Connecting BI and Partner Tools to Databricks Lakehouse

Summary

This video demonstrates how to connect Databricks Lakehouse data directly to the tools business users rely on—including Excel, Google Sheets, Slack, Microsoft Teams, M365 Copilot, Power Apps, and Power Automate—without spinning up a separate data warehouse.
Live demos show Genie answering natural-language data questions inside Slack and Microsoft Teams, pulling from the same governed Genie spaces that data analysts use and making accurate analytics accessible to any employee regardless of SQL skill.
The Excel connector supports scheduled data imports and direct SQL queries from spreadsheets, while Genie skills extend beyond answering questions to triggering automated actions within business workflows.

Connecting BI and Partner Tools to Databricks Lakehouse

Did you know you can connect your favorite BI and collaboration tools directly to Databricks Lakehouse—without spinning up an extra data warehouse? In this video, we’ll explore how Databricks integrates seamlessly with tools like Excel, Google Sheets, Copilot Studio, Slack, Microsoft Teams, M365 Copilot, and more. You’ll see how these integrations empower your business users to tap into the full power of the Databricks Platform, including accessing Genie’s trusted insights directly into many of the applications they already use every day.
Talk By: Matt Holzapfel, Sr Product Adoption Architect, Databricks ; Toussaint Webb, Sr. Product Manager, Databricks ;

Chapters

FAQs

How does Genie work inside Slack and Microsoft Teams?

Genie is connected to Slack and Teams as a messaging integration, allowing users to send natural-language data questions directly within their communication tools. Genie routes these questions to the same governed Genie space used by data analysts, runs the appropriate SQL against the Databricks warehouse, and returns an answer in the channel within about a minute.

What can the Databricks Excel connector do?

The Excel connector allows business users to import live data from Databricks into spreadsheets through scheduled imports or direct SQL queries, without requiring SQL knowledge for basic use cases. This gives spreadsheet users a governed, up-to-date data source rather than relying on exported CSV files.

What are Genie skills and how do they extend beyond answering questions?

Genie skills allow Genie to take actions within business workflows in addition to answering analytical questions, moving it from a read-only query assistant to an active participant. This means Genie can trigger downstream processes such as sending notifications or updating records based on the data it retrieves from Databricks.

How does the Power Platform integration with Databricks work?

The Power Platform integration exposes Databricks data through a connector compatible with both Power Apps and Power Automate, allowing citizen developers to build low-code applications and automated workflows that pull from governed lakehouse data. This extends the reach of Databricks analytics into the Microsoft ecosystem's application and automation layers.

