Top 25+ SQL Projects on GitHub You Should Explore in 2025

Did you know that SQL ranked seventh on the programming languages list in 2025, with a rating of 2.87%? This highlights the growing demand for SQL projects on GitHub, where developers can refine their skills and tackle data management and query optimization challenges.

SQL projects on GitHub offer a practical platform for learning advanced query optimization, data normalization, and transactional integrity. These projects span real-world use cases, from complex joins and nested queries to indexing strategies and database performance tuning. 

By engaging with these projects, you’ll better understand relational databases, data manipulation techniques, and scalable solutions. Completing these projects enhances your proficiency in SQL, allowing you to tackle sophisticated problems in modern database systems.

In this blog, we will explore 25+ SQL Projects on GitHub that are relevant in modern data-driven organizations. 

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25+ Must-Explore SQL Projects on GitHub in 2025

SQL continues to be a critical tool for data manipulation, complex querying, data integration, and reporting in modern software systems. Learning advanced SQL techniques like window functions, recursive queries, and query optimization is essential for solving real-world problems efficiently. GitHub repositories provide a collaborative space for SQL practitioners, offering opportunities to work on real-world projects. 

If you want to learn advanced computational skills to help you make successful SQL projects, the following courses can help you succeed.

• Executive Diploma in Machine Learning and AI with IIIT-B
• Generative AI Mastery Certificate for Data Analysis
• AI-Powered Full Stack Development Course by IIITB

Here is an overview of the top SQL projects on GitHub.

Now that you’ve seen an overview of the top SQL projects on GitHub, let’s explore them in detail.

SQL Projects GitHub with Source Code for Beginners

Beginner SQL projects on GitHub focus on foundational concepts such as database schema design, query optimization, and data manipulation. These projects will help you develop core SQL skills, including crafting efficient SELECT statements, working with joins, and performing aggregation and filtering operations.

Here are the top SQL projects for begineers:

1. SQL Database for Online Store

An SQL database for an online store serves as the backbone for managing essential data like products, customers, orders, and payments. By designing normalized tables, you can create a structure that efficiently stores and queries transactional data. This project helps you learn how to manage relationships between different entities and ensures that your e-commerce system can handle large amounts of data with integrity.

Technology Stack and Tools:

• PostgreSQL: PostgreSQL is a powerful, open-source relational database management system, ideal for handling large-scale e-commerce data.
• SQL: A standardized programming language for managing and querying data in relational databases.
• DBMS (Database Management System): DBMS hosts and manages your database, ensuring performance and scalability.

Key Skills Gained:

• Database Design: Learn how to design normalized relational databases with clear relationships between tables such as customers and orders.
• SQL Querying: Gain expertise in writing complex SQL queries involving joins, aggregations, and subqueries to efficiently manage your e-commerce data.
• Data Integrity: Maintain data consistency and integrity using constraints like primary keys, foreign keys, and unique fields.

Applications:

• E-commerce Websites: Use the database to track products, customers, orders, and payments, ensuring smooth transactions.
• Inventory Management: Integrate the database into retail platforms to manage stock levels, order history, and customer details.
• Customer Analytics: Expand the database to analyze customer behavior, predictive analysis, and improve marketing strategies.

Code Example:

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    email VARCHAR(100) UNIQUE,
    phone VARCHAR(15),
    address VARCHAR(255)
);

Output:

Table 'customers' created with columns: customer_id, first_name, last_name, email, phone, address.

Output Explanation:

In this example, the customers table is created with essential fields for storing customer information. The customer_id is the primary key, ensuring each record is unique, and the email field is marked as unique to prevent duplicate entries.

2. Social Media Data Analysis Dashboard

This project involves creating an SQL database to analyze social media data such as user activity, posts, comments, and interactions. By using SQL to manage large datasets, you can derive actionable insights that help businesses improve their marketing strategies and engagement. This is a great SQL project on GitHub to contribute to, especially for those interested in social media analytics.

Technology Stack and Tools:

• MySQL/PostgreSQL: Use these database management systems to store and handle vast amounts of social media interaction data.
• SQL: Write queries to analyze user engagement, activity, and trends across different social media platforms.
• Data Visualization Tools: Tools like Tableau and Power BI can visualize your SQL queries, turning data into actionable insights.

Key Skills Gained:

• Aggregation Functions: Learn to use SQL’s GROUP BY, COUNT(), and SUM() for analyzing user activity across different platforms.
• Complex Query Design: Write complex queries to filter user interactions, find engagement trends, and measure campaign success.
• Data Visualization: Use external tools like Tableau and Power BI to visually represent social media metrics, helping businesses track performance.

Applications:

• Social Media Analytics Platforms: Build dashboards for tracking engagement, likes, shares, and more.
• Marketing Departments: Provide insights into campaign effectiveness and audience engagement.
• Predictive Analytics: Use historical social media data to predict future trends and improve business strategies.

Code Example:

CREATE TABLE authors (
    author_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50)
);

CREATE TABLE books (
    book_id INT PRIMARY KEY,
    title VARCHAR(100),
    author_id INT,
    genre VARCHAR(50),
    publication_year INT,
    FOREIGN KEY (author_id) REFERENCES authors(author_id)
);

CREATE TABLE borrowers (
    borrower_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    email VARCHAR(100)
);

CREATE TABLE loans (
    loan_id INT PRIMARY KEY,
    book_id INT,
    borrower_id INT,
    loan_date DATE,
    due_date DATE,
    return_date DATE,
    FOREIGN KEY (book_id) REFERENCES books(book_id),
    FOREIGN KEY (borrower_id) REFERENCES borrowers(borrower_id)
);

Output:

-- Insert data into authors
INSERT INTO authors (author_id, first_name, last_name)
VALUES (1, 'J.K.', 'Rowling'), (2, 'George', 'Orwell');

-- Insert data into books
INSERT INTO books (book_id, title, author_id, genre, publication_year)
VALUES (1, 'Harry Potter and the Sorcerer\'s Stone', 1, 'Fantasy', 1997),
      (2, '1984', 2, 'Dystopian', 1949);

-- Insert data into borrowers
INSERT INTO borrowers (borrower_id, first_name, last_name, email)
VALUES (1, 'Suraj', 'Patel', 'suraj.patel@example.com'),
      (2, 'Naina', 'Chettri', 'naina.chettri@example.com');

-- Insert data into loans
INSERT INTO loans (loan_id, book_id, borrower_id, loan_date, due_date)
VALUES (1, 1, 1, '2025-05-10', '2025-06-10'),
      (2, 2, 2, '2025-05-11', '2025-06-11');

Output Explanation:

You’ve set up tables for authors, books, borrowers, and loans with relationships between them. Sample data has been inserted, linking books and borrowers through the loans table.

Also Read: 15 Major Social Media Trends in 2025

3. Real-Time Stock Market Data Analysis

In this SQL project on GitHub, you will create an SQL database to store and analyze real-time stock market data. The database will track stock prices, trading volumes, and market trends, which are key data points for stock market analysis.

Technology Stack and Tools:

• MySQL/PostgreSQL: Store historical and real-time stock market data for analysis.
• SQL: Perform trend analysis and create reports based on stock performance.
• Alpha Vantage API: Fetch real-time stock data using this API.

Key Skills Gained:

• Real-Time Data Management: Learn to manage real-time data ingestion from external APIs and store it in SQL databases.
• Trend Analysis: Use SQL queries to analyze stock data over time and find trends.
• Optimizing Queries: Improve SQL query performance for handling large datasets with indexing and other optimization techniques.
  Applications:
• Stock Market Tools: Build applications that track stock performance, allowing users to make informed decisions.
• Trading Platforms: Integrate SQL databases for real-time stock analysis in trading platforms.
• Portfolio Management: Use SQL for tracking investments and portfolio performance.

Code Exmple:

CREATE TABLE stock_prices (
    stock_id INT PRIMARY KEY,
    symbol VARCHAR(10),
    price DECIMAL(10, 2),
    volume INT,
    date TIMESTAMP
);

Output:

-- Insert data into stock_prices table
INSERT INTO stock_prices (stock_id, symbol, price, volume, date)
VALUES (1, 'AAPL', 145.30, 1000000, '2025-05-20 09:00:00'),
      (2, 'GOOGL', 2734.56, 750000, '2025-05-20 09:00:00'),
      (3, 'AMZN', 3368.00, 500000, '2025-05-20 09:00:00');

Output Explanation:

The stock_prices table is designed to store stock symbols, prices, volumes, and timestamps. Data is inserted for three stocks: AAPL, GOOGL, and AMZN, allowing you to track stock performance.

Also Read: Stock Market Prediction Using Machine Learning [Step-by-Step Implementation]

4. Library Management System

In this project, the goal is to create an SQL database to manage a library's inventory, including books, authors, borrowers, and due dates. This is a typical SQL project on GitHub where you can manage records efficiently using relational databases.

Technology Stack and Tools:

• MySQL/PostgreSQL: Design a relational DBSM with MySQL and PostgreSQL for books, authors, and borrowers.
• SQL: Write queries to manage book availability, overdue fines, and reservations.
• Python with Django: Build a web interface with Python and Django to interact with your SQL database.

