Delta Tables in Databricks

Batch process all your records to store structured outputs in a Delta Table in Databricks.

The Delta Tables in Databricks connector provides two implementations:

• The SQL-based implementation enables you to have Unstructured write its processed data directly to a table in Unity Catalog. If you use this implementation, you do not need to provide a volume as described in the following requirements.
• The volume-based implementation enables you to have Unstructured write its processed data as a file to a volume in Unity Catalog. Unstructured then uses that file in the volume to write the file’s data to a table in Unity Catalog. The file remains in the volume.

The requirements are as follows.

• A Databricks account on AWS, Azure, or GCP.

• A workspace within the Datbricks account for AWS, Azure, or GCP.

• One of the following compute resources within the workspace:

  • A SQL warehouse for AWS, Azure, or GCP.
  • A cluster for AWS, Azure, or GCP.

• The SQL warehouse’s or cluster’s Server Hostname and HTTP Path values for AWS, Azure, or GCP.

• Unity Catalog enabled in the workspace for AWS, Azure, or GCP.

• Within Unity Catalog:

  • A catalog for AWS, Azure, or GCP.
  • A schema for AWS, Azure, or GCP within that catalog,
  • A table for AWS, Azure, or GCP within that schema.

  This table must contain the following column names and their data types:

  CREATE TABLE IF NOT EXISTS `<catalog-name>`.`<schema-name>`.elements (
      id STRING NOT NULL PRIMARY KEY,
      record_id STRING,
      element_id STRING,
      text STRING,
      embeddings ARRAY<FLOAT>,
      type STRING,
      date_created TIMESTAMP,
      date_modified TIMESTAMP,
      date_processed TIMESTAMP,
      permissions_data STRING,
      filesize_bytes FLOAT,
      url STRING,
      version STRING,
      record_locator STRING,
      category_depth INT,
      parent_id STRING,
      attached_filename STRING,
      filetype STRING,
      last_modified TIMESTAMP,
      file_directory STRING,
      filename STRING,
      languages ARRAY<STRING>,
      page_number STRING,
      links STRING,
      page_name STRING,
      link_urls STRING,
      link_texts STRING,
      sent_from STRING,
      sent_to STRING,
      subject STRING,
      section STRING,
      header_footer_type STRING,
      emphasized_text_contents STRING,
      emphasized_text_tags STRING,
      text_as_html STRING,
      regex_metadata STRING,
      detection_class_prob FLOAT,
      is_continuation BOOLEAN,
      orig_elements STRING,
      coordinates_points STRING,
      coordinates_system STRING,
      coordinates_layout_width FLOAT,
      coordinates_layout_height FLOAT
  );
  

• Within Unity Catalog, a volume for AWS, Azure, or GCP within the same schema as the table.

• For Databricks personal access token authentication to the workspace, the Databricks personal access token value for AWS, Azure, or GCP. This token must be for the workspace user who has the appropriate access permissions to the catalog, schema, table, volume, and cluster or SQL warehouse,

• For Databricks managed service principal authentication (using Databricks OAuth M2M) to the workspace:

  • A Databricks managed service principal. This service principal must have the appropriate access permissions to the catalog, schema, table, volume, and cluster or SQL warehouse.
  • The service principal’s UUID value.
  • The OAuth Secret value for the service principal.

  To get this information, see Steps 1-3 of the instructions for AWS, Azure, or GCP.

  For Azure Databricks, this connector only supports Databricks managed service principals. Microsoft Entra ID managed service principals are not supported.

The Delta Tables in Databricks connector dependencies:

pip install "unstructured-ingest[databricks-delta-tables]"

You might also need to install additional dependencies, depending on your needs. Learn more.

The following environment variables:

• DATABRICKS_HOST - The Databricks cluster’s or SQL warehouse’s Server Hostname value, represented by --server-hostname (CLI) or server_hostname (Python).
• DATABRICKS_HTTP_PATH - The cluster’s or SQL warehouse’s HTTP Path value, represented by --http-path (CLI) or http_path (Python).
• DATABRICKS_TOKEN - For Databricks personal access token authentication, the token’s value, represented by --token (CLI) or token (Python).
• DATABRICKS_CLIENT_ID - For Databricks managed service principal authenticaton, the service principal’s UUID value, represented by --client-id (CLI) or client_id (Python).
• DATABRICKS_CLIENT_SECRET - For Databricks managed service principal authenticaton, the service principal’s OAuth Secret value, represented by --client-secret (CLI) or client_secret (Python).
• DATABRICKS_CATALOG - The name of the catalog in Unity Catalog, represented by --catalog (CLI) or catalog (Python).
• DATABRICKS_DATABASE - The name of the schema (database) inside of the catalog, represented by --database (CLI) or database (Python). The default is default if not otherwise specified.
• DATABRICKS_TABLE - The name of the table inside of the schema (database), represented by --table-name (CLI) or table_name (Python). The default is elements if not otherwise specified.

For the SQL-based implementation, add these environment variables:

• DATABRICKS_RECORD_ID_KEY - The name of the column that uniquely identifies each record in the table, represented by --record-id-key (CLI) or record_id_key (Python).
• The maximum number of records to be transmitted per batch, represented by --batch-size (CLI) or batch_size (Python). The default is 50 if not otherwise specified.

For the volume-based implementation, add these environment variables:

• DATABRICKS_SCHEMA - The name of the schema (database) inside of the catalog, represented by --schema (CLI) or schema (Python). This name of this database (schema) must be the same as the value of the DATABRICKS_DATABASE environment variable and is required for compatiblity. The default is default if not otherwise specified.
• DATABRICKS_VOLUME - The name of the volume inside of the schema (database), represented by --volume (CLI) or volume (Python).
• DATABRICKS_VOLUME_PATH - Optionally, a specific path inside of the volume that you want to start accessing from, starting from the volume’s root, represented by --volume-path (CLI) or volume_path (Python). The default is to start accessing from the volume’s root if not otherwise specified.

These environment variables:

• UNSTRUCTURED_API_KEY - Your Unstructured API key value.
• UNSTRUCTURED_API_URL - Your Unstructured API URL.

Now call the Unstructured Ingest CLI or the Unstructured Ingest Python library. The source connector can be any of the ones supported. This example uses the local source connector: