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Implementation

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.

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Requirements

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Required Azure Configurations

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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

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Databricks

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: 
spark.hadoop.fs.azure.account.key..blob.core.windows.net
Once you’ve clicked “Confirm”, your cluster will need to restart before you can apply the transformation again.

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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.

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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.

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Configuration

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.
databricks_definition.py
import featureform as ff

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

azure_blob = ff.register_blob_store(
    name="azure-quickstart",
    description="An azure blob store provider to store offline and inference data"
    container_name="my_company_container"
    # 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
    root_path="my_company_container/path/to/specific/directory"
    account_name=""
    account_key=""
)

spark = ff.register_spark(
    name="spark_provider",
    executor=databricks,
    filestore=azure_blob
)

transactions = spark.register_file(
    name="transactions",
    variant=variant,
    # Must be an absolute path using the abfss:// protocol
    file_path="abfss://@.dfs.core.windows.net/transactions.csv",
)

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Mutable Configuration Fields

  • description
  • username (Executor)
  • password (Executor)
  • token (Executor)
  • account_key (File Store)

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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. Spark