How to load data from Microsoft Dataverse to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Microsoft Dataverse data into Databricks Lakehouse within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Microsoft Dataverse connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Microsoft Dataverse data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Microsoft Dataverse to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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How to Sync to Manually

Step 1: Access Dataverse Data via API

Begin by accessing your Dataverse data using the Microsoft Dataverse API. You will need to authenticate using OAuth 2.0 to obtain an access token. Use this token to make HTTP requests to the Dataverse Web API and retrieve the desired data. Familiarize yourself with the API documentation to understand the available endpoints and query capabilities.

Once you have access to the data via the API, write a script in a programming language like Python or PowerShell to extract the data and convert it into CSV format. Ensure that you handle pagination if your data set is large, and include error handling to manage potential API request failures.

Set up your Databricks environment if you haven't already. This involves creating a Databricks workspace and cluster. Ensure that your cluster is configured with the necessary resources and libraries, such as AWS or Azure configurations, depending on your cloud service provider.

After exporting the data to CSV files, upload these files to your cloud storage service linked with Databricks, such as AWS S3 or Azure Blob Storage. Use the respective cloud storage service's SDK or CLI tools to securely transfer the files to the designated storage bucket or container.

In your Databricks notebook, mount the cloud storage location where you uploaded the CSV files. This involves using the Databricks File System (DBFS) mount command to create a mount point that allows Databricks to access data from your cloud storage seamlessly.

Use Databricks notebooks to write a script that reads the CSV files from the mounted storage location. Utilize Spark's `read.csv` function to load the data into a DataFrame. Ensure that you define the appropriate schema and handle data cleaning and transformations as necessary to prepare the data for further processing.

Finally, write the DataFrame to the Databricks Lakehouse using the Delta Lake format for optimized storage and performance. Use the `write.format("delta").save("/path/to/delta/table")` method to save the data. This format supports ACID transactions and enables efficient data management and querying in the Databricks Lakehouse.
By following these steps, you will successfully move data from Microsoft Dataverse to the Databricks Lakehouse without relying on third-party connectors or integrations.