>>Live from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Welcome back everyone to the cubes live coverage of Microsoft ignite here in the sunshine state. I'm your host, Rebecca, co-hosting alongside of Stu Miniman. We are joined by Theresa Miller. She is the principal technologist technology advocacy group at Cohesity. Thank you so much for coming on the cube. Thank you for having me. It's a pleasure. So you are an an it veteran and a Microsoft MVP MVP. Uh, you're here to talk about some of the integrated solutions with Microsoft at Cohesity. But this conference is really all about community and I'd love to have you riff a little bit on what it means to be part of this community, to be such a key player and to be partnered with Mark, Microsoft and how you all come together here. Right. So outside of Cohesity, being part of the MVP program is really a pretty spectacular thing. Um, it does take a lot of work and commitment to it. >>Um, I always tell people to pace themselves if they're going to try to, uh, work toward, um, a Microsoft MVP. But it really is about, um, helping others, teaching others and being involved. Um, now from the Cohesity side, I do see a lot of benefits to both. Um, one kinda supports the other and vice versa. Cohesity's doing a lot of really spectacular things in the Microsoft space as a partner. Um, specifically it really, um, is a nice relationship. Um, we have a lot of products that support like Azure stack. We just announced this week for example. Um, we're in the office three 65 space protecting data there and, um, traditional Azure backflow, uh, backups as well. >>Yeah. So Theresa, I, I'd love if you could bring us inside the community a bit more. So, you know, this show has been around for decades, wasn't called ignite back in the day. It was tech ed. Uh, but you know, one of the things that was highlighted in the keynote is community and the future of jobs in the citizen developer. Uh, and moving people from, you know, they might've been four, been, you know, configuring their, a server to run email in their office. And today I'll office three 65 takes care of a lot of that. So bring us through as to how the community's helping. Introducing new people as well as the people that have been here for a while. Helping them learn new skills and uh, be ready for the, the, you know, cloud, AI, you know, world that we live in today. >>Yeah. The community around office three 65 is pretty amazing. I get to network and be involved with some pretty spectacular people in that space. Um, there are some podcasts that people can definitely check out. There's always new writing that happens that covers the various angles of what people need to learn to make sure they're successful. And, um, you know, it, it really is pretty tight knit group, um, to be a part of. There've been so many new product announcements and new developments that they're being announced here. What are you most interested in? What are you most excited about in terms of getting it in front of your customers? So from the Cohesity side, there's two different angles that I'm really interested in getting in front of the customers. Um, one being the office three 65 piece exchange online and one drive specifically and how data protection can get them to that next level. I'm also, uh, very excited to talk to companies about, um, a tool we have called cloud spin that will help them take advantage of 2008 servers end of life. They can, um, Microsoft has a nice program that will allow customers to stick with their 2008 servers if they go to the cloud and we can help them get there, um, as well as protecting their Azure workloads. >>Yeah. Up to Theresa, bring us inside the, the customers today we all talk about cloud journeys and what they're doing. Uh, what's going well, uh, you know, what, what, what are some of the challenges that they have day to day and, uh, the, the type of activities that they do at a show like here, uh, to, to help further their businesses. >>Yeah. Enterprises, I think the long term is going to be hybrid. Everybody's going to be maintaining some level of on-prem as well as cloud. And so with that in mind, you really need, um, I'm gonna put my Cohesity hat back on here. You really do need a solution that can support you in both areas, from a single location. Um, if you try to piece it together, you're, you're not going to have a single solution to manage everything. You're going to have kind of a confusing a mess at the end of the day. So having a single solution to help support that hybrid journey is a big deal. >>Yeah, no, I think you bring up a great point as a, you know, customers went when they move to SAS, when they moved to public cloud, you know, data protection security are even more important than they were when I had it in the four walls of my, my data center. Uh, so having a solution that can span those environments, uh, and companies like Cohesity are doing that. We're actually, uh, in, in our analysis this morning, we talked about Microsoft in many ways, uh, in the announcements that they're making this week, uh, is putting out their vision for how they think it should be. Uh, and uh, well Microsoft has positions in all of the, all of the environments that we know that there's work that we all need to do to make that hybrid environment of today and tomorrow even better. >>One other thing I'd like to add to that is I think a lot of enterprises assume when they go to the cloud that their data is just protected. And so I like to really call out the differentiation between what, what a cloud provider like Microsoft offers and your corporate data. At the end of the day, your corporate data is your corporate data. If you're attacked by ransomware, if there's not a backup of some form or a copy of that data, then you may not ever be able to recover it. And so, um, cloud companies like Microsoft are really great at making sure you have disaster recovery and high availability. Um, companies like Cohesity help with the backup, the point in time backup that protects your data for the long run. I mean, what you just described with companies not even realizing, just assuming that once they're in the cloud, everything is safe and secure. >>Is, would you describe companies as naive to, to what the cloud can offer? I mean, how would you, how, how are companies thinking about this and approaching this particularly when it becomes, when it comes to those, those disasters? Right. I certainly don't think companies are naive. I think we don't know what we don't know. Right. And I think sometimes too, like I've talked to even enterprise peers, people that I've, I've worked with in the past that you think you have the most secure system ever and stuff just happens. Things happen. And so backup and recovery in an ad in a way becomes an insurance policy. Even you'll, you may not ever need it, but if you do, you'll be glad you had it. >>Teresa, one of the things that socked into Dell spent a lot of time this morning talking about was trust. So talk a little bit about the relationship customers have with Microsoft. You know, Microsoft is in a great resurgence, uh, under Satya Nadella, uh, went from a company that most of us didn't spend much time talking to, uh, to now. Microsoft is once again a front and center for, for the people that have been, you know, in the trenches working with that D did they have a different relationship with Microsoft today to, does the word trust resonate with you and your peers when they think about Microsoft? >>It absolutely does resonate. Things have changed a lot since Satya has, has stepped in. I absolutely do trust what they're doing and I think a lot of companies do, uh, where they're growing pains as part of that process. Absolutely. Did it take time? Absolutely. But yes, I do see a lot of enterprise customers trusting what Microsoft is doing and what does that mean? I mean, what, so what, so how does that manifest itself at a time where there is deep distrust and skepticism toward a lot of big technology companies? How do you think that that that trust is, is, is enabling Microsoft to go from strength to strength? I find them to be a very transparent company. I think when they're, there have been issues in the past, they've always been very forthcoming about it and transparency does go a long way for the customer. Plus, I've seen a lot of stability out of the products, um, and a lot of enhancements and improvements across the board that also, um, create that trust that companies want. >>Theresa, you're going be doing some speaking this week for people that don't have the chance to, to see it in person. It gives a little taste of what a, you'll be sharing with everyone at the show here. >>Yeah, absolutely. So I have three theater sessions this week, one of which we'll cover it burnout. I brought in a panel of some Microsoft MVPs from around the world. I wanted that global perspective and we're going to really kind of hammer through what people are facing, but also how they can avoid burnout. So that's one of them. The second one that I'm doing is a Cohesity sponsored session, but I actually take on a subject matter expert approach in that session where I talk about backup and recovery from an office three 65 and Azure perspective, what Microsoft offers and some of the challenges that I see companies needing to consider if they move forward with those native options. And then I'll talk a little bit about Cohesity in that session where I'll cover more. We may be able to help if, um, some of those challenges are concerning to customers. >>And then the third session that I'm doing is my MVP session on exchange online security where I'll dive into exchange online protection and advanced threat protection. >> So I'd love to talk about the first session because it just sounds so interesting. I mean this is the time and it's extremely tight job market. Everyone's being asked to do more with less. There's a lot of industries that face burnout and stress. What is, what are the special challenges that it industry workers face, would you say? So I'm gonna step back a few years in my career I did go through burnout and I think that for me, and I think this will, will correlate and translate to probably what others feeling burnout are going through is one, I didn't even realize I was burned out when I was burned out. That's one issue. But stepping back when I finally realized it, cause I knew I needed a change, I took the leap, I made a change looking back. >>Um, I didn't take lunch breaks. I didn't take long enough vacations. And how I start my day makes a big difference. I work out every morning, even if it's just a light jog, I'm taking time for me. Every day has really gone a long way, um, to how I feel, which not only benefits me and my quality of life, it benefits my employer as well. So that's kind of my nutshell version of what I'm, what I think some of we'll talk about what are the tools that Microsoft is introducing or reintroducing as Microsoft teams. And it has a lot of those time management, uh, attributes and characteristics to it. Some of the, the email reminders and one if there's one, uh, element that will not, if you want to write an email to a colleague, it will say, wait, let's make sure this person is actually not working right now. >>It's 10 o'clock at night. Why don't we send it the next morning at seven 30. So it's waiting for them when they, they get debt that get in. How, how much do you think Microsoft is thinking about these things? Tech burnout, stress, uh, employee satisfaction in terms of the way they are. They're bringing this humanity to the tools that they're putting out there. So I think the tools say a lot for that already, but you bring it, you bring up a good point. And actually one of the things I've noticed from some of the Microsoft employees, and I haven't quite done it yet, but I'm very tempted to, you'll see in a lot of their email signatures that, you know, even though they have sent that email, maybe they didn't wait til the person's working at seven 30 in the morning, but they understand that everybody has different working hours. It's okay to wait, cause you're right, that can be a stressor too. If you don't know when to disconnect and you're checking email all night long and email could drag you right into work when you're not expecting. So it's a great point. Great. Well Theresa, thank you so much for coming on the cube. It was a pleasure tapping you on. Thank you so much for having me. I'm Rebecca Knight for Stu Miniman. Stay tuned for more of the cubes live coverage of Microsoft ignite.

>> Announcer: Live from Las Vegas, it's theCUBE. Covering VMworld 2017, brought to you by VMware and its ecosystem partners. (techno music) >> Good morning, welcome to day three of VMworld 2017. This is theCUBE's continuing coverage of this big event in Las Vegas. I am Lisa Martin with my co-host, Stu Miniman, and we're very excited to be joined by a couple of gals in tech. We have Phoummala Schmitt, you are the infrastructure lead for Independence Blue Cross, and Theresa Miller, the founder of the 24x7 IT Connection. Welcome. >> Thank you. >> Thank you for having us. >> So, Phoummala, let's start with you. You are a very leading female in the technology and software space. Tell us about yourself. What do you do, what inspires you as a female leader in technology? >> I'm currently in infrastructure lead at Independence Blue Cross. I manage unified communications applications, Exchange, Skype for Business. What excites me is the ability to show young women that you can do anything. When I was younger, I was told that I didn't understand math very well, so I couldn't be in IT, well, look at me here. So, it's inspiring. I feel inspired when little girls tell me, I want to do something technical. >> That's awesome, Theresa, what about you? What's your journey been like? >> My journey started over 20 years ago by accident, while I was studying at the University of Wisconsin-Whitewater. There was an opportunity to study IT. It made sense to me, and I dropped my accounting major just like that as soon as IT came to the forefront. I've been doing technology ever since, and today, now, I have 24x7 IT Connection. We do writing, we do webinars, and we also do IT consulting. >> So, you guys work together with the 24x7 IT Connection. Tell us a little bit more about that. I know that, Phoummala, you mentioned Microsoft, and here we are at VMworld. Tell us about what kind of topics you cover on that 24x7 IT Connection. >> We cover a broad range of topics. I usually cover the Microsoft, Exchange, Skype world. I actually blog for Theresa, but we do a podcast together, the Current Status, and we talk about all sorts of technology, storage, networking, and just the current trends. And we actually do it on video, through YouTube, and we have a glass of wine. We make it so it's like a casual conversation with your friends, you know, you're just chattin' and talkin' tech, like here at the conference. >> Uh-- >> Just to add to that, so, from the blogging and like the approach we use, it really is about what's relevant to us. So, like she said, we're covering end-user computing, it might be Microsoft, it might be Linux. It just depends. We have several female writers and one male, and it's really about what's relevant, what's going on in their world, because then you know it's going on in someone else's. >> Yeah, Phoummala, you've been in the center of a really interesting transition we've been seeing in the marketplace. You know, I think about my career in tech, you know, deploying servers for email, and that whole push. Microsoft, huge push to get everybody onto Office 365. >> Phoummala: Yes. >> You know, where that lives, we talk about, you know, software's eating the world. You know, so, give us that journey of applications for you. How's that change your role, some of the dynamics? Sounds like you might need a glass of wine after talking through some of these topics, yeah. >> Yeah, I actually started in the server world, server and infrastructure. I was racking and stacking servers, deploying VMs, and then, at the same time, I was also managing Exchange, but as my career progressed, I kind of left that storage and server background and decided, you know what, applications. I wanted to focus a little bit more and really embrace the application world. Since I had that server background, that was my job, it just seems I could actually deploy these applications a lot better, because I understand the underlying foundations behind it, Exchange, and the cloud, so, right now, you know the push is to be in the cloud. Where I work, and a lot of organizations like ourselves, we don't go to the cloud yet. We're just not there. So, there is a very strong push. Eventually, I suspect we'll all be in the cloud. I mean, that's just, it's not if, it's when. >> Yeah, so, but I want to dig down just a tiny bit more, because, you know, most people in the VMware community know Microsoft pretty well. The relationship with Microsoft and their applications with virtualization, and now with cloud, is a really interest dynamic, so you've gone against some of what Microsoft said in the past, kind of do what's best for your organization, why don't you explain some of that to our audience, yeah. >> Yeah, so, Exchange. The preferred architecture for Exchange is, or Exchange 2016, 2013, is to be physical servers, with DAS, direct-attached storage, which is, you know, not what most people are. I mean, it's a virtualized world now. I don't know any company that isn't virtualized. So, I've taken the approach, what is the best situation or deployment for your organization. Yes, there is the preferred architecture, but it's not, I don't look at it as the Holy Grail, or the Bible. I look at what is best for the organization. What are your requirements? So, if the requirement is to reduce data center cost, reduce some rack space, and you can't go to the cloud yet, due to other requirements, let's look at alternative solutions that still follow some of the guidance. So, you know, yes, I break away from it, but it's what's best for your business, because not everybody can deploy physical. >> Yeah, Theresa, I have to imagine you cover a lot of this. You know, what's really happening with customers versus, you know, no offense to our friends on the vendor side, but, you know, they always think its what's right, as opposed to the person doing it, knows what is right for their environment. That's one of the challenges of IT, right? There is no one way to do things, so. >> Every organization is going to take a different path and journey, and that might be to the cloud, that might not be yet. That might be a combination, that could be hybrid IT, I think is another term that we keep hearing where, maybe I have some applications in the cloud, and some that will always remain on prem, but it has to match the culture and the fit of the business, or you won't be successful with any IT project. >> So, ladies, we're at VMworld 2017, given both of your thoughts in terms of, we need to do what's best for the business, Phoummala, let me start with you. What are some of the things that you're hearing, are you hearing other peers of yours echo the same feelings and sentiments? >> Yes, when we're out in the field, you know, I'm talkin' to people, and it's, yeah, it's we're not there yet, we want to go there, yeah, but we can't do it, we don't have the infrastructure, we don't have the resources. And oftentimes, you know, our vendors, they, they forget that budgets, there's constraints, you know, resourcing. So, you know, my word to them is be patient with us. We want to go there, we like your products, but there's so many other factors in play, especially when you work for a large enterprise. You know, there's politics, and large enterprises just take time to do things, especially certain industries, healthcare, financial sectors. You have certain regulations that you have to follow, and in order to get to the cloud, or whatever, you know, the latest trend is, we may have to modify certain policies that are in place and then there's a downward effect, because let's say we want to go to the cloud, then you have to go to, you know, your security department. What regulations or what retention policies do you have to change? And that may, you know, that may take time. So, it's not like it's going to' happen today. >> Theresa, same question, but I guess, maybe, no, maybe a different question. In terms of your podcast, have you heard anything here that's inspired the next conversation that you guys want to have with your glass of wine? >> So, I really think, it's probably going to revolve around cloud again, and in terms of, the other thing I keep seeing is analytics. Everybody's talking about analytics. >> Lisa: Yes. >> And I think that's a really interesting conversation, 'cause it means so many different things. The depth, what are you going to analyze? How do you manage that data? So I could see it being a combination of those topics, and even maybe separate. >> Yeah, yeah, Phoummala, you and I were talking before the interview. Think about this community here. It used to be, you know, it was like hypervisor, virtualizing, we were all in this journey to virtualize. Now, it's a little bit fragmented because there's so many different areas. Analytics, absolutely huge people. Security, lots of people going there. This whole cloud discussion, on all the different apps. What are you seeing in the community? What are the topic areas? Is that, you know, is that a challenge to the community? VMware, the VMworld community was a pretty tight-knit community, and now it feels, you know, while there's great connections and great people, it's broken into a few different pieces. What's your reaction to that? >> I mean, I do feel there's sort of a, not a disconnect, but there's so many different aspects, and I think that's just the evolution of IT. We've evolved to the point where it's beyond IT, it's beyond the technical approaches. It is, um, it's almost like it's, IT's just another business department. We're a business. We provide services to the other business units, and it's just that evolution of, we're service providers, all of us. Whether we are in the data center, or we are an apps develop, we are providing a service to somebody, and we have customers. >> Do you find that that's an advantage? We were talking to some guests earlier this week that, I think it was an analyst from ESG that was saying, you know, you can show that certain problems with storage, certain costs, aren't IT's problem, it's a business problem. Is that an advantage what you just kind of talked about, Phoummala, in terms of getting eyes and ears of the business to provide, okay, this is a business challenge, we need to provide the right expertise, the right funding, to support these services that are needed? >> I definitely think so. I mean, just from my own experience. Understanding what the business wants and needs is huge. And then just puttin' yourself in their shoes. What do they need, what can we do to make their jobs better? So that person, you know, clicking the button of submitting our payroll, or, you know, putting a purchase order in, what can we do to make that better? So, you know, it's one of the things I always do when I'm looking at projects. What value is this going to bring for our business? And, I think, that's just the way IT has evolved to. We're not the programmers in the basement anymore, you know, with the lights turned off and just coding away. We're all business analysts now. Because, at the end of the day, it's our paycheck, too. So, these products that we're hearing about, at the end of the day, it affects us, it affects our business, and the bottom line. >> And what is the website of 24x7 IT Connection that people can see and hear the value that you bring to the community? >> It's 24x7, so that's the 24 by 7, and then itconnection.com. And so, like I said, we share a lot of really great stuff. We have something new every week, so it's definitely worth checking out. >> Well, ladies, thank you so much for joining Stu and myself this morning and sharing your journeys into IT, as well as your insights, what you've learned from the show, what excites you, and where people can go to find more information about the expertise that you bring to the community. We want to thank you for watching again. We are theCUBE live from day three of VMworld 2017. I am Lisa Martin, for my esteemed co-host, Stu Miniman, thanks for watching. We'll be right back. (techno music)

