With Databricks, you can leverage your Databricks cluster to compute transformations and training sets. Featureform however does not handle storage in non-local mode, so it is necessary to separately register a file store provider like Azure to store the results of its computation.


Required Azure Configurations

Azure Blob Storage

If you encounter the following error, or one similar to it, when registering a transformation, you may need to disable certain default container configurations:

Caused by: Operation failed: "This endpoint does not support BlobStorageEvents or SoftDelete. Please disable these account features if you would like to use this endpoint."

To disable these configurations, you can navigate to Home > Storage Accounts > YOUR STORAGE ACCOUNT, and select “Data Protection” under the “Data Management” section. Then uncheck:

  • Enable soft delete for blobs

  • Enable soft delete for containers


If you encounter the following error, or one similar to it, when registering a transformation, you may need to add credentials for your Azure Blob Storage account to your Databricks cluster:

Cannot read the python file abfss://@//featureform/scripts/spark/offline_store_spark_runner_py.
Please check driver logs for more details.

To add Azure Blob Store account credentials to your Databricks cluster, you’ll need to:

  • Launch your Databricks workspace from the Azure portal (e.g. by clicking “Launch Workspace” from the “Overview” page of your Databricks workspace)

  • Select “Compute” from the left-hand menu and click on your cluster

  • Click “Edit” and then select “Advanced Options” tab to show the “Spark Config” text input field

  • Add the following configuration to the “Spark Config” text input field: 

Once you’ve clicked “Confirm”, your cluster will need to restart before you can apply the transformation again.

Transformation Sources

Using Databricks as an Offline Store, you can define new transformations via SQL and Spark DataFrames. Using either these transformations or preexisting tables in your file store, a user can chain transformations and register columns in the resulting tables as new features and labels.

Training Sets and Inference Store Materialization

Any column in a preexisting table or user-created transformation can be registered as a feature or label. These features and labels can be used, as with any other Offline Store, for creating training sets and inference serving.


To configure a Databricks store as a provider, you need to have a Databricks cluster. Featureform automatically downloads and uploads the necessary files to handle all necessary functionality of a native offline store like Postgres or BigQuery.
import featureform as ff

databricks = ff.DatabricksCredentials(
    # You can either use username and password ...
    # ... or host and token

azure_blob = ff.register_blob_store(
    description="An azure blob store provider to store offline and inference data"
    # Will either be the container name or the container name plus a path if you plan read/write
    # to a specific directory in your container

spark = ff.register_spark(

transactions = spark.register_file(
    # Must be an absolute path using the abfss:// protocol

Mutable Configuration Fields

  • description

  • username (Executor)

  • password (Executor)

  • token (Executor)

  • account_key (File Store)

Dataframe Transformations

Because Featureform supports the generic implementation of Spark, transformations written in SQL and Dataframe operations for the different Spark providers will be very similar except for the file_path or table name.