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Databricks

Databricks

PROD
As per the documentation here, note that we only support metadata tag extraction for databricks version 13.3 version and higher.
In this section, we provide guides and references to use the Databricks connector. Configure and schedule Databricks metadata and profiler workflows from the OpenMetadata UI:

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How to Run the Connector Externally

To run the Ingestion via the UI you’ll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

External Schedulers

Get more information about running the Ingestion Framework Externally

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Requirements

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Python Requirements

We have support for Python versions 3.9-3.11
To run the Databricks ingestion, you will need to install: 
pip3 install "openmetadata-ingestion[databricks]"

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Permission Requirement

To enable full functionality of metadata extraction, profiling, usage, and lineage features in OpenMetadata, the following permissions must be granted to the relevant users in your Databricks environment.

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Metadata and Profiling Permissions

These permissions are required on the catalogs, schemas, and tables from which metadata and profiling information will be ingested. 
GRANT USE CATALOG ON CATALOG <catalog_name> TO `<user>`;
GRANT USE SCHEMA ON SCHEMA <schema_name> TO `<user>`;
GRANT SELECT ON TABLE <table_name> TO `<user>`;
Ensure these grants are applied to all relevant tables for metadata ingestion and profiling operations.

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Usage and Lineage

These permissions enable OpenMetadata to extract query history and construct lineage information. 
GRANT SELECT ON SYSTEM.QUERY.HISTORY TO `<user>`;
GRANT USE SCHEMA ON SCHEMA system.query TO `<user>`;
These permissions allow access to Databricks system tables that track query activity, enabling lineage and usage statistics generation.
Adjust <user>, <catalog_name>, <schema_name>, and <table_name> according to your specific deployment and security requirements.

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Unity Catalog

If you are using unity catalog in Databricks, then checkout the Unity Catalog connector.

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Metadata Ingestion

1

Visit the Services Page

Click `Settings` in the side navigation bar and then `Services`. The first step is to ingest the metadata from your sources. To do that, you first need to create a Service connection first. This Service will be the bridge between OpenMetadata and your source system. Once a Service is created, it can be used to configure your ingestion workflows.Visit Services Page
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Create a New Service

Click on _Add New Service_ to start the Service creation.Create a new Service
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Select the Service Type

Select Databricks as the Service type and click _Next_.Select Service
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Name and Describe your Service

Provide a name and description for your Service.

Service Name

OpenMetadata uniquely identifies Services by their **Service Name**. Provide a name that distinguishes your deployment from other Services, including the other Databricks Services that you might be ingesting metadata from. Note that when the name is set, it cannot be changed.Add New Service
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Configure the Service Connection

In this step, we will configure the connection settings required for Databricks. Please follow the instructions below to properly configure the Service to read from your sources. You will also find helper documentation on the right-hand side panel in the UI.Configure Service connection

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Connection Details

1

Connection Details

When using a Hybrid Ingestion Runner, any sensitive credential fields—such as passwords, API keys, or private keys—must reference secrets using the following format:
password: secret:/my/database/password
This applies only to fields marked as secrets in the connection form (these typically mask input and show a visibility toggle icon). For a complete guide on managing secrets in hybrid setups, see the Hybrid Ingestion Runner Secret Management Guide.
  • Host and Port: Enter the fully qualified hostname and port number for your Databricks deployment in the Host and Port field.
  • Token: Generated Token to connect to Databricks.
  • HTTP Path: Databricks compute resources URL.
  • connectionTimeout: The maximum amount of time (in seconds) to wait for a successful connection to the data source. If the connection attempt takes longer than this timeout period, an error will be returned.
  • Catalog: Catalog of the data source(Example: hive_metastore). This is optional parameter, if you would like to restrict the metadata reading to a single catalog. When left blank, OpenMetadata Ingestion attempts to scan all the catalog.
  • DatabaseSchema: databaseSchema of the data source. This is optional parameter, if you would like to restrict the metadata reading to a single databaseSchema. When left blank, OpenMetadata Ingestion attempts to scan all the databaseSchema.
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Advanced Configuration

Database Services have an Advanced Configuration section, where you can pass extra arguments to the connector and, if needed, change the connection Scheme.This would only be required to handle advanced connectivity scenarios or customizations.
  • Connection Options (Optional): Enter the details for any additional connection options that can be sent to database during the connection. These details must be added as Key-Value pairs.
  • Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent during the connection. These details must be added as Key-Value pairs. Advanced Configuration
3

Test the Connection

Once the credentials have been added, click on Test Connection and Save the changes.Test Connection
4

Configure Metadata Ingestion

In this step we will configure the metadata ingestion pipeline, Please follow the instructions belowConfigure Metadata IngestionConfigure Metadata Ingestion

