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Documentation Index

Fetch the complete documentation index at: https://openmetadata-feat-feat-2mbfixdeploy.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

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

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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.

External Schedulers

Get more information about running the Ingestion Framework Externally

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Requirements

There are two types of metadata we ingest from Looker:
  • Dashboards & Charts
  • LookML Models For the project metadata being ingested:
  • We get the actual LookML Project an Explore or View is developed in.
  • For Dashboards, we use the folder name from the UI, since there is no other hierarchy involved there. In terms of permissions, we need a user with access to the Dashboards and LookML Explores that we want to ingest. You can create your API credentials following these docs. However, LookML Views are not present in the Looker SDK. Instead, we need to extract that information directly from the GitHub repository holding the source .lkml files. In order to get this metadata, we will require a GitHub token with read only access to the repository. You can follow these steps from the GitHub documentation.
The GitHub credentials are completely optional. Just note that without them, we won’t be able to ingest metadata out of LookML Views, including their lineage to the source databases. Moreover, Looker lineage only supports LookML views configured with sql_table_name and derived_table in plain SQL. We do not yet support liquid variables.

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Python Requirements

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

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Metadata Ingestion

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

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1. Define the YAML Config

This is a sample config for Looker:

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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.