From your first SELECT
to window functions.

Return first_name, last_name, salary from employees with explicit ORDER BY and LIMIT 20.

Return exactly 4 requested columns from employees and rename two with business aliases.

Return top 15 salaries with tie-break order by employee_id.

Return distinct department_id values sorted ascending.

Return employee contact columns and normalize output column names with aliases.

Return rows 21-40 using LIMIT/OFFSET with stable ordering.

Return employees with salary, plus a derived salary_band CASE expression.

Build a compact preview query that verifies table shape and data quality.

Demonstrate two different deterministic ordering strategies and compare outputs.

Refactor a messy SELECT into clean formatting with clear aliases.

Filter employees by department, salary floor, and hire date threshold.

Exclude rows by multi-rule policy with grouped OR conditions.

Return hires within a target month range with explicit boundaries.

Return employees missing email/phone with clear null logic.

Find employee emails with specific domain patterns and case handling.

Rewrite a complex negative filter into a readable positive equivalent.

Classify rows into compensation ranges and filter by selected segments.

Implement three business policies in one WHERE clause with comments.

Use IN/NOT IN safely and explain null pitfalls in output notes.

Run count checks before/after each condition to prove logic correctness.

Return headcount per department with deterministic ordering.

Return avg, min, max salary per department with readable aliases.

Keep only groups above salary or volume thresholds using HAVING.

Compare COUNT(*) vs COUNT(DISTINCT employee_id) by group.

Build category revenue totals with grouped order metrics.

Add count-based quality checks next to each aggregate metric.

Group rows by computed range bucket and aggregate each bucket.

Return 6+ aggregate metrics in one compact management table.

Validate expected number of groups and total row coverage.

Use FILTER (WHERE ...) for conditional aggregates.

Join employees to departments with clear aliases and deterministic sorting.

Keep all departments and include employee counts including zero-member groups.

Join employees, departments, and jobs in one clean result set.

Identify records with no matching lookup table rows.

Prevent duplicates by pre-aggregating line-item table before join.

Join orders, customers, and order_items with quality-safe counts.

Join appointments to doctors and patients for operational reporting.

Compare INNER vs LEFT row counts and explain differences.

Create a query that flags suspicious foreign-key gaps across tables.

Deliver a final join report with row-quality validation columns.

Use subquery to flag employees above global average salary.

Use correlated subquery against department-level average.

Build a CTE chain for cleaned base rows and final summary output.

Return top spenders using staged CTE logic from orders and items.

Use EXISTS to keep only rows with qualifying related records.

Refactor nested subquery into cleaner CTE architecture.

Run each CTE stage independently and validate intermediate row counts.

Compare derived table approach with CTE approach for same business question.

Create a 3+ stage analysis pipeline with explicit business-rule stages.

Design a repeatable CTE template for recurring KPI requests.

Rank salaries within each department using PARTITION BY.

Compare RANK and DENSE_RANK outputs for tied salaries.

Compute running totals by order date and customer.

Use LAG to calculate period-over-period change percentage.

Return top 3 earners per department using window filtering.

Use ROW_NUMBER to sequence hires by department and date.

Compute rolling 3-period average with explicit frame clause.

Use NTILE(4) to segment salaries into quartiles.

Use FIRST_VALUE and LAST_VALUE in partitioned windows.

Validate partition sizes and ranking consistency in final output.