >>Start with an Instagram. Real basically. Yeah. Okay. >>Yeah. Hi. Go. I would just say from an S3 perspective, over the course of the last several years, we've really seen workloads shift from just backup and recovery and static images on websites to data lake analytics applications. And you continue to see that here. And I can tell you that some of these scaled applications are running at enormous mind blowing scale, right? And so, so every year we come here, we talk to customers, and it's just every year it sort of blows me away. And I've been in the storage industry for a long time and it's just is, it blows me away. Just the scale at customers are running in >>And >>Blowing scale. And when it comes to backup, let me just say that it's easy to back up and recover a single object, but doing an easy thing, a billion or 10 billion times over, that's actually quite hard. >>And just to, just to bold that a little bit, just pull out my highlighter. S3 now has over 280 trillion objects. That's a lot. >>That's a lot of objects. >>Yeah. You are not, you are not kidding. When you talk about scale, I mean, this is the most scalable. >>That's not solution's not there. Yeah. That, that's right. And we wake up every, we have a culture of durability and we wake up every single day to raise the bar on the fundamentals and make sure that every single one of those objects is protected and safe. >>Okay. You, I, >>I can't imagine worrying about two, two 80 trillion different things. >>Let's go. You're Instagram real >>For me again, you know, between S3 and us, we are two players out there that are really, you know, processing the data at the end of the day, right? And so I'm very excited about, you know, what we are going to do more and more with the instant restore capability where we can integrate third party services on top of it that can do more things with the data that is not, not passively sitting, but now becomes active data that you can analyze and do things with. So that's something where we take this to the next level is something that I'm super excited about. >>There's a lot to be excited about and, and we're excited to have you. We're excited to hear what happens next. Excited to see more collaboration like this. Paul Pon, thank you so much for joining us here on the show. Thank all of you from for tuning into our continuous wall to wall super thrilling live coverage of AWS reinvent here in fabulous Las Vegas, Nevada, with John Furrier. I'm Savannah Peterson. We're the cube, the leading source for high tech coverage.

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit '22 live from Caesar's Forum in Las Vegas. I'm Lisa Martin, my cohost Dave Vellante, we've been here the last day and a half unpacking a lot of news, a lot of announcements, talking with customers and partners, and we have another great session coming for you next. We've got a customer and a partner talking tech and data mash. Please welcome Mitesh Shah, VP in market strategy at Elation. >> Great to be here. >> and Ash Naseer great, to have you, senior director of data engineering at Warner Brothers Discovery. Welcome guys. >> Thank you for having me. >> It's great to be back in person and to be able to really get to see and feel and touch this technology, isn't it? >> Yeah, it is. I mean two years or so. Yeah. Great to feel the energy in the conference center. >> Yeah. >> Snowflake was virtual, I think for two years and now it's great to kind of see the excitement firsthand. So it's wonderful. >> Th excitement, but also the boom and the number of customers and partners and people attending. They were saying the first, or the summit in 2019 had about 1900 attendees. And this is around 10,000. So a huge jump in a short time period. Talk a little bit about the Elation-Snowflake partnership and probably some of the acceleration that you guys have been experiencing as a Snowflake partner. >> Yeah. As a snowflake partner. I mean, Snowflake is an investor of us in Elation early last year, and we've been a partner for, for longer than that. And good news. We have been awarded Snowflake partner of the year for data governance, just earlier this week. And that's in fact, our second year in a row for winning that award. So, great news on that front as well. >> Repeat, congratulations. >> Repeat. Absolutely. And we're going to hope to make it a three-peat as well. And we've also been awarded industry competency badges in five different industries, those being financial services, healthcare, retail technology, and Median Telcom. >> Excellent. Okay. Going to right get into it. Data mesh. You guys actually have a data mesh and you've presented at the conference. So, take us back to the beginning. Why did you decide that you needed to implement something like data mesh? What was the impetus? >> Yeah. So when people think of Warner brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of HBO, you think of TNT, you think of CNN, we have 30 plus brands in our portfolio and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against as an example, HBO, if Game of Thrones is going on. >> So, okay. So the, the impetus was to serve those lines of business better. Actually, given that you've got these different brands, it was probably easier than most companies. Cause if you're, let's say you're a big financial services company, and now you have to decide who owns what. CNN owns its own data products, HBO. Now, do they decide within those different brands, how to distribute even further? Or is it really, how deep have you gone in that decentralization? >> That's a great question. It's a very close partnership, because there are a number of data sets, which are used by all the brands, right? You think about people browsing websites, right? You know, CNN has a website, Warner brothers has a website. So for us to ingest that data for each of the brands to ingest that data separately, that means five different ways of doing things and you know, a big environment, right? So that is where our team comes into play. We ingest a lot of the common data sets, but like I said, any unique data sets, data sets regarding theatrical as an example, you know, Warner brothers does it themselves, you know, for streaming, HBO Max, does it themselves. So we kind of operate in partnership. >> So do you have a centralized data team and also decentralized data teams, right? >> That's right. >> So I love this conversation because that was heresy 10 years ago, five years ago, even, cause that's inefficient. But you've, I presume you've found that it's actually more productive in terms of the business output, explain that dynamic. >> You know, you bring up such a good point. So I, you know, I consider myself as one of the dinosaurs who started like 20 plus years ago in this industry. And back then, we were all taught to think of the data warehouse as like a monolithic thing. And the reason for that is the technology wasn't there. The technology didn't catch up. Now, 20 years later, the technology is way ahead, right? But like, our mindset's still the same because we think of data warehouses and data platforms still as a monolithic thing. But if you really sort of remove that sort of mental barrier, if you will, and if you start thinking about, well, how do I sort of, you know, federate everything and make sure that you let folks who are building, or are closest to the customer or are building their products, let them own that data and have a partnership. The results have been amazing. And if we were only sort of doing it as a centralized team, we would not be able to do a 10th of what we do today. So it's that massive scale in, in our company as well. >> And I should have clarified, when we talk about data mesh are we talking about the implementing in practice, the octagon sort of framework, or is this sort of your own sort of terminology? >> Well, so the interesting part is four years ago, we didn't have- >> It didn't exist. >> Yeah. It didn't exist. And, and so we, our principle was very simple, right? When we started out, we said, we want to make sure that our brands are able to operate independently with some oversight and guidance from our technology teams, right? That's what we set out to do. We did that with Snowflake by design because Snowflake allows us to, you know, separate those, those brands into different accounts. So that was done by design. And then the, the magic, I think, is the Snowflake data sharing where, which allows us to sort of bring data in here once, and then share it with whoever needs it. So think about HBO Max. On HBO Max, You not only have HBO Max content, but content from CNN, from Cartoon Network, from Warner Brothers, right? All the movies, right? So to see how The Batman movie did in theaters and then on streaming, you don't need, you know, Warner brothers doesn't need to ingest the same streaming data. HBO Max does it. HBO Max shares it with Warner brothers, you know, store once, share many times, and everyone works at their own pace. >> So they're building data products. Those data products are discoverable APIs, I presume, or I guess maybe just, I guess the Snowflake cloud, but very importantly, they're governed. And that's correct, where Elation comes in? >> That's precisely where Elation comes in, is where sort of this central flexible foundation for data governance. You know, you mentioned data mesh. I think what's interesting is that it's really an answer to the bottlenecks created by centralized IT, right? There's this notion of decentralizing that the data engineers and making the data domain owners, the people that know the data the best, have them be in control of publishing the data to the data consumers. There are other popular concepts actually happening right now, as we speak, around modern data stack. Around data fabric that are also in many ways underpinned by this notion of decentralization, right? These are concepts that are underpinned by decentralization and as the pendulum swings, sort of between decentralization and centralization, as we go back and forth in the world of IT and data, there are certain constants that need to be centralized over time. And one of those I believe is very much a centralized platform for data governance. And that's certainly, I think where we come in. Would love to hear more about how you use Elation. >> Yeah. So, I mean, elation helps us sort of, as you guys say, sort of, map, the treasure map of the data, right? So for consumers to find where their data is, that's where Elation helps us. It helps us with the data cataloging, you know, storing all the metadata and, you know, users can go in, they can sort of find, you know, the data that they need and they can also find how others are using data. So it's, there's a little bit of a crowdsourcing aspect that Elation helps us to do whereby you know, you can see, okay, my peer in the other group, well, that's how they use this piece of data. So I'm not going to spend hours trying to figure this out. You're going to use the query that they use. So yeah. >> So you have a master catalog, I presume. And then each of the brands has their own sub catalogs, is that correct? >> Well, for the most part, we have that master catalog and then the brands sort of use it, you know, separately themselves. The key here is all that catalog, that catalog isn't maintained by a centralized group as well, right? It's again, maintained by the individual teams and not only in the individual teams, but the folks that are responsible for the data, right? So I talked about the concept of crowdsourcing, whoever sort of puts the data in, has to make sure that they update the catalog and make sure that the definitions are there and everything sort of in line. >> So HBO, CNN, and each have their own, sort of access to their catalog, but they feed into the master catalog. Is that the right way to think about it? >> Yeah. >> Okay. And they have their own virtual data warehouses, right? They have ownership over that? They can spin 'em up, spin 'em down as they see fit? Right? And they're governed. >> They're governed. And what's interesting is it's not just governed, right? Governance is a, is a big word. It's a bit nebulous, but what's really being enabled here is this notion of self-service as well, right? There's two big sort of rockets that need to happen at the same time in any given organization. There's this notion that you want to put trustworthy data in the hands of data consumers, while at the same time mitigating risk. And that's precisely what Elation does. >> So I want to clarify this for the audience. So there's four principles of database. This came after you guys did it. And I wonder how it aligns. Domain ownership, give data, as you were saying to the, to the domain owners who have context, data as product, you guys are building data products, and that creates two problems. How do you give people self-service infrastructure and how do you automate governance? So the first two, great. But then it creates these other problems. Does that align with your philosophy? Where's alignment? What's different? >> Yeah. Data products is exactly where we're going. And that sort of, that domain based design, that's really key as well. In our business, you think about who the customer is, as an example, right? Depending on who you ask, it's going to be, the answer might be different, you know, to the movie business, it's probably going to be the person who watches a movie in a theater. To the streaming business, to HBO Max, it's the streamer, right? To others, someone watching live CNN on their TV, right? There's yet another group. Think about all the franchising we do. So you see Batman action figures and T-shirts, and Warner brothers branded stuff in stores, that's yet another business unit. But at the end of the day, it's not a different person, it's you and me, right? We do all these things. So the domain concept, make sure that you ingest data and you bring data relevant to the context, however, not sort of making it so stringent where it cannot integrate, and then you integrate it at a higher level to create that 360. >> And it's discoverable. So the point is, I don't have to go tap Ash on the shoulder, say, how do I get this data? Is it governed? Do I have access to it? Give me the rules of it. Just, I go grab it, right? And the system computationally automates whether or not I have access to it. And it's, as you say, self-service. >> In this case, exactly right. It enables people to just search for data and know that when they find the data, whether it's trustworthy or not, through trust flags, and the like, it's doing both of those things at the same time. >> How is it an enabler of solving some of the big challenges that the media and entertainment industry is going through? We've seen so much change the last couple of years. The rising consumer expectations aren't going to go back down. They're only going to come up. We want you to serve us up content that's relevant, that's personalized, that makes sense. I'd love to understand from your perspective, Mitesh, from an industry challenges perspective, how does this technology help customers like Warner Brothers Discovery, meet business customers, where they are and reduce the volume on those challenges? >> It's a great question. And as I mentioned earlier, we had five industry competency badges that were awarded to us by Snowflake. And one of those four, Median Telcom. And the reason for that is we're helping media companies understand their audiences better, and ultimately serve up better experiences for their audiences. But we've got Ash right here that can tell us how that's happening in practice. >> Yeah, tell us. >> So I'll share a story. I always like to tell stories, right? Once once upon a time before we had Elation in place, it was like, who you knew was how you got access to the data. So if I knew you and I knew you had access to a certain kind of data and your access to the right kind of data was based on the network you had at the company- >> I had to trust you. >> Yeah. >> I might not want to give up my data. >> That's it. And so that's where Elation sort of helps us democratize it, but, you know, puts the governance and controls, right? There are certain sensitive things as well, such as viewership, such as subscriber accounts, which are very important. So making sure that the right people have access to it, that's the other problem that Elation helps us solve. >> That's precisely part of our integration with Snowflake in particular, being able to define and manage policies within Elation. Saying, you know, certain people should have access to certain rows, doing column level masking. And having those policies actually enforced at the Snowflake data layer is precisely part of our value product. >> And that's automated. >> And all that's automated. Exactly. >> Right. So I don't have to think about it. I don't have to go through the tap on their shoulder. What has been the impact, Ash, on data quality as you've pushed it down into the domains? >> That's a great question. So it has definitely improved, but data quality is a very interesting subject, because back to my example of, you know, when we started doing things, we, you know, the centralized IT team always said, well, it has to be like this, Right? And if it doesn't fit in this, then it's bad quality. Well, sometimes context changes. Businesses change, right? You have to be able to react to it quickly. So making sure that a lot of that quality is managed at the decentralized level, at the place where you have that business context, that ensures you have the most up to date quality. We're talking about media industry changing so quickly. I mean, would we have thought three years ago that people would watch a lot of these major movies on streaming services? But here's the reality, right? You have to react and, you know, having it at that level just helps you react faster. >> So data, if I play that back, data quality is not a static framework. It's flexible based on the business context and the business owners can make those adjustments, cause they own the data. >> That's it. That's exactly it. >> That's awesome. Wow. That's amazing progress that you guys have made. >> In quality, if I could just add, it also just changes depending on where you are in your data pipeline stage, right? Data, quality data observability, this is a very fast evolving space at the moment, and if I look to my left right now, I bet you I can probably see a half-dozen quality observability vendors right now. And so given that and given the fact that Elation still is sort of a central hub to find trustworthy data, we've actually announced an open data quality initiative, allowing for best-of-breed data quality vendors to integrate with the platform. So whoever they are, whatever tool folks want to use, they can use that particular tool of choice. >> And this all runs in the cloud, or is it a hybrid sort of? >> Everything is in the cloud. We're all in the cloud. And you know, again, helps us go faster. >> Let me ask you a question. I could go on forever in this topic. One of the concepts that was put forth is whether it's a Snowflake data warehouse or a data bricks, data lake, or an Oracle data warehouse, they should all be inclusive. They should just be a node on the mesh. Like, wow, that sounds good. But I haven't seen it yet. Right? I'm guessing that Snowflake and Elation enable all the self-serve, all this automated governance, and that including those other items, it's got to be a one-off at this point in time. Do you ever see you expanding that scope or is it better off to just kind of leave it into the, the Snowflake data cloud? >> It's a good question. You know, I feel like where we're at today, especially in terms of sort of technology giving us so many options, I don't think there's a one size fits all. Right? Even though we are very heavily invested in Snowflake and we use Snowflake consistently across the organization, but you could, theoretically, could have an architecture that blends those two, right? Have different types of data platforms like a teradata or an Oracle and sort of bring it all together today. We have the technology, you know, that and all sorts of things that can make sure that you query on different databases. So I don't think the technology is the problem, I think it's the organizational mindset. I think that that's what gets in the way. >> Oh, interesting. So I was going to ask you, will hybrid tables help you solve that problem? And, maybe not, what you're saying, it's the organization that owns the Oracle database saying, Hey, we have our system. It processes, it works, you know, go away. >> Yeah. Well, you know, hybrid tables I think, is a great sort of next step in Snowflake's evolution. I think it's, in my opinion, I, think it's a game changer, but yeah. I mean, they can still exist. You could do hybrid tables right on Snowflake, or you could, you know, you could kind of coexist as well. >> Yeah. But, do you have a thought on this? >> Yeah, I do. I mean, we're always going to live in a time where you've got data distributed in throughout the organization and around the globe. And that could be even if you're all in on Snowflake, you could have data in Snowflake here, you could have data in Snowflake in EMEA and Europe somewhere. It could be anywhere. By the same token you might be using. Every organization is using on-premises systems. They have data, they naturally have data everywhere. And so, you know, this one solution to this is really centralizing, as I mentioned, not just governance, but also metadata about all of the data in your organization so that you can enable people to search and find and discover trustworthy data no matter where it is in your organization. >> Yeah. That's a great point. I mean, if you have the data about the data, then you can, you can treat these independent nodes. That's just that. Right? And maybe there's some advantages of putting it all in the Snowflake cloud, but to your point, organizationally, that's just not feasible. The whole, unfortunately, sorry, Snowflake, all the world's data is not going to go into Snowflake, but they play a key role in accelerating, what I'm hearing, your vision of data mesh. >> Yeah, absolutely. I think going forward in the future, we have to start thinking about data platforms as just one place where you sort of dump all the data. That's where the mesh concept comes in. It is going to be a mesh. It's going to be distributed and organizations have to be okay with that. And they have to embrace the tools. I mean, you know, Facebook developed a tool called Presto many years ago that that helps them solve exactly the same problem. So I think the technology is there. I think the organizational mindset needs to evolve. >> Yeah. Definitely. >> Culture. Culture is one of the hardest things to change. >> Exactly. >> Guys, this was a masterclass in data mesh, I think. Thank you so much for coming on talking. >> We appreciate it. Thank you so much. >> Of course. What Elation is doing with Snowflake and with Warner Brothers Discovery, Keep that content coming. I got a lot of stuff I got to catch up on watching. >> Sounds good. Thank you for having us. >> Thanks guys. >> Thanks, you guys. >> For Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22. We'll be back after a short break. (upbeat music)