(energetic music) >> Okay, we're back with Justin Borgman, CEO of Starburst, Richard Jarvis is the CTO of EMIS Health, and Teresa Tung is the cloud first technologist from Accenture. We're on to lie number three. And that is the claim that today's "Modern Data Stack" is actually modern. So (chuckles), I guess that's the lie. Or, is that it's not modern. Justin, what do you say? >> Yeah, I think new isn't modern. Right? I think it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually, are exactly the same as what we've had for 40 years. Rather than Teradata, you have Snowflake. Rather than Informatica, you have Fivetran. So, it's the same general stack, just, y'know, a cloud version of it. And I think a lot of the challenges that have plagued us for 40 years still maintain. >> So, let me come back to you Justin. Okay, but there are differences, right? You can scale. You can throw resources at the problem. You can separate compute from storage. You really, there's a lot of money being thrown at that by venture capitalists, and Snowflake you mentioned, its competitors. So that's different. Is it not? Is that not at least an aspect of modern dial it up, dial it down? So what do you say to that? >> Well, it is. It's certainly taking, y'know what the cloud offers and taking advantage of that. But it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data's still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same structural constraints that exist with the old enterprise data warehouse model on-preem still exist. Just yes, a little bit more elastic now because the cloud offers that. >> So Teresa, let me go to you, 'cause you have cloud-first in your title. So, what's say you to this conversation? >> Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud as we know it, maybe data lake, data warehouse in the central place, that's not even how the cloud providers are looking at it. They have use query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our- the future goes, right? That's going to very much fall the same thing. There was going to be more edge. There's going to be more on-premise, because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers, right? So, there's a lot of reasons why the modern, I guess, the next modern generation of the data stack needs to be much more federated. >> Okay, so Richard, how do you deal with this? You've obviously got, you know, the technical debt, the existing infrastructure, it's on the books. You don't want to just throw it out. A lot of conversation about modernizing applications, which a lot of times is, you know, of microservices layer on top of legacy apps. How do you think about the Modern Data Stack? >> Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just 'cause you can scale CPU and storage doesn't mean you can get more people to use your data to generate you more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business not just the technology itself. >> Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five seven years cloud obviously has given a different pricing model. Derisked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm taking away that that's not enough. Based on what Richard just said, the modern data stack has to serve the business and enable the business to build data products. I buy that. I'm you a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about you know, the, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >> Of how it should look like or, or how >> Yeah. What it should be? >> Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I certainly agree with that. So by no means, are we suggesting that, you know Snowflake or what Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of of idealism. They had the benefit of starting with a clean slate that does not reflect the vast majority of enterprises. And even those companies, as they grow up, mature out of that ideal state, they go by a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really future proof yourself from the inevitable change that you will you won't encounter over time. >> So thank you. So Theresa, based on what Justin just said, I I might take away there is it's inclusive whether it's a data mart, data hub, data lake, data warehouse, just a node on the mesh. Okay. I get that. Does that include Theresa on, on Preem data? Obviously it has to. What are you seeing in terms of the ability to, to take that data mesh concept on Preem I mean most implementations I've seen and data mesh, frankly really aren't, you know adhering to the philosophy there. Maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing, HelloFresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >> I mean, I think it's a killer case for data mesh. The fact that you have valuable data sources on Preem, and then yet you still want to modernize and take the best of cloud. Cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both world. You can start using the data products on Preem, or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or or maybe just tapping into better analytics to get better insights, right? So you're going to be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >> Okay. Thank you. So Richard, you know, talking about data as product wonder if we could give us your perspectives here what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >> So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients, demographics about their their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight and in any business that's clearly not a desirable outcome but when that insight is so critical as it might be in healthcare or some security settings you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured managed way, even if that data comes from a variety of different sources in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >> So that data product through whatever APIs is is accessible, it's discoverable, but it's obviously got to be governed as well. You mentioned appropriately provided to internally. >> Yeah. >> But also, you know, external folks as well. So the, so you've, you've architected that capability today? >> We have and because the data is standard it can generate value much more quickly and we can be sure of the security and value that that's providing, because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context what does this data mean, and what does it mean to process this data for a particular use case. >> Yeah, it makes sense. It's got the context. If the, if the domains on the data, you know you got to cut through a lot of the, the centralized teams, the technical teams that that data agnostic, they don't really have that context. All right, let's end. Justin. How does Starburst fit into this modern data stack? Bring us home. >> Yeah. So I think for us it's really providing our customers with, you know the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know and optionality provides the ability to reduce costs store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know incorporated into our offering as well you can really create and, and curate, you know data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know model and make that an appropriate compliment to you know, the modern data stack that people have today. >> Excellent. Hey, I want to thank Justin, Teresa, and Richard for joining us today. You guys are great. Big believers in the in the data mesh concept, and I think, you know we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are going to be available on the cube.net for on demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and they have awesome resources. Lots of data mesh conversations over there and really good stuff in, in the resource section. So check that out. Thanks for watching the "Data Doesn't Lie... or Does It?" made possible by Starburst data. This is Dave Vellante for the Cube, and we'll see you next time. (upbeat music)

>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. And what if this also included enterprise security and fast performance with Starburst enterprise, you can provide your data consumers with a single point of secure access to all of your data, no matter where it lives with features like strict, fine grained, access control, end to end data encryption and data masking Starburst meets the security standards of the largest companies. Starburst enterprise can easily be deployed anywhere and managed with insights where data teams holistically view their clusters operation and query execution. So they can reach meaningful business decisions faster, all this with the support of the largest team of Trino experts in the world, delivering fully tested stable releases and available to support you 24 7 to unlock the value in all of your data. You need a solution that easily fits with what you have today and can adapt to your architecture. Tomorrow. 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>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data-driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting cost could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data marts, data hubs, and yes, even data lakes were broken and left us wanting from more welcome to the data doesn't lie, or doesn't a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have featured parody with the data lake or vice versa is the so-called modern data stack, simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for hit that was acquired by Teradata. And when I got to Teradata, of course, Teradata is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience show? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know, right. You actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like star Oxley, for things like security for certainly very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited Jamma, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come. I think that's the story at Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenants of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about SAR, brain Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but what does that mean? Does that mean the E D w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's going to be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems, maybe either those that either source systems for the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to use all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got to, you know, domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue are two, you know, challenges, self-serve infrastructure let's park that for a second. And then in your industry, the one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at emus is we have a single security layer that sits on top of our data match, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin, I mean, Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, do an analytic queries and with data that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah. I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively a almost eCommerce like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself, >>Okay. G guys grab a sip of water. After this short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence, keep it right there. >>Your company has more data than ever, and more people trying to understand it, but there's a problem. Your data is stored across multiple systems. It's hard to access and that delays analytics and ultimately decisions. The old method of moving all of your data into a single source of truth is slow and definitely not built for the volume of data we have today or where we are headed while your data engineers spent over half their time, moving data, your analysts and data scientists are left, waiting, feeling frustrated, unproductive, and unable to move the needle for your business. But what if you could spend less time moving or copying data? What if your data consumers could analyze all your data quickly? >>Starburst helps your teams run fast queries on any data source. We help you create a single point of access to your data, no matter where it's stored. And we support high concurrency, we solve for speed and scale, whether it's fast, SQL queries on your data lake or faster queries across multiple data sets, Starburst helps your teams run analytics anywhere you can't afford to wait for data to be available. Your team has questions that need answers. Now with Starburst, the wait is over. You'll have faster access to data with enterprise level security, easy connectivity, and 24 7 support from experts, organizations like Zolando Comcast and FINRA rely on Starburst to move their businesses forward. Contact our Trino experts to get started. >>We're back with Jess Borgman of Starburst and Richard Jarvis of EVAs health. Okay, we're gonna get to lie. Number two, and that is this an open source based platform cannot give you the performance and control that you can get with a proprietary system. Is that a lie? Justin, the enterprise data warehouse has been pretty dominant and has evolved and matured. Its stack has mature over the years. Why is it not the default platform for data? >>Yeah, well, I think that's become a lie over time. So I, I think, you know, if we go back 10 or 12 years ago with the advent of the first data lake really around Hudu, that probably was true that you couldn't get the performance that you needed to run fast, interactive, SQL queries in a data lake. Now a lot's changed in 10 or 12 years. I remember in the very early days, people would say, you you'll never get performance because you need to be column there. You need to store data in a column format. And then, you know, column formats we're introduced to, to data apes, you have Parque ORC file in aro that were created to ultimately deliver performance out of that. So, okay. We got, you know, largely over the performance hurdle, you know, more recently people will say, well, you don't have the ability to do updates and deletes like a traditional data warehouse. >>And now we've got the creation of new data formats, again like iceberg and Delta and Hodi that do allow for updates and delete. So I think the data lake has continued to mature. And I remember a, a quote from, you know, Kurt Monash many years ago where he said, you know, know it takes six or seven years to build a functional database. I think that's that's right. And now we've had almost a decade go by. So, you know, these technologies have matured to really deliver very, very close to the same level performance and functionality of, of cloud data warehouses. So I think the, the reality is that's become a line and now we have large giant hyperscale internet companies that, you know, don't have the traditional data warehouse at all. They do all of their analytics in a data lake. So I think we've, we've proven that it's very much possible today. >>Thank you for that. And so Richard, talk about your perspective as a practitioner in terms of what open brings you versus, I mean, look closed is it's open as a moving target. I remember Unix used to be open systems and so it's, it is an evolving, you know, spectrum, but, but from your perspective, what does open give you that you can't get from a proprietary system where you are fearful of in a proprietary system? >>I, I suppose for me open buys us the ability to be unsure about the future, because one thing that's always true about technology is it evolves in a, a direction, slightly different to what people expect. And what you don't want to end up is done is backed itself into a corner that then prevents it from innovating. So if you have chosen a technology and you've stored trillions of records in that technology and suddenly a new way of processing or machine learning comes out, you wanna be able to take advantage and your competitive edge might depend upon it. And so I suppose for us, we acknowledge that we don't have perfect vision of what the future might be. And so by backing open storage technologies, we can apply a number of different technologies to the processing of that data. And that gives us the ability to remain relevant, innovate on our data storage. And we have bought our way out of the, any performance concerns because we can use cloud scale infrastructure to scale up and scale down as we need. And so we don't have the concerns that we don't have enough hardware today to process what we want to do, want to achieve. We can just scale up when we need it and scale back down. So open source has really allowed us to maintain the being at the cutting edge. >>So Jess, let me play devil's advocate here a little bit, and I've talked to Shaak about this and you know, obviously her vision is there's an open source that, that the data meshes open source, an open source tooling, and it's not a proprietary, you know, you're not gonna buy a data mesh. You're gonna build it with, with open source toolings and, and vendors like you are gonna support it, but to come back to sort of today, you can get to market with a proprietary solution faster. I'm gonna make that statement. You tell me if it's a lie and then you can say, okay, we support Apache iceberg. We're gonna support open source tooling, take a company like VMware, not really in the data business, but how, the way they embraced Kubernetes and, and you know, every new open source thing that comes along, they say, we do that too. Why can't proprietary systems do that and be as effective? >>Yeah, well, I think at least with the, within the data landscape saying that you can access open data formats like iceberg or, or others is, is a bit dis disingenuous because really what you're selling to your customer is a certain degree of performance, a certain SLA, and you know, those cloud data warehouses that can reach beyond their own proprietary storage drop all the performance that they were able to provide. So it is, it reminds me kind of, of, again, going back 10 or 12 years ago when everybody had a connector to Haddo and that they thought that was the solution, right? But the reality was, you know, a connector was not the same as running workloads in Haddo back then. And I think similarly, you know, being able to connect to an external table that lives in an open data format, you know, you're, you're not going to give it the performance that your customers are accustomed to. And at the end of the day, they're always going to be predisposed. They're always going to be incentivized to get that data ingested into the data warehouse, cuz that's where they have control. And you know, the bottom line is the database industry has really been built around vendor lockin. I mean, from the start, how, how many people love Oracle today, but our customers, nonetheless, I think, you know, lockin is, is, is part of this industry. And I think that's really what we're trying to change with open data formats. >>Well, that's interesting reminded when I, you know, I see the, the gas price, the tees or gas price I, I drive up and then I say, oh, that's the cash price credit card. I gotta pay 20 cents more, but okay. But so the, the argument then, so let me, let me come back to you, Justin. So what's wrong with saying, Hey, we support open data formats, but yeah, you're gonna get better performance if you, if you keep it into our closed system, are you saying that long term that's gonna come back and bite you cuz you're gonna end up, you mentioned Oracle, you mentioned Teradata. Yeah. That's by, by implication, you're saying that's where snowflake customers are headed. >>Yeah, absolutely. I think this is a movie that, you know, we've all seen before. At least those of us who've been in the industry long enough to, to see this movie play over a couple times. So I do think that's the future. And I think, you know, I loved what Richard said. I actually wrote it down. Cause I thought it was an amazing quote. He said, it buys us the ability to be unsure of the future. Th that that pretty much says it all the, the future is unknowable and the reality is using open data formats. You remain interoperable with any technology you want to utilize. If you want to use spark to train a machine learning model and you want to use Starbust to query via sequel, that's totally cool. They can both work off the same exact, you know, data, data sets by contrast, if you're, you know, focused on a proprietary model, then you're kind of locked in again to that model. I think the same applies to data, sharing to data products, to a wide variety of, of aspects of the data landscape that a proprietary approach kind of closes you in and locks you in. >>So I, I would say this Richard, I'd love to get your thoughts on it. Cause I talked to a lot of Oracle customers, not as many te data customers, but, but a lot of Oracle customers and they, you know, they'll admit, yeah, you know, they're jamming us on price and the license cost they give, but we do get value out of it. And so my question to you, Richard, is, is do the, let's call it data warehouse systems or the proprietary systems. Are they gonna deliver a greater ROI sooner? And is that in allure of, of that customers, you know, are attracted to, or can open platforms deliver as fast in ROI? >>I think the answer to that is it can depend a bit. It depends on your businesses skillset. So we are lucky that we have a number of proprietary teams that work in databases that provide our operational data capability. And we have teams of analytics and big data experts who can work with open data sets and open data formats. And so for those different teams, they can get to an ROI more quickly with different technologies for the business though, we can't do better for our operational data stores than proprietary databases. Today we can back off very tight SLAs to them. We can demonstrate reliability from millions of hours of those databases being run at enterprise scale, but for an analytics workload where increasing our business is growing in that direction, we can't do better than open data formats with cloud-based data mesh type technologies. And so it's not a simple answer. That one will always be the right answer for our business. We definitely have times when proprietary databases provide a capability that we couldn't easily represent or replicate with open technologies. >>Yeah. Richard, stay with you. You mentioned, you know, you know, some things before that, that strike me, you know, the data brick snowflake, you know, thing is, oh, is a lot of fun for analysts like me. You've got data bricks coming at it. Richard, you mentioned you have a lot of rockstar, data engineers, data bricks coming at it from a data engineering heritage. You get snowflake coming at it from an analytics heritage. Those two worlds are, are colliding people like PJI Mohan said, you know what? I think it's actually harder to play in the data engineering. So I E it's easier to for data engineering world to go into the analytics world versus the reverse, but thinking about up and coming engineers and developers preparing for this future of data engineering and data analytics, how, how should they be thinking about the future? What, what's your advice to those young people? >>So I think I'd probably fall back on general programming skill sets. So the advice that I saw years ago was if you have open source technologies, the pythons and Javas on your CV, you commander 20% pay, hike over people who can only do proprietary programming languages. And I think that's true of data technologies as well. And from a business point of view, that makes sense. I'd rather spend the money that I save on proprietary licenses on better engineers, because they can provide more value to the business that can innovate us beyond our competitors. So I think I would my advice to people who are starting here or trying to build teams to capitalize on data assets is begin with open license, free capabilities, because they're very cheap to experiment with. And they generate a lot of interest from people who want to join you as a business. And you can make them very successful early, early doors with, with your analytics journey. >>It's interesting. Again, analysts like myself, we do a lot of TCO work and have over the last 20 plus years. And in world of Oracle, you know, normally it's the staff, that's the biggest nut in total cost of ownership, not an Oracle. It's the it's the license cost is by far the biggest component in the, in the blame pie. All right, Justin, help us close out this segment. We've been talking about this sort of data mesh open, closed snowflake data bricks. Where does Starburst sort of as this engine for the data lake data lake house, the data warehouse fit in this, in this world? >>Yeah. So our view on how the future ultimately unfolds is we think that data lakes will be a natural center of gravity for a lot of the reasons that we described open data formats, lowest total cost of ownership, because you get to choose the cheapest storage available to you. Maybe that's S3 or Azure data lake storage, or Google cloud storage, or maybe it's on-prem object storage that you bought at a, at a really good price. So ultimately storing a lot of data in a deal lake makes a lot of sense, but I think what makes our perspective unique is we still don't think you're gonna get everything there either. We think that basically centralization of all your data assets is just an impossible endeavor. And so you wanna be able to access data that lives outside of the lake as well. So we kind of think of the lake as maybe the biggest place by volume in terms of how much data you have, but to, to have comprehensive analytics and to truly understand your business and understand it holistically, you need to be able to go access other data sources as well. And so that's the role that we wanna play is to be a single point of access for our customers, provide the right level of fine grained access controls so that the right people have access to the right data and ultimately make it easy to discover and consume via, you know, the creation of data products as well. >>Great. Okay. Thanks guys. Right after this quick break, we're gonna be back to debate whether the cloud data model that we see emerging and the so-called modern data stack is really modern, or is it the same wine new bottle? When it comes to data architectures, you're watching the cube, the leader in enterprise and emerging tech coverage. >>Your data is capable of producing incredible results, but data consumers are often left in the dark without fast access to the data they need. Starers makes your data visible from wherever it lives. Your company is acquiring more data in more places, more rapidly than ever to rely solely on a data centralization strategy. Whether it's in a lake or a warehouse is unrealistic. A single source of truth approach is no longer viable, but disconnected data silos are often left untapped. We need a new approach. One that embraces distributed data. One that enables fast and secure access to any of your data from anywhere with Starburst, you'll have the fastest query engine for the data lake that allows you to connect and analyze your disparate data sources no matter where they live Starburst provides the foundational technology required for you to build towards the vision of a decentralized data mesh Starburst enterprise and Starburst galaxy offer enterprise ready, connectivity, interoperability, and security features for multiple regions, multiple clouds and everchanging global regulatory requirements. The data is yours. And with Starburst, you can perform analytics anywhere in light of your world. >>Okay. We're back with Justin Boardman. CEO of Starbust Richard Jarvis is the CTO of EMI health and Theresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie it's it is it's is that it's not modern. Justin, what do you say? >>Yeah. I mean, I think new isn't modern, right? I think it's the, it's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exist just yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess, the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, a microservices layer on top of leg legacy apps. How do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more, more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Well thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. De-risked experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that. I'm a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and, and principles there >>Of, of how it should look like or, or how >>It's yeah. What it should be. >>Yeah. Yeah. Well, I think, you know, in, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of it was starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go buy a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, my takeaway there is it's inclusive, whether it's a data Mar data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include there on Preem data? O obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on Preem? I mean, most implementations I've seen in data mesh, frankly really aren't, you know, adhering to the philosophy. They're maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data. Me, the fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both worlds. