This eBook introduces modern data preparation concepts for business and technical users alike. It explains why data preparation consumes most analytics effort and compares traditional ETL, spreadsheets, SQL, and cloud-based approaches. The guide covers data quality, transformation, profiling, automation, and production pipelines, emphasizing self-service and collaboration. It also highlights how machine learning enhances data preparation by detecting errors and recommending transformations. Designed as an accessible primer, the book positions data preparation as a critical enabler of analytics, AI, and data-driven decision-making.

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