Run Looker using the Airflow SDK
| Stage | PROD |
|---|---|
| Dashboards | |
| Charts | |
| Owners | |
| Tags | |
| Datamodels | |
| 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:
Requirements
OpenMetadata 0.12 or laterTo deploy OpenMetadata, check the Deployment guides.
There are two types of metadata we ingest from Looker:
- Dashboards & Charts
- LookML Models
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.
Python Requirements
To run the Looker ingestion, you will need to install:
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
1. Define the YAML Config
This is a sample config for Looker:
Source Configuration - Service Connection
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.
Source Configuration - Source Config
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".
- 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.
Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.
Workflow Configuration
The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
Workflow Configs for Security Provider
We support different security providers. You can find their definitions here.
Openmetadata JWT Auth
- 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. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.
2. Prepare the Ingestion DAG
Create a Python file in your Airflow DAGs directory with the following contents:
Import necessary modules
The Workflow class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
Default arguments for all tasks in the Airflow DAG.
- Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.
- config: Specifies config for the metadata ingestion as we prepare above.
- metadata_ingestion_workflow(): This code defines a function
metadata_ingestion_workflow()that loads a YAML configuration, creates aWorkflowobject, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
- DAG: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
- For more Airflow DAGs creation details visit here.
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.