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds. That, again, going back to Richard's point, that is meaningful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, talking about data as product, wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight. And in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured, managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to researchers. >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned you, you appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have, and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use case? >>Yeah, it makes sense. It's got the context. If the, if the domains own the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's send Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Theresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave Valante for the cube, and we'll see you next time. >>The explosion of data sources has forced organizations to modernize their systems and architecture and come to terms with one size does not fit all for data management today. Your teams are constantly moving and copying data, which requires time management. And in some cases, double paying for compute resources. Instead, what if you could access all your data anywhere using the BI tools and SQL skills your users already have. 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>>Hello, everyone. Welcome to the cubes coverage, extended coverage of the V IP gala event. Earlier in the day, we were at the Monaco crypto summit, where we had 11 years, all the fault leaders here in MoCo coming together. It's a global event. It's an inner circle. It's a beginning, it's an ELG overall event. It's a kernel of the best of the best from finance entrepreneurship government coming together here with the gala event at the yacht club in Monaco. And we got a great lineup here. We have Sherman elder wish from decentralized investment group here with me. She and I was just talking and we're gonna have a great conversation. Welcome to the cube. Thank >>You so much. Thank you for having me. >>It's kind of our laid back to not only have an anchored desk, but we're kind of have conversations. You know, one of the things that we've been talking about is, you know, the technology innovation around decentralized, right? You've been an entrepreneur 9, 9, 9 years. Yes. Plus you're in a region of the world right now where it's exploding. You're in Dubai. Tell your story. You're in Dubai. There's a lot of action what's happening. >>So to Dubai is, is really the bridge between the east and the west. And it's grown. I've, I've had the privilege of witnessing Dubai's growth for over 16 years now. So I've been based in Dubai for 16 years. I'm originally from Jordan, lived in 11 countries. You can call me a global nomad home is where my suitcases and where I, you know, where I'm, I'm literally with my friends and community and the work that I do. So I've been there and I've witnessed this grow through working with the government there as well. So nine years ago, I jumped into the world of entrepreneurship. I specialize in art and education. Also, I work extensively now in decentralized with decentralized investment group. So we specialize in defi game five and also digital assets. So it's a beautiful time to be in Dubai right now. And witness that growth in web three, there's going to be a summit that's actually happening in September. And so it's attracting all the global leaders there with the government there. So they're really investing in, >>You know, the date on that. >>Sorry, >>You know the date on that? Yeah. Oh, >>September. They're going to be September, either 27th or >>28th. So later in the month, >>Yes. Later in the month of September. Okay. So it's very exciting to be a part >>Of that. Well, I love you're on here cause I want, first of all, you look fabulous. Great. Oh, thank you. Great event. Everyone's dressed up here. But one of the things I've been passionate about is women in tech. And I know you've got a project now you're working on this. Yes. Not only because it's it's needed. Yeah, but they're taking over. There's a lot of growth. Absolutely. The young entrepreneurs, young practitioners, absolutely young women all around the world. Absolutely. And we did a five region women in tech on March 7th with Stanford university, amazing. And Amazon web services. And I couldn't believe the stories. So we're gonna do more. And I want to get your take on this because there are stories that need to be told. Absolutely. What are, what are the, some of the stories that you're seeing, some of the, some of the cautionary tales, some of the successes, >>Well, you have, I mean the middle east right now is really a space, especially in Dubai, in the UAE, the growth of women in entrepreneurship, the support that we have from incubators, there, there is a hunger for growth and learning and innovation. And that is the beauty of being there. There are so many incredible stories, not one that I could say right now, but each and every story is exquisite and extraordinary. And what's really amazing is that you have the community there that supports one another, especially women in tech. I'm, I'm actually one of the co-founders of made for you global, which is a tech platform, which attracts entrepreneurship, female entrepreneurs, and really helping them kind of grow to their potential or maximize their potential. And we're actually going to have it on web three as well and integrate it within the blockchain. So there's a lot of, there's a lot of passion for, for growth in women, in tech and, and there's so many incredible stories to come, not just one, so many. And I invite you to come to Dubai so I can introduce you to all >>These incredible. So I'm really glad you're inclusive about men. >>Of course, we're inclusive >>About men, >>You know, men and women. I mean, it's a community that brings together these ideas. >>Yeah. I will say I had to go the microphone one time because I love doing the Stanford women in data science, but they, and we have female, a host. I just wanna do the interviews right there. So smart. I said, Chuck, can we have the female interviews cuz you know, like, okay, but they included me. Oh yes. But in all serious. Now this is a major force because women entrepreneurship make up 50% of the, the target audience of all products. Absolutely. So if, why, why isn't there more developers and input into the products and policies, right? That shape our society. This has been one of those head scratching moments and we're making progress, but not fast enough. >>Absolutely. And you know what, especially after COVID, so after COVID we all learned the lessons of the hybrid models of being more flexible of being more innovative of being making use of our time more effectively. And we've witnessed like an increase in women in tech over the years and especially in web three and decentralized investment group invest heavily into women and in tech as well, >>Give some examples of some things you're working on right now, projects you're investing in. So >>We're, well, everything that we do is inclusive of women. So with game five, for example, we specialize greatly in game five through our subsidiary company, based in the us, it's called X, Y, Z, Z Y it's gaming. And actually many of our creative team are women who are the developers behind the scenes who are bringing it to life. A lot of basically we're trying to educate the public as well about how to get meta mask wallets and to enter into this field. It's all about education and growing that momentum to be able to be more and more inclusive. >>Do you think you can help us get a cube host out there? Of course, of course they gotta be dynamic. Of course smart of course and no teleprompter of >>Course. And we would love for you to come so that we can really introduce you to >>All well now, now that COVID is over. We got a big plan on going cube global, digging it out in 2019, we had London, Bahrain, Singapore, amazing Dubai, Korea. Amazing. And so we wanted to really get out there and create a node, right? And open source kind of vibe where right. The folks all around the world can connect through the network effects. And one thing I noticed about the women in tech, especially in your area is the networking is really high velocity. Absolutely people like the network out there is that, do you see that as well? Absolutely. >>Because it's a, it's a city of transition, you know? So that's the beauty of Dubai, it's positioning power. And also it's a very innovative hub. And so with all of these summits that are coming up, it's attracting the communities and there's lots of networking that happens there. And I think more and more we're seeing with web three is that it is all about the community. It's all about bringing everyone together. >>Well, we got people walking through the sets. See, that's the thing that about a cocktail party. You got people walking through the set that's good. Made, had some color. Rachel Wolfson is in the house. Rachel is here. That's Rachel Woodson. If you didn't recognize her she's with coin Telegraph. Oh bless. I don't know who they, the Glo is as they say, but that's how he went cool to me. All right. So betting back to kinda what you're working on. Have you been to Silicon valley lately? Because you're seeing a lot of peering where people are looking at web three and saying, Hey, Silicon valley is going through a transition too. You're seeing beacons of outposts, right? Where you got people moving to Miami, you got Dubai, you got Singapore, you got, you know, Japan, all these countries. Now there's a, there's a network effect. >>Absolutely. It's all about. And honestly, when I see, I mean, I've been to Miami so many times this year for all the web three events and also in Austin and GTC as well. And what you see is that there is this ripple effect that's happening and it is attracting more and more momentum because the conversations are there and the openness to work together. It's all about partnerships and collaboration. This is a field which is based on collaboration communities. >>Awesome. What are some of the advice advice you have for women out there that are watching around being an entrepreneur? Because we were talking before we came on camera about it's hard. It's not easy. It's not for the faint of heart. Yeah. As Theresa Carlson, a friend of mine used, used to say all the time entrepreneurship was a rollercoaster. Of course, what's your advice don't give up or stay strong. What's your point of view? >>Honestly, if you're passionate about what you do. And I know it sounds very cliche. It's really important to stay focused, moving forward, always. And really it's about partnerships. It's about the ability to network. It's the ability to fail as well. Yeah. And to learn from your mistakes and to know when to ask for help. A lot of the times, you know, we shy away from asking for help or because we're embarrassed, but we need to be more open to failing, to growing and to also collaborating with one another. >>Okay. So final question for you while I got, by the way, you're an awesome guest. Oh, thank you. What are you what's next for you? What are you working on right now? Next year? What's on your goal list. What's your project? What's >>Your top goal? Oh my gosh. >>Top three, >>Top three, definitely immersing myself more into web three. Web three is definitely the future getting made for you global on the ground and running in terms of the networking aspect in a female entrepreneurship, more and more giving back as well. So using web three for social good. So a lot more charitable, innovative kind of campaigns that we hope to host within the web three community to be able to educate, to innovate and also help those that are, that need it the most as >>Well. Shaman, thank you for coming on the cube. I really appreciate it. And thanks for coming on. Thank you >>So much. >>I'm so grateful. Okay. You watching the queue, we're back in the more coverage here at the after party of the event, it's the VIP gala with prince Albert and all the top guests in Monica leaning into crypto I'm John furier. Thanks for watching.

(upbeat music) >> Okay, welcome back everyone. This is theCube's extended hours. Things are starting to settle down. Dinner has started being served. We're here with Prince Alvarez, has an invitation for the VIP gala. Part of Monaco leaning into crypto, we're reporting on it. Not our normal set, more of an after hours vibe. We're here with Iman Bashir, found of Craftly.ai. Thanks for coming on theCube. >> Thanks for having me. >> So I love your story. You're a coder, built some code, started a company. Now you're the CEO. You hired some people to wrap around support you. Now you're running the show. What a great story. How did you get here? Give me the origination story >> Basically, for as long as I can remember, I've been an entrepreneur. My parents have stories of me being too young to babysit, but I would create a babysitting agency and have sent babysitters out or I would sell my lunches. Throughout school, I would always find some kind of entrepreneurship endeavor. And when I came out of school, I kept finding myself lacking the necessary skills to really do a startup. And so that's when I discovered coding. And I took myself through coding boot camps, and I started websites and I'm like, no, I want something more perpetual. I want to make money when I sleep. And then from there I found search engine optimization. So how to get to the top of Google. And I started working really quickly with like really big companies. And immediately I realized my full budget's spent on copywriting. And so that's when I discovered you could have that written by AI. Not going to replace you, but it's there to enhance you. And so built an AI copywriter. >> And so what does it do? >> Basically you type in a couple words. It could be anything, any use case. So product descriptions for eCommerce, blog articles for any company really, web copy, even does songs. Or your next breakup text, which we'll get to that. But it does basically anything. You type in a couple words and it generates text for you. All original and plagiarism free. >> Okay. Can you write our blog post for us? >> Yeah. >> Say, hey, we're covering the crypto-con in Monaco. >> Could even do a summary of this interview, yeah. >> Well, we get that transcribed in the cloud. We'll get that in a second. First. I love the story. Okay. So now you're the CEO. Great application. So imagine you're scraping pages. You looking at summaries, doing any extraction, looking at word combinations. What were some of the tech behind it? >> We use, we leverage a bunch of different models. We use GPT3, which was founded by Sam Altman, Elon Musk, all major players that basically allows you to pull 175 billion parameters of data. Anything before it was 2 billion. So now you're talking, I'm able to pull like basically the whole internet. And from there we added different models to provide learning and to get the best quality AI out there. There's a lot of bad quality. And so from there, we're able to take it, mix and match, and have it formulate the best things. >> So where are you now? So you're in your journey, the CEO, how many people in your staff, what's the status? >> So right now we have eight full time people and a bunch of contractors. Before I was the lead developer. But now as the company's growing, I have to take a backseat and be more in a sales role versus being the one to develop it every single day. And so right now we're hiring more developers as we go. >> So the funding options must be off the charts. Offers coming in left and right. >> So tons, but definitely we're in a different market environment than we were two months ago. So as you may have heard, crypto's down, possibly, but so we were - >> On temporary basis, not truly down. >> But in the beginning, I wanted to hold as much equity as possible by bootstrapping, proving concept, doing it. I have a lot of the background and skillset to have it there. So I hired the best people. And once we prove concept, we were prepared to raise and then the market kind of slowed us down. So right now, luckily our company is self-funded and supporting itself. >> Good. >> So we're making money. We're profitable. >> What you want to do. >> Yeah. >> As much as possible. >> Yeah. And so right now we do, we are looking for growth options, funding options. We're talking to a lot of people. That's why I'm here in Monaco. But it's a good place to not be desperate. It's a good place to not need the money, but. >> You know, I always said when I was running companies and to my team, my a friend gave me great advice. You can't go out of business when you have money in the bank. >> Yeah. >> So don't run out of money. >> Basically. Luckily our product, it's a subscription basis and it's a monthly, so we're making money immediately. >> All right. So I got to ask. What's the biggest challenge you've had and putting this because it's great, great story. You're really impressive. Great vision. You coded your own product. Now you got put the team around you. >> Yeah. >> What's been the challenge. How have you handled the grind? Cuz it's, the joy in the grind can be fun. >> Yeah. >> But then it gets complicated. Start adding people to the mix and you got to get milestones. You're self-funding. Which by the way, self-funding is the hardest part. >> Yeah. >> It is difficult. >> Yeah. >> Most people think like raising a big round is the top of the mountain. No, no, no, no. Self-funding is the A-1 player. That's an A play move, A player move right there. >> Definitely. I would say if I were to go back I would get funded a lot earlier, especially with the market conditions eight months ago. But one of the biggest struggles I would, I feel I have faced was just being a younger founder. Sometimes you're, there's imposter syndrome in your, within yourself. But otherwise, a lot of times people don't take you seriously immediately. Everyone always assumes that I'm someone's girlfriend at an event. Or I, they say that's cute when talking about your business. And so you have to deal with that. Yeah. Or one time I was at a conference and someone asked how I funded the company. And I said, I created ancillary revenue streams to be able to support it. And their response was, oh, I love it when my OnlyFans funds my business. And that, immediately, >> Oh my God, that is... That is total. >> But now I use it as fire to ignite me and kind of prove everyone wrong. But I definitely would say that the journey of falling in love with the journey and realizing that no matter how big you get, your problems only get bigger. So it's choosing the right problems to solve and realizing that every day there's going to be a fire. Just living in the moment. >> Well, you're such an inspiration to me and anyone. I'm going to share your story because what you just talked about, a lot of people, being a startup, you're eating glass, you're falling on your face. You're tripping all the time. Hopefully you don't get hurt. But when people make comments like that to you, given how smart you are, and how brilliant you are, how beautiful you are, that is just unacceptable. And I think that is just a really weird thing. Like that has to change. It's like, its so unacceptable. >> I feel like the world's heading in the right direction and it's up to people to use those setbacks to ignite them and push them forward, which I'm trying to. >> You know, I was, I read a book about trauma and how trauma defines you, right? >> Yeah. >> And trauma is little trauma, family trauma and and trauma's defined >> All perspective. >> Trauma's defined as not like, oh, something dire, like little things could be like little traumas. Oh yeah, I was offended by my brother. This happened there. So experiences define you. >> Yeah. >> And I think one of the things that you just mentioned is you've made it stronger, made you stronger. >> Yeah. >> The comments made you stronger. >> Oh, I definitely see, even everything that I've been through. And this is the same for a lot of first time founders. All my previous companies I've had the blessing of working with like an older mentor that had done it before. This was the first where I kind of was on my own. And when you do that, now, if I look back on the last year and a half, I could probably do the same thing in a week. Once you do it the first time you really do learn. >> I'll just tell you. You're brilliant, beautiful. You're very impressive. >> Thank you. >> Theresa Carlson, who used to run all Amazon's web services, business and public sector. She's a Renaissance woman. She's an amazing friend. Great power. That's she always, she always says to me, and she's like you know, my father was a basketball coach. I can handle with those men. And she would say, but she said it with proud, like leaning in like, hey, that's life. I'll take what life gives me. And I think that's a lesson we're seeing more of. Because you're seeing a lot more women in tech. I did 30 interviews in Europe, the past March 7th. Okay. In three weeks. >> Yeah. >> So a lot of stuff. Well, thanks for coming on. We got the events starting. I'll let you go. Thanks for sharing your story. >> Thanks for having me. >> Well, what's next for you? What's next? >> Next is, I'm going to I'm going to build the word processor of the future and be the future of writing. >> Okay. And thank you for coming out. >> There you go. I appreciate it. All right. This is theCube coverage here at the event. we'll be back with more after this break. (upbeat music)

>>Okay. We're back with Justin Boorman CEO of Starburst. Richard Jarvis is the CTO of EMI health and Teresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie or it's it is it's is that it's not modern, Justin, what do you say? >>Yeah, I mean, I think new isn't modern, right? I think it's, the's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just this, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data's still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exists just, yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, of microservices layer on top of leg legacy apps. Ho how do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Oh thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. Drisk experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that I'm, you know, a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, the paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and principles there >>Of, of how it should look like or, or how >>Yeah. What it should be? >>Yeah. Yeah. Well, I think, you know, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go by a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, I might take away there is it's inclusive, whether it's a data Mart, data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include Theresa on, on Preem data? Obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on pre I mean most implementations I've seen and data mesh, frankly really aren't, you know, adhering to the philosophy there. Maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data mesh. The fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both world. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds that, again, going back to Richard's point, that is needful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, you're talking about data as product. Wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight and in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured and managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to research is >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the right, correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use >>Case? Yeah, it makes sense. It's got the context. If the, if the domains on the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's end, Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Teresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave ante for the, and we'll see you next time.

