 Hello and welcome to this discussion on the introduction to the Dell Data Lakehouse with Dell Technologies and Starburst. I'm Rob Streche, Managing Director with theCUBE Research. Today we'll speak with Vrshank Jain, Product Manager at Dell Technologies and Harrison Johnson, VP of Technology Partners in Business Development about how organizations are leaning into data lakes and experiencing challenges with data management as they try to monetize their data as data products. Well, welcome to the show. Thanks for having us. Thanks for having us. So, why don't we start with you, Vrshank? I think that one of the things that is key that people are always trying to understand is what are some of the challenges as you go out, because you talk to customers of all various different sizes. What are some of the key challenges they're having getting into data lakes? Yeah, I think it really starts with AI, right? And everybody's trying to get to AI and generate AI. But every single conversation of AI starts to get into, well, what about the data? And I think the rapid pace of the growth of the data and as well as the proliferation of the data sources is really what customers are facing with. They need a modern data platform to really simplify all of that. But when they look at the choices that they have, that's either go to the cloud or stay on premise. But when you go to the cloud, yeah, there's some ease of use, obviously some really good innovation there. But it's a non-starter for a lot of companies because they're not really inclined to move any of their sensitive data to the cloud or sort of the loss of control or even the cost around using the cloud. But even when you stay on premise, you start to deal with a lot of DIY solutions, a lot of open source that you have to go and manage and stand up on your own. So really two suboptimal choices, I think people need a better answer. So I think that better answer is basically an easy button that sort of works with their data gravity versus against it. And then they need help from an expertise perspective, right? They need a bench of people who just know exactly what they're doing. They can come in and they can help customers stand this up, run and manage at scale. I think that's a big challenge. Yeah, definitely because I think even nowadays their budgets are not necessarily getting any larger. And having to reskill people or have people with multiple hats on really is tough. And I think don't kind of bridging off of that. I think that when you start to look at from your vantage point, how is it that platform engineering and data engineering are becoming kind of forefront and center for these organizations? Yeah, I think their role has evolved significantly, right? If you think about any sort of modern data platform, it's made up of three large things, right? Number one is the infrastructure. Then you have the technology, sort of the platform that makes the data platform. And then you have this extended set of tools or technologies or solutions that you want to make work with the rest of the platform. And there's a lot of innovation that's happening at every single layer. There's a lot of disruption happening at every single layer. And the data piece continues to pose a lot of challenges. So I think what customers have started to do is have more of a platform thinking versus a piece part thinking, right? People need sort of a platform that does two things, right? One, give them a lot of ease of use and scale and ability to modernize as they go along, but also gives them choice to make sure that if tomorrow a new technology comes along, they should be able to use it. They don't get locked into something that they made a decision on maybe five years ago. So I think all of this is, I guess the problems are exacerbated because if you think about what the business team is doing, they're putting a lot more pressure on the IT teams. They're saying, I need this done faster and I need this done cheaper. And then the IT teams are sitting there thinking, look, I have the business teams wanting more on one side. I have the technology that's proliferating at an unprecedented rate on the other side. They're really getting stuck. And I think the list of things that they have to manage is growing so much. You think about software operating systems, Kubernetes experiences, servers, storage, protocols, data formats. I mean, the list is endless. So I think ultimately people are going to start to reject niche piece parts and they're going to start to want more end-to-end holistic solutions that are easy. Yeah, it totally makes sense. But so give us a little insight into why Dell with Starburst. Yeah, I think it really started maybe a couple of years ago when we were starting to look at this landscape that I just described. Clearly customers needed a better answer. But we wanted to work with a partner who shared our ethos for where I think the world is going, which is a lot more distributed data. And maybe the answer is not necessarily centralizing to another data sort of, let's say a data lake or a data warehouse, which really is a walled garden today. We're talking, I think we wanted to work with the company that agreed with us in the sense that some of this data is always going to live everywhere else and some of this is going to get centralized. And so we needed a technology that does both really well. The federation side of the house, where you're trying to get access to data spread across all your environments, as well as really high performance access to data that's now centralized in your data lake. So I think that's where the most urgent need is, which is let's get access into the hands of the data analyst or the data scientist and then let's centralize the most high quality data or the most valuable data. I think Starburst does both really, really well. And I think combined with the strengths that we bring, I think one plus one is really equal to three at this point. I think that you're on target with the whole evolution of IT into platform engineering and data engineering and how