Stop watching tutorials. Write queries against Stripe revenue, cohort retention, and user attribution tables with instant sub-5ms feedback.
| billing_month | mrr | mom_growth_pct |
|---|---|---|
| 2024-03-01 | $68,900 | +27.1% |
| 2024-02-01 | $54,200 | +27.5% |
| 2024-01-01 | $42,500 | null |
Checks row counts, columns, and data values against reference benchmarks.
b2b_saasTry it in the IDEA tight feedback loop designed for learning SQL by writing queries, not watching slides.
Tackle realistic B2B SaaS MRR, e-commerce checkouts, and customer churn tables—not toy bookstore databases.
Execute queries locally with full PostgreSQL power, rich keyboard shortcuts, and sub-5ms runtime.
Get automated visual diffs on row counts, column types, and data values. Learn from mistakes instantly without guessing.
Move beyond isolated syntax drills. Connect queries into cohesive business investigations to build portfolio-grade analytical skills.
Investigate why monthly recurring revenue is dropping by auditing dormant accounts, computing activity lag gaps, measuring MAU stickiness, and building a multi-month cohort retention matrix.
Isolate active enterprise and growth subscriptions from historical canceled contracts.
Find paying accounts that logged zero telemetry events in the past 60 days.
Measure day-over-day gaps between logins using window lag functions to predict churn danger.
Evaluate weekly engagement frequency to determine product stickiness ratios.
Group users by signup month and track retention decay over subsequent calendar months.
76 structured problems organized into 6 tracks. Start with simple filters or jump straight to window functions.
Test-drive 1 problem from each track: Fundamentals, Aggregations, Joins, Window Functions, CTEs, and Business Analytics.
Master core SQL filters, multi-column ordering, pattern matching, and CASE WHEN logic.
A table with subscription_id, company_name, plan_tier, and mrr sorted by highest MRR first.
Unique list of acquisition_channel and country_code with no null or unknown values, alphabetically sorted.
List of waiting emergency patients under Resuscitation or Emergent triage, sorted chronologically.
Express delivery orders >= $250 still pending or processing, sorted by price descending.
High-performing engineers earning under $160k, sorted by rating descending and salary ascending.
High-risk non-US wire transfers between $10k and $50k, sorted by highest risk score first.
Online top-rated XL and Black tier drivers eligible for surge dispatch.
Paid tier accounts missing payment provider credentials, sorted by creation date.
Budget wireless and Bluetooth audio products sorted by price ascending.
Customer profiles with computed loyalty tier labels sorted by lifetime spend.
Inpatients in clinical distress requiring ICU escalation, sorted by lowest oxygen saturation.
Dispute tickets classified by escalation priority and sorted by disputed amount.
SaaS metrics, GMV, chargeback ratios, fleet SLA adherence, and grouped HAVING filters.
Aggregate table showing tier, total_mrr, active_customers, and avg_seats for tiers with >= 2 active accounts.
Summary table of category, total_orders, total_gmv, and refund_rate_pct sorted by highest refund rate first.
Summary of department_name, total_admissions, avg_stay_days, and max_stay_days for wards with >= 2 admissions.
High-risk merchants with dispute rate >= 1.0%, sorted by highest dispute rate.
Delivery metrics per zone showing total packages, average transit time, and on-time percentage.
Department compensation metrics including min, max, avg, and wage spread, sorted by spread descending.
Plan tiers showing total events, unique active workspaces, and events per workspace.
Summary of promo code sales metrics with net revenue >= $500, sorted by highest net revenue.
Summary of pharmaceutical spending per drug category sorted by total cost.
High-volume ATMs requiring priority cash restocking, sorted by cash dispensed.
Summary of ride requests, completed rides, and completion percentage per vehicle tier.
Summary of lost MRR and churned accounts per industry sector with lost MRR >= $3,000.
Relational data modeling, dormant account detection, multi-table joins, and FULL OUTER reconciliations.
List of customer_id, company_name, account_manager, and plan_name for customers with zero activity records.
Campaign performance metrics with campaign_name, channel, budget_spent, revenue_generated, and roas.
Roster of admitted inpatients with attending physician details, sorted by ward room.
List of unreceived purchase orders sorted chronologically by expected delivery date.
Org chart hierarchy showing employee name, title, and manager name.
