Managing SQL code at scale is one of the biggest challenges in data engineering. As data teams grow and pipelines become more complex, traditional approaches to SQL development quickly become unwieldy.

This LinkedIn Learning course explores how dbt (data build tool) transforms the way we think about SQL development, bringing software engineering best practices to analytics engineering.

Course Approach

Real-World Problem Solving: Each chapter presents actual situations and challenges that data engineers face, with focused code examples showing practical solutions.

Hands-On Implementation: The course covers both basic and advanced dbt concepts through working examples rather than theoretical explanations.

Production-Ready Techniques: Learn to build maintainable, testable SQL transformations that scale with your organization.

What You’ll Learn

The course covers essential dbt concepts including:

  • Schema design fundamentals for maintainable data models
  • Generating SQL model files efficiently and consistently
  • Table materializations and when to use different strategies
  • Implementing CTEs (Common Table Expressions) within dbt models
  • SQL unit tests to ensure data quality and catch regressions
  • Code organization patterns for large dbt projects

Why dbt Matters

Traditional SQL development often involves:

  • Copy-pasting code across multiple files
  • Manual dependency management
  • No testing framework
  • Difficult collaboration and code review processes

dbt addresses these challenges by providing:

  • Modularity: Break complex transformations into manageable pieces
  • Dependencies: Automatic resolution of table and view dependencies
  • Testing: Built-in data quality testing framework
  • Documentation: Generate and maintain data documentation automatically
  • Version Control: Treat analytics code like software with proper CI/CD

Who This Course Is For

This course is designed for:

  • Data engineers working with SQL transformations
  • Analytics engineers building data models
  • Data analysts who want to improve their SQL workflow
  • Anyone managing complex SQL codebases looking for better organization

The course assumes familiarity with SQL but doesn’t require prior dbt experience.

Real-World Applications

Throughout the course, we tackle common data engineering challenges:

  • Building dimensional models for analytics
  • Handling slowly changing dimensions
  • Creating reusable macros for complex logic
  • Implementing data quality checks
  • Managing environments (dev, staging, production)

Key Takeaways

dbt brings software engineering discipline to analytics engineering. By treating SQL transformations as code, teams can build more reliable, maintainable data pipelines.

The tool has fundamentally changed how many organizations approach data transformation, moving from ad-hoc SQL scripts to well-structured, tested, and documented data models.

You can find the course on LinkedIn Learning.

Questions about dbt or data engineering practices? Feel free to reach out.