Run Amundsen using the Airflow SDK

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

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

Requirements

Before this, you must ingest the database / messaging service you want to get metadata for. For more details click here

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.

Python Requirements

To run the Amundsen 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 Amundsen.

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

This is a sample config for Amundsen:

Source Configuration - Service Connection

hostPort: Host and port of the Amundsen Neo4j Connection. This expect a URI format like: bolt://localhost:7687.

username: Username to connect to the Amundsen. This user should have privileges to read all the metadata in Amundsen.

password: Password to connect to the Amundsen.

maxConnectionLifeTime: Maximum connection lifetime for the Amundsen Neo4j Connection.

validateSSL: Enable SSL validation for the Amundsen Neo4j Connection.

encrypted: Enable encryption for the Amundsen Neo4j Connection.

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