Food Delivery Dataset for Analysts

• orders.customer_id →references customers.customer_idmany to one: each Orders row points to one Customers row
• orders.delivery_id →references deliveries.delivery_idmany to one: each Orders row points to one Deliveries row

1. Calculate the average delivery time per region using the deliveries and orders tables.

   Join deliveries and orders on delivery_id. Extract the hour from delivery_time. Group by delivery_region.

   tables: deliveries, orders

2. Identify the top 10 customers by order frequency using the customers and orders tables.

   Join customers and orders on customer_id. Count orders per customer. Order by count descending and take the top 10.

   tables: customers, orders

3. Determine the peak hours for food delivery orders by analyzing order_time in the orders table.

   Extract the hour from the order_time column. Group by hour and count the number of orders. Find the hour with the maximum count.

   tables: orders

4. Create a Power BI dashboard showing total order value by region and customer.

   Join all three tables. Use visuals to show SUM(orders.order_value) by deliveries.delivery_region and customers.customer_name.

   tables: customers, orders, deliveries

Table names match the SQLite file and the SQL script.

SELECT c.city, AVG(o.order_value) AS average_order_value
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.city
ORDER BY average_order_value DESC
LIMIT 20;

SELECT d.delivery_region, COUNT(o.order_id) AS total_orders
FROM deliveries d
JOIN orders o ON d.delivery_id = o.delivery_id
GROUP BY d.delivery_region
ORDER BY total_orders DESC
LIMIT 20;

SELECT c.customer_name, COUNT(o.order_id) AS order_count
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_name
ORDER BY order_count DESC
LIMIT 1;

SELECT STRFTIME('%Y-%m', d.delivery_time) AS delivery_month, AVG(STRFTIME('%s', d.delivery_time) - STRFTIME('%s', o.order_date)) AS avg_delivery_seconds
FROM deliveries d
JOIN orders o ON d.delivery_id = o.delivery_id
GROUP BY delivery_month
ORDER BY delivery_month
LIMIT 20;

Excel

Load each table into a separate Excel sheet. Use Power Query to join tables based on their keys (e.g., customer_id, delivery_id). Create a PivotTable from the joined data to summarize order values by region.

Power BI

Create a star schema by connecting the 'orders' table (fact table) to 'customers' and 'deliveries' tables (dimension tables) using their respective keys. Build measures like Total Order Value (SUM(orders[order_value])) and Average Delivery Time (AVERAGE(deliveries[delivery_time])).

SQL

Load the SQLite or SQL relational download. The tables are pre-defined with primary and foreign keys. Join 'orders' with 'customers' on 'customer_id' and with 'deliveries' on 'delivery_id' to analyze relationships.

• CSV (zip)One CSV file per table, zipped
• Excel workbookOne worksheet per table
• SQLite databaseA ready-to-query database file with every table
• Parquet (zip)One Parquet file per table, zipped
• SQL scriptCREATE TABLE with primary and foreign keys, then INSERTs
• CSVA single CSV file
• JSONA single JSON file