LookML is a data modeling language for describing dimensions, fields, aggregates and relationships based on SQL.
LookML is powerful because it:
- Is all about reusability: Most data analysis requires the same work to be done over and over again. You extract raw data, prepare it, deliver an analysis... and then are never able touse any of that work again. This is hugely inefficient, since the next analysis often involves many of the same steps. With LookML, once you define a dimension or a measure, you continue to build on it, rather than having to rewrite it again and again.
- Empowers end users: The data model that data analysts and developers create in LookML condenses and encapsulates the complexity of SQL, it and lets analysts get the knowledge about what their data means out of their heads so others can use it. This enables non-technical users to do their jobs — building dashboards, drilling to row-level detail, and accessing complex metrics — without having to worry about what’s behind the curtain.
- Allows for data governance: By defining business metrics in LookML, you can ensure that Looker is always a credible single source of truth.
The Looker application uses a model written in LookML to construct SQL queries against a particular database that business analysts can Explore on. For an overview on the basics of LookML, see What is LookML?
A LookML project is a collection of LookML files that describes a set of related views, models, and Explores.
- A view (.view files) contains information about how to access or calculate information from each table (or across multiple joined tables). Here you’ll typically define the view, its dimensions and measures, and its field sets.
- A model (.model file) contains information about which tables to use and how they should be joined together. Here you’ll typically define the model, its Explores, and its joins.
- An Explore is the starting point for business users to query data, and it is the end result of the LookML you are writing. To see the Explores in this project, select an Explore from the Explore menu.
Ad-hoc data discovery is one of Looker’s most powerful and unique features. As you evaluate use cases for your trial, consider what business areas you would like to explore. Open the Explore menu in the main navigation to see the Explores you are building.
To support a multi-developer environment, Looker is integrated with Git for version control. Follow these directions to set up Git for your project. To edit LookML, expand the Develop drop-down and toggle on Development Mode. In Development Mode, changes you make to the LookML model exist only in your account until you commit the changes and push them to your production model.
To learn more about LookML and how to develop visit:
This repository contains several markdown files that serve as context and guidelines for the development of the project:
project_context_summary.md: Provides a comprehensive summary of the project schema, dimensions, measures, and overall data structure for the Rx dataset.project_dashboard_prompts.md: Contains a list of standard prompts and requirements used to generate the LookML dashboards in the project.available_visualizations.md: A manifest of all available custom and marketplace visualizations installed in the Looker environment.
The agent SKILL.md file (.agents/skills/lookml-modeling-guidelines/SKILL.md) has been recently updated to enforce rigorous LookML modeling guidelines, best practices for Looker MCP tool usage, and co-development patterns. This ensures all new code and dashboard generated by AI agents adhere to high standards of performance, security, and maintainability.