>>In 2011, early Facebook employee and Cloudera co-founder Jeff Ocker famously said the best minds of my generation are thinking about how to get people to click on ads. And that sucks. Let's face it more than a decade later organizations continue to be frustrated with how difficult it is to get value from data and build a truly agile data driven enterprise. What does that even mean? You ask? Well, it means that everyone in the organization has the data they need when they need it. In a context that's relevant to advance the mission of an organization. Now that could mean cutting costs could mean increasing profits, driving productivity, saving lives, accelerating drug discovery, making better diagnoses, solving, supply chain problems, predicting weather disasters, simplifying processes, and thousands of other examples where data can completely transform people's lives beyond manipulating internet users to behave a certain way. We've heard the prognostications about the possibilities of data before and in fairness we've made progress, but the hard truth is the original promises of master data management, enterprise data, warehouses, data, Mars, data hubs, and yes, even data lakes were broken and left us wanting for more welcome to the data doesn't lie, or does it a series of conversations produced by the cube and made possible by Starburst data. >>I'm your host, Dave Lanta and joining me today are three industry experts. Justin Borgman is this co-founder and CEO of Starburst. Richard Jarvis is the CTO at EMI health and Theresa tongue is cloud first technologist at Accenture. Today we're gonna have a candid discussion that will expose the unfulfilled and yes, broken promises of a data past we'll expose data lies, big lies, little lies, white lies, and hidden truths. And we'll challenge, age old data conventions and bust some data myths. We're debating questions like is the demise of a single source of truth. Inevitable will the data warehouse ever have feature parody with the data lake or vice versa is the so-called modern data stack simply centralization in the cloud, AKA the old guards model in new cloud close. How can organizations rethink their data architectures and regimes to realize the true promises of data can and will and open ecosystem deliver on these promises in our lifetimes, we're spanning much of the Western world today. Richard is in the UK. Teresa is on the west coast and Justin is in Massachusetts with me. I'm in the cube studios about 30 miles outside of Boston folks. Welcome to the program. Thanks for coming on. Thanks for having us. Let's get right into it. You're very welcome. Now here's the first lie. The most effective data architecture is one that is centralized with a team of data specialists serving various lines of business. What do you think Justin? >>Yeah, definitely a lie. My first startup was a company called hit adapt, which was an early SQL engine for IDU that was acquired by Teradata. And when I got to Teradata, of course, Terada is the pioneer of that central enterprise data warehouse model. One of the things that I found fascinating was that not one of their customers had actually lived up to that vision of centralizing all of their data into one place. They all had data silos. They all had data in different systems. They had data on-prem data in the cloud. You know, those companies were acquiring other companies and inheriting their data architecture. So, you know, despite being the industry leader for 40 years, not one of their customers truly had everything in one place. So I think definitely history has proven that to be a lie. >>So Richard, from a practitioner's point of view, you know, what, what are your thoughts? I mean, there, there's a lot of pressure to cut cost, keep things centralized, you know, serve the business as best as possible from that standpoint. What, what is your experience, Joe? >>Yeah, I mean, I think I would echo Justin's experience really that we, as a business have grown up through acquisition, through storing data in different places sometimes to do information governance in different ways to store data in, in a platform that's close to data experts, people who really understand healthcare data from pharmacies or from, from doctors. And so, although if you were starting from a Greenfield site and you were building something brand new, you might be able to centralize all the data and all of the tooling and teams in one place. The reality is that that businesses just don't grow up like that. And, and it's just really impossible to get that academic perfection of, of storing everything in one place. >>Y you know, Theresa, I feel like Sarbanes Oxley kinda saved the data warehouse, you know? Right. But you actually did have to have a single version of the truth for certain financial data, but really for those, some of those other use cases, I, I mentioned, I, I do feel like the industry has kinda let us down. What's your take on this? Where does it make sense to have that sort of centralized approach versus where does it make sense to maybe decentralized? >>I, I think you gotta have centralized governance, right? So from the central team, for things like swans Oxley, for things like security, for certain very core data sets, having a centralized set of roles, responsibilities to really QA, right. To serve as a design authority for your entire data estate, just like you might with security, but how it's implemented has to be distributed. Otherwise you're not gonna be able to scale. Right? So being able to have different parts of the business really make the right data investments for their needs. And then ultimately you're gonna collaborate with your partners. So partners that are not within the company, right. External partners, we're gonna see a lot more data sharing and model creation. And so you're definitely going to be decentralized. >>So, you know, Justin, you guys last, geez, I think it was about a year ago, had a session on, on data mesh. It was a great program. You invited JAK, Dani, of course, she's the creator of the data mesh. And her one of our fundamental premises is that you've got this hyper specialized team that you've gotta go through. And if you want anything, but at the same time, these, these individuals actually become a bottleneck, even though they're some of the most talented people in the organization. So I guess question for you, Richard, how do you deal with that? Do you, do you organize so that there are a few sort of rock stars that, that, you know, build cubes and, and the like, and, and, and, or have you had any success in sort of decentralizing with, you know, your, your constituencies, that data model? >>Yeah. So, so we absolutely have got rockstar, data scientists and data guardians. If you like people who understand what it means to use this data, particularly as the data that we use at emos is very private it's healthcare information. And some of the, the rules and regulations around using the data are very complex and, and strict. So we have to have people who understand the usage of the data, then people who understand how to build models, how to process the data effectively. And you can think of them like consultants to the wider business, because a pharmacist might not understand how to structure a SQL query, but they do understand how they want to process medication information to improve patient lives. And so that becomes a, a consulting type experience from a, a set of rock stars to help a, a more decentralized business who needs to, to understand the data and to generate some valuable output. >>Justin, what do you say to a, to a customer or prospect that says, look, Justin, I'm gonna, I got a centralized team and that's the most cost effective way to serve the business. Otherwise I got, I got duplication. What do you say to that? >>Well, I, I would argue it's probably not the most cost effective and, and the reason being really twofold. I think, first of all, when you are deploying a enterprise data warehouse model, the, the data warehouse itself is very expensive, generally speaking. And so you're putting all of your most valuable data in the hands of one vendor who now has tremendous leverage over you, you know, for many, many years to come, I think that's the story of Oracle or Terra data or other proprietary database systems. But the other aspect I think is that the reality is those central data warehouse teams is as much as they are experts in the technology. They don't necessarily understand the data itself. And this is one of the core tenets of data mash that that jam writes about is this idea of the domain owners actually know the data the best. >>And so by, you know, not only acknowledging that data is generally decentralized and to your earlier point about, so Oxley, maybe saving the data warehouse, I would argue maybe GDPR and data sovereignty will destroy it because data has to be decentralized for, for those laws to be compliant. But I think the reality is, you know, the data mesh model basically says, data's decentralized, and we're gonna turn that into an asset rather than a liability. And we're gonna turn that into an asset by empowering the people that know the data, the best to participate in the process of, you know, curating and creating data products for, for consumption. So I think when you think about it, that way, you're going to get higher quality data and faster time to insight, which is ultimately going to drive more revenue for your business and reduce costs. So I think that that's the way I see the two, the two models comparing and con contrasting. >>So do you think the demise of the data warehouse is inevitable? I mean, I mean, you know, there Theresa you work with a lot of clients, they're not just gonna rip and replace their existing infrastructure. Maybe they're gonna build on top of it, but the, what does that mean? Does that mean the ed w just becomes, you know, less and less valuable over time, or it's maybe just isolated to specific use cases. What's your take on that? >>Listen, I still would love all my data within a data warehouse would love it. Mastered would love it owned by essential team. Right? I think that's still what I would love to have. That's just not the reality, right? The investment to actually migrate and keep that up to date. I would say it's a losing battle. Like we've been trying to do it for a long time. Nobody has the budgets and then data changes, right? There's gonna be a new technology. That's gonna emerge that we're gonna wanna tap into. There's gonna be not enough investment to bring all the legacy, but still very useful systems into that centralized view. So you keep the data warehouse. I think it's a very, very valuable, very high performance tool for what it's there for, but you could have this, you know, new mesh layer that still takes advantage of the things. I mentioned, the data products in the systems that are meaningful today and the data products that actually might span a number of systems. Maybe either those that either source systems, the domains that know it best, or the consumer based systems and products that need to be packaged in a way that be really meaningful for that end user, right? Each of those are useful for a different part of the business and making sure that the mesh actually allows you to lose all of them. >>So, Richard, let me ask you, you take, take Gemma's principles back to those. You got, you know, the domain ownership and, and, and data as product. Okay, great. Sounds good. But it creates what I would argue or two, you know, challenges self-serve infrastructure let's park that for a second. And then in your industry, one of the high, most regulated, most sensitive computational governance, how do you automate and ensure federated governance in that mesh model that Theresa was just talking about? >>Well, it absolutely depends on some of the tooling and processes that you put in place around those tools to be, to centralize the security and the governance of the data. And, and I think, although a data warehouse makes that very simple, cause it's a single tool, it's not impossible with some of the data mesh technologies that are available. And so what we've done at EMI is we have a single security layer that sits on top of our data mesh, which means that no matter which user is accessing, which data source, we go through a well audited well understood security layer. That means that we know exactly who's got access to which data field, which data tables. And then everything that they do is, is audited in a very kind of standard way, regardless of the underlying data storage technology. So for me, although storing the data in one place might not be possible understanding where your source of truth is and securing that in a common way is still a valuable approach and you can do it without having to bring all that data into a single bucket so that it's all in one place. >>And, and so having done that and investing quite heavily in making that possible has paid dividends in terms of giving wider access to the platform and ensuring that only data that's available under GDPR and other regulations is being used by, by the data users. >>Yeah. So Justin mean Democrat, we always talk about data democratization and you know, up until recently, they really haven't been line of sight as to how to get there. But do you have anything to add to this because you're essentially taking, you know, doing analytic queries and with data, that's all dispersed all over the, how are you seeing your customers handle this, this challenge? >>Yeah, I mean, I think data products is a really interesting aspect of the answer to that. It allows you to, again, leverage the data domain owners, people know the data, the best to, to create, you know, data as a product ultimately to be consumed. And we try to represent that in our product as effectively, almost eCommerce, like experience where you go and discover and look for the data products that have been created in your organization. And then you can start to consume them as, as you'd like. And so really trying to build on that notion of, you know, data democratization and self-service, and making it very easy to discover and, and start to use with whatever BI tool you, you may like, or even just running, you know, SQL queries yourself. >>Okay. G guys grab a sip of water. After the short break, we'll be back to debate whether proprietary or open platforms are the best path to the future of data excellence. Keep it right there.

>>Hello, everyone. Welcome to the cubes coverage here. New York city for AWS Amazon web services summit 2022. I'm John furrier, host of the cube with Dave ante. My co-host. We are breaking it down, getting an update on the ecosystem. As the GDP drops, inflations up gas prices up the enterprise continues to grow. We're seeing exceptional growth. We're here on the ground floor. Live at the Summit's packed house, 10,000 people. Eric Bradley's here. Chief STR at ETR, one of the premier enterprise research firms out there, partners with the cube and powers are breaking analysis that Dave does check that out as the hottest podcast in enterprise. Eric. Great to have you on the cube. Thanks for coming on. >>Thank you so much, John. I really appreciate the collaboration always. >>Yeah. Great stuff. Your data's amazing ETR folks watching check out ETR. They have a unique formula, very accurate. We love it. It's been moving the market. Congratulations. Let's talk about the market right now. This market is booming. Enterprise is the hottest thing, consumers kind of in the toilet. Okay. I said that all right, back out devices and, and, and consumer enterprise is still growing. And by the way, this first downturn, the history of the world where hyperscalers are on full pumping on all cylinders, which means they're still powering the revolution. >>Yeah, it's true. The hyperscalers were basically at this two sun system when Microsoft and an AWS first came around and everything was orbiting around it. And we're starting to see that sun cool off a little bit, but we're talking about a gradient here, right? When we say cool off, we're not talking to shutdown, it's still burning hot. That's for sure. And I can get it to some of the macro data in a minute, if that's all right. Or do you want me to go right? No, go go. Right. Yeah. So right now we just closed our most recent survey and that's macro and vendor specific. We had 1200 people talk to us on the macro side. And what we're seeing here is a cool down in spending. We originally had about 8.5% increase in budgets. That's cool down is 6.5 now, but I'll say with the doom and gloom and the headlines that we're seeing every day, 6.5% growth coming off of what we just did the last couple of years is still pretty fantastic as a backdrop. >>Okay. So you, you started to see John mentioned consumer. We saw that in Snowflake's earnings. For example, we, we certainly saw, you know, Walmart, other retailers, the FA Facebooks of the world where consumption was being dialed down, certain snowflake customers. Not necessarily, they didn't have mentioned any customers, but they were able to say, all right, we're gonna dial down, consumption this quarter, hold on until we saw some of that in snowflake results and other results. But at the same time, the rest of the industry is booming. But your data is showing softness within the fortune 500 for AWS, >>Not only AWS, but fortune 500 across the board. Okay. So going back to that larger macro data, the biggest drop in spending that we captured is fortune 500, which is surprising. But at the same time, these companies have a better purview into the economy. In general, they tend to see things further in advance. And we often remember they spend a lot of money, so they don't need to play catch up. They'll easily more easily be able to pump the brakes a little bit in the fortune 500. But to your point, when we get into the AWS data, the fortune 500 decrease seems to be hitting them a little bit more than it is Azure and GCP. I >>Mean, we're still talking about a huge business, right? >>I mean, they're catching up. I mean, Amazon has been transforming from owning the developer cloud startup cloud decade ago to really putting a dent on the enterprise as being number one cloud. And I still contest that they're number one by a long ways, but Azure kicking ass and catching up. Okay. You seeing people move to Azure, you got Charlie bell over there, Sean, by former Amazonians, Theresa Carlson, people are going over there, there there's lift over at Azure. >>There certainly is. >>Is there kinks in the arm or for AWS? There's >>A couple of kinks, but I think your point is really good. We need to take a second there. If you're talking about true pass or infrastructure is a service true cloud compute. I think AWS still is the powerhouse. And a lot of times the, the data gets a little muddied because Azure is really a hosted platform for applications. And you're not really sure where that line is drawn. And I think that's an important caveat to make, but based on the data, yes, we are seeing some kinks in the armor for AWS. Yes. Explain. So right now, a first of all caveat, 40% net score, which is our proprietary spending metric across the board. So we're not like raising any alarms here. It's still strong that said there are declines and there are declines pretty much across the board. The only spot we're not seeing a decline at all is in container, spend everything else is coming down specifically. We're seeing it come down in data analytics, data warehousing, and M I, which is a little bit of a concern because that, that rate of decline is not the same with Azure. >>Okay. So I gotta ask macro, I see the headwinds on the macro side, you pointed that out. Is there any insight into any underlying conditions that might be there on AWS or just a chronic kind of situational thing >>Right now? It seems situational. Other than that correlation between their big fortune 500, you know, audience and that being our biggest decline. The other aspect of the macro survey is we ask people, if you are planning to decline spend, how do you plan on doing it? And the number two answer is taking a look at our cloud spend and auditing it. So they're kind say, all right, you know, for the last 10 years it's been drunken, sail or spend, I >>Was gonna use that same line, you know, >>Cloud spend, just spend and we'll figure it out later, who cares? And then right now it's time to tighten the belts a little bit, >>But this is part of the allure of cloud at some point. Yeah. You, you could say, I'm gonna, I'm gonna dial it down. I'm gonna rein it in. So that's part of the reason why people go to the cloud. I want to, I wanna focus in on the data side of things and specifically the database. Let, just to give some context if, and correct me if I'm, I'm a little off here, but snowflake, which hot company, you know, on the planet, their net score was up around 80% consistently. It it's dropped down the last, you know, quarter, last survey to 60%. Yeah. So still highly, highly elevated, but that's relative to where Amazon is much larger, but you're saying they're coming down to the 40% level. Is that right? >>Yeah, they are. And I remember, you know, when I first started doing this 10 years ago, AWS at a 70%, you know, net score as well. So what's gonna happen over time is those adoptions are gonna get less and you're gonna see more flattening of spend, which ultimately is going to lower the score because we're looking for expansion rates. We wanna see adoption and increase. And when you see flattening a spend, it starts to contract a little bit. And you're right. Snowflake also was in the stratosphere that cooled off a little bit, but still, you know, very strong and AWS is coming down. I think the reason why it's so concerning is because a it's within the fortune 500 and their rate of decline is more than Azure right >>Now. Well, and, and one of the big trends you're seeing in database is this idea of converging function. In other words, bringing transaction and analytics right together at snowflake summit, they added the capability to handle transaction data, Mongo DB, which is largely mostly transactions added the capability in June to bring in analytic data. You see data bricks going from data engineering and data science now getting into snowflake space and analytics. So you're seeing that convergence Oracle is converging with my SQL heat wave and their core databases, couch base couch base is doing the same. Maria do virtually all these database companies are, are converging their platforms with the exception of AWS. AWS is still the right tool for the right job. So they've got Aurora, they've got RDS, they've got, you know, a dynamo DV, they've got red, they've got, you know, going on and on and on. And so the question everybody's asking is will that change? Will they start to sort of cross those swim lanes? We haven't seen it thus far. How is that affecting the data >>Performance? I mean, that's fantastic analysis. I think that's why we're seeing it because you have to be in the AWS ecosystem and they're really not playing nicely with others in the sandbox right now that now I will say, oh, Amazon's not playing nicely. Well, no, no. Simply to your point though, that there, the other ones are actually bringing in others at consolidating other different vendor types. And they're really not. You know, if you're in AWS, you need to stay within AWS. Now I will say their tools are fantastic. So if you do stay within AWS, they have a tool for every job they're advanced. And they're incredible. I think sometimes the complexity of their tools hurts them a little bit. Cause to your point earlier, AWS started as a developer-centric type of cloud. They have moved on to enterprise cloud and it's a little bit more business oriented, but their still roots are still DevOps friendly. And unless you're truly trained, AWS can be a little scary. >>So a common use case is I'm gonna be using Aurora for my transaction system and then I'm gonna ETL it into Redshift. Right. And, and I, now I have two data stores and I have two different sets of APIs and primitives two different teams of skills. And so that is probably causing some friction and complexity in the customer base that again, the question is, will they begin to expand some of those platforms to minimize some of that friction? >>Well, yeah, this is the question I wanted to ask on that point. So I've heard from people inside Amazon don't count out Redshift, we're making, we're catching up. I think that's my word, but they were kind of saying that right. Cuz Redshift is good, good database, but they're adding a lot more. So you got snowflake success. I think it's a little bit of a jealousy factor going on there within Redshift team, but then you got Azure synapse with the Synap product synapse. Yep. And then you got big query from Google big >>Query. Yep. >>What's the differentiation. What are you seeing for the data for the data warehouse or the data clouds that are out there for the customers? What's the data say, say to us? >>Yeah, unfortunately the data's showing that they're dropping a little bit whose day AWS is dropping a little bit now of their data products, Redshift and RDS are still the two highest of them, but they are starting to decline. Now I think one of the great data points that we have, we just closed the survey is we took a comparison of the legacy data. Now please forgive me for the word legacy. We're gonna anger a few people, but we Gotter data Oracle on-prem, we've got IBM. Some of those more legacy data warehouse type of names. When we look at our art survey takers that have them where their spend is going, that spends going to snowflake first, and then it's going to Google and then it's going to Microsoft Azure and, and AWS is actually declining in there. So when you talk about who's taking that legacy market share, it's not AWS right now. >>So legacy goes to legacy. So Microsoft, >>So, so let's work through in a little context because Redshift really was the first to take, you know, take the database to the cloud. And they did that by doing a one time license deal with par XL, which was an on-prem database. And then they re-engineered it, they did a fantastic job, but it was still engineered for on-prem. Then you along comes snowflake a couple years later and true cloud native, same thing with big query. Yep. True cloud native architecture. So they get a lot of props. Now what, what Amazon did, they took a page outta of the snowflake, for example, separating compute from storage. Now of course what's what, what Amazon did is actually not really completely separating like snowflake did they couldn't because of the architecture, they created a tearing system that you could dial down the compute. So little nuances like that. I understand. But at the end of the day, what we're seeing from snowflake is the gathering of an ecosystem in this true data cloud, bringing in different data types, they got to the public markets, data bricks was not able to get to the public markets. Yeah. And think is, is struggling >>And a 25 billion evaluation. >>Right. And so that's, that's gonna be dialed down, struggling somewhat from a go to market standpoint where snowflake has no troubles from a go to market. They are the masters at go to market. And so now they've got momentum. We talked to Frank sluman at the snowflake. He basically said, I'm not taking the foot off the gas, no way. Yeah. We, few of our large, you know, consumer customers dialed things down, but we're going balls to the >>Wall. Well, if you look at their show before you get in the numbers, you look at the two shows. Snowflake had their summit in person in Vegas. Data bricks has had their show in San Francisco. And if you compare the two shows, it's clear, who's winning snowflake is blew away from a, from a market standpoint. And we were at snowflake, but we weren't at data bricks, but there was really nothing online. I heard from sources that it was like less than 3000 people. So >>Snowflake was 1900 people in 2019, nearly 10,000. Yeah. In 2020, >>It's gonna be fun to sort of track that as a, as an odd caveat to say, okay, let's see what that growth is. Because in fairness, data, bricks, you know, a little bit younger, Snowflake's had a couple more years. So I'd be curious to see where they are. Their, their Lakehouse paradigm is interesting. >>Yeah. And I think it's >>And their product first company, yes. Their go to market might be a little bit weak from our analysis, but that, but they'll figure it out. >>CEO's pretty smart. But I think it's worth