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit '22 live from Caesar's Forum in Las Vegas. I'm Lisa Martin, my cohost Dave Vellante, we've been here the last day and a half unpacking a lot of news, a lot of announcements, talking with customers and partners, and we have another great session coming for you next. We've got a customer and a partner talking tech and data mash. Please welcome Mitesh Shah, VP in market strategy at Elation. >> Great to be here. >> and Ash Naseer great, to have you, senior director of data engineering at Warner Brothers Discovery. Welcome guys. >> Thank you for having me. >> It's great to be back in person and to be able to really get to see and feel and touch this technology, isn't it? >> Yeah, it is. I mean two years or so. Yeah. Great to feel the energy in the conference center. >> Yeah. >> Snowflake was virtual, I think for two years and now it's great to kind of see the excitement firsthand. So it's wonderful. >> Th excitement, but also the boom and the number of customers and partners and people attending. They were saying the first, or the summit in 2019 had about 1900 attendees. And this is around 10,000. So a huge jump in a short time period. Talk a little bit about the Elation-Snowflake partnership and probably some of the acceleration that you guys have been experiencing as a Snowflake partner. >> Yeah. As a snowflake partner. I mean, Snowflake is an investor of us in Elation early last year, and we've been a partner for, for longer than that. And good news. We have been awarded Snowflake partner of the year for data governance, just earlier this week. And that's in fact, our second year in a row for winning that award. So, great news on that front as well. >> Repeat, congratulations. >> Repeat. Absolutely. And we're going to hope to make it a three-peat as well. And we've also been awarded industry competency badges in five different industries, those being financial services, healthcare, retail technology, and Median Telcom. >> Excellent. Okay. Going to right get into it. Data mesh. You guys actually have a data mesh and you've presented at the conference. So, take us back to the beginning. Why did you decide that you needed to implement something like data mesh? What was the impetus? >> Yeah. So when people think of Warner brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of HBO, you think of TNT, you think of CNN, we have 30 plus brands in our portfolio and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against as an example, HBO, if Game of Thrones is going on. >> So, okay. So the, the impetus was to serve those lines of business better. Actually, given that you've got these different brands, it was probably easier than most companies. Cause if you're, let's say you're a big financial services company, and now you have to decide who owns what. CNN owns its own data products, HBO. Now, do they decide within those different brands, how to distribute even further? Or is it really, how deep have you gone in that decentralization? >> That's a great question. It's a very close partnership, because there are a number of data sets, which are used by all the brands, right? You think about people browsing websites, right? You know, CNN has a website, Warner brothers has a website. So for us to ingest that data for each of the brands to ingest that data separately, that means five different ways of doing things and you know, a big environment, right? So that is where our team comes into play. We ingest a lot of the common data sets, but like I said, any unique data sets, data sets regarding theatrical as an example, you know, Warner brothers does it themselves, you know, for streaming, HBO Max, does it themselves. So we kind of operate in partnership. >> So do you have a centralized data team and also decentralized data teams, right? >> That's right. >> So I love this conversation because that was heresy 10 years ago, five years ago, even, cause that's inefficient. But you've, I presume you've found that it's actually more productive in terms of the business output, explain that dynamic. >> You know, you bring up such a good point. So I, you know, I consider myself as one of the dinosaurs who started like 20 plus years ago in this industry. And back then, we were all taught to think of the data warehouse as like a monolithic thing. And the reason for that is the technology wasn't there. The technology didn't catch up. Now, 20 years later, the technology is way ahead, right? But like, our mindset's still the same because we think of data warehouses and data platforms still as a monolithic thing. But if you really sort of remove that sort of mental barrier, if you will, and if you start thinking about, well, how do I sort of, you know, federate everything and make sure that you let folks who are building, or are closest to the customer or are building their products, let them own that data and have a partnership. The results have been amazing. And if we were only sort of doing it as a centralized team, we would not be able to do a 10th of what we do today. So it's that massive scale in, in our company as well. >> And I should have clarified, when we talk about data mesh are we talking about the implementing in practice, the octagon sort of framework, or is this sort of your own sort of terminology? >> Well, so the interesting part is four years ago, we didn't have- >> It didn't exist. >> Yeah. It didn't exist. And, and so we, our principle was very simple, right? When we started out, we said, we want to make sure that our brands are able to operate independently with some oversight and guidance from our technology teams, right? That's what we set out to do. We did that with Snowflake by design because Snowflake allows us to, you know, separate those, those brands into different accounts. So that was done by design. And then the, the magic, I think, is the Snowflake data sharing where, which allows us to sort of bring data in here once, and then share it with whoever needs it. So think about HBO Max. On HBO Max, You not only have HBO Max content, but content from CNN, from Cartoon Network, from Warner Brothers, right? All the movies, right? So to see how The Batman movie did in theaters and then on streaming, you don't need, you know, Warner brothers doesn't need to ingest the same streaming data. HBO Max does it. HBO Max shares it with Warner brothers, you know, store once, share many times, and everyone works at their own pace. >> So they're building data products. Those data products are discoverable APIs, I presume, or I guess maybe just, I guess the Snowflake cloud, but very importantly, they're governed. And that's correct, where Elation comes in? >> That's precisely where Elation comes in, is where sort of this central flexible foundation for data governance. You know, you mentioned data mesh. I think what's interesting is that it's really an answer to the bottlenecks created by centralized IT, right? There's this notion of decentralizing that the data engineers and making the data domain owners, the people that know the data the best, have them be in control of publishing the data to the data consumers. There are other popular concepts actually happening right now, as we speak, around modern data stack. Around data fabric that are also in many ways underpinned by this notion of decentralization, right? These are concepts that are underpinned by decentralization and as the pendulum swings, sort of between decentralization and centralization, as we go back and forth in the world of IT and data, there are certain constants that need to be centralized over time. And one of those I believe is very much a centralized platform for data governance. And that's certainly, I think where we come in. Would love to hear more about how you use Elation. >> Yeah. So, I mean, elation helps us sort of, as you guys say, sort of, map, the treasure map of the data, right? So for consumers to find where their data is, that's where Elation helps us. It helps us with the data cataloging, you know, storing all the metadata and, you know, users can go in, they can sort of find, you know, the data that they need and they can also find how others are using data. So it's, there's a little bit of a crowdsourcing aspect that Elation helps us to do whereby you know, you can see, okay, my peer in the other group, well, that's how they use this piece of data. So I'm not going to spend hours trying to figure this out. You're going to use the query that they use. So yeah. >> So you have a master catalog, I presume. And then each of the brands has their own sub catalogs, is that correct? >> Well, for the most part, we have that master catalog and then the brands sort of use it, you know, separately themselves. The key here is all that catalog, that catalog isn't maintained by a centralized group as well, right? It's again, maintained by the individual teams and not only in the individual teams, but the folks that are responsible for the data, right? So I talked about the concept of crowdsourcing, whoever sort of puts the data in, has to make sure that they update the catalog and make sure that the definitions are there and everything sort of in line. >> So HBO, CNN, and each have their own, sort of access to their catalog, but they feed into the master catalog. Is that the right way to think about it? >> Yeah. >> Okay. And they have their own virtual data warehouses, right? They have ownership over that? They can spin 'em up, spin 'em down as they see fit? Right? And they're governed. >> They're governed. And what's interesting is it's not just governed, right? Governance is a, is a big word. It's a bit nebulous, but what's really being enabled here is this notion of self-service as well, right? There's two big sort of rockets that need to happen at the same time in any given organization. There's this notion that you want to put trustworthy data in the hands of data consumers, while at the same time mitigating risk. And that's precisely what Elation does. >> So I want to clarify this for the audience. So there's four principles of database. This came after you guys did it. And I wonder how it aligns. Domain ownership, give data, as you were saying to the, to the domain owners who have context, data as product, you guys are building data products, and that creates two problems. How do you give people self-service infrastructure and how do you automate governance? So the first two, great. But then it creates these other problems. Does that align with your philosophy? Where's alignment? What's different? >> Yeah. Data products is exactly where we're going. And that sort of, that domain based design, that's really key as well. In our business, you think about who the customer is, as an example, right? Depending on who you ask, it's going to be, the answer might be different, you know, to the movie business, it's probably going to be the person who watches a movie in a theater. To the streaming business, to HBO Max, it's the streamer, right? To others, someone watching live CNN on their TV, right? There's yet another group. Think about all the franchising we do. So you see Batman action figures and T-shirts, and Warner brothers branded stuff in stores, that's yet another business unit. But at the end of the day, it's not a different person, it's you and me, right? We do all these things. So the domain concept, make sure that you ingest data and you bring data relevant to the context, however, not sort of making it so stringent where it cannot integrate, and then you integrate it at a higher level to create that 360. >> And it's discoverable. So the point is, I don't have to go tap Ash on the shoulder, say, how do I get this data? Is it governed? Do I have access to it? Give me the rules of it. Just, I go grab it, right? And the system computationally automates whether or not I have access to it. And it's, as you say, self-service. >> In this case, exactly right. It enables people to just search for data and know that when they find the data, whether it's trustworthy or not, through trust flags, and the like, it's doing both of those things at the same time. >> How is it an enabler of solving some of the big challenges that the media and entertainment industry is going through? We've seen so much change the last couple of years. The rising consumer expectations aren't going to go back down. They're only going to come up. We want you to serve us up content that's relevant, that's personalized, that makes sense. I'd love to understand from your perspective, Mitesh, from an industry challenges perspective, how does this technology help customers like Warner Brothers Discovery, meet business customers, where they are and reduce the volume on those challenges? >> It's a great question. And as I mentioned earlier, we had five industry competency badges that were awarded to us by Snowflake. And one of those four, Median Telcom. And the reason for that is we're helping media companies understand their audiences better, and ultimately serve up better experiences for their audiences. But we've got Ash right here that can tell us how that's happening in practice. >> Yeah, tell us. >> So I'll share a story. I always like to tell stories, right? Once once upon a time before we had Elation in place, it was like, who you knew was how you got access to the data. So if I knew you and I knew you had access to a certain kind of data and your access to the right kind of data was based on the network you had at the company- >> I had to trust you. >> Yeah. >> I might not want to give up my data. >> That's it. And so that's where Elation sort of helps us democratize it, but, you know, puts the governance and controls, right? There are certain sensitive things as well, such as viewership, such as subscriber accounts, which are very important. So making sure that the right people have access to it, that's the other problem that Elation helps us solve. >> That's precisely part of our integration with Snowflake in particular, being able to define and manage policies within Elation. Saying, you know, certain people should have access to certain rows, doing column level masking. And having those policies actually enforced at the Snowflake data layer is precisely part of our value product. >> And that's automated. >> And all that's automated. Exactly. >> Right. So I don't have to think about it. I don't have to go through the tap on their shoulder. What has been the impact, Ash, on data quality as you've pushed it down into the domains? >> That's a great question. So it has definitely improved, but data quality is a very interesting subject, because back to my example of, you know, when we started doing things, we, you know, the centralized IT team always said, well, it has to be like this, Right? And if it doesn't fit in this, then it's bad quality. Well, sometimes context changes. Businesses change, right? You have to be able to react to it quickly. So making sure that a lot of that quality is managed at the decentralized level, at the place where you have that business context, that ensures you have the most up to date quality. We're talking about media industry changing so quickly. I mean, would we have thought three years ago that people would watch a lot of these major movies on streaming services? But here's the reality, right? You have to react and, you know, having it at that level just helps you react faster. >> So data, if I play that back, data quality is not a static framework. It's flexible based on the business context and the business owners can make those adjustments, cause they own the data. >> That's it. That's exactly it. >> That's awesome. Wow. That's amazing progress that you guys have made. >> In quality, if I could just add, it also just changes depending on where you are in your data pipeline stage, right? Data, quality data observability, this is a very fast evolving space at the moment, and if I look to my left right now, I bet you I can probably see a half-dozen quality observability vendors right now. And so given that and given the fact that Elation still is sort of a central hub to find trustworthy data, we've actually announced an open data quality initiative, allowing for best-of-breed data quality vendors to integrate with the platform. So whoever they are, whatever tool folks want to use, they can use that particular tool of choice. >> And this all runs in the cloud, or is it a hybrid sort of? >> Everything is in the cloud. We're all in the cloud. And you know, again, helps us go faster. >> Let me ask you a question. I could go on forever in this topic. One of the concepts that was put forth is whether it's a Snowflake data warehouse or a data bricks, data lake, or an Oracle data warehouse, they should all be inclusive. They should just be a node on the mesh. Like, wow, that sounds good. But I haven't seen it yet. Right? I'm guessing that Snowflake and Elation enable all the self-serve, all this automated governance, and that including those other items, it's got to be a one-off at this point in time. Do you ever see you expanding that scope or is it better off to just kind of leave it into the, the Snowflake data cloud? >> It's a good question. You know, I feel like where we're at today, especially in terms of sort of technology giving us so many options, I don't think there's a one size fits all. Right? Even though we are very heavily invested in Snowflake and we use Snowflake consistently across the organization, but you could, theoretically, could have an architecture that blends those two, right? Have different types of data platforms like a teradata or an Oracle and sort of bring it all together today. We have the technology, you know, that and all sorts of things that can make sure that you query on different databases. So I don't think the technology is the problem, I think it's the organizational mindset. I think that that's what gets in the way. >> Oh, interesting. So I was going to ask you, will hybrid tables help you solve that problem? And, maybe not, what you're saying, it's the organization that owns the Oracle database saying, Hey, we have our system. It processes, it works, you know, go away. >> Yeah. Well, you know, hybrid tables I think, is a great sort of next step in Snowflake's evolution. I think it's, in my opinion, I, think it's a game changer, but yeah. I mean, they can still exist. You could do hybrid tables right on Snowflake, or you could, you know, you could kind of coexist as well. >> Yeah. But, do you have a thought on this? >> Yeah, I do. I mean, we're always going to live in a time where you've got data distributed in throughout the organization and around the globe. And that could be even if you're all in on Snowflake, you could have data in Snowflake here, you could have data in Snowflake in EMEA and Europe somewhere. It could be anywhere. By the same token you might be using. Every organization is using on-premises systems. They have data, they naturally have data everywhere. And so, you know, this one solution to this is really centralizing, as I mentioned, not just governance, but also metadata about all of the data in your organization so that you can enable people to search and find and discover trustworthy data no matter where it is in your organization. >> Yeah. That's a great point. I mean, if you have the data about the data, then you can, you can treat these independent nodes. That's just that. Right? And maybe there's some advantages of putting it all in the Snowflake cloud, but to your point, organizationally, that's just not feasible. The whole, unfortunately, sorry, Snowflake, all the world's data is not going to go into Snowflake, but they play a key role in accelerating, what I'm hearing, your vision of data mesh. >> Yeah, absolutely. I think going forward in the future, we have to start thinking about data platforms as just one place where you sort of dump all the data. That's where the mesh concept comes in. It is going to be a mesh. It's going to be distributed and organizations have to be okay with that. And they have to embrace the tools. I mean, you know, Facebook developed a tool called Presto many years ago that that helps them solve exactly the same problem. So I think the technology is there. I think the organizational mindset needs to evolve. >> Yeah. Definitely. >> Culture. Culture is one of the hardest things to change. >> Exactly. >> Guys, this was a masterclass in data mesh, I think. Thank you so much for coming on talking. >> We appreciate it. Thank you so much. >> Of course. What Elation is doing with Snowflake and with Warner Brothers Discovery, Keep that content coming. I got a lot of stuff I got to catch up on watching. >> Sounds good. Thank you for having us. >> Thanks guys. >> Thanks, you guys. >> For Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22. We'll be back after a short break. (upbeat music)

