Build reliable data pipelines, trusted data products, and resilient data platforms that deliver accurate information on time and give the business what they need to make the right decisions.
Data engineers, data architects, analytics engineers, data leaders, and platform teams will find this a practical field guide for moving beyond "it works" and toward dependable, production-ready data systems. Analyze the real problems behind broken data systems: fragile scripts, silent failures, missing checks, unclear requirements, poor observability, and teams stuck in constant firefighting.
Apply the Reliable Data Platform Framework (RDPF) to every stage of the data pipeline lifecycle, from requirement understanding and robust design to appropriate architecture, suitable technology, high performance, and precise timing. The book explains how to build data pipelines that are idempotent, observable, testable, recoverable, scalable, and aligned with business strategy.
Evaluate the practices that separate trusted data platforms from unreliable ones, including data quality checks, data contracts, CI/CD pipelines, smart recovery, error alerting, destination data validation, documentation, clean code style, and reliability culture. Written from real-world data engineering experience, this book avoids theory for theory's sake and focuses on the patterns, principles, and habits that help teams prevent midnight emergencies and reduce costly rework.
Create a stronger foundation for data engineering, data governance, cloud data platforms, analytics, machine learning, business intelligence, and modern data products. Whether you are building your first pipeline or managing hundreds across a global organization, Reliable Data Platform Framework will help you design systems that earn trust, protect the single source of truth, and turn reliability into a daily engineering practice.
