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 — Build an End-to-End Data Platform

A capstone build integrating Kafka, Spark, Airflow, Delta Lake or Iceberg, and Grafana/Prometheus into one end-to-end data platform.

3 — Build a Lakehouse on Kubernetes

A capstone build of a lakehouse on Kubernetes — the Spark Operator, Airflow, MinIO, Trino, and Iceberg working together.

4 — Staff-Level Architecture Case Studies

Staff-level architecture case studies from Netflix, Uber, Airbnb, LinkedIn, Meta, and Google's data platforms.

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.