Keep Your LookML Data Models Catalogued and Compliant

The Challenge

As organizations scale their Looker deployments, maintaining control over the data model layer becomes increasingly complex. Data governance teams face:

Documentation debt — LookML models evolve faster than documentation teams can keep up, creating gaps in data dictionaries and lineage maps
Naming convention drift — Without automated checks, dimension and measure names gradually diverge from organizational standards
Unauthorized changes — New explores, dimensions, or database connections appear without proper review processes
Compliance blind spots — Auditors require proof of data lineage and model integrity that can only be assembled through time-consuming manual review
Knowledge concentration — Model expertise is siloed in individual analysts who become single points of failure for governance knowledge

The Autohive Solution

Autohive’s GoogleLooker integration retrieves comprehensive LookML metadata programmatically—models, explores, dimensions, measures, and database connections—enabling automated governance workflows that run continuously without manual intervention.

Complete Model Metadata Retrieval

Pull the full structure of any LookML model automatically, including all explores, dimensions, and measures with their definitions, labels, and descriptions. Build a living catalog of your entire data model layer.

Automated Naming Convention Validation

Schedule regular audits that check all dimensions and measures against your organization’s naming standards. Flag violations immediately and route them to the appropriate team for remediation—before they compound into larger inconsistencies.

Database Connection Inventory

Automatically retrieve and track all available database connections in your Looker instance. Maintain an up-to-date inventory of data sources, supporting both security audits and data lineage documentation.

Change Detection and Alerting

Compare current model metadata against previous snapshots to detect changes. When new explores, dimensions, or connections appear, automatically trigger review workflows and notify data governance teams.

Benefits

Continuous governance — Model audits run automatically on schedule, not just when someone has time
Living documentation — Data catalogs update automatically as LookML models evolve
Faster compliance — Produce audit-ready lineage documentation in minutes rather than days
Proactive quality control — Catch naming violations and undocumented changes before they become governance issues
Scalable oversight — Govern complex multi-model Looker instances without adding headcount to your data team

How It Works

Connect to your Looker instance — Configure the GoogleLooker integration with your authentication credentials
Define governance rules — Specify naming conventions, required fields, documentation standards, and change notification criteria
Schedule automated audits — Set Autohive to retrieve model metadata at your preferred cadence (daily, weekly, on-change)
Generate documentation — Automatically build and update data catalogs, lineage maps, and model inventories from retrieved metadata
Alert on violations — Route governance findings to the right teams via Slack, email, or ticketing systems for prompt resolution

Getting Started

Sign up at app.autohive.com
Connect the GoogleLooker integration from the marketplace
Define your organization’s LookML governance standards and documentation requirements
Build an automated audit workflow using model metadata retrieval
Deploy your agents and maintain continuous visibility into your entire data model layer