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Getting Started With Lakeflow Connect

Summary

  • No-Code Data Ingestion: Lakeflow Connect provides built-in connectors for SaaS applications, databases and file sources, enabling data ingestion with just a few clicks.
  • Integrated Governance: The solution is fully integrated with the Databricks Data and AI platform for built-in governance, observability, and CI/CD automation.
  • Ready-to-Use Analytics: Hundreds of customers ingest data from Salesforce, ServiceNow, Google Analytics and more to unlock immediate data intelligence for analytics and AI.

Getting Started With Lakeflow Connect

Watch: Getting Started With Lakeflow Connect
Talk by Giselle Goicochea, Sr. Product Marketing Manager, Databricks ; Peter Pogorski, Senior Product Manager, Databricks
Hundreds of customers are already ingesting data with Lakeflow Connect from SQL Server, Salesforce, ServiceNow, Google Analytics, SharePoint, PostgreSQL and more to unlock the full power of their data. Lakeflow Connect introduces built-in, no-code ingestion connectors from SaaS applications, databases and file sources to help unlock data intelligence. In this demo-packed session, you’ll learn how to ingest ready-to-use data for analytics and AI with a few clicks in the UI or a few lines of code. We’ll also demonstrate how Lakeflow Connect is fully integrated with the Databricks Data and AI platform for built-in governance, observability, CI/CD, automated pipeline maintenance and more. Finally, we’ll explain how to use Lakeflow Connect in combination with downstream analytics and AI tools to tackle common business challenges and drive business impact.
Here’s more to explore:
Production ready data pipelines for analytics and AI: https://www.databricks.com/solutions/data-engineering
The Big Book of Data Engineering: https://www.databricks.com/resources/ebook/big-book-data-engineering-2nd-edition
See all the product announcements from Data + AI Summit: https://www.databricks.com/events/dataaisummit-2025-announcements

Chapters

FAQs

What data sources does Lakeflow Connect support?

Lakeflow Connect supports SaaS applications like Salesforce and ServiceNow, databases including SQL Server and PostgreSQL, file sources and more for flexible data ingestion.

How does Lakeflow Connect integrate with Databricks?

It is fully integrated with the Databricks Data and AI platform and provides built-in governance, observability, CI/CD, and automated pipeline maintenance.

How does Lakeflow Connect compare to Fivetran for ingestion?

This video introduces Lakeflow Connect on the Databricks Data and AI platform as a data integration solution. Unlike extract-transform-load (ETL) tools like Fivetran, Lakeflow Connect specializes in bringing data from multiple sources directly into your lakehouse with minimal transformation overhead. The video demonstrates configuration and operational benefits for organizations consolidating data infrastructure.

What data sources can Genie One connect to?

This video covers getting started with Lakeflow Connect on the Databricks Data and AI platform. While focused on Lakeflow, this video discusses integration with Databricks ecosystem tools including Genie. Through Lakeflow Connect and Genie integration, you can connect to and query data from numerous sources including SaaS applications, databases, and cloud storage systems.

How to ingest Salesforce and Workday data without building custom API scripts?

This video demonstrates Lakeflow Connect's capabilities for ingesting popular SaaS data sources like Salesforce and Workday on the Databricks Data and AI platform. Rather than building custom API integrations, Lakeflow Connect provides pre-built connectors that handle authentication, schema detection, and incremental updates automatically. This approach dramatically reduces engineering effort and time-to-value for data integration projects.

