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Databricks

DataHub supports integration with Databricks ecosystem using a multitude of connectors, depending on your exact setup.

Databricks Hive​

The simplest way to integrate is usually via the Hive connector. The Hive starter recipe has a section describing how to connect to your Databricks workspace.

Databricks Unity Catalog (new)​

The recently introduced Unity Catalog provides a new way to govern your assets within the Databricks lakehouse. If you have enabled Unity Catalog, you can use the unity-catalog source (see below) to integrate your metadata into DataHub as an alternate to the Hive pathway.

Databricks Spark​

To complete the picture, we recommend adding push-based ingestion from your Spark jobs to see real-time activity and lineage between your Databricks tables and your Spark jobs. Use the Spark agent to push metadata to DataHub using the instructions here.

Watch the DataHub Talk at the Data and AI Summit 2022​

For a deeper look at how to think about DataHub within and across your Databricks ecosystem, watch the recording of our talk at the Data and AI Summit 2022.

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Module unity-catalog​

Incubating

Important Capabilities​

CapabilityStatusNotes
Asset Containers✅Enabled by default
Column-level Lineage✅Enabled by default
Descriptions✅Enabled by default
Detect Deleted Entities✅Optionally enabled via stateful_ingestion.remove_stale_metadata
Domains✅Supported via the domain config field
Platform Instance✅Enabled by default
Schema Metadata✅Enabled by default
Table-Level Lineage✅Enabled by default

This plugin extracts the following metadata from Databricks Unity Catalog:

  • metastores
  • schemas
  • tables and column lineage

Prerequisities​

CLI based Ingestion​

Install the Plugin​

pip install 'acryl-datahub[unity-catalog]'

Starter Recipe​

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
  type: unity-catalog
  config:
    workspace_url: https://my-workspace.cloud.databricks.com
    token: "mygenerated_databricks_token"
    #metastore_id_pattern:
    #  deny:
    #    - 11111-2222-33333-44-555555
    #catalog_pattern:
    #  allow:
    #    - my-catalog
    #schema_pattern:
    #  deny:
    #    - information_schema
    #table_pattern:
    #  allow:
    #    - test.lineagedemo.dinner
    # First you have to create domains on Datahub by following this guide -> https://datahubproject.io/docs/domains/#domains-setup-prerequisites-and-permissions
    #domain:
    #  urn:li:domain:1111-222-333-444-555:
    #    allow:
    #      - main.*

    stateful_ingestion:
      enabled: true

pipeline_name: acme-corp-unity


# sink configs if needed

Config Details​

Note that a . is used to denote nested fields in the YAML recipe.

View All Configuration Options
Field [Required]TypeDescriptionDefaultNotes
include_column_lineage [✅]booleanOption to enable/disable lineage generation. Currently we have to call a rest call per column to get column level lineage due to the Databrick api which can slow down ingestion.True
include_table_lineage [✅]booleanOption to enable/disable lineage generation.True
platform_instance [✅]stringThe instance of the platform that all assets produced by this recipe belong toNone
token [✅]stringDatabricks personal access tokenNone
workspace_name [✅]stringName of the workspace. Default to deployment name present in workspace_urlNone
workspace_url [✅]stringDatabricks workspace urlNone
env [✅]stringThe environment that all assets produced by this connector belong toPROD
catalog_pattern [✅]AllowDenyPatternRegex patterns for catalogs to filter in ingestion. Specify regex to match the catalog name{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
catalog_pattern.allow [❓ (required if catalog_pattern is set)]array(string)None
catalog_pattern.deny [❓ (required if catalog_pattern is set)]array(string)None
catalog_pattern.ignoreCase [❓ (required if catalog_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
domain [✅]map(str,AllowDenyPattern)A class to store allow deny regexesNone
domain.key.allow [❓ (required if domain is set)]array(string)None
domain.key.deny [❓ (required if domain is set)]array(string)None
domain.key.ignoreCase [❓ (required if domain is set)]booleanWhether to ignore case sensitivity during pattern matching.True
metastore_id_pattern [✅]AllowDenyPatternRegex patterns for metastore id to filter in ingestion.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
metastore_id_pattern.allow [❓ (required if metastore_id_pattern is set)]array(string)None
metastore_id_pattern.deny [❓ (required if metastore_id_pattern is set)]array(string)None
metastore_id_pattern.ignoreCase [❓ (required if metastore_id_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
schema_pattern [✅]AllowDenyPatternRegex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
schema_pattern.allow [❓ (required if schema_pattern is set)]array(string)None
schema_pattern.deny [❓ (required if schema_pattern is set)]array(string)None
schema_pattern.ignoreCase [❓ (required if schema_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
table_pattern [✅]AllowDenyPatternRegex patterns for tables to filter in ingestion. Specify regex to match the entire table name in catalog.schema.table format. e.g. to match all tables starting with customer in Customer catalog and public schema, use the regex 'Customer.public.customer.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow [❓ (required if table_pattern is set)]array(string)None
table_pattern.deny [❓ (required if table_pattern is set)]array(string)None
table_pattern.ignoreCase [❓ (required if table_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
stateful_ingestion [✅]StatefulStaleMetadataRemovalConfigUnity Catalog Stateful Ingestion Config.None
stateful_ingestion.enabled [❓ (required if stateful_ingestion is set)]booleanThe type of the ingestion state provider registered with datahub.None
stateful_ingestion.ignore_new_state [❓ (required if stateful_ingestion is set)]booleanIf set to True, ignores the current checkpoint state.None
stateful_ingestion.ignore_old_state [❓ (required if stateful_ingestion is set)]booleanIf set to True, ignores the previous checkpoint state.None
stateful_ingestion.remove_stale_metadata [❓ (required if stateful_ingestion is set)]booleanSoft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.True

Troubleshooting​

No data lineage captured or missing lineage​

Check that you meet the Unity Catalog lineage requirements.

Also check the Unity Catalog limitations to make sure that lineage would be expected to exist in this case.

Lineage extraction is too slow​

Currently, there is no way to get table or column lineage in bulk from the Databricks Unity Catalog REST api. Table lineage calls require one API call per table, and column lineage calls require one API call per column. If you find metadata extraction taking too long, you can turn off column level lineage extraction via the include_column_lineage config flag.

Code Coordinates​

  • Class Name: datahub.ingestion.source.unity.source.UnityCatalogSource
  • Browse on GitHub

Questions​

If you've got any questions on configuring ingestion for Databricks, feel free to ping us on our Slack