they're coming together and trying to understand that. What are you seeing from the Starburst side that really drove you from a customer requirements perspective to say, hey, partnering with Dell made a lot of sense? Yeah, absolutely. I think it was a pretty easy decision, a natural decision for us because of its alignment with what we're hearing from our customers. We're hearing meet me where I am, meaning support me in my existing architecture and then support me or be able to support me as I evolve. We're hearing simplicity and optionality, meaning make it really easy to use, make sure I can tap into my existing tool set and also have the future ability to evolve along with the technology landscape. And then the last piece is just around being able to operate in a world where data might not just be in one place. This idea that there might be all the data on-prem, but even on-prem it exists in multiple regions and multiple systems. Maybe it's a hybrid architecture with cloud and on-prem, maybe it's multiple clouds. Just this idea that data strategies will require hybrid capability going forward. And for us, there was a natural alignment with Dell, not just in terms of ethos, which Prashant mentioned. And you'll hear most of our answers. You can tell we've spent so much time together building this. But yeah, there's a shared ethos between the two companies. And then I think the Dell Data Lakehouse powered by Starburst really couples world-class architecture and Dell's market-leading infrastructure solutions, particularly their AI and analytics compute-powered solutions as well as, and probably most importantly, their object store offering with Starburst governance and access layer, which allows customers to sort of defeat the idea that, you know, the market, the public cloud era, most AI and analytics vendors in general, have left the customers on-prem and the data on-prem behind and have sort of created this move to cloud or cloud-only ultimatum for most of our customers. And while we all acknowledge that the cloud has a critical role in our customers' future, I think the why Dell specifically for Starburst was the commitment to building a simple streamlined offering that would allow them to successfully operate in their current state, drive business value, and then be able to evolve comfortably without having to disrupt the business. Yeah, I think it's very critical and we talk about it a lot on this program and other programs that there's really becoming a balance between where net new apps are being built between, you know, cloud, they're being built cloud-native, but they're also being built cloud-native on-premise and in the cloud. And we're seeing actually in the data that we have with our partner ETR, it's becoming really within balance from a 50-50 perspective. But how do you see this playing out for Starburst customers going forward as well? And what can customers of this product set, this solution and platform really expect in the future going forward? Yeah, absolutely. So I think the most important thing that I think we're going to be centered around in terms of how the products developed and the way we support our customers is really going to be simplicity. That's going to be the name of the game. Vrashank mentioned earlier that the option for you as a customer, if some or all of your data is on-prem, is to do it yourself, right? You are the architect of your own future, meaning you are completely responsible for piecing together a cloud-like experience. And what we're going to do together is really change that paradigm for customers. We're going to give them two things. We're going to give them a powerful, integrated, complete, open offering that allows them to plug into their architecture today, be able to add incremental value throughout their transformation journey, and also be able to take advantage of new technology that none of us could even imagine here sitting at the table. And then the second piece is the unparalleled growth of data coupled with the unparalleled demand for access to data for everyone has really made a lake an important center of gravity from a cost perspective. And then coupling the Starburst engine or other lake engines with that brings about the lake house paradigm. The lake house is really only available today, if not only available today in the public cloud, right? And the reality is for most of the customers that we support, which is the large global enterprise, the cloud is absolutely a part of their strategy, but it's tied directly to their architecture that they already have and their footprint on-prem and their need to be able to operate between cloud and premise. And so I think you're going to see a really thoughtful approach around how do you make a customer successful in a hybrid architecture versus most of the other offerings in the market which are pretty focused on getting all the data into one place, their place. And I think we certainly are looking to help customers centralize data when it makes sense, but also allowing them to exist in a world where data might be everywhere. I think that's the key is that it's simplicity and choice of where the data is so that you can bring it together when you need it. And I think obviously we'd be remiss and you kind of hit on it a little bit, but if we don't talk about AI, we'll get kicked out of the club or something like that. So how do you really see AI and how do you see Dell's infrastructure really supporting that and customers' AI strategies and data apps going forward? Yeah, I think data and AI are both very big strategic priorities for the company. It's no surprise that obviously AI has been a big growth story for us in the last maybe a couple of years. But like I said, every conversation about AI eventually gets to, well, what's your data strategy that's going to feed this AI strategy? And it's becoming pretty clear that the modern data strategy is essentially a modern data lake house. I think there's a race to get to sort of enabling that AI and therefore there's a race to get to this modern data platform. And