Transcript

Hey everyone, I'm here with Scott Guthrie, EVP of Cloud and AI at Microsoft, and a legal team, CEO of Databricks. So Ali, tell us a little bit about what data Brexit is. Yeah. So what Databricks helps you do is take data that you have inside of an organization, structured, unstructured, organize it all in open formats, then put governance on it so you can secure and lock it down. And then on top of that, you can do all kinds of things ranging from. Analytics to all the way to building agents and AI and it's all of this is integrated into the whole Azure ecosystem. So it's integrated with every product that you have inside of Azure. So that's kind of what I love that. I love that. And Scott, you know, going off of what Ali has said, what is the relationship between Databricks and Microsoft? How do you work together and for how long? Well, we've been partners for well over 8 years that we've actually had. A first party service, meaning it's Microsoft branded Azure data bricks. I was just catching up with Ali before this event in terms of I remember an event we did in New York, we're trying to figure out it was 2016 or 2017 where we first showed it to the world. And it's been a fantastic partnership. We've got more than 20,000 enterprises around the world that are using it today on Azure. And you know it's really been fantastic for both companies and then more importantly for our customers because it really enables that open. Flexible way to kind of leverage your data and use your data more right and I'm gonna hang there a little bit because the importance for customers and this partnership and why it's lasted for so long, can you walk us through a little bit of what that's been like Well, I think the thing that both data Bricks and Microsoft very focused on is how do we make our customers successful and data is at the heart of every business and how do we let customers securely store their. Data. How do they actually unify more of their data assets in one place and ultimately be able to get BI value out? So our Power BI integration together is one key area. How do you do reports in query analytics? And then ultimately, in this era of AI, how do you actually train AI models? And then ultimately, how do you surface AI agents And a lot of the work that we've done across Microsoft Copilot and actual 365 Copilot and for example, data. There's Genie is about, you know, integrating the agent so that, you know, you can have one seamless experience in the context of AI across your entire data state. Yeah. And it's so important, you know, put the customer at the center, which is what you both have been doing on this partnership has been doing. So Ali, tell us more about how Azure data Bricks works with Microsoft and Azure ecosystems. How is it beneficial to the customer? Yeah, So the way it works is that Azure Databricks first of all, integrates with Entra, so all of the identities. If you have an extra flow right through Azure data bricks, and then of course, all of the data is stored in open formats. And what we're really excited about here now is that any data that you have in Databricks that you've organized with Unity Catalogs, you've set up your governance, your security, you can now mirror data that you have in one lake into Unity Catalog and access it without any data migration. It's zero copy. And you'll be able to do the same thing in the other direction as well. And any data that you had stored in Adls in Azure. Bricks we'll be able to also with 0 copy mirror and make it available also in one lake. So the integration works really, really well. Then of course, it's also integrated with the whole AI stack. So things like Agent 365, things like Foundry, things like Copilot Studio. So it's seamlessly integrated into all of these things. Even Power Apps is a very, very popular integration between Azure Databricks. People love building these Powerapps. It's no code, low code then you can build, especially these days you can vibe code anything you like. Uh, but at the core of it is again, that data that's so important to the enterprises and then making sure that you've actually locked it down so that you ensure privacy, right? Right. And I think for a lot of customers, actually all of our customers, security is important. A unified platform is important. So Scott, tell us a little bit about what's new for the Azure data bricks around data analytics and what big news are you sharing? Well, I think the biggest news probably this week between the two companies is the integration we're doing across. Are one lake offering at Microsoft and Unity Catalog which is Databricks is offering and as Allie mentioned just before that ability to kind of seamlessly integrate the data together without needing to copy it really enables organizations to leverage the data that they have every time you have to copy there's some latency in the copying there's extra cost in the copying and at the end of the day if you can actually. Seamlessly integrate into your lake house. Data from all of your existing systems of record and across all your different analytic systems, you can leverage that data and get more value out of it faster, cheaper, and better and so like that's a huge announcement for us in terms of that integration again across 1 lake and across Unity catalog and that really unlocks the entire data bricks and Microsoft ecosystem in one unified governed way and I think the other big announcement is around the work we're doing around agents and. So that ability again with all the announcements we're doing this week around Microsoft 365 Copilot. Agent 365 and all the work we're doing around governance right now, we'll work with Databricks Genie, which is Databricks's AI agent. And that ability to kind of have agents composed with agents to solve real world problems, I think is what every organization is looking for. And it really unlocks A tremendous amount of value. And I think what I'm hearing from you too is that you are listening to the customer, you're giving the customer what they want, also what they need, right, which is super. Important. So Ali, talk to me a little bit about how this all comes together, right What is the product experiences that customers can have from this and then what can you show us here? Yeah, I mean honestly more than me talking about it, maybe if you just look at the demo, take us through yeah, yeah. So OK, so if we next look at the screen here So what we're looking at here is Unicap Log, which is a central place where you set up all the governance of Azure data bricks so. You can have all your data there, all of your models, all of your unstructured data. But what we're doing now here is that we've actually set up. You can see here, we're clicking on one link and 1 lake is now seamlessly available, 0 copy mirrored in and you can see it in unit catalog. We also have SAP Azure Databricks here. So we can see actually data that we have from SAP could be ERP data that you have and it's stored here. So OK, so we've set up all our data in uni Catalog. We've governed it. We have access to 1 leaked data. We have access to SAP data. So next, let's start asking some