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 — Distributed Computing Fundamentals

The distributed-systems fundamentals underneath every big-data engine — parallel processing, distributed execution, cluster computing, scheduling, and resource management.

3 — Batch Processing Frameworks

The batch processing framework landscape beyond Spark — Hadoop MapReduce, Tez, Flink's batch mode, and Apache Beam's unified batch/stream model.

4 — Stream Processing

Stream processing engines — Spark Streaming, Structured Streaming, Flink, Kafka Streams, Beam — and the watermarks, windowing, state management, and event-time-vs-processing-time semantics they all have to solve.

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.