Description

Introduction:

dbt (data build tool) is a modern analytics engineering tool. It is used to transform raw data into clean and reliable datasets. These transformations run directly inside cloud data warehouses using SQL. Therefore, organizations can follow the ELT approach instead of traditional ETL. In addition, dbt improves collaboration through version control, automated testing, and modular development. As a result, teams can build scalable analytics workflows across modern data platforms.

Learner Prerequisites:

• Basic SQL knowledge such as SELECT, JOIN, and GROUP BY
• Understanding of relational databases and data warehouse concepts
• Familiarity with reporting or analytics workflows
• Awareness of ETL and ELT concepts
• Basic understanding of Git is recommended

Table of Contents

1. Introduction to dbt for Data Analysts and Engineers

1.1 What dbt is and why it is important in modern analytics engineering
1.2 How dbt fits into the modern data stack architecture
1.3 Difference between ELT and traditional ETL workflows
1.4 Key benefits of dbt for analysts and engineers
1.5 Overview of dbt ecosystem and integrations

2. Setting Up dbt Environment

2.1 Installing dbt Core and setting up dbt Cloud
2.2 Configuring profiles and project structure correctly
2.3 Connecting dbt with cloud data warehouses
2.4 Creating and running your first dbt project
2.5 Understanding project folders and configuration files

3. Core dbt Concepts

3.1 Understanding models, sources, seeds, and snapshots
3.2 Materializations such as table, view, and incremental
3.3 How DAG (Directed Acyclic Graph) works in dbt
3.4 Using ref() and source() for dependencies
3.5 Execution flow in dbt projects

4. Data Modeling Fundamentals in dbt

4.1 Basics of dimensional modeling for analytics
4.2 Designing fact and dimension tables
4.3 Building star and snowflake schema structures
4.4 Choosing between normalization and denormalization
4.5 Best practices for scalable data models

5. Building and Managing dbt Models

5.1 Writing modular SQL models for reuse
5.2 Splitting complex queries into staging and marts
5.3 Managing dependencies using ref() function
5.4 Organizing models into layers for clarity
5.5 Standard naming conventions for dbt projects

6. Data Testing and Quality Assurance

6.1 Built-in dbt tests like unique, not null, and relationships
6.2 Creating custom tests for business rules
6.3 Applying test-driven development in dbt workflows
6.4 Ensuring data accuracy and consistency
6.5 Monitoring data quality in production pipelines

7. Documentation and Data Lineage

7.1 Generating dbt documentation site
7.2 Adding descriptions to models and columns
7.3 Understanding lineage graphs in dbt
7.4 Sharing documentation across teams
7.5 Keeping documentation updated using CI/CD

8. Macros and Jinja in dbt

8.1 Introduction to Jinja templating in dbt
8.2 Creating reusable macros for SQL automation
8.3 Using variables and logic in dbt
8.4 Reducing repetitive SQL using macros
8.5 Advanced macro usage for scalability

9. Deployment and Workflow Orchestration

9.1 Running dbt in development and production
9.2 Scheduling dbt jobs using dbt Cloud or tools like Airflow
9.3 Integrating dbt with CI/CD pipelines
9.4 Managing dev, staging, and production environments
9.5 Monitoring and troubleshooting dbt runs

10. Advanced Analytics Engineering Practices

10.1 Using incremental models for large datasets
10.2 Improving query performance in dbt
10.3 Optimizing transformations for scale
10.4 Managing environments in enterprise setups
10.5 Handling complex dependencies in large projects

11. dbt for Data Analysts Use Cases

11.1 Building clean datasets for reporting and dashboards
11.2 Enabling self-service analytics for business users
11.3 Creating KPI-ready data models
11.4 Simplifying SQL for analytics teams
11.5 Supporting faster ad-hoc analysis

12. dbt for Data Engineers Use Cases

12.1 Building scalable transformation pipelines
12.2 Automating workflows in modern data stacks
12.3 Integrating dbt with orchestration tools like Airflow
12.4 Designing production-grade data models
12.5 Supporting enterprise data architecture

Conclusion:

This dbt training provides a structured learning path for both data analysts and engineers. It builds strong foundations in analytics engineering concepts and workflows. Moreover, learners gain practical experience in data modeling, testing, and deployment. In addition, the course prepares professionals to design scalable and production-ready data pipelines in modern cloud environments.