A 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.

The database did not make the mistake.

The meaning was lost somewhere in the query.

SQL Analytics for Real-World Data bridges the gap between knowing SQL commands and using SQL to produce reliable, decision-ready analysis.

Instead 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.

Inside, you'll learn how to:

Translate vague business questions into explicit analytical definitions
Define the correct grain before joins, aggregations, and window calculations
Prevent fanout and metric duplication when combining one-to-many and many-to-many relationships
Choose INNER, LEFT, EXISTS, anti-join, temporal, and reconciliation patterns based on analytical intent
Build trustworthy business metrics with clear populations, numerators, denominators, and time boundaries
Avoid misleading averages, incorrect distinct counts, and aggregation errors
Structure complex analysis with readable, testable CTE pipelines
Master window functions using partitioning, ordering, and explicit frames
Use ranking, LAG, LEAD, running totals, rolling calculations, and share-of-total analysis
Build period-over-period and year-over-year reporting correctly
Handle missing dates, incomplete periods, business calendars, and timezone boundaries
Create cohort and retention analyses without confusing entry, activity, maturity, and reactivation
Build funnels that respect sequence, timing, repeated attempts, and session boundaries
Analyze customer behavior, repeat purchases, revenue concentration, subscriptions, MRR movement, and churn
Calculate product engagement, service levels, throughput, backlog, operational latency, and percentile-based metrics
Diagnose wrong query results systematically instead of adding DISTINCT and hoping for the best
Improve query performance using pruning, execution plans, cardinality awareness, indexing, pre-aggregation, and cost-aware design
Turn ad hoc SQL into production analytics with tests, contracts, reconciliation, version control, freshness checks, and observability
Write analytical logic that remains understandable across PostgreSQL, SQL Server, MySQL, BigQuery, Snowflake, DuckDB, and other platforms
Work effectively with semi-structured data, modern cloud warehouses, semantic layers, and AI-assisted SQL
Communicate results so that stakeholders understand not only the number, but exactly what the number means

The book's central discipline is simple:

Every query has a grain. Every metric has a definition. Every transformation changes meaning.

The analyst's job is to control those changes deliberately.

The manuscript reinforces this with a practical workflow: define, inspect, transform, validate, and present—rather than building a complicated final query first and trying to debug the answer afterward.

The diagram on page 17 makes the same philosophy visual: business question → analytical definition → data transformation → validation → decision-ready result. SQL is only one part of the reasoning process.

Don't just write SQL that runs. Write SQL that proves what the data means.

"Synopsis" may belong to another edition of this title.

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SQL Analytics for Real-World Data

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