How to load data from Metabase to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Metabase 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 Metabase 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 Metabase 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 Metabase

Begin by exporting the data you need from Metabase. Access the relevant dashboard or query within Metabase that contains the data you intend to move. Use the export functionality to download the data in a CSV format. Ensure that the CSV file adequately represents the data you wish to transfer, including all necessary fields and rows.

Set up your local environment to handle data processing. Ensure you have Python installed, as it will be used to interact with both the CSV file and Databricks. Install necessary libraries such as `pandas` for data manipulation and `databricks-cli` for interacting with your Databricks workspace.

Use Python and the Pandas library to load and process the exported CSV file. Write a script to read the CSV file into a Pandas DataFrame. Perform any necessary data cleaning or transformations to prepare the data for loading into Databricks. This may include handling missing values, renaming columns, or converting data types.

Authenticate your local environment with Databricks. Use the `databricks-cli` to log in to your Databricks workspace. You will need to set up a Databricks personal access token, which can be generated in the Databricks User Settings. Use the command line to log in using the token, ensuring you have access to the desired Lakehouse environment.

Use the `databricks-cli` to upload the processed CSV file to the Databricks File System (DBFS). This involves running a command that specifies the local path of your CSV file and the destination path in DBFS. For example, use `databricks fs cp` to copy files to DBFS, making sure the file is accessible for further processing within Databricks.

Access your Databricks workspace, and open a new notebook. Use PySpark or Databricks SQL to create a table from the uploaded CSV file. Load the CSV file from DBFS into a DataFrame within the notebook, then write a command to save the DataFrame as a table in your Lakehouse. Ensure that the schema aligns with your expectations and the data types are correctly inferred.

After the table is created, perform a series of checks to verify that the data has been accurately transferred. This involves running queries in Databricks to inspect the data, checking row counts, and ensuring data types and values match what was exported from Metabase. Adjust any discrepancies as necessary to finalize the data migration process.

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