GOOGLE CLOUD
Developing Data Models with LookML
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.
What you will learn
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 optimize SQL query performance
Who this course is for
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
Level
Intermediate
Duration
1 x 8 hour session
Language
Delivered in English
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 TOPICS
Module 1: Introduction to Looker and LookML
LookML basics, Looker development environment
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
Dimensions, measures
Create dimensions and measures to curate data attributes used by business users
Module 3: Project Version Control
Git within Looker, project version control
Module 4: Model Files
SQL within Looker, Explores, joins, symmetric aggregations, filters
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 business users
Module 5: Derived Tables
Derived tables, best practices
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
Caching, datagroups
Explain how Looker uses caching to optimize SQL query performance
Use datagroups to manage caching policies in Looker
Ref: C-LOOKDM-I-02
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