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---
title: Run the Databricks Connector Externally
slug: /connectors/database/databricks/yaml
---
{% connectorDetailsHeader
name="Databricks"
stage="PROD"
platform="OpenMetadata"
availableFeatures=["Metadata", "Query Usage", "Lineage", "Column-level Lineage", "Data Profiler", "Data Quality", "dbt", "Tags", "Sample Data"]
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unavailableFeatures=["Owners", "Stored Procedures"]
/ %}
{% note %}
As per the [documentation](https://docs.databricks.com/en/data-governance/unity-catalog/tags.html#manage-tags-with-sql-commands) here, note that we only support metadata `tag` extraction for databricks version 13.3 version and higher.
{% /note %}
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:
- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
- [Query Usage](#query-usage)
- [Lineage](#lineage)
- [Data Profiler](#data-profiler)
- [Data Quality](#data-quality)
- [dbt Integration](#dbt-integration)
{% partial file="/v1.6/connectors/external-ingestion-deployment.md" /%}
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## Requirements
### Python Requirements
{% partial file="/v1.6/connectors/python-requirements.md" /%}
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To run the Databricks ingestion, you will need to install:
```bash
pip3 install "openmetadata-ingestion[databricks]"
```
### 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.
### Metadata and Profiling Permissions
These permissions are required on the catalogs, schemas, and tables from which metadata and profiling information will be ingested.
```sql
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.
### Usage and Lineage
These permissions enable OpenMetadata to extract query history and construct lineage information.
```sql
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.
{% note %}
Adjust <user>, <catalog_name>, <schema_name>, and <table_name> according to your specific deployment and security requirements.
{% /note %}
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## Metadata Ingestion
All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/databricksConnection.json)
you can find the structure to create a connection to Databricks.
In order to create and run a Metadata Ingestion workflow, we will follow
the steps to create a YAML configuration able to connect to the source,
process the Entities if needed, and reach the OpenMetadata server.
The workflow is modeled around the following
[JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)
### 1. Define the YAML Config
This is a sample config for Databricks:
{% codePreview %}
{% codeInfoContainer %}
#### Source Configuration - Service Connection
{% codeInfo srNumber=1 %}
**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.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**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.
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**hostPort**: Enter the fully qualified hostname and port number for your Databricks deployment in the Host and Port field.
{% /codeInfo %}
{% codeInfo srNumber=4 %}
**token**: Generated Token to connect to Databricks.
{% /codeInfo %}
{% codeInfo srNumber=5 %}
**httpPath**: Databricks compute resources URL.
{% /codeInfo %}
{% codeInfo srNumber=6 %}
**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.
{% /codeInfo %}
{% partial file="/v1.6/connectors/yaml/database/source-config-def.md" /%}
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{% partial file="/v1.6/connectors/yaml/ingestion-sink-def.md" /%}
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{% partial file="/v1.6/connectors/yaml/workflow-config-def.md" /%}
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#### Advanced Configuration
{% codeInfo srNumber=7 %}
**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.
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{% /codeInfo %}
{% codeInfo srNumber=8 %}
**Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to database during the connection. These details must be added as Key-Value pairs.
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- In case you are using Single-Sign-On (SSO) for authentication, add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "sso_login_url"`
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="filename.yaml" %}
```yaml {% isCodeBlock=true %}
source:
type: databricks
serviceName: local_databricks
serviceConnection:
config:
type: Databricks
```
```yaml {% srNumber=1 %}
catalog: hive_metastore
```
```yaml {% srNumber=2 %}
databaseSchema: default
```
```yaml {% srNumber=3 %}
token: <databricks token>
```
```yaml {% srNumber=4 %}
hostPort: <databricks connection host & port>
```
```yaml {% srNumber=5 %}
httpPath: <http path of databricks cluster>
```
```yaml {% srNumber=6 %}
connectionTimeout: 120
```
```yaml {% srNumber=7 %}
# connectionOptions:
# key: value
```
```yaml {% srNumber=8 %}
# connectionArguments:
# key: value
```
{% partial file="/v1.6/connectors/yaml/database/source-config.md" /%}
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{% partial file="/v1.6/connectors/yaml/ingestion-sink.md" /%}
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{% partial file="/v1.6/connectors/yaml/workflow-config.md" /%}
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{% /codeBlock %}
{% /codePreview %}
{% partial file="/v1.6/connectors/yaml/ingestion-cli.md" /%}
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{% partial file="/v1.6/connectors/yaml/query-usage.md" variables={connector: "databricks"} /%}
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{% partial file="/v1.6/connectors/yaml/lineage.md" variables={connector: "databricks"} /%}
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{% partial file="/v1.6/connectors/yaml/data-profiler.md" variables={connector: "databricks"} /%}
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{% partial file="/v1.6/connectors/yaml/auto-classification.md" variables={connector: "databricks"} /%}
{% partial file="/v1.6/connectors/yaml/data-quality.md" /%}
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## dbt Integration
You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).