Scenario

A SQLite database contains sales data across multiple tables that need to be analyzed together.

Task

Write a Python script at /home/interview/analyze_sales.py that:

1. Connects to /home/interview/sales.db
2. Executes a SQL query using INNER JOIN to combine customers, orders, and order_items tables (customers → orders → order_items), filtering for orders with status 'completed'
3. Loads the results into a pandas DataFrame
4. Calculates total_amount (quantity × unit_price) rounded to 2 decimal places for each order item
5. Calculates customer_total (sum of all total_amounts per customer) rounded to 2 decimal places and adds it as a column
6. Calculates revenue_percentage showing each customer's percentage of total revenue (customer_total / overall_total × 100), rounded to 2 decimal places
7. Exports the result to /home/interview/sales_report.csv

Note: pandas and sqlite3 are already installed. The output will have customer-level metrics (customer_total, revenue_percentage) repeated for each order item row.

Database Schema

• customers: id, name, email, city, country
• orders: id, customer_id, order_date, status
• order_items: id, order_id, product_name, quantity, unit_price