Full transcript

[00:08] Uh, welcome everyone. How are you all doing? Good. Good. That's a bit more energy than I expected uh on Thursday after the summit. Um, so yeah, it's been an action-packed summit. We've had a lot of announcements, uh, events. I'm sure you
[00:20] all have been to your fair share of happy hours. Uh, so thank you all for for taking the time to join us today, uh, at the end of the summit. We hope that the next 40 minutes will be informative, interesting, engaging, and
[00:32] you walk away with something interesting. With that said, my name is Tucant. I'm a senior product manager at Data Bricks where I focus on our partner ecosystem. Uh, so that just means I work on
[00:44] bringing all the cool stuff that's in data bicks and making it available in partner tools like Excel, Google Sheets, Slack, Teams, the list goes on and on. Um, so yeah, let's go ahead and get
[00:56] started. Uh behind me you'll see that I have Slack up. Uh and I want you all to imagine that I'm not a senior product manager at Data Bricks, but I'm a regional manager for retail stores. Uh
[01:09] and I have a report that I need to share with my boss uh coming up this week. And I need to get access to some data. Unfortunately, I don't know where the data is. I don't know how to run a SQL query. Uh I'm really quite useless uh in
[01:23] that in that capacity. And so I go to my my data channel within Slack uh and I post a question to my data analyst looking to get help. And I'm doing this early on because I know it's going to take him some time to run the question
[01:36] uh do an analysis and get us an answer uh before my report that I need to do by the end of the week. So I posted the question in Slack. Uhoh. I was up late last night at the Chain
[01:50] Smoker show. So, I did not have a chance to put together this report, but I know how to get this done quickly.
[02:03] Um, all right. So, this is just our secret that I didn't put together the the report. Hey, Tissant. Good news. Bad news. Bad news. Don't have your report yet. Good news. I hooked up Slack to
[02:17] Genie yesterday. And so now whenever we have a question about how sales looked, which stores are top performers, you just go into Slack, you hit Genie, send
[02:32] a message to Genie. That will hit the exact same Genie space that Molly, our CMO, has been using to answer all of her questions. It'll hit the exact same data um that she's looking at in order to to
[02:45] run her reports. um and it will give us um an answer in in under a minute. And so all of us can use Slack now in order to start answering these these questions. And so instead of you hitting
[02:58] me up late at night wanting to know about sales, just ask ask Genie in the in the Slack channel. Um everything is is there. And if you click view in Genie, then you could you could drill in and see that same
[03:11] >> Genie space. >> There we go. And it's ultra secure. >> Yeah, you just >> All right. So, flipping into the presentation here. So, that was just one example of the types of of integrations
[03:24] that um we will um walk through today. Uh we Yep. Uh I'm I'm Matt. Um by the way, I'm a product adoption um architect. I focus on um data warehousing more more generally. Um and
[03:39] uh for this talk today we're going to talk about um some of the the great integrations and um connectivity that we have for analytics, collaboration, apps and and automation. Um and so you saw
[03:52] the the Slack um integration which is just one piece of the the overall story. really the the goal is to ensure that um no matter where your business users work, whatever their favorite tool is,
[04:04] that they can still hit that um same governed uh set of of data using the lakehouse as the underlying engine. Um, one of the things that, uh, you've likely heard a lot about, uh, this week,
[04:17] um, is all of the kind of great capabilities around, um, our, uh, single unified, um, platform for, um, data and AI. U,, one of the kind of key pieces of
[04:29] that is the lakehouse. Um, and a key part of the value of of the lakehouse um is the fact that it's it's open by design, which also means that we have this um incredibly rich um open
[04:43] ecosystem. We're going to focus on a couple of kind of common patterns. One where um folks are using things like Slack and Sheets, another where you're using the Microsoft Office um suite. Um but the the number of integrations kind
[04:56] of go well um well well beyond uh beyond that. When you use these integrations, a key part of the value and a key part of kind of the value of what you saw with with
[05:08] Genie is that you're hitting the lakehouse. And so you're still using the same warehouse compute um that you would be using if you were just writing a SQL query within within a notebook. And so you're still benefiting from um that uh
[05:20] kind of best-in-class price performance that we have on the lakehouse. Um, and that's one of the main reasons that um, you've seen so many kind of amazing logos here um, over the past week and
