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[00] – dbt by Example Course

kerrache.massipssa

This course is designed to help you master dbt (Data Build Tool) through hands-on practice. Rather than just theory, we’ll walk through real-world examples, applying dbt’s features step by step.

You’ll start with the fundamentals—understanding dbt’s architecture—before progressing to advanced transformations, testing, performance optimization, and automation. Throughout the course, we will build a fully functional analytics pipeline for an e-commerce business, applying dbt features at each stage.

Who Is This Course For? #

  • Data Analysts
  • Data Engineers
  • Business Intelligence Professionals
  • Anyone working with SQL-based transformations

Prerequisites #

  • Basic knowledge of SQL
  • Familiarity with Data Warehousing concepts (optional)
  • Understanding of ETL/ELT workflows (optional)

Course Curriculum #

Module 1: Introduction to dbt #

Module 2: Setting Up Your dbt Environment #

  • Installing dbt (Local vs. Cloud)
  • Setting up dbt with:
    • Postgres
    • Snowflake
    • BigQuery
    • Redshift
  • Configuring the profiles.yml file

Module 3: Working with dbt Models #

Module 4: dbt Testing & Data Quality #

  • The importance of data testing
  • Writing dbt tests for:
    • Unique constraints
    • Null checks
    • Referential integrity
  • Custom tests using schema.yml
  • Advanced testing with Great Expectations and dbt

Module 5: dbt Documentation & Version Control #

  • Generating and viewing dbt documentation (dbt docs generate)
  • Adding descriptions to models, columns, and sources
  • Version control with Git + dbt
  • Collaborative workflows in dbt Cloud

Module 6: Advanced dbt Concepts #

  • Using seeds and sources in dbt
  • Refactoring large dbt projects with macros
  • Implementing hooks & operations
  • Using Jinja templating in dbt
  • dbt packages & dependencies

Module 7: dbt Performance Optimization #

  • Understanding incremental models in dbt
  • Performance tuning for:
    • Snowflake
    • BigQuery
    • Redshift
    • Postgres
  • Managing dependencies in large dbt projects
  • Optimizing dbt compilation & execution

Module 8: Deploying dbt in Production #

  • CI/CD with dbt Cloud
  • Automating dbt workflows using Airflow
  • dbt integration with orchestration tools (Prefect, Dagster)
  • Error handling and logging in dbt

Module 9: dbt for Analytics & BI Teams #

  • How dbt fits into the Modern Data Stack
  • Using dbt for data marts and business intelligence
  • Integrating dbt with:
    • Looker
    • Metabase
    • Tableau
  • Best practices for maintaining dbt projects in a team setting

Module 10: Capstone Project #

  • Project Goal: Build an end-to-end dbt transformation pipeline
  • Tools: Choose between Postgres, Snowflake, or BigQuery
  • Tasks:
  • Create source tables and define staging models
  • Implement incremental models
  • Write tests and document your models
  • Optimize for performance
  • Deploy the project using dbt Cloud + CI/CD

Learning Path #

This schema outlines how you progress through the course.

Project workflow #

This diagram illustrates how raw data will flow through the dbt models.

dbt Project Structure #

This schema represents how we will organize the dbt models throughout the course.

dbt_project/
│── models/
│   │── staging/
│   │   │── stg_orders.sql       # Cleaned order data
│   │   │── stg_consumers.sql    # Cleaned consumer data
│   │
│   │── intermediate/
│   │   │── int_completed_orders.sql      # Filtered completed orders
│   │   │── int_not_completed_orders.sql  # Filtered pending orders
│   │
│   │── final/
│   │   │── final_top_consumers.sql   # Aggregated customer spending
│   │   │── final_sales_performance.sql # Monthly revenue trends
│   
│── seeds/
│   │── product_categories.csv  # Static reference data for products
│   │── country_codes.csv       # Country mapping data
│
│── tests/
│   │── schema.yml  # Data integrity tests (unique, not null, etc.)
│
│── dbt_project.yml  # Project configuration
│── README.md        # Documentation

Conclusion #

By following this step-by-step approach, you’ll gain practical experience with dbt while building a real-world e-commerce analytics pipeline.

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