Full transcript

0:00 Summit welcome and intro

All right, let's go ahead and get started. Um, couple things here. So, welcome to the data and AI summit. We have the distinct um, honor of being one of the first sessions right out of the gate on Tuesday. Um this is a slide we have to show very important um presentation forformational purposes only. Of course as we continue to develop this product it may contain forward-looking statements. All right. So it's fitting we're up first on Tuesday since we want to help you learn all the basics on LakeFlow Connect and efficient data ingestion into your lakehouse and how to get started.
The rest of the week we have a ton more LakeFlow Connect sessions here at Summit from our SMEES and from our customers. So please be sure to check your schedule. All right, so let's introduce ourselves. Uh. Uh I'm the LakeFlow Connect product marketing lead. Um previously PMM for a few years at AWS. um Pagorski is a LakeFlow Connect product manager um and previously a PM at Microsoft. He'll be up here shortly um doing an amazing demo that we have for you today.
All right, so we've got lots we want to cover today uh so you can learn more about LakeFlow Connect. We'll start kind of with the current data ingestion landscape, probably things you're dealing with right now. We'll talk about the basics of the product. Um like I said, will do that demo on LakeFlow Connect in action um with a fictitious Spark paper company. Uh we'll review our road map, some exciting releases, how to get started, uh reference some customer stories, and finally we'll wrap with some frequently asked questions, uh and obviously take questions from you all.

1:34 Data ingestion challenges

All right, so let's talk about your data and what you are probably dealing with now. Now, before you can take advantage of data bricks, you have to be able to access your data. And that data is probably all over the place. You have it in SAS applications like Salesforce, Workday, Service Now, Google Analytics. You have databases like SQL Server, Postgress, Oracle. You have file sources, message buses, and APIs and so on.
So, it can be really difficult to get that data into your lakehouse so that you can do meaningful things with it. So, what are the challenges? To start, pipelines are hard to set up, creating efficiencies. SAS APIs have a ton of quirks and can be pretty difficult to wrap up on. They also have they also change a lot so it's hard to keep up with them. Uh database pipelines are even harder to configure and manage especially without impacting the source.
So customers do a lot of work to build these pipelines as you know to optimize them and particularly to maintain them. So spending time on all this can get super expensive and it slows your time to value. It doesn't help these pipelines often require a specialized data engineer. So the people who need to use this data downstream the analyst, the ML engineer get blocked. This is the opposite of democratization with silos and low productivity.
You might not even want to democratize the data when you have a patchwork of pipelines because it's so hard to govern it effectively. There is no holistic data view and issues with unified access control lineage monitoring. So at the end of the day, you're not fully utilizing your data. Your teams are probably running into pipeline outages and you have underutilized data. You might not you might even have some security risks.
So data bricks has done a ton of work already to address this. Let's review some of the existing solutions that we can help you contextualize where we are today with our portfolio. So at the most customizable level of the stack, we have structured streaming. This is where oh sorry this is an API for incremental stream processing in near real time. Engineers love the performance the scalability fall tolerance but over time they asked for more automation and less overhead.

3:49 Introduction to LakeFlow

They wanted better autoscaling data quality constraints failure recovery and so on. So we added DT a framework for building reliable and scalable data pipelines. You define the transformations to perform on your data, manage orchestration, monitoring, data quality, errors, and customers love that reduce the complexity um while also improving the performance of their pipelines. Then we heard more demand for something that's even easier to set up, even more hands-off, even more when you're dealing with those complex data sources.
You don't want to spend your time learning about those SAS APIs and all of their edge cases. you don't want to deal with the networking complexity of on-prem databases. This is why we launched manage connectors on LakeFlow Connect. We recently went G for Salesforce and Workday and we'll talk about those here shortly. Um, but it's important to note that LakeFlow Connect now is not synonymous with connectors. For example, autoloader can currently be used to ingest from cloud storage with both structured streaming and DT.
We also offer connectors for Kafka, Kinesis, Pub Sub and Pulsar at those layers and over time we intend to provide more managed versions of those same connectors in the Lakeflow connect layer 2. So in the fullness of time instead of focusing on what data sources I have or what layer of the stack I'm in, the question will be how much management versus customization do I actually want. So, LakeFlow Connect, as I said before at the very beginning, is efficient data ingestion into your lakehouse.
Look at all those data sources. We'll get into the specifics of each release and road map a little bit later. Now, before we dig into LakeFlow Connect and what it actually is, it's important to take a step back and ask what is LakeFlow? You'll hear lots more about Lake Flow here at Summit. Just a show of hands. How many of you know what LakeFlow is? Okay, a few of you. So, data bricks lakeflow is a unified intelligent solution for data engineering built on the data intelligence platform.

