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Open FinderRunnable SQL practice
40 SQL analytics challenges with validated answers.
Practice cohorts, retention, window functions, revenue analysis, and data engineering SQL patterns with small datasets you can run locally.
SELECT
sex,
passenger_class,
COUNT(*) AS total_passengers,
ROUND(100.0 * AVG(survived), 2) AS survival_rate_pct
FROM passengers
GROUP BY sex, passenger_class
ORDER BY sex, passenger_class;
40
challenge folders
100%
snapshot-validated outputs
4
structured learning paths
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Open ViewerRun it locally
Copy the SQL commands, compare against the validated snapshot, and inspect the source on GitHub.
Open RepositoryChallenge finder
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Filter the challenge set by topic and difficulty, then open a readable viewer with the prompt, schema, solution, and expected output.
Study paths
Start where the skill gap is.
Analytics Foundations
Grouping, ranking, time-based aggregation, percent-of-total analysis, and customer-level metrics.
Retention and Cohorts
Monthly retention, cohort offsets, repeat behavior, retention matrices, and rolling trends.
Revenue and Growth
Daily revenue, date spines, Pareto analysis, month-over-month growth, and customer contribution share.
Data Engineering SQL
Renewal state logic, SCD Type 2 history, incremental loads, stockout risk, conversion windows, and payment reconciliation.
Validation
CI checks the answers, not just the syntax.
Each challenge has an expected.json snapshot. The validator executes the schema and solution in SQLite, then compares the actual result columns and rows against the expected output.
python scripts/validate_challenges.pyContribute
Bring a realistic analytics question.
The roadmap lists challenge ideas for inventory, attribution, SaaS metrics, support operations, and data quality. Requests and small fixes are welcome when they keep the examples runnable and easy to review.