Data Engineering

A book-shaped table of contents for data engineering: foundations and lifecycle, data modeling, storage systems, ingestion and CDC, distributed processing (Spark/Flink), SQL mastery, workflow orchestration, data quality, platform and cloud architecture, pipeline observability, security and governance, performance engineering, system design, and MAANG interview preparation through capstone builds — cross-linking the existing observability book instead of duplicating it.

1 — Building a Data Platform

The architectural decisions behind building a data platform — core platform components, data mesh vs. data fabric, and centralized vs. federated ownership models.

2 — Storage Architecture

Tiered storage architecture — hot, warm, and cold tiers — and the lifecycle policies that move data between them automatically.

Data Engineering

A book-shaped table of contents for data engineering: foundations and lifecycle, data modeling, storage systems, ingestion and CDC, distributed processing (Spark/Flink), SQL mastery, workflow orchestration, data quality, platform and cloud architecture, pipeline observability, security and governance, performance engineering, system design, and MAANG interview preparation through capstone builds — cross-linking the existing observability book instead of duplicating it.

1 — What is Data Engineering?

How data engineering evolved into its own discipline, how the role differs from analytics engineering and data science, and the foundational distinctions (batch vs. streaming, OLTP vs. OLAP) that shape the rest of this book.