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Azure Data Factory

Azure Data Factory

PROD
In this section, we provide guides and references to use the Azure Data Factory connector. Configure and schedule Azure Data Factory metadata and profiler workflows from the OpenMetadata UI:

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.

Requirements

Data Factory Versions

The Ingestion framework uses Azure Data Factory APIs to connect to the Data Factory and fetch metadata. You can find further information on the Azure Data Factory connector in the docs.

Permissions

Ensure that the service principal or managed identity you’re using has the necessary permissions in the Data Factory resource (Reader, Contributor or Data Factory Contributor role at minimum).

Python Requirements

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

Metadata Ingestion

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

2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
metadata ingest -c <path-to-yaml>
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.