Distributed Data Systems in Production: Fix Slow Databases, Handle Failures Safely, and Build Scalable Applications Without Costly Rewrites

By Clinton S. Dunavant

Distributed Data Systems in Production: Fix Slow Databases, Handle Failures Safely, and Build Scalable Applications Without Costly Rewrites

Problem

Your database is getting slower. Your application is becoming harder to scale. And every new infrastructure change seems to introduce another problem.

How do you improve database performance, prevent data inconsistencies, and handle distributed system failures without rebuilding an application that already works?

For backend developers, database engineers, software architects, and platform engineers, these challenges become increasingly difficult as applications grow.

Slow SQL queries, inefficient indexes, race conditions, duplicate transactions, replication lag, and failed background jobs can undermine reliability. Adding servers, introducing microservices, or switching databases without understanding the underlying problems can increase costs and complexity.

You need more than a system that works. You need one that remains dependable under real production conditions.

Solution

Distributed Data Systems in Production provides a practical, engineering-focused approach to diagnosing performance problems, protecting data integrity, and building scalable, fault-tolerant applications.

Through clear explanations, hands-on Python and SQL examples, architectural diagrams, and progressive implementation exercises, you'll learn how to improve existing systems while avoiding unnecessary rewrites.

Inside, you'll learn to:

Fix slow databases: Diagnose expensive SQL queries, optimize PostgreSQL indexes, improve connection pooling, implement caching, and measure performance with realistic benchmarks.

Prevent costly data errors: Use ACID transactions, concurrency control, idempotency, and consistency guarantees to protect critical business operations.

Scale applications safely: Understand database replication, partitioning, sharding, distributed consistency, failover, and recovery strategies.

Build reliable distributed workflows: Implement event-driven systems with Apache Kafka and Debezium, analytical pipelines with DuckDB and Parquet, and durable workflows with Temporal.

Prepare for modern AI applications: Design persistent agent memory, hybrid retrieval with PostgreSQL and pgvector, secure MCP data services, and controlled agent-to-database interactions.

You'll also explore OpenTelemetry observability, disaster recovery, database migrations without downtime, infrastructure cost optimization, and production deployment practices.

This book is designed for developers and engineers with foundational programming, SQL, and API knowledge who want to build more reliable data-intensive applications.

Proof

The practical approach is built around one evolving application-not disconnected technical demonstrations.

You'll progressively develop an order and inventory management system using PostgreSQL, Python, FastAPI, Docker, and supporting distributed technologies.

The book follows a Build, Break, Measure, and Repair methodology: implement a feature, expose its weaknesses through controlled tests, measure the results, and apply targeted improvements.

Dedicated chapters cover production incidents, backward-compatible migrations, reliability testing, and operational handoffs. Reference implementations, SQL examples, testing procedures, recovery commands, and production-readiness checklists provide practical material you can adapt to your own projects.

By the end, you'll be better equipped to diagnose bottlenecks, make informed architectural decisions, recover from failures, and evolve applications without unnecessary infrastructure complexity.

Build applications that stay fast, protect critical data, and scale with confidence.

• Format: Paperback
• Language: English
• ISBN: B0HNDMHGMF
• ISBN13: 9798163110421
• Release Date: October 2026
• Publisher: Independently Published
• Length: 264 Pages
• Weight: 1.02 lbs.
• Dimensions: 0.6" x 7.0" x 10.0"