Looker Connector

This section provides guides and references to use the Looker connector. Configure and schedule Looker metadata and profiler workflows from the Collate UI:

Requirements
Metadata Ingestion
Lineage
Troubleshooting

Requirements

Collate ingests two types of metadata from Looker:

Dashboards & Charts
LookML Models For the project metadata being ingested:
The actual LookML Project an Explore or View is developed in.
For Dashboards, the folder name from the UI, since there is no other hierarchy involved there. In terms of permissions, a user with access to the Dashboards and LookML Explores to be ingested is required. Create your API credentials following these docs. However, LookML Views are not present in the Looker SDK. Instead, that information must be extracted directly from the GitHub repository holding the source .lkml files. To get this metadata, a GitHub token with read only access to the repository is required. Follow these steps from the GitHub documentation.

Tip: The GitHub credentials are completely optional. Without them, Collate cannot 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. Liquid variables are not yet supported.

Entity Mapping

The Looker connector maps Looker assets to Collate entities as follows:

Looker Asset	Collate Entity	Description
Dashboards	Dashboard	Looker dashboards are mapped to Collate Dashboard entities.
Dashboard Elements (tiles)	Chart	Visualization tiles within a Looker dashboard are mapped to Collate Chart entities.
Explores and Views (LookML)	Data Models	LookML Explores and Views are mapped to Collate Data Model entities.
Folders (Dashboards) / LookML Projects (Explores & Views)	Project field	The project field means something different depending on the entity: for a Dashboard it stores the Looker folder name, while for an Explore or View it stores the LookML project (Git repository) it belongs to.

Example Structure

Looker Structure:

Folder: Sales Team
└── Dashboard: Revenue Overview
    ├── Tile: Monthly Revenue
    └── Tile: Regional Breakdown
LookML Project: sales_analytics
├── Explore: revenue
└── View: orders

Collate Structure:

Dashboard: "Revenue Overview" (Project: "Sales Team")
├── Chart: "Monthly Revenue"
└── Chart: "Regional Breakdown"
Data Model: "revenue" (Project: "sales_analytics")
Data Model: "orders" (Project: "sales_analytics")

This mapping ensures that:

Looker dashboards appear as Collate dashboards, organized by their Looker folder
Dashboard tiles appear as charts underneath their dashboard
LookML Explores and Views appear as data models, organized by their LookML project
The project field is sourced differently depending on entity type: a Looker folder for Dashboards, a LookML project for Data Models

Metadata Ingestion

To ingest metadata from Looker, you need to create a service connection. The service connects Looker with Collate. Once you create a service, Collate automatically starts ingesting metadata.

Step 1: Add New Service

In the left navigation, click Connections.
On the Connections page, click Add New Service.

Step 2: Select a Service and Connector

From the service type dropdown, select Dashboard Services, then click the Looker connector tile.

Step 3: Add Service Name and Description

Enter a unique, descriptive Service Name. Collate identifies services by their service name. Enter a name that distinguishes this deployment from other Looker services you are ingesting metadata from.
Optional: Enter a Description for the service.

Note: The service name cannot be changed after it is set.

Step 4: Configure Connection Options

Specify where ingestion runs, provide your source credentials, and verify the connection.

Select Ingestion Runner

Select an Ingestion Runner: the runner where the ingestion pipeline will execute.

Enter Connection Details

Enter the connection details for Looker. The right-hand panel in the UI displays inline help for each field.

Host and Port: URL to the Looker instance, for example, https://my-company.region.looker.com.
Client ID: User’s Client ID to authenticate to the SDK. This user should have privileges to read all the metadata in Looker.
Client Secret: User’s Client Secret for the same ID provided. To ingest LookML Views, provide your GitHub credentials:
Repository Owner: The owner (user or organization) of a GitHub repository. For example, in https://github.com/open-metadata/OpenMetadata, the owner is open-metadata.
Repository Name: The name of a GitHub repository. For example, in https://github.com/open-metadata/OpenMetadata, the name is OpenMetadata.
API Token: Token to use the API. This is required for private repositories and to avoid hitting API rate limits. Follow these steps 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. Set Repository Permissions to Read-only for Contents.

Test Connection

Once the credentials have been added, click on Test Connection and Save the changes.

Step 5: Configure Ingestion Options

In the What to Ingest step, use filter patterns to control which assets Collate ingests from your dashboard service. Filter patterns use regular expressions applied to asset names.

How Filter Patterns Work

Include: Add one or more comma-separated regular expressions. Collate ingests only assets whose names match at least one expression. Leave blank to include all assets.
Exclude: Add one or more comma-separated regular expressions. Collate skips any asset whose name matches an expression. Leave blank to exclude nothing.

Rules match asset names using one of five expressions:

contains: matches any name containing the value. For example, sales matches my_sales_data and sales_2024.
starts with: matches names beginning with the value. For example, prod_ matches prod_db and prod_schema.
ends with: matches names ending with the value. For example, _raw matches events_raw and logs_raw.
is exactly: matches the exact name only. For example, analytics matches only analytics.
matches regex: matches names using a regular expression. For example, ^prod_.*_v\d+$ matches prod_events_v1.

When both Include and Exclude are set, Exclude takes priority.

Tip: Leave all filter patterns empty to ingest all dashboards, charts, and data models available in the source.