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of the International Women's Showcase for 2022. I'm your host, Lisa Martin. I'm really excited because for the first time in my CUBE career of six years, I have a father daughter duo maybe the first time in CUBE's history. Frank and Emma Arrigo from AWS join me guys it's great to have you on the program. >> Great to be here thank you. >> So, Emma, let's go ahead and start with you. Talk to us about how you got to AWS and a little bit about your background. >> Yes, thanks Lisa. So I've joined AWS as a recent graduate from university. So I did my masters of data science and I was going through the grad, the grad job hunt applying for all these different places. And AWS appeared on my radar for an intern program. And Frank was there at the time and so I was like, "Should I do it?" But I still applied cause it was a great program. And so I went did that internship for three months over the summer of 2019-2020, and then I went back and finished my degree. And another grad role came up for in AWS in Tech U to be an associate solutions architect. And so I was approached to apply for that. And I got through to that program and joined the team almost a year ago in March, 2021 through Tech U and yeah, that's how I ended up at AWS. >> Excellent and so Frank, this is a pretty unique situation, father daughter duo at AWS let alone Amazon, let alone probably a lot of companies. Talk to me about it the parental lens. >> Yeah, look it is unique, there's a few family connections within AWS, but you know, definitely here in Australia, it's really rare, but I think the family connection is, you know Emma and we've got four kids, I've got four kids in total. So Emma has three brothers, you know, I've lived in tech, my entire career and so they've been part of it. You know, we've lived in the States, lived in Seattle for a couple of years. And so they'd come to the office and see what dad did. And so it wasn't a big surprise for them to understand what the role was and what we did, so, you know, they kind of grew up with it. And you know, when the opportunity came up for Emma, did the internship, I was excited for it because it was in a different area. It was working in a startup team doing some interesting work that really lined up with some of the interest Emma had. And so she kind of learned what it was like to be Amazonian through that internship and that was... I call that a long audition for a job. And she was then able to join Tech U program, which is a early career bootcamp, I like to think of it, which is the six month program to help our grads learn some of the fundamental skills because the value of a solutions architect or some of these other tech roles is you need experience. You need to have been in the game a while to be a trusted advisor to a customer. And it's hard to do that when you're a grad. So the bootcamp gives them the practical experience and then they get another six months on the job experience where they develop those skills and hone it and get ready to, you know, be a trusted advisor to the customers. >> Right, and that's such a great... I'm sure that's a tremendous opportunity to learn how to become that trusted advisor, especially from peers, such as yourself and I want to go back to you. Talk to me about your interest in IT, in data science. Was this something that you were always interested in primary school or in high school? Or was this something that kind of came on later on? >> Yeah, so my interest in tech kind of emerged as I went along in my education. So when I was younger, I really wanted to be an orthodontist for some reason. I don't know why. And then you just sort of in year eight and like early school sort of didn't really know what I wanted to do. Just sort of going through just trying to survive as a teenage girl at high school at an all girls school, didn't really have many, didn't really have career aspirations, I guess, and then one year I attended a information day at a university about engineering and that just really sparked my interest, I don't know why, but I was like, I've always been obsessed with like factories and those types of things and how things are made. And so that really just sparked my interest and I never really thought of it before. And so then that put STEM engineering on my radar and then I guess spoke with it about with the parents. And then they mentioned that tech would be a like IT, Information Technology would be really useful. And so then we approached the school to ask if I could do IT in year 11. So that's sort of our second last year of high school. And they said, "No, we couldn't do IT." I couldn't go to the boys' school to do IT. That girls don't do it or that not good at it. And I wasn't allowed, and they wouldn't let me do physics either. So I moved school in for the final two of high school to be able to do IT and physics to help, you know, get to the course I wanted to do. And so that was my journey into STEM. So it wasn't really on my radar, but then events like this and at university isn't it? Organizations sparked my interest. And then still when I entered university, I didn't know exactly what I wanted to major in nor where I wanted to work would never have thought it would be where, with my father, like I was aware of the world of IT and everything, but I wouldn't, if you'd asked me in first year, it wouldn't have been that I would probably, we would've said, I don't know an academic or something. I don't know. And then, but again, as the university went on and you attend networking events or club things, you sort of learn a bit more about the ecosystem. And then that's where yeah. Tech company sort of became where I was looking for jobs and roles for when I finished up. So that was kind of my journey to... >> So what I love though, that you and Frank, this is going to be a question for you, how Emma was told. "No, you can't study IT. No, you can't study physics. You can't go to the boys school and do that either." Talk to me about that, Frank, from your perspective as a parent of a daughter, and you said, I think she's got three brothers lucky, Emma, but talk to me about that from your perspective, in terms of going, my daughter has really has an in a strong interest in this and they're telling her no we're going to pivot and actually change schools to be able give her the opportunities that she wants to pursue. >> Yeah. Look, as a parent, we were shocked. You know, it was just an unexpected response, you know, in a lot of ways, the school that she was at was more of a finishing school than anything else, you know, preparing young ladies for marriage and, you know, career as a, I don't know, I will leave it at that. So we were really disappointed. And so very quickly we looked at other alternatives and other options and we pulled Emma out of school and we knew it was like the last two years are critical in Australia. We don't have a middle school and a senior school, it's all one, you know, combined thing. But those last two years are all about getting ready for university. And so we made a really tough call and we picked her up, dropped her into a totally new school. It was co-ed school. And then when we told her previous, her girl's school. I actually spoke to the vice principal and he said, "Oh, I can't believe you're sending her to a co-ed school. She's going to struggle 'cause boys are so much better in tech." And I was totally, I was lost for words, right? Because I felt back in my career and I had some amazing female managers, leaders, role models in my time that I worked for and I followed and they were always struggling because, you know, they were in the minority, but they were incredible, you know, technologists and leaders. And I just couldn't believe it. So as parents we made the tough call. We picked Emma up. We put her into another high school and she flourished, you know, Emma started a club, she got convolved with a whole bunch of other things. When she graduated, the teachers felt that she'd been there six years, right? The whole time of it. So she really made a mark, made an impact at this school and so much so that her younger brother then followed and went to that school and completed his high school there as well. But it, we just can't believe it. And we tell it everyone, this story, you know, we name the school, we won't name. We choose not to name them here, but we name the school because we just think it's really terrible guidance and terrible advice. Like we want people to follow their passion. I tell my kids and I tell the folks when I speak to, you know, early career folks, follow your passion first, guess what the job will appear. Right? You know, there'll be the... The work will come if you do something that you love. And then the second piece that I always say is, "Every future job is going to be a tech job." Technology is embedded in everything that we do. So the fact that you say, "A girl can't do technology," you're limiting yourselves, right? You don't want to think that, you want to think about the possibilities rather than the things you can't do. It's the things you can do. And the things that you haven't even thought about doing. So that's why, you know, it was so exciting to see that experience with Emma, and just seeing her grow through that and she became a bit of a STEM advocate at a high school as well. So, she saw the value of her role model that helped her. And she wants to be a.... Continue being a role model for others as well, which again, I think is admirable, right? It's about- >> Absolutely. >> Shining a light and leading and as a parent, irrespective that we work at the same company as a parent, that's what you want to see. You want to see your kids aim high and inspire others. That's what she does. >> Well, she's already been a role model too, I mean, to your younger brother, but one of the things that we say often, and theCUBE does a lot for women in technology events. And I'm fortunate to get to host a lot of those, we say, "You can't be what you can't see." So needing to have those role models who are visible. Now, it doesn't have to be female necessarily. and Frank you mentioned that you had female mentors and role models and in your illustrious career. But the important point is being able to elevate women into positions where others can see and can identify, "Oh, there's a role model. There's somebody that might be a mentor for me, or a sponsor down the road, it's critically important." And as of course, we look at the numbers in tech, women in technical roles are still quite low, but Emma, tell me a little bit about, you've been through the program. You talked about that. What are some of the things that you feel in like the last six months that you've been able to learn that had you not had this opportunity, maybe you wouldn't have. >> You know, I think that's a great point. So as a solutions architect, I get to be both technical. So hands on building an AWS, helping customers solve their problems, whether it be a data leak or I don't know, an image recognition tool to look for garbage dumped on the street or, and also thinking from the business perspective for the customers, so that's a fun part as the, of the role, but things I get to do. So currently I'm working on a demo for the conference in Sydney. So I'm building a traffic detection model using some computer vision and IOT so I get to bring my data science background to this build and also learn about new areas like IOT, Internet Of Things; Technologies. So that's been a really fun project and yeah, just having the ability to play around on AWS, we have... >> Right. Well, the exposure in the experiences is priceless. You can't put a price on that, but being able to get into the environment, learn it from a technical perspective, learn it from a practical perspective. And then of course get all the great things about getting to interact with customers and learning how different industries work, you mentioned you were in public sector. That just must be a field of dreams, I would imagine. >> I know. >> In some senses for you, right? >> Really have lucked out. I know it's, I'm like, "Wow, this my job is to play around with some new service, just because need to know about that for the customer meeting. Like I'm building a chatbot or helping build a chatbot for a customer, at the university. So yeah, things like that make it very, yeah. It's a pretty amazing role. >> It sounds, it sure sounds like it. And sounds like you're are excelling at it tremendously. Let me ask you Emma. For young girls who might be in a similar situation to where you were not that long ago with the school telling you, "No, you can't do IT." "No, you can't do physics." So you actually switched schools. What would you tell those young girls who might be in that situation about hearing the word, "No." And would you advise them to embrace a career in technology? >> Yeah, I would say that it really..... What makes me so sad is if my family didn't know about tech and had my... Supported me through that like if I would've just gone, "Oh, okay. I won't do it." You know what I mean? Like that just makes me really sad. How many people have missed out on studying what they wanted to study. So by having those types of experiences, so what I would say as advice is, "Back yourself, find supporters, whether it be your family or a teacher that you really sort of connect with, to be able to support you and through these decisions." And yeah, I think having those sponsors in a way, your advocates to help you make those choices and help support you through those choices. >> Yeah. I agree. And I have a feeling you're going to be one of those sponsors and mentors, if you aren't already Emma, I have a feeling that's just around the corner from you. So Frank, last question to you. What's the overall lesson here, if we look at statistics, I mentioned some of the stats about, you know, women in technical roles as usually less than 25% globally. But also we see data that shows that companies are more profitable and more performant when there's at least 30% of the executive suite it's women. So from your parental perspective, and from an Amazonian perspective, Frank, what's the lesson here? >> Well, look from an Amazonian perspective, we need to make sure that we have a team that represents our customers, right? And our customers aren't all boys. You know, they're not all blokes, as we say down here. So you've got to have a team that is made up of what represents your customers. So I think that's the Amazonian view. And so diverse perspectives, diverse experience, diverse backgrounds is what does that. The other from a parent, you know, I said it earlier. I think every future job is a tech job. And I think it's really important that as kids come through, you know, primary school, high school, whatever, they're prepared for that, they're already consumers of technology. You know, they need to be creators or, or participate in that environment. And I can give you an example, a few years ago, I worked for, at a large telco here. And we actually invested in a thing called code club, which was aimed at primary school kids, kids in grade four, five and six. So elementary school for my friends in America, it's kids in grade four, five and six. And they were learning how to use scratch. Scratch is this interactive tool like building lego to write programming and believe it or not, there were more girls interested and were part of code club. It was probably 60-40 was the ratio of young girls doing it compared to boys because it was creative, it was a creative outlet, they were building stuff and assembling and making these things that they loved to make. Right. But then what we saw was there'd be a drop off at high school, whether it's curriculum related or interests or distractions, I don't know what it is, but there things get lost along the way along high school. But I see it at the primary school stage at elementary school that the interest is there. So I think part of it is, there needs to be a bit of a switch up in education or other opportunities outside of school to really foster and nurture and develop this interest because it really does take all kinds to be successful in the role. And Emma talked about a chatbot that she's building and that's a conversational thing. I can't see geek boys having being able to impact and create a interesting conversation, right. Then there's other areas that seems to be skewed and biased based on a predominantly male view of the world. So we need the tech, the industry needs these diverse perspectives and these diverse views, because, you know, to your point, it's going to impact the bottom line. It's going to also deliver a better product and it's going to reflect society. It's going to reflect the customers that are using it because we're made up of every, every race and color, creed, gender. And we need a team that represents that. >> Exactly. I couldn't agree more. Well, it sounds like the Arrigo family are quite the supporters of this, but also we need more of both of you. We need more of the sponsors and the parents who are encouraging the kids and making the right decisions to help them get along that path. And we need more folks like Emma and more women that we can see, "Wow, look what she's doing in such a short time period. We want to be just like that." So you guys are, have both been fantastic. I thank you so much for joining me at the International. Women's Showcase, more power to your family. We need more folks like you guys, so great work. Keep it up. >> Thank you. >> Thanks Lisa. >> For Frank and Emma Arrigo, I'm Lisa Martin. You're watching theCUBE's coverage of International Women's Showcase 2022. (soothing music)

(upbeat music) >> Hey everyone. Welcome back to Las Vegas. The Cube is live. I can't say that enough. We are alive at AWS re:Invent 2021. Lisa Martin with Dave Nicholson. Hey Dave. >> Hey Lisa. >> Having a good day so far. >> So far, so good. >> We have an alumni back with us. We have about a hundred segments on the cube at AWS remit. We've got one of our original alumni back with us. Zaki Bajwa joins us the global head of partner solution engineers at Stripe. Zaki welcome back. >> Thank you, Lisa, thank you, Dave. Pleasure to be here. >> Lisa: Isn't it great to be back in person? >> Love it. Love it. Can't do a whiteboard virtually, you can, it's not the same. >> It's not the same and all those conversations I'm sure that you've had with partners and with customers the last couple of days that you just can't replicate that over zoom. >> Zaki: Exactly. >> So just for anyone who doesn't understand, AWS has a massive ecosystem of partners. So we'll get to talk about Stripe and AWS, but for anyone that doesn't know what Stripe is, give us the lowdown. You guys started 10 years ago. Talk to us about Stripe, the business strategy, what it's like today. >> Yeah, sure. So you guys know Stripe started 10 years ago by two brothers, John and Patrick Collison. And they've really focused on the developer and helping the developers accelerate digital commerce. Why? Cause the status quo at the time was one where a developer needed to, you know, build banking relationships with issuing banks, merchant banks, card networks, payment networks, tax liabilities, data compliance, and all of these manual processes that they had to deal with. So what Stripe aspires to do is build a complete commerce platform. Leveraging our integrated suite of products that is really allowing us to build what we call the global payments and treasury network. So if you think about the global payment and treasury network or what we call the G P T N it's meant to not only help abstract all of that complexity from a global payment infrastructure point of view, but also help move money in a simple and borderless and a programmable way just like we do in the internet. So that's the core essence of Stripe is to build this global payment treasury network to allow for money movement to happen in a simple and borderless manner. >> Simple and borderless two key things there. How has the business strategy evolved in the last 10 years and specifically in the last 20, 22 months? >> Yeah. Great question. So as you can imagine with COVID, you know, David you can order a cup of coffee or a brand new car, and that whole direct to consumer model has accelerated in COVID right. We've accelerated ourselves going to upwards of 6,000 employees. We've been able to answer or manage upwards of 170 billion API requests in the last 12 months alone. Right we deliver upwards of five nines from a availability performance point of view. That means 13 seconds of downtime or less a month. And we're doing this originally starting off for the developer David as you talked about allowing developers to deliver, you know, what I call process payments, accept payments and reconcile payments. But the evolution that you're talking about Lisa has really led to three key areas of focus that our users are requesting from us. And Stripe's first operating principle is really that user first mentality similar to the Amazons where we listen to our users and they're really asking for three key areas of focus. Number one is all around modernizing their digital commerce. So this is big enterprises coming to us and saying, whether I'm a uni lever or a Ford, how do you help me with a direct to consumer a e-commerce type platform? Number one. Secondly, is companies like Deliveroo and Lyft creating what we call marketplaces. Also think about Twitter and clubhouse, more solopreneurs entrepreneurs kind of marketplaces. Third is all around SaaS business models. So think about slack and Atlassian. That are customer vivers and accelerating the journey with us around digitizing digital commerce. So that's the first area of evolution. The second area is all around what we call embedded FinTech. So we know just like Amazon helped accelerate infrastructure as a service, platform as a service and function as a service. We're helping accelerate FinTech as a service. So we believe every company in every industry aspires to add more and more FinTech capabilities in their core services that they offer to their customers. So think about a Shopify or a Lyft they're adding more FinTech capabilities, leveraging Stripe APIs that they offer to their consumers. Likewise, when you think about a Monzo bank or a and 26, what we call Neo banks. They're creating more banking as a service component so a second area of evolution is all around FinTech as a service or embedded FinTech. And the third area of focus again, listen to our users is all around users are saying. Hey, Stripe, you have our financial data. How do you help us more with business operations and automating and optimizing our business operations? So this is revenue management, revenue reconciliation, financial reporting, all of the business processes, you and I know, code to cash, order to cash, pay to procure. Help us automate, optimize, and not just optimize, but help us create net new business models. So these are the three key areas of evolution that we've seen modernizing digital commerce, embedded FinTech, and then certainly last but not least business operations and automating that. >> And your target audience is the developers. Or are you having conversations now that are more, I mean, this is like transformative to industries and disruptive. Are you having conversations higher up in the chain? >> Great, great question. And this is the parallel with Amazon, just like Amazon started with developers, AWS. And then what up to the C-suite, if you will, we're seeing the same exact thing. Obviously our DNA is developer first making it intuitive, natural easy for developers to build on Stripe. But we're seeing more and more C-suite leaders come to us and saying, help us evolve our business model, help us modernize and digitize net new business models to get new revenue streams. So those parallel work streams are both developer mindset and C-suite led is certainly a big evolution for us. And we're looking to learn from our Amazon friends as to the success that they've had there. >> Do you have any examples of projects that developers have proposed that were at first glance, completely outlandish? Something that, you know, is there any sort of corner of the chart use case where Stripe didn't think of it, some developer came up with the idea, maybe it can't be done yet. If you have an example of that, that would be very interesting. >> Yeah, I'll give you two examples. So as I said, we're definitely a user first entity. That's our operating principle. We always think about the user. So let me go to developers and say, what are you struggling with? What are you thinking about? What are the next set of things you need from us? And a simple comment around tax started to come up and do you know in the U S there's 11,000 tax jurisdictions that you and you're selling something online have to abide to these different jurisdictions. So one of the things that we then evolved into is created a Stripe tax product, which initially users or developers were really struggling with and working on. So we created a Stripe tax product. We've done an acquisition called tax jar that helps us accelerate that journey for tax. The other one is this notion of low code that we see in the marketplace right now, where developers saying. Hey, give me more embeddables on top of the primitives that you've created