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ThoughtSpot

ThoughtSpot

BETA
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]"

Metadata Ingestion

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:

Securing ThoughtSpot Connection with SSL in OpenMetadata

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.