Adventure Works Data Pipeline with dbt and BigQuery

Overview

This project was developed as part of the Analytics Engineer certification from Indicium Academy. It implements a comprehensive data pipeline for ingesting and transforming Adventure Works SAP data using dbt Core and BigQuery. The solution follows modern data engineering best practices with a three-layer architecture: staging for raw data standardization and cleaning, intermediate for transformation logic, and marts for final dimensional models organized by business subject. The project includes automated testing, comprehensive documentation, and processes 64 source tables to deliver analytics-ready data models.

Key Highlights

• Implemented a three-layer data architecture (staging, intermediate, marts) for the Adventure Works SAP dataset using dbt Core and BigQuery
• Developed comprehensive data models with automated testing and documentation to ensure data quality and reliability
• Processed and transformed 64 source tables from SAP into analytics-ready dimensional models organized by business subject
• Created staging layer for raw data standardization and cleaning, intermediate layer for transformation logic, and marts for final business-ready models
• Implemented data engineering best practices including version control, testing, and comprehensive documentation
• Delivered analytics-ready data models that enable business intelligence and data-driven decision making

Technical Approach

The project follows a three-layer data architecture: staging layer for raw data standardization and cleaning, intermediate layer for transformation logic and preparation, and marts layer for final dimensional models organized by business subject.

Additional Resources