ANALYTICS ENGINEERING FROM SOURCE TO INSIGHT: A Practical Introduction to Data Modeling, Transformation, Testing, and Delivery
Reseña del libro "ANALYTICS ENGINEERING FROM SOURCE TO INSIGHT: A Practical Introduction to Data Modeling, Transformation, Testing, and Delivery"
A polished dashboard can still be wrong. When source tables are poorly understood, joins multiply rows, definitions drift, and late records arrive unnoticed, even a clean report can lose the trust of the people who rely on it. Writing a query is only one part of the job. Reliable analytics also requires clear requirements, sound models, controlled transformations, meaningful tests, documentation, monitoring, and careful delivery. This practical textbook presents the full workflow between raw operational data and decision-ready reporting. Across twenty-one connected chapters, it shows how to translate an unclear business request into a well-defined analytical output, then carry that work through profiling, ingestion, transformation, dimensional modeling, validation, deployment, performance tuning, governance, and reporting. Inside, you will learn how to: • define business questions, metrics, dimensions, data grain, source requirements, and acceptance criteria; • profile unfamiliar data for keys, nulls, duplicates, inconsistent values, and source-system limitations; • use SQL joins, common table expressions, window calculations, dates, types, and null handling safely; • design fact and dimension tables, preserve useful history, and avoid double counting; • build reusable transformation layers, incremental processes, backfills, and repeatable loads; • test relationships and business rules, reconcile totals, document lineage, schedule dependencies, monitor failures, and communicate known limitations. Worked examples, expected outputs, review questions, practical exercises, templates, checklists, a glossary, and a connected retail capstone help turn the concepts into practice. The explanations also examine trade-offs, so you can judge when a design is appropriate instead of treating one approach as correct in every environment. The learning path also connects technical choices to the way analytical data is used. You will consider how model dependencies affect deployment order, how freshness and incidents should be communicated, how query plans expose unnecessary work, and how ownership and access rules help keep reporting assets useful after their first release. This guide is intended for aspiring analytics engineers, data analysts moving toward engineering work, business intelligence developers, junior data engineers, technical students, and practitioners who want a coherent reference across the analytics lifecycle. Basic familiarity with simple queries and joins is useful, but advanced platform experience is not required. Open the book and begin developing analytical systems that people can inspect, maintain, and trust from source to report.