I think customers are asking us to basically make that journey more simple for them. So I think from our perspective, right, data and AI are sort of very, very tightly coupled. But it's not just AI from our perspective, like you talked about multiple clouds. I think this intersects with our sort of existing strategy for the multi-cloud. And we define multi-cloud as not just on-premise or not just one cloud or not just even multiple clouds. It's actually all of the above. And so having a solution that lets customers access the data across their clouds and on-premise, maybe even a call load, maybe even an edge location is really the name of the game. So I think that lake house has to adapt to this type of a notion versus being another centralized sort of repository. So I think the timing is perfect really for us to partner with Starbucks and get into this market because people are going to need this more and more going forward. Yeah. And I think that's a good segue because, I mean, again, that's where Starbucks lives is really the intersection of data and data apps and bringing the data to where it needs to be utilized and kind of, you know, you can think of it as either bringing the AI to the data or the data to the AI. But to that point, multi-cloud hybrid cloud is true. And we're seeing it everywhere. Where do you see from a strategy and around AI that, again, the onus is on those organizations to really bring and feed the AI that data? How is it that you see that Starbucks is really providing value in that space? How are you bringing the data to the AI, I guess you could say? Yeah, absolutely. Yeah, we're as excited as everyone else about the, you know, the value that AI can bring particularly and, you know, bringing the value of data to the forefront of business decision making. We have a couple of areas where we're, you know, either already delivering outcomes or investigating and investing in the first ones I would argue for any organization is sort of like table stakes, which is just to make sure that the in-product customer experience is infused with AI. So just sort of simplifying the in-product experience. A good example of that would be natural language to via SQL. So the ability to either speak natural language that will then write SQL or vice versa. But that's sort of an example of our foray into simplifying the Starburst offerings with AI. The second one is actually something that we already do. And it's supporting traditional ML and AI use cases. I never thought I'd have to preface machine learning and AI with the word traditional, but I will in this case. And we played a role for a long time in the discovery and the wrangling and the governance of the model creation workflow. Just helping data sciences, data engineers and data consumers in general be able to get everything they need and package it up the right way to then feed it into a model. The third area is around furthering the platform's capability for analytics to lean in even more to supporting that predictive AI and traditional ML use case. We have ongoing conversations with customers, with partners and with Dell on how the Dell Data Lakehouse can really lean in there. And then the last one is around generative AI and in particular value creation in combining unstructured and structured data. And how do you do that in a way that allows one to enrich the other? How do you do that in a way that that's easy and reliable? And a lot of that effort is actually going to probably come from this partnership where we're going to leverage our ability to securely access any structured data with Dell's leading footprint in storing, protecting unstructured data. Yeah, I think that we've kind of beat around the whole solution a little bit in kind of the use cases. So let's kind of dive in a little bit for Sean, if you could kind of lead off with some of the key features and highlights from the infrastructure and data perspective and what the outcomes for those teams that would be using this product could be. Yeah, so I'd like to describe the solution essentially in four big parts. If you think about sort of the most basic components being your compute infrastructure, which obviously we're really good at, and we're tailoring it to particularly this type of workload. We have our leading object storage platforms that's going to basically serve as the Data Lakehouse, sort of the Data Lake where you could store some of that data. You have the Dell Data Analytics Engine, which is a new introduction into the market powered by Starburst. So that brings the massively paralyzed performance of the Starburst engine with inbuilt access control, encryption, masking, the ability to create data products, support for both long running queries as well as ad hoc analytics. So really sort of the full package of pretty much anything you'll want to do from a query perspective. But that's not it, right? And I'll pause there because I think a lot of customers when they do DIY, they tend to get these piece parts and they try to stitch it all together on their own. We're introducing another sort of piece of the puzzle here, which will help complete the stack, which is the Dell Data Lakehouse system software. And that system software really converts this piece parts into something a lot more integrated, a lot more turnkey. And it gives significant benefits to both the IT and the data teams, right? From the day zero of when you're sort of trying to purchase this to deploying this, we can take that deployment experience from weeks to, I guess, months to weeks, right? Because we're shipping the entire integrated solution from the infrastructure all the way to the software stack. We're taking on support for the entire stack, which means customers don't have to triage anymore. They don't have to second guess themselves in terms of who to call if something breaks. It's all 1-800 Dell from that perspective, right? And then ongoing from a day one, day two perspective, you get much better lifecycle management that's pre-integrated. It comes shipped into the box. So hardware alerts, things