Key Skills Gained:

• Database Design: Learn to create relational databases with multiple tables and foreign keys.
• Complex Queries: Write SQL queries for checking the status of books and overdue fines.
• Reporting: Create reports that track library usage, book checkouts, and overdue items.

Applications:

• Library Systems: Use this system for managing public libraries, school libraries, or university libraries.
• Reservation Management: Build tools for handling book reservations and tracking overdue fines.

Code Example:

CREATE TABLE authors (
    author_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50)
);

CREATE TABLE books (
    book_id INT PRIMARY KEY,
    title VARCHAR(100),
    author_id INT,
    genre VARCHAR(50),
    publication_year INT,
    FOREIGN KEY (author_id) REFERENCES authors(author_id)
);

CREATE TABLE borrowers (
    borrower_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    email VARCHAR(100)
);

CREATE TABLE loans (
    loan_id INT PRIMARY KEY,
    book_id INT,
    borrower_id INT,
    loan_date DATE,
    due_date DATE,
    return_date DATE,
    FOREIGN KEY (book_id) REFERENCES books(book_id),
    FOREIGN KEY (borrower_id) REFERENCES borrowers(borrower_id)
);

Output:

-- Insert data into authors
INSERT INTO authors (author_id, first_name, last_name)
VALUES (1, 'J.K.', 'Rowling'), (2, 'George', 'Orwell');

-- Insert data into books
INSERT INTO books (book_id, title, author_id, genre, publication_year)
VALUES (1, 'Harry Potter and the Sorcerer\'s Stone', 1, 'Fantasy', 1997),
      (2, '1984', 2, 'Dystopian', 1949);

-- Insert data into borrowers
INSERT INTO borrowers (borrower_id, first_name, last_name, email)
VALUES (1, Ramesh, 'Desai', 'ramesh.desai@example.com'),
      (2, 'Radhika', 'Singh', 'radhika.singh@example.com');

-- Insert data into loans
INSERT INTO loans (loan_id, book_id, borrower_id, loan_date, due_date)
VALUES (1, 1, 1, '2025-05-10', '2025-06-10'),
      (2, 2, 2, '2025-05-11', '2025-06-11');

Output Explanation:

You’ve set up tables for authors, books, borrowers, and loans with relationships between them. Sample data has been inserted, linking books and borrowers through the loans table.

Also Read: Library Management System Project Java: Design, Features, and Code

5. SQL for Budget Tracking and Analysis

This project helps build an SQL database to track personal or business budgets, including income, expenses, and financial goals. SQL queries are used to analyze spending and generate reports, making it an ideal SQL project on GitHub for anyone interested in finance.

Technology Stack and Tools:

• MySQL/PostgreSQL: These databases are used to store financial transactions and budget data.
• SQL: Write queries to categorize spending, generate financial reports, and track goals.
• Excel: Import/export data from SQL to Excel for analysis.

Key Skills Gained:

• Financial Tracking: Design a database to track income and expenses over different periods.
• Aggregation: Use SQL functions like SUM(), AVG(), and GROUP BY to analyze spending and budget adherence.
• Reporting: Generate reports to track monthly expenses and financial forecasts.

Applications:

• Personal Finance: Build tools for personal budgeting and expense tracking.
• Business Financial Tracking: Design systems to track company finances, revenue, and expenditure.

Input:

CREATE TABLE transactions (
    transaction_id INT PRIMARY KEY,
    transaction_type VARCHAR(10), -- 'income' or 'expense'
    amount DECIMAL(10, 2),
    category VARCHAR(50),
    transaction_date DATE
);

Output:

-- Insert data into transactions
INSERT INTO transactions (transaction_id, transaction_type, amount, category, transaction_date)
VALUES (1, 'income', 2000.00, 'Salary', '2025-05-01'),
      (2, 'expense', 150.00, 'Groceries', '2025-05-02'),
      (3, 'expense', 50.00, 'Transport', '2025-05-02');

Output Explanation:

The transactions table stores both income and expenses, allowing you to track financial transactions. Sample data has been inserted to track salary, groceries, and transport expenses.

6. Employee Salary Distribution Analysis

This project builds an SQL database to manage and analyze employee salary distributions across roles, departments, and pay grades. By applying data analysis techniques, you can gain insights into salary trends and fairness in an Indian organizational context. As part of data science applications, this project helps HR departments and business analysts make data-driven decisions on compensation structures.

Technology Stack and Tools:

• MySQL/PostgreSQL: Store employee data, including salaries, roles, and departments.
• SQL: Perform complex queries to analyze salary trends and generate detailed reports.
• Data Visualization Tools: Use Tableau or Power BI to visualize salary distributions and trends across departments.

Key Skills Gained:

• Data Aggregation: Use SQL aggregation functions like SUM(), AVG(), and GROUP BY to identify salary distributions across roles and departments.
• Data Analysis: Analyze salary trends using advanced SQL queries to detect discrepancies and ensure fair compensation practices.
• Report Generation: Use SQL to generate salary reports and use data visualization tools for presentation of compensation structures.

Applications:

• HR Systems: Manage and optimize employee compensation structures.
• Payroll Systems: Integrate SQL databases with payroll systems to streamline salary distribution and reporting.
• Data Science: Use statistical models and data science techniques to predict compensation patterns and optimize employee benefits.

Code Example:

CREATE TABLE employees (
    employee_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    department VARCHAR(50),
    salary DECIMAL(10, 2),
    city VARCHAR(50)
);

CREATE TABLE departments (
    department_id INT PRIMARY KEY,
    department_name VARCHAR(50)
);

Output:

-- Insert data into employees table
INSERT INTO employees (employee_id, first_name, last_name, department, salary, city)
VALUES (1, 'Ravi', 'Sharma', 'Engineering', 80000.00, 'Mumbai'),
      (2, 'Priya', 'Singh', 'Marketing', 60000.00, 'Delhi'),
      (3, 'Amit', 'Verma', 'HR', 50000.00, 'Bangalore');

-- Insert data into departments table
INSERT INTO departments (department_id, department_name)
VALUES (1, 'Engineering'),
      (2, 'Marketing'),
      (3, 'HR');

Output Explanation:

The employees table contains employee salary data, including Indian cities like Mumbai, Delhi, and Bangalore. The departments table contains data about different company departments.

7. Movie Database Analysis

This project creates an SQL database to manage a movie collection, including directors, genres, actors, and ratings. It helps in analyzing movie popularity and trends based on user ratings and viewing history. The system can be extended to create a data science-driven recommendation engine.

Technology Stack and Tools:

• MySQL/PostgreSQL: Use relational databases to store movie data such as titles, ratings, and genres.
• SQL: Write complex queries to analyze movie trends and generate recommendations.
• Pandas: Perform data analysis and cleaning in Python  with Pandas to enhance movie data.

Key Skills Gained:

• Data Modeling: Design a relational database schema for managing movie-related data.
• SQL Queries: Use advanced SQL techniques to query large datasets and extract insights on movie popularity and trends.
• Data Visualization: Use tools like Tableau or Power BI to visualize trends and ratings across movies and genres.

Applications:

• Movie Recommendation Engines: Develop systems that recommend movies based on user preferences using data science techniques.
• Content Management Systems: Manage and use data science techniques to recommend movies based on user preferences, and optimize movie catalogs for streaming platforms.
• Data Science: Apply data analysis techniques to predict user preferences and trends in the entertainment industry.

Code Example:

CREATE TABLE movies (
    movie_id INT PRIMARY KEY,
    title VARCHAR(100),
    director VARCHAR(50),
    genre VARCHAR(50),
    release_year INT
);

CREATE TABLE ratings (
    rating_id INT PRIMARY KEY,
    movie_id INT,
    user_id INT,
    rating INT,
    review_date TIMESTAMP,
    FOREIGN KEY (movie_id) REFERENCES movies(movie_id)
);

Output:

-- Insert data into movies table
INSERT INTO movies (movie_id, title, director, genre, release_year)
VALUES (1, 'Dilwale Dulhania Le Jayenge', 'Aditya Chopra', 'Romance', 1995),
      (2, 'Lagaan', 'Ashutosh Gowariker', 'Drama', 2001);

-- Insert data into ratings table
INSERT INTO ratings (rating_id, movie_id, user_id, rating, review_date)
VALUES (1, 1, 101, 5, '2025-05-20 10:00:00'),
      (2, 2, 102, 4, '2025-05-20 11:00:00');

Output Explanation:

The movies table stores titles, director, genre, and release year data, with Indian movie examples like Dilwale Dulhania Le Jayenge and Lagaan. The ratings table tracks user ratings and reviews for each movie.

8. Shipping Logistics Optimization

This project creates an SQL database to optimize shipping logistics, including inventory, shipping routes, and delivery times. By applying data analysis techniques, you can streamline supply chains and reduce operational costs. REST APIs can be used for real-time route optimization.