on top of the APIs. So we went leveraging what our customers have already done, created things like a checkout capability, which is a simple redirect highly customized for conversion, which you can just integrate to one API. You have a full checkout capability. You can embed that into your platform, which didn't exist before and needed you to really integrate into different APIs. So all of these capabilities are what developers have really focused on and built that we've done leverage and Excel on. >> Yeah, I think between Lisa and myself, we've paid taxes in about 7,000 of those >> Lisa: Yeah, probably. >> Not 11,000 jurisdictions, but all the various sales taxes and everything else. So we're sort of familiar with it. >> I think so, so here we are, you know, on the floor at re-invent. Great, as we said to be back in person, the 10th annual, but with, as each year goes by AWS has a ecosystem of partners gets bigger and bigger. The flywheel gets, I don't know, I think faster and faster, the number of announcements that came out yesterday and today talk to us about some of the common traits that Stripe and AWS share. >> Yeah. So I've mentioned a few of them. One is certainly the user first mentality where we're listening to users. That tax example is a perfect one of how do we decide new features, new capability based on user first, Amazon does that better than anyone else. Second is that developer mindset focus on the developer. Those will be the core persona we target give you an example, Lyft, we all know Lyft. They wanted to create instant payouts for their drivers. So their developers came to us and say, our developers don't want to get paid. I'm sorry. Our drivers don't want to get paid in a week or two weeks. So we work with their developers who create a instant payout mechanism. Now in six months, over 40% of their drivers are using Stripe instant payout powered by Stripe. And that's a developer first mindset again, back to AWS. And then the third is really around the go to market. And the market opportunity is very similar. You talked about the developer persona and the C-suite very similar to Amazon. But also we're not just catering to enterprise and strategic big customers. We are just so much focused on startups, SMB, mid-market, digital native, just like Amazon is. And I would say the last parallel, which is probably the most important one is innovation. I come from enterprise software where we looked at monthly, quarterly, biannual, annual release cycles. Well, as Stripe, all of that goes out the door just like Amazon. We may have a hundred to a thousand APIs in motion at any time in alpha beta production. And just like Amazon we're iterating and releasing new innovations consistently. So I would say that's probably the most important one that we have with Amazon. >> So a lot of synergies there like deep integrated trusted partner synergies it sounds like. >> Agreed, definitely and then we're seeing this. I was going more as we are going more up market. We're seeing a demand for end to end solutions that require integrations with a CRM vendor for customer 360 with our accounting vendor for pivotal procure order to cash, billing accounting with a e-commerce company like Adobe Magento to do better econ. So more end to end solutions with these tech partners, we're working with our GSI to help deliver those end to end solutions. And certainly, but not least the dev agencies who are still sort of our core constituents that help us keep relevant with those developers. >> You mentioned this at the outset, but some things bear repeating. Can you go into a little more detail on the difference between me wanting to start up a business and take credit cards as payment 10 years ago? Let's say versus today, how much of the friction have you removed from that system? >> It is literally an hour to two hour process versus weeks and months before. >> But what are those steps? Like who would I, you mentioned this, again you mentioned this already, but the go through that, go through that again who would I have to reach out to, to make this happen? And we were talking, you know, relationships with banks, et cetera, et cetera. >> Yeah. So it starts at initiating and registering that company. So imagine you going and having to register a company today, you can do that with a Stripe Atlas product in a matter of hours, get your EIN number, get your tax jurisdictions on your registration as a Delaware entity within the U S you can be anywhere at globally and go do that within a matter of one hour. That's number one, you start there. From there, then it's a matter of embedding payment embeddables within your e-commerce platform, marketplace platform, et cetera. As you've heard us talk about seven lines of code to get payments going, you can quickly onboard accept payments, process payments, reconcile payments all within an hour. And that's just the start. But now you get into more complex use cases around marketplaces and multi-party connection. Multi-party payouts, different commission rates, different subscription models. Think about a flat tier model, a metered tier model, all of these different things that we've abstracted and allow you to just use one to three different integrations to help accelerate and use that in your digital commerce platform. So all of these different workflows have is what we've automated through our APIs. >> Dave: That's unbelievable. >> Yeah. >> It really is. >> It is unbelievable, the amount of automation and innovation that's gone on in such a short time period. What are some of the things as we kind of wrap up here that we can look forward to from stripe from a roadmap perspective, technology wise, partner wise? >> Yes. I mean, we have a slew of data you can imagine billions of billions of transactional data. And you guys know what we do with data is we're looking at fraud prevention. We're looking at, we have a product called radar that looks at fraud, we're doing acceptance, adaptive acceptance to do more AIML learned data and authorization. We're also looking at how do we feed a lot of this financial data into the right mechanisms to allow you to then create new business models on top of this, whether it's cross sell upsell to new user business capture. As well as you know, one of the things I did not talk about, which coming from a farming background is this notion of Stripe climate. Where we have upwards of 2000 companies across 37 countries that are leveraging our Stripe climate product to give back to tech advanced companies that are helping in carbon offset. And super exciting times there from an ESG environmental social governance point of view. So all of those combined is what excites us about the future at Stripe. >> Wow. The future seems unlimited. Lots going on. >> Super excited. Zaki, thank you so much for joining Dave and me talking about what's going on with Stripe. All the innovation that's going on. The synergies with AWS and what's coming down the pipe. We appreciate your insights and your time. >> Thank you, Lisa, thank you, David. Appreciated All right. For Dave Nicholson, I'm Lisa Martin. You're watching the Cube. The global leader in live tech coverage. (lighthearted piano music)

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and Alkira. I looked at many a company and Alkira seemed the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

>>Welcome everybody to the cubes live coverage of AWS reinvent 2021. We're here in the main hall. Yes, this is a physical event. It's a hybrid event, probably the industry's most important hybrid event in the year. We're super excited to be here. Of course, last year during the lockdown, reinvent was purely virtual. This year. They go in hybrid 20 plus thousand people. I hear the whisper numbers like 25, 20 7,000 hundreds of thousands of people online. The cubes here, two sets, we've got two remote studios, super excited. I'd like to introduce my co-host David Nicholson. He'll be here all week with us. Uh, John furrier is also here, Lisa Martin for the cubes wall-to-wall coverage. And we're so psyched to start off this session with Kenneth Chestnut. Who's the head of technology partnerships at Stripe. Stripe's an amazing company, Ken. Great to see you. Thanks for coming on. >>Thanks for having me, Dave and David. I greatly appreciate it. How about this? >>Right. Finally live event. We've done a few. We probably done four or five this year, but >>It's good to be back in person. It is. Yeah, absolutely. It's >>A Stripe. I mean, wow. Can a powering the new economy. Tell us a little bit more for those people who may not be familiar with Stripe. They probably use it without even knowing it when they sign it away. Yeah. So tell us about the >>Well, uh, Stripe was founded in 2010 by two brothers, Patrick and John Colson. And really it was from their first business and realizing how hard it was to actually charge for things on online. Um, you had to acquire a relationship with, uh, with a gateway provider to accept payments. You had to acquire a relationship with a, with a acquiring bank. Um, and you had to do that for each and every country that you wanted to service. Uh, so the same way that AWS reduced the barrier in terms of not having to procure, spend millions of dollars on storage, computers, networking, uh, effectively, what we we've done at Stripe is reduce the barriers around economic infrastructure, accepting payments online, >>Use that undifferentiated heavy lifting for payments. So describe Ken, what it was like kind of pre Stripe. You would literally have to install servers, get storage and put, put software on there, get a database. And then what if you had any money left over, you can actually do some business, but, but describe the sort of what the experience is like with Stripe. >>Sure. So, uh, the R R with, with Stripe, we literally talk about seven lines of code. So we, we allow any developer to, um, uh, provide a set of APIs for any developer to accept payments on online. And we do the undifferentiated heavy lifting in terms of accepting payments, accepting those payments, processing them revenue, reporting, and reconciliation, um, all ensuring compliance and security. Um, so it's like you said, uh, taking care of the undifferentiated heavy lifting are around accepting payments online in the enabling >>The enabler. There is the cloud. I mean, it was 2009, 2010. You guys were founded, the cloud was only like three years old. Right. And so you had to really sort of take a chance on leveraging the cloud or maybe early on you just installed it yourself and said, this isn't going to scale. So maybe tell us how you sort of leverage the cloud. >>Sure. Um, so we're a long time, uh, AWS, uh, customer and user, um, uh, back in the early days of, of Stripe in the early days of, of AWS. And we've just grown, uh, with, with AWS and the ecosystem. And it's interesting because a lot of, uh, a lot of the companies that have been built on, on AWS and grown to be successful, they're also Stripe customers as well. So they use Stripe for their economic infrastructure. >>We use Stripe, we run our company on AWS and we use Stripe. It it's true. The integration took like minutes. It was so simple. Hey it, test it, make sure it scales. But so what, what's the stack look like? What is there, is there such thing as a payment stack? What's the technology stuff? >>Sure. So we initially started with payments and being able to accept payments, uh, on online. Uh we've we brought in out our, our, our Stripe product portfolio now to effectively provide economic, uh, infrastructure for the internet. So that could be accepting payments. Uh, it could be setting up marketplaces. So companies like Lyft and Deliveroo, uh, use Stripe to power their marketplaces with their, with their drivers and, and, um, uh, delivers, um, uh, we provide, uh, a product called radar that, uh, that, um, prevents fraud, uh, around, around the globe. Um, based upon the data that we're seeing from our, from our customers, um, we have, uh, issuing and treasury so that companies can provide their users or their merchants with banking services. So loans, uh, issuing credit cards. So we we've really broadened out the product portfolio of Stripe to provide sort of economic infrastructure for the internet. So >>We talked about strike being in the cloud from an infrastructure perspective and how that enables certain things, but that in and of itself, doesn't change the dynamics around sovereignty and governance from country to country. Sure. Uh, I imagine that the global nature of AWS sort of dovetails with your strategy, but how, how do you address that? It's one thing to tell me in Northern California, you can process payments for me, but now globally go across 150 countries. How do you make that work? Yeah, >>Uh, absolutely. So we, we establish relationships, uh, within, within each company country that we operate in we're in about 47, uh, countries, uh, today, um, and that's rapidly expanding so that companies can, can process or accept payments and do, uh, financial transactions within, within, within those countries. So we're in 47 countries today. We, we accept a multitude of different payment, uh, different currencies, different payment types. So the U S is very, uh, credit card focused. But if you go to other, other parts of the globe, it could be a debit cards. It could be, um, uh, wallets, uh, uh, Google pay, Ali pay, uh, others. So really it's, uh, providing sort of the payment methods that users prefer in, in the different countries, uh, and meeting and meeting those users where, where they are. >>Are you out of the box compliant? What integration is required to do that? Uh, what about things like data sovereignty, is that taken care of by the cloud provider or you guys, and where, w w where does, where does AWS end and you guys pick up? Yes, >>We're, we're PCI compliant. Um, we, we leverage AWS as our, as our infrastructure, um, to grow, grow and scale. So, um, one of the things that we're, we're proud of is, uh, through, throughout 2020 and 2021, we've, we've had 11 nines of, uh, of, of, uh, or five nines of uptime, um, even through, um, uh, black Friday and cyber Monday. So providing AWS provides that, that infrastructure, which we built on top of to provide, uh, you know, five nines of uptime for our, for our users. >>You describe in more detail, Kenya, your ecosystem. I mean, you're responsible for tech partnerships. What does that ecosystem, how I paint a picture of it? >>Sure. So, um, uh, a number of users want to be able to use Stripe with, with their other, uh, it infrastructure and, and their business processes. So a customer may start, uh, with a salesperson may start with a quote or order, uh, in, in Salesforce, want to automate the invoicing and billing and payment of that with, with Stripe and then, uh, reconcile re revenue and an ERP solution like SAP or Oracle or NetSuite or into it, um, in the case of, of small, medium businesses. So really, um, what we're focused on is building out that, that ecosystem to allow, uh, um, our, our customers to streamline their business processes, um, and, and integrate Stripe into their existing it infrastructure and, and business processes. >>You mentioned a lot of different services, but broadly speaking, if I think about payments, correct me if I'm wrong, but you were one of the early, uh, sort of software companies, if I can call you that, um, platforms, whatever, but to really focus on a usage based pricing, but how do I, how do I engage with you? What's, what's the pricing model. Maybe you could describe that a little. >>Sure. So the pricing model is very, very transparent. Uh, it's on, it's on the website. So, uh, we, we take a, um, a percentage of each transaction. So literally you can, you can set up a, a Stripe account it's self-service, um, uh, we, we take a 2.9% plus 30 cents on every, uh, Tran transaction. Um, we don't, you don't start getting, um, uh, charged until, uh, you start accepting payments from your, from your customers or from your users. >>Um, can you give us a sense of the business scope, maybe any metrics you can share, customers, whatever. >>Sure. So there's a couple of things we can share publicly, just in terms of the size of the business. I think since, uh, since 2020, uh, more than 2 million businesses have launched on, on Stripe. Uh, so, uh, 2 million in, in, in, in 2020, um, we've, uh, uh, in the past 12 months, we've, uh, uh, uh, processed over 173 billion, uh, API calls. Uh, we do we process about, um, uh, hundreds of billions of, of, of, uh, payment volume, uh, every, every year. Um, if you look at sort of the macros of the business, the business is growing faster than the broader e-commerce space. So the amount of payment volume that we did in this past year is more than the entire industry did when Patrick and John founded the company. And in 2010, just to give you a, uh, an idea of the, the, the size of the business and sort of the pace of the business >>You're growing as e-commerce grows, but you're also stealing share from other sort of traditional payment systems. Okay. So that's a nice flywheel effect. And of course, Stripe's a private company they've raised well over a billion dollars of Peter teal, and it wasn't original founders, so are funders. So, you know, that's, he's talking scale. I want to go back to something you said about radar. Sure. So there's tech in your stack fraud detection, right. So some of >>That in machine learning, right. >>So, and so you guys, I mean, are you a technology company, are you a F a FinTech company? What are you? >>We're a software company. We provide software and we provide technology for developers, uh, to make online businesses and make, uh, uh, commerce, uh, more seamless and more frictionless >>Cloud-first API first. I mean, maybe describe how that is different maybe than, you know, the technical debt that's been built up over, you know, decades with traditional payment systems. >>Yes, it's very similar to the early, earlier days of AWS where a lot of tech forward companies leveraged Stripe, um, to, um, whether it be large enterprises to transform their businesses and move online, or, or, uh, uh, startups and developers that want to, uh, start a new business online and, and do that, uh, as quickly and seamlessly as possible. So it's, it's quite the gamut from large enterprises that are digitally transforming themselves companies like Marske and, and NASDAQ and others, as well as, uh, um, startups and developers that have started their businesses and born on born on Stripe. So >>When you talk about a startup, how small of an entity makes sense, uh, when you think of, if you look at, from an economic perspective, lowering the friction associated with transactions can lift up a large part of the world with sort of, you know, w with very, very small businesses. Is that something that this is all about? >>Yeah, absolutely. So, like I said, you know, two, 2 million business have sub launched on, on, on Stripe, uh, in, in the past year. And, and those businesses vary, but it could be literally a, a developer or a, uh, uh, a small, uh, SMB that wants to be able to accept payments on online. And it can just set up a Stripe account and start accepting payments. >>Yeah. So this is not a one hit wonder, um, lay out the vision for Stripe, right? I mean, you're, you're a platform, uh, you're, you're becoming a fundamental ingredient of the digital economy sounds pre pandemic. That was all a bunch of buzzwords, but today we all know how important that is, but what lay out the vision for us can, >>Yeah, it really are. The mission of Stripe is to grow the GDP of the internet. Um, and, and so what that means is, uh, more and more our, our, our basic belief is more and more and more businesses, uh, will, will, uh, go, go online, uh, with, uh, with the pandemic that that was, uh, accelerated. But I think that the general trend of businesses moving online, uh, will continue to accelerate, and we want to provide, uh, economic infrastructure to support those businesses. Um, you know, um, uh, uh, Andreessen talked about sort of software, software eating the world well fit. Our belief has FinTech is eating software. So in, in the fullness of time, I think the opportunity is for, uh, any, any company to be a financial services company. And we want to empower any company that wants to, or any user that wants to be a financial services company to, to provide the economic infrastructure for them to do so. >>And, and, you know, I mean your data company in that sense, you're moving bits around, you know, and those datas, I like to say data's eating software, you know, cause really you gotta have your data act together. Absolutely. And that's an evolving, I mean, you guys started to, to 2010, I would imagine your data strategy has evolved quite dramatically. Yeah. >>It's a great, it's a great call out Dave. Uh, one of our other products is a product called Sigma. So Sigma allows, uh, merchants or our customers to query payment and transaction data. So they want to be able to understand who, who, who are their customers, what are the payment methods that those customers prefer in different countries, in different regions? Um, so we're, we're starting to have some interesting use cases, um, working with, with AWS and other partners when you can start combining payment and transaction data in Stripe with other data to understand customer segmentation, customer 360 lifetime value of a customer customer acquisition costs, being able to close the books faster in your ERP, because you can apply that payment and transaction data to your general ledger to, to close the books faster at the end of the month or at the end of the, at the end of the year. So, uh, yeah, we we're, um, uh, as, as more and more companies are using Stripe, um, they want to be able to take advantage of that data and combine it with other, other sources of data to drive business. >>Yeah. You mentioned some of those key metrics that are, that are so important to companies today. I'll give you the last word re-invent this hall is packed, um, a little bit surprising, frankly, you know, but, uh, but exciting. Uh, what are you looking forward to this? >>Yeah, I'm just looking forward to meeting people in person again, it's, uh, it's great to be here and, and, you know, uh, uh, we have a strong relationship with AWS. We have lots of partners in, in, in common here, uh, as well, both consulting partners and technology partners. So really looking forward to meeting with partners and customers, and especially as we, as we plan for next year and, uh, launching our, our, our partner program beginning of next year. Uh, there's a lot of, uh, uh, groundwork and things to learn from, from here. As we, as we, we, we, we launch our, our, our partner business formula next >>I'll bet. Looking forward to that, Ken, thanks so much for coming to the cure. You so much. It was great to have a chat at the time. All right. And we want to thank our sponsors, uh, AWS, of course, and also AMD who's making the editorial segments that we bring you this week possible for Dave Nicholson. I'm Dave Volante. You're watching the cube at AWS reinvent 2021. Keep it right there, right back.