questions from this data. OK? OK, So what we're going to do next is that we're going to go in and this is Jeannie. So Jeannie lets, you know, select tables that you have in Unity catalogs. It could be in one lake, it could be in SAP, and you can start asking questions in English from the data. So I'm going to select one of these that's there. And we're going to ask question about this VAT receipts here. But what's really interesting about it now actually we'll construct queries for you as if it was an analyst and it will give you the answers without hosting. So you can see here it's giving you the dollar amounts. Now the great thing is you can always bring in a subject matter expert that can look at the query if you want. And you can edit the query and it can make sure that that query, there are no errors in it. And if they fix it, they'll remember it will get smarter over time. You can of course, click here. You can see it's actually extracted all this English descriptions about all the information that you have in Unicap log. So it's used generative AI to describe that. And you can, of course, go and add your own instructions in English if you want to make the room smarter, smart, OK, you can add more sort of things that, you know, you think can guide it better. Yeah, we like smarter. Yeah. So that's that's Jeannie. And now we have our genie there. We can ask questions about it in English. But what if we also have a bunch of unstructured data? So here we're going to build an agent next in agent bricks. So this is a knowledge assistant that's running on one like you can see and. Inside of it, we can set it up and configure it so it has access to these CFO nodes and some financial documents. And we can debug it here on the right. So you can see how it would be if we ask it in English questions, it can now actually go and interrogate that unstructured data. It thinks through it, it comes up with a plan, and then of course give you the answers. But the nice thing about the answer is that it gives you is that you can actually get receipts. You can actually see where did it get exactly this information from and you can start iterating. On that quality so we can see here it's extracted information from these PDFs and the best part of this is that you can actually now give it instructions to tune it so that it gets smarter over time right OK so that's just now we have this we have two ways of asking questions let's say we want to put together a multi agent system so we want to string together multiple of these agents so this is a multi agent supervisor that we're looking at here so we have our unstructured data we have our structured data at the bottom also we're giving the access to just. Search. So we're giving it MCP server that it can actually just go off to and do web search on. So now you have an agent that can both interrogate the structured data, generating SQL queries for you automatically, the unstructured data. And if there's something it doesn't know, it can actually go off to the web and search the web for that information. So let's give it a try. So we ask a question again in English. But now the multi agent supervisor will figure out a strategy and see where can it best find this information out of these three sort of agents that we've given it. So you can see here, this shows you actually the queries that it's gonna make so we can actually debug them again and making sure that they're doing it the right way. And if the answers are not what we want. Can actually go ahead and look at how we actually received those. So let's look at the traces. And you can see the details of the traces, all the agents that were called, all the invocations. You can actually debug them and make sure that they're doing it the right way. You can look at the timeline here on the X axis, you can see how long it took, and if there's something too long, you can fix that. And make the agent just perfect for that use case that you have. Uh, so that's how this works. But what I'm really excited about is here when you put it all together, yeah, inside Microsoft next. So what we've done now is we take any, we put it inside Copilot Studio and we actually integrated into Teams. So we should be able to now ask all of these agents directly in teams the questions that we have and they'll actually go through again, governed by Unicap log, ensuring the correctness that we just iterated through. And it can do that for you. But the best part of that, all of this is available right now, but what's coming next is that you'll be able to actually take. These agents and put them in Agent 365 and that way you can have digital coworkers inside office that you can actually talk to and the routing your questions to the right agent for the right job. And it'll just be available there for you, seamlessly integrated with Azure ecosystem, part of Microsoft 365. And I love that what all that you demoed is available for the customer right now. And you demoed a lot there. So tell me, what's the one take away that you want the customer to walk away? From this right now, yeah, that, you know, it's really important that you can govern and secure your data. And once you've done that, you can build these agents and they're seamlessly integrated into the whole Azure ecosystem, whether it's Extra or it's Agent 365 or Foundry or Copilot Studio or Powerapps, it's just integrated into that whole ecosystem. And therefore, you don't need to think about how do we make this work? It's just seamless and to use. Scott, tell us a little bit about how this partnership enables customers to leverage all their data investments. Well, I think, yeah, I think it's kind of all he mentioned. I think one of the things that's most powerful is the fact that it really enables you to integrate with everything, both in terms of what's consuming the data, but then also where you pull the data from. So for example, SAP data bricks integration that Ali showed allows you to kind of take your SAP instance running on Azure and seamlessly be able to access the data. And importantly, it's not a copy of the data. You're accessing the live data and so. Accounts receivable changes in the next minute it's reflected and that ability to kind of have that data be live and then integrate it with your employees through 5. Having digital agents that work with your employees through our Agent 365, all integrated with a single common security model that you can govern around and reason around, really enables organizations to leverage all their data but also do it. That much, much faster. Yeah. And, and be able to really integrate AI and drive business outcomes with it, which is what everyone's looking for. Yeah. And I think what's brilliant about this and also powerful is that this partnership provides customers with seamless access to their live data. And I think that's really, really incredible. And so thank you both for joining me. Where can customers go to learn more? Well, there's actually 2 sessions, I think one at 1:00, one at 2. OK. Both on Azure Databricks, so check those out and please attend those. Yes, thank you both for joining us. Thanks for having us. Thank you.