In this section, we provide guides and references to use the ThoughtSpot connector.
Configure and schedule ThoughtSpot 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
To access the ThoughtSpot APIs and import liveboards, charts, and data models from ThoughtSpot into OpenMetadata, you need appropriate permissions on your ThoughtSpot instance.
- The minimum required role is typically “Developer” or higher, depending on your ThoughtSpot security model.
- For lineage extraction, ensure TML (ThoughtSpot Modeling Language) export is enabled for your user.
Python Requirements
We have support for Python versions 3.9-3.11
To run the ThoughtSpot ingestion, you will need to install:
pip3 install "openmetadata-ingestion[thoughtspot]"
All connectors are defined as JSON Schemas.
Here
you can find the structure to create a connection to ThoughtSpot.
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
1. Define the YAML Config
This is a sample config for ThoughtSpot:
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.