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and now Alkira. I looked at many of companies and I'm curious in the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

>>from the Cube Studios >>in Palo Alto and >>Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hey, welcome back here. Ready? Jeff Frick here with the Cube. We're doing a special presentation today really talking about AI and making ai really with two companies that are right in the heart of the Dell EMC as well as Intel. So we're excited to have a couple Cube alumni back on the program. Haven't seen him in a little while. First off from Intel. Lisa Spelman. She is the corporate VP and GM for the Xeon Group in Jersey on and Memory Group. Great to see you, Lisa. >>Good to see you again, too. >>And we've got Ravi Pinter. Conte. He is the SBP server product management, also from Dell Technologies. Ravi, great to see you as well. >>Good to see you on beast. Of course, >>yes. So let's jump into it. So, yesterday, Robbie, you guys announced a bunch of new kind of ai based solutions where if you can take us through that >>Absolutely so one of the things we did Jeff was we said it's not good enough for us to have a point product. But we talked about hope, the tour of products, more importantly, everything from our workstation side to the server to these storage elements and things that we're doing with VM Ware, for example. Beyond that, we're also obviously pleased with everything we're doing on bringing the right set off validated configurations and reference architectures and ready solutions so that the customer really doesn't have to go ahead and do the due diligence. Are figuring out how the various integration points are coming for us in making a solution possible. Obviously, all this is based on the great partnership we have with Intel on using not just their, you know, super cues, but FPG's as well. >>That's great. So, Lisa, I wonder, you know, I think a lot of people you know, obviously everybody knows Intel for your CPU is, but I don't think they recognize kind of all the other stuff that can wrap around the core CPU to add value around a particular solution. Set or problems. That's what If you could tell us a little bit more about Z on family and what you guys are doing in the data center with this kind of new interesting thing called AI and machine learning. >>Yeah. Um, so thanks, Jeff and Ravi. It's, um, amazing. The way to see that artificial intelligence applications are just growing in their pervasiveness. And you see it taking it out across all sorts of industries. And it's actually being built into just about every application that is coming down the pipe. And so if you think about meeting toe, have your hardware foundation able to support that. That's where we're seeing a lot of the customer interest come in. And not just a first Xeon, but, like Robbie said on the whole portfolio and how the system and solution configuration come together. So we're approaching it from a total view of being able to move all that data, store all of that data and cross us all of that data and providing options along that entire pipeline that move, um, and within that on Z on. Specifically, we've really set that as our cornerstone foundation for AI. If it's the most deployed solution and data center CPU around the world and every single application is going to have artificial intelligence in it, it makes sense that you would have artificial intelligence acceleration built into the actual hardware so that customers get a better experience right out of the box, regardless of which industry they're in or which specialized function they might be focusing on. >>It's really it's really wild, right? Cause in process, right, you always move through your next point of failure. So, you know, having all these kind of accelerants and the ways that you can carve off parts of the workload part of the intelligence that you can optimize betters is so important as you said Lisa and also Rocket and the solution side. Nobody wants General Ai just for ai sake. It's a nice word. Interesting science experiment. But it's really in the applied. A world is. We're starting to see the value in the application of this stuff, and I wonder you have a customer. You want to highlight Absalon, tell us a little bit about their journey and what you guys did with them. >>Great, sure. I mean, if you didn't start looking at Epsilon there in the market in the marketing business, and one of the crucial things for them is to ensure that they're able to provide the right data. Based on that analysis, there run on? What is it that the customer is looking for? And they can't wait for a period of time, but they need to be doing that in the near real time basis, and that's what excellent does. And what really blew my mind was the fact that they actually service are send out close to 100 billion messages. Again, it's 100 billion messages a year. And so you can imagine the amount of data that they're analyzing, which is in petabytes of data, and they need to do real time. And that's all possible because of the kind of analytics we have driven into the power It silver's, you know, using the latest of the Intel Intel Xeon processor couple with some of the technologies from the BGS side, which again I love them to go back in and analyze this data and service to the customers very rapidly. >>You know, it's funny. I think Mark Tech is kind of an under appreciated ah world of ai and, you know, in machine to machine execution, right, That's the amount of transactions go through when you load a webpage on your site that actually ideas who you are you know, puts puts a marketplace together, sells time on that or a spot on that ad and then lets people in is a really sophisticated, as you said in massive amounts of data going through the interesting stuff. If it's done right, it's magic. And if it's done, not right, then people get pissed off. You gotta have. You gotta have use our tools. >>You got it. I mean, this is where I talked about, you know, it can be garbage in garbage out if you don't really act on the right data. Right. So that is where I think it becomes important. But also, if you don't do it in a timely fashion, but you don't service up the right content at the right time. You miss the opportunity to go ahead and grab attention, >>right? Right. Lisa kind of back to you. Um, you know, there's all kinds of open source stuff that's happening also in the in the AI and machine learning world. So we hear things about tense or flow and and all these different libraries. How are you guys, you know, kind of embracing that world as you look at ai and kind of the development. We've been at it for a while. You guys are involved in everything from autonomous vehicles to the Mar Tech. Is we discussed? How are you making sure that these things were using all the available resources to optimize the solutions? >>Yeah, I think you and Robbie we're just hitting on some of those examples of how many ways people have figured out how to apply AI now. So maybe at first it was really driven by just image recognition and image tagging. But now you see so much work being driven in recommendation engines and an object detection for much more industrial use cases, not just consumer enjoyment and also those things you mentioned and hit on where the personalization is a really fine line you walk between. How do you make an experience feel good? Personalized versus creepy personalized is a real challenge and opportunity across so many industries. And so open source like you mentioned, is a great place for that foundation because it gives people the tools to build upon. And I think our strategy is really a stack strategy that starts first with delivering the best hardware for artificial intelligence and again the other is the foundation for that. But we also have, you know, Milat type processing for out of the Edge. And then we have all the way through to very custom specific accelerators into the data center, then on top about the optimized software, which is going into each of those frameworks and doing the work so that the framework recognizes the specific acceleration we built into the CPU. Whether that steel boost or recognizes the capabilities that sit in that accelerator silicon, and then once we've done that software layer and this is where we have the opportunity for a lot of partnership is the ecosystem and the solutions work that Robbie started off by talking about. So Ai isn't, um, it's not easy for everyone. It has a lot of value, but it takes work to extract that value. And so partnerships within the ecosystem to make sure that I see these are taking those optimization is building them in and fundamentally can deliver to customers. Reliable solution is the last leg of that of that strategy, but it really is one of the most important because without it you get a lot of really good benchmark results but not a lot of good, happy customer, >>right? I'm just curious, Lee says, because you kind of sit in the catbird seat. You guys at the core, you know, kind of under all the layers running data centers run these workloads. How >>do you see >>kind of the evolution of machine learning and ai from kind of the early days, where with science projects and and really smart people on mahogany row versus now people are talking about trying to get it to, like a citizen developer, but really a citizen data science and, you know, in exposing in the power of AI to business leaders or business executioners. Analysts, if you will, so they can apply it to their day to day world in their day to day life. How do you see that kind of evolving? Because you not only in it early, but you get to see some of the stuff coming down the road in design, find wins and reference architectures. How should people think about this evolution? >>It really is one of those things where if you step back from the fundamentals of AI, they've actually been around for 50 or more years. It's just that the changes in the amount of computing capability that's available, the network capacity that's available and the fundamental efficiency that I t and infrastructure managers and get out of their cloud architectures as allowed for this pervasiveness to evolve. And I think that's been the big tipping point that pushed people over this fear. Of course, I went through the same thing that cloud did where you had maybe every business leader or CEO saying Hey, get me a cloud and I'll figure out what for later give me some AI will get a week and make it work, But we're through those initial use pieces and starting to see a business value derived from from those deployments. And I think some of the most exciting areas are in the medical services field and just the amount, especially if you think of the environment we're in right now. The amount of efficiency and in some cases, reduction in human contact that you could require for diagnostics and just customer tracking and ability, ability to follow their entire patient History is really powerful and represents the next wave and care and how we scale our limited resource of doctors nurses technician. And the point we're making of what's coming next is where you start to see even more mass personalization and recommendations in that way that feel very not spooky to people but actually comforting. And they take value from them because it allows them to immediately act. Robbie reference to the speed at which you have to utilize the data. When people get immediately act more efficiently. They're generally happier with the service. So we see so much opportunity and we're continuing to address across, you know, again that hardware, software and solution stack so we can stay a step ahead of our customers, >>Right? That's great, Ravi. I want to give you the final word because you guys have to put the solutions together, it actually delivering to the customer. So not only, you know the hardware and the software, but any other kind of ecosystem components that you have to bring together. So I wonder if you can talk about that approach and how you know it's it's really the solution. At the end of the day, not specs, not speeds and feeds. That's not really what people care about. It's really a good solution. >>Yeah, three like Jeff, because end of the day I mean, it's like this. Most of us probably use the A team to retry money, but we really don't know what really sits behind 80 and my point being that you really care at that particular point in time to be able to put a radio do machine and get your dollar bills out, for example. Likewise, when you start looking at what the customer really needs to know, what Lisa hit upon is actually right. I mean what they're looking for. And you said this on the whole solution side house. To our our mantra to this is very simple. We want to make sure that we use the right basic building blocks, ensuring that we bring the right solutions using three things the right products which essentially means that we need to use the right partners to get the right processes in GPU Xen. But then >>we get >>to the next level by ensuring that we can actually do things we can either provide no ready solutions are validated reference architectures being that you have the sausage making process that you now don't need to have the customer go through, right? In a way. We have done the cooking and we provide a recipe book and you just go through the ingredient process of peering does and then off your off right to go get your solution done. And finally, the final stages there might be helped that customers still need in terms of services. That's something else Dell technology provides. And the whole idea is that customers want to go out and have them help deploying the solutions. We can also do that we're services. So that's probably the way we approach our data. The way we approach, you know, providing the building blocks are using the right technologies from our partners, then making sure that we have the right solutions that our customers can look at. And finally, they need deployment. Help weaken due their services. >>Well, Robbie, Lisa, thanks for taking a few minutes. That was a great tee up, Rob, because I think we're gonna go to a customer a couple of customer interviews enjoying that nice meal that you prepared with that combination of hardware, software, services and support. So thank you for your time and a great to catch up. All right, let's go and run the tape. Hi, Jeff. I wanted to talk about two examples of collaboration that we have with the partners that have yielded Ah, really examples of ah put through HPC and AI activities. So the first example that I wanted to cover is within your AHMAD team up in Canada with that team. We collaborated with Intel on a tuning of algorithm and code in order to accelerate the mapping of the human brain. So we have a cluster down here in Texas called Zenith based on Z on and obtain memory on. And we were able to that customer with the three of us are friends and Intel the norm, our team on the Dell HPC on data innovation, injuring team to go and accelerate the mapping of the human brain. So imagine patients playing video games or doing all sorts of activities that help understand how the brain sends the signal in order to trigger a response of the nervous system. And it's not only good, good way to map the human brain, but think about what you can get with that type of information in order to help cure Alzheimer's or dementia down the road. So this is really something I'm passionate about. Is using technology to help all of us on all of those that are suffering from those really tough diseases? Yeah, yeah, way >>boil. I'm a project manager for the project, and the idea is actually to scan six participants really intensively in both the memory scanner and the G scanner and see if we can use human brain data to get closer to something called Generalized Intelligence. What we have in the AI world, the systems that are mathematically computational, built often they do one task really, really well, but they struggle with other tasks. Really good example. This is video games. Artificial neural nets can often outperform humans and video games, but they don't really play in a natural way. Artificial neural net. Playing Mario Brothers The way that it beats the system is by actually kind of gliding its way through as quickly as possible. And it doesn't like collect pennies. For example, if you play Mary Brothers as a child, you know that collecting those coins is part of your game. And so the idea is to get artificial neural nets to behave more like humans. So like we have Transfer of knowledge is just something that humans do really, really well and very naturally. It doesn't take 50,000 examples for a child to know the difference between a dog and a hot dog when you eat when you play with. But an artificial neural net can often take massive computational power and many examples before it understands >>that video games are awesome, because when you do video game, you're doing a vision task instant. You're also doing a >>lot of planning and strategy thinking, but >>you're also taking decisions you several times a second, and we record that we try to see. Can we from brain activity predict >>what people were doing? We can break almost 90% accuracy with this type of architecture. >>Yeah, yeah, >>Use I was the lead posts. Talk on this collaboration with Dell and Intel. She's trying to work on a model called Graph Convolution Neural nets. >>We have being involved like two computing systems to compare it, like how the performance >>was voting for The lab relies on both servers that we have internally here, so I have a GPU server, but what we really rely on is compute Canada and Compute Canada is just not powerful enough to be able to run the models that he was trying to run so it would take her days. Weeks it would crash, would have to wait in line. Dell was visiting, and I was invited into the meeting very kindly, and they >>told us that they started working with a new >>type of hardware to train our neural nets. >>Dell's using traditional CPU use, pairing it with a new >>type off memory developed by Intel. Which thing? They also >>their new CPU architectures and really optimized to do deep learning. So all of that sounds great because we had this problem. We run out of memory, >>the innovation lab having access to experts to help answer questions immediately. That's not something to gate. >>We were able to train the attic snatch within 20 minutes. But before we do the same thing, all the GPU we need to wait almost three hours to each one simple way we >>were able to train the short original neural net. Dell has been really great cause anytime we need more memory, we send an email, Dell says. Yeah, sure, no problem. We'll extended how much memory do you need? It's been really simple from our end, and I think it's really great to be at the edge of science and technology. We're not just doing the same old. We're pushing the boundaries. Like often. We don't know where we're going to be in six months. In the big data world computing power makes a big difference. >>Yeah, yeah, yeah, yeah. The second example I'd like to cover is the one that will call the data accelerator. That's a publisher that we have with the University of Cambridge, England. There we partnered with Intel on Cambridge, and we built up at the time the number one Io 500 storage solution on. And it's pretty amazing because it was built on standard building blocks, power edge servers until Xeon processors some envy me drives from our partners and Intel. And what we did is we. Both of this system with a very, very smart and elaborate suffering code that gives an ultra fast performance for our customers, are looking for a front and fast scratch to their HPC storage solutions. We're also very mindful that this innovation is great for others to leverage, so the suffering Could will soon be available on Get Hub on. And, as I said, this was number one on the Iot 500 was initially released >>within Cambridge with always out of focus on opening up our technologies to UK industry, where we can encourage UK companies to take advantage of advanced research computing technologies way have many customers in the fields of automotive gas life sciences find our systems really help them accelerate their product development process. Manage Poor Khalidiya. I'm the director of research computing at Cambridge University. Yeah, we are a research computing cloud provider, but the emphasis is on the consulting on the processes around how to exploit that technology rather than the better results. Our value is in how we help businesses use advanced computing resources rather than the provision. Those results we see increasingly more and more data being produced across a wide range of verticals, life sciences, astronomy, manufacturing. So the data accelerators that was created as a component within our data center compute environment. Data processing is becoming more and more central element within research computing. We're getting very large data sets, traditional spinning disk file systems can't keep up and we find applications being slowed down due to a lack of data, So the data accelerator was born to take advantage of new solid state storage devices. I tried to work out how we can have a a staging mechanism for keeping your data on spinning disk when it's not required pre staging it on fast envy any stories? Devices so that can feed the applications at the rate quiet for maximum performance. So we have the highest AI capability available anywhere in the UK, where we match II compute performance Very high stories performance Because for AI, high performance storage is a key element to get the performance up. Currently, the data accelerated is the fastest HPC storage system in the world way are able to obtain 500 gigabytes a second read write with AI ops up in the 20 million range. We provide advanced computing technologies allow some of the brightest minds in the world really pushed scientific and medical research. We enable some of the greatest academics in the world to make tomorrow's discoveries. Yeah, yeah, yeah. >>Alright, Welcome back, Jeff Frick here and we're excited for this next segment. We're joined by Jeremy Raider. He is the GM digital transformation and scale solutions for Intel Corporation. Jeremy, great to see you. Hey, thanks for having me. I love I love the flowers in the backyard. I thought maybe you ran over to the Japanese, the Japanese garden or the Rose Garden, Right To very beautiful places to visit in Portland. >>Yeah. You know, you only get him for a couple. Ah, couple weeks here, so we get the timing just right. >>Excellent. All right, so let's jump into it. Really? And in this conversation really is all about making Ai Riel. Um, and you guys are working with Dell and you're working with not only Dell, right? There's the hardware and software, but a lot of these smaller a solution provider. So what is some of the key attributes that that needs to make ai riel for your customers out there? >>Yeah, so, you know, it's a it's a complex space. So when you can bring the best of the intel portfolio, which is which is expanding a lot, you know, it's not just the few anymore you're getting into Memory technologies, network technologies and kind of a little less known as how many resources we have focused on the software side of things optimizing frameworks and optimizing, and in these key ingredients and libraries that you can stitch into that portfolio to really get more performance in value, out of your machine learning and deep learning space. And so you know what we've really done here with Dell? It has started to bring a bunch of that portfolio together with Dell's capabilities, and then bring in that ai's V partner, that software vendor where we can really take and stitch and bring the most value out of that broad portfolio, ultimately using using the complexity of what it takes to deploy an AI capability. So a lot going on. They're bringing kind of the three legged stool of the software vendor hardware vendor dental into the mix, and you get a really strong outcome, >>right? So before we get to the solutions piece, let's stick a little bit into the Intel world. And I don't know if a lot of people are aware that obviously you guys make CPUs and you've been making great CPIs forever. But there's a whole lot more stuff that you've added, you know, kind of around the core CPU. If you will in terms of of actual libraries and ways to really optimize the seond processors to operate in an AI world. I wonder if you can kind of take us a little bit below the surface on how that works. What are some of the examples of things you can do to get more from your Gambira Intel processors for ai specific applications of workloads? >>Yeah, well, you know, there's a ton of software optimization that goes into this. You know that having the great CPU is definitely step one. But ultimately you want to get down into the libraries like tensor flow. We have data analytics, acceleration libraries. You know, that really allows you to get kind of again under the covers a little bit and look at it. How do we have to get the most out of the kinds of capabilities that are ultimately used in machine learning in deep learning capabilities, and then bring that forward and trying and enable that with our software vendors so that they can take advantage of those acceleration components and ultimately, you know, move from, you know, less training time or could be a the cost factor. But those are the kind of capabilities we want to expose to software vendors do these kinds of partnerships. >>Okay. Ah, and that's terrific. And I do think