Data is evidence of past activity - frozen moments in time, waiting to be uncovered and analyzed. But without understanding the context in which data is created, it remains just 'stuff.' Building this understanding takes time. To turn raw data into meaningful information, we must continuously refine our interpretations - and adapt our data solutions accordingly. Flexibility is key. A truly effective data solution must evolve alongside our understanding, supported by a structured approach, automation, and code generation. Data Engine Thinking presents an end-to-end methodology for designing and delivering data solutions that can evolve. This book guides you on a journey with FastChangeCo, a fictitious company struggling to keep up with today's business demands. Faced with an increasingly complex and fast-changing system landscape, they embark on a mission to develop a future-proof data solution - one that accelerates the incorporation of new requirements and overcomes integration challenges. Supported by detailed conceptual and practical explanations, we follow FastChangeCo as they transform their fragmented, inflexible data platforms into a solution that fully meets the business' needs. Along the way, you'll learn how to:Define a clear vision and strategy for data Select the right frameworks for a scalable architecture Overcome integration challenges Tackle the complexities of 'time' in data Deliver an architecture that is designed for change Implement code generation and automation Solve real-world problems when working with data Whether you're a data architect or modeler, engineer, or business leader, Data Engine Thinking will provide the principles, patterns, and practical techniques to create a data solution that adapts - just like your organization does.