Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

A crash course on core data architecture patterns for analytics engineers, covering relational databases, data warehouses (Inmon vs. Kimball), data lakes, lakehouses (Delta Lake, Iceberg, Hudi), data mesh, and event-driven architecture. Each pattern is explained with its strengths, weaknesses, and ideal use cases, using real-world analogies. The central argument is that understanding these architectural trade-offs is essential because daily decisions about data structure, storage, and transformation compound into the foundation of an entire analytics ecosystem.

Why Every Analytics Engineer Needs to Understand Data Architecture

1. Relational Database — The Fine Old Wine

2. Relational Data Warehouse — The Analyst’s Playground

3. Data Lake — The Promise and the Swamp

4. Data Lakehouse — The Best of Both Worlds

5. Data Mesh — Decentralizing Data Ownership

6. Event-Driven Architecture — The Gossipy Neighbor