that's a big part of the story that a lot of people are probably not as aware of that. There are a lot of these optimization opportunities that you guys have been leveraging for a while. So shifting gears a little bit, right? AI and machine learning is all about the data. And in doing a little research for this, I found actually you on stage talking about some company that had, like, 350 of road off, 315 petabytes of data, 140,000 sources of those data. And I think probably not great quote of six months access time to get that's right and actually work with it. And the company you're referencing was intel. So you guys know a lot about debt data, managing data, everything from your manufacturing, and obviously supporting a global organization for I t and run and ah, a lot of complexity and secrets and good stuff. So you know what have you guys leveraged as intel in the way you work with data and getting a good data pipeline. That's enabling you to kind of put that into these other solutions that you're providing to the customers, >>right? Well, it is, You know, it's absolutely a journey, and it doesn't happen overnight, and that's what we've you know. We've seen it at Intel on We see it with many of our customers that are on the same journey that we've been on. And so you know, this idea of building that pipeline it really starts with what kind of problems that you're trying to solve. What are the big issues that are holding you back that company where you see that competitive advantage that you're trying to get to? And then ultimately, how do you build the structure to enable the right kind of pipeline of that data? Because that's that's what machine learning and deep learning is that data journey. So really a lot of focus around you know how we can understand those business challenges bring forward those kinds of capabilities along the way through to where we structure our entire company around those assets and then ultimately some of the partnerships that we're gonna be talking about these companies that are out there to help us really squeeze the most out of that data as quickly as possible because otherwise it goes stale real fast, sits on the shelf and you're not getting that value out of right. So, yeah, we've been on the journey. It's Ah, it's a long journey, but ultimately we could take a lot of those those kind of learnings and we can apply them to our silicon technology. The software optimization is that we're doing and ultimately, how we talk to our enterprise customers about how they can solve overcome some of the same challenges that we did. >>Well, let's talk about some of those challenges specifically because, you know, I think part of the the challenge is that kind of knocked big data, if you will in Hadoop, if you will kind of off the rails. Little bit was there's a whole lot that goes into it. Besides just doing the analysis, there's a lot of data practice data collection, data organization, a whole bunch of things that have to happen before. You can actually start to do the sexy stuff of AI. So you know, what are some of those challenges. How are you helping people get over kind of these baby steps before they can really get into the deep end of the pool? >>Yeah, well, you know, one is you have to have the resource is so you know, do you even have the resource is if you can acquire those Resource is can you keep them interested in the kind of work that you're doing? So that's a big challenge on and actually will talk about how that fits into some of the partnerships that we've been establishing in the ecosystem. It's also you get stuck in this poc do loop, right? You finally get those resource is and they start to get access to that data that we talked about. It start to play out some scenarios, a theorize a little bit. Maybe they show you some really interesting value, but it never seems to make its way into a full production mode. And I think that is a challenge that has faced so many enterprises that are stuck in that loop. And so that's where we look at who's out there in the ecosystem that can help more readily move through that whole process of the evaluation that proved the r a y, the POC and ultimately move that thing that capability into production mode as quickly as possible that you know that to me is one of those fundamental aspects of if you're stuck in the POC. Nothing's happening from this. This is not helping your company. We want to move things more quickly, >>right? Right. And let's just talk about some of these companies that you guys are working with that you've got some reference architectures is data robot a Grid dynamics H 20 just down the road in Antigua. So a lot of the companies we've worked with with Cube and I think you know another part that's interesting. It again we can learn from kind of old days of big data is kind of generalized. Ai versus solution specific. Ai and I think you know where there's a real opportunity is not AI for a sake, but really it's got to be applied to a specific solution, a specific problem so that you have, you know, better chatbots, better customer service experience, you know, better something. So when you were working with these folks and trying to design solutions or some of the opportunities that you saw to work with some of these folks to now have an applied a application slash solution versus just kind of AI for ai's sake. >>Yeah. I mean, that could be anything from fraud, detection and financial services, or even taking a step back and looking more horizontally like back to that data challenge. If if you're stuck at the AI built a fantastic Data lake, but I haven't been able to pull anything back out of it, who are some of the companies that are out there that can help overcome some of those big data challenges and ultimately get you to where you know, you don't have a data scientist spending 60% of their time on data acquisition pre processing? That's not where we want them, right? We want them on building out that next theory. We want them on looking at the next business challenge. We want them on selecting the right models, but ultimately they have to do that as quickly as possible so that they can move that that capability forward into the next phase. So, really, it's about that that connection of looking at those those problems or challenges in the whole pipeline. And these companies like data robot in H 20 quasi. Oh, they're all addressing specific challenges in the end to end. That's why they've kind of bubbled up as ones that we want to continue to collaborate with, because it can help enterprises overcome those issues more fast. You know more readily. >>Great. Well, Jeremy, thanks for taking a few minutes and giving us the Intel side of the story. Um, it's a great company has been around forever. I worked there many, many moons ago. That's Ah, that's a story for another time, but really appreciate it and I'll interview you will go there. Alright, so super. Thanks a lot. So he's Jeremy. I'm Jeff Frick. So now it's time to go ahead and jump into the crowd chat. It's crowdchat dot net slash make ai real. Um, we'll see you in the chat. And thanks for watching

(gentle music) >> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi, I'm Stu Miniman. And this is a special CUBE conversation. We've been talking a lot, of course for many years about the ascendancy of cloud. And today in 2020, multicloud is a big piece of the discussion. And we're really happy to help unveil coming out of stealth Alkira, which is helping the networking challenges when it comes to multicloud and I have the two co-founders, they are brothers. I have Amir, who is the CEO and Atif, who is the CTO, the Khan brothers, thank you so much for joining us, and congratulation on the launch of the company. >> Thank you Stu for having us on the Show. It's a pleasure to see you again. >> All right, so Amir, we've had you on the program. Your previous company that you've done was of course Viptela, the two of you have worked together at, I believe, five companies, a successful companies. Acquired the most recent one into Cisco. So, Amir, obviously, strong networking team, your brother, the CTO is going to talk to us about the engineering but give us just the story of Alkira, what you've been building and now ready to unveil to the world. >> Certainly, Stu, so when around 2018 timeframe, we started looking into the next big problem to solve in the industry, which was not only a substantial from the market size perspective, but also from the customers perspective was solving a major pain point. So when we started looking into the cloud customers and started talking to our customers, they were struggling from the cloud networking perspective, even in a single cloud, and it was a new environment for them and they had to understand all the nitty gritty details of each one of these clouds and when you go to multicloud environment, it becomes exponentially complicated to address not only connectivity, but how to deploy services like firewall and other services, including load balancers and IP address management, et cetera, and remote access. So we started digging deeper into this problem and started working with the customers and took a clean sheet of paper and came up with a very comprehensive approach to offering a solution which is as-a Service. This time, we are not shipping any hardware software it is just like any other SaaS application, you just come to our portal just drag and drop, literally draw out your network and click on provision. And come back after 40 minutes or so your whole global cloud infrastructure is up and running. >> All right, Atif your brother laid out a pretty broad vision there, any of us from the networking world, we know there's a lot of complexity there. And therefore it takes a lot of work, when I want to do things simply, as-a Service is a huge growth area bring us inside the engineering challenges that you and the team have been working on to build this solution. >> Certainly, Stu, so we've been working both Amir and myself in the networking industry for more than 25 years now. And the way we have worked and what we have believed in is that we need to solve customer problems. We never believe in doing a science project. So here also we started working with customers as we have always done in the past. We understood the customers pain points, the challenges they were facing, especially in this case and in cloud networking space, multicloud networking space, based on the user requirements, users, or the customers use cases, we started building our service. And here what we have built as a complete network as-a Service. It's a multicloud network as-a service, which not only provides connectivity to multiple clouds, but also addresses the needs for bringing in networking services, as well as security services, making sure that you have a full policy based infrastructure on top of it, you have deep visibility into the clouds as well as into on-premise end to end visibility, end to end monitoring, troubleshooting. And all of it is delivered to you as-a service. So that's what we have been doing here at Alkira. >> Excellent! So when we've looked at multicloud, of course, every cloud, they have some similar things, they have some different things. They all tend to do things a little bit differently. One of the secret sauces that have been talked about for the last few years is the SD-WAN space, like you had built with the tele to help really enable those environments. So Atif we've got a diagram here, which I think will help explain a little bit as to where out here and how it plugs into these different environments, walk us through a little bit what we're seeing here, and what you're actually doing at Alkira. >> So here we are building a global unified, multicloud network. It's consumed as a service. Think of it as consuming it just like you would consume any other SaaS, like our SaaS application. So you come to Alkira's portal, you register. And then there you go, and you start building your global multicloud unified network with integrated services. So here what you see is a Alkira's cloud services exchange which comprises of the cloud exchange points. You can bring these up these cloud exchange points up anywhere on the globe. You can decide like what networking services security services you need in these cloud exchange points, you can connect to multiple clouds. From there, you can bring your existing on-prem connectivity into the CXPs. All these CXPs have a full mesh of overlay, high speed, low latency connectivity among each other. So there is a full network which comes up between these CXPs. And the whole infrastructure scales with customers as our customers scale. So it's a horizontally scalable, very highly redundant and resilient infrastructure, which we had built. >> All right, so, Amir now that we understand the basics of the technology, you've got some strong investors including Sequoia, Kleiner Perkins, give us what is being announced that you're coming out of stealth, where are you with the product? How many employees you have? And where are you with the discussion of customer adoption. >> So Stu we are obviously, bringing this to the market, and we will be announcing it on April 15. It's available for the customers to consume our solution as a service on that day. So they are welcome to reach out to us and we'll be happy to help them. And as a matter of fact, just come to our website and register for the service. And yeah, I mean you rightly said that we have a superstar team of not only the venture capital companies, but also the board members representing those companies, the Bill Coffin and Mamun Ahmed, who the leading VCs are on the board of our company, including myself and Atif. >> All right, Amir I'd love to actually bring up the second slide that we have here. Walk us through you said the service, how do people get started? How do they understand, walk us through what they do. >> So the biggest challenge when we started looking into these problems, Stu was that it was very complicated. You had to piecemeal bring up instances in the cloud and stitch them together. And when you try to integrate the services, that was a different challenge for the customers. So we want to make sure that it was so simple and clean, that the customer didn't even have to think about any underlying construct on any of the clouds, they should not have to worry about learning each individual part from the networking perspective. So here's your portal, you just come, step one is come to our portal register. Step two is you start drawing your network based on your intent, what on-prem connectivity you want to bring into this service, what type of services you need, like a lot of firewalls and then what pilots you need to connect and everything happens seamlessly, from on-prem, prem through services into the cloud, across multiple clouds. It's a seamless service that we have created and with full analytics capabilities and full governance built in. >> All right, so Atif bring us into what this means for customers, how do they manage it? Is this the networking team? Is it the cloud architects? What API's are there? How does this fit into kind of what customers are doing today? And solve some of those challenges that we laid out earlier in the discussion. >> Yes, from the customer's perspective, as I said, it's completely delivered as a service. Customers come to our portal, they draw out the network, they select the services, they click on provision and the whole network comes up within minutes. So the main thing here is that from a customer's point of view, if they are connecting to different clouds, they don't need to understand any of the underlying specifics or underlying constructs of any of the cloud in order to bring up connectivity. So what we are doing here is we are abstracting the cloud chair. So we are building a virtual cloud network. So if you think of, if you compare with what we did in the previous life, we virtualized the WAN. So here what we are doing is we are virtualizing the cloud network, so underlying doesn't matter which cloud you sit on which cloud you need to connect to, which networking services, whether a cloud native services or whether you want to consume Alkira services, or we also support like customer bringing in third party services as well. So it's all offered from our platform all offered is service to the customer. Again, no expertise required in any of the underlying networking constructs of any of these clouds. >> Give us what we should be looking at from a technology roadmap from Alkira, through the rest of 2020. >> Good question, Stu. So as I mentioned earlier, our roadmap is dictated by customer requirements, so we prioritize what customers need from us. So we have come out with a scalable platform, we have come out with a marketplace for networking services in there. In the near term, we'll be expanding our marketplace with more services. We will be addressing more use cases and when I talk about use cases, I can give you some examples. Like there's, you not just only need connectivity into cloud, you might have different requirements from throughput perspective or bandwidth perspective or different services that you need to contend your cloud when you may have certain applications such as Internet facing application where you need like traffic coming in from the internet, inbound to those applications, you might need services like a load balancer, like an external load balancer in our services exchange. You might also need like a firewall, you might need traffic engineering, or sorry, service chaining capabilities where you chain service through multiple traffic through multiple of these services like a firewall and a load balancer. So we built a platform which gives you all those capabilities going forward, we will be adding more services more use cases to it. We have a long ways ahead of us and we will be putting a lot of effort in delivering a roadmap as we go. >> All right, so Amir your technical team definitely has their hands full and robust roadmap to work on. Give us the high level, what we should be looking for Alkira, for people that are out there, multicloud and networking tends to get talked a lot. There's many big companies and small ones. What will separate Alkira from the rest of the market today? And what should we be looking to see the company's progression through 2020? >> Yeah, thanks for asking that. Yeah, certainly. I mean, from the solution perspective, Atif said that it's so fundamentally important to have a very strong basis. And that's what we have done. We are bringing out a certain number of services and now we will continue to grow on that we'll create a big marketplace. We will continue to improve on which clouds we connect to and how and we will building our own services in certain cases as well. Now, building a technology is just one piece of it, we have to go out to market with a company that the customers can trust every single department in that company, whether it's sales or how they do business with us all the business back end pieces, after we sorted out and that's what we've been working with. And then go to market partners, that is very, very important, support is very important. So let me spend a little bit of time on go to market strategy. We have been working with the service providers so that we can extend our reach not only to the large customers, but also to mid-size customers across the globe. So you will see us in the future announcing major service provider, partnerships, as well as we've been working with large SIS, WAAS and system integration partners. And also we have taking a slightly different approach this time because it's a service. So we are going with telecom master agents, which have been working with the service providers, the cloud providers, the cable providers, as a channel, and they have a huge reach into the customer base. So we have a very comprehensive strategy not only from the go to market perspective and the technology perspective, but also how we are going to support our customers and continue to build our relationship to build a lasting company. >> Yeah, Amir super important point there. Absolutely, we've seen the maturation and change in the service providers, as today they are working with many of the public cloud providers and they're, as you said, the close touch point and a trusted partner for customers. All right, so before I let you go, you two are brothers, everybody in today's day and age is spending even more time with family but your situation you've worked together for a long time. What keeps bringing the two of you together, working together and talk about that bond? >> So I mean we're a very close knit family, we have four brothers and one sister, and obviously Atif and I have been the closest because we have been working together for the longest, we've at least work in five different companies together, our families traveled together, we have three daughters each, we live about five minutes, walk from each other. And we just have this bond where we not only have the family close, but also very close knit friends circle, which we both hang out with, and we obviously have common interests in the sports as well. We play squash and tennis and workout. So Atif if you want to take a stab at that also. >> Yeah, so we've always been very close. In fact, we've been together for the last like, ever since I can remember like even college days, we were roommates for some time also, we have our circle of friends, is the same old source. So, again, we are very close. And we worked well together so we complement each other's skills. And it's worked out in the past. Hopefully it will work out again. And I look forward to working with them for many, many more years to come. >> Amir and Atif thank you so much for sharing the coming out of stealth. After all, Alkira we definitely look forward to watching your progress and seeing how you're helping customers in this multicloud world. Thank you for joining us. >> Stu thank you so much. >> Thank you for having us. >> All right, I'm Stu Miniman. And thank you so much for watching this special CUBE conversation on theCUBE. (gentle music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation I am stupid a man and this is a special cube conversation we've been talking a lot of course for many years about the ascent of cloud and today in 2020 multi cloud is a big piece of the discussion and we're really happy to help unveil coming out of cell al kiram which is helping the networking challenges when it comes to multi cloud and I have the two co-founders they are brothers I have Amir who is the CEO and a DIF who is the CTO the Khan brothers thank you so much for joining us and congratulations on the launch of the company thank you sue for having us on the show it's a pleasure to see you again all right so Amir we've had you on the program your previous company that you've done was of course the fella you the two of you have worked together at I believe five companies successful companies acquired you know the most recent one into Cisco so a mirror obviously you know you know strong networking theme your brother the CTO I was going to talk to us about the engineering but give us you know just the the story of Al Kyra what you've been building and now ready to unveil to the world certainly needs to so in around 2018 timeframe we started looking into the next big problem to solve in the industry which was not only a substantial you know from the market size perspective but also from the customers perspective was solving a major pain point so when we started looking into the cloud customers and started talking to our customers they were struggling from the cloud networking perspective even in a single cloud and it was a new environment for them and they had to understand all the nitty-gritty details of each one of these clouds and when you go to multi cloud environment it becomes exponentially complicated to address not only connectivity but how to deploy services like firewall and other services including low balancers and IP address management etc and remote access so we started digging deeper into this problem and start working with the customers and took a clean sheet of paper and came up with a very comprehensive approach to offering a solution which is as a service this time we are not shipping any hardware or software it is you know just like any other SAS application you just come to our portal I just drag and drop literally draw out your network and click on provision and you know come back after 40 minutes or so your full global cloud infrastructure is up and running so out if your brother laid out a pretty broad vision there any of us from the networking world we know there's a lot of complexity there and therefore it takes a lot of work when I want to do things simply as a service is you know a huge growth area bring us inside the engineering challenges that you and the team have been working on to build this solution second let's do so we've been working both our men and myself in the networking industry for more than 25 years now and our the way we have worked and what we have believed in is that we need to solve customer problems we never believed in like doing a science project so here also we started working with customers as we have always done in the past we understood the customers pain points the challenges they were facing especially in this case and in cloud networking space multi-cloud networking space based on the user requirements users or the customers use cases we started the building a service and here what we have built is a complete network as a service