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Starts 6 June 2025 10:38

Ends 6 June 2025

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SQL and Jinja for dbt

Master SQL optimization and Jinja templating in dbt to create efficient data models, automate transformations, and implement dynamic references for scalable, maintainable workflows.
via Pluralsight

659 Courses


43 minutes

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Overview

Enhancing dbt workflows leads to efficient and maintainable data models. In this course, SQL and Jinja for dbt, you’ll gain the ability to write optimized dbt models, automate transformations, and apply Jinja scripting for dynamic data references.

First, you’ll explore best practices for writing efficient SQL in dbt models. Next, you’ll discover how to enhance dbt transformations using Jinja templating for dynamic schema and table references.

Finally, you’ll learn how to automate SQL workflows using dbt macros, enabling code reusability, logic-driven queries, and debugging strategies. When you’re finished with this course, you’ll have the skills and knowledge to optimize dbt models, streamline SQL workflows, and apply Jinja-based automation for scalable data transformations.

Syllabus

  • Introduction to dbt and its Workflow
  • Overview of dbt and its Role in Data Engineering
    Understanding dbt Models and Their Structure
    Introduction to SQL and Jinja in the Context of dbt
  • Writing Efficient SQL in dbt Models
  • Best Practices for SQL in dbt
    Leveraging dbt’s Compilation Process
    Techniques for Optimizing Query Performance
  • Using Jinja Templating in dbt
  • Introduction to Jinja and Its Syntax
    Dynamic Schema and Table References
    Conditional Logic and Looping with Jinja
    Tips for Troubleshooting Jinja Code
  • Enhancing dbt Transformations with Jinja
  • Creating Dynamic Queries with Jinja
    Using Jinja to Manage Configurations
    Implementing Jinja for Data Validation and Cleaning
  • Automating SQL Workflows with dbt Macros
  • Understanding Macros in dbt
    Writing Custom Macros for Reusable Logic
    Implementing Logic-Driven Queries via Macros
    Debugging Strategies for Macros
  • Case Studies: Applying SQL and Jinja for dbt
  • Real-world Examples of Optimized dbt Models
    Case Study: Dynamic Data Models with Jinja
    Automating Complex Transformations with Macros
  • Final Project
  • Developing a Comprehensive dbt Model
    Incorporating SQL Best Practices, Jinja, and Macros
    Peer Review and Feedback
  • Conclusion and Next Steps
  • Recap of Key Learnings
    Resources for Continued Learning
    Exploring Advanced Topics in dbt and Data Engineering

Taught by

Pinal Dave


Subjects

Business