Description

Introduction

dbt (data build tool) is a modern data transformation framework that enables analytics engineering by applying software engineering practices to SQL-based data workflows. It helps teams build modular, testable, and version-controlled data pipelines directly in the data warehouse. As a result, organizations can support scalable analytics and real-world data transformation use cases more effectively.

Moreover, dbt follows the ELT approach, where raw data is transformed after loading into cloud platforms like Snowflake, Google BigQuery, or Amazon Redshift. In addition, it improves collaboration between engineers and analysts by standardizing transformations and ensuring better data quality. Therefore, teams can manage data workflows in a more consistent and reliable manner.

Learner Prerequisites

• Basic understanding of SQL (SELECT, JOIN, GROUP BY)
• Familiarity with data warehouses like Snowflake, BigQuery, or Redshift
• Fundamental knowledge of data pipelines and ETL/ELT concepts
• Basic exposure to Git and version control concepts (recommended)
• Understanding of analytics or BI reporting workflows (optional but helpful)

Table of Contents

1. Introduction to Real-World Data Workflows with dbt

1.1 Overview of modern data engineering workflows
1.2 Role of dbt in ELT architecture
1.3 Batch vs real-time transformation concepts
1.4 Core dbt project structure and components

2. Setting Up dbt Environment and Project Configuration

2.1 Installing dbt and choosing adapters
2.2 Initializing a dbt project
2.3 Configuring profiles.yml and setting up environments
2.4 Connecting dbt to cloud data warehouses

3. Data Sources and Staging Layer Design

3.1 Understanding raw data ingestion layers
3.2 Defining sources in dbt
3.3 Building staging models for standardization
3.4 Applying naming conventions and folder structuring

4. Building Transformation Models in dbt

4.1 Model types: staging, intermediate, and marts
4.2 Writing modular SQL models for reuse
4.3 Using ref() for dependency management
4.4 Designing reusable transformation logic

5. Advanced Data Transformation Techniques

5.1 Incremental models for performance optimization
5.2 Handling slowly changing dimensions (SCDs)
5.3 Using window functions for complex aggregations
5.4 Creating reusable macros with Jinja templating

6. Testing and Data Quality Assurance

6.1 Built-in dbt tests such as unique, not null, relationships
6.2 Creating custom tests for business rules
6.3 Applying data validation strategies in pipelines
6.4 Monitoring and resolving data quality issues

7. Documentation and Data Lineage

7.1 Generating dbt documentation
7.2 Understanding DAG and lineage graphs
7.3 Adding descriptions and metadata to models
7.4 Building a structured data catalog view

8. Orchestration and Workflow Automation

8.1 Running dbt models using CLI
8.2 Scheduling dbt jobs using Airflow or dbt Cloud
8.3 Managing job dependencies and execution order
8.4 Handling failures and retry mechanisms

9. Performance Optimization and Cost Efficiency

9.1 Optimizing SQL queries in dbt models
9.2 Choosing materializations: table, view, incremental
9.3 Using partitioning and clustering strategies
9.4 Reducing warehouse compute costs effectively

10. CI/CD and Production Deployment Practices

10.1 Version control using Git integration
10.2 Managing pull request workflows for dbt projects
10.3 Automating tests in CI pipelines
10.4 Deploying dbt projects in production environments

11. Real-World End-to-End dbt Project Case Study

11.1 Business requirement analysis and scoping
11.2 Designing multi-layer dbt architecture
11.3 Building analytics-ready datasets for dashboards
11.4 Troubleshooting real-world pipeline issues

Conclusion

This training provides a practical understanding of real-world data workflows using dbt. In addition, learners gain hands-on experience in building scalable transformation pipelines and implementing strong data quality practices. As a result, they will be able to design production-ready analytics workflows with confidence.

Furthermore, the course prepares participants to improve data reliability, streamline transformations, and support better business decision-making. Ultimately, it enables teams to deliver well-structured and high-quality datasets in modern analytics environments.