it's a multi cloud met work as a service which not only provides connectivity to multiple routes but also addresses the needs for bringing in networking services as well as security services making sure that you have a full policy based infrastructure on top of it you have deep visibility into into the clouds as well as into on-premise into and visibility into and monitoring troubleshooting and all of it is delivered to you as a service so that's what we have been doing here at ELQ here excellent so when we look at multi-cloud of course you know every cloud they have some similar things they have some different things they all tend to do things a little bit differently you know one of the secret sauces that have been talked about for the last few years is ESP BAM space like you and built with Nutella to help really enable those environments so if we've got a diagram here which I think will help explain a little bit as you know we're out here it how it plugs into these different environments walk us through a little bit what we're seeing here and what you're actually doing a tell Kira so here we are building a global unifying the multi cloud Network it's consumed as a service think of it as consuming it just like you would consume any other SAS like our SAS issue so you come to lqs portal you register and then there you go and you start building your global multi-cloud unified network with integrated services so here what you see is is a Elka's cloud services exchange with comprises of cloud exchange points you can bring these up these cloud exchange points up anywhere on the globe you can decide like what networking services security services you need in these cloud exchange points you can connect the multiple clouds from there you can bring your existing on-premise connector matiee into the CX PS all these CX B's have a full mesh of overlay high speed low latency connectivity among each other so there is a full network which comes up between these CX B's and this the whole infrastructure scales with customers as as a customer scale so it's a horizontally scalable veil a very highly redundant and resilient infrastructure which we have both all right so armor now that we understand the basics of the technology you've got some strong investors including Sequoia kleiner perkins give us you know what is being announced day you're coming out of stealth where are you with the product you know how many employees you have and where are you with the discussion of customer adoption so stew we're obviously bringing this to the market and we will be announcing it on April 15th it's available for the customers to consume our solution as a service on that day so they are welcome to reach out to us and we'll be happy to help them and as a matter of fact just come to our website and register for the service and yeah we rightly said that we have a superstar team of not only the venture capital companies but also the board members representing those companies the bill Cochran and mamoon Hamid Wright who the leading VCS are on the board of our company including myself inactive all right I'm all right love to actually bring up the second slide that we have here walk us through you said you know the service you know how do people get started how do they understand you know what would walk us through what what they do so the biggest challenge when we started looking into these problems you know Stu was that it was very complicated you have to piecemeal bring up instances and the cloud and stitch them together and when you try to integrate the services that was a different challenge for the customers right so we wanted to make sure that it was so simple and clean that the customer didn't even have to think about any underlying construct on any of the clouds they should not have to worry about learning each individual power from the you know networking perspective so here's your portal you just come you know step one is come to a portal or register step two is you start drawing your network based on your intent what on-prem an activity you want to bring into this service what type of services you need like all all the firewalls and then you know what pilots you need to connect and everything happens seamlessly the from on pram pram through services into the cloud and across multiple clouds it's a seamless service that we have created and with full analytics capabilities and full governance built in alright so I'll to bring us into what this means for customers you know how do they manage it you know is this the networking team is it the cloud architects you know what api's are there how does this fit into kind of what customers are doing today and you know solve some of those challenges that we laid out earlier in the discussion yes trauma from the customers perspective it's as I said it's it's completely delivered as a service customers come to our portal they draw out the network they select the services they click on provision and the whole network comes up within minutes so the main thing here is that from a customer's point of view if they are connecting to different clouds they don't need to understand any of the underlying specifics or underlying constructs of any of the of the cloud in order to bring can I bring up connectivity so we what we are doing here is we are abstracting the clouds here so we are building a virtual cloud network so if you if you think of if you compare it with what we did in the in the previous life be virtualized the when so here would be a doing is we are virtualizing the cloud network so underlying doesn't matter which cloud you sit on which cloud you need to connect to which networking services whether cloud native services or whether you you want to consume our care services or we also support like customer bringing in third-party services as well so it's all all offered from our platform all offered is a service for to the customer again no expertise required in any of the underlying networking constructs of any of these cards give us what we should be looking at from a technology roadmap from Akira through the rest of 2020 good question as to so as I mentioned earlier our roadmap is dictated by customer requirements so we prioritize what customers need from us so we have come out with a scalable platform we have come out with a marketplace for networking services in there in the near term we'll be expanding our market place with more services we will be addressing more use cases and when I talk about use cases I can give you some examples like there's a view you not just only need connectivity into cloud you might have different requirements from from throughput perspective or bandwidth perspective or different services that you need to front-end your cloud but you may have certain applications such as internet basing application where you eat like traffic coming in from the internet inbound to those applications you might need services like a load balancer like an external load balancer in our services exchange you might also need like a firewall you might need traffic engineering or sorry service eaning capability is where you would chain service through multiple or traffic through multiple of these services like a firewall in the load balancer so we have built a platform which gives you all those capabilities going forward we will be adding more services more use cases to it we have a long ways ahead of us and we will be putting all our effort in delivering a roadmap as we go all right so Amma your technical team definitely has their hands full and uh you know robust after work on uh give us the the high-level what we should be looking for out Kira for people that are out there you know multi-cloud and networking you know tend to get talked a lot there's many big companies and some small ones what will separate al Kira from the rest of the market today and what should we be looking to see the company's progression through 2020 yeah thanks for asking that yeah certainly I mean you know from the solution perspective out it's said that you know it's so fundamentally important to have a very strong basis right and that's what we have done we are bringing out a certain number of services and now we will continue to grow on that will create a big marketplace we will continue to improve on which clouds we connect to and how and we will be building our own services in certain cases as well now building a technology is just one piece of it we have to go out to market with a company that the customers can trust every single you know the department in that company whether it's sales or how they do business with us all the business back-end pieces have to be sorted out and that's what we've been working with and you know then go to market partners that is very very important right support is very important so let me spend a little bit of time on go to market strategy we have been working with the service riders so that we can extend our reach not only to the large customers but also to midsize customers across the globe so you will see us in the future announcing major service water partnerships as well as we've been working with large sis bars and system integration in a partners and also we have taking a slightly different approach this time because it's a service so we are going with telecom master agents which have been you know working with the service providers the cloud providers the cable providers as a channel and they have a huge reach into the customer base so we we have a very comprehensive strategy not only from the go to market in the technology perspective but also how we are going to support our customers and continue to build a relationship to build a lasting company yeah I'm a super important point there absolutely we've seen the maturation and change in the service providers as today they are working with many of the public cloud providers and they're as you said a close touch point and a trusted partner of our customers all right so before I let you go you know YouTuber brothers everybody in today's day and age is spending even more time with family but you know your your situation you've worked together for a long time what keeps bringing the two of you together working together and then talk about that ball so I mean we're very close-knit family we have four brothers and one sister and obviously active and I have been the closest because we have been working together for the longest we have at least work in five different companies together our families travel together we have three daughters each we live about five minutes you know walk from each other and we you know just have this bond where we not only have you know the family close but also very close-knit friends a circle which we both hang out with and we you know obviously have common interest in the sports as well we play squash and tennis and work out so after four if they want to take a stab at it but also yeah so we've always been very close in fact we've been together for the last like ever since I can remember like even even college days he was we were roommates for for some time also he ever say we have like our circle of friends is the same also so again we're very close and we work well together so we complement each other's skills and and it's it's worked out in the past hopefully it will work out again and I look forward to working with them for many many more years to come yeah well I'm or not - thank you so much for sharing the the coming out of stealth for Al Kyra we definitely look forward to watching your progress and you know seeing how you're helping customers in this multi-cloud world thank you for joining us - thank you so much thank you for having us all right I'm Stu minimun and thank you so much for watching this special cube conversation on the cube [Music]

>> Narrator: Hi, everyone, thanks for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data. My name is Sue LeClaire, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Mauro Barbieri, lead architect of analytics at Philips. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. And we'll answer as many questions as we're able to during that time. Any questions that we don't get to we'll do our best to answer them offline. Alternatively, you can also visit the vertical forums to post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And yes, this virtual session is being recorded, and we'll be available to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Mauro, over to you. >> Thank you, good day everyone. So medical imaging systems such as MRI scanners, interventional guided therapy machines, CT scanners, the XR system, they need to provide hospitals, optimal clinical performance but also predictable cost of ownership. So clinicians understand the need for maintenance of these devices, but they just want to be non intrusive and scheduled. And whenever there is a problem with the system, the hospital suspects Philips services to resolve it fast and and the first interaction with them. In this presentation you will see how we are using big data to increase the uptime of our medical imaging systems. I'm sure you have heard of the company Phillips. Phillips is a company that was founded in 129 years ago in actually 1891 in Eindhoven in Netherlands, and they started by manufacturing, light bulbs, and other electrical products. The two brothers Gerard and Anton, they took an investment from their father Frederik, and they set up to manufacture and sale light bulbs. And as you may know, a key technology for making light bulbs is, was glass and vacuum. So when you're good at making glass products and vacuum and light bulbs, then there is an easy step to start making radicals like they did but also X ray tubes. So Philips actually entered very early in the market of medical imaging and healthcare technology. And this is what our is our core as a company, and it's also our future. So, healthcare, I mean, we are in a situation now in which everybody recognize the importance of it. And and we see incredible trends in a transition from what we call Volume Based Healthcare to Value Base, where, where the clinical outcomes are driving improvements in the healthcare domain. Where it's not enough to respond to healthcare challenges, but we need to be involved in preventing and maintaining the population wellness and from a situation in which we episodically are in touch with healthcare we need to continuously monitor and continuously take care of populations. And from healthcare facilities and technology available to a few elected and reach countries we want to make health care accessible to everybody throughout the world. And this of course, has poses incredible challenges. And this is why we are transforming the Philips to become a healthcare technology leader. So from Philips has been a concern realizing and active in many sectors in many sectors and realizing what kind of technologies we've been focusing on healthcare. And we have been transitioning from creating and selling products to making solutions to addresses ethical challenges. And from selling boxes, to creating long term relationships with our customers. And so, if you have known the Philips brand from from Shavers from, from televisions to light bulbs, you probably now also recognize the involvement of Philips in the healthcare domain, in diagnostic imaging, in ultrasound, in image guided therapy and systems, in digital pathology, non invasive ventilation, as well as patient monitoring intensive care, telemedicine, but also radiology, cardiology and oncology informatics. Philips has become a powerhouse of healthcare technology. To give you an idea of this, these are the numbers for, from 2019 about almost 20 billion sales, 4% comparable sales growth with respect to the previous year and about 10% of the sales are reinvested in R&D. This is also shown in the number of patents rights, last year we filed more than 1000 patents in, in the healthcare domain. And the company is about 80,000 employees active globally in over 100 countries. So, let me focus now on the type of products that are in the scope of this presentation. This is a Philips Magnetic Resonance Imaging Scanner, also called Ingenia 3.0 Tesla is an incredible machine. Apart from being very beautiful as you can see, it's a it's a very powerful technology. It can make high resolution images of the human body without harmful radiation. And it's a, it's a, it's a complex machine. First of all, it's massive, it weights 4.6 thousand kilograms. And it has superconducting magnets cooled with liquid helium at -269 degrees Celsius. And it's actually full of software millions and millions of lines of code. And it's occupied three rooms. What you see in this picture, the examination room, but there is also a technical room which is full of of of equipment of custom hardware, and machinery that is needed to operate this complex device. This is another system, it's an interventional, guided therapy system where the X ray is used during interventions with the patient on the table. You see on the left, what we call C-arm, a robotic arm that moves and can take images of the patient while it's been operated, it's used for cardiology intervention, neurological intervention, cardiovascular intervention. There's a table that moves in very complex ways and it again it occupies two rooms, this room that we see here and but also a room full of cabinets and hardwood and computers. This is another another characteristic of this machine is that it has to operate it as it is used during medical interventions, and so it has to interact with all kind of other equipment. This is another system it's a, it's a, it's a Computer Tomography Scanner Icon which is a unique, it is unique due to its special detection technology. It has an image resolution up to 0.5 millimeters and making thousand by thousand pixel images. And it is also a complex machine. This is a picture of the inside of a compatible device not really an icon, but it has, again three rotating, which waits two and a half turn. So, it's a combination of X ray tube on top, high voltage generators to power the extra tube and in a ray of detectors to create the images. And this rotates at 220 right per minutes, making 50 frames per second to make 3D reconstruction of the of the body. So a lot of technology, complex technology and this technology is made for this situation. We make it for clinicians, who are busy saving people lives. And of course, they want optimal clinical performance. They want the best technology to treat the patients. But they also want predictable cost of ownership. They want predictable system operations. They want their clinical schedules not interrupted. So, they understand these machines are complex full of technology. And these machines may have, may require maintenance, may require software update, sometimes may even say they require some parts, horrible parts to be replaced, but they don't want to have it unplanned. They don't want to have unplanned downtime. They would hate send, having to send patients home and to have to reschedule visits. So they understand maintenance. They just want to have a schedule predictable and non intrusive. So already a number of years ago, we started a transition from what we call Reactive Maintenance services of these devices to proactive. So, let me show you what we mean with this. Normally, if a system has an issue system on the field, and traditional reactive workflow would be that, this the customer calls a call center, reports the problem. The company servicing the device would dispatch a field service engineer, the field service engineer would go on site, do troubleshooting, literally smell, listen to noise, watch for lights, for, for blinking LEDs or other unusual issues and would troubleshoot the issue, find the root cause and perhaps decide that the spare part needs to be replaced. He would order a spare part. The part would have to be delivered at the site. Either immediately or the engineer would would need to come back another day when the part is available, perform the repair. That means replacing the parts, do all the needed tests and validations. And finally release the system for clinical use. So as you can see, there is a lot of, there are a lot of steps, and also handover of information from one to between different people, between different organizations even. Would it be better to actually keep monitoring the installed base, keep observing the machine and actually based on the information collected, detect or predict even when an issue is is going to happen? And then instead of reacting to a customer calling, proactively approach the customer scheduling, preventive service, and therefore avoid the problem. So this is actually what we call Corrective Service. And this is what we're being transitioning to using Big Data and Big Data is just one ingredient. In fact, there are more things that are needed. The devices themselves need to be designed for reliability and predictability. If the device is a black box does not communicate to the outside world the status, if it does not transmit data, then of course, it is not possible to observe and therefore, predict issues. This of course requires a remote service infrastructure or an IoT infrastructure as it is called nowadays. The passivity to connect the medical device with a data center in enterprise infrastructure, collect the data and perform the remote troubleshooting and the predictions. Also the right processes and the right organization is to be in place, because an organization that is, you know, waiting for the customer to call and then has a number of few service engineers available and a certain amount of spare parts and stock is a different organization from an organization that actually is continuously observing the installed base and is scheduling actions to prevent issues. And in other pillar is knowledge management. So in order to realize predictive models and to have predictive service action, it's important to manage knowledge about failure modes, about maintenance procedures very well to have it standardized and digitalized and available. And last but not least, of course, the predictive models themselves. So we talked about transmitting data from the installed base on the medical device, to an enterprise infrastructure that would analyze the data and generate predictions that's predictive models are exactly the last ingredient that is needed. So this is not something that I'm, you know, I'm telling you for the first time is actually a strategic intent of Philips, where we aim for zero unplanned downtime. And we market it that way. We also is not a secret that we do it by using big data. And, of course, there could be other methods to to achieving the same goal. But we started using big data already now well, quite quite many years ago. And one of the reasons is that our medical devices already are wired to collect lots of data about the functioning. So they collect events, error logs that are sensor connecting sensor data. And to give you an idea, for example, just as an order of magnitudes of size of the data, the one MRI scanner can log more than 1 million events per day, hundreds of thousands of sensor readings and tens of thousands of many other data elements. And so this is truly big data. On the other hand, this data was was actually not designed for predictive maintenance, you have to think a medical device of this type of is, stays in the field for about 10 years. Some a little bit longer, some of it's shorter. So these devices have been designed 10 years ago, and not necessarily during the design, and not all components were designed, were designed with predictive maintenance in mind with IoT, and with the latest technology at that time, you know, progress, will not so forward looking at the time. So the actual the key challenge is taking the data which is already available, which is already logged by the medical devices, integrating it and creating predictive models. And if we dive a little bit more into the research challenges, this is one of the Challenges. How to integrate diverse data sources, especially how to automate the costly process of data provisioning and cleaning? But also, once you have the data, let's say, how to create these models that can predict failures and the degradation of performance of a single medical device? Once you have these models and alerts, another challenge is how to automatically recommend service actions based on the probabilistic information on these possible failures? And once you have the insights even if you can recommend action still recommending an action should be done with the goal of planning, maintenance, for generating value. That means balancing costs and benefits, preventing unplanned downtimes without of course scheduling and unnecessary interventions because every intervention, of course, is a disruption for the clinical schedule. And there are many more applications that can be built off such as the optimal management of spare parts supplies. So how do you approach this problem? Our approach was to collect into one database Vertica. A large amount of historical data, first of all historical data coming from the medical devices, so event logs, parameter value system configuration, sensor readings, all the data that we have at our disposal, that in the same database together with records of failures, maintenance records, service work orders, part replacement contracts, so basically the evidence of failures and once you have data from the medical devices, and data from the failures in the same database, it becomes possible to correlate event logs, errors, signal sensor readings with records