[05:32] over 60% of the the Fortune 500 use the the lakehouse. Get that worldclass um, price performance, but also you're able to do that on top of um, whatever tools you you prefer to uh to work in. Um, now
[05:45] uh, we know that um, many business users don't live in the data platform. Um, early in my my career, um, I worked at at Dell as an as an analyst and, um, you would have to kind of rip Excel out of
[05:59] my my cold dead hands before ever before I would ever get rid of that. Like, I just liked working in spreadsheets. Didn't want to write queries. Didn't want to have to negotiate for for data data access. Like, just give me a spreadsheet. I know what I'm doing. I
[06:11] know how to do a pivot table. I know how to write macros. Um, and one of the things that is um, a big focus of ours is ensuring that those people like like myself who prefer to work in a spreadsheet that we could be empowered to work on that same copy of data so
[06:25] that everyone is speaking the same language. Metrics are aligned. There's no confusion over what were the sales last weekend. Um, because someone ran a report or pulled a report Sunday afternoon, we had more sales throughout
[06:38] the the weekend, things changed. We want everyone to be able to connect to live governed um data. Um a key part of the challenge of of doing that is um when you're working with all of these these
[06:50] different tools, all of these um different integration points, they all have kind of their own language in in in a sense. And so making it so that that data can stay governed, can actually work for everyone, and you could have a
[07:03] single source of of truth. That's really the the hard problem. I think we'll we'll focus a lot on the integrations and the user experience around that which is is super important. Um but kind of underneath that, the fact that we're
[07:16] we're working on a consistent set of governed data that that's really the kind of core value proposition. Um and so our our mission um within the uh the ecosystem um group is is really to
[07:28] empower organizations um to connect to their trusted data to whatever tool they love and do that securely and seamlessly because it's it's one thing to go into data bricks run a query hit export to
[07:40] CSV put that into Google Drive and use that in a sheet. I've done that a lot I will admit. Um, but it's kind of another thing and it's much better for for everyone to connect directly to the
[07:52] lakehouse and then be able to um query the latest data query the same data that the analysts the data scientists other colleagues are are using and and
[08:04] reviewing. Um and so um with with that I'll go through um another example here. Um once again kind of looking at uh a a sporting goods retailer data is in and data
[08:16] bricks. And as I I mentioned um I really really uh really like uh spreadsheets. Um and so I'm going to crouch down here in the corner of the stage um because
[08:29] our podium's a little small. um go into uh Google Sheets um and within Google Sheets we have this this extension um where I can launch a data bricks um
[08:42] connector um so it was over here the uh data bricks connector um and I could create a import and so here I've hooked up to that that workspace um you can see um here I'm using the serverless started
[08:55] warehouse the same warehouse um that um I use um in other parts of the uh the workspace. So I'm using that uh kind of scalable um compute. Select my catalog.
[09:07] I want to look at some sales information. Um specifically concerned about POS transactions. Now the um uh kind of options here. I could create a pivot table right on the fly. So I could set
[09:20] this up. So hey, just pivot the data for me automatically. In this case, I just want to see the last uh 10,00 transactions. I'm going to hit save and import. Um, the save piece is really important. Um,
[09:33] because I am doing an import, but I'm also saving this this query. I didn't have to write any code. I didn't have to actually write out the query. Um, but
[09:47] what makes this um, super valuable is now I have my my table. If I wanted to add a chart arbitrarily, I could I can do that. I have my my regular report
[09:59] Um, but now with this, if I want to create a schedule, I can create a new schedule. Import it here. Let's do this every day at 9:00 a.m. So I'm never caught off
[10:11] guard. I create that schedule. And now every day at 9:00 a.m. I have the schedule that runs. I can hit refresh,
[10:25] view my schedules. It's going to do a refresh for me. I can see what I uh was looking at. View that run history. And
[10:42] there you go. I'll refresh that again.
[10:59] and my chart stays up to date. So I can do this on a recurring schedule, keep my data fresh and work within my preferred tool which is Google Sheets. If I want to create more schedules can can do that as well.
[11:12] this on. >> Perfect. >> Back to the slides. >> Okay. Um, so now I'm going to cover more generally how we handle this the
