The Ultimate Power Query Cookbook for Power BI and Excel (Dominick Raimato)（Z-Library）

# The Ultimate Power Query Cookbook for Power BI and Excel — Reading Guide ## 【One-Line Pitch】 A practical, recipe-driven guide for anyone who needs to clean, connect, and transform data in Power Query—whether you're a beginner looking for step-by-step help or an experienced analyst wanting to optimize your data models for speed and maintainability. ## 【Book Arc】 - **Opening (~0%–9%)**: Introduces the book's purpose and audience, positioning Power Query as the essential tool for data transformation in both Excel and Power BI. The author shares his background and sets expectations for a hands-on, recipe-based learning experience. - **Early (~15%–27%)**: Lays the foundation by walking through connecting to data sources—files, folders, SharePoint, web APIs, databases (SQL, Snowflake, BigQuery, etc.), and third-party SaaS platforms. Also covers managing data types, which the author stresses is not as straightforward as it seems due to implications for model bloat and performance. - **Early-to-Middle (~24%–42%)**: Moves into core transformation techniques: transforming columns (headers, formatting, extraction, merging, pivoting), cleansing columns (filtering, replacing values/errors, removing duplicates), and creating new columns (conditional, custom, from-example). Introduces combining queries via merging and appending, plus aggregations with Group By. - **Middle (~42%–48%)**: Covers advanced integration topics—using Python, R, and AI within Power Query for predictive analytics and text/vision functions. Then dives into indexing for star schema design, parameters for simplifying connections and enabling incremental refresh, and functions for repeatable transformations. - **Late (~48%+)**: Grounds the technical content in a real-world narrative (the story of Andrew, a finance analyst spending three days on repetitive report assembly) to illustrate why Power Query matters—eliminating manual, error-prone work and freeing time for actual analysis. ## 【Key Takeaways】 - **Power Query solves the "repeatable transformation" problem** (Early): The book opens with a relatable case—an analyst spending days on reports that leaders barely open—to show that Power Query's core value is automating repetitive data prep. This frames every recipe that follows. - **Connection strategy matters as much as transformation** (Early): Chapters 2–5 systematically cover file-based, web-based, database, and third-party sources, including gotchas like gateway requirements and potential costs. The goal is to get you connected to at least one source you can practice with immediately. - **Data types are not created equal** (Early): Choosing the right data types has downstream implications for your data model and can contribute to "bloat." The book emphasizes understanding efficiencies and optimizing types upstream rather than fixing them later. - **Column transformations are the daily bread of Power Query** (Middle): From promoting headers to splitting, merging, and pivoting columns, the book covers the full toolkit. A key theme is maintaining query folding during transformations to preserve performance. - **Cleansing is about intentionality** (Middle): Filtering, replacing values/errors, filling up/down, and removing duplicates are covered with attention to "considerations"—meaning you should understand the trade-offs of each cleansing action before applying it. - **Group By and merging are your aggregation power tools** (Middle): Aggregating data in Power Query (rather than later in the model) reduces row counts and streamlines queries. The book covers join types, fuzzy matching, and appending with use cases for each. - **Parameters are underused and transformational** (Middle): Parameters simplify connections, manage multiple queries at once, and are critical for setting up Incremental Refresh in Power BI. They also enable template files for repeatable work. - **Functions turn one-time work into reusable assets** (Middle): Creating functions from queries lets you apply repeatable transformations with minimal overhead—time zone conversion, lookups, and date table creation are all demonstrated as function use cases. ## 【Reading Tips】 - **Skim the front matter** (~0%–9%): The author bio, acknowledgements, and reviewer notes add little technical value. Jump straight to Chapter 1 when you want to start learning. - **Deep-read Chapters 2–6** (~15%–24%): These connection and data-type chapters are foundational. If you're a beginner, follow along with your own data source; if you're experienced, focus on the "gotchas" and optimization notes that the author flags. - **Use Chapters 7–10 as a reference** (~24%–42%): The transformation, cleansing, column creation, and combining chapters are recipe-based—skim for the technique you need rather than reading linearly. Pay special attention to the "considerations" sections at the end of each chapter. - **Don't skip Chapter 13 (Parameters)** (~27%): The author explicitly calls parameters one of the most transformational tools in Power Query and notes they're often ignored. This chapter is worth a careful read even if you're experienced. - **Treat Chapter 11 (Python, R, AI) as an overview** (~24%): The book explicitly states it won't teach you to build AI models—it focuses on how to apply them in Power Query. Read this for awareness, not depth. ## 【Coverage Limits】 This guide is based on the book's table of contents, chapter objectives, and introductory material. Detailed recipe steps, code samples, and specific connector walkthroughs are not covered here—the excerpts provide structure and intent, not full technical instructions. ##