Technology Stack and Tools:

• MySQL/PostgreSQL: Manage inventory, shipping data, and supplier information.
• SQL: Write queries to optimize delivery routes and track inventory levels.
• Google Maps API: Use for real-time delivery route optimization and location-based logistics analysis.

Key Skills Gained:

• Logistics Data Management: Learn to efficiently structure and store logistics and shipping data.
• Data Analysis: Apply SQL queries to analyze delivery times, inventory levels, and shipment efficiency.
• Route Optimization: Use external APIs, such as Google Maps API, to integrate real-time data and optimize delivery routes.

Applications:

• Supply Chain Management: Develop systems to optimize shipping and track inventory in real-time.
• Logistics Platforms: Build applications that provide actionable insights into shipping logistics.
• Data Science: Use data analysis and machine learning models to predict future shipping demands and improve operational efficiency.

Code Example:

CREATE TABLE shipments (
    shipment_id INT PRIMARY KEY,
    product_name VARCHAR(50),
    quantity INT,
    origin_city VARCHAR(50),
    destination_city VARCHAR(50),
    delivery_date DATE
);

CREATE TABLE inventory (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(50),
    stock_quantity INT,
    warehouse_location VARCHAR(50)
);

Output:

-- Insert data into shipments table
INSERT INTO shipments (shipment_id, product_name, quantity, origin_city, destination_city, delivery_date)
VALUES (1, 'Electronics', 50, 'Mumbai', 'Chennai', '2025-05-25'),
      (2, 'Clothing', 200, 'Delhi', 'Kolkata', '2025-05-26');

-- Insert data into inventory table
INSERT INTO inventory (product_id, product_name, stock_quantity, warehouse_location)
VALUES (1, 'Electronics', 150, 'Mumbai Warehouse'),
      (2, 'Clothing', 500, 'Delhi Warehouse');

Output Explanation:

The shipments table tracks product shipments from one city to another, with Indian cities like Mumbai, Delhi, Chennai, and Kolkata. The inventory table stores stock levels and locations of products in warehouses.

Also Read: What is Logistics Management? Overview, Types & Process

9. Inventory Forecasting Model

This project involves building an SQL database to track inventory levels and use historical data to forecast future inventory needs. The project can provide optimized inventory predictions by integrating data analysis techniques and machine learning models.

Technology Stack and Tools:

• MySQL/PostgreSQL: Store and manage inventory data, including stock levels and sales history.
• SQL: Use SQL queries to identify trends and generate inventory forecasts.
• Scikit-learn: Build and train forecasting models using machine learning.

Key Skills Gained:

• Inventory Data Management: Structure databases to track and manage inventory across different product categories.
• Trend Analysis: Use SQL to analyze historical sales data and identify patterns.
• Machine Learning: Integrate data science techniques to build forecasting models using libraries like Scikit-learn.

Applications:

• Retail Inventory Management: Optimize stock levels and reduce waste by accurately predicting future demand for effective inventory management.
• E-commerce: Integrate forecasting models into e-commerce platforms to manage stock levels efficiently.
• Data Science: Apply data analysis and machine learning techniques to improve accuracy in forecasting inventory levels.

Code Example:

CREATE TABLE inventory_levels (
    inventory_id INT PRIMARY KEY,
    product_name VARCHAR(50),
    quantity_in_stock INT,
    last_updated DATE
);

CREATE TABLE sales (
    sale_id INT PRIMARY KEY,
    product_name VARCHAR(50),
    sale_date DATE,
    quantity_sold INT
);

Output:

-- Insert data into inventory_levels table
INSERT INTO inventory_levels (inventory_id, product_name, quantity_in_stock, last_updated)
VALUES (1, 'Smartphone', 100, '2025-05-15'),
      (2, 'Laptop', 50, '2025-05-14');

-- Insert data into sales table
INSERT INTO sales (sale_id, product_name, sale_date, quantity_sold)
VALUES (1, 'Smartphone', '2025-05-10', 30),
      (2, 'Laptop', '2025-05-12', 20);

Output Explanation:

The inventory_levels table stores product inventory data, and sales tracks product sales over time. This data can be used to analyze and forecast future inventory needs using machine learning techniques.

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10. Food Delivery Service Data Analysis

This project builds an SQL database to analyze food delivery data, such as orders, customer ratings, and delivery times. By performing data analysis and integrating REST APIs for real-time tracking, the system can optimize the delivery process.

Technology Stack and Tools:

• SQL: Write queries to analyze service times, customer satisfaction, and restaurant performance.
• Google Maps API: Use for real-time delivery tracking and optimization.
• Power BI/Tableau: Visualize key metrics such as delivery time, customer satisfaction, and restaurant ratings.

Key Skills Gained:

• Transactional Data Management: Track customer orders, ratings, and delivery efficiency using SQL.
• Service Time Analysis: Use SQL to analyze the efficiency of delivery times and identify trends.
• Performance Reporting: Generate reports to track delivery efficiency and customer satisfaction.

Applications:

• Food Delivery Platforms: Analyze and optimize delivery processes for improved customer service.
• Restaurant Performance: Use data analysis to track restaurant performance and improve operations.
• Customer Insights: Build systems to track customer feedback and improve service offerings.

Code Example:

CREATE TABLE food_orders (
    order_id INT PRIMARY KEY,
    customer_name VARCHAR(50),
    restaurant_name VARCHAR(50),
    order_date TIMESTAMP,
    delivery_time INT, -- in minutes
    rating INT
);

CREATE TABLE delivery_locations (
    delivery_id INT PRIMARY KEY,
    order_id INT,
    latitude DECIMAL(9, 6),
    longitude DECIMAL(9, 6),
    FOREIGN KEY (order_id) REFERENCES food_orders(order_id)
);

Output:

-- Insert data into food_orders table
INSERT INTO food_orders (order_id, customer_name, restaurant_name, order_date, delivery_time, rating)
VALUES (1, 'Neha Sharma', 'Biryani Palace', '2025-05-20 12:00:00', 30, 5),
      (2, 'Amit Verma', 'Pasta Hut', '2025-05-20 12:30:00', 45, 4);

-- Insert data into delivery_locations table
INSERT INTO delivery_locations (delivery_id, order_id, latitude, longitude)
VALUES (1, 1, 19.0760, 72.8777), -- Mumbai coordinates
      (2, 2, 28.7041, 77.1025); -- Delhi coordinates

Output Explanation:

The food_orders table tracks food orders, including customer names, restaurant names, delivery times, and ratings. The delivery_locations table stores delivery location coordinates for real-time tracking.

11. Project Management Dashboard

This project creates an SQL database to track project management data, such as tasks, deadlines, teammates, and progress. By using data visualization tools, project performance can be monitored in real-time.

Technology Stack and Tools:

• MySQL/PostgreSQL: Manage project data, including tasks, deadlines, and resources.
• SQL: Write queries to track task completion, team performance, and project timelines.
• Power BI: Use for data visualization to create interactive dashboards that monitor project progress.

Key Skills Gained:

• Project Data Management: Learn how to design databases that track project milestones, deadlines, and resources.
• Data Querying: Write SQL queries to track task completion and team performance.
• Data Visualization: Use Power BI or Tableau to create real-time dashboards for project monitoring.

Applications:

• Project Management Software: Build platforms that help teams track progress, allocate resources, and meet deadlines.
• Business Analytics: Use data visualization to improve decision-making in project management.
• Resource Allocation: Analyze team performance and project timelines to optimize resource allocation.

Code Example:

CREATE TABLE projects (
    project_id INT PRIMARY KEY,
    project_name VARCHAR(100),
    start_date DATE,
    end_date DATE,
    status VARCHAR(50)
);

CREATE TABLE tasks (
    task_id INT PRIMARY KEY,
    project_id INT,
    task_name VARCHAR(100),
    assignee VARCHAR(50),
    due_date DATE,
    completion_status VARCHAR(50),
    FOREIGN KEY (project_id) REFERENCES projects(project_id)
);

Output:

-- Insert data into projects table
INSERT INTO projects (project_id, project_name, start_date, end_date, status)
VALUES (1, 'Website Development', '2025-05-01', '2025-07-01', 'In Progress'),
      (2, 'Mobile App Launch', '2025-05-05', '2025-06-30', 'Completed');

-- Insert data into tasks table
INSERT INTO tasks (task_id, project_id, task_name, assignee, due_date, completion_status)
VALUES (1, 1, 'Design Website Layout', 'Ravi Kumar', '2025-05-10', 'Completed'),
      (2, 2, 'Create App Marketing Plan', 'Priya Patel', '2025-05-15', 'Pending');

Output Explanation:

The projects table stores details of each project, including start and end dates, project status, and project name. The tasks table tracks the status of each task and assigns them to team members for efficient management.

Discover how to use SQL for hypothesis formulation and solving complex business problems effectively with upGrad's free course on Introduction to Business Analytics.

For beginners, SQL projects GitHub will help master skills like creating databases and writing queries to manipulate data. For intermediate learners, check out the following SQL projects for data analysis GitHub.

