Cloud-Native SQL Programming

Cloud-Native SQL Programming: Optimize Database Performance and Reduce Compute Costs in Snowflake, BigQuery, and PostgreSQL

For decades, an inefficient SQL query was merely a technical oversight that caused a slow report. In the era of cloud-native systems, it is a financial liability. When platforms charge by scanned data, active warehouse time, or elastic consumption, poorly written code becomes an ongoing operational drain. Are you ready to stop leaking budget and start making every query earn its cost?

Cloud-Native SQL Programming provides a definitive roadmap for engineers, analysts, and architects who need to write SQL that is both correct and cost-efficient. Mason Davidson shifts the focus from isolated syntax to treating SQL as a control surface for performance, infrastructure behavior, and cloud spending. This book provides the architectural precision required to master the three dominant platforms of the modern data ecosystem: Snowflake, BigQuery, and PostgreSQL.

By reading this book, you will acquire high-impact skills to:

Maximize PostgreSQL Throughput: Align memory usage with physical hardware through shared_buffers tuning, implement huge_pages, and configure io_uring for non-blocking disk operations.

• Optimize Snowflake Credits: Right-size virtual warehouses, manage multi-cluster concurrency for heavy traffic, and leverage serverless features like Query Acceleration to handle outliers without overspending.

• Slash BigQuery Costs: Implement strict partition-aware design and clustering to minimize bytes scanned while utilizing Information Schema audits to identify and halt cost leaks.

• Ensure System Stability: Deploy zero-downtime migration patterns and automated maintenance strategies that prevent performance degradation as your data scales.

Stop guessing why your cloud bill is high. Elevate your engineering standards and build high-performance systems that scale cleanly without draining the budget. Secure your copy today.