Developing Data Models with LookML

Module 1: Introducing Looker and LookML

• Articulate the benefits of using LookML for data modeling and analysis.

• Identify the primary components of the Looker user interface.

• Identify the target users and functions of key Looker UI elements.

• Define core Looker platform and LookML terminology.

• Understand the basic integration between Looker and Git for version control.

• Describe the LookML development lifecycle, including writing, validation, merging, and deployment processes.

• Recognize how Looker reads, parses, and writes SQL.

• Explain the relationship between SQL and the LookML modeling language.

Module 2: Data Modeling Using LookML

• Detail the hierarchical layers contained within a LookML project.

• Convert between user-defined and LookML dashboards.

• Construct dimensions and measures within a Looker Explore, defining appropriate data types, formats, and calculations.

• Locate dimensions, measures, and dashboards in the Looker IDE.

• Understand how dimensions and measures connect IDE development to Explore usage.

• Model complex dimensions for enhanced user experience in Looker.

• List the features and basic functionality of a LookML dashboard.

• Convert between user-defined and LookML dashboards.

Module 3: Modeling Explores for Your Users

• Create new Explores and filters in the Looker IDE.

• Connect Explores and filters to end-user data exploration.

• Understand symmetric aggregation for data analysis in Looker.

Module 4: Working with Derived Tables

• Understand derived tables, including SQL, native, and persistent types, and their respective purposes.

• Identify appropriate use cases for derived tables and select the optimal type based on specific requirements.

• Explain how derived tables enhance data analysis efficiency and effectiveness in Looker.

• Locate the creation points for SQL, native, and persistent derived tables within the Looker UI.

• Describe the process of creating SQL, native, and persistent derived tables in Looker.

• Identify the two key parameters used in native derived tables.

• Identify optional parameters for optimizing native derived table performance and functionality.

• Define caching and datagroups in the context of Looker.

• Determine when to implement caching and datagroups for query optimization.

• Evaluate the impact of caching and datagroups on overall Looker performance and user experience.

This course is primarily intended for the following participants:

• 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.

Prerequisite:

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.