Intermediate SQL Data Analysis Project GitHub with Source Code

SQL projects for data analysis GitHub will focus on more complex queries involving subqueries, window functions, and data aggregations to obtain insights. You will also focus on optimizing query performance and working with large datasets to recognize trends and patterns.

Here are the top intermediate SQL projects for data analysis GitHub.

12. E-commerce Revenue Breakdown

This project analyzes e-commerce revenue by tracking sales across product categories, customer segments, and periods. The goal is to gain valuable insights into revenue trends, helping businesses optimize their offerings and marketing strategies. 

Technology Stack and Tools:

• MySQL/PostgreSQL: Use relational databases to store and manage sales, transactions, and customer data.
• SQL: Perform detailed SQL queries involving aggregations, joins, and time-based analysis of revenue data.
• Google Data Studio: Visualize revenue trends, identify high-performing products, and forecast future sales using data visualization tools.
• AI and ML (Machine Learning): Integrate AI models and machine learning algorithms to predict revenue shifts and optimize business strategies based on historical data.

Key Skills Gained:

• Advanced SQL Queries: Learn to write complex SQL queries for multi-table joins, aggregations, and advanced date functions to analyze sales and revenue.
• Revenue Analysis: Use SQL to break down revenue by categories, customer segments, and time periods to understand which products and customers are driving sales.
• Data Insights: Derive actionable insights from transaction data, identifying key drivers of revenue and applying them to business strategy.
• AI and ML Models: Implement ML algorithms using Python libraries like Scikit-learn to predict future revenue and seasonal trends.

Applications:

• Revenue Breakdown: Break down revenue by product categories to identify top-performing items and optimize inventory.
• Customer Segmentation: Identify high-value customers and tailor marketing efforts to improve engagement and conversion rates.
• Seasonal Revenue Prediction: Analyze historical data to predict future revenue trends during peak seasons or promotional periods.

Code Example:

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(100),
    category VARCHAR(50),
    price DECIMAL(10, 2)
);

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    city VARCHAR(50),
    email VARCHAR(100) UNIQUE
);

CREATE TABLE transactions (
    transaction_id INT PRIMARY KEY,
    product_id INT,
    customer_id INT,
    quantity INT,
    total_amount DECIMAL(10, 2),
    transaction_date DATE,
    FOREIGN KEY (product_id) REFERENCES products(product_id),
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);

Output:

-- Insert data into products table
INSERT INTO products (product_id, product_name, category, price)
VALUES (1, 'Smartphone', 'Electronics', 25000.00),
      (2, 'Laptop', 'Electronics', 50000.00),
      (3, 'Headphones', 'Accessories', 1500.00);

-- Insert data into customers table
INSERT INTO customers (customer_id, first_name, last_name, city, email)
VALUES (1, 'Ravi', 'Sharma', 'Mumbai', 'ravi.sharma@example.com'),
      (2, 'Priya', 'Singh', 'Delhi', 'priya.singh@example.com'),
      (3, 'Amit', 'Verma', 'Bangalore', 'amit.verma@example.com');

-- Insert data into transactions table
INSERT INTO transactions (transaction_id, product_id, customer_id, quantity, total_amount, transaction_date)
VALUES (1, 1, 1, 2, 50000.00, '2025-05-10'),
      (2, 2, 2, 1, 50000.00, '2025-05-11'),
      (3, 3, 3, 3, 4500.00, '2025-05-12');

Output Explanation:

The products table tracks product information, including product names, categories, and prices. The customers table stores customer details, including names, cities, and emails. The transactions table logs sales data, linking products and customers through foreign keys.

If you want to learn how you can streamline your data analysis with AI, check out upGrad’s Master’s Degree in Artificial Intelligence and Data Science. The program will help youi learn critical tools and Python libraries for effective data analysis. 

13. Website Traffic Analysis

This project focuses on building an SQL database to analyze website traffic data, such as page views, user behavior, and session duration. The goal is to gain valuable insights into how traffic flows through your website, which traffic sources are most effective, and where users tend to drop off. Businesses can optimize user experience, increase engagement, and improve conversion rates by analyzing this data.

Technology Stack and Tools:

• Google Analytics API: Import real-time traffic data directly into your SQL database, providing up-to-date insights on user activity.
• MySQL/PostgreSQL: Manage website traffic logs, user session data, and analytics data in relational databases.
• SQL: Perform complex queries to analyze user activity, page views, and session duration.
• HTML/CSS: HTML or CSS are used for tracking the page layout and styles to correlate traffic with page design.
• HTTP: Track HTTP status codes to analyze user interactions with website resources.

Key Skills Gained:

• Advanced SQL Queries: Learn how to write complex SQL queries for multi-table joins, aggregations, and advanced filtering to analyze website traffic.
• Data Analysis: Identify key traffic patterns, user drop-off points, and user engagement metrics using SQL.
• Data Visualization: Use tools like Google Data Studio to visualize trends, user behavior, and traffic sources, which can help inform decisions about improving the website.

Applications:

• Traffic Source Analysis: Understand which traffic sources (e.g., organic search, social media, paid ads) drive the most traffic to your website.
• User Engagement Optimization: Track where users drop off in their journey on the website and optimize the design to reduce bounce rates.
• Goal Completion Tracking: Analyze how traffic correlates with goal completions, such as form submissions, purchases, or other user actions.

Code Example:

SELECT ts.source_name, SUM(tl.session_duration) AS total_session_duration
FROM traffic_logs tl
JOIN user_sessions us ON tl.log_id = us.session_id
JOIN traffic_sources ts ON us.source_id = ts.source_id
GROUP BY ts.source_name
ORDER BY total_session_duration DESC;

Output:

source_name       | total_session_duration
------------------|------------------------
Organic Search    | 1800
Paid Search       | 240
Social Media      | 600

Output Explanation:

This query calculates the total session duration for each traffic source, helping businesses identify the most engaging channels. The result can guide marketing strategies, such as increasing efforts in high-performing channels like Organic Search.

Also Read: How to Use Google Analytics: Comprehensive Guide For Beginners

14. Healthcare Data Analysis

This project focuses on analyzing healthcare data, such as patient records and treatment plans, to discover trends in patient demographics and treatment effectiveness. By utilizing SQL and R for advanced data analysis, healthcare providers can gain valuable insights into patient care patterns and treatment outcomes. This is a vital project for those looking to contribute to healthcare data analytics and build SQL projects on GitHub.

Technology Stack and Tools:

• R: Use R for additional data analysis and predictive modeling, especially when integrating machine learning techniques for patient outcome prediction.
• MySQL/PostgreSQL: Manage patient and healthcare data in relational databases, ensuring secure storage and fast access for analysis.
• SQL: Perform complex queries to analyze patient records, treatment types, and outcomes across different patient demographics.
• Java/JavaScript: Use Java to integrate machine learning algorithms in healthcare applications and JavaScript to build interactive healthcare dashboards and visualizations.

Key Skills Gained:

• SQL for Healthcare Datasets: Learn to write complex SQL queries to retrieve and analyze large healthcare datasets, focusing on patient demographics, medical histories, and treatment outcomes.
• Patient Demographics Analysis: Use SQL to aggregate and analyze patient data based on age, gender, location, and treatment history.
• Predictive Modeling: Implement R for analyzing the effectiveness of different treatments and using machine learning models to predict patient outcomes.

Applications:

• Healthcare Utilization Patterns: Identify trends in healthcare utilization by age, gender, or geographical location, helping optimize resources and healthcare delivery.
• Treatment Effectiveness: Analyze and compare the effectiveness of different treatment plans, identifying which ones yield the best outcomes based on patient demographics.
• Hospital Admissions and Patient Flow: Predict hospital admissions and patient flow to optimize staffing and resource allocation.

Code Example:

CREATE TABLE patients (
    patient_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    age INT,
    gender VARCHAR(10),
    city VARCHAR(50)
);

CREATE TABLE treatments (
    treatment_id INT PRIMARY KEY,
    treatment_name VARCHAR(100),
    treatment_type VARCHAR(50)
);

CREATE TABLE patient_treatments (
    patient_id INT,
    treatment_id INT,
    treatment_start_date DATE,
    treatment_end_date DATE,
    outcome VARCHAR(50),
    FOREIGN KEY (patient_id) REFERENCES patients(patient_id),
    FOREIGN KEY (treatment_id) REFERENCES treatments(treatment_id)
);

Output:

-- Insert data into patients table
INSERT INTO patients (patient_id, first_name, last_name, age, gender, city)
VALUES (1, 'Ravi', 'Sharma', 45, 'Male', 'Mumbai'),
      (2, 'Priya', 'Singh', 34, 'Female', 'Delhi'),
      (3, 'Amit', 'Verma', 50, 'Male', 'Bangalore');

-- Insert data into treatments table
INSERT INTO treatments (treatment_id, treatment_name, treatment_type)
VALUES (1, 'Chemotherapy', 'Cancer'),
      (2, 'Dialysis', 'Kidney Disease');

-- Insert data into patient_treatments table
INSERT INTO patient_treatments (patient_id, treatment_id, treatment_start_date, treatment_end_date, outcome)
VALUES (1, 1, '2025-05-01', '2025-05-30', 'Positive'),
      (2, 2, '2025-06-01', '2025-06-30', 'Neutral'),
      (3, 1, '2025-04-15', '2025-05-15', 'Negative');

Output Explanation:

The patients table stores patient information, including age, gender, and city (Indian cities like Mumbai, Delhi, and Bangalore). The treatments table stores information about medical treatments (e.g., Chemotherapy, Dialysis). The patient_treatments table links patients to treatments and stores treatment outcomes.

