Auto Ingest dbt-core

Learn how to automatically ingest dbt-core artifacts into OpenMetadata using the simplified metadata ingest-dbt CLI command that reads configuration directly from your dbt_project.yml file.

This feature eliminates the need for separate YAML configuration files. All configuration is done directly in your existing dbt_project.yml file.

Overview

The metadata ingest-dbt command provides a streamlined way to ingest dbt artifacts into OpenMetadata by:

Reading configuration directly from your dbt_project.yml file
Automatically discovering dbt artifacts (manifest.json, catalog.json, run_results.json)
Supporting comprehensive filtering and configuration options

Prerequisites

dbt project setup: You must have a dbt project with a valid dbt_project.yml file
dbt artifacts: Run dbt compile or dbt run to generate required artifacts in the target/ directory
OpenMetadata service: Your database service must already be configured in OpenMetadata
OpenMetadata Python package: Install the OpenMetadata ingestion package

pip install "openmetadata-ingestion[dbt]"

Quick Start

1. Configure your dbt_project.yml

Add the following variables to the vars section of your dbt_project.yml file:

vars:
  # Required OpenMetadata configuration
  openmetadata_host_port: "https://your-openmetadata-server-url/endpoint"
  openmetadata_jwt_token: "your-jwt-token-here"
  openmetadata_service_name: "your-database-service-name"

Environment Variables: For security, you can use environment variables instead of hardcoding sensitive values. See the Environment Variables section below for supported patterns.

2. Generate dbt artifacts

cd your-dbt-project
dbt compile  # or dbt run

3. Run the ingestion

If you’re already in your dbt project directory:

metadata ingest-dbt

Or if you’re in a different directory:

metadata ingest-dbt -c /path/to/your/dbt-project

Environment Variables

For security and flexibility, you can use environment variables in your dbt_project.yml configuration instead of hardcoding sensitive values like JWT tokens. The system supports three different environment variable patterns:

Supported Patterns

Pattern	Description	Example
`${VAR}`	Shell-style variable substitution	`"${OPENMETADATA_TOKEN}"`
`{{ env_var("VAR") }}`	dbt-style without default	`"{{ env_var('OPENMETADATA_HOST') }}"`
`{{ env_var("VAR", "default") }}`	dbt-style with default value	`"{{ env_var('SERVICE_NAME', 'default-service') }}"`

Environment Variables Example

# dbt_project.yml
vars:
  # Using shell-style variables
  openmetadata_host_port: "${OPENMETADATA_HOST_PORT}"
  openmetadata_jwt_token: "${OPENMETADATA_JWT_TOKEN}"

  # Using dbt-style variables
  openmetadata_service_name: "{{ env_var('OPENMETADATA_SERVICE_NAME') }}"

  # Using dbt-style with defaults
  openmetadata_dbt_classification_name: "{{ env_var('DBT_CLASSIFICATION', 'dbt_tags') }}"
  openmetadata_search_across_databases: "{{ env_var('SEARCH_ACROSS_DB', 'false') }}"

Then set your environment variables:

export OPENMETADATA_HOST_PORT="https://your-openmetadata-server-url/endpoint"
export OPENMETADATA_JWT_TOKEN="your-jwt-token"
export OPENMETADATA_SERVICE_NAME="your-database-service"

Alternative: Using .env Files For local development, you can create a .env file in your dbt project directory:

# .env file in your dbt project root
OPENMETADATA_HOST_PORT=https://your-openmetadata-server-url/endpoint
OPENMETADATA_JWT_TOKEN=your-jwt-token
OPENMETADATA_SERVICE_NAME=your-database-service

Note: The system automatically loads environment variables from .env files in both the dbt project directory and the current working directory. Environment variables set in the shell take precedence over .env file values.

Error Handling: If a required environment variable is not set and no default is provided, the ingestion will fail with a clear error message indicating which variable is missing.

Configuration Options

Required Parameters

Parameter	Description
`openmetadata_host_port`	OpenMetadata server URL (must start with `https://`)
`openmetadata_jwt_token`	JWT token for authentication
`openmetadata_service_name`	Name of the database service in OpenMetadata

Optional Parameters

Parameter	Default	Description
`openmetadata_dbt_update_descriptions`	`true`	Update table/column descriptions from dbt
`openmetadata_dbt_update_owners`	`true`	Update model owners from dbt
`openmetadata_include_tags`	`true`	Include dbt tags as OpenMetadata tags
`openmetadata_search_across_databases`	`false`	Search for tables across multiple databases
`openmetadata_dbt_classification_name`	`null`	Custom classification name for dbt tags

Filter Patterns

Control which databases, schemas, and tables to include or exclude:

vars:
  # ... required config above ...

  # Filter patterns using regex
  openmetadata_database_filter_pattern:
    includes: ["production_*", "analytics_*"]
    excludes: ["temp_*", "test_*"]

  openmetadata_schema_filter_pattern:
    includes: ["public", "marts", "staging"]
    excludes: ["information_schema", "temp_*"]

  openmetadata_table_filter_pattern:
    includes: [".*"]
    excludes: ["temp_.*", "tmp_.*", "dbt_.*"]

Complete Example

# dbt_project.yml
name: 'my_analytics_project'
version: '1.0.0'
config-version: 2

profile: 'my_analytics_project'
model-paths: ["models"]
# ... other dbt settings ...

vars:
  # OpenMetadata Configuration - Using Environment Variables
  openmetadata_host_port: "${OPENMETADATA_HOST_PORT}"
  openmetadata_jwt_token: "{{ env_var('OPENMETADATA_JWT_TOKEN') }}"
  openmetadata_service_name: "{{ env_var('OPENMETADATA_SERVICE_NAME', 'postgres_analytics') }}"