of failures and records of part replacement and maintenance operations. And we did that also with a specific approach. So we, we create integrated teams, and every integrated team at three figures, not necessarily three people, they were actually multiple people. But there was at least one business owner from a service organization. And this business owner is the person who knows what is relevant, which use case are relevant to solve for a particular type of product or a particular market. What basically is generating value or is worthwhile tackling as an organization. And we have data scientists, data scientists are the one who actually can manipulate data. They can write the queries, they can write the models and robust statistics. They can create visualization and they are the ones who really manipulate the data. Last but not least, very important is subject matter experts. Subject Matter Experts are the people who know the failure modes, who know about the functioning of the medical devices, perhaps they're even designed, they come from the design side, or they come from the service innovation side or even from the field. People who have been servicing the machines in real life for many, many years. So, they are familiar with the failure models, but also familiar with the type of data that is logged and the processes and how actually the systems behave, if you if you if you if you allow me in, in the wild in the in the field. So the combination of these three secrets was a key. Because data scientist alone, just statisticians basically are people who can all do machine learning. And they're not very effective because the data is too complicated. That's why you more than too complex, so they will spend a huge amount of time just trying to figure out the data. Or perhaps they will spend the time in tackling things that are useless, because it's such an interesting knows much quicker which data points are useful, which phenomenon can be found in the data or probably not found. So the combination of subject matter experts and data scientists is very powerful and together gathered by a business owner, we could tackle the most useful use cases first. So, this teams set up to work and they developed three things mainly, first of all, they develop insights on the failure modes. So, by looking at the data, and analyzing information about what happened in the field, they find out exactly how things fail in a very pragmatic and quantitative way. Also, they of course, set up to develop the predictive model with associated alerts and service actions. And a predictive model is just not an alert is just not a flag. Just not a flag, only flag that turns on like a like a traffic light, you know, but there's much more than that. It's such an alert is to be interpreted and used by highly skilled and trained engineer, for example, in a in a call center, who needs to evaluate that error and plan a service action. Service action may involve the ordering a replacement of an expensive part, it may involve calling up the customer hospital and scheduling a period of downtime, downtime to replace a part. So it has an impact on the clinical practice, could have an impact. So, it is important that the alert is coupled with sufficient evidence and information for such a highly skilled trained engineer to plan the service session efficiently. So, it's it's, it's a lot of work in terms of preparing data, preparing visualizations, and making sure that old information is represented correctly and in a compact form. Additionally, These teams develop, get insight into the failure modes and so they can provide input to the R&D organization to improve the products. So, to summarize these graphically, we took a lot of historical data from, coming from the medical devices from the history but also data from relational databases, where the service, work orders, where the part replacement, the contact information, we integrated it, and we set up to the data analytics. From there we don't have value yet, only value starts appearing when we use the insights of data analytics the model on live data. When we process live data with the module we can generate alerts, and the alerts can be used to plan the maintenance and the maintenance therefore the plant maintenance replaces replacing downtime is creating value. To give an idea of the, of the type of I cannot show you the details of these modules, all of these predictive models. But to give you an idea, this is just a picture of some of the components of our medical device for which we have models for which we have, for which we call the failure modes, hard disk, clinical grade monitoring, monitors, X ray tubes, and so forth. This is for MRI machines, a lot of custom hardware and other types of amplifiers and electronics. The alerts are then displayed in a in a dashboard, what we call a Remote monitoring dashboard. We have a team of remote monitoring engineers that basically surveyors the install base, looks at this dashboard picks up these alerts. And an alert as I said before is not just one flag, it contains a lot of information about the failure and about the medical device. And the remote monitor engineer basically will pick up these alerts, they review them and they create cases for the markets organization to handle. So, they see an alert coming in they create a case. So that the particular call center in in some country can call the customer and schedule and make an appointment to schedule a service action or it can add it preventive action to the schedule of the field service engineer who's already supposed to go to visit the customer for example. This is a picture and high-level picture of the overall data person architecture. On the bottom we have install base install base is formed by all our medical devices that are connected to our Philips and more service network. Data is transmitted in a in a secure and in a secure way to our enterprise infrastructure. Where we have a so called Data Lake, which is basically an archive where we store the data as it comes from, from the customers, it is scrubbed and protected. From there, we have a processes ETL, Extract, Transform and Load that in parallel, analyze this information, parse all these files and all this data and extract the relevant parameters. All this, the reason is that the data coming from the medical device is very verbose, and in legacy formats, sometimes in binary formats in strange legacy structures. And therefore, we parse it and we structure it and we make it magically usable by data science teams. And the results are stored in a in a vertica cluster, in a data warehouse. In the same data warehouse, where we also store information from other enterprise systems from all kinds of databases from SQL, Microsoft SQL Server, Tera Data SAP from Salesforce obligations. So, the enterprise IT system also are connected to vertica the data is inserted into vertica. And then from vertica, the data is pulled by our predictive models, which are Python and Rscripts that run on our proprietary environment helps with insights. From this proprietary environment we generate the alerts which are then used by the remote monitoring application. It's not the only application this is the case of remote monitoring. We also have applications for particular remote service. So whenever we cannot prevent or predict we cannot predict an issue from happening or we cannot prevent an issue from happening and we need to react on a customer call, then we can still use the data to very quickly troubleshoot the system, find the root cause and advice or the best service session. Additionally, there are reliability dashboards because all this data can also be used to perform reliability studies and improve the design of the medical devices and is used by R&D. And the access is with all kinds of tools. So Vertica gives the flexibility to connect with JDBC to connect dashboards using Power BI to create dashboards and click view or just simply use RM Python directly to perform analytics. So little summary of the, of the size of the data for the for the moment we have integrated about 500 terabytes worth of data tables, about 30 trillion data points. More than eighty different data sources. For our complete connected install base, including our customer relation management system SAP, we also have connected, we have integrated data from from the factory for repair shops, this is very useful because having information from the factory allows to characterize components and devices when they are new, when they are still not used. So, we can model degradation, excuse me, predict failures much better. Also, we have many years of historical data and of course 24/7 live feeds. So, to get all this going, we we have chosen very simple designs from the very beginning this was developed in the back the first system in 2015. At that time, we went from scratch to production eight months and is also very stable system. To achieve that, we apply what we call Exhaustive Error Handling. When you process, most of people attending this conference probably know when you are dealing with Big Data, you have probably you face all kinds of corner cases you feel that will never happen. But just because of the sheer volume of the data, you find all kinds of strange things. And that's what you need to take care of, if you want to have a stable, stable platform, stable data pipeline. Also other characteristic is that, we need to handle live data, but also be able to, we need to be able to reprocess large historical datasets, because insights into the data are getting generated over time by the team that is using the data. And very often, they find not only defects, but also they have changed requests for new data to be extracted to distract in a different way to be aggregated in a different way. So basically, the platform is continuously crunching data. Also, components have built-in monitoring capabilities. Transparent transparency builds trust by showing how the platform behaves. People actually trust that they are having all the data which is available, or if they don't see the data or if something is not functioning they can see why and where the processing has stopped. A very important point is documentation of data sources every data point as a so called Data Provenance Fields. That is not only the medical device where it comes from, with all this identifier, but also from which file, from which moment in time, from which row, from which byte offset that data point comes. This allows to identify and not only that, but also when this data point was created, by whom, by whom meaning which version of the platform and of the ETL created a data point. This allows us to identify issues and also to fix only the subset of when an issue is identified and fixed. It's possible then to fix only subset of the data that is impacted by that issue. Again, this grid trusts in data to essential for this type of applications. We actually have different environments in our analytic solution. One that we call data science environment is more or less what I've shown so far, where it's deployed in our Philips private cloud, but also can be deployed in in in public cloud such as Amazon. It contains the years of historical data, it allows interactive data exploration, human queries, therefore, it is a highly viable load. It is used for the training of machine learning algorithms and this design has been such that we it is for allowing rapid prototyping and for large data volumes. In other environments is the so called Production Environment where we actually score the models with live data from generation of the alerts. So this environment does not require years of data just months, because a model to make a prediction does not need necessarily years of data, but maybe some model even a couple of weeks or a few months, three months, six months depending on the type of data on the failure which has been predicted. And this has highly optimized queries because the applications are stable. It only only change when we deploy new models or new versions of the models. And it is designed optimized for low latency, high throughput and reliability is no human intervention, no human queries. And of course, there are development staging environments. And one of the characteristics. Another characteristic of all this work is that what we call Data Driven Service Innovation. In all this work, we use the data in every step of the process. The First business case creation. So, basically, some people ask how did you manage to find the unlocked investment to create such a platform and to work on it for years, you know, how did you start? Basically, we started with a business case and the business case again for that we use data. Of course, you need to start somewhere you need to have some data, but basically, you can use data to make a quantitative analysis of the current situation and also make it as accurate as possible estimate quantitative of value creation, if you have that basically, is you can justify the investments and you can start building. Next to that data is used to decide where to focus your efforts. In this case, we decided to focus on the use cases that had the maximum estimated business impact, with business impact meaning here, customer value, as well as value for the company. So we want to reduce unplanned downtime, we want to give value to our customers. But it would be not sustainable, if for creating value, we would start replacing, you know, parts without any consideration for the cost of it. So it needs to be sustainable. Also, then we use data to analyze the failure modes to actually do digging into the data understanding of things fail, for visualization, and to do reliability analysis. And of course, then data is a key to do feature engineering for the development of the predictive models for training the models and for the validation with historical data. So data is all over the place. And last but not least, again, these models is architecture generates new data about the alerts and about the how good the alerts are, and how well they can predict failures, how much downtime is being saved, how money issues have been prevented. So this also data that needs to be analyzed and provides insights on the performance of this, of this models and can be used to improve the models found. And last but not least, once you have performance of the models you can use data to, to quantify as much as possible the value which is created. And it is when you go back to the first step, you made the business value you you create the first business case with estimates. Can you, can you actually show that you are creating value? And the more you can, have this fitness feedback loop closed and quantify the better it is for having more and more impact. Among the key elements that are needed for realizing this? So I want to mention one about data documentation is the practice that we started already six years ago is proven to be very valuable. We document always how data is extracted and how it is stored in, in data model documents. Data Model documents specify how data goes from one place to the other, in this case from device logs, for example, to a table in vertica. And it includes things such as the finish of duplicates, queries to check for duplicates, and of course, the logical design of the tables below the physical design of the table and the rationale. Next to it, there is a data dictionary that explains for each column in the data model from a subject matter expert perspective, what that means, such as its definition and meaning is if it's, if it's a measurement, the use of measure and the range. Or if it's a, some sort of, of label the spec values, or whether the value is raw or or calculated. This is essential for maximizing the value of data for allowing people to use data. Last but not least, also an ETL design document, it explains how the transformation has happened from the source to the destination including very important the failure and the strategy. For example, when you cannot parse part of a file, should you load only what you can parse or drop the entire file completely? So, import best effort or do all or nothing or how to populate records for which there is no value what are the default values and you know, how to have the data is normalized or transform and also to avoid duplicates. This again is very important to provide to the users of the data, if full picture of all the data itself. And this is not just, this the formal process the documents are reviewed and approved by all the stakeholders into the subject matter experts and also the data scientists from a function that we have started called Data Architect. So to, this is something I want to give about, oh, yeah and of course the the documents are available to the end users of the data. And we even have links with documents of the data warehouse. So if you are, if you get access to the database, and you're doing your research and you see a table or a view, you think, well, it could be that could be interesting. It looks like something I could use for my research. Well, the data itself has a link to the document. So from the database while you're exploring data, you can retrieve a link to the place where the document is available. This is just the quick summary of some of the of the results that I'm allowed to share at this moment. This is about image guided therapy, using our remote service infrastructure for remotely connected system with the right contracts. We can achieve we have we have reduced downtime by 14% more than one out of three of cases are resolved remotely without an engineer having to go outside. 82% is the first time right fixed rate that means that the issue is fixed either remotely or if a visit at the site is needed, that visit only one visit is needed. So at that moment, the engineer we decided the right part and fix this straightaway. And this result on average on 135 hours more operational availability per year. This therefore, the ability to treat more patients for the same costs. I'd like to conclude with citing some nice testimonials from some of our customers, showing that the value that we've created is really high impact and this concludes my presentation. Thanks for your attention so far. >> Thank you Morrow, very interesting. And we've got a number of questions that we that have come in. So let's get to them. The first one, how many devices has Philips connected worldwide? And how do you determine which related center data workloads get analyzed with protocols? >> Okay, so this is just two questions. So the first question how many devices are connected worldwide? Well, actually, I'm not allowed to tell you the precise number of connected devices worldwide, but what I can tell is that we are in the order of tens of thousands of devices. And of all types actually. And then, how would we determine which related sensor gets analyzed with vertica well? And a little bit how I set In the in the presentation is a combination of two approaches is a data driven approach and the knowledge driven approach. So a knowledge driven approach because we make maximum use of our knowledge of the failure modes, and the behavior of the medical devices and of their components to select what we think are promising data points and promising features. However, from that moment on data science kicks in, and it's actually data science is used to look at the actual data and come up with quantitative information of what is really happening. So, it could be that an expert is convinced that the particular range of value of a sensor are indicative of a particular failure. And it turns out that maybe it was too optimistic on the other way around that in practice, there are many other situations situation he was not aware of. That could happen. So thanks to the data, then we, you know, get a better understanding of the phenomenon and we get the better modeling. I bet I answered that, any question? >> Yeah, we have another question. Do you have plans to perform any analytics at the edge? >> Now that's a good question. So I can't disclose our plans on this right now, but at the edge devices are certainly one of the options we look at to help our customers towards Zero Unplanned Downtime. Not only that, but also to facilitate the integration of our solution with existing and future hospital IT infrastructure. I mean, we're talking about advanced security, privacy and guarantee that the data is always safe remains. patient data and clinical data remains does not go outside the parameters of the hospital of course, while we want to enhance our functionality provides more value with our services. Yeah, so edge definitely very interesting area of innovation. >> Another question, what are the most helpful vertica features that you rely on? >> I would say, the first that comes to mind, to me at this moment is ease of integration. Basically, with vertica, we will be able to load any data source in a very easy way. And also it really can be interfaced very easily with old type of ions as an application. And this, of course, is not unique to vertica. Nevertheless, the added value here is that this is coupled with an incredible speed, incredible speed for loading and for querying. So it's basically a very versatile tool to innovate fast for data science, because basically we do not end up another thing is multiple projections, advanced encoding and compression. So this allows us to perform the optimizations only when we need it and without having to touch applications or queries. So if we want to achieve high performance, we Basically spend a little effort on improving the projection. And now we can achieve very often dramatic increases in performance. Another feature is EO mode. This is great for for cloud for cloud deployment. >> Okay, another question. What is the number one lesson learned that you can share? >> I think that would my advice would be document control your entire data pipeline, end to end, create positive feedback loops. So I hear that what I hear often is that enterprises I mean Philips is one of them that are not digitally native. I mean, Philips is 129 years old as a company. So you can imagine the the legacy that we have, we will not, you know, we are not born with Web, like web companies are with with, you know, with everything online and everything digital. So enterprises that are not digitally native, sometimes they struggle to innovate in big data or into to do data driven innovation, because, you know, the data is not available or is in silos. Data is controlled by different parts of the organ of the organization with different processes. There is not as a super strong enterprise IT system, providing all the data, you know, for everybody with API's. So my advice is to, to for the very beginning, a creative creating as soon as possible, an end to end solution, from data creation to consumption. That creates value for all the stakeholders of the data pipeline. It is important that everyone in the data pipeline from the producer of the data to the to the consumers, basically in order to pipeline everybody gets a piece of value, piece of the cake. When the value is proven to all stakeholders, everyone would naturally contribute to keep the data pipeline running, and to keep the quality of the data high. That's the students there. >> Yeah, thank you. And in the area of machine learning, what types of innovations do you plan to adopt to help with your data pipeline? >> So, in the error of machine learning, we're looking at things like automatically detecting the deterioration of models to trigger improvement action, as well as connected with active learning. Again, focused on improving the accuracy of our predictive models. So active learning is when the additional human intervention labeling of difficult cases is triggered. So the machine learning classifier may not be able to, you know, classify correctly all the time and instead of just randomly picking up some cases for a human to review, you, you want the costly humans to only review the most valuable cases, from a machine learning point of view, the ones that would contribute the most in improving the classifier. Another error is is deep learning and was not working on it, I mean, but but also applications of more generic anomaly detection algorithms. So the challenge of anomaly detection is that we are not only interested in finding anomalies but also in the recommended proper service actions. Because without a proper service action, and alert generated because of an anomaly, the data loses most of its value. So, this is where I think we, you know. >> Go ahead. >> No, that's, that's it, thanks. >> Okay, all right. So that's all the time that we have today for questions. I want to thank the audience for attending Mauro's presentation and also for your questions. If you weren't able to, if we weren't able to answer your question today, I'd ask let we'll let you know that we'll respond via email. And again, our engineers will be at the vertica, on the vertica quorums awaiting your other questions. It would help us greatly if you could give us some feedback and rate the session before you sign off. Your rating will help us guide us as when we're looking at content to provide for the next vertica BTC. Also, note that a replay of today's event and a PDF copy of the slides will be available on demand, we'll let you know when that'll be by email hopefully later this week. And of course, we invite you to share the content with your colleagues. Again, thank you for your participation today. This includes this breakout session and hope you have a wonderful day. Thank you. >> Thank you