15. Employee Performance Analysis

This project analyzes employee performance data, such as productivity rates, goal completion rates, and attendance, to identify top performers, detect performance trends, and recognize factors affecting employee productivity. By leveraging SQL and data analysis tools like Tableau, this project aims to provide actionable insights to HR departments and management for optimizing workforce performance.

Technology Stack and Tools:

• MySQL/PostgreSQL: Use relational databases to store and manage employee performance data, including productivity metrics, attendance records, and goal completions.
• SQL: Write complex queries to aggregate, filter, and analyze performance data to identify trends and areas for improvement.
• Tableau: Visualize employee performance trends, productivity, and goal completion in easy-to-read, actionable dashboards.
• TensorFlow/PyTorch: Use machine learning frameworks to build predictive models for employee performance, providing insights into future productivity and potential improvements.

Key Skills Gained:

• SQL Querying: Learn to write complex SQL queries for aggregating employee performance data based on metrics like productivity, attendance, and goals.
• Trend Analysis: Use SQL functions like GROUP BY, SUM(), and AVG() to analyze employee productivity trends and identify patterns in performance.
• Data Visualization: Use Tableau to visualize performance metrics, making it easier for decision-makers to interpret key performance indicators (KPIs).
• Machine Learning: Implement TensorFlow or PyTorch to predict future employee performance based on historical data.

Applications:

• Productivity vs. Attendance: Analyze the correlation between employee attendance and productivity, helping to identify factors that contribute to high performance.
• Top Performer Identification: Identify high-performing teams and individuals who consistently meet or exceed performance goals.
• Goal Completion: Measure employee performance against the goals set by the company, identifying areas for improvement and recognizing top achievers.
• Real-Time Dashboards: Develop real-time dashboards for HR managers to track employee performance and make immediate interventions where necessary.

Code Example:

CREATE TABLE employees (
    employee_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    department VARCHAR(50),
    hire_date DATE
);

CREATE TABLE performance_metrics (
    performance_id INT PRIMARY KEY,
    employee_id INT,
    productivity_score DECIMAL(5, 2),
    goals_completed INT,
    attendance_rate DECIMAL(5, 2),
    review_date DATE,
    FOREIGN KEY (employee_id) REFERENCES employees(employee_id)
);

Output:

-- Insert data into employees table
INSERT INTO employees (employee_id, first_name, last_name, department, hire_date)
VALUES (1, 'Ravi', 'Sharma', 'Engineering', '2020-01-15'),
      (2, 'Priya', 'Singh', 'Marketing', '2019-04-22'),
      (3, 'Amit', 'Verma', 'Sales', '2021-06-30');

-- Insert data into performance_metrics table
INSERT INTO performance_metrics (performance_id, employee_id, productivity_score, goals_completed, attendance_rate, review_date)
VALUES (1, 1, 88.5, 5, 95.0, '2025-05-15'),
      (2, 2, 76.3, 3, 98.0, '2025-05-15'),
      (3, 3, 92.0, 6, 97.5, '2025-05-15');

Output Explanation:

The employees table stores basic employee information, including their department and hire date. The performance_metrics table tracks employee performance, including productivity scores, goals completed, and attendance rates, linked to the employees table via employee_id.

16. Retail Purchase Prediction

This project aims to predict future retail purchases based on historical sales data, customer behavior, and market trends. By leveraging SQL for aggregating and querying historical data, and integrating ML models like those in Scikit-learn, businesses can predict future buying patterns and optimize inventory and marketing strategies accordingly.

Technology Stack and Tools:

• Scikit-learn: Use Scikit-learn to build machine learning models that predict customer purchasing behavior based on historical sales data and customer data.
• PostgreSQL: Store and query historical sales data and customer behavior patterns in relational databases, ensuring efficient data management and retrieval.
• SQL: Write complex queries to aggregate sales data, customer demographics, and product purchases, preparing the data for predictive analysis.

Key Skills Gained:

• SQL Querying: Write advanced SQL queries to track customer purchasing behavior and aggregate sales data over different periods.
• Predictive Modeling: Use Scikit-learn and machine learning to predict future purchase trends based on historical data, identifying patterns in customer behavior and market trends.
• Data Aggregation: Learn to aggregate large datasets and prepare them for predictive modeling using SQL.
• Customer Segmentation: Analyze customer demographics and behavior to predict what products will be purchased in the future.

Applications:

• Product Purchase Prediction: This involves predicting what products customers are likely to buy, helping to optimize inventory and marketing strategies.
• Inventory Optimization: Optimize inventory levels by predicting future sales trends and avoiding stockouts or overstocking.
• Targeted Campaigns: Predict customer behavior to create targeted marketing campaigns, which improve customer engagement and increase conversion rates.

Code Example:

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    gender VARCHAR(10),
    city VARCHAR(50),
    date_of_birth DATE
);

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(100),
    category VARCHAR(50),
    price DECIMAL(10, 2)
);

CREATE TABLE transactions (
    transaction_id INT PRIMARY KEY,
    customer_id INT,
    product_id INT,
    quantity INT,
    total_amount DECIMAL(10, 2),
    transaction_date DATE,
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id),
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

Output:

-- Insert data into customers table
INSERT INTO customers (customer_id, first_name, last_name, gender, city, date_of_birth)
VALUES (1, 'Ravi', 'Sharma', 'Male', 'Mumbai', '1985-01-10'),
      (2, 'Priya', 'Singh', 'Female', 'Delhi', '1990-07-25'),
      (3, 'Amit', 'Verma', 'Male', 'Bangalore', '1988-03-15');

-- Insert data into products table
INSERT INTO products (product_id, product_name, category, price)
VALUES (1, 'Smartphone', 'Electronics', 25000.00),
      (2, 'Laptop', 'Electronics', 50000.00),
      (3, 'Headphones', 'Accessories', 1500.00);

-- Insert data into transactions table
INSERT INTO transactions (transaction_id, customer_id, product_id, quantity, total_amount, transaction_date)
VALUES (1, 1, 1, 2, 50000.00, '2025-05-10'),
      (2, 2, 2, 1, 50000.00, '2025-05-11'),
      (3, 3, 3, 3, 4500.00, '2025-05-12');

Output Explanation:

The customers table contains customer information, including gender, city, and date_of_birth. The products table stores product information, such as product name, category, and price. The transactions table records sales transactions, linking customers and products through foreign keys, and stores transaction details such as quantity and total amount spent.

17. Sales Region Performance Analysis

This project aims to analyze sales data across multiple regions to determine performance differences and optimize sales strategies. Using SQL, PostgreSQL, and Power BI, the project will help compare sales trends, customer behavior, and regional performance, leading to data-driven decision-making. This analysis allows businesses to adapt their sales strategies based on the strengths and weaknesses of each region.

Technology Stack and Tools:

• PostgreSQL: Manage and store regional sales data, allowing for fast querying and aggregation.
• Power BI: Visualize regional sales performance to identify trends and generate actionable insights.
• SQL: Write complex queries to aggregate and compare sales performance across different regions.
• Docker: Containerize the application for consistent development, testing, and production environments.
• AWS/Azure Databricks: Use cloud services to store, process, and analyze large volumes of regional sales data, enabling scalable and real-time analysis.

Key Skills Gained:

• SQL Querying: Write advanced SQL queries to aggregate and compare sales data across multiple regions.
• Regional Sales Analysis: Identify trends and performance differences across geographic areas using SQL functions and aggregations.
• Data Visualization: Use Power BI to generate interactive dashboards that visualize regional sales trends.
• Cloud Integration: Learn to use AWS and Azure Databricks for processing and analyzing large datasets in a scalable environment.
• Containerization: Utilize Docker to package the solution, ensuring that it runs consistently across different environments.

Applications:

• Compare Sales Trends: Compare sales performance in different regions to identify areas that require attention or have high potential for growth.
• Predict Future Sales: Use historical sales data to predict future performance in each region, helping with forecasting and inventory management.
  Marketing Strategy: Tailor marketing efforts to specific regions based on performance data, optimizing campaigns and promotions for better engagement.