pointing out. It's like two different philosophies, right? It is. Snowflake is come into our data cloud. That's their proprietary environment. They're the, they think of the iPhone, right? End to end. We, we guarantee it's all gonna work. And we're in control. Snowflake is like, Hey, open source, no, bring in data bricks. I mean data bricks, open source, bring in this tool that too, now you are seeing snowflake capitulate a little bit. They announce, for instance, Apache iceberg support at their, at the snowflake summit. So they're tipping their cap to open source. But at the end of the day, they're gonna market and sell the fact that it's gonna run better in native snowflake. Whereas data bricks, they're coming at it from much more of an open source, a mantra. So that's gonna, you know, we'll see who look at, you had windows and you had apple, >>You got, they both want, you got Cal and you got Stanford. >>They both >>Consider, I don't think it's actually there yet. I, I find the more interesting dynamic right now is between AWS and snowflake. It's really a fun tit for tat, right? I mean, AWS has the S three and then, you know, snowflake comes right on top of it and announces R two, we're gonna do one letter, one number better than you. They just seem to have this really interesting dynamic. And I, and it is SLT and no one's betting against him. I mean, this guy's fantastic. So, and he hasn't used his war chest yet. He's still sitting on all that money that he raised to your point, that data bricks five, their timing just was a little off >>5 billion in >>Capital when Slootman hasn't used that money yet. So what's he gonna do? What can he do when he turns that on? He finds the right. >>They're making some acquisitions. They did the stream lit acquisitions stream. >>Fantastic >>Problem. With data bricks, their valuation is underwater. Yes. So they're recruiting and their MNAs. Yes. In the toilet, they cannot make the moves because they don't have the currency until they refactor the multiple, let the, this market settle. I I'm, I'm really nervous that they have to over factor the >>Valuation. Having said that to your point, Eric, the lake house architecture is definitely gaining traction. When you talk to practitioners, they're all saying, yeah, we're building data lakes, we're building lake houses. You know, it's a much, much smaller market than the enterprise data warehouse. But nonetheless, when you talk to practitioners that are actually doing things like self serve data, they're building data lakes and you know, snow. I mean, data bricks is right there. And as a clear leader in, in ML and AI and they're ahead of snowflake, right. >>And I was gonna say, that's the thing with data bricks. You know, you're getting that analytics at M I built into it. >>You know, what's ironic is I remember talking to Matt Carroll, who's CEO of auDA like four or five years ago. He came into the office in ma bro. And we were in temporary space and we were talking about how there's this new workload emerging, which combines AWS for cloud infrastructure, snowflake for the simple data warehouse and data bricks for the ML AI, and then all now all of a sudden you see data bricks yeah. And snowflake going at it. I think, you know, to your point about the competition between AWS and snowflake, here's what I think, I think the Redshift team is, you know, doesn't like snowflake, right. But I think the EC two team loves it. Loves it. Exactly. So, so I think snowflake is driving a lot of, >>Yeah. To John's point, there is plenty to go around. And I think I saw just the other day, I saw somebody say less than 40% of true global 2000 organizations believe that they're at real time data analytics right now. They're not really there yet. Yeah. Think about how much runway is left and how many tools you need to get to real time streaming use cases. It's complex. It's not easy. >>It's gonna be a product value market to me, snowflake in data bricks. They're not going away. Right. They're winning architectures. Yeah. In the cloud, what data bricks did would spark and took over the Haddo market. Yeah. To your point. Now that big data, market's got two players, in my opinion, snow flicking data, bricks converging. Well, Redshift is sitting there behind the curtain, their wild card. Yeah. They're wild card, Dave. >>Okay. I'm gonna give one more wild card, which is the edge. Sure. Okay. And that's something that when you talk about real time analytics and AI referencing at the edge, there aren't a lot of database companies in a position to do that. You know, Amazon trying to put outposts out there. I think it runs RDS. I don't think it runs any other database. Right. Snowflake really doesn't have a strong edge strategy when I'm talking the far edge, the tiny edge. >>I think, I think that's gonna be HPE or Dell's gonna own the outpost market. >>I think you're right. I'll come back to that. Couch base is an interesting company to watch with Capella Mongo. DB really doesn't have a far edge strategy at this point, but couch base does. And that's one to watch. They're doing some really interesting things there. And I think >>That, but they have to leapfrog bongo in my >>Opinion. Yeah. But there's a new architecture emerging at the edge and it's gonna take a number of years to develop, but it could eventually from an economic standpoint, seep back into the enterprise arm base, low end, take a look at what couch base is >>Doing. They hired an Amazon guard system. They have to leapfrog though. They need to, they can't incrementally who's they who >>Couch >>Base needs to needs to make a big move in >>Leap frog. Well, think they're trying to, that's what Capella is all about was not only, you know, their version of Atlas bringing to the cloud couch base, but it's also stretching it out to the edge and bringing converged database analytics >>Real quick on the numbers. Any data on CloudFlare, >>I was, I've been sitting here trying to get the word CloudFlare out my mouth the whole time you guys were talking, >>Is this another that's innovated in the ecosystem. So >>Platform, it was really simple for them early on, right? They're gonna get that edge network out there and they're gonna steal share from Akamai. Then they started doing exactly what Akamai did. We're gonna start rolling out some security. Their security is fantastic. Maybe some practitioners are saying a little bit too much, cuz they're not focused on one thing or another, but they are doing extremely well. And now they're out there in the cloud as well. You >>Got S3 compare. They got two, they got an S3 competitor. >>Exactly. So when I'm listening to you guys talk about, you know, a, a couch base I'm like, wow, those two would just be an absolute fantastic, you know, combination between the two of them. You mean >>CloudFlare >>Couch base. Yeah. >>I mean you got S3 alternative, right? You got a Mongo alternative basically in my >>Opinion. And you're going and you got the edge and you got the edge >>Network with security security, interesting dynamic. This brings up the super cloud date. I wanna talk about Supercloud because we're seeing a trend on we're reporting this since last year that basically people don't have to spend the CapEx to be cloud scale. And you're seeing Amazon enable that, but snowflake has become a super cloud. They're on AWS. Now they're on Azure. Why not tan expansion expand the market? Why not get that? And then it'll be on Google next, all these marketplaces. So the emergence of this super cloud, and then the ability to make that across a substrate across multiple clouds is a strategy we're seeing. What do you, what do you think? >>Well, honestly, I'm gonna be really Frank here. The, everything I know about the super cloud I know from this guy. So I've been following his lead on this and I'm looking forward to you guys doing that conference and that summit coming up from a data perspective. I think what you're saying is spot on though, cuz those are the areas we're seeing expansion in without a doubt. >>I think, you know, when you talk about things like super cloud and you talk about things like metaverse, there's, there's a, there, there look every 15 or 20 years or so this industry reinvents itself and a new disruption comes out and you've got the internet, you've got the cloud, you've got an AI and VR layer. You've got, you've got machine intelligence. You've got now gaming. There's a new matrix, emerging, super cloud. Metaverse there's something happening out there here. That's not just your, your father's SAS or is or pass. Well, >>No, it's also the spend too. Right? So if I'm a company like say capital one or Goldman Sachs, my it spend has traditionally been massive every year. Yes. It's basically like tons of CapEx comes the cloud. It's an operating expense. Wait a minute, Amazon has all the CapEx. So I'm not gonna dial down my budget. I want a competitive advantage. So next thing they know they have a super cloud by default because they just pivoted their, it spend into new capabilities that they then can sell to the market in FinTech makes total sense. >>Right? They're building out a digital platform >>That would, that was not possible. Pre-cloud >>No, it wasn't cause you weren't gonna go put all that money into CapEx expenditure to build that out. Not knowing whether or not the market was there, but the scalability, the ability to spend, reduce and be flexible with it really changes that paradigm entire. >>So we're looking at this market now thinking about, okay, it might be Greenfield in every vertical. It might have a power law where you have a head of the long tail. That's a player like a capital one, an insurance. It could be Liberty mutual or mass mutual that has so much it and capital that they're now gonna scale it into a super cloud >>And they have data >>And they have the data tools >>And the tools. And they're gonna bring that to their constituents. Yes, yes. And scale it using >>Cloud. So that means they can then service the entire vertical as a service provider. >>And the industry cloud is becoming bigger and bigger and bigger. I mean, that's really a way that people are delivering to market. So >>Remember in the early days of cloud, all the banks thought they could build their own cloud. Yeah. Yep. Well actually it's come full circle. They're like, we can actually build a cloud on top of the cloud. >>Right. And by the way, they can have a private cloud in their super cloud. Exactly. >>And you know, it's interesting cause we're talking about financial services insurance, all the people we know spend money in our macro survey. Do you know the, the sector that's spending the most right now? It's gonna shock you energy utilities. Oh yeah. I was gonna, the energy utilities industry right now is the one spending the most money I saw largely cuz they're playing ketchup. But also because they don't have these type of things for their consumers, they need the consumer app. They need to be able to do that delivery. They need to be able to do metrics. And they're the they're, they're the one spending right >>Now it's an arms race, but the, the vector shifts to value creation. So >>It's it just goes back to your post when it was a 2012, the trillion dollar baby. Yeah. It's a multi-trillion dollar baby that they, >>The world was going my chassis post on Forbes, headline trillion dollar baby 2012. You know, I should add it's happening. That's >>On the end. Yeah, exactly. >>Trillions of babies, Eric. Great to have you on the key. >>Thank you so much guys. >>Great to bring the data. Thanks for sharing. Check out ETR. If you're into the enterprise, want to know what's going on. They have a unique approach, very accurate in their survey data. They got a great market basket of, of, of, of, of data questions and people and community. Check it out. Thanks for coming on and sharing with. >>Thank you guys. Always enjoy. >>We'll be back with more coverage here in the cube in New York city live at summit 22. I'm John fur with Dave ante. We'll be right back.

>> Narrator: TheCUBE presents HPE Discover 2022. Brought to you by HPE. >> Welcome back to Las Vegas. Lisa Martin and Dave Vellante here bringing you day one of theCUBE's coverage of HPE Discover 22. We've had a lot of great conversations so far. Just a few hours in. We have two of our alumni back with us. Powerhouses, two powerhouses from Nutanix. Two for the price of one. Mandy Dhaliwal joins us. The CMO of 90 days at Nutanix. It's great to see you. Congratulations on the gig. >> Thanks Lisa. It's great to be here and great to be at Nutanix. >> Isn't it? And Tarkan Maner, the Chief Commercial Officer at Nutanix. Welcome back Tarkan. >> Great to see you guys. >> So this is only day one of the the main show Tarkan. We've been hearing a lot about cloud as an operating model. We've heard your CEO Rajiv talking about it. Break that down from Nutanix's point of view. >> Yeah, look at the end, the tech conference we are talking a lot technology but at the end it is all about outcomes. I saw Keith was here earlier, you know, GreenLake's story. We were on a session earlier. Everything is about business outcomes for the customers. And obviously our partner Ecosystems, NBC all these double technologies come together and become an open model. And our customers are moving from a CAPEX model, old school model, what I call dinosaur model, into an OPEX model, subscription model. Which Nutanix basically the category creator for this, in a hybrid multi-cloud fashion. One platform, one experience, any app, any user, anytime, and make it count. Let the customers focus on business outcomes. Let us deal with infrastructure for you. >> What are some of the key outcomes that you're seeing customers achieve? We've seen so much change in the last couple of years. >> Tarkan: Right. >> A lot of acceleration. >> Tarkan: Right. >> Every company has to be a data company today to compete. >> Right. >> What are some of the outcomes that you're really proud of? >> So look, at the end of the, day's it's all about digital transformation and it's a big loaded word. But at the end of the day every company is trying to get digitized. And hybrid multicloud is the only way to get there in a cost effective way. So that cost is a big story. Highly secure. Manageable, available, reliable, total cost ownership definitely depressed and take the complexity out. Let us deal with the infrastructure for you. You focus on your time to market, and the best applications for the best users. >> So Mandy, I remember, you know you talked about your category creator Tarkan, and I remember Stu Miniman and I, were in the Wikibon offices. We were just getting started and he said, "Dave you got to come in here." And Dhiraj was on the phone. They were describing this new category and I was blown away. I'm like, wow, that's like the cloud but you know, for on-prem. So what does the, what does the cloud operating model mean to Nutanix Mandy? >> Really, what we're trying to do is become this common cloud platform across Core, Edge and Cloud. We're known for our strength in HCI on premise. We have capability across. So it's really important for us to share this story with the market. Now, also one of the reasons I joined. You know this story needs to be told in a bigger fashion. So I'm here to really help evolve this category. We've won HCI, right? What's next? So stay tuned. >> So we call that super cloud. I call it. >> Yes, I love that name. >> So it, but it needs has meaning, right? >> Right. >> It's a new layer. It's not just, oh, I run on Azure. I run an Aw or running green. >> Mandy: Right. >> It's actually a common infrastructure. >> Yes. >> Common experience across maybe and even out to the edge. >> Mandy: Right. >> Right. So is, is that, do you guys see that or do you think this is just a little buzzword that Dave made up? >> No, I think it has legs. And I think at the core of it, it's simplicity and elegance. And if you look, and, and again, I'm drinking the the champagne, right? We have that we architected for that. We've solved that problem. So we now can extend it to become ubiquitous in the market. So it's, it's an amazing place to be because we've got the the scale, frankly, and the breadth now of the technology platform to be able to go deliver that super cloud. >> And you have to do the work, right? You, you, you have to hide all the complexity- >> Mandy: Yeah. >> Of whether it's AWS, Azure, Google, GreenLake wherever you go on prem. >> Mandy: Right. >> And not only that, as you know Dave, many people think about cloud, they automatically think about public cloud. AWS, Azure, or Google. Guess what? We have customers. Some of the workloads and apps running on a local country. If you're in Singapore, on Singtel, and your, if you're in Switzerland on Swisscom, if you're in Japan on NTT, guess what? Our cloud runs also on those clouds. For those customers who want the data, gravity, local issues with the security and privacy laws in the local country then all this SI you have HCI, Emphasis VIDPro, Accenture, CAPS, JAM, and ITCS. They have also cloud services. What we build as Mandy said as the creator, make the platform run anywhere. So the customers can move data, apps, workloads from cloud to cloud. From private to public and within public, from public to public. From AWS to Singtel. From Singtel to Swisscom to Azure, doesn't matter. We want to make sure one platform one experience, any app, any user. >> And at least a lot of those guys are building on OpenStack. We don't talk about OpenStack anymore. But a lot of the local telcos they actually it's alive and well and actually growing. >> So you, you make it sound simple. So I got to ask you as the chief marketing officer how do you message that simplicity and actually make it tangible for customers? >> That's a great question. It's really about the customer story, right? How do we share that we're able to take something that took months to deploy and have it done in in days, minutes, right? So there's a lot of those kinds of stories that you'll see across the platform coming. We're getting a lot more messaging around that. We're also tightening up the message to be more easily conveyed. So that's a lot of the stuff that I'm working on right now and really super excited. You know, we've got leading retailers, financial services institutions, public sector agencies that are running on our platform. So we've got this amazing cadre of customers and their stories just need to be told. >> That voice of the customer is so powerful. >> Mandy: Yep. >> As you well know Tarkan. That's, that's the objective voice right? That is ideally articulating your value proposition. >> Yeah. >> Validating that helping other customers understand this, these are the outcomes we are achieving. >> Mandy: Right? >> You can do the same. >> Mandy: Right. >> And, and different personas. >> Mandy: Right. >> It's not one customer fits all right. You heard Home Depot, Daniel with Antonio on the keynote. The stores, the distribution center, the warehousing and their service department, their mobile app all that data has to move from place to place. And we want to make sure it's cost effective. It's secure. And not only for the system, people like Daniel but also for application developers. Dave, you talked about, you know, Open Source, OpenStack, a lot of new application development is all open source. >> Mandy: Yep. >> And we need to also gear toward them and give them a platform, a hybrid multicloud platform. So they can build applications and then run applications and manage lifecycle applications anywhere in simple ways securely. So this platform is not only for running applications but also build a new set of digital transformation driven applications. >> So what are you doing with GreenLake especially in that context, right? 'Cause that's what we're looking for. Is like are people going to build applications on top. Maybe it's the incumbents. It might not be startups, but what what are you doing there? >> Right. So look, I'll give you the highlights on this. I know you talked to Keith again we had the session earlier. We have about 2000 plus customers. Customers are moving from a CapEx model to an OPEX model. They like the subscription side of the business and basically our strategy and many is leading this globally making cloud on your terms. So you have the control, you move from CapEx to OPEX and we bring the data in cloud to you. So you can manage the data securely, privately build your applications, and then they're ready. You can move applications based on microservices capabilities we deliver to different cloud as, as you wish. >> So then what are you hearing from customers? What are they most excited about right now given the massive potential that you're about to unleash? >> It it's really about best in class, right? So you get these these amazing technologies to come together. We abstract the complexity away for the customer. So HP GreenLake brings economic benefit. Nutanix brings experience. So you couple those two. And all of a sudden they've got time to value. Like they've never had before. Add on top of that the skills gap that we've got in the market, right? The new breed of folks that are deploying and managing these applications just don't have an appetite for complexity like they did in the old world. So we've got elegance, that's underpinning our architecture and simplicity and ease of use that learn that really translates into customer delight. So that's our secret sauce. >> You talk about time to value. Sorry, Dave. Time to value is no joke as a marketer. Talk to me about what does that mean from a translation perspective? Because these days, one of the things we learned in the pandemic, other than everyone had no patience and still probably doesn't is that access to realtime data no longer a, oh, it's awesome. It's Fanta, it's, it's table stakes. It is it's, what's going to help companies succeed. And those not. So from a time to value perspective, talk a little bit more about that as really impactful to every industry. >> Right, And, and, and underpin underpinning, all of it is that simplicity and ease of use, right? So if I can pick up and have portability across all aspects of my platform, guess what? I've got a single pane of glass that's that I'm able to manage my entire infrastructure through. That's really powerful. So I don't have to waste time doing an undifferentiated heavy lifting, all of a sudden there's huge value there in simplicity and ease of use, right? So it translates for things that would take months and you know, hundreds of developers all of a sudden you can vend out infrastructure in a way that's performant, reliable, scalable and all of a sudden, right? Everybody's happy. People are not losing sleep anymore because they know they've got a reliable way of deploying and managing and running their infrastructure. >> Perfect example for you very quick. Just is very exciting. Mandy and I, were in the session, Texas Children's Hospital. >> Yeah. >> Theresa Montag. I mean, Tonthat, she's the head of infrastructure, with Keith, with us you should listen to the patient care Pediatric, you know, oncology, realtime data. Hip regulation, highly regulated industry data. Gravity is super important. State laws, city laws, healthcare laws. The data cannot go to a public cloud service but has to be cloud driven, cloud enabled and data driven and eccentric on the site. But cloud operating model, Nutanix again with GreenLake, delivers a subscription methodology, a you know, OPEX model and delivers desktop service cloud native applications, supporting all these tools like epic all happening in healthcare. >> You guys have a high net promoter score. What, what got you there? And what's going to keep you there in the future. >> It's underpinned by the technology itself and also our outstanding support team right. We hear our competitors' customers call us for support first, before they call our competitors. If you can't take that to the bank, what can you, right. It's crazy. They, our customers tell us this >> Dave: Really? >> Really. >> It's pretty validating. >> Yeah. >> Yeah, help us with has help us with this XYZ stuff. Yeah. >> And it becomes even more important with this new cloud era. >> Yes. >> As you're moving the data, the applications to different places, they want the same experience. And look as a company, we spent the investment. It's not free. >> Mandy: Yeah. >> It costs us a lot of money to make that happen. One of the best support organizations I've been in industry for 30 years, I've never seen this kind of a maniacal focus on customer service. And without that success will not come. >> Yeah, I mean, I've met a lot of Nutanix customers at the various shows over the years. Ridden in taxis bus rides, you know, cocktail parties. They're, they're an interesting bunch, right. They, they were kind of leading edge early on. They saw the light bulb went off, they adopted. >> Right. >> Right, so, and think about thinking about aligning with where they're going where are they going and how is Nutanix aligning with them? >> There's, there's so much complexity in the world, right? So we're abstracting away the complexity. Not all workloads are meant to run in an either or situation. >> Right. >> Right, and we're hearing from IDC as well in, in, by 2026, 75% of workloads are going to be misplaced. How do they have a strategic partner? That's going to help them run their organization effectively and efficiently. We become that open and neutral player in the market. That can be the trusted advisor for them to help with workload placement optimization. They're standardizing, they're consolidating they're modernizing, they're transforming. There's a lot going on right. And so how do they come to somebody? That's voice of reason that also is well networked across the ecosystem. And that interoperability is key and yes, I'm still drinking the Kool-Aid, but it, I see it. It's, it's a tremendous story. >> Switzerland with weapons. (everyone laughing) >> You said it, you said it, Dave. >> And also one other thing it's important competition makes us better not bitter. >> Yeah. >> We have the best best partner network, 10,000 plus partners but more than numbers, quality, constantly working theater. And some of our partners also are competitors. We compete with them and we deliver solutions this way. Customers don't have to forklift out forklift in Nutanix. We leverage their past investment, current investment so they can tie Nutanix in different ways for different workloads, not one size fits all. We have multiple solutions, multiple ways you know, small, medium, large, extra large D in terms of scale and different workloads from the, you know Edge to the Cloud. And to at the end of the day to data as a whole, as you heard from HP today, our strategy, our roadmaps super aligned. That's why we were having a lot of success with GreenLake as well. >> Mandy, can you talk a last question about the partner ecosystem that Tarkan mentioned? How were you leveraging that to, to modify the messaging that you talked about? You've only been here almost 90 days. >> Mandy: Right. >> How is the partner ecosystem going to be a facilitator of the Nutanix brand and messaging and the reach? >> They're, they're tremendous, right? Because we're able to now, like we're doing here, right. Be able to reach into their customer base, and showcase our stories in a purpose built way right. This is, this is reality and solutions that we're driving for the customers with like-minded problems, like-minded people so they can see that. And so we do that across the, the ecosystem and all of a sudden, we've got this rolling thunder if you will. So it's up to us to, to, to really hone in on the right narrative and hand it to them and have them run with it that there's going to be practices built on this, even in a deeper way, moving forward. I see it, you know, we've done, I've done this before in my career. And so I've got conviction that we're on the right track and, you know, watch the space. >> Dot, dot, dot, to be continued. Watch the space. You heard it here on theCUBE. Mandy, Tarkan, thank you so much for joining Dave and me talking about the power of Nutanix with HPE, what you're doing and what you're enabling customers to achieve. It's transformative and, and best of luck. You'll have to come back in the next 90 days so we can see some of those customer stories. >> Absolutely. Absolutely, would love to, thank you. >> Thanks guys. >> Mandy: Yeah. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from the show floor of HPE Discover 22. Day one coverage continues after a short break.