[11:24] spreadsheet connectivity and some of the details. Uh, so let's start with with why we built this. Um, love it or hate it. And as Matt mentioned, he used to love it. Uh, now that he's on the data platform side, he
[11:37] might hate it. Um, business users want to be in Excel and Google Sheets because it can solve millions of use cases for them, whether that's ad hoc analysis, uh, financial planning, forecasting,
[11:50] whatever the case is. And so, we need to be able to get the information that's in the lakehouse uh, into Google Sheets in a way that's scalable, governed, and and all of those good things. And so, that's what we set out to solve when we built these connectors to uh, Excel and Google
[12:04] Sheets. Um, so three things that that came up consistently when doing customer interviews around uh challenges with connecting data bricks to Excel and
[12:16] Google Sheets is number one architectural complexity, right? uh customers had to build their own connectors, procure a third-party, come up with complicated processing pipelines
[12:28] with Python and push it into like just things that were they were entirely too difficult uh to achieve a very simple task of I need my business user to have data in Excel so that they can do uh whatever it is that they need to do to
[12:40] get their job done. Uh number two is that there's a lot of issues with stale data, right? If you were not planning on building a a Python script or or procuring a third party, that meant you likely were emailing a CSV uh to your
[12:54] business users, which is not a governed approach, but then also soon as you send that email, it's already out of date the information that you're you're accessing. Uh and then the third issue is that it created a lot of bottlenecks on your analyst team who are being
[13:07] inundated with requests uh to get those uh queries run and and get the CSVs for their business users. Uh so we we set out to to uh solve all these problems with with our connectors. So I'll start
[13:20] with the Excel connector. Uh today it's in public preview. Uh so anyone with the data bricks workspace can can use the connector. Uh at a high level it helps you solve two core tasks, right? Number
[13:33] one, you can import data. Uh you do this by selecting a table or metric view, applying filters, and then loading the data in. It doesn't require any SQL. Uh so your business users don't have to be
[13:45] upscaled in terms of of SQL. But if they are comfortable writing SQL, they can also do that as well. The second task that it allows you to accomplish is that you can write data back to Unity catalog. Uh I know that gives uh pause
[13:59] and panic to quite a few data platform teams. Um so users can only do this if they have permission, right? If they do not have permission in Unity catalog, they cannot create a new table.
[14:12] Uh these operations uh as I mentioned are are entirely at a table level. There's a few other features uh in this connector such as we have custom functions uh so that you can directly embed a SQL query within uh Excel as
[14:27] well as uh we can access queries that you've saved in your data bicks workspace and bring them into Excel. Right? So if you wrote a SQL query for your business user, you just say, "Hey, use this query, click run." Uh they'll
[14:39] be able to do that as well from from Excel. All right, my Wi-Fi decided to not work. Let's try again.
[15:01] Okay, well that's awkward. Um, so th this was meant to be a slide on uh Google Sheets. I don't know why it's not loading right now. Um, but essentially Google Sheets connector is largely the same and as Matt was showing, uh, you're able to pull in data without ever having
[15:14] to write SQL. Uh, the one benefit of the the Google Sheets connector today is that you can schedule refreshes of your queries. Uh, so if you know that you have a report due every morning at 9:00 a.m., 10 a.m., you need an update every
[15:26] hour, uh, you can do that. And under the hood, all we're doing is creating a job within data bricks uh that's running and then pushing that data to uh Google Sheets on a schedule. Uh and and so we have the Excel
[15:39] connector that's in public preview. We have the Google Sheets connector which is uh generally available. And in both cases, we have customers who are uh trusting both of those platforms to democratize access to their lakehouse
[15:51] for their business users and who are rolling it out uh to their entire organization. Um today uh two quick things on on what's what's on the road map for both of these connectors. Number one is we'll be
[16:03] integrating Genie uh into both experiences. This means that they won't even have to point and click anymore. They'll just be able to ask a natural language question, get a response back, and then we'll load that data into uh
[16:15] Excel and Google Sheets. And then the second thing is that for Excel in particular, we'll also be adding the schedule refresh capability. And we expect that both of these capabilities would uh launch by the end of uh data bricks quarter uh which is end of July.