Code Example:

CREATE TABLE regions (
    region_id INT PRIMARY KEY,
    region_name VARCHAR(100)
);

CREATE TABLE sales (
    sale_id INT PRIMARY KEY,
    region_id INT,
    product_name VARCHAR(100),
    quantity_sold INT,
    total_sales DECIMAL(10, 2),
    sale_date DATE,
    FOREIGN KEY (region_id) REFERENCES regions(region_id)
);

CREATE TABLE customer_behavior (
    behavior_id INT PRIMARY KEY,
    region_id INT,
    customer_id INT,
    frequency INT,  -- frequency of purchase
    avg_purchase_value DECIMAL(10, 2),
    FOREIGN KEY (region_id) REFERENCES regions(region_id)
);

Output:

-- Insert data into regions table
INSERT INTO regions (region_id, region_name)
VALUES (1, 'North India'),
      (2, 'South India'),
      (3, 'East India'),
      (4, 'West India');

-- Insert data into sales table
INSERT INTO sales (sale_id, region_id, product_name, quantity_sold, total_sales, sale_date)
VALUES (1, 1, 'Smartphone', 200, 500000.00, '2025-05-10'),
      (2, 2, 'Laptop', 150, 750000.00, '2025-05-10'),
      (3, 3, 'Headphones', 300, 450000.00, '2025-05-10'),
      (4, 4, 'Smartwatch', 100, 250000.00, '2025-05-10');

-- Insert data into customer_behavior table
INSERT INTO customer_behavior (behavior_id, region_id, customer_id, frequency, avg_purchase_value)
VALUES (1, 1, 101, 5, 25000.00),
      (2, 2, 102, 3, 50000.00),
      (3, 3, 103, 7, 15000.00),
      (4, 4, 104, 2, 20000.00);

Output Explanation:

The regions table stores regional information (e.g., North India, South India, etc.). The sales table tracks product sales across regions, with product names, quantities sold, and total sales values. The customer_behavior table logs customer purchasing behavior, such as the frequency of purchases and average purchase value, per region.

Also read: Top 15 AWS Project Ideas for Beginners in 2025

18. Customer Segmentation for Marketing

The project seeks to segment customers based on their behavior and purchasing patterns. By creating different customer groups, businesses can analyze how effective various marketing campaigns are for each segment. SQL and machine learning clustering algorithms like K-Means (via Scikit-learn) will help identify distinct customer segments based on their attributes. 

Technology Stack and Tools:

• Scikit-learn: Run clustering algorithms (like K-Means) to segment customers based on their behaviors and purchasing patterns.
• MySQL/PostgreSQL: Store and manage customer data such as demographics, purchasing history, and engagement metrics.
• SQL: Use SQL queries to segment customers, aggregate behavior data, and prepare datasets for clustering analysis.
• Python: Python libraries (e.g., Pandas, Scikit-learn) are used to perform clustering and analyze customer segments.

Key Skills Gained:

• SQL Querying: Write SQL queries to extract relevant customer data based on various behaviors and attributes, such as purchase frequency, product preferences, and location.
• Customer Segmentation: Apply machine learning clustering algorithms like K-Means clustering to segment customers based on their purchasing behavior and demographics.
• Marketing Strategy Optimization: Analyze customer segments to identify the most effective marketing strategies and tailor campaigns accordingly.
• Data Preparation for Clustering: Use SQL to prepare and clean customer data, transforming it into a format suitable for Scikit-learn clustering.

Code Example:

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    gender VARCHAR(10),
    city VARCHAR(50),
    join_date DATE
);

CREATE TABLE purchases (
    purchase_id INT PRIMARY KEY,
    customer_id INT,
    product_name VARCHAR(100),
    purchase_date DATE,
    amount DECIMAL(10, 2),
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);

CREATE TABLE customer_engagement (
    engagement_id INT PRIMARY KEY,
    customer_id INT,
    last_login DATE,
    pages_visited INT,
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);

Output:

-- Insert data into customers table
INSERT INTO customers (customer_id, first_name, last_name, gender, city, join_date)
VALUES (1, 'Ravi', 'Sharma', 'Male', 'Mumbai', '2022-01-10'),
      (2, 'Priya', 'Singh', 'Female', 'Delhi', '2021-06-15'),
      (3, 'Amit', 'Verma', 'Male', 'Bangalore', '2020-03-22');

-- Insert data into purchases table
INSERT INTO purchases (purchase_id, customer_id, product_name, purchase_date, amount)
VALUES (1, 1, 'Smartphone', '2025-05-10', 25000.00),
      (2, 2, 'Laptop', '2025-05-11', 50000.00),
      (3, 3, 'Headphones', '2025-05-12', 1500.00);

-- Insert data into customer_engagement table
INSERT INTO customer_engagement (engagement_id, customer_id, last_login, pages_visited)
VALUES (1, 1, '2025-05-09', 20),
      (2, 2, '2025-05-10', 15),
      (3, 3, '2025-05-11', 30);

Output Explanation:

The customers table stores customer demographic information such as gender, city, and join date. The purchases table records each customer’s purchases, linking to the customers table via customer_id.

Also Read: Segmentation in Marketing: Get Started with Effective Strategies

19. Supplier Performance Monitoring

This project aims to monitor supplier performance, focusing on delivery times, product quality, and contract compliance, to ensure effective supply chain management. By leveraging SQL for querying supplier data, Tableau for visualizing performance metrics, and real-time performance monitoring systems, businesses can optimize their supply chain operations and make data-driven decisions.

Technology Stack and Tools:

• Tableau to create dashboards to track supplier performance over time.
• MySQL to store supplier-related data.
• SQL to query and aggregate supplier performance metrics.

Key Skills Gained:

• Write SQL queries to track supplier delivery times and product quality.
• Analyze supplier reliability and contract compliance.
• Create performance reports to evaluate supplier effectiveness.

Applications:

• Track delivery times, contract compliance, and quality for each supplier.
• Identify underperforming suppliers and take corrective actions.
• Monitoring supplier pricing trends to negotiate better contract costs.

Code Example:

CREATE TABLE suppliers (
    supplier_id INT PRIMARY KEY,
    supplier_name VARCHAR(100),
    contact_info VARCHAR(100),
    region VARCHAR(50)
);

CREATE TABLE delivery_performance (
    delivery_id INT PRIMARY KEY,
    supplier_id INT,
    delivery_date DATE,
    scheduled_delivery_date DATE,
    delivery_status VARCHAR(50),
    FOREIGN KEY (supplier_id) REFERENCES suppliers(supplier_id)
);

CREATE TABLE product_quality (
    quality_id INT PRIMARY KEY,
    supplier_id INT,
    product_name VARCHAR(100),
    quality_rating DECIMAL(5, 2),  -- Rating between 1 and 5
    inspection_date DATE,
    FOREIGN KEY (supplier_id) REFERENCES suppliers(supplier_id)
);

Output:

-- Insert data into suppliers table
INSERT INTO suppliers (supplier_id, supplier_name, contact_info, region)
VALUES (1, 'ABC Ltd.', 'abc@abc.com', 'North India'),
      (2, 'XYZ Pvt. Ltd.', 'xyz@xyz.com', 'South India');

-- Insert data into delivery_performance table
INSERT INTO delivery_performance (delivery_id, supplier_id, delivery_date, scheduled_delivery_date, delivery_status)
VALUES (1, 1, '2025-05-01', '2025-05-01', 'On Time'),
      (2, 2, '2025-05-03', '2025-05-02', 'Late');

-- Insert data into product_quality table
INSERT INTO product_quality (quality_id, supplier_id, product_name, quality_rating, inspection_date)
VALUES (1, 1, 'Smartphone', 4.8, '2025-05-01'),
      (2, 2, 'Laptop', 3.5, '2025-05-02');

Output Explanation:

The suppliers table stores basic supplier information. The delivery_performance table tracks delivery performance metrics, including the actual delivery date, scheduled delivery date, and delivery status (e.g., On Time, Late).

20. Product Pricing Optimization

This project aims to optimize product pricing by analyzing sales data, market demand, and competitor pricing to identify the optimal price points for products. By leveraging SQL for querying and aggregating data, and Scikit-learn for predictive pricing models, businesses can adjust their prices dynamically, improving their competitive position in the market and maximizing revenue.

Technology Stack and Tools:

• Scikit-learn to develop predictive pricing models.
• PostgreSQL to store sales, pricing, and competitor data.
• SQL to query pricing data and conduct price elasticity analysis.

Key Skills Gained:

• Writing SQL queries to analyze sales data and competitor pricing.
• Developing predictive models for pricing optimization.
• Using SQL to identify optimal price points.

Applications:

• Adjusting product prices based on demand, competitor pricing, and sales history.
• Identifying customer price sensitivity and optimizing pricing strategies.
• Monitoring competitor pricing to stay relevant in the market.