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

>>Hello. Welcome back to the cubes coverage of splunk.com virtual this year. I'm John ferry, host of the cube. And one of the great reasons of great reasons of being on site with the team here is we have to bring remote guests in real guests from all no stories, too small. We bring people into the cube to have the right conversations. We've got Brooke Cunningham area, VP of global partner marketing experience. Brooke, welcome to the cube. Thanks for coming on. >>Hey, thank you, John. This is my sixth dot conflict, but this is actually my first time being on the cube. So I'm delighted. >>Great to have you on these new hybrid events. We can bring people in. You don't have to be here. All the execs are here, the partners are here. Great news is happening all around the world. You guys just announced a new partner program for the cloud called partner verse program. This is kind of, you know, mostly partner news is okay. Okay. Partner news partner ecosystem. But I think this is an important story because Splunk is kind of going to the next level of scale. That's to me is my observations walking away from the keynote, a lot of the partners, great technology, great platform, a lot of growth with cloud. We had formula one on you guys have a growing ecosystem. What is the new announcement partner versus about? >>Yes. Thanks, John. And you are spot on. We are growing for scale and Splunk's partner ecosystem is 2200 strong and we were so delighted to have so much partner success highlighted today on the keynotes. And specifically we have announced an all new spunk Splunk partner program called the Splunk partner verse. So we're taking it to new frontiers for our partners, really built for the cloud to help our partners lean into those cloud transformations with their customer. >>Great. Fro can you walk me through some of the numbers inside the numbers for a second? How many partners do you have and what is this program about specifically? >>Yeah, so 2200 partners that we featured some amazing stories in the keynotes today, around some of the momentum we have with partners like AWS, a center blue buoyant, a partner that just recently rearchitected all of their managed services from Splunk enterprise to Splunk cloud, because as they put it, Splunk is the only solution that can truly offer that hybrid solution for their customers. So all new goodness for our partners to help them lean in, to get enabled around all of the Splunk products, as well as to differentiate, differentiate their offerings with a new badging system. And we're going to help our partners really take that to the market by extending and expanding our marketing and creating an all new solutions catalog for our partners to differentiate themselves to their customers. >>You mentioned a couple things I want to double down on this badging thing, get in some of the nuances, but I want to just point out that, you know, and get your reaction to this when you see growth. And I saw this early on with AWS early on, when they performing, when you start to see the ecosystem grow like this, you start to see more enablement. You see more, money-making going on more, more, um, custom solutions, more agility you. So you started to see these things develop around you guys. So what does all this badging mean? How what's in it for me as a partner? Like how do I win on this? >>Yeah, great question. So first of all, John partner listening is a big part of what we do here at Splunk. And it's specifically a major part of what I do in my role. So we create a lot of forums to get that real deal partner feedback. What do they need to be successful with their customers? Especially as Splunk continues to expand our portfolio. And we heard some really clear feedback from our partners. Number one, they need more enablement faster, especially all those new products. They really want to get enabled around new product areas like observability, their customers are asking for it. They secondly told us that being able to differentiate themselves to customers was key. And that showing that they had core expertise around specific solution areas, types of services, as well as specializations. For example, some of our partners that are authorized learning partners, they really want it to be able to showcase these skills and differentiate that to their customers in the market. And it's not a role for us at Splunk to really help them do that. And so we took that feedback and really incorporated it into this new program, badging specifically will help to address some of those things I mentioned. So for example, a lot of badging around those use case areas, security, observability, AOD migrations, as well as specializations. Like I mentioned, for things like, uh, partners that are doing, uh, learning specific partners that are really helping us extend our reach in, in different international markets and so on. >>Okay. Let me just ask a question on the badge if you don't mind. Um, so you mentioned, you mentioned almost like you were going through like verticals is badging to be much more about discovery from a client customer, uh, end user customer standpoint. Are you looking to create kind of much more categorical differentiation is what's the, what, what's the purpose of the badge? Cause I noticed it was like different verticals. I heard security and >>Yeah, so I would say it's think of it as both. So for example, our partners go to market with us in many different ways. Some of them are selling servicing building. So there'll be partner motion badges to really differentiate the different ways that they're supporting customers from a go-to-market approach and then additional badging to help really identify some of those specialization areas around whether that's clunky use cases, specializations and more, uh, for example, a specific badge that we're rolling out right here at.com is around cloud migrations and partners will be able to get started to get engaged on that badge in preparation for our full-scale launch in February, we'll, they'll start to be able to take advantage of learning pathways, get their teams skilled up, and that will then unlock some new incentives as well as, uh, benefits that they can take advantage of things like accessing or of the Splunk's I've experience and the proof of concept platform and really giving their teams more, uh, capability. And, >>You know, I such a recent cross in the hallway here at dot confidence. She was, she and I were talking about how AI and data is enabling a lot of people to create these solutions. So, you know, you got kind of this almost like Amazon web services dynamic, where it's growing really fast and we're hearing stories, how data is driving value. We had formula one on the cube, the keynotes were giving some examples as you start to see this momentum kind of scaling up to the next level, if you're enabling customers, which you are with data, the monetization or the economic shifts, right? So it's healthy ecosystems, the partners create solutions, they deal with the customer, they're making some money, right? So, so can you share your vision on the unit on the economic equation of how partners are tapping into this? Because I almost imagine, um, a thousand flowers are blooming and then you start to see more value being created and Splunk also gets a cut of it, but there's, there should be that kind of deck. And you can talk about that. >>Yeah, absolutely. In fact, one of the things that I have the opportunity to do with our partners is study our partners, success and profitability. And some of the things that we learned from those studies with our partners is that what's really helping our partners to grow their practices with Blanca and their profitability with that business is really the stickiness that they have with their customers, being able to deliver solutions and services and really be those subject matter experts for their customers. And we know that our most successful and profitable partners are servicing their customers across the Splunk cases. So for example, many of our partners came from a security background and they are super deep, super knowledgeable around security, and they are trusted by their customers as the, you know, subject matter experts around security. And so many of them are starting to lean in on some of the new, additional use cases. Observability is a hot topic with our partners right now it's a new and emerging use cases case for them to transition some of the same sets of data that they are addressing in their current appointments with our customers and bring new value with those new use cases. But that's where we're seeing partner profitability growth. >>I love the channel dynamic. There we go, indirect and real and value creation. I got to ask you about the day-to-day dynamic. Of course we all know about the mark injuries and story. Software's eating the world, okay. Software ate the world. Okay. Now that's done. Now we're data is continuing to drive the value proposition. And so that's going to have an impact on how customer your partners serve their customers, ultimately your customer at the end of the day. How, how is that happening? And from a success standpoint, how would you talk to, uh, where people are on the progress of bringing the most innovative solutions? What, where's the headroom, where do you see that going Brook >>There's? I would say there's just endless opportunity here. And we just see so much innovation in our partner ecosystem to create purpose built solutions for their customers business problems. And that's where I think the value of the data comes to life. Really turning that data into doing as is really the Matic for all the things that we're talking about here, uh, at.com 21, that our partners really see these opportunities and then can replicate some of those same solutions for other customers in the same spaces. So for example, you know, really specialized solutions for healthcare where they're, uh, providing, you know, access to all the data across the hospital, or, um, you heard in guard's keynote about unlocking the value of SAP data. This is just a huge opportunity accessing all that data and really turning that data into doing. And we'll be talking even more about the new SAP relationship and the value for the partner ecosystem to go address those FP data sets in their customers. We'll be talking more about that on our partner feature session, which is tomorrow in day two of dotcom. >>Well, you guys to have a nice mix of business in the partner ecosystem from, you know, small boutiques to high-end system integrators and everything in between, I noticed you're doing a lot with censure. Could you talk about how you guys are partnering with the large global system integrators because they're becoming their own clouds. So, you know, as Jerry Chen at Greylock says, are these castles being built in the cloud with real competitive advantage with data? Again, this is a new phenomenon in the past really two years, you're starting to see explosion of, of scale and refactoring business models with data. What's your, what's your reaction to that? >>Absolutely. In fact, we are really leading in with some of these global systems integrators, and you've heard this exciting news in Theresa Carlson's portion of the keynote earlier today, where we've announced a partner, a center partner business group together. And we're so excited about the center and Splunk partner business group. It's going to elevate the Splunk and essential partnership eCenter has invested in thousands and thousands of joint professionals that are skilled up on flunk. They are building a purpose patients. We have so many amazing examples where Splunk and essential work together to solve real life problems. For example, there's a joint solution that helps address anti-human trafficking. Uh, there's a joint solution that helped with vaccine tracking. I mean, just really powerful examples that are just really extending value to customers and solving real life, data problems. >>Well, you guys have a lot of momentum, bro. Congratulations on the success and partner versus we're going to follow it again. It was built for the cloud. I know it's in the headline. It says flunked launches, new partner program for the cloud. Was there a partner program for the on premises and what's different about on the cloud? Was it kind of new, everything is cloud what's that? What does that mean? That statement? Yeah, >>Absolutely. So we, you know, as we've all seen, customers are leaning into the class that growth to the movement, to the cloud, just accelerated during COVID. And so part of that feedback that I referenced earlier that we heard from our partners, they said, we need help. We need help moving faster. And so that's really the underpinning of the all-new Splunk partner vers program is to really that acceleration to skill up our partners and give them the tools to be successful. And so with that, we did want to rebrand and reinvigorate it to really signal this newness. And as it was mentioning earlier, when we were talking about the badges, it's really about making sure we're providing the partners the right enablement so that they can be ready and able to support their customers on this journey, to the cloud, as well as the access, the resources, the support and the marketing so that they can be successful and really featured their expertise and value in the market. >>Well, Brooke, I want to get one final question before we go. Cause I know you have a lot of experience in the partner ecosystems and over your career. And we just interviewed the formula one CEO, uh, Zach brown, and, and they've been very popular with the, with the Netflix series driving to survive. And I was joking with him driving value with data as channel partners and your partners look to the post pandemic survive and thrive trend that people are going through right now. What should they be thinking about when they look at partner versus, and how Splunk can help them drive an advantage, not only just survive, but to actually drive to an advantage. >>I, I just see this as an opportunity for partners that haven't already leaned into the cloud and helping their customers migrate to the cloud now is the time rapid five acceleration is just essential for organizations to reach their most critical missions and their outcomes. And this one partner versus program is a significant move forward for Splunk partners. And we want to pursue a massive market opportunity focused on the cloud with our partners, for our customers. So I just really encourage our partners to engage, participate and join us on this journey. >>Well, it's a lot of evidence to support this vision. Uh, with pandemic, we saw refab replatforming and refactoring the businesses in the cloud at speeds, that unprecedented deployments. So, uh, cloud can, can bring that scale and speed to the table. It's really incredible. So thank you very much for coming on the cube remotely. Thanks have you had, >>Thank you. This was a delight. Really appreciate the time, John and very excited to have my first opportunity to be a >>Okay. You're a cube alumni. We are here in the studios, Splunk studios for their virtual event here with all the top executives and partners bringing in guests remotely. It's a virtual event. So we'll be back in person. I'm Jennifer, the cube. Thanks for watching.

>>Mhm. >>The company Nutanix was founded as the world was coming out of the financial crisis in 2009 Cop Computing was still in its infancy but had shown the way for what was possible with automation and simplification of infrastructure provisioning and management at scale. Now what Nutanix did is it brought cloud concepts to data centers and created the market for hyper converged infrastructure, a software defined architecture that eliminated stovepipes in the heavy lifting Associated with traditional compute networking in storage management. Now in the first part of the next decade, Nutanix essentially set the standard for this new world, building a loyal customer base, reaching escape velocity and successfully going public in 2016. Fast forward to 2021 and much has changed. Cloud is no longer knew rather it's become a staple of the digital economy as we exit the isolation economy. The cloud is much different today. It's expanding to on prem and out to the edge. New connections are being made in hybrid and across cloud models and as such, connecting and managing infrastructure across these new clouds to create a facile experience for users irrespective of where the data lives. Has become a major priority for organizations. They don't want to waste time and money on making the plumbing work. But that's easier said than done as the market is evolving. So is Nutanix to meet these new customer challenges and opportunities and with me ahead of dot next the major event of the year for Nutanix customers is Monica Monica Kumar who is the senior vice president of marketing and cloud go to market for the company Monica always great to see you welcome back to the cube. >>Thank you so much. Dave I'm so happy to be here again. >>Okay, so you heard my little narrative upfront, what's your perspective on the cloud market and where your customers are in their journey? >>Well, as you said, Dave Cloud is a critical enabler for rapid growth for organizations now, it's no longer just uh you know, nice to have, it's become a must have for organizations to survive and thrive in this digital economy. Uh In fact I follow a lot of um surveys that are happening around cloud adoption and one of the key trends that's coming out is it's no longer just about I. T. Practitioners adopting cloud. In fact, 78% of C. X. O. S are looking to cloud to speed up transformation of the entire businesses. You know, 80% of business executives are looking to cloud to mitigate their risks of their companies and 87% of the executives view cloud as critical to achieving their corporate growth goals. So what we are now realizing is that hybrid multi cloud is becoming the preferred model Which means there is no one cloud that customers are using, they're using the right cloud for the right workload. In fact, according to Gartner Group, 81% of public cloud users are using more than two providers. So what's happening is increasingly businesses are relying on multiple public clouds and on premises to meet their needs and are looking for that flexibility and that's delivered by different cloud providers. Um We've done our own survey called Nutanix Enterprise cloud and that we do it every year and 86% of respondents in the last service said hybrid cloud is the ideal operating model. So the Net Net that we're hearing from our customers is cloud is not a destination, it's an operating model. Customers want the right cloud for the right workload and the right applications. >>Okay, awesome. So the world, great setup. Thank you. So the world is moving to multi cloud. I think there's not no debate on that and that is really the mainstream. That's the norm. Talk about where Nutanix fits into this new world. >>Absolutely. So we're at an inflection point as organizations are grappling with this complexity. Now, obviously you can imagine the more computing environments to use this complexity in running and managing those hybrid solutions across multiple clouds. When Nutanix is focused singularly on is making that cloud complexity invisible. So our customers can focus on their business outcomes. We are solving the complexity of running and managing multiple clouds, just like we did for infrastructure and data centers a decade ago when we first started as a company. Now with the Nutanix start platform enabling our customers to seamlessly connect their private and public clouds simply move applications, data licenses across any cloud, optimize the work replacement and costs all while leveraging a consistent set of services, tools and processes. So for us it's really, really crucial that we give customers the choice to pick the hardware. Of the choice, the cloud of their choice, the virtual machines, they want to deploy the containers and data and help them realize their entire hybrid multi cloud strategy. It's all about giving our customers that peace of mind to deploy and operate the apps and data across multiple clouds with ease and flexibility. >>All right, let's talk about dot next my I think I'm pretty sure my first dot next was the first one ever, which I think was 2015. It was pre I P O. The focus is obviously evolving what's the focus this year? >>Well, dot next has evolved to become the industry's leading hybrid multi cloud conference. It's almost here. It's taking place next week, september 28th, 23rd and this year's event will bring together it and cloud professionals from around the globe to explore the latest trends, solutions, best practices and hybrid, multi cloud technology. Now we're obviously gonna, you know, future a lot of thought leaders from within the industry as well as in general, you know, people that impact our lives in a positive manner. And we're going to really focus on topics around hybrid multi cloud hyper converged infrastructure, private cloud ABM organization, you know, kubernetes containers, how do you figure out which after deploy where? So you're gonna see a lot of focus on hybrid multi colored solutions this year we're going to have lots of real world stories, hands on labs, best practices for practitioners. And again as I said all the tools that attendees need to go back and then put to practice some of the hybrid multi cloud strategies that they would learn and dark next >>talk a little bit more Monica about the what's in it for me for for attendees, what can they expect? What are they going to be able to take away from from this conference? >>Well, so as I said, a conferences both for business leaders and I. T. Leaders and practitioners. So for the business leaders, as I said, they'll get to hear from the latest industry visionaries around where the world of cloud is moving to, what are the latest and greatest innovations and hybrid multi cloud technologies uh and how can they make the businesses more competitive? How can they, you know, create more business value for the organization by using these technologies. For the IOT practitioners, they will go away as I said, learning from their peers in how they are adopting cloud, what are some of the myths around cloud computing. Get some information on deployment details and the benefits some of the piers are realizing since they moved to new tenants for example, in general, since they've adopted, you know, hybrid multi cloud solutions, they will also be able to connect with their industry peers, access democrat pounds. Uh in fact one of the major uh spotlights and not next will be the test drive live uh practitioners can get hands on our technology and really test drive it during the event itself and learn how to create a hybrid cloud within an hour, learn how to deploy databases with a click of a button for example, so lots of great goodies there and oh by the way we have some amazing external speakers as well besides our own, you know engineers, executives and so on. We have a whole roster of third party speakers too. >>That's awesome. Now, you know, one of the other things too is one of the ways you were able to reach escape velocity as a company is you had a strong partner ecosystem I presume is going to be a partner network participating as well. >>Yes, absolutely, thank you for reminding me about that partnerships is very, very, very, very important in Nutanix. You know, it does take a village, we have a full day dedicated to our partners and partner technology and solutions. It's called the part exchange. It's on Monday September 20, so again we hope that you all will participate but also you'll see partners are embedded uh in our september 21st and 22nd agenda and programme as well which is the main two days of dot next. So partners are in our life and blood, they're part of our ecosystem. >>That's great. What's next for Nutanix as you head into 20, >>Well before I go there, I do want to focus on a couple more featured speakers. So for those of you who are interested in cybersecurity, we will have Theresa Patton, who is the first female white house C I O and a leading cybersecurity expert. She'll be speaking. I'm actually interviewing her as well. We have Rachel, so johnny who is the founder of Girls who code and marshall plan for moms. We have Gary Vaynerchuk who's the ceo of Winner Media who is an author and entrepreneur. So I do hope that folks will plan to join if not for the core hybrid, multi colored content but also for these amazing speakers and last but not least. Hey, if none of this excites you then we do have some amazing entertainment. We have john taylor of Duran, Duran and the electric fondue coke, Romeo also headlining our day to keynote. >>So fantastic. I love it. Okay, go ahead please. >>Well I was gonna say so now let me talk about So what's next? Well for us, what's next is really helping customers realize their full hybrid, multi cloud strategy and empower them to make the right cloud decisions. So in fact one of the things you're gonna see us launch next week is also a new brand campaign. It's called cloud on your terms and you'll see that all over plastered all over dot next and so on. We are fully invested in our customer success to help them build, run and operate anywhere to help them easily migrate to public cloud or stay on premises if they choose to. And ultimately to make cloud complexity invisible for our customers, >>you know uh cloud your way kind of thing. I love that. And I and I failed to mention one of the first conferences I went to next, I met some developers and I was like whoa, cool. Because you guys one of the first that really truly do infrastructure as a code and bring that on prem and now it's going across clouds. So September 20 you kick off the partner day, is that right? And then the big keynote start the 21st right >>And go through the 20 >>third. Yes, >>yes. And we have a lot of on demand content as well around the keynote. So it's gonna be a packed packed set of agenda and days and you can choose whatever content you want to attend and participate in. >>Excellent. You guys always put under great program so go there register, we'll see you there, Monica. Always a pleasure. Thanks so much. >>Thank you so much for having me. I really appreciate it. >>All right. And we'll see you at dot next. This is Dave Volonte for the cube. >>Mhm mm

