 Hey, welcome back to theCUBE's continuing coverage of AWS re-invent 2021. I'm your host, Lisa Martin. This is day two, our first full day of coverage, but day two, we have two live sets here with AWS and its ecosystem partners, two remote sets, over 100 guests on the program. We're going to be talking about the next decade of cloud innovation, and I'm pleased to welcome back two CUBE alumni to the program. Justin Borgman is here, the co-founder and CEO of Starburst, and Teresa Tong, cloud first chief technologist at Accenture. Guys, welcome back to theCUBE. Thank you. Thank you for having me. Good to have you back. So Teresa, I was doing some research on you, and I see you are the most 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 that. Well, I get to think about the future of cloud. And if you think about cloud, it powers everything, the experiences in our everyday lives, in our homes, in our car and our stores. So pretty much I get to be CUBE, right? The rest of Accenture's James Vaughn. And you're CUBE, I like that. I'm CUBE, yes. Wow, what a great analogy. Justin, talk to me a little bit. I know Starburst 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 market that you saw a few years ago and said we have an idea to solve this? Sure. Starburst offers a distributed query engine, which essentially means we're able to run SQL queries on data anywhere. 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 analyzed that data. And 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 a number of the internet companies run their analytics. And so our idea was basically to take that, commercialize that, 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? And every company these days has to be a data company. If they're not, there's a competitor in the 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 concept, 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 around 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 having these really, 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 they're products, you really need to make sure that you're doing the right things. But with data mesh, we're not talking about a single tool here, right? This is more of 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 talk to me about when you're having customer conversations, that obviously organizations need to have a core data strategy that runs the business. They need to be able to democratize, really truly democratize data access across all business units. 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, every business is data-driven today and the pandemic, if anything, has accelerated digital transformation and 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 Datamesh resonate 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 Datamesh is a framework for thinking about that that not only acknowledges that reality but also embraces 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 Datamesh paradigm and it's been exciting to work with customers as they think about that strategy and I think that essentially every company in the space is in transition, whether they're moving 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 trying to wrestle with how to grasp that. And we know that there's so much value in data. 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 that real-time is no longer a nice to have, it is essential for businesses in every organization. So Teresa, let's talk about how Accenture and servers are working together to take the Datamesh from a concept of framework and put this into production, into execution. Yeah, I mean, many clients are already doing some aspect of the Datamesh. 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? And Amazon is 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, 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. What are some of the key outcomes, Teresa, that Datamesh 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 being 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 predicted, 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 kind of game out scenarios about what if I did things differently. All of this in a very virtualized, data-driven fashion. 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 to like customers, right? And really achieve the competitive advantage that businesses want to have. Just 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 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 that's been obviously good for companies like us and like Accenture, because there's a lot of work to be done out there. But I think it's a transition from a storage-centric mindset to more of an analytic-centric mindset. I think traditionally data warehousing has been all about moving data into one central place 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'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've been focusing on with our customers. Shorten that time to insight to get that value out of the data faster. Exactly. Is there, like I said, the real time is no longer a nice to have. It's an absolute differentiator for folks in every business and 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 the access to that data, be able to do the analytics to make the decisions based on what the data say. Are you finding, let's talk 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 will start with you and then Justin will head over to you. Well a lot of this is powered by the cloud. 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 is it gives you a framework to look at and tap into multi-cloud, on-prem, edge data. Data that can't be moved because it is 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 its scales. Doesn't talk to me about it from your perspective, the GTM. Yeah, so I mean I think Data Mesh is really about people, process and technology. I think Theresa alluded to it as a strategy. 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 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. The last 22 months have been hard on everyone for every reason. How are you facilitating? I'm curious. I'd like to get Theresa to start with you, your perspectives on how are together is Starburst and Accenture with the power of AWS helping to drive that cultural change within organizations? Cause like we talked about Justin there, nobody has extra time to waste on anything these days. Well, 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 the laggards. So before pandemic is times two, post pandemic times five. So there's a needed 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 data mesh help facilitate that? Cause we talk about technical debt and it can really add up. Well, a lot of people use us or think about us as an abstraction layer above the different data sources that they have. So they may have legacy data sources today. Then maybe they want to move off of over time. Could be classical data warehouses, other classical 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 scientist from where the data lives. And I think that gives people a lot of freedom and a lot of optionality. And I think the only constant is change. 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 SaaS platform now available on all three major clouds. Of course here we are at AWS. This is a big directional shift for Starburst, talk to me about that. It is, you know, I think there's great precedent within open source enterprise software companies like MongoDB or Confluent who started with a self-managed product much the way that we did. And then moved in the direction of creating a SaaS 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. You know, Starburst was already powerful. This makes it powerful and easy. And in our view, can hopefully expand the market to thousands of potential customers that can now leverage this technology in a faster, easier way. And Justin, sticking with you for a minute, talk to me about kind of where you're going in, where Starburst is heading in terms of support for the data mesh architecture across industries. Yeah, so a couple things that we've done recently and what we're doing as we speak. One is we introduced a new capability we call Stargate. Now Stargate is a connector between Starburst clusters. So you're going to have a Starburst cluster in let's say Azure, a Starburst cluster in AWS, a Starburst cluster maybe in AWS West and AWS East. And this basically pushes the processing to where the data lives. So again, living within this construct 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 ability to join across those different clouds, but also data sovereignty constraints, right? Increasingly important, especially in Europe, but increasingly everywhere. And if your data's in Switzerland it needs to stay in Switzerland. So Stargate is 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 so we think that's a big deal about kind of enabling a data mesh on a global scale. 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 Teresa said, it's really all about making this practical and finding quick wins that customers can deploy in their data mesh journey. Right, those quick wins are key. So Teresa, last question to you, where should companies go to get started today? Obviously everybody's got, we're still in this work from anywhere. Environment companies have tons of data, tons of sources of data. Data infrastructure is already in place. How do they go and get started with data mesh? I think they should start looking at their data projects and thinking about them as 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 there. And then thinking about who are the products that your product could work better with just like any other product. It's 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 theCUBE at ReInvent talking about data mesh, really how you can help organizations and the industry put that together and what's going on at servers. We appreciate your time. Thanks again. All right, for my guests, I'm Lisa Martin. You're watching theCUBE's coverage of AWS ReInvent 2021. theCUBE is the leader in global live tech coverage. We'll be right back.