Course Description

Course Overview

The Developing Data Models with LookML (DDMLML) course is designed to provide individuals with the knowledge and skills needed to create and maintain scalable data models using LookML, the modeling language of Looker. This course covers the fundamentals of LookML, including data modeling best practices, creating reusable models, and optimizing query performance.

Prerequisites

To enroll in the DDMLML course, participants should have a solid understanding of data analysis concepts, familiarity with SQL, and experience with Looker or similar business intelligence tools. Prior knowledge of relational databases and data modeling principles is recommended. Participants should also have access to a Looker instance or demo environment to practice the concepts covered in the course.

Methodology

The DDMLML course follows a blended learning approach, combining theoretical instruction, demonstrations, and hands-on labs. Participants will engage in instructor-led sessions where LookML concepts and best practices are explained. They will also have access to a Looker instance or demo environment to gain practical experience in working with LookML and developing data models. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to LookML and Data Modeling

Overview of LookML and its role in data modeling

Understanding the LookML development workflow

Best practices for designing scalable and maintainable data models

LookML Basics and Syntax

LookML files, projects, and views

LookML syntax, objects, and declarations

Templating and reusable code in LookML

Building Dimensional Models

Understanding dimensions and dimension groups

Defining relationships between dimensions and views

Implementing hierarchies and time-based dimensions

Advanced LookML Modeling Techniques

Working with derived tables and persistent derived tables

Creating aggregate tables and materialized views

Implementing custom measures and calculations

Performance Optimization and Query Management

Optimizing LookML models for query performance

Using caching and pre-aggregation techniques

Query optimization and managing expensive queries

LookML Testing, Version Control, and Best Practices

Writing unit tests for LookML models

Version control and collaboration with LookML

LookML best practices for maintainability and scalability

Outcome

By the end of the DDMLML course, participants will have:

• Developed a comprehensive understanding of LookML and its role in data modeling
• Acquired practical knowledge in building scalable and maintainable data models with LookML
• Gained expertise in advanced LookML techniques for dimension modeling, custom calculations, and query optimization
• Learned best practices for version control, testing, and performance optimization in LookML development
• Gained hands-on experience through practical labs and exercises
• Prepared to develop and maintain efficient data models using LookML in their organizations

Labs

The DDMLML course includes hands-on labs that provide participants with practical experience in working with LookML and developing data models. Some examples of lab exercises include:

• Creating LookML projects and files
• Defining dimensions, measures, and relationships in LookML
• Building hierarchies and time-based dimensions
• Implementing derived tables and persistent derived tables
• Creating custom calculations and measures in LookML
• Optimizing LookML models for query performance
• Writing unit tests for LookML models
• Using version control with LookML and collaborating on development

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in developing efficient and scalable data models using LookML, allowing them to develop practical skills in leveraging LookML’s capabilities for data modeling and analysis.