This SQL project models a food ordering ecosystem involving hotels, customer orders, and user registrations.
It demonstrates strong hands-on skills in SQL querying, filtering, aggregation, pattern matching, and real-world business analysis.
The project is ideal for SQL fresher roles, Data Analyst interviews, and academic portfolios.
The database consists of three main tables:
Stores information about hotels, items sold, pricing, city, and hotel type.
| Column | Description |
|---|---|
| Hotel_Name | Name of the hotel |
| Contact_Number | Contact number |
| City | City location |
| Item | Food item provided |
| Cost | Item price |
| Timings | Opening hours |
| Htype | Hotel type (Cafe, Bakery, Biryani, FastFood) |
Stores food order transaction details.
| Column | Description |
|---|---|
| OID | Order ID |
| Cname | Customer name |
| Item | Ordered item |
| Amount | Total bill amount |
| Quantity | Quantity ordered |
| City | Order city |
Stores registered customer information.
| Column | Description |
|---|---|
| Cust_ID | Customer ID |
| Cname | Customer name |
| Phone | Phone number |
| Email address | |
| Location | City |
| Age | Customer age |
| FoodPreference | Veg / Non-Veg |
- Total number of cafes in Hyderabad & Bangalore
- Hotels providing specific items (Samosa, French Fries)
- Hotels with names ending in a specific pattern
- Average cost of Biryani by city
- Hotels opening at a specific time
- City-wise hotel distribution
- Maximum item cost in a city
- Total biryani orders by city
- Customers ordering specific food items
- Orders by payment type
- Pattern-based customer name searches
- Total revenue by city
- Unique items ordered
- Orders count across multiple cities
- Veg vs Non-Veg preference count
- City-wise customer registrations
- Age-based customer segmentation
- Customers from specific cities
- Pattern-based name filtering
- Senior customer identification
CREATE TABLE,ALTER TABLEINSERT INTOSELECT,WHERE,GROUP BY- Aggregate functions:
COUNT,SUM,AVG,MAX LIKEpattern matchingIN,BETWEEN,NOT IN- Data filtering & business logic queries
- Hyderabad and Bangalore have high cafe density
- Biryani is the most frequently ordered item
- Digital payments (GPay, PhonePe) are widely used
- Senior customers show strong registration presence
- Veg and Non-Veg preferences vary significantly by city
- SQL (MySQL / SQL Server compatible)
- Relational Database Concepts
- Data Analysis & Query Optimization
Hotels_Orders_Registrations_SQL.sql
(Includes table creation, data insertion, and all analytical queries)
This project reflects real-world SQL usage, covering:
- Data modeling
- Business-driven query writing
- Pattern searching
- Analytical thinking
It is well-suited for SQL Developer, Data Analyst, and Entry-Level IT roles.
This project is part of my SQL portfolio, showcasing practical database handling and analytical query skills.