Course Outline:
Module 1 - Introducing Looker and LookML
Topics
- Looker and LookML
- The Looker user interface
- Example 1: The Looker IDE
- LookML project version control
- Example 2: Git workflow in Looker
- How Looker writes SQL
Objectives
- 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.
Activities
- 2 demos
- 1 quiz
Module 2 - Data Modeling Using LookML
Topics
- Anatomy of a LookML project
- Modeling dimensions
- Example 3: Creating dimensions using LookML
- Modeling measures
- Example 4: Modeling measures using LookML
- Dimension and measure modeling logic
- LookML dashboards
- Lab 1: Creating dimensions and measures with LookML
Objectives
- 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.
Activities
- 1 quiz
- 2 demos
- 1 lab
Module 3 - Modeling Explores for Your Users
Topics
- Modeling new Explores
- Using LookML to filter Explores
- Understanding symmetric aggregation
Objectives
- 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.
Activities
- 1 quiz
Module 4 - Working with Derived Tables
Topics
- Introducing derived tables
- Types of derived tables
- Example 5: Using SQL derived tables
- Example 6: Using native derived tables
- Native derived table parameters
- Using persistent derived tables
- Caching and datagroups
- Implementing datagroups in Looker
- Lab 2: Creating Derived Tables with LookML
Objectives
- 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, and persistent derived tables within the Looker UI.
- Describe the process of creating SQL, 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.
Activities
- 2 demos
- 1 quiz
- 1 lab, Introduction to Looker and LookML, Creating Dimensions and Measures, Project Version Control, Model Files, Derived Tables, Caching and Datagroups
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