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 — Data Validation

Validating data as it moves — constraints, assertions, Great Expectations, Deequ, and schema validation.

3 — Metadata Management

The metadata layer that makes data discoverable and trustworthy — data catalogs, lineage tracking, schema registries, and data discovery tooling.

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.