Developing Data Models with LookML
Fundamental 1 day
This course is an intermediate-level training that introduces the fundamentals of LookML for Looker developers and provides guided demos and hands-on practice with writing LookML code.
To get the most out of this course, participants should have a basic understanding of SQL, Git, and the Looker business user experience. For learners with no previous experience as data explorers in Looker, it is recommended to first complete Analyzing and Visualizing Data in Looker.
Define LookML basic terms and building blocks; Use the Looker Integrated Development Environment (IDE) and project version control to modify LookML projects; Create dimensions and measures to curate data attributes used by business users; Create and design Explores to make data accessible to business users; Use derived tables to instantaneously create new tables; Use caching and datagroups in Looker to speed up SQL queries
Data developers who are responsible for data curation and management within their organizations; Data analysts interested in learning how data developers use LookML to curate and manage data in their organization’s Looker instance.
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Introduction to Looker and LookML
- Define Looker and the functionality it provides for curating data.
- Define LookML basic terms and building blocks.
- Use the Looker Integrated Development Environment (IDE) to modify LookML projects.
Module 2: Creating Dimensions and Measures
- Create dimensions and measures to curate data attributes used by business users.
Module 3: Project Version Control
- Implement version control with Git to manage and track changes in LookML projects.
Module 4: Model Files
- Explain how Looker utilizes SQL on the back end to translate user requests to query results.
- Create and design Explores to make data accessible to business users.
- Use joins to establish relationships between data tables.
- Leverage symmetric aggregation to ensure the accuracy of aggregated metrics.
- Implement filters to preselect data provided to end users.
Module 5: Derived Tables
- Define the two types of derived tables in Looker.
- Create ephemeral and persistent derived tables.
- List best practices for creating derived tables.
Module 6: Caching and Datagroups
- Explain how Looker uses caching to speed up SQL queries.
- Use datagroups to manage caching policies in Looker.