DBT: Syllabus

Mastering DBT: Data Transformation and Validation with Python

DBT (Data Build Tool) is a modern data transformation framework that allows analysts and engineers to transform raw data into analytics-ready datasets. This book provides a hands-on approach to mastering DBT, focusing on data transformation, validation, testing, and best practices for maintaining data quality in PostgreSQL environments.

Module 1: Introduction to DBT and Modern Data Transformation#

• What is DBT? Why is it important for modern data workflows?
• Key concepts: Models, Seeds, Snapshots, Macros, and Tests
• Installing and setting up DBT with PostgreSQL
• Understanding DBT project structure

Module 2: Building Data Models in DBT#

• Creating and managing DBT models
• Using Common Table Expressions (CTEs) for transformations
• Best practices for structuring DBT models
• Incremental models and partitioning strategies

Module 3: Data Testing and Validation with DBT#

• Understanding DBT tests: Schema, Data, and Custom tests
• Implementing unique, not-null, and referential integrity tests
• Writing custom tests in SQL and Python
• Debugging and troubleshooting failed tests

Module 4: Documentation and Data Lineage in DBT#

• Auto-generating documentation for DBT models
• Understanding data lineage and dependencies
• Using DBT Docs to visualize model relationships
• Maintaining version-controlled documentation

Module 5: Integrating DBT with PostgreSQL and Cloud Data Warehouses#

• Connecting DBT to PostgreSQL for data transformations
• Deploying DBT in cloud environments (Snowflake, BigQuery, Redshift)
• Running DBT transformations with Airflow
• Best practices for managing database performance with DBT

Module 6: Advanced DBT Features and Performance Optimization#

• Using DBT Macros and Jinja for reusable code
• Parameterizing models with variables and seeds
• Optimizing query performance with materializations
• Handling large-scale transformations efficiently

Module 7: CI/CD and DBT Deployment#

• Implementing version control for DBT projects with Git
• Running DBT in CI/CD pipelines for automated validation
• Deploying DBT models in production environments
• Monitoring DBT runs and debugging performance issues

Module 8: Data Governance and Security in DBT#

• Implementing role-based access control for DBT projects
• Managing sensitive data with DBT configurations
• Auditing and tracking changes in transformation logic
• Compliance and regulatory considerations for data pipelines

Hands-On Projects

Project 1: Building a Data Warehouse with DBT and PostgreSQL#

• Set up a DBT project for transforming raw data
• Implement data models for business reporting
• Generate documentation and validate transformations

Project 2: Automating Data Quality Checks in ETL Pipelines#

• Define DBT tests to ensure data consistency
• Integrate DBT tests with CI/CD pipelines
• Monitor and alert on data quality issues

Project 3: Implementing Incremental Transformations for Large Datasets#

• Optimize DBT models for incremental data updates
• Use snapshots for tracking historical changes
• Implement partitioning strategies for scalability

Project 4: Creating a Data Validation Framework for PostgreSQL#

• Build reusable data quality checks for production databases
• Implement automated documentation and lineage tracking
• Deploy validation processes with DBT Cloud or Airflow

Project 5: Deploying a Scalable DBT Pipeline in Production#

• Run DBT transformations in a cloud environment
• Secure and optimize workflows for large-scale transformations
• Implement monitoring and performance tuning strategies

References#

• DBT Official Documentation
• PostgreSQL Documentation
• DBT Best Practices
• Data Validation Techniques