If you know basic SQL but struggle to apply it in production environments, or you are preparing for data engineering interviews and want experience beyond simple SELECT statements, this book was written for you.

SQL for Data Engineering bridges the gap between tutorial SQL and the skills that data engineering roles actually demand — ETL pipelines, warehouse design, query optimization, and the architectural thinking that separates engineers who build reliable data systems from those who only query them.

What you will learn:

Advanced querying — joins, CTEs, subqueries, window functions, and analytical SQL patterns
Data transformation — cleaning, standardisation, safe casting, and deduplication techniques
ETL architecture — incremental loading, staging patterns, Slowly Changing Dimensions, and idempotent pipeline design
Warehouse design — star schemas, dimensional modelling, partitioning, indexing, and materialised views
Performance optimisation — EXPLAIN ANALYZE, execution plans, query tuning, and production-grade optimisation
Semi-structured data — JSON and JSONB processing, schema drift handling, and event pipeline patterns
Cloud platforms — Snowflake, BigQuery, Redshift, and Databricks with practical SQL for each
AI workflows — prompt engineering for SQL tasks, AI-assisted debugging, and the risks of generated code

Four industry projects you will build:

E-Commerce Analytics Warehouse
SaaS Subscription Metrics Platform
Banking Transaction Fraud Monitoring System
Food Delivery ETL and Operations Pipeline

Each project covers a realistic business problem, complete ETL pipeline, warehouse schema, KPI queries, and interview preparation — designed to go directly into your portfolio.

You also get:

Integrated interview Q&A in every chapter drawn from real hiring panels
Resume bullet points after every chapter and project
A complete career roadmap for aspiring data engineers
Appendices covering SQL functions, data types, troubleshooting, glossary, and a tool landscape guide

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