going down, adding and removing nodes from the cluster, adding and removing data catalogs or sort of connectors, if you will, into the platform, inbuilt security. I think there's a lot that we're sort of packaging in that makes this sort of an easy button while not making any of these things locked in, right? So the data formats are open. So you're not restricted, you're not sort of, you're not picking a horse, right? You're basically saying, I can keep any data I want on this lake house. The engine is obviously based on open source Trino and that's really, really extendable. And then we're providing a whole host of services around it. And I think, you know, being Dell, that's sort of one of the biggest draws for a lot of companies to work with us is because we provide amazing deployment services, support services, and then a lot of professional services that we're adding into this mix. So if you think about the data lake house, a lot of companies may be able to buy the software, but it takes another effort to even implement it, see the ROI and then grow from there. So we're providing implementation services and residency services to sort of complete the package here. So I think both for data teams who are looking to get a modern data platform that's highly performing, open, they're in luck. And for IT teams who are looking for cost effective, easy button and simplicity, I think they're also in luck, I guess. So it's really the best of both worlds. Yeah, no, I mean, I think that's part of it is that day zero to day one stuff, I think people get, and you know, maybe they're big enough that they can throw people at that. I think it becomes okay, understanding, not only do I need to know the roadmap, if I was going to say use open source Trino or something like that, I need to know that plus the roadmap for every other component that goes along with that entire stack in my modern data stack. And the more components you have there, it becomes really hard to support ongoing. I guess this isn't the only place, you know, or the only game in town. There's a lot of startups and established companies that are really aiming at this. What is it that, you know, really makes this solution unique? Yeah, so I mean, I can start and then add to that. But I think it's really the combination of your best to breed in every single sense, right? You start from the query engine with Starburst, right? It's a market leader, accelerates time to value, super simple to use. You have the object storage that's already sort of, you know, geared for performance and security. You have amazing compute platforms that we're already building. And then you add sort of this lake house system software to the mix. That's a combination that doesn't really exist in the market today. And then it's not just that, right? I mean, we're not even stopping there. Then you add the experts that a lot of companies, you know, maybe barring a few do need to take this from an idea to a reality. So I think the one plus two plus three plus four really is sort of the unique value proposition. Yeah. And I think just to just to add to that, I think this is the only purpose built offering of its kind for customers deployed in an on-prem or hybrid architecture. I think there's an understandable retrofitting of existing offerings to try to solve future problems or current problems. And I think this is the this will be the only offering in the market that's actually purpose built to solve these problems. And because of the combined commitment to create a cloud experience on-prem, but not focused on barring the customer from leveraging cloud, we're able to bring a lot of capability that you see Starburst and similar vendors bringing to customers in the cloud on-prem. An example being warp speed. That's one of many things that Starburst does for its customers that adds a tremendous performance gain that's actually only available in the cloud because of our ability to control the infrastructure configuration with our SaaS offering. And by partnering with Dell, we're able to bring cloud like capability. And again, this is one of many examples that would traditionally only be available in a public cloud to customers on-prem. And then the last thing I'll mention that, you know, you there are many folks trying to figure out what's their role in this stack, both longstanding and new players of the game. I would say that Dell and Starburst's ecosystem first, partner first mentality to supporting customers and going to market is also a pretty like massive differentiation between most of the other games in town. If you look at some of the other organizations from a technology perspective and from an advisory and services perspective that are going to be, you know, voicing their opinions and support about around this offering, I think that is, you know, as important as anything in terms of customer success because the platform we defined is certainly a center of gravity for a customer data strategy. But also it plays a role in a much bigger picture that we've taken into consideration and we've engaged a lot of the necessary parties. And so I think you're going to see customers able to move a lot faster with our offering than they may be either piecemealing things together or potentially leaning into a smaller, more per, you know, more targeted, you know, outcome oriented offering. Yeah, no, it totally makes sense. And I think, again, your guys comment that people want the ease of use of being in the cloud but want it on premise. And I totally agree on the privacy and the protection and compliance and governance, especially when the data is your intellectual property. So I think this solution sounds awesome to people and they should check it out. So I'm excited to have you guys launch this and introduce it here. So, you know, thank you both for being on today and, you know, welcome back anytime. Thanks for having us. Thank you for having us. Yeah. Thank you for watching this introduction to the Dell Data Lake House on the Cube, the leader in high tech enterprise analysis and coverage.