Are your data pipelines groaning under the weight of billions of rows? Do slow queries and brittle transformations threaten your analytics SLAs? Modern Data Engineering with SQL in 2025 equips you to conquer these challenges with proven, practical techniques.

In this comprehensive guide, you'll discover how to architect, transform, and tune big data workflows using nothing but SQL-and a few carefully chosen orchestration tools. From mastering explicit column selection and optimal data types to writing modular CTEs and LATERAL joins, this book shows you exactly how to build pipelines that are both powerful and maintainable. You'll learn to leverage advanced aggregates, window functions, and user-defined functions in Python or JavaScript, and to harness open table formats like Iceberg and Delta Lake for ACID-compliant lakes. Along the way, you'll integrate orchestration with Airflow, Prefect, and dbt, bake in governance and data-quality checks, and exploit serverless engines and AI-powered tuners for next-level performance.

What you'll gain:

• Efficient SQL patterns for big data: explicit SELECT, predicate pushdown, and dynamic filters

• Modular transformations with CTEs, LATERAL, and refactored subqueries for clarity and speed

• Advanced analytics using ROLLUP, window frames, percentiles, and statistical functions

• Scalable joins and CDC strategies to handle massive, hybrid-cloud architectures

• Performance tuning via indexes, partitioning, clustering keys, and execution-plan interpretation

• Pipeline orchestration & CI/CD with Airflow, Prefect, and dbt for automated, reliable deployments

• Governance & privacy controls: row-level security, masking, lineage, and GDPR/CCPA compliance

• Future-proof insights on serverless SQL, data mesh, federated analytics, and continuous learning resources

Ready to transform your SQL skills into a high-impact data engineering toolkit? Pick up Modern Data Engineering with SQL in 2025 today and start building pipelines that scale, perform, and adapt to tomorrow's data demands.