6:12 Benefits of LakeFlow Connect

So, as you can see here, you have three components. Today, we are only talking about the first ingestion with connect. Okay. So, what does efficient ingestion into your lakehouse mean? Let's get into some of the key benefits of LakeFlow Connect. First, it's simple and lowmaintenance. You can set up a productionready pipeline in just a few steps. And once you've set a connector up, it's also fully managed with schema evolution, observability, alerts, automated retries, sedd type 2, and more.
This lowers your maintenance costs, but more generally, it means you can spend less time moving data and more getting value from that data. Second, it's efficient end to end. This is because it uses incremental reads and writes. And since ingestion is just step one, we actually write the data to support incremental processing downstream as it works its way through the Medallion architecture. Along the way, it has a simple costefficient pricing model, which we'll talk about more that's simply based on the compute used by your pipelines.
Finally, it's unified with your existing data bricks tools with built-in governance, so you can take advantage of all the goodness that comes from data bicks. For example, this product is governed by Unity catalog and is orchestrated via data bricks workflows. It also integrates with data bricks asset bundles for CI/CD and lakehouse monitoring for data quality. All right, so I'm going to talk about the releases very quickly.

7:58 Releases and roadmap

We'll kind of go through some of the ones you may have seen and may have heard of. Um we have a customer of Porsche uh Porsche Holding uh one of the leading auto manufacturers uh in Europe who is saying 85% faster production using this connector and you'll hear from them at summit as well. LakeFlow Connect for workday also released GI and this is a critical um you know connector for many of our customers to help unlock the data from workday reports.
Um, so you can actually have those centralized analytics on your employee workflow, sorry, workforce. Um, Google Analytics, this is new, just went GA. Uh, so you have builtin uh integration for GA for event level data. This was a very um in demand request from our customers. So really excited about this one. All fields are supported. We also have LakeFlow Connect for service. Now uh this also went GI uh this is new and you're able to um basically get uh integration with service now so you can do things for analytics uh and feature road mapping.
SharePoint is also new. Uh this is one that is available in beta. Um so this is available to most customers. Uh and it has support for history tracking, pipeline observability, UC lineage. Um but it supports all formats as unstructured data. Sorry, there is a little issue with the font. And then SFTP is also new. Um public preview is coming soon. So for those of you who want to access this, uh please make sure to reach out to your account teams uh so you can get access to this.
But right now it's a private preview. And finally um actually we have two more uh from Postgress SQL. Another one that's been highly requested by customers. Uh this supports all major variations and on prem. Um private preview is available now. So you do need to request that from your accounts teams. And finally SQL server. We are in public preview right now. Uh GA is coming soon. Uh it supports many SQL server environments.
Um as you can see listed here. uh change tracking CDC um SCD type two is coming soon um but all data types supported including geometric data types all right so that was quite a lot uh let's just do a quick recap so when we think about our connectors we've got applications file sources databases so you can see where the current release status is for that and actually if you check out our website um you can also see this uh through the documentations where we have extensive um step-by-step instructions on how to get set up uh troubleshooting and all those uh FAQ questions that you might have.
All right. And then in addition to this, I'm happy to announce a new set of connectors. Now, these are different uh in the sense that we're trying to expand our breath of data sources uh to include databases and data warehouses. will actually cover these more in depth in his demo. Um so, he'll give you some more context u about that. And speaking of, is going to do his amazing demo about this Spark Paper Company. So, let's turn it.

