Description
Data engineering is the backbone of modern business intelligence, yet navigating the complexities of roles and tools can be challenging for new and experienced professionals alike. However, data engineering sits at the core of modern analytics. As organizations scale their use of data, they need robust architecture, reliable pipelines, and strong governance to turn raw data into trusted insights.
This book follows the journey of data from source to insight. It defines the data engineering role, presents reference architectures, and explains how to model, secure, and govern data for analytics. Subsequent chapters cover CI/CD, ETL versus ELT, infrastructure operations, data quality, operations, AI, and supporting processes.
By the end of this book, the readers will possess the competency to build, design, and operate end-to-end data platforms, collaborate effectively with analysts and data scientists, and apply repeatable patterns to build secure, scalable, and high-quality data solutions.
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