(upbeat music) >> Narrator: From around the globe. It's theCUBE. With digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome to theCUBE's digital coverage of IBM Think 2021. I'm Lisa Martin. Next joining me is Leo LaBranche, Director of global strategic initiatives at AWS. Leo, welcome to theCUBE. >> Thank you, happy to be here. >> So talk to me about AWS and IBM what's going on there with their relationship. What are some of the things that are significant for both partners? >> Yeah, absolutely, IBM's relationship really started with us around 2016. I would say it was a little bit more opportunistic at the time. We knew there was an opportunity to go to market together. We knew there were some great things we could do for our customers. But we hadn't quite cracked to crack the code so to speak on when and where and why we were going to partner at that point. You fast forward into this sort of 2017 to 2019 timeframe. And we became, I'd say a lot more intentional about how we were going to go to market, where we were going to invest areas such as SAP, et cetera. Were an early one that we identified and I'd say the ball really started rolling sort of in the 2018 timeframe. A combination of a number of different things occurred, you know, the acquisition of Red Hat, obviously, you know Red Hat is a very significant, was a very significant partner with AWS, prior to the acquisition. And so post acquisition, you combine that with ramping up a workforce, focused on AWS, combined with a number of different competencies that IBM really invested in, around migration as an example, or SAP. And, you know, the, the ball really starting to roll quickly after that, you know, I'd say the last 18 months or so we both invested significantly in the relationship expansion around the world really, and joined resources and capability to make sure that we're going to markets sort of partnered intentional way rather than sort of an opportunistic. >> Oh, go ahead. >> So I'd say so far, that's absolutely been paying off in that we are seeing a number of wins all around the world across a broad set of industries, as well as the broad set of technologies. So, you know, the strength of, of IBM's consulting services in particular, but also their software combined with the strength of our platform has really proven to be successful for our customers. >> So you said started in 2016, really started taking shape in the last couple of years, that Red Hat acquisition. Talk to me about what's in this for customers. I imagine customers that are expanding or needing to move workloads into the cloud, or maybe more of a hybrid cloud approach. What are some of the big benefits that customers are going to gain from this partnership? >> Yeah, absolutely. And the reality is IBM has a long and storied history and relationship with their customers, right? They run and manage many of the workloads. They really know the customer's business incredibly well. They have domain expertise and industry and then the technology expertise from a professional services perspective to really help navigate the waters and determine what the right strategy is around moving to the cloud, right? You combine that with the depth and breadth of the skills and capabilities and services that AWS provides. And the fact that IBM has invested significantly in making sure their professional services are deeply steeped in our technology and capabilities. It's a great combination of really understanding the customer's needs. Plus the art of the possible, honestly, when it comes to technology that we provide, really can accelerate both and mitigate risk when it comes to moving to the cloud. >> That risk mitigation is key. So you guys recently, AWS recently launched if I'm going to get this right. Red Hat OpenShift Service on AWS or ROSA. Can you talk to me a little bit about ROSA? >> Yeah so, Red Hat obviously very well known, and ultimately adopted within the enterprise. We have built a fully managed service around Red Hat on AWS. What that means is you'll have access to essentially the capabilities that that Red Hat would normally provide but all containerized within a solution that allows you to get access to AWS services, right. The other benefit here is normally you would get sort of a multi-vendor with invoicing and cost model, right? Where you get billed from Red Hat, get billed from Amazon. You get billed from IBM. In this case, it's essentially a wholistic service in which there's a single sort of invoicing and vendor relationship, right. So it's combination of capabilities that normally would be provided via Red Hat combined with access to cloud and all the interfaces and capabilities around OpenShift, et cetera, that you could do there. Plus a more interesting and beneficial commercial model. >> So streamlined pricing models, streamlined operating model for customers. Talk to me about some of the customers that have adopted it. Give me a look into some of the industries where you've seen good adoption and some of the results that they're gaining so far. (loud engine buzz in background) >> Yeah one second, sorry, it's like insanely loud. >> Man's voice: No worries, let's just take a pause. We can just, so yeah we'll go right as if Lisa just finished the question. So just take a breather as long as it needs. And then whenever you're ready whenever that's died down, just like give it a beat give it like a second and then just right as if she just asked the question. >> Answer the question then. >> Man's voice: Yeah. >> All right. >> Man's voice: I'll cut it out as if nothing happened. >> Give me two minutes. So actually on your question, I know the answer from things that I've done recently, but was there an official answer Theresa I'm supposed to give on this? >> Teresa: No, not really I mean, I think what you're talking about on Red Hat specifically >> Right, ROSA's early adoption. >> Teresa: Yeah, no I mean, there there's a product page and stuff, it's really about just the ability of customers to be able to run those solutions on the AWS console it is really the, the gist of it. And that it's fully integrated. >> I'm not sure some of the examples I know of are publicly refrenceable. >> Lisa: That's okay, you could just say, you know, customer in XYZ industry, that's totally fair. I'm not so worried about that. >> Teresa: Yeah I don't know if so ROSA. Lisa, ROSA was just launched in March and so it's brand new so I don't have the customer stories yet. So that's why I don't have them listed for Leo. >> Lisa: Oh, that's fine, that's totally fine. Maybe we can talk about, you know, since the launch was just around the corner, some of the things that have been going on, the momentum interest from customers, questions conversations can be more like that as you're launching the GTM. >> Yeah, and there's certainly a couple of industries that they have targeted I'm going to go with that as well as a couple of customers, like, >> Teresa: Thank you, Lisa. >> Lisa: Sure, of course. >> I think they went around the corner. (Lisa laughs) >> Lisa: All right, let me know and I'll re-ask the question. I'll tweak it a little bit. >> Yeah, go ahead. >> Lisa: All right, so talk to me about, ROSA just launched very recently. Talk to me about customer interest, adoption. Maybe some of the industries in particular if you're seeing any industry that's kind of really leading edge here and taking advantage of this new managed service. >> Yeah, absolutely, so no big surprise, right? The the existing customer base that currently uses Red Hat Linux, and some of the options in OpenShift, et cetera that are out today are then the right customers to potentially look at this when it comes to moving forward. You know, industry-wise certainly there are areas within financial services, banking, insurance, et cetera. We're also seeing some around manufacturing, a little less so, but some in media and telco as well. So it's, it's a broad swath of any applicability of Red Hat and OpenShift is somewhat universal but the early customer bases has largely been sort of in those three areas. >> What I'm curious what are the key target audiences are these, Red Hat customers are these AWS customers. IBM all three? >> Yeah. I mean, there isn't necessarily the perfect customer that we're necessarily looking for, as much as if there are existing customers that are currently using Linux or using Red Hat. If there are someone who, a customer who currently has a relationship with either AWS or IBM there's an opportunity to essentially look at it from any of the angles. If you're already on cloud or you've already experienced AWS in some shape or form there's an opportunity to potentially to leverage ROSA, to further expand that capability and also have some more flexibility so to speak. If you're already using IBM as a professional services provider and advisory firm then they absolutely have the expertise and understanding of this product set to help you understand how it could be best leveraged, right. So you can kind of look at it from either of the dimensions. If it's a customer that's completely new to all of us then we're happy to talk to you. But it's something that will definitely take a little bit more explanation to understand as to why you should, or shouldn't consider us with this multicloud OpenShift type solution. >> Got it, let's shift gears a bit and talk about SAP. When we think about customers looking to migrate SAP workloads to the cloud, looking at the right cloud providers those are really big, challenging strategic decisions for leadership to make. Talk to me about why when you're in those conversations AWS is the best choice. >> Absolutely, I mean, really AWS, let's say with SAP and with with many of our services is really looking to give all the options that you could conceivably need or want in order to engage in cloud migration and transformation. press AP specifically, right? There are a number of different options, right. You could go for a lift and shift or upgrade from many databases to a suite on SAP HANA could potentially look to modernize and leverage cloud services, post migration as well. And then the sort of final pinnacle of that is a complete transformation to S four or S four HANA as far as why AWS specifically beyond just choice, you know, from a cost perspective, it's pretty compelling. And we have some pretty compelling business and use cases around ultimately the cost savings that come when you move from an on-premise SAP implementation to cloud beyond that, usually the cloud migration itself is an opportunity to condense or reduce the number of instances you're paying for, from an SAP perspective, which then further reduces cost. From a reliability perspective, you know, AWS is the world's most secure, extensive reliable cloud infrastructure, right? Any of the instances that you put on AWS are instantly I'd say fairly instantly provisioned in such a way that they are provided across multiple what we call Availability Zones which is giving you sort of the resiliency and the stability that really no other cloud provider can provide. On the security front, I mean this is really a unique position in that AWS plus IBM and the security, the depth in security services you know, numerous years of professional services work that IBM has done in the security space. You know, they have roughly 8,000 or so cybersecurity experts within IBM. So the combination of their expertise in security plus the security of our platform is a great combination. I'd say the final one is around performance, right? AWS offers many more cloud native options around certified SAP instances, specifically all the way from 256 gigabyte option all the way up to 24 terabytes which is the largest of its kind. And as those who have implemented SAP know it's a very resource intensive. So having the ability to do that from a performance perspective is a key differentiator for sure. >> Talk to me from your opinion about why IBM for SAP on AWS, why should customers go that direction for their projects? >> Yeah, you know, IBM has over 40 years of experience in implementing SAP for their customers right. And they've done, I think it's over 6,000 SAP migrations, 40,000 global SAP consultants around the world. Right, so from a capability and depth of experience, you know, there's a lot of nuance to doing it. SAP implementation, particularly one that's then moving from on-prem to the cloud. You know, they've got the experience right. Beyond that they have industry specific solutions that are pre-configured. So I think that there's 12 industry specific solutions pre-configured for SAP, it allows, you know roughly 20 to 30% acceleration when it comes to implementation of platforms. So combination of just depth of experience, depth of capability combined with these solutions to accelerate are all key reasons for sure. >> The acceleration you bring up, sorry is interesting because we saw in the last year the acceleration of digital transformation projects and businesses needing to pivot again and again, and again to figure out how to survive and be successful in this very dynamic market in which we're still living. Anything industry-wise specific that you saw that was really driving the acceleration and the use cases for ROSA in the last year? >> Yeah so, you know SAP, we saw an interesting trend as a result of what's everyone's been experiencing in the last year with COVID, et cetera. You know, many organizations postponed large ERP implementations and large SAP migrations, because of what you just said, right. They weren't entirely sure what would need to be done in order to survive either a competitive threats or more just the global threats that were occurring. So what we saw was, really none of the transformations went away. They, were put on hold for a period of time let's say six to nine months ago maybe even a year ago almost. In lieu of I would say more top line revenue generating or innovative type solutions that maybe were focused specifically at, you know, the changing dynamic with COVID. Since then we've seen a combination of those new ideas, right? Combination of the new innovation around healthcare of course, but also public sector and, you know a lot around employment and then engagement there. We've seen a combination of those new ideas and new innovations with the original goal of optimizing transforming SAP ERP, et cetera. And then combining the two to allow access to the data, that sits inside the SAP implementation the SAP. Combine the data in SAP with all these new innovations and then ultimately use that to sort of capitalize on what the future businesses are going to be. That's been huge, it's been very interesting to see some organizations completely change their business model over the course of the last 12 months. In ways they probably had never intended to before right? But it's, absolutely become an opportunity in a time of a lot of challenges. >> Agreed there are silver linings and we've seen a lot of those interesting opportunities to your point that businesses probably would never have come up with had there not been a forcing function like we've been living with. Leo thank you for joining me today. Talking to me about what's going on with IBM and AWS. We'll be excited to follow what happens with ROSA as it continues to roll out. And we appreciate you joining us on the program. >> Absolutely thank you for your time. >> For Leo Labrunch I'm Lisa Martin. You're watching theCUBE's digital coverage of IBM think 2021. (upbeat music)

>> Narrator: From around the globe, It's theCUBE. With digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome to theCUBE's digital coverage of IBM Think 2021. I'm Lisa Martin. Next joining me is Leo LaBranche, Director of global strategic initiatives at AWS. Leo, welcome to theCUBE. >> Thank you, happy to be here. >> So talk to me about AWS and IBM what's going on there with their relationship. What are some of the things that are significant for both partners? >> Yeah, absolutely, IBM's relationship really started with us around 2016. I would say it was a little bit more opportunistic at the time. We knew there was an opportunity to go to market together. We knew there were some great things we could do for our customers. But we hadn't quite cracked to crack the code so to speak on when and where and why we were going to partner at that point. You fast forward into this sort of 2017 to 2019 timeframe. And we became, I'd say a lot more intentional about how we were going to go to market, where we were going to invest areas such as SAP, et cetera. Were an early one that we identified and I'd say the ball really started rolling sort of in the 2018 timeframe. A combination of a number of different things occurred, you know, the acquisition of Red Hat, obviously, you know Red Hat is a very significant, was a very significant partner with AWS, prior to the acquisition. And so post acquisition, you combine that with ramping up a workforce, focused on AWS, combined with a number of different competencies that IBM really invested in, around migration as an example, or SAP. And, you know, the, the ball really starting to roll quickly after that, you know, I'd say the last 18 months or so we both invested significantly in the relationship expansion around the world really, and joined resources and capability to make sure that we're going to markets sort of partnered intentional way rather than sort of an opportunistic. >> Oh, go ahead. >> So I'd say so far, that's absolutely been paying off in that we are seeing a number of wins all around the world across a broad set of industries, as well as the broad set of technologies. So, you know, the strength of, of IBM's consulting services in particular, but also their software combined with the strength of our platform has really proven to be successful for our customers. >> So you said started in 2016, really started taking shape in the last couple of years, that Red Hat acquisition. Talk to me about what's in this for customers. I imagine customers that are expanding or needing to move workloads into the cloud, or maybe more of a hybrid cloud approach. What are some of the big benefits that customers are going to gain from this partnership? >> Yeah, absolutely. And the reality is IBM has a long and storied history and relationship with their customers, right? They run and manage many of the workloads. They really know the customer's business incredibly well. They have domain expertise and industry and then the technology expertise from a professional services perspective to really help navigate the waters and determine what the right strategy is around moving to the cloud, right? You combine that with the depth and breadth of the skills and capabilities and services that AWS provides. And the fact that IBM has invested significantly in making sure their professional services are deeply steeped in our technology and capabilities. It's a great combination of really understanding the customer's needs. Plus the art of the possible, honestly, when it comes to technology that we provide, really can accelerate both and mitigate risk when it comes to moving to the cloud. >> That risk mitigation is key. So you guys recently, AWS recently launched if I'm going to get this right. Red Hat OpenShift Service on AWS or ROSA. Can you talk to me a little bit about ROSA? >> Yeah so, Red Hat obviously very well known, and ultimately adopted within the enterprise. We have built a fully managed service around Red Hat on AWS. What that means is you'll have access to essentially the capabilities that that Red Hat would normally provide but all containerized within a solution that allows you to get access to AWS services, right. The other benefit here is normally you would get sort of a multi-vendor with invoicing and cost model, right? Where you get billed from Red Hat, get billed from Amazon. You get billed from IBM. In this case, it's essentially a wholistic service in which there's a single sort of invoicing and vendor relationship, right. So it's combination of capabilities that normally would be provided via Red Hat combined with access to cloud and all the interfaces and capabilities around OpenShift, et cetera, that you could do there. Plus a more interesting and beneficial commercial model. >> So streamlined pricing models, streamlined operating model for customers. Talk to me about some of the customers that have adopted it. Give me a look into some of the industries where you've seen good adoption and some of the results that they're gaining so far. (loud engine buzz in background) >> Yeah one second, sorry, it's like insanely loud. >> Man's voice: No worries, let's just take a pause. We can just, so yeah we'll go right as if Lisa just finished the question. So just take a breather as long as it needs. And then whenever you're ready whenever that's died down, just like give it a beat give it like a second and then just right as if she just asked the question. >> Answer the question then. >> Man's voice: Yeah. >> All right. >> Man's voice: I'll cut it out as if nothing happened. >> Give me two minutes. So actually on your question, I know the answer from things that I've done recently, but was there an official answer Theresa I'm supposed to give on this? >> Teresa: No, not really I mean, I think what you're talking about on Red Hat specifically >> Right, ROSA's early adoption. >> Teresa: Yeah, no I mean, there there's a product page and stuff, it's really about just the ability of customers to be able to run those solutions on the AWS console it is really the, the gist of it. And that it's fully integrated. >> I'm not sure some of the examples I know of are publicly refrenceable. >> Lisa: That's okay, you could just say, you know, customer in XYZ industry, that's totally fair. I'm not so worried about that. >> Teresa: Yeah I don't know if so ROSA. Lisa, ROSA was just launched in March and so it's brand new so I don't have the customer stories yet. So that's why I don't have them listed for Leo. >> Lisa: Oh, that's fine, that's totally fine. Maybe we can talk about, you know, since the launch was just around the corner, some of the things that have been going on, the momentum interest from customers, questions conversations can be more like that as you're launching the GTM. >> Yeah, and there's certainly a couple of industries that they have targeted I'm going to go with that as well as a couple of customers, like, >> Teresa: Thank you, Lisa. >> Lisa: Sure, of course. >> I think they went around the corner. (Lisa laughs) >> Lisa: All right, let me know and I'll re-ask the question. I'll tweak it a little bit. >> Yeah, go ahead. >> Lisa: All right, so talk to me about, ROSA just launched very recently. Talk to me about customer interest, adoption. Maybe some of the industries in particular if you're seeing any industry that's kind of really leading edge here and taking advantage of this new managed service. >> Yeah, absolutely, so no big surprise, right? The the existing customer base that currently uses Red Hat Linux, and some of the options in OpenShift, et cetera that are out today are then the right customers to potentially look at this when it comes to moving forward. You know, industry-wise certainly there are areas within financial services, banking, insurance, et cetera. We're also seeing some around manufacturing, a little less so, but some in media and telco as well. So it's, it's a broad swath of any applicability of Red Hat and OpenShift is somewhat universal but the early customer bases has largely been sort of in those three areas. >> What I'm curious what are the key target audiences are these, Red Hat customers are these AWS customers. IBM all three? >> Yeah. I mean, there isn't necessarily the perfect customer that we're necessarily looking for, as much as if there are existing customers that are currently using Linux or using Red Hat. If there are someone who, a customer who currently has a relationship with either AWS or IBM there's an opportunity to essentially look at it from any of the angles. If you're already on cloud or you've already experienced AWS in some shape or form there's an opportunity to potentially to leverage ROSA, to further expand that capability and also have some more flexibility so to speak. If you're already using IBM as a professional services provider and advisory firm then they absolutely have the expertise and understanding of this product set to help you understand how it could be best leveraged, right. So you can kind of look at it from either of the dimensions. If it's a customer that's completely new to all of us then we're happy to talk to you. But it's something that will definitely take a little bit more explanation to understand as to why you should, or shouldn't consider us with this multicloud OpenShift type solution. >> Got it, let's shift gears a bit and talk about SAP. When we think about customers looking to migrate SAP workloads to the cloud, looking at the right cloud providers those are really big, challenging strategic decisions for leadership to make. Talk to me about why when you're in those conversations AWS is the best choice. >> Absolutely, I mean, really AWS, let's say with SAP and with with many of our services is really looking to give