LookML (Looker Modeling Language) is Looker's data modeling language used to define dimensions, aggregates, calculations, and table relationships in a SQL database. [1]
It acts as a semantic layer between your raw database (such as BigQuery, Snowflake, or Redshift) and business users. When a user builds a query in Looker, LookML translates those interactions into optimized SQL queries automatically. [2, 3]
Core Concepts and Components
- DRY Style (Don't Repeat Yourself): You write complex SQL logic and business rules once in LookML, and Looker reuses the code across multiple reports to prevent duplication and calculation errors.
- Projects: A collection of LookML files version-controlled together via a Git repository.
- Models (
.model.lkml): Files that define which database tables are used and how they join together. - Explores: User-friendly analysis playgrounds built from pre-joined tables where business users can explore data without writing code.
- Views (
.view.lkml): Files that map to database tables and define the structure. - Dimensions: Raw data fields or attributes (e.g., customer name, product ID, date).
- Measures: Calculated or aggregated values based on dimensions (e.g., total sales, average order value, count). [1, 2, 3, 4, 5]
Why LookML is Used
- Single Source of Truth: Establishes consistent definitions for metrics (like "active user" or "revenue") so all teams pull the same numbers. [6]
- Version Control: Integrates with Git so changes can be tracked, reviewed, tested, and rolled back safely. [3, 5]
- Empowers Business Users: Allows non-technical users to query data via simple point-and-click interfaces while abstracting complex SQL scaffolding. [1, 3]
Learn more about building and structuring models in the official Google Cloud Looker Documentation. [1]
Would you like to see a basic code example of a LookML view file and model file, or do you want to learn how Git version control works inside Looker?
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