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 — SQL Interview Problems

SQL interview problems by difficulty — easy, medium, and hard — with a dedicated focus on window function problems.

2 — Spark Interview Questions

Spark interview questions covering architecture, optimization techniques, debugging approaches, and performance tuning.

3 — Data Engineering System Design Interviews

How to run an open-ended data engineering system design interview — framing trade-offs, capacity estimation, and bottleneck analysis.

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.