Developing Data Models with LookML
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Learning Track
Looker and Looker Studio
Delivery methods
On-Site, Virtual
Duration
1 day
This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
Course objectives
- 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
Audience
- 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.
Prerequisites
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.
Course outline
- 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
- Create dimensions and measures to curate data attributes used by business users
- Implement version control with Git to manage and track changes in LookML projects
- 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
- Define the two types of derived tables in Looker
- Create ephemeral and persistent derived tables
- List best practices for creating derived tables
- Explain how Looker uses caching to speed up SQL queries
- Use datagroups to manage caching policies in Looker