Filter Options The Dashboard, Chart, and Data Model sections each include the following filter options:

Dashboard: Controls which dashboards Collate ingests from the source.
Chart: Controls which charts within the ingested dashboards are included.
Data Model: Controls which data models are included in metadata ingestion.

Each section provides the following controls:

Scan Mode: Choose between the following scan modes:
- Scan all: Ingests every asset of that type the connector can access. This is the default.
- Only specific: Enables include rules so only assets matching at least one rule are ingested.
Exclude system toggle: Use this toggle to automatically filter out system-reserved names defined by the connector—for example, Exclude system databases for the Databases section.
Always exclude: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules.
Preview: Shows a real-time summary of what will be in scope based on your current rules.
Include rules (available only in Only specific mode): Click + Add to define a rule. Added rules appear as chips. An asset is included if it matches any rule.

Tip: If AutoPilot is enabled, usage tracking, data lineage, and other downstream workflows start automatically after the first metadata ingestion completes.

Step 6: Create & Deploy

Click Create & Deploy to deploy the agent and start the first metadata ingestion run. Collate saves the service configuration and immediately begins pulling metadata from the source. To monitor ingestion progress or view the service you just added, go to Connections in the left navigation and select your service.

Configure Metadata Agent and Schedule Ingestion

The Metadata Agent extracts dashboards, charts, data models, and other structural metadata from your source and keeps your Collate catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click Create & Deploy, Collate automatically deploys a Metadata Agent for this service and triggers the first ingestion run. View its status and run history from the Agents tab on the service detail page. To configure the additional Metadata Agent and schedule ingestion, follow these steps:

In the left navigation, click Connections and select your service.
Click the Agents tab.
Click Add Agent and select Metadata from the dropdown. For some services, the dropdown is not available and clicking Add Agent takes you directly to the agent configuration page.

On the Configure Ingestion page, do the following and click Next.

Name this Ingestion: Enter a unique recognizable name for this ingestion pipeline.

Agent Setup: Configure ingestion parameters. The following fields are available:

Field	Default	Description
Db Service Prefixes	—	List of database service name prefixes used to resolve lineage between dashboards and database assets. Accepted formats: `DBService`, `DBService.Database`, `DBService.Database.Schema`, `DBService.Database.Schema.Table`.
Query Parser Type	Auto	Select the SQL parser used for lineage extraction from dashboard queries. Auto selects the best available parser automatically. SqlGlot is a high-performance parser with excellent dialect support; falls back to SqlParse on failure. SqlFluff is a comprehensive parser with strong dialect support; falls back to SqlParse on failure.
Mark Deleted Dashboards	On	Soft-delete dashboards in Collate when they are removed from the source. Associated lineage is also deleted.
Mark Deleted Data Models	On	Soft-delete data models in Collate when they are removed from the source. Associated lineage is also deleted.
Mark Deleted Charts	On	Soft-delete charts in Collate when they are removed from the source.
Include Data Models	On	Ingest data model metadata.
Include Draft Dashboards	On	Ingest dashboards that are in draft state.
Include Usage	On	Collect and ingest usage statistics for dashboards.

Filter Patterns: Apply include or exclude rules to scope which dashboards, charts, data models, and projects this agent ingests. These follow the same filter options described in Step 5.

Scope & Behaviour: Control what metadata to include and how to handle deletions. Toggle each option on or off based on your needs:

Toggle	Default	Description
Enable Debug Log	Off	Sets the ingestion log level to DEBUG. Useful for troubleshooting.
Include Current Owners	Off	When enabled, replaces the current owner with a new owner from the source if the current owner is null. Recommended to keep enabled during the first metadata ingestion to capture owner information.
Include Tags	On	Ingest tags from the source.
Override Metadata	Off	When on, source values overwrite existing descriptions, tags, owners, and display names in Collate. When off, Collate only updates fields that have no existing value.
Override Lineage	Off	When on, existing lineage is replaced with newly extracted lineage on each run.

On the Schedule Interval page, set when the agent runs:
- Schedule: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression.
- On-Demand: No automatic schedule; trigger the agent manually when needed.
Click Add to deploy the agent.

Lineage

Lineage in Collate shows you which database tables power each dashboard. When lineage is set up, you can trace any dashboard back to the exact source tables in your database. There is no separate lineage agent or lineage pipeline — lineage is collected as part of the same metadata ingestion workflow. To enable lineage, open the Configure Metadata Agent section above, scroll to the Lineage Information section in the agent setup, and enter one or more database service names in the Db Service Prefixes field.

Configuring Db Service Prefixes is optional but recommended — it restricts table matching to specific database services. If left blank, Collate attempts to match source tables across all ingested database services.

What you enter	What it matches
`SnowflakeProd`	All tables in the Snowflake service named `SnowflakeProd`
`SnowflakeProd.sales_db`	Tables in the `sales_db` database within `SnowflakeProd`
`SnowflakeProd.sales_db.public`	Tables in the `public` schema within `sales_db`

Tip: If your dashboards pull data from multiple database services, add each service as a separate entry in the Db Service Prefixes field.

Troubleshooting

Looker Troubleshooting

Learn more about how to troubleshoot common Looker connector issues and resolve configuration or ingestion errors.

Connectors

Connectors

Requirements