all the options that you could conceivably need or want in order to engage in cloud migration and transformation. press AP specifically, right? There are a number of different options, right. You could go for a lift and shift or upgrade from many databases to a suite on SAP HANA could potentially look to modernize and leverage cloud services, post migration as well. And then the sort of final pinnacle of that is a complete transformation to S four or S four HANA as far as why AWS specifically beyond just choice, you know, from a cost perspective, it's pretty compelling. And we have some pretty compelling business and use cases around ultimately the cost savings that come when you move from an on-premise SAP implementation to cloud beyond that, usually the cloud migration itself is an opportunity to condense or reduce the number of instances you're paying for, from an SAP perspective, which then further reduces cost. From a reliability perspective, you know, AWS is the world's most secure, extensive reliable cloud infrastructure, right? Any of the instances that you put on AWS are instantly I'd say fairly instantly provisioned in such a way that they are provided across multiple what we call Availability Zones which is giving you sort of the resiliency and the stability that really no other cloud provider can provide. On the security front, I mean this is really a unique position in that AWS plus IBM and the security, the depth in security services you know, numerous years of professional services work that IBM has done in the security space. You know, they have roughly 8,000 or so cybersecurity experts within IBM. So the combination of their expertise in security plus the security of our platform is a great combination. I'd say the final one is around performance, right? AWS offers many more cloud native options around certified SAP instances, specifically all the way from 256 gigabyte option all the way up to 24 terabytes which is the largest of its kind. And as those who have implemented SAP know it's a very resource intensive. So having the ability to do that from a performance perspective is a key differentiator for sure. >> Talk to me from your opinion about why IBM for SAP on AWS, why should customers go that direction for their projects? >> Yeah, you know, IBM has over 40 years of experience in implementing SAP for their customers right. And they've done, I think it's over 6,000 SAP migrations, 40,000 global SAP consultants around the world. Right, so from a capability and depth of experience, you know, there's a lot of nuance to doing it. SAP implementation, particularly one that's then moving from on-prem to the cloud. You know, they've got the experience right. Beyond that they have industry specific solutions that are pre-configured. So I think that there's 12 industry specific solutions pre-configured for SAP, it allows, you know roughly 20 to 30% acceleration when it comes to implementation of platforms. So combination of just depth of experience, depth of capability combined with these solutions to accelerate are all key reasons for sure. >> The acceleration you bring up, sorry is interesting because we saw in the last year the acceleration of digital transformation projects and businesses needing to pivot again and again, and again to figure out how to survive and be successful in this very dynamic market in which we're still living. Anything industry-wise specific that you saw that was really driving the acceleration and the use cases for ROSA in the last year? >> Yeah so, you know SAP, we saw an interesting trend as a result of what's everyone's been experiencing in the last year with COVID, et cetera. You know, many organizations postponed large ERP implementations and large SAP migrations, because of what you just said, right. They weren't entirely sure what would need to be done in order to survive either a competitive threats or more just the global threats that were occurring. So what we saw was, really none of the transformations went away. They, were put on hold for a period of time let's say six to nine months ago maybe even a year ago almost. In lieu of I would say more top line revenue generating or innovative type solutions that maybe were focused specifically at, you know, the changing dynamic with COVID. Since then we've seen a combination of those new ideas, right? Combination of the new innovation around healthcare of course, but also public sector and, you know a lot around employment and then engagement there. We've seen a combination of those new ideas and new innovations with the original goal of optimizing transforming SAP ERP, et cetera. And then combining the two to allow access to the data, that sits inside the SAP implementation the SAP. Combine the data in SAP with all these new innovations and then ultimately use that to sort of capitalize on what the future businesses are going to be. That's been huge, it's been very interesting to see some organizations completely change their business model over the course of the last 12 months. In ways they probably had never intended to before right? But it's, absolutely become an opportunity in a time of a lot of challenges. >> Agreed there are silver linings and we've seen a lot of those interesting opportunities to your point that businesses probably would never have come up with had there not been a forcing function like we've been living with. Leo thank you for joining me today. Talking to me about what's going on with IBM and AWS. We'll be excited to follow what happens with ROSA as it continues to roll out. And we appreciate you joining us on the program. >> Absolutely thank you for your time. >> For Leo Labrunch I'm Lisa Martin. You're watching theCUBE's digital coverage of IBM think 2021. (upbeat music)

>>From around the globe. It's the queue with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hello, and welcome back to the cubes coverage of ADFS reinvent 2020 it's virtual this year because of the pandemic we can't be in person normally would do in these interviews face to face, but we're here remote. I'm your host, John furrier. We're the cube virtual and we're here with Teresa Carlson, who is the chief and heads up the public sector business, uh, for AWS and also now has industries, which is a lot of the verticals and just continues to, um, have great leadership and continues to do well in the business. I Theresa great to see you for the eighth consecutive cube interview you've been on every year and we thank you for coming on big year this year. Thanks for coming on. Great to see you. >>Thank you, John. Thank you for having me. It's hard to believe it's eight years already. Wow, go ahead. >>Well, first of all, I want to say congratulations. Um, the first year you will run, you never wavered. You always had a North star. Um, you had the Amazonia and kind of way, um, you told us what you were going to do and you did it. The CIA came on board and the dots just connected. So congratulations this year more than ever, um, during your keynote. And re-invent, even though it was virtual, um, again, you're raising the bar on the theme leadership and making use of the data two major themes this year on your keynote because of the pandemic. And just because of the cloud computing benefits are all kind of coming together. You're helping more people than ever doing a more public service with cloud when it needs it. The most. This has been a big story. Share your, your reaction to that. >>Yeah. Well, John, thank you again for having me in your coverage of reinvent. It's been three weeks of, wow. I mean, three weeks we do one hour a day three, uh, that COVID, you know, we're still, we're still not dead, right? The vaccinations are out. People are starting to, I saw on the television yesterday here in the U S the first nurse that was vaccinated. Uh, but for us, I will tell you the data side of this piece during COVID has been huge. I mean, huge. It has been, you know, our customers have always said data is golden for them, right. Uh, but during COVID, we have actually seen the use of data, just go up like crazy and not just the use of it, but, um, I will say it's multiple data lakes that are used hydrating multiple data lakes and using that data to merge. >>So if you think about economic data and health data and putting those data sets together in a way that they have deeper understanding of what's happening within their community, their state, their, their, uh, their country. So we've seen emerging of data, uh, in a big way. If you think about the vaccinations themselves, uh, John, that wouldn't have been possible to move this fast without the use of scalable compute, processing and analytics in a way like no one has ever seen it. And, uh, it's, it's, it's pretty amazing. And I don't think we'll ever go back. And also I'll just say sharing of that data has changed. Researchers are now much more open to sharing that data air cord 19 a research site that we've done has thousands of researchers on it. Now, hundreds of thousands of views on it with people sharing research about COVID and think about that. I mean, research has always been held tightly, and now we're really starting to see them open up and share that data so that we can move much faster. >>I think doing that public service with the data has always been a killer idea. We talked about national parks being kind of open for the people over the years now, super computing and data. You guys do a great job doing that, but the other area that you're getting a lot of press on and, and rightfully so is an area that I know is close to your heart, as well as our mission, which is getting people trained up on cloud computing. And you've done this for years, but this year more importantly, with all the pressure and all the need, you guys have offered, offering a huge training skills training for 29 million people globally. I saw that on the news, I saw you on doing some TV interviews on this. It's been all over the press has been getting a lot of great buzz. Can you tell me more about what that is? >>Yeah. So part of my, when I picked up bear industry business units also picked up our training and certification organization that is ran by Maureen Lonergan. I know you've had Maureen on your show before too, and then I have education, which is run by Kimma Jarris in the U S and max, uh, Peterson internationally. And we are now we've merged so that we have a model that we can teach and train around the world in a much more scalable way that this announcement was about going into 200 Kemp countries and territories training, 29 million people by 2025 free do free skills training and making that available job through multiple different programs and scaling those. So we'll take the programs we have and we'll scale those app much more rapidly. And then now we'll also look for new programs that we need to run in parallel because that's what we do. >>We have to look around corners. Also make sure that we have the right programs and, you know, I've lived, I've lived, you know, they're all amazing, but near and dear to my heart has always been our AWS educate, which we started, uh, for ages 14 and up to at the university and high school level, to be able to start to bring on those cloud skills. Then we added badging and credentialing onto that. And from there, you can go into the air Academy, which you can actually get certifications as a solution architect. Uh, but we've, we've added so many more, uh, our program restart now, which has been really, which is about training. Those who are jobless or an underserved communities and socioeconomic depressed areas. Uh, and I love that program. I told a story about an individual in Boston who had opened a training center, a gym he's a fitness trainer, and he had to close it, uh, because you know, COVID, and he went through our 12 week. >>We restart training program and now has a job with a company there in Boston. And I just love those kind of stories where you know, that you're putting people to work. And I think for us, there's thousands and thousands of jobs around the world, just in any city, if you, if you search on cloud computing jobs open, I just looked in New York when I was on CNBC. I looked in New York and there are 10,000 cloud jobs just there in New York. And I just did a quick search. So there's always jobs, and we've got to make sure that we're skilling them so they can go now fill those jobs. And that will help us close that gap. Uh, John, which we still have a big one, uh, to get all the jobs filled that are out there. >>That's a great mission. And I got to say, it's super important because one is cloud computing. There's openings for this kind of new, the new paradigm, which is now mainstream and playing out on, in real time, as, as Andy was talking about, but also the global it markets being reshaped by cloud computing. So you have the intersection of those two, which is a new skill. You can't just take it and make a cloud. You've got to bring it together. So it's a great opportunity for someone to come into the industry and level up pretty quickly. You don't have to have the 20 years of experience to do this. It's you can come in instantly level up, have a great job. >>You know, it's the one thing John, I hear all the time around the world before from like when I would go and speak with university chancellors and presidents and just professors, they would say, Hey, you know, AWS, we need you to do the micro-credentialing along the way. And this was pre COVID when they said, we need to get your students want to work while they're in school. Well now more than ever, it's important. And we also, John Luke, just in September, over 800,000 women left the workplace. That is a trend that we do not want and we can not sustain. And so doing, you know, doing programs like this virtually that you can do self paced environments, intensive environments. We want to make, we want to make these programs fit for whatever the individual needs. So it's not just a one size fits all. We want to make sure that the programs that we're providing will fit the needs of the individuals doing the training. And I, I particularly am, uh, I want to push this with their, you know, inclusion and diversity of the individuals that we need to get into the workplace, but it is pretty alarming when you see that many women leaving the workplace, you know, when a choice is being made right now, we're seeing women take the brunt of that. And we want to make sure that they have the opportunity to work virtually train themselves and get those new jobs that are out in tech. >>Well, that's one of the questions I had for you. I'll just jump to that. Now I'll get back to some of the other ones, but the customers that pivot to remote work and learning, uh, it's changing. And, you know, I was, um, riffing on an interview. Um, I think it was with one of your public sector customers, the future of work. And if you just think about the word work workforce, workplace workload work flows, the notion of work is now impacted. And you mentioned the diversity piece. This is an opportunity. So how should people think about this, uh, relearning? So we don't lose people and we actually get a net positive inbound migration to the workforce. >>You know, the flexibility I had, I did a fireside chat with Andrew Nooney. Um, he was the former CEO of PepsiCo and chairman, and is now on our Amazon board, uh, for re-invent. And she talked about, you know, being your authentic self, uh, curiosity, but one of her big points is women in the workplace. Uh, and she's gonna publish a new book soon, and it's going to be really focused on kind of equity policy, uh, areas of need that we have to focus on to make sure that we have at women being able to tackle both the home issues and being able to work and taking advantage of that plus 50%. And I would say the virtual opportunity is really fantastic, especially for, um, all levels of socioeconomic individuals, because you can work part-time full-time, you can work virtually. And I do believe while we all want to get back into the workplace. >>I think for me, I'm a social animal. I'd love to be there sitting beside you, John, you know, I think for a lot of us, we are, we kind of yearn to be back in the office, but there's also a lot that working from home, um, is, is much more achievable for them, right? Especially with childcare if school day, if it's a short day, because the schools and allowing flexibility with work is going to be really important and COVID has taught us that that is possible. My team did not miss a beat during COVID. I tell ya, it's like unbelievable. Our business, uh, has, has really kinda been on fire because public sector. And if you look at the other industries, I've picked up financial services, uh, energy and telecommunications and training and certification. These are all that had to keep going. Uh, governments were moving faster than ever. >>So our team was really busy. Um, I've had individuals asked me, well, how did you manage the downtowns? Like we didn't have any downtime. Like literally day one, we were like 24 seven and the teams were working with it pretty much every government around the world because COVID moved so quickly and all virtually. And I will have to say, John, I was really skeptical in the beginning about how is this? How, how are we going to do this? Um, but the teams really, we figured out how to operate. You know, you had to, it's a new muscle. You kind of have to build that virtual work muscle and figure out how you manage your day, how you fit things in. And then there's the point that people think you're always available because you are at home, right? So you can never, that you can't possibly not be available because you know, you're, you are sitting at home. And then there's the many times where people's cats walk across and kind of with their tail on their face. And that dog child were at REMS in with the diaper. And you know, it's all, you, you have to have grace and humor about all this. Sometimes T like you can't take everything so seriously. And perhaps we've learned that, um, work and life can blend a little bit more, right? That you can, you can have that when a lot of people, when they talk about work-life balance, now we have work-life harmony. >>You know, you and I have talked about this before. If you can tap whoever taps, the diversity of talent will always let me win the game and not just, um, diversity in terms of gender or background role. I mean, if you can tap the virtual space, you're a winner because there's talent out there that can be aggregated in, and there's no stigma associated with anything. So, you know, this is, I think Andy kinda, uh, expressed that to me. And, and he heard it in his keynote where he said, Hey, people are a square, but you can get more participation. I think that is a real positive, um, upside. And I love the perspective of this new muscle. I totally agree. You need to, you need to have that >>Square. I mean, we've, we've actually chatted. I don't know if we'll ever go back to having big rooms with people in it, because you have a voice, you have a face. And I do believe, especially for women, uh, John, who can not always speak up, it's an opportunity for them to have their own space. They ha they can have their own voice. All individuals cause centers. They have great ideas, but they don't always value them. So having, you know, when you, each person has their own square, you can actually kind of see, well, who's, who's has an opinion. Who's spoken up. Who, who do I want to call on here and ask them if they have an opinion? So I like the idea of everybody having their own space when you're having a meeting. If you have to be virtual, because you get lost in translation, especially if you have that large leader in the room and everybody else's around them, then sometimes they only kind of adhere to their voice. This is an opportunity for others to really have that pool. >>I was just, I saw a joke on Twitter from a friend that said, Hey, I run all the meetings now because I can mute people. So if someone starts talking, you're muted bye-bye. So again, this is a whole new muscle great stuff. Well, since you've, since you brought up your role, I know you have a new expanded role. Could you take a minute to explain what that is? Because I'm still not clear. I know you've been doing an amazing job. I've written about, uh, your initial successes, and now you continue to do well with public sector and believe me, I've exploding. I see it. We're reporting on it. Public service is changing with digital transformation, but these other things, what are you working on? What are the new areas? Yeah, so I >>Just passed my 10th year. I'm starting my 11th year and it's been like amazing building this public sector business. I, I, and our government customers. Wow. The innovation and education during COVID has been pretty off the charts, which I don't think I'll slow down. And then a few months ago I was asked to take on our, uh, our training and certification org and our evangelist in solution architecture org, along with the industry business units of, uh, finance, telecommunications, and energy. And then, uh, John, if you remembering June, I announced our aerospace and satellite industry business unit. So, uh, these are the ones that we have right now are very regulated. A lot of them are, you know, very closely aligned to regulated industry. Um, you know, there could be others that are not as regulated, but the ones right now, if you think about aerospace, satellite, financial services, telecommunications in, in, in energy. >>So they, for me, um, they're very, it can tell a lot of the work I've been doing in building public sector, because when I go into a country today, when my teams go in, we generally always have to work with these groups. So if you think about telecommunications, we have to go in and make sure that we're working on our networking, our connectivity, and we negotiate and work with those telco providers. Same with the energy companies, both large ones and small ones. We go in and we work to build a power purchasing agreements, you know, solar power, uh, renewable energy to power our data centers and make sure that we're giving back to the grid. So we have that partnership. And then in the financial sector, I've had our, uh, I've had all of our regulators anyway, like FINRA fed reserve. Um, I R S treasury. >>So I've already, I've always had all the regulators. So now working with the, uh, you know, the additional, the banking, the investment sector, capital markets, it's very, it's, it seems so natural if that makes sense. And now diving into the upstream and downstream stream of supply chain for both that energy and telco and what a fantastic time now for telcos with 5g. I mean, I've been saying for two or three years that I thought this would be a huge opportunity for telecommunications companies to actually look for new, uh, work streams for their customers. And I mean, edge, you know, now our connect or call centers that they can do and take advantage of that. So I'm actually really excited. Uh, John seeing seven of new opportunities and, you know, renewable the new energy, uh, startups that are out there, the things I'm seeing, power, solar, nuclear, um, and then seeing a lot of the larger energy companies take on these projects. It's a lot of fun. And, um, I'm very excited now to continue to meet those customers. I got to meet a lot during re-invent. I love their energy. Yeah. I love kind of learning about what they're looking to solve. And, and I'm also just looking forward to helping them, um, with the connections that we've already been doing in government. I think it's a really nice combination of working together. Now. >>I, I see it as, um, what you've done with public sector was take a partnership approach to an old standing industry, changed them quickly, get the transformation, build the relationships, get the successes and establish that transformation and this needed versus the organically developing, you know, stuff. That's going to be the cloud startups and whatnot. Those are going to use Amazon, but you're a transformational leader. >>John, if I could just save for a minute, if you think about re-invention, you're at re-invent and a lot of these are going through massive reinvention, uh, you know, again, 5g with telco renewables, uh, with energy and then financial services where everything is kind of moving to an online model and digital model with different types of currencies that they have to deal with. It's, it's really perfect for cloud and what we offer. So I think the opportunity, um, to dive in and really partner with these industries and aerospace and Salado. Oh my gosh. It's just, I have to say, I really do believe cloud computing is, um, the perfect kind of step forward with all these industries for reinvention and innovation, which they're all moving towards. >>Well, Theresa, you're a re-invention leader. Uh, we've covered it. And now we've got all new territory for you to work on. Um, bring your playbook, you know, people-centric partner results are charging Theresa, thank you for your time. Great to have you on. Great to see you. Wish you, we were in person in real life again soon. Thank you for coming on. >>Yeah, John, thank you. Happy holidays. I look forward to seeing you next year. >>Okay. This is the cubes coverage of AWS reinvented. We have Teresa Carlson, she heads up the public sector. She's the chief of the whole public sector, and now taking on other industries to bring that playbook, the reinvention to the industries, really a big part of the Amazon web services, vision and cultural change. That's going on with the pandemic reach rechanging and reformatting and refactoring industries. That's what's going on in the big picture and a lot of gay tech under the hood. I'm John for your host. Thanks for watching.