From Kimball to Lakehouse: The Evolution of Data Storage (with Python Demo)

Data Storage Architecture: Deconstructing Warehouse, Lake, and Lakehouse 🏛️ In modern data engineering, choosing the right storage architecture is critical. Based on the data_engineering_book , this guide breaks down the core differences between traditional Warehouses, Data Lakes, and the modern Lakehouse, while providing a hands-on Delta Lake demo. 1. Warehouse vs. Lake vs. Lakehouse Understanding the core philosophy of each architecture is the first step toward a successful design. Architecture Definition Design Philosophy Warehouse (Kimball/Inmon) Structured, integrated, non-volatile storage using Star/Snowflake schemas. Schema-on-Write. Optimized for fast BI reporting and business logic. Data Lake A vast repository for raw data (Structured/Unstructured) with no strict schema. Schema-on-Read. Optimized for data exploration, ML, and low-cost storage. Data Lakehouse A hybrid architecture bringing warehouse management to the data lake. Best of both. Retains lake flexibility with wareho