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Building Reliable Data Systems: How to Design, Scale, Operate, and Evolve Production Data Platforms
Paperback
Publisher: Independently Published
Published: Sep 23 2026
Pages: 270
Weight: 1.04
Height: 0.57 Width: 7.00 Depth: 10.00
Language: English
Modern applications depend on data systems that must do far more than simply store information. They must remain fast under load, recover from failure, scale across services and regions, preserve consistency, protect sensitive data, and evolve without disrupting production.
BUILDING RELIABLE DATA SYSTEMS is a practical, technically rigorous guide to designing, scaling, operating, and evolving dependable production data platforms. Written for software engineers, data engineers, backend developers, platform engineers, architects, and technical leaders, the book explains the principles and engineering decisions behind systems that must work reliably in the real world.
Inside, you will learn how to reason about data models, storage engines, indexing, caching, partitioning, replication, transactions, consistency, distributed-system failures, messaging, streaming, change data capture, analytical platforms, search, vector retrieval, observability, data quality, security, disaster recovery, migrations, and cost-aware architecture.
Rather than focusing on one database, cloud provider, or temporary technology trend, the book teaches durable principles, practical trade-offs, failure scenarios, architecture patterns, diagrams, and concise implementation examples that can be applied across different technologies and environments.
You will learn not only how data systems work, but how to make better decisions when those systems face growth, partial failures, traffic spikes, stale data, schema changes, regional outages, security requirements, and continuous production change.
If you are responsible for systems where reliability, scalability, maintainability, and data correctness matter, this book will help you move beyond architecture diagrams and build platforms that can be trusted in production.
Build systems that scale. Design for failure. Operate with evidence. Evolve without breaking what already works.
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