11:39 Demo: Salesforce ingestion

Awesome. So, as Shazelle was mentioning, I'm going to be talking about the Spark Paper Company today. And usually I'm a product manager at data bricks, but for the demo purpose today, I'm a data engineer at the Spark Paper Company. So, the Spark Paper Company is a regional paper company with branches across the Northeast. And the Spark Paper Company has a lot of disperate data sources that have created information silos where information is limited to branches and departments.
This prevents comprehensive business visibility and results in suboptimal critical decision-m. And then there's our CEO, who wants to make her decisions based on the based on data. And she wants the confidence that this data is accurate. and she wants the ability to deep dive into any specific trend at any point in time. And then we have. He's our data engineer. He's trying to keep up with the constant asks from the CEO and he's struggling to keep data from all these disperate data sources centralized and up to date.
So for the purposes of this demo, I'm going to be. So, what disperate data sources do I need to ingest and transform data from in order to answer questions? First, we have Salesforce includes all of the customer details and opportunities. Then, we have Google Analytics, which includes all of the marketing activities. Terod data which includes alltorical records of customer purchases and then finally SQL server which includes the information about our suppliers and our inventory and before a lakeflow connect I probably would have ingested this data using a combination of different tools for example I might need extraction software that's running on a virtual machine and this means I need to think about what kind of infrastructure to allocate what scale scale I'm going to need and how I can keep that software up.
I might create a message bus that subscribes to all of the events that are generated from my source. And then finally, I'd write custom code to capture those events in the notebook and then use structured streaming to get the data into my lake. And while this solution works, it's probably going to take me a long time to set up. I'm then also going to need to worry about the ongoing maintenance and if there's a change on one of the source APIs, it's possible things could break entirely.
Now, with connect, I can ingest data into the lakehouse without that complexity. All I need to focus on is what data I need to ingest. So now we'll start in my data bricks workspace. Here I can see all of the connectors and previews that I'm able to ingest from. I'm going to click on Salesforce to first bring in data about our customers and our sales opportunities. Once in here, I'll give the pipeline a name. I'm going to call it the Spark Paper Salesforce pipeline.
And then I'll select a location for the bet log. So even though this is a fully managed service, you're still able to find your critical event logs and cluster logs. So you can be confident that you know what's going on under the hood. And then finally, I'll select a Unity catalog connection which stores my Salesforce credentials. If I didn't already have one, I could easily create a new one right here using OOTH. And once I select that, I'm ready to create my pipeline and fetch the source schema.
So I'll click next. And on this step, the connector is pulling the schema of the data that's available for B to ingest. and it's restricted to whatever data that the authenticated user actually has access to. So, we're always going to respect your source permissions and only pull those tables. And now we know that the schema can evolve over time and link flow connect can already handle a lot of different types of schema evolution.
For example, it will automatically ingest new columns. So, I'll select a couple of tables to ingest. First, I'll select the account table to determine what customers we have at Spark Paper Company. And then I'll search for the opportunities table to determine how we can expand our market. And as I'm doing that, you can see that users can select and deselect individual columns without having to adjust the entire table.
So that means if you have certain junk columns that you don't actually want to adjust, you can simply exclude them or choose to include them again. And once I've selected the data to ingest, I'm going to decide where to put it within my lakehouse. I can use a destination schema that already exists or I could create a new one in line. And if you're using our API, you could also write to multiple destinations per pipeline. for example, table table A to schema A and table B to schema B.
So now I'm going to click through and it will validate all of the setup I've just done on my source and destination. And then once we get to the settings page, I can choose to create a new schedule for my pipeline or I can just keep the default schedule that was already created. I can also create notifications to determine when my pipeline succeeded or when it failed. So finally after all that setup I'll save and run my pipeline and we can see data start to ingest from Salesforce.
So I'll be taken to the pipeline monitoring page where I can monitor the status of all the tables that I've ingested and I can see records ingested into both the account and opportunity tables. And next, I want to actually ingest my historical sales data from parad data to determine how my branch sales are performing compared to my online sales. So to do this, I'm going to use one of our new expanded breath ingestion connectors, which is all just announced.