Code Example:

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(100),
    category VARCHAR(50),
    base_price DECIMAL(10, 2)
);

CREATE TABLE sales (
    sale_id INT PRIMARY KEY,
    product_id INT,
    quantity_sold INT,
    sale_price DECIMAL(10, 2),
    sale_date DATE,
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

CREATE TABLE competitor_pricing (
    competitor_id INT PRIMARY KEY,
    product_id INT,
    competitor_price DECIMAL(10, 2),
    pricing_date DATE,
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

Output:

-- Insert data into products table
INSERT INTO products (product_id, product_name, category, base_price)
VALUES (1, 'Smartphone', 'Electronics', 25000.00),
      (2, 'Laptop', 'Electronics', 50000.00),
      (3, 'Headphones', 'Accessories', 1500.00);

-- Insert data into sales table
INSERT INTO sales (sale_id, product_id, quantity_sold, sale_price, sale_date)
VALUES (1, 1, 100, 24000.00, '2025-05-01'),
      (2, 2, 50, 48000.00, '2025-05-02'),
      (3, 3, 200, 1400.00, '2025-05-03');

-- Insert data into competitor_pricing table
INSERT INTO competitor_pricing (competitor_id, product_id, competitor_price, pricing_date)
VALUES (1, 1, 24500.00, '2025-05-01'),
      (2, 2, 49500.00, '2025-05-02'),
      (3, 3, 1350.00, '2025-05-03');

Output Explanation:

The products table stores basic product information, including the base price. The sales table tracks the quantity sold, sale price, and the date of each sale. The competitor_pricing table tracks the prices set by competitors for each product.

Intermediate SQL projects for data analysis GitHub will help teach you to work with tables using joins and also the generation of reports for dashboards. If you’re looking to explore projects for experienced learners, check out the following section.

Advanced SQL Projects for Data Analysis GitHub with Source Code for Professionals

The advanced SQL projects for data analysis GitHub require you to work with techniques like optimization, large dataset management, and advanced analytics. You will be building real-world data challenges such as predictive modeling and performance tuning through these projects.

Here are some of the advanced SQL data analysis project GitHub. 

21. Sales Trend Analysis

The project analyzes sales data over time to identify patterns, seasonality, and trends. By exploring factors such as pricing, customer behavior, and seasonal fluctuations, the project helps businesses understand their sales performance and forecast future trends. The project can deliver actionable decision-making insights through SQL queries and Power BI dashboards.

Technology Stack and Tools:

• Power BI: Visualize sales trends using interactive dashboards, enabling stakeholders to understand sales patterns and forecast future performance.
• MySQL/PostgreSQL: Store and query sales data, customer information, and promotional data.
• SQL: Write complex SQL queries to perform time-series analysis, identify trends, and merge sales data over different periods.
• Python: Use machine learning models for advanced sales forecasting, including predictive models like ARIMA or LSTM for time series prediction.

Key Skills Gained:

• SQL Time-Series Analysis: Learn to perform time-series analysis in SQL, such as identifying seasonal patterns, aggregating sales data by periods, and detecting trends.
• Data Merging and Filtering: Use advanced SQL techniques to merge and filter data over different time periods, preparing the data for analysis.
• Data Visualization: Gain proficiency using Power BI to create dashboards highlighting sales trends, seasonality, and future forecasts.
• Sales Forecasting: Implement techniques to predict future sales trends based on historical data.

Applications:

• Sales Trend Prediction: Use historical data to predict future sales trends and aid in inventory planning and resource allocation.
• Seasonality Identification: Identify seasonal peaks and lows to effectively manage inventory, promotions, and stock levels.
• Promotion Impact Analysis: Measure the effectiveness of promotional campaigns on sales trends, allowing businesses to optimize marketing strategies.

Code Example:

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(100),
    category VARCHAR(50),
    base_price DECIMAL(10, 2)
);

CREATE TABLE sales (
    sale_id INT PRIMARY KEY,
    product_id INT,
    quantity_sold INT,
    sale_price DECIMAL(10, 2),
    sale_date DATE,
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

CREATE TABLE promotions (
    promo_id INT PRIMARY KEY,
    product_id INT,
    promo_type VARCHAR(50),
    start_date DATE,
    end_date DATE,
    discount DECIMAL(5, 2),  -- Discount percentage
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

Output:

-- Insert data into products table
INSERT INTO products (product_id, product_name, category, base_price)
VALUES (1, 'Smartphone', 'Electronics', 25000.00),
      (2, 'Laptop', 'Electronics', 50000.00),
      (3, 'Headphones', 'Accessories', 1500.00);

-- Insert data into sales table
INSERT INTO sales (sale_id, product_id, quantity_sold, sale_price, sale_date)
VALUES (1, 1, 100, 24000.00, '2025-05-10'),
      (2, 2, 50, 48000.00, '2025-05-11'),
      (3, 3, 200, 1400.00, '2025-05-12');

-- Insert data into promotions table
INSERT INTO promotions (promo_id, product_id, promo_type, start_date, end_date, discount)
VALUES (1, 1, 'Flash Sale', '2025-05-01', '2025-05-03', 10.00),
      (2, 2, 'Holiday Discount', '2025-05-05', '2025-05-10', 15.00);

Output Explanation:

The products table stores basic product information, including product name, category, and base price. The sales table tracks quantity sold, sale price, and sale date for each product. The promotions table tracks promotional activities, including the discount percentage, start and end dates, and promotion type.

22. Customer Churn Prediction

The Customer Churn Prediction project aims to analyze historical customer behavior and transaction data to predict customer attrition (churn). By identifying factors that contribute to churn, businesses can take proactive actions to retain valuable customers. This project uses SQL for data cleaning, transformation, and feature extraction before feeding the data into machine learning models for churn prediction.

Technology Stack and Tools:

• Scikit-learn to implement machine learning models
• MySQL to manage and query customer data.
• SQL to calculate churn rates and identify at-risk customers.

Key Skills Gained:

• Writing SQL queries to segment customer behavior.
• Developing predictive models to forecast customer churn.
• Understanding key factors influencing customer churn and retention strategies.

Applications:

• Targeting at-risk customers with retention offers.
• Identifying the behaviors that indicate churn.
• Developing targeted loyalty programs to reduce churn.

Code Example:

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    gender VARCHAR(10),
    city VARCHAR(50),
    join_date DATE,
    last_active DATE
);

CREATE TABLE transactions (
    transaction_id INT PRIMARY KEY,
    customer_id INT,
    transaction_date DATE,
    transaction_amount DECIMAL(10, 2),
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);

CREATE TABLE customer_complaints (
    complaint_id INT PRIMARY KEY,
    customer_id INT,
    complaint_date DATE,
    complaint_type VARCHAR(50),
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);

Output:

-- Insert data into customers table
INSERT INTO customers (customer_id, first_name, last_name, gender, city, join_date, last_active)
VALUES (1, 'Ravi', 'Sharma', 'Male', 'Mumbai', '2020-01-10', '2025-05-01'),
      (2, 'Priya', 'Singh', 'Female', 'Delhi', '2019-06-15', '2025-04-28'),
      (3, 'Amit', 'Verma', 'Male', 'Bangalore', '2021-02-22', '2025-05-05');

-- Insert data into transactions table
INSERT INTO transactions (transaction_id, customer_id, transaction_date, transaction_amount)
VALUES (1, 1, '2025-05-01', 1000.00),
      (2, 2, '2025-04-25', 2500.00),
      (3, 3, '2025-05-03', 1500.00);

-- Insert data into customer_complaints table
INSERT INTO customer_complaints (complaint_id, customer_id, complaint_date, complaint_type)
VALUES (1, 1, '2025-04-20', 'Shipping Delay'),
      (2, 2, '2025-03-15', 'Product Defect');

Output Explanation:

The customers table stores customer demographics and activity data. The transactions table tracks customer transactions, including transaction amounts and dates. The customer_complaints table records any complaints made by customers, which could correlate with churn.

23. Website Conversion Rate Optimization

The Website Conversion Rate Optimization project focuses on analyzing website data to identify issues in the user journey and develop strategies to improve conversion rates. By using SQL for analyzing user flow, bounce rates, and conversion metrics, businesses can pinpoint friction points and optimize the user experience, leading to higher conversions.

Technology Stack and Tools:

• Google Analytics API: Pull website traffic data, including user behavior, page visits, bounce rates, and conversions.
• SQL: Write advanced SQL queries to track user flow, identify conversion bottlenecks, and analyze the impact of specific website elements on conversion rates.
• Node.js: Node.js Integrate backend services for data collection and handling API requests.
• React.js/Next.js: Build front-end user interfaces with React.js and Next.js to display conversion analytics and track real-time data.

Key Skills Gained:

• Advanced SQL Querying: Write SQL queries to analyze user flow, track conversion rates, and examine bounce rates across different pages and sessions.
• Conversion Funnel Analysis: Identify key pages where users drop off and propose strategies to optimize the conversion funnel.
• A/B Testing Analysis: Analyze the results of A/B tests to determine which website elements (e.g., CTAs, forms) lead to the highest conversion rates.
• Data-Driven Strategies: Implement actionable strategies to improve conversion rates based on user behavior data.

Applications:

• Conversion Funnel Optimization: Identify stages in the user journey where visitors drop off and propose solutions to reduce friction and improve conversion rates.
• A/B Testing: Conduct A/B tests to analyze which website features and layouts improve user engagement and conversion rates.
• Friction Point Identification: Use data to identify friction points in the user journey and improve overall user experience to drive more conversions.