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Metadata Ingestion Options

If the owner’s name is openmetadata, you need to enter [email protected] in the name section of add team/user form, click here for more info.
  • Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
  • Database Filter Pattern (Optional): Use to database filter patterns to control whether or not to include database as part of metadata ingestion.
    • Include: Explicitly include databases by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all databases with names matching one or more of the supplied regular expressions. All other databases will be excluded.
    • Exclude: Explicitly exclude databases by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all databases with names matching one or more of the supplied regular expressions. All other databases will be included.
  • Schema Filter Pattern (Optional): Use to schema filter patterns to control whether to include schemas as part of metadata ingestion.
    • Include: Explicitly include schemas by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all schemas with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
    • Exclude: Explicitly exclude schemas by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all schemas with names matching one or more of the supplied regular expressions. All other schemas will be included.
  • Table Filter Pattern (Optional): Use to table filter patterns to control whether to include tables as part of metadata ingestion.
    • Include: Explicitly include tables by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded.
    • Exclude: Explicitly exclude tables by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included.
  • Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug.
  • Mark Deleted Tables (toggle): Set the Mark Deleted Tables toggle to flag tables as soft-deleted if they are not present anymore in the source system.
  • Mark Deleted Tables from Filter Only (toggle): Set the Mark Deleted Tables from Filter Only toggle to flag tables as soft-deleted if they are not present anymore within the filtered schema or database only. This flag is useful when you have more than one ingestion pipelines. For example if you have a schema
  • includeTables (toggle): Optional configuration to turn off fetching metadata for tables.
  • includeViews (toggle): Set the Include views toggle to control whether to include views as part of metadata ingestion.
  • includeTags (toggle): Set the ‘Include Tags’ toggle to control whether to include tags as part of metadata ingestion.
  • includeOwners (toggle): Set the ‘Include Owners’ toggle to control whether to include owners to the ingested entity if the owner email matches with a user stored in the OM server as part of metadata ingestion. If the ingested entity already exists and has an owner, the owner will not be overwritten.
  • includeStoredProcedures (toggle): Optional configuration to toggle the Stored Procedures ingestion.
  • includeDDL (toggle): Optional configuration to toggle the DDL Statements ingestion.
  • queryLogDuration (Optional): Configuration to tune how far we want to look back in query logs to process Stored Procedures results.
  • queryParsingTimeoutLimit (Optional): Configuration to set the timeout for parsing the query in seconds.
  • useFqnForFiltering (toggle): Regex will be applied on fully qualified name (e.g service_name.db_name.schema_name.table_name) instead of raw name (e.g. table_name).
  • Incremental (Beta): Use Incremental Metadata Extraction after the first execution. This is done by getting the changed tables instead of all of them. Only Available for BigQuery, Redshift and Snowflake
    • Enabled: If True, enables Metadata Extraction to be Incremental.
    • lookback Days: Number of days to search back for a successful pipeline run. The timestamp of the last found successful pipeline run will be used as a base to search for updated entities.
    • Safety Margin Days: Number of days to add to the last successful pipeline run timestamp to search for updated entities.
  • Threads (Beta): Use a Multithread approach for Metadata Extraction. You can define here the number of threads you would like to run concurrently. For further information please check the documentation on Metadata Ingestion - Multithreading
Note that the right-hand side panel in the OpenMetadata UI will also share useful documentation when configuring the ingestion.
5

Schedule the Ingestion and Deploy

Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.If something doesn’t look right, click the Back button to return to the appropriate step and change the settings as needed.After configuring the workflow, you can click on Deploy to create the pipeline.Schedule the Workflow
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View the Ingestion Pipeline

Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.View Ingestion Pipeline
If AutoPilot is enabled, workflows like usage tracking, data lineage, and similar tasks will be handled automatically. Users don’t need to set up or manage them - AutoPilot takes care of everything in the system.
1

Reverse Metadata

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Description Management

Databricks supports description updates at all levels:
  • Database level
  • Schema level
  • Table level
  • Column level

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Owner Management

Databricks supports owner management at the following levels:
  • Database level
  • Schema level
  • Table level
Databricks does not support to set null as owner.

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Tag Management

Databricks supports tag management at all levels:
  • Database level
  • Schema level
  • Table level
  • Column level

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Custom SQL Template

Databricks supports custom SQL templates for metadata changes. The template is interpreted using python f-strings.Here are examples of custom SQL queries for metadata changes:
-- Set table tags
ALTER TABLE {database}.{schema}.{table} SET TAGS {tags};
The list of variables for custom SQL can be found here.For more details about reverse metadata ingestion, visit our Reverse Metadata Documentation.

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Requirements for Reverse Metadata

In addition to the basic ingestion requirements, for reverse metadata ingestion the user needs:
-- Catalog grants
GRANT USE CATALOG ON CATALOG `<catalog>` TO `<principal>`;
GRANT MANAGE ON CATALOG `<catalog>` TO `<principal>`;

-- Schema grants
GRANT USE SCHEMA ON SCHEMA `<catalog>`.`<schema>` TO `<principal>`;
GRANT MANAGE ON SCHEMA `<catalog>`.`<schema>` TO `<principal>`;
GRANT APPLY TAG ON SCHEMA `<catalog>`.`<schema>` TO `<principal>`;

-- Table grants
GRANT MANAGE ON TABLE `<catalog>`.`<schema>`.`<table>` TO `<principal>`;
GRANT APPLY TAG ON TABLE `<catalog>`.`<schema>`.`<table>` TO `<principal>`;

Usage Workflow

Learn more about how to configure the Usage Workflow to ingest Query information from the UI.

Lineage Workflow

Learn more about how to configure the Lineage from the UI.

Profiler Workflow

Learn more about how to configure the Data Profiler from the UI.

Data Quality Workflow

Learn more about how to configure the Data Quality tests from the UI.

dbt Integration

Learn more about how to ingest dbt models’ definitions and their lineage.