Introduction

dbt (data build tool) is a modern analytics engineering framework used to transform data inside the warehouse using SQL. It helps teams convert raw data into analytics-ready datasets in a structured way. In addition, dbt supports modular development, testing, documentation, and version control. Therefore, data teams can build scalable and maintainable pipelines with consistency. Moreover, it integrates easily with modern cloud data warehouses and follows the ELT approach, where transformations happen after data loading.

Learner Prerequisites

• Basic understanding of SQL (SELECT, JOIN, GROUP BY, etc.)
• Familiarity with relational databases and data models
• Awareness of data warehousing concepts (tables, schemas, ETL/ELT)
• Basic knowledge of Git and version control concepts
• Understanding of command-line or terminal usage
• Prior exposure to analytics or reporting tools is helpful but not mandatory

Table of Contents

1. dbt Project Overview and Architecture

1.1 Introduction to dbt and analytics engineering concepts
1.2 Overview of dbt project structure and components
1.3 Understanding dbt execution flow step by step
1.4 Role of DAG in dbt project relationships
1.5 How dbt fits into modern data stack

2. Setting Up a dbt Project

2.1 Installing dbt Core and dbt Cloud
2.2 Creating a new dbt project using CLI
2.3 Configuring profiles.yml and environments
2.4 Connecting dbt to a data warehouse
2.5 Running initial dbt commands and validation steps

3. Data Modeling in dbt

3.1 Understanding staging, intermediate, and marts layers
3.2 Designing modular and reusable models
3.3 Defining naming conventions and folder structure
3.4 Working with sources and freshness checks
3.5 Applying best practices for scalable modeling

4. Jinja and Macros in dbt

4.1 Introduction to Jinja templating in dbt
4.2 Creating reusable macros for SQL logic
4.3 Using variables and control flow in dbt
4.4 Understanding ref, source, and dependencies clearly
4.5 Building dynamic and reusable models efficiently

5. Testing and Data Quality Assurance

5.1 Built-in dbt tests like unique and not null
5.2 Writing custom tests for validation needs
5.3 Applying testing strategies across models
5.4 Increasing overall test coverage in projects
5.5 Automating data quality checks effectively

6. Documentation and Lineage

6.1 Generating dbt documentation easily
6.2 Understanding data lineage and DAG visualization
6.3 Adding descriptions for models and columns
6.4 Using documentation blocks effectively
6.5 Maintaining consistent documentation across projects

7. Incremental Models and Performance Optimization

7.1 Understanding incremental models in dbt
7.2 Using materializations like table and view
7.3 Optimizing SQL performance for efficiency
7.4 Using partitions and clustering techniques
7.5 Reducing run time and warehouse costs

8. dbt Packages and Reusability

8.1 Installing and managing dbt packages
8.2 Using popular community packages effectively
8.3 Creating reusable macros and components
8.4 Standardizing transformations across projects
8.5 Improving reuse and maintainability of code

9. Version Control and Collaboration

9.1 Using Git with dbt projects
9.2 Implementing branching strategies effectively
9.3 Managing pull requests and code reviews
9.4 Improving team collaboration workflows
9.5 Handling multi-environment deployments smoothly

10. Deployment and Orchestration

10.1 Running dbt in production environments
10.2 Scheduling jobs using dbt Cloud or Airflow
10.3 Building CI/CD pipelines for dbt projects
10.4 Managing dev, test, and prod environments
10.5 Monitoring and troubleshooting dbt runs

Conclusion

This dbt training provides a complete foundation in modern analytics engineering practices. It covers the full lifecycle of data transformation, including modeling, testing, documentation, and deployment. In addition, it emphasizes best practices for building modular and maintainable data pipelines. Therefore, learners gain practical skills to work confidently in real-world data environments. Ultimately, this training prepares participants to design and manage production-grade dbt projects efficiently.