Compliance violation list of high-value wires sent to unverified accounts.
Trips performed with uninspected vehicles sorted by fare amount.
Complete plan-to-feature matrix with enabled status flag.
Flagged prescriptions where prescribed drug substance matches documented patient allergies.
Reconciliation table classifying each transaction as Matched, Missing in Bank, or Missing in Ledger.
High-value abandoned cart sessions with cart value >= $100.
Two-leg transit routes from Chicago with layover hub and total flight hours.
MoM revenue acceleration, regional rankings, running totals, moving averages, and NTILE quartiles.
Time-series table showing month_date, revenue, prev_month_revenue, and growth_pct.
Filtered table with rep_name, region, total_bookings, and regional_rank containing only rank 1 and 2 reps per region.
Time-series showing entry_date, daily_net, and running_balance.
User login history with next session date and gap in days.
Daily sales alongside 3-day rolling window average.
Patient telemetry readings showing previous heart rate and BPM delta.
Transaction log showing the incremental card usage sequence number.
Drivers segmented into 4 earnings quartiles (1 = top 25%, 4 = bottom 25%).
Employee salary percentile within department on a 0.00 to 1.00 scale.
Monthly expansion MRR alongside cumulative cohort expansion total.
Order history showing order amount alongside the customer first purchase value.
ER patients categorized into 4 wait time quartiles.
Ledger lines showing running balance per account.
Trip log showing preceding dropoff timestamp per driver.
Modular enterprise pipelines, logo churn rates, repeat purchase cohorts, and Recursive CTE hierarchies.
Single row summary with total_accounts, churned_accounts, and churn_rate_pct.
Single row summary showing new_mrr, expansion_mrr, contraction_mrr, and net_new_mrr.
Summary with total_customers, repeat_customers, and repeat_rate_pct within 30 days.
Single row summary showing total discharges, readmissions within 30 days, and readmission rate percentage.
Summary of client count and total assets grouped by wealth tier.
Employee hierarchy showing reporting depth distance from the executive level.
Summary of stops, total driving time, and shift violation status per route.
Power users utilizing at least 3 distinct modules and logging >= 10 actions.
Customer lifetime spend with quartile segmentation.
Surgical rooms with total surgery minutes and utilization percentage.
Monthly paydown schedule showing remaining loan balance after 4 periods.
At-risk inventory items with 10 or fewer days of remaining stock.
Cohort retention matrices, JSONB audit telemetry, RFM segmentation, and AML structuring forensics.
Extracted telemetry metrics with log_id, feature_name, duration_ms, and status for vector_search_v2 successes.
Cohort table displaying cohort_month, activity_month, and active_users.
Flagged wire transfers matching sanctioned names on official watchlists.
Extracted service error metrics for all fatal incidents.
Customer profiles with Recency, Frequency, and Monetary metrics.
Conversion funnel counts and admission rate percentage from the emergency department.
Vehicles overdue for preventative maintenance sorted by overdue miles.
Departmental turnover rates and resignation counts.
Executable arbitrage pairs where Exchange A bid exceeds Exchange B ask.
Single row summary showing DAU, MAU, and stickiness percentage.
Purchase funnel step counts and overall conversion percentage.
Summary of pathogen isolates and susceptibility percentage.
Geographic dispatch zones with calculated surge pricing multipliers.
Suspected smurfing bank accounts with 2 or more sub-$10k deposits.
The SQL features most commonly used in real jobs and interview technical screens.
LAG, LEAD, DENSE_RANK, running sums, and partitioned rankings.
Common Table Expressions, multi-step queries, and modular pipeline design.
INNER, LEFT, FULL OUTER, anti-joins, and multi-table joins.
GROUP BY, HAVING filters, conditional sums, and distinct counts.
Extracting JSON objects, array filtering, and semi-structured querying.
Cohort retention, marketing attribution, churn risk, and MRR expansion.
Designed for practical querying skills, not puzzle trivia.
Practice window functions and multi-table CTEs to prepare for technical screens and daily analytics jobs.
Learn modern PostgreSQL analytical queries and JSONB extraction without setting up local database instances.
Gain the ability to query retention models and validate product metrics independently.
Common questions about in-browser execution, supported features, and pricing.
No registration required. 18 comprehensive starter problems are completely free.