[16:30] Uh so with that I'll do a demo quickly of uh Excel. Let me just load the screen up again. Um
[16:45] all right. Okay. So the addin pops up here. I'll be prompted to to log into my data bricks workspace. Uh and again very similar to to Google Sheets, no SQL is is required. I can go in uh search for my
[17:00] table that I'm looking for. In this case, I'll select a metric view. Uh because say for example, I want to build a pivot table about quarterly sales. Um expand this out a bit, make this a bit
[17:13] bigger so I can see all of the dimensions and measures that are in my metric view. And at this point, I can just drag and drop uh whatever it is that I'm interested in in using. Uh so I'll pull over quarter. Uh I want store
[17:26] name. And then I'm just interested in the revenue. Um at this point, if I were to uh import the results, it's going to run
[17:38] a query on the data brick side. And because we're using pivot tables, we're going to aggregate the data first and then load it into Excel so that we avoid the the Excel limitations on the the number of rows that can be brought in.
[17:51] Uh so while this is is loading, wait a second.
[18:07] All right. That's not expected. >>
[18:25] All right. Today is not my day. Um, this I've never seen that error before. Uh, but the the the expectation was that the data would be loaded in um as a as a pivot table. Um, let me just try this
[18:37] really quickly. uh select for okay something is down right now so I will need to check that after this call um but uh the addin besides the the
[18:53] issue I I am clearly showing as the demo here um is expected to load the data in to Excel without me having to uh write any SQL queries or uh do anything along those lines and as I mentioned you also
[19:06] have the rightback capabilities Um, so you would just select the range of data that you're looking to write back. Uh, select the catalog and and schema that you're interested to writing the data to. Create the table name. Uh,
[19:20] let me just do this really quickly. And the whole thing is just not going to be my friend today. Um, but uh, the rightback capabilities would be would be at a table level. Um, let me go back to the the slides here.
[19:36] um hand it off to Matt to talk about Slack and Teams integrations. >> All right, let's get into collaboration tools. Um the only thing more terrifying than demoing your own product is demoing
[19:49] your own product embedded in another product. And so yeah, >> you know, Tissant, I feel for you, man. >> Yeah, trying to figure it out right now.
[20:02] ready AI cooworker. We've been we've been talking a lot about about Genie, the kind of problem space that that we're trying to to solve. Um but but more broadly um AI has worked so well in
[20:15] coding environments and for engineers because the whole kind of DevOps tool chain and the whole kind of software development life cycle has really been designed around making it easy to hand off and coordinate across um very large
[20:29] distributed set of engineers where you have rich context built into the repo. people are expected to leave good comments about what they built um and and and how it works. That doesn't really exist in in the enterprise as
[20:42] much. If you're working in marketing analytics, you need to go and and have a conversation with someone on, oh, we used to measure campaigns one way 6 months ago. We completely changed it. We haven't written it down, but
[20:55] institutional knowledge exists, so we kind of know how to how to look at it. Um, and that's what one of the things that just makes AI um tools more generally kind of outside of of coding applications um has has made them um
[21:08] very difficult. Um, and business users who are trying to to use AI and throw AI on on top of um their their business data are really kind of feeling a lot of that pain because one very often it's the case where you just don't have um uh
[21:22] business context. the number of spreadsheets that are just floating around in our in our enterprise with with no context except the person who got the spreadsheet received an email and that email probably had the context. They didn't copy and paste that email
[21:34] when they dropped it into SharePoint and so the context is is separated. It's very fragmented. Um and how do you actually govern all of this? Should we put the email um into SharePoint or into
[21:46] somewhere else but then everyone can see the email? So it becomes just really really difficult for how do you actually make it so that AI can work um for your business users um with Genie not only
[21:59] kind of the interface that um uh we showed a little bit ago but with just the whole Genie um product suite things we're doing with ontologies for for example a big part of that is because Genie is designed to be data smart AI
[22:12] that understands your business context AI um doesn't as our Ali, our CEO says, you know, AI doesn't have a intelligence problem, it has a context problem. Um,
[22:24] and so a big part of what what we're trying to do is is really um solve um that uh context problem. um as we're making great progress with uh things like Unity catalog ontologies and
