How to load data from Braze to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Braze 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 Braze 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 Braze 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: Export Data from Braze

Start by exporting the data from Braze. Utilize Braze's data export functionality, such as the Currents or Data Export feature, to download data files. You can schedule exports in common formats like CSV or JSON, which are suitable for processing and loading into Databricks.

Once the data is exported, you'll need to transfer it to a local or cloud storage system that you have access to. Use secure methods such as SFTP for local transfers or AWS S3/Azure Blob for cloud storage. Ensure data integrity during the transfer by verifying checksums.

Set up your Databricks environment by creating a new cluster if necessary. Ensure that you have the appropriate permissions and configurations in place, including access to the storage location where your data resides.

Use the Databricks CLI or Databricks web interface to upload your exported data files to the Databricks File System (DBFS). Place the files in a designated directory structure that will make them easy to access and manage within Databricks.

In Databricks, create external tables to reference the data files stored in DBFS. Use Spark SQL to define the schema of your data, specifying file formats and paths. This allows you to query the data directly and efficiently within Databricks.

Utilize Spark's powerful data processing capabilities to transform and clean your data. Write Spark SQL or PySpark scripts to address any data quality issues, convert data types, and aggregate data as needed. This step ensures your data is ready for analysis.

Finally, load the transformed data into the Databricks Lakehouse. You can use Spark to write the data into Delta Lake tables, which offer ACID transactions and scalable metadata handling. Confirm that the data is correctly loaded by running validation queries.

By following these steps, you can efficiently move data from Braze to the Databricks Lakehouse without relying on third-party connectors or integrations.