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Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, cofounder of Claypot AI, considers each design decision in the context of how it can help your system as a whole achieve its objectives.

A decade ago, developers built information-sharing software by writing a lot of custom code in a single code base, then adding layers of caching. Now we build information systems: interdependent software and services, data platforms, and event streams. Many of our software-to-systems initiatives fail, though, and when they do, we double down on traditional linear approaches. But linear thinking cannot resolve systems challenges. We need to shift to systems thinking.