[22:38] solving that context problem. The other end of the problem and the other piece that we are solving that you saw first with um Slack um but also exists in
[22:50] Teams and um M365 copilot is you want to have that context. You want to have that ability to answer those questions wherever you you work because um it's
[23:02] kind of one thing to just solve the problem of like hey all of our data is nicely organized and tagged and people um who have access to to data bricks understand it but to actually drive value get consumption take action on the
[23:16] data you need to get that into the tools that that you um that you use and you have um and so we have the the Slack integration in public preview today. You saw that at the the very beginning. We
[23:29] within within data bicks use this um all the time. It's tagged in um a bunch of our our channels. It's really um kind of raised the bar on the quality of conversation and the speed at which we
[23:43] can answer questions um as a result of having that in in Slack and being able to collaborate um on that. Um, but we've also um rolled that out in beta within u Microsoft Teams and M365 C-pilot. You
[23:56] didn't um get to see the configuration for um Slack and the way that works, but um it's really you have the the Genie app. You point it at the workspace and then once it's connected to the the
[24:08] workspace, then from there you could start to ask um Genie those questions um and then it will look for the appropriate Genie space answer. um uh and then um help get you the the
[24:21] insights you're looking for. Um and kind of to the uh the initial framing on the the broader um problem statement, you know, the the goal here is to really make Genie much more than than a
[24:34] chatbot. Um you saw a little bit of here I could tag Genie and get an answer to my my question. Um, but I can also follow up with Genie and say, "Hey, um, give me this answer 9:00 a.m. every
[24:47] Tuesday." And when you schedule that, then you'll get an email 9:00 a.m. every Tuesday with that report and with that information that answers your your question. So, uh, Genie becomes much
[25:00] more of another co-worker. Um, and, uh, beyond just being able to answer those questions, you also have a concept within Genie of of skills. And so people who um have worked in things like claude
[25:12] code where you have a skill where a skill is basically a um a repeatable um uh kind of workflow or activity um that's been condensed into um a
[25:24] executable form. Um then we point at the skill and say you know hey run this type of analysis. It has the routine in order to run that and is able to do that so that you get um reliability on those
[25:36] repeatable um uh repeatable tasks. where where we're going uh next with Genie within the context of these integrations. The the part that I'm most
[25:49] excited about is going from answers to to actions. Uh because often times a Slack conversation um will uh kind of look like uh hey Genie, how many
[26:01] migrations uh did we do over the past 3 months? and it will produce a report and say here's how many migrations went live here here's how many migrations are in a P. I'll drill in, look at an individual
[26:15] migration, and then I might want to leave a comment and say, "Oh, this was tagged as a snowflake migration. Let's say it's actually a Big Query migration." And where we can um where we're going is being able to have Genie
[26:28] be able to actually make those updates, take take that that action. Um, and so being able to work with those different MCPs that are kind of being put in place just all across the enterprise so that Genie can be a very very very productive
[26:41] um, co-worker where when I see something and want it to take action, it's able to uh, to do that because it actually has the interfaces and um, the connectivity. Um, we'll flip over and uh, go into
[26:54] another demo around uh, retail and collaboration. >> All right. All right. So, let me open Teams. Um, so same way in Slack, we have an app in Teams called Data Bricks Genie, uh, that
[27:08] you can chat with in one-on-one conversations. Uh, you can also appment mention it in uh channels or or groups. Um, to Matt's point, this is the main configuration point uh that we have in
[27:21] the uh integration. So, you select your workspace and then you select the scope of Genie, right? So in this particular workspace, I don't have any Genie spaces which have been renamed to Genie agents.
[27:33] Uh but if I were, I would have the choice between picking a specific uh Genie agent to have my conversations with if I know that it's going to have all the context and all the information that I'm looking for, or if I really
[27:45] don't don't know what information uh I'm interested in, uh then I can leave the conversation with with Genie uh who can look across my entire workspace, click across my tables, dashboards, volumes, etc. and and find the the correct answer