17:38 Demo: Database ingestion

So these connectors enable you to ingest from sources without the need to make any source modification. You don't need to enable CDC. You don't need to create replication slots. You can just get started right away. And we're able to incrementally inest data by leveraging both a primary key and a cursor column. And if these columns aren't actually available on a table, we're still able to ingest the data, but we will do so using a batchbased method.
And these connectors are coming soon and they'll support all of the sources which are currently available via lakehouse federation. So coming back to the add data page this time I'm going to select the ter data tile and then once I'm in the wizard I'll once again provide a name for the pipeline. I'll call it spark paper ter data pipeline and then I'll once again select an event log location within the spark paper company catalog.
I'll then after selecting the schema select a Unity catalog connection that I've already created to my terod data instance and then I'm ready to create my pipeline and continue. So on this step the connector is once again pulling the schema of data that's available for VHS. Here I'm going to select from the terror data historical catalog and select the customer order and product table. For each of these tables I'm going to select a primary key and a cursor column so that they can be ingested incrementally.
For customers it will be the customer ID and the last updated column. For orders it will be an order ID and then a last update column. And finally for product it will be product ID and the last updated column as well. So once I do that we can move on to select the destination schema catalog. Here I'll pick to ingest into the terod data historical sales schema and then I can save and continue. And then once again on the settings page I can choose to create a new schedule for my pipeline or I can just keep a default schedule that's been created.
And I could also create a new notification. So I'll click to save and run the pipeline. And just like with Salesforce pipeline, I'm going to land on the monitoring page where I can see the status of the tables that I've selected for ingestion. I can see that I've ingested 5,000 records from the customer and orders table and then 10 records from the product table. So now that I've completed ingesting my data, I've also set up other pipelines to ingest data from Postgress and Google Analytics.

20:51 Demo: DLT transformations

I'm still going to need to transform and orchestrate the data that I'm bringing in so that it's useful for my organization. I'm going to need to clean and standardize that data across the sources so that I know I have high quality data that's providing me most accurate insights and best uh business outcomes. So, I'm going to need to join my data across sources. For example, I'll need to join my marketing data from Google Analytics with my sales data from Terara data in order to determine the influence of a marketing campaign on our sales.
And to do all of this, I'm going to create a DLT pipeline. It needs declarative statements to create materialized views along the Medallion architecture. So I'll now go into the LakeFlow DLT pipeline editor to do my transformations. In this IDE like editor, I can see all of my source code and a generated DAG which shows the relationship between all of my tables. So I pre-created this pipeline with three transformation files representing the bronze, silver, and gold layers of the medallion architecture.
Let's jump in and take a look at some of the transformations that I'm performing at the silver layer with the data that I ingested using flow connect. For the first materialized view that I'm creating, I want to ensure that I have high quality data on the historical sales within my organization. I've heard that some of our salespeople have been recording as a sale as being made both online and at a branch in order to inflate the online sales numbers.
And there's a young exec really hoping to prove that the company's bet on the online store was worth it. So, I want to remove the junk data. I'm going to create an expectation which will drop any record that was recorded as both a branch and an online sale. And then I'm also going to want to join our sales data with the product data that have been adjusted from data. And this will enable me to see the product name related to each of our sales.
Once I do that, I can click on the table in the tag to see some of the sample data. And with that sample data, I can check if my transformations work exactly as I expected. So, in this table, I can see the sales type and the branch location. And it looks like Scranton has been selling a lot of new printers and copy paper. I'll then also need to join and clean my data from the other ingested sources. And the great part about LakeFlow Connect is that it makes it really easy to create a materialized view as it ingests data using streaming tables.
So we'll only compute changes for the records that are updated rather than needing to recomputee the entire table each time the source table is updated. So now that I've actually transformed my interest data to be high quality, I'm going to use an AI dashboard to provide my organization with fast, secure visual insights. And then I'm going to use Genie to help my organization ask deep questions about our data. So I've started by creating an AI dashboard.

