An interactive relational database workspace designed for software engineers and data analysts. Write real SQL queries against an isolated in-memory engine, receive instant ground-truth evaluation, and learn with an AI tutor that coaches you without spoiling answers.
No simulated or mocked tables. Every query runs directly against an enterprise retail schema (ShopDB) with customers, orders, itemized transactions, inventory, and reviews.
SELECT
c.first_name || ' ' || c.last_name AS customer_name,
c.city,
COUNT(o.order_id) AS total_orders,
ROUND(SUM(o.total_amount), 2) AS lifetime_spend
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, customer_name, c.city
HAVING COUNT(o.order_id) >= 2
ORDER BY lifetime_spend DESC
LIMIT 5;| customer_name | city | total_orders | lifetime_spend |
|---|---|---|---|
| Vihaan Kumar | Bangalore | 3 | 71,596 |
| Aarav Sharma | Delhi | 3 | 63,096 |
| Diya Patel | Mumbai | 2 | 5,998 |
| Ananya Singh | Chennai | 2 | 4,097 |
| Arjun Reddy | Hyderabad | 2 | 4,498 |
Designed progressively from first relational principles to production query optimization and index utilization.
Tables, relational models, primary/foreign keys, and data types.
SELECT columns, arithmetic aliases, DISTINCT sets, and calculated fields.
Comparison operators, LIKE pattern matching, BETWEEN ranges, and NULL logic.
ORDER BY ascending/descending, tie-breaking multi-sort, and LIMIT / OFFSET.
COUNT, SUM, AVG, MIN, MAX with GROUP BY and post-aggregate HAVING clauses.
INNER JOIN, LEFT/RIGHT OUTER JOIN, FULL OUTER JOIN, CROSS JOIN, and self joins.
Scalar subqueries, IN / NOT IN subqueries, EXISTS, and correlated rows.
Conditional branching (CASE WHEN), bucketing revenue tiers, and KPI reporting.
Modular WITH clauses, chained CTEs, and clean procedural query structures.
OVER, PARTITION BY, ROW_NUMBER(), RANK(), DENSE_RANK(), and LEAD/LAG.
UNION, UNION ALL, INTERSECT, and EXCEPT row-level set algebra.
EXPLAIN QUERY PLAN, B-Tree indexes, scan types, and query performance tuning.
Every practice problem is framed as a concrete engineering ticket or data request inspired by top technology firms.
Reconcile settlement volumes across payment gateways (UPI, credit cards, debit cards) and identify high-value charge allocations.
Detect catalog items whose inventory dropped below safety thresholds and aggregate warehouse replenishment orders.
Compute regional store footprints, staff payroll totals per urban center, and dense ranking within metropolitan hubs.
When you get stuck, the built-in AI tutor analyzes your SQL syntax and database schema to guide you step-by-step—without giving away the solution.
Start with a conceptual nudge, escalate to column and table references, and only view structural templates if needed.
Demystifies SQLite error messages (syntax errors, ambiguous columns, missing GROUP BY expressions) with plain explanations.
Listen to lessons and tutor feedback through native text-to-speech with customized pitch, rate, and multi-language support (English & Hindi).
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