In this section, we provide guides and references to use the Spline connector.
Configure and schedule Spline 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
The Spline connector supports lineage of data source of type jdbc or dbfs i.e. The Spline connector would be able to extract lineage if the data source is either a jdbc connection or the data source is a Databricks instance.
Currently we do not support data source of type AWS S3 or any other cloud storage, which also means that the lineage for external tables from Databricks will not be extracted.
You can refer this documentation on how to configure Databricks with Spline.
Python Requirements
We have support for Python versions 3.9-3.11
To run the Spline ingestion, you will need to install:
pip3 install "openmetadata-ingestion"
All connectors are defined as JSON Schemas.
Here
you can find the structure to create a connection to Spline.
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 Spline:
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.