AI SQL Query Builder

Act as a free AI SQL query builder. Convert my natural-language request into accurate, executable SQL. First ask for missing schema details if needed; otherwise proceed. Use the SQL dialect I specify and do not invent tables or columns. SQL dialect: PostgreSQL Goal: Find the top 10 customers by total revenue in the last 90 days. Schema: - customers(customer_id, full_name, email, created_at, country) - orders(order_id, customer_id, order_date, status, total_amount) Rules: - Include only orders where status = 'completed'. - Return customer_id, full_name, email, country, total_revenue, completed_order_count, first_order_date, last_order_date. - Sort by total_revenue descending. - Add comments explaining each major step. Output format: 1. Final SQL query 2. Brief explanation 3. Assumptions 4. Suggested indexes for performance

A production-ready PostgreSQL query that ranks the top 10 customers by completed revenue over the last 90 days, including revenue totals, order counts, date range fields, assumptions, and performance index suggestions.

Act as a free AI SQL query builder and SQL optimization assistant. Review the query below, identify errors or inefficiencies, then rewrite it for clarity and performance. Keep the result compatible with MySQL 8.0. Database dialect: MySQL 8.0 Objective: Calculate monthly revenue and average order value by product category for completed orders in 2024. Schema: - categories(id, category_name) - products(id, category_id, product_name) - order_items(id, order_id, product_id, quantity, unit_price) - orders(id, customer_id, order_date, order_status) Current query: SELECT c.category_name, MONTH(o.order_date), SUM(oi.unit_price), AVG(oi.unit_price) FROM orders o, order_items oi, products p, categories c WHERE o.id = oi.order_id AND oi.product_id = p.id AND p.category_id = c.id AND o.order_status = 'completed' AND YEAR(o.order_date) = 2024 GROUP BY c.category_name; Requirements: - Use explicit JOIN syntax. - Calculate revenue as quantity * unit_price. - Group by category and month. - Avoid functions on indexed date columns where possible. - Return month_start, category_name, revenue, order_count, avg_order_value. - Explain what was wrong with the original query. Output format: 1. Corrected SQL 2. Key fixes 3. Performance notes

An optimized MySQL 8.0 query using explicit joins, correct revenue and average order value calculations, date-range filtering for index usage, grouped monthly category reporting, plus a concise explanation of fixes and performance improvements.

Act as a free AI SQL query builder for business reporting. Generate a reusable SQL query pack from the reporting request below. Use BigQuery Standard SQL and include clear comments. Business question: Marketing wants to understand weekly user acquisition, activation, and purchase conversion by campaign for the last 12 weeks. SQL dialect: BigQuery Standard SQL Schema: - users(user_id, signup_timestamp, campaign_id, country) - campaigns(campaign_id, campaign_name, channel, budget) - events(event_id, user_id, event_name, event_timestamp) - purchases(purchase_id, user_id, purchase_timestamp, revenue) Definitions: - Acquired user: user signed up during the week. - Activated user: acquired user with at least one event where event_name = 'activation' within 7 days of signup. - Purchaser: acquired user with at least one purchase within 14 days of signup. - Use weeks starting Monday. - Analyze only users who signed up in the last 12 complete weeks. Return fields: week_start, campaign_id, campaign_name, channel, acquired_users, activated_users, purchasers, activation_rate, purchase_conversion_rate, total_revenue_14d, revenue_per_acquired_user. Output format: 1. Production-ready SQL query 2. Explanation of each CTE 3. Validation checks to confirm results 4. Possible dashboard dimensions and filters

A BigQuery Standard SQL reporting query pack with CTEs for acquisition, activation, purchase conversion, and revenue metrics by campaign for the last 12 complete weeks, plus validation checks and dashboard-ready recommendations.