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Home/Connectors/Dashboard/Metabase/Airflow

Run Metabase using the Airflow SDK

StagePROD
Dashboards
Charts
Owners
Tags
Datamodels
Lineage

In this section, we provide guides and references to use the Metabase connector.

Configure and schedule Metabase metadata and profiler workflows from the OpenMetadata UI:

Requirements

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

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.

Note: We have tested Metabase with Versions 0.42.4 and 0.43.4.

Python Requirements

To run the Metabase ingestion, you will need to install:

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Metabase.

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

1. Define the YAML Config

Source Configuration - Service Connection

username: Username to connect to Metabase, for ex. user@organization.com. This user should have access to relevant dashboards and charts in Metabase to fetch the metadata.

password: Password of the user account to connect with Metabase.

hostPort: The hostPort parameter specifies the host and port of the Metabase instance. This should be specified as a string in the format http://hostname:port or https://hostname:port. For example, you might set the hostPort parameter to https://org.metabase.com:3000.

Source Configuration - Source Config

The sourceConfig is defined here:

  • dbServiceNames: Database Service Names for ingesting lineage if the source supports it.
  • dashboardFilterPattern, chartFilterPattern, dataModelFilterPattern: Note that all of them support regex as include or exclude. E.g., "My dashboard, My dash.*, .*Dashboard".
  • includeOwners: 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.
  • includeTags: Set the 'Include Tags' toggle to control whether to include tags in metadata ingestion.
  • includeDataModels: Set the 'Include Data Models' toggle to control whether to include tags as part of metadata ingestion.
  • markDeletedDashboards: Set the 'Mark Deleted Dashboards' toggle to flag dashboards as soft-deleted if they are not present anymore in the source system.

Sink Configuration

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

Workflow Configuration

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

filename.yaml
Workflow Configs for Security Provider
We support different security providers. You can find their definitions here.
Openmetadata JWT Auth
  • JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.
  • You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.
2. Prepare the Ingestion DAG
Create a Python file in your Airflow DAGs directory with the following contents:
Import necessary modules
The Workflow class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
Default arguments for all tasks in the Airflow DAG.
  • Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.
  • config: Specifies config for the metadata ingestion as we prepare above.
  • metadata_ingestion_workflow(): This code defines a function metadata_ingestion_workflow() that loads a YAML configuration, creates a Workflow object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
  • DAG: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
  • For more Airflow DAGs creation details visit here.
Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.
filename.py