SQL in a Week A Fast-Track Guide to Writing and Optimizing Queries Like a Pro (Bhumika Shah, Devtosh Dubey) (z-library.sk, 1lib.sk, z-lib.sk)

【One-Line Pitch】 A seven-day, hands-on sprint from your first `SELECT` to writing joins and optimized queries, built around a single running company dataset. Best for absolute beginners and career-switchers who want practical, interview-relevant SQL fast rather than a theory-heavy reference. 【Book Arc】 - **Opening (~0%–10%)**: Frames SQL as a career skill even in the AI era, then gets you set up—installing DB Browser for SQLite and creating a small multi-table company database (employees, departments, projects, assignments, timesheets) that anchors every later example. - **Early (~10%–30%)**: Covers the fundamentals of talking to a database: what tables and data types are, how to write a clean first `SELECT`, and why formatting and avoiding `SELECT *` matter from day one. - **Early–Middle (~30%–45%)**: Moves into filtering and shaping results with `WHERE`, comparison and logical operators, `ORDER BY`, and comments—framed as answering real business questions like "who earns the most?" - **Middle (~45%–55%)**: Introduces joins: why data lives in separate tables, table relationships/cardinality (one-to-many, many-to-many), and `INNER`/`LEFT` joins across two or more tables. - **Late (~55%+)**: Shifts to aggregation (grouping and summarizing numbers) and then to performance habits—indexing join keys, avoiding `SELECT *`, and preferring `INNER` over `OUTER` joins when logic allows. - **Ending**: Each day closes with a recurring recap block (key concepts, best practice, common mistakes, and an "SQL in the age of AI" note), reinforcing retention over the week. 【Key Takeaways】 - **SQL is framed as judgment, not just syntax** (Opening): The authors argue AI can draft queries but you must validate logic and catch missing filters—so understanding *why* a query works is the real skill. - **One dataset carries the whole week** (Opening–Early): A small employee/project schema is built once and reused, so each new concept lands on familiar data instead of abstract examples. - **Readable queries are a habit, not a nicety** (Early): Capitalized keywords, multi-line formatting, aliases, and avoiding `SELECT *` are taught as professional defaults from the first query. - **Filtering and sorting answer business questions** (Early–Middle): `WHERE`, operators (`BETWEEN`, `LIKE`, `IN`, `IS NULL`), and logical `AND/OR/NOT` are the tools for turning raw tables into targeted answers. - **Relationships explain join results** (Middle): Understanding cardinality upfront prevents confusion over "duplicate-looking" rows when joining departments, employees, and projects. - **Joins have a cost—choose the lightest one** (Middle): Index join keys, join only what you need, and prefer `INNER` over `OUTER` joins unless unmatched rows genuinely matter. - **Aggregation turns rows into insight** (Late): Grouping and summarizing is where numbers become decisions—though the excerpts only sketch this stage, not its full detail. - **Each day ends with a mistake checklist** (Throughout): Recurring "common mistakes" and "best practice" recaps make the book easy to review and self-test against. 【Reading Tips】 - **Do the setup, don't skip it.** Build the sample database in Day 1; every later example depends on it, and typing the schema yourself teaches relationships. - **Deep-read Days 1–3** (basics, filtering, joins)—these are the load-bearing skills. Skim the AI-era sidebars if you're short on time; they're motivational rather than technical. - **Treat the end-of-day recaps as flashcards.** The key-takeaways and common-mistakes blocks are the highest-value review material for interview prep. - **Watch for the aggregation and optimization chapters**—the excerpts cover them thinly, so plan to supplement with hands-on practice on your own data. - **Type every query by hand** rather than copying; the formatting habits are part of the lesson. 【Coverage Limits】 This guide reflects the book's framing, setup, and early-to-middle chapters in detail; the later aggregation and optimization material is only partially represented in the excerpts, so specifics there are inferred from chapter headings and recaps rather than fully verified.