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Run the Looker Connector Externally

StagePROD
Dashboards
Charts
Owners
Tags
Datamodels
Projects
Lineage

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:

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.

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

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.

To run the Looker ingestion, you will need to install:

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

This is a sample config for Looker:

clientId: Specify the Client ID to connect to Looker. It should have enough privileges to read all the metadata.

clientSecret: Client Secret to connect to Looker.

hostPort: URL to the Looker instance.

githubCredentials (Optional): GitHub API credentials to extract LookML Views' information by parsing the source .lkml files. There are three properties we need to add in this case:

  • repositoryOwner: The owner (user or organization) of a GitHub repository. For example, in https://github.com/open-metadata/OpenMetadata, the owner is open-metadata.
  • repositoryName: The name of a GitHub repository. For example, in https://github.com/open-metadata/OpenMetadata, the name is OpenMetadata.
  • token: Token to use the API. This is required for private repositories and to ensure we don't hit API limits.

Follow these steps in order to create a fine-grained personal access token.

When configuring, give repository access to Only select repositories and choose the one containing your LookML files. Then, we only need Repository Permissions as Read-only for Contents.

The sourceConfig is defined here:

  • dbServiceNames: Database Service Names for ingesting lineage if the source supports it.
  • dashboardFilterPattern, chartFilterPattern, dataModelFilterPattern: Note that all of them support regex as include or exclude. E.g., "My dashboard, My dash.*, .*Dashboard".
  • projectFilterPattern: Filter the dashboards, charts and data sources by projects. Note that all of them support regex as include or exclude. E.g., "My project, My proj.*, .*Project".
  • includeOwners: Set the 'Include Owners' toggle to control whether to include owners to the ingested entity if the owner email matches with a user stored in the OM server as part of metadata ingestion. If the ingested entity already exists and has an owner, the owner will not be overwritten.
  • includeTags: Set the 'Include Tags' toggle to control whether to include tags in metadata ingestion.
  • includeDataModels: Set the 'Include Data Models' toggle to control whether to include tags as part of metadata ingestion.
  • markDeletedDashboards: Set the 'Mark Deleted Dashboards' toggle to flag dashboards as soft-deleted if they are not present anymore in the source system.

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

Logger Level

You can specify the loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with DEBUG will give you far more traces for identifying issues.

JWT Token

JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.

SSL Configuration

If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set verifySSL to ignore, or have it as validate, which will require you to set the sslConfig.certificatePath with a local path where your ingestion runs that points to the server certificate file.

Find more information on how to troubleshoot SSL issues here.

filename.yaml

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

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.