This dataset contains filtered restaurant information based on actual customer order activity, focusing on restaurants that served a sample of 5,000 distinct customers.

• Contains data on restaurants that served 5,000 unique customers who placed at least one order.
• Filtered using a subquery to include only restaurants tied to active customer transactions.
• Each row is a restaurant record from the app.restaurant table.
• Enriched via a JOIN with app.orders to ensure restaurants have real customer interactions.
• Useful for analyzing:
  • Customer-restaurant relationships
  • Restaurant exposure and customer reach
  • Performance benchmarking for active restaurants
• Ideal for downstream analysis such as:
  • Customer preferences
  • Marketing segmentation
  • Restaurant engagement scoring

This dataset contains structured order-level data used to analyze transaction patterns, revenue generation, and customer activity across a food delivery platform.

• Captures each order placed on the platform, including order ID, customer ID, restaurant ID, order date, and total price.
• Includes important status fields such as payment_status and is_cancelled for identifying successful transactions.
• Enables time-series analysis using the order_date field to explore daily, weekly, or seasonal trends.
• Supports revenue tracking and performance benchmarking across customers and restaurants.
• Useful for calculating metrics like average order value, order frequency, and churn indicators.
• Can be joined with customer, restaurant, and order_items tables for richer insights.
• Ideal for demand forecasting, customer behavior analysis, and operational reporting.

This dataset contains structured order-level data used to analyze transaction patterns, revenue generation, and customer activity across a food delivery platform.

• Captures each order placed on the platform, including order ID, customer ID, restaurant ID, order date, and total price.
• Includes important status fields such as payment_status and is_cancelled for identifying successful transactions.
• Enables time-series analysis using the order_date field to explore daily, weekly, or seasonal trends.
• Supports revenue tracking and performance benchmarking across customers and restaurants.
• Useful for calculating metrics like average order value, order frequency, and churn indicators.
• Can be joined with customer, restaurant, and order_items tables for richer insights.
• Ideal for demand forecasting, customer behavior analysis, and operational reporting.

This dataset provides detailed information on menu items offered by restaurants on the platform, useful for pricing, availability, and performance analysis.

• Contains each menu item's ID, name, description, price, and availability status.
• Linked to the restaurant_id to support restaurant-level product analysis.
• Enables evaluation of pricing strategies and menu diversity across restaurants.
• Helps identify top-performing and underperforming items based on sales (when joined with order data).
• Useful for assessing item availability, seasonal offerings, and product lifecycle.
• Supports menu optimization by analyzing popularity, pricing trends, and item attributes.
• Ideal for building recommendation systems, menu engineering dashboards, and profitability models.

This dataset contains customer-generated reviews and ratings, enabling analysis of satisfaction and service quality across restaurants.

• Includes fields like review_id, customer_id, restaurant_id, rating, and review text.
• Supports evaluation of customer sentiment and dining experience.
• Enables tracking of average ratings and feedback trends over time.
• Useful for identifying top-rated restaurants and flagging service issues.
• Can be joined with orders and restaurant data to enrich analysis.
• Ideal for sentiment analysis, quality assurance, and performance benchmarking.
• Helps improve customer satisfaction, retention, and operational feedback loops.

This document outlines advanced SQL-based use cases to evaluate restaurant and menu performance within a food delivery platform.

• Includes five real-world use cases covering ranking, trend analysis, and profitability.
• Covers essential SQL skills like window functions, aggregations, and conditional logic.
• Guides learners through performance benchmarking and operational efficiency analysis.
• Uses a dataset containing restaurant, menu_item, orders, order_items, and reviews tables.
• Offers business context, technical objectives, and expected outcomes for each task.
• Ideal for learners and analysts seeking hands-on experience in SQL-driven business intelligence.
• Supports data-driven decision-making for pricing, customer satisfaction, and growth planning.

This dataset captures operational details of food delivery orders and is designed to support performance analysis and logistics optimization:

Provides structured delivery data from a food delivery application.

Contains unique identifiers for orders and delivery partners.

Includes timestamps for pickup and drop-off events.

Tracks delivery status (is_delivered) and delay reasons.

Offers estimated vs. actual delivery time comparisons.

Enables analysis of delivery efficiency and operational bottlenecks.

Useful for performance monitoring of delivery operations and partners.

Supports use cases in logistics optimization, SLA tracking, and time-series analysis.