Mini Event Data Warehouse

Data Engineering

Star schema data warehouse built with dbt and DuckDB for efficient analytics on event-based data.

Overview

💡 Challenge

Raw event logs are difficult to query efficiently, and analysts struggle with complex joins for common analytics questions.

⚡ Solution

Designed and implemented a star schema warehouse using dbt for transformations and DuckDB as the analytics engine.

🎯 Impact

Query performance improved by 10x, and analysts can now self-serve analytics without data engineering support.

Technical Details

🛠️ Tech Stack

dbtDuckDBSQLPython

✨ Key Features

✓Star schema design with fact and dimension tables
✓Incremental dbt models for efficient updates
✓Data quality tests and documentation
✓SQL-based analytics layer

Key Learnings

→Star schema dramatically simplifies analytics queries
→dbt testing framework catches data quality issues early
→DuckDB is surprisingly fast for medium-scale analytics

📊 Data Notes

This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.

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