23:57 Demo: AI and BI insights

Here I can choose the data set which will be used to generate visualizations. I'm going to select the gold tables that I generated in the DT pipeline. And once the data sets are added, I can quickly and easily use AI to create new visualizations in addition to the ones that I've already created. Here I can see that we sell a lot more from our branches compared to our online stores. But now I want to compare what the total number of sales are across each of the branch locations.
I can simply click to add a visualization. I will then select the data set that I want to use for this visualization. And then I will ask the assistant to show what the total sales are by branch location. And then very quickly I should see a graph comparing the sales across the different branch locations. And we can see that Scranton is by far leading the pack. But what if, our CEO, is looking at these graphs and wants to ask even deeper questions.
To help her do that, I can create a genie space which is configured on the context of the data in this dashboard. So, in the past, she might wake me up at 2 am to ask those deep questions, but now she will be able to selfs serve via genie spaces. And since we're using LakeFlow Connect to bring in the data that feeds these dashboards, I can be confident that the context of these answers is using the most up-to-date data from our sources.
So, let's ask Genie some questions. For example, I want to know who my top salesperson has been each month and how much their total sales amounted to. Genie will then think based on the data from our goal tables and will provide me with a report which shows the top salesperson each month. So just wait for it and then the report will be generated. And now if I want to dive even deeper, I could ask another follow-up question.
I want to determine who my top salesperson is based on the number of months in which they've had the most sales. So we'll generate another report using those gold tables. And then once that comes back, we can see it's probably tied amongst, and Stanley. So Genie Spaces makes it really quick and easy to answer any question that you might have about your data. And finally, I'm going to need to orchestrate all of the components that I have just put together.
I want to make sure that any time new data is ingested into my gold tables that the dashboard is updated. And I'm going to use workflows to orchestrate all of this across my ingestion pipelines, ETL pipelines, and AI dashboards. So within workflows, I'm able to create a DAG with all of the ingestion, transformation, and dashboard tasks, which also includes the dependencies between them. Here, I created four different ingestion tasks representing my ingestion pipelines, ingesting from four different sources.

26:56 Demo: Workflow orchestration

And if I wanted to, I could click to add a new task for a new notebook, a pipeline, or a dashboard. And then in the dependency graph, I've made the ETL pipeline dependent on all four ingestion pipeline tasks. So this means the ETL pipeline will run and update my gold table if any data lands in any of our sources. So this is going to ensure that my dashboards are updated as soon as new data is available. And then if I want to sk I'm going to want to add a schedule or a trigger to actually determine when this workflow runs.
And here I'm going to add a schedule so that the workflow will run once per hour. And this is also going to trigger my attestion pipeline tasks to run every hour. So to recap what you just saw in the demo, we are able to quickly and easily ingest incremental data into our bronze layer from Salesforce, Google Analytics, terror data, and SQL server using linkflow connect. We then used linkflow DLT to clean up our data and joined it with data from other sources and we created materialized views.

28:26 Future features and pricing

And then with our data in our gold layer, we were able to generate visualizations uh to help our organization make decisions via AI dashboards and provided the opportunity for to have all of her questions answered by asking them directly in a GD space. Finally, we orchestrated all of this using a workflow. Now, looking forward, at the same time that we're improving connector breath, we're also focusing on deepening the feature set of these connectors.
So, this is going to include things like query push down, which will allow you to filter data that you pull in, for example, to improve the performance of the initial data load. It also includes Apple inestion which will allow you to ingest permissions from data sources that are commonly used for AI downstream. For example, to limit the output of a chatbot based on a user. It also includes column level masking which allows you to mask data mid-flight as you're ingesting it.
For example, um to mask PII coming from your HR system. And how is this priced? So all of our connectors are powered by a simple and efficient and entirely compute-based pricing model. You simply pay for the compute that's needed to run your pipelines as well as any networking egress fees that may apply from your source. So pipelines for SAS sources like Salesforce and Workday run exclusively on serless and they're going to incur serless DPU charges.
For database sources like SQL server, the ingestion pipeline will run on serverless. However, ingestion gateways may run on classic or serless depending on the source. So, as a result, you may receive a combination of classic and serverless DLT DPU charges. I'm now going to hand it back to answer some of our most frequently asked questions. Cool. Awesome. Am I back on? Yes. Okay. Well, that was a great demo. Um, really end to end uh for LakeFlow Connect and I'm sure you'll have questions.

