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I recently built a Data Engineering project that simulates how a coffee shop chain can process real-time orders and deliver instant product suggestions, while also supporting end-of-day batch analytics. The pipeline combines streaming, batch processing based on Lakehouse architecture, data quality validation, and SCD Type 2 dimension modeling — using Kafka, Spark, Airflow, Delta Lake, Redis, and MinIO. It’s fully documented and production-simulated from scratch.
Documentation is public, so feel free to explore and share your thoughts!
GitHub repo &amp; docs: <a href="https://lnynhi02.github.io/coffee-sales-dp-docs/" target="_blank" rel="noopener nofollow">https://lnynhi02.github.io/coffee-sales-dp-docs/</a>

Data Engineering

A comprehensive data engineering project demonstrates building a complete pipeline for a coffee shop chain that processes real-time orders and provides instant product recommendations while supporting batch analytics. The implementation uses modern tools including Kafka for streaming, Spark for processing, Airflow for orchestration, Delta Lake for storage, Redis for caching, and MinIO for object storage. The project showcases Lakehouse architecture, data quality validation, and SCD Type 2 dimension modeling with full documentation and production-ready simulation.

Data Engineer Project: From Streaming Orders to Batch Insights — A Coffee Shop Chain’s Data Pipeline

Very nice and informative especially for the begginers that has a basic knowledge.