{"product_id":"sql-analytics-for-real-world-data-corin-halstead-9798176041552","title":"SQL Analytics for Real-World Data: Solve Business Questions with Joins, Windows, CTEs, and Analytical Queries","description":"\u003cp\u003e\u003cb\u003eKnowing SQL syntax is not the same as knowing how to answer a business question correctly.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eA query can run perfectly and still produce the wrong number.\u003c\/p\u003e\u003cp\u003eA join can silently duplicate revenue. A retention calculation can change because of one misplaced filter. A rolling average can cover seven rows without covering seven days. A dashboard can report a perfectly calculated metric whose denominator nobody actually agreed on.\u003c\/p\u003e\u003cp\u003eThe database did not make the mistake.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe meaning was lost somewhere in the query.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eSQL Analytics for Real-World Data\u003c\/b\u003e bridges the gap between knowing SQL commands and using SQL to produce reliable, decision-ready analysis.\u003c\/p\u003e\u003cp\u003eInstead of teaching isolated tricks on toy tables, this practical guide shows you how professional analysts reason through messy data, ambiguous business questions, changing grains, multiple relationships, incomplete records, and production-scale analytical workflows.\u003c\/p\u003e\u003cp\u003eInside, you'll learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eTranslate vague business questions into explicit analytical definitions\u003c\/li\u003e\n\u003cli\u003eDefine the correct grain before joins, aggregations, and window calculations\u003c\/li\u003e\n\u003cli\u003ePrevent fanout and metric duplication when combining one-to-many and many-to-many relationships\u003c\/li\u003e\n\u003cli\u003eChoose INNER, LEFT, EXISTS, anti-join, temporal, and reconciliation patterns based on analytical intent\u003c\/li\u003e\n\u003cli\u003eBuild trustworthy business metrics with clear populations, numerators, denominators, and time boundaries\u003c\/li\u003e\n\u003cli\u003eAvoid misleading averages, incorrect distinct counts, and aggregation errors\u003c\/li\u003e\n\u003cli\u003eStructure complex analysis with readable, testable CTE pipelines\u003c\/li\u003e\n\u003cli\u003eMaster window functions using partitioning, ordering, and explicit frames\u003c\/li\u003e\n\u003cli\u003eUse ranking, LAG, LEAD, running totals, rolling calculations, and share-of-total analysis\u003c\/li\u003e\n\u003cli\u003eBuild period-over-period and year-over-year reporting correctly\u003c\/li\u003e\n\u003cli\u003eHandle missing dates, incomplete periods, business calendars, and timezone boundaries\u003c\/li\u003e\n\u003cli\u003eCreate cohort and retention analyses without confusing entry, activity, maturity, and reactivation\u003c\/li\u003e\n\u003cli\u003eBuild funnels that respect sequence, timing, repeated attempts, and session boundaries\u003c\/li\u003e\n\u003cli\u003eAnalyze customer behavior, repeat purchases, revenue concentration, subscriptions, MRR movement, and churn\u003c\/li\u003e\n\u003cli\u003eCalculate product engagement, service levels, throughput, backlog, operational latency, and percentile-based metrics\u003c\/li\u003e\n\u003cli\u003eDiagnose wrong query results systematically instead of adding DISTINCT and hoping for the best\u003c\/li\u003e\n\u003cli\u003eImprove query performance using pruning, execution plans, cardinality awareness, indexing, pre-aggregation, and cost-aware design\u003c\/li\u003e\n\u003cli\u003eTurn ad hoc SQL into production analytics with tests, contracts, reconciliation, version control, freshness checks, and observability\u003c\/li\u003e\n\u003cli\u003eWrite analytical logic that remains understandable across PostgreSQL, SQL Server, MySQL, BigQuery, Snowflake, DuckDB, and other platforms\u003c\/li\u003e\n\u003cli\u003eWork effectively with semi-structured data, modern cloud warehouses, semantic layers, and AI-assisted SQL\u003c\/li\u003e\n\u003cli\u003eCommunicate results so that stakeholders understand not only the number, but exactly what the number means\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe book's central discipline is simple: \u003c\/p\u003e\u003cp\u003e\u003cb\u003eEvery query has a grain. Every metric has a definition. Every transformation changes meaning.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThe analyst's job is to control those changes deliberately.\u003c\/p\u003e\u003cp\u003eThe manuscript reinforces this with a practical workflow: \u003cb\u003edefine, inspect, transform, validate, and present\u003c\/b\u003e-rather than building a complicated final query first and trying to debug the answer afterward.\u003c\/p\u003e\u003cp\u003eThe diagram on page 17 makes the same philosophy visual: \u003cb\u003ebusiness question → analytical definition → data transformation → validation → decision-ready result\u003c\/b\u003e. SQL is only one part of the reasoning process.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDon't just write SQL that runs. Write SQL that proves what the data means.\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Corin Halstead\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798176041552\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/21\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 172\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.48lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.43d","brand":"Corin Halstead","offers":[{"title":"Paperback","offer_id":50780097609983,"sku":"9798176041552","price":18.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/sql-analytics-for-real-world-data-corin-halstead-9798176041552","provider":"WR Book House","version":"1.0","type":"link"}