  # Optional Settings
  openmetadata_dbt_update_descriptions: true
  openmetadata_dbt_update_owners: true
  openmetadata_include_tags: true
  openmetadata_dbt_classification_name: "{{ env_var('DBT_CLASSIFICATION', 'dbt_analytics_tags') }}"

  # Filtering
  openmetadata_database_filter_pattern:
    includes: ["analytics", "data_warehouse"]
    excludes: ["temp_db", "test_db"]

  openmetadata_table_filter_pattern:
    includes: [".*"]
    excludes: ["temp_.*", "tmp_.*", "test_.*"]

Command Options

metadata ingest-dbt [OPTIONS]

Options:
  -h, --help                   Show help message and exit
  -c, --dbt-project-path PATH  Path to the dbt project directory (default: current directory)

Note: Global options like --version, --log-level, and --debug are available at the main metadata command level:

metadata --version                    # Show version information
metadata --log-level DEBUG ingest-dbt -c /path/to/project  # Set log level
metadata --debug ingest-dbt -c /path/to/project           # Enable debug mode

Artifacts Discovery

The command automatically discovers artifacts from your dbt project’s target/ directory:

Artifact	Required	Description
`manifest.json`	✅ Yes	Model definitions, relationships, and metadata
`catalog.json`	Optional	Table and column statistics from `dbt docs generate`
`run_results.json`	Optional	Test results from `dbt test`

Generate All Artifacts

dbt compile              # Generate manifest.json
dbt docs generate        # Generate catalog.json (requires database connection)
dbt test                 # Generate run_results.json

What Gets Ingested

Model Definitions: Queries, configurations, and relationships
Lineage: Table-to-table and column-level lineage
Documentation: Model and column descriptions
Data Quality: dbt test definitions and results
Tags & Classification: Model and column tags
Ownership: Model owners and team assignments

Error Handling & Troubleshooting

Common Issues

Issue	Solution
`dbt_project.yml not found`	Ensure you’re in a valid dbt project directory
`Required configuration not found`	Add `openmetadata_*` variables to your `dbt_project.yml`
`manifest.json not found`	Run `dbt compile` or `dbt run` first
`Invalid URL format`	Ensure `openmetadata_host_port` includes protocol (`https://`)
`Environment variable 'VAR' is not set`	Set the required environment variable or provide a default value
`Environment variable not set and no default`	Either set the environment variable or use the `{{ env_var('VAR', 'default') }}` pattern

Debug Mode

Enable detailed logging:

metadata --debug ingest-dbt -c .

Best Practices

Security

Always use environment variables for sensitive data like JWT tokens

Multiple patterns supported for flexibility:

vars:
  # Shell-style (simple and widely supported)
  openmetadata_host_port: "${OPENMETADATA_HOST_PORT}"
  openmetadata_jwt_token: "${OPENMETADATA_JWT_TOKEN}"

  # dbt-style (consistent with dbt conventions)
  openmetadata_service_name: "{{ env_var('OPENMETADATA_SERVICE_NAME') }}"

  # dbt-style with fallbacks (recommended for optional settings)
  openmetadata_dbt_classification_name: "{{ env_var('DBT_CLASSIFICATION', 'dbt_tags') }}"

Never commit sensitive values directly to version control

Filtering

Use specific patterns to exclude temporary/test tables
Filter based on your organization’s naming conventions
Exclude system schemas and databases

Automation

Integrate into CI/CD pipelines
Run after successful dbt builds
Set up scheduled ingestion for regular updates

CI/CD Integration

# .github/workflows/dbt-ingestion.yml
name: dbt and OpenMetadata Ingestion

on:
  push:
    branches: [main]

jobs:
  dbt-run-and-ingest:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.12'

      - name: Install dependencies
        run: |
          pip install dbt-core dbt-postgres
          pip install "openmetadata-ingestion[dbt]"

      - name: Run dbt
        run: |
          dbt deps
          dbt compile
          dbt run
          dbt test
          dbt docs generate
        env:
          DBT_PROFILES_DIR: .

      - name: Ingest to OpenMetadata
        run: metadata ingest-dbt -c .
        env:
          OPENMETADATA_HOST_PORT: ${{ secrets.OPENMETADATA_HOST_PORT }}
          OPENMETADATA_JWT_TOKEN: ${{ secrets.OPENMETADATA_JWT_TOKEN }}
          OPENMETADATA_SERVICE_NAME: ${{ secrets.OPENMETADATA_SERVICE_NAME }}

Next Steps

After successful ingestion:

Explore your data in the OpenMetadata UI
Configure additional dbt features like tags, tiers, and glossary
Set up data governance policies and workflows
Schedule regular ingestion for keeping metadata up-to-date

For additional troubleshooting, refer to the dbt Troubleshooting Guide.

Connectors

Connector

Ingestion

Auto Ingest dbt-core

Auto Ingest dbt-core

Overview

Prerequisites

Quick Start

1. Configure your dbt_project.yml

2. Generate dbt artifacts

3. Run the ingestion

Environment Variables

Supported Patterns

Environment Variables Example

Configuration Options

Required Parameters

Optional Parameters

Filter Patterns

Complete Example

Command Options

Artifacts Discovery

Generate All Artifacts

What Gets Ingested

Error Handling & Troubleshooting

Common Issues

Debug Mode

Best Practices

Security

Filtering

Automation

CI/CD Integration

Next Steps