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.

2 — Data Lifecycle

The end-to-end journey data takes from generation through collection, ingestion, storage, processing, serving, consumption, governance, and eventual archival.

3 — Data Engineering Principles

The cross-cutting engineering principles — scalability, reliability, maintainability, data quality, idempotency, fault tolerance, cost, security, and observability — that every pipeline in this book is judged against.

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 — Batch Ingestion

Batch ingestion patterns — ETL vs. ELT, bulk vs. incremental loads, CDC-driven loads, and snapshot loading strategies.