Each of the services support providing a Custom Connector. It should be a Python class available in the Python environment running the ingestion process (e.g., EC2 instance, Airflow host, Docker Image...). It should also match specific constraints on the methods to implement and how to send the Entities to be created.
In this guide, we'll walk through a possible implementation. The example is based on a Database Service, but the process is the same for Pipelines, Dashboard or Messaging services.
This guide is based on a working example in the OpenMetadata Demos repository: link.
We'd recommend to go through the example to better understand how all the pieces should look like.
Step 1 - Prepare your Connector
A connector is a class that extends from
metadata.ingestion.api.source.Source. It should implement
all the required methods (docs).
In connector/my_awesome_connector.py you have a minimal example of it.
Note how the important method is the
next_record. This is the generator function that will be iterated over
to send all the Create Entity Requests to the
Sink. Read more about the
Step 2 - Yield the data
Sink is expecting Create Entity Requests. To get familiar with the Python SDK and understand how to create
the different Entities, a recommended read is the Python SDK docs.
We do not have docs and examples of all the supported Services. A way to get examples on how to create and fetch
other types of Entities is to directly refer to the
ometa integration tests.
Step 3 - Prepare the package installation
We'll need to package the code so that it can be shipped to the ingestion container and used there. In this demo
you can find a simple
setup.py that builds the
Step 4 - Prepare the Ingestion Image
If you want to use the connector from the UI, the Python environment running the ingestion process should contain
the new code you just created. For example, if running via Docker, the
openmetadata-ingestion image should be
aware of your new package.
We will be running the demo against the OpenMetadata version
0.12.2, therefore, our Dockerfile looks like:
# Base image from the right version FROM openmetadata/ingestion:0.12.2 # Let's use the same workdir as the ingestion image WORKDIR ingestion USER airflow # Install our custom connector # For a PROD image, this could be picking up the package from your private package index COPY connector connector COPY setup.py . RUN pip install --no-deps .
Step 5 - Run OpenMetadata with the custom Ingestion image
We have a
Makefile prepared for you to run
make run. This will get OpenMetadata up in Docker Compose using the
custom Ingestion image.
Step 6 - Configure the Connector
In the example we prepared a Database Connector. Thus, go to
Database Services > Add New Service > Custom
and set the
Source Python Class Name as
Note how we are specifying the full module name so that the Ingestion Framework can import the Source class.