[27:58] based on uh the ontology graph that we have within within data bricks. Um so if I go here and I ask a question about which stores has been performing the best in the month of uh June, Genie
[28:12] is going to uh receive the question. is going to start the process of thinking. Uh and then in these one-on-one conversations, you'll be able to see uh what thought process uh Genie is taking to to uh return the response to the
[28:25] particular uh question that you've asked. Um and as uh uh Matt mentioned uh the the questions come back relatively
[28:38] quickly. uh and then back to the the point I can provide feedback to the question so that I can give signals to uh Genie whether the ontology the the way it answered the question uh was
[28:50] correct and what I was expecting. Uh and then I also have the the sources that were used to answer that question if I want to investigate uh and dig into the to the answers more. I'm glad that demo decided to be
[29:02] friendly with me today. Uh cuz yeah, it's been a been a long day today. Um all right so the last one is on on apps and automation. Uh I want to cover how
[29:14] you can connect uh data bricks to to a few uh uh platforms uh in this space. And so the main challenges here is I have a lot of data. It's always
[29:26] changing. Um how do I keep that up to date in particular workflows that I need uh to have synced? So we saw that in in Google Sheets, but we'll we'll talk about that uh in a few other integrations uh here as well. Uh so
[29:40] there's there's two main integrations that I want to call out. Uh the first is our power platform connector. Uh this connects to Power Apps, Power Automate uh and Copilot Studio so that you can build apps on top of data bricks, build
[29:52] automation flows on top of data bricks uh and connect uh Genie to Copilot Studio agents. And you can do all of this without ever having to copy data uh into power platform or into uh to data
[30:04] versse. Uh the queries are sent directly to data bricks and then we return the results so that you can visualize in in the particular platform within uh power platform. Uh currently that's GA on Azure. Uh it's public preview on AWS and
[30:17] GCP but we'll be taking that one to GA relatively soon. And then the second one is uh PowerBI tasks within uh data bricks link flow. And so what this allows you to do is orchestrate your semantic models directly from within
[30:30] data bricks. Uh this is currently in in in public preview as well. Uh and we plan to take this to GA soon. Um and so I I uh mentioned some of the things that we'll be working on. But to get those
[30:44] products to GA one, we'll be adding incremental refresh to PowerBI task. Uh today it's a full refresh. You want to make sure that you're not wasting uh money unnecessarily. So you're only loading the data that needs to be loaded
[30:56] into to PowerBI and that should be launching relatively soon. The second is that we'll be integrating lakebase within uh the power platform connector so that you can get all of the the benefits of uh the fast reads fast
[31:08] rights that that lakebase provides in addition to um the analytical capabilities of uh lakehouse. And then the the third is bringing OOTH support to AWS and GCP to make sure that all the
[31:21] governance that you set up in data bricks uh is is respected when you're using AWS or GCP with with power platform. Um so we set out to have this conversation
[31:34] to talk about three ways that you can connect uh govern data to the data bricks platform across analytics, collaboration, uh apps and automation. Um and uh we hope that uh with this
[31:48] presentation um what you'll take from it is that one we're doing our best to try to meet business users where they are to make your jobs easier uh so that they can see the value of the data bricks platform in the tools where they're
[32:01] doing work and also remove burdens from uh data teams having to be the bottleneck on on getting access to uh data and information. We want to do this in a way that maintains uh data bricks as the source of truth. so you don't
[32:14] start recreating data silos with these connectors. And then third, doing that in a in a governed way so that all the work you've done to set up Unity catalog is respected. Um, so where can you go to get started? Um, I recommend enabling
[32:28] the the uh Excel, Google Sheets, Slack, Teams integrations in your uh data bricks workspace account console. Um, we have a ton of blogs and documentation about all of these integrations uh that
[32:40] can can help you get started. And then um we have demo videos as well. And the last thing uh data bicks uh really wants Genie to become that AI co-worker for you and for your business users. So I
[32:52] recommend trying that out. Uh and the data bricks team is really open to feedback. So uh feel free to share that with your account teams or or reach out on Reddit, LinkedIn. Uh we we'll make sure to respond there. Um so thank you all for your time. Um
[33:07] you. Thank you.

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