30:53 Frequently asked questions

Um, but we're really excited about being able to showcase all those features here for you today. Um, one of the things we'll take some time and then I want to leave time for questions uh is to kind of showcase some of the most popular questions we typically get. uh and then I'll leave it for open Q&A. All right. So, does LakeFlow Connect cover all of ingestion? Well, we talked about this at the beginning. Uh and so, one of the things that we've kind of, you know, expanded, if you will, the umbrella of Lake Flow Connect is that yes, uh it covers all of ingestion, uh as it relates to data bricks, uh and the broader Lake Flow, um three components we discussed at the beginning.
And so this covers manage connectors, you know, DLT, uh, which it covers autoloader, Kafka, Kinesis, uh, and structured streaming. The key thing to remember is it's really how, um, much you want to manage versus how much you want to customize. The next one is how does LakeFlow connect work. And so there are distinctions um between obviously SAS connectors and database connectors but really um the basic components come down to you know the sources, the connection, the ingestion pipeline and the destination.
Um you can see all of this uh amazing um or hear more about these uh in two deep dive sessions that we're having here at Summit. Uh we're actually going to have one just on SAS connectors um and one on database connectors. So the database connectors will actually be hosted by. We'll get into that. Um but those will be more technical in nature and so you can get really into the specifics around um how to work with the connectors and then is the data encrypted?
So, you know, one of the things um that is great about data bricks um that you're able to really take advantage of is the data intelligence platform. um you have you know built-in governance from Unity catalog which includes data encryption and so when we get questions like is data encrypted in transit uh is data encrypted at rest you know it does vary based on SAS uh and databases but u yes the ingested data lives in UC storage uh which is great and it's encrypted uh and there is no difference from other UC data sets and then what features do each of these these connectors currently support.
So there's lots and lots of questions around well you know do you support this you know such as UI API pipeline authoring or support for incremental ingestion or does it support SCD type two um basically we have in our documentation a feature compatibility chart I like to point this out because um it helps solve a lot of these questions so docs. databicks. com data bricks. com there's we we continually update the site um as we're releasing new connectors uh and then finally how can we influence the product roadmap so we get lots and lots of requests um as I mentioned the documentation website is updated uh as they're released but please please please I cannot stress this enough reach out to your account teams uh to nominate any new connectors and we actually do hear all of the feedback and this helps uh drive the road map.
Um so with that, I'll just very quickly um go through some of the recommended sessions. So if you want to take a photo of this um these are the ones specifically for LakeFlow Connect. We've done all the work for you um across Tuesday, Wednesday, and Thursday. Uh and then I'll just give a shout out to a few of the ones that I really think are can't miss. They're all great, but um specifically the SAS connectors uh database session that I mentioned before, uh this is uh with uh and from Porsche Holding.

34:41 Recommended sessions and Q&A

Uh they're going to do a fantastic session. Um we've got who's going to do another fantastic session for databases uh with and then a session on Wednesday afternoon which is the broader lakeflow session with Bal and. This is really exciting about the future of data engineering. Uh a couple of our uh great leaders uh from data bricks and we've got another one that's more on the AIBI side um which is about intelligent document processing with uh and uh which talks about unstructured data and focuses on the SFTP um with lakeflow connect.
And then finally, this one is a can't miss um with and um about how you can eliminate hops in your streaming architecture. This follows this uh the keynote on Thursday. Um so definitely check those out. Okay, so we'll have we have time for just a few questions. If people want to ask anything, feel free to go to the mics uh that are there. Um and we will stay just a couple more minutes. Yep. Any questions? Thanks everyone.
Thanks everyone for joining and please complete your surveys. These really help um improve our content. Thank you.

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