Code Example:

CREATE TABLE users (
    user_id INT PRIMARY KEY,
    session_id VARCHAR(100),
    user_agent VARCHAR(100),
    country VARCHAR(50),
    city VARCHAR(50)
);

CREATE TABLE page_visits (
    visit_id INT PRIMARY KEY,
    user_id INT,
    page_url VARCHAR(255),
    visit_time TIMESTAMP,
    FOREIGN KEY (user_id) REFERENCES users(user_id)
);

CREATE TABLE conversions (
    conversion_id INT PRIMARY KEY,
    user_id INT,
    conversion_time TIMESTAMP,
    product_id INT,
    purchase_amount DECIMAL(10, 2),
    FOREIGN KEY (user_id) REFERENCES users(user_id)
);

Output:

-- Insert data into users table
INSERT INTO users (user_id, session_id, user_agent, country, city)
VALUES (1, 'ABC123', 'Mozilla/5.0', 'India', 'Mumbai'),
      (2, 'DEF456', 'Chrome/91.0', 'India', 'Delhi'),
      (3, 'XYZ789', 'Safari/537.36', 'India', 'Bangalore');

-- Insert data into page_visits table
INSERT INTO page_visits (visit_id, user_id, page_url, visit_time)
VALUES (1, 1, 'https://www.example.com/product/1', '2025-05-10 10:15:00'),
      (2, 2, 'https://www.example.com/product/2', '2025-05-10 10:30:00'),
      (3, 3, 'https://www.example.com/product/3', '2025-05-10 11:00:00');

-- Insert data into conversions table
INSERT INTO conversions (conversion_id, user_id, conversion_time, product_id, purchase_amount)
VALUES (1, 1, '2025-05-10 10:20:00', 1, 5000.00),
      (2, 2, '2025-05-10 10:35:00', 2, 7000.00);

Output Explanation:

The users table tracks user-specific data such as user agent, location, and session ID. The page_visits table stores page visits, including the URL and visit time. The conversion table records successful conversions, including purchase details like product ID and purchase amount.

If you want to learn advanced ReactJS applications, check out upGrad’s React.js For Beginners. The 14-hour free certification will help you learn essential React components and analytical skills that are necessary for scalable data operations. 

24. Employee Attrition Analysis

The Employee Attrition Analysis project aims to identify the key factors that contribute to employee turnover by analyzing historical employee data such as job satisfaction, performance, tenure, and survey results. By leveraging SQL for aggregating, correlating, and segmenting data, businesses can uncover patterns in employee attrition, helping HR teams develop strategies to retain top talent.

Technology Stack and Tools:

• Power BI to visualize attrition trends
• SQL for aggregating, correlating, and segmenting metrics related to performance
• MySQL to store employee data

Key Skills Gained:

• Develop SQL queries to identify key attrition drivers.
• Categorize employee data by department, role, and tenure for deeper analysis.
• Develop data-driven strategies to improve retention.

Applications:

• Identify departments with high attrition to design better retention programs.
• Use historical data to predict future attrition rates.
• Analyze survey results and link them to attrition patterns.

Code Example:

CREATE TABLE employees (
    employee_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    department VARCHAR(50),
    role VARCHAR(50),
    job_satisfaction DECIMAL(3, 2), -- Rating between 0 and 1
    performance_score DECIMAL(3, 2), -- Rating between 0 and 1
    hire_date DATE,
    termination_date DATE
);

CREATE TABLE employee_surveys (
    survey_id INT PRIMARY KEY,
    employee_id INT,
    survey_date DATE,
    satisfaction_score DECIMAL(3, 2), -- Survey score between 0 and 1
    feedback VARCHAR(255),
    FOREIGN KEY (employee_id) REFERENCES employees(employee_id)
);

Output:

-- Insert data into employees table
INSERT INTO employees (employee_id, first_name, last_name, department, role, job_satisfaction, performance_score, hire_date, termination_date)
VALUES (1, 'Ravi', 'Sharma', 'Engineering', 'Software Engineer', 0.85, 0.92, '2020-01-10', '2025-05-01'),
      (2, 'Priya', 'Singh', 'Marketing', 'Manager', 0.78, 0.75, '2019-06-15', '2025-04-20'),
      (3, 'Amit', 'Verma', 'Sales', 'Sales Executive', 0.90, 0.80, '2021-02-22', NULL);

-- Insert data into employee_surveys table
INSERT INTO employee_surveys (survey_id, employee_id, survey_date, satisfaction_score, feedback)
VALUES (1, 1, '2025-04-15', 0.90, 'Satisfaction with current projects is high.'),
      (2, 2, '2025-04-10', 0.70, 'Struggling with work-life balance.'),
      (3, 3, '2025-04-20', 0.85, 'Happy with the work environment.');

Output Explanation:

The employee table stores demographics, job satisfaction, performance scores, and employment dates. The employee_surveys table tracks survey results, capturing employee feedback and satisfaction scores.

25. Inventory Management Optimization

This project optimizes inventory levels based on historical sales data and demand forecasting. Using SQL to track inventory levels, sales trends, and demand patterns, businesses can minimize stockouts, prevent overstocking, and optimize inventory turnover. The project also uses Excel for advanced analysis and forecasting based on the data stored in MySQL.

Technology Stack and Tools:

• Excel for analyzing and forecasting based on SQL data
• SQL to calculate stock levels, demand forecasting, and inventory turnover.
• MySQL to manage inventory data and sales history.

Key Skills Gained:

• Write SQL queries to track stock levels and sales trends.
• Implementing demand forecasting models using historical data.
• Analyzing inventory turnover to optimize stock levels.

Applications:

• Predicting future inventory needs based on historical trends.
• Improving inventory turnover to reduce storage costs.
• Ensuring stock levels are optimized to meet demand without overstocking.

Code Example:

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(100),
    category VARCHAR(50),
    price DECIMAL(10, 2)
);

CREATE TABLE inventory (
    inventory_id INT PRIMARY KEY,
    product_id INT,
    stock_level INT,
    restock_date DATE,
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

CREATE TABLE sales (
    sale_id INT PRIMARY KEY,
    product_id INT,
    quantity_sold INT,
    sale_date DATE,
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

Output:

-- Insert data into products table
INSERT INTO products (product_id, product_name, category, price)
VALUES (1, 'Smartphone', 'Electronics', 25000.00),
      (2, 'Laptop', 'Electronics', 50000.00),
      (3, 'Headphones', 'Accessories', 1500.00);

-- Insert data into inventory table
INSERT INTO inventory (inventory_id, product_id, stock_level, restock_date)
VALUES (1, 1, 200, '2025-06-01'),
      (2, 2, 50, '2025-05-25'),
      (3, 3, 500, '2025-06-10');

-- Insert data into sales table
INSERT INTO sales (sale_id, product_id, quantity_sold, sale_date)
VALUES (1, 1, 100, '2025-05-10'),
      (2, 2, 50, '2025-05-11'),
      (3, 3, 300, '2025-05-12');

Output Explanation:

The products table stores product details like product name, category, and price. The inventory table tracks stock levels and restock dates. The sales table stores transaction data, including quantity sold and sale dates.

26. Market Basket Analysis

The Market Basket Analysis project focuses on analyzing customer transaction data to identify associations between frequently bought products. By using SQL to manage transaction data and the Apriori Algorithm for frequent itemset mining. Businesses can uncover product associations and design targeted promotions or bundling strategies to optimize sales and inventory.

Technology Stack and Tools:

• Apriori Algorithm: Apply the Apriori algorithm to identify frequent itemsets and associations between products in transactional data.
• MySQL/PostgreSQL: Store customer transaction data and perform SQL queries to aggregate product purchases and analyze frequent itemsets.
• SQL: Write SQL queries to find product associations and generate insights using frequent itemset mining techniques.

Key Skills Gained:

• SQL Querying: Write advanced SQL queries to analyze transaction data and identify frequent product associations.
• Frequent Itemset Mining: Use the Apriori Algorithm to find associations between products and discover frequent itemsets in customer transactions.
• Data-Driven Promotions: Design product bundles and promotional strategies based on product combinations that are frequently bought together.
• Inventory Optimization: Optimize inventory management by understanding which products are often purchased together.

Applications:

• Product Bundling: Create product bundles based on frequent combinations of products that customers often purchase together, enhancing customer experience and sales.
• Targeted Promotions: Run promotions or discounts for products that are frequently bought together, increasing sales and improving customer satisfaction.
• Inventory Optimization: Improve inventory management by stocking frequently purchased products, reducing stockouts and overstocking.

Code Example:

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(100),
    category VARCHAR(50),
    price DECIMAL(10, 2)
);

CREATE TABLE transactions (
    transaction_id INT PRIMARY KEY,
    transaction_date DATE
);

CREATE TABLE transaction_items (
    transaction_item_id INT PRIMARY KEY,
    transaction_id INT,
    product_id INT,
    quantity INT,
    FOREIGN KEY (transaction_id) REFERENCES transactions(transaction_id),
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

Output:

-- Insert data into products table
INSERT INTO products (product_id, product_name, category, price)
VALUES (1, 'Smartphone', 'Electronics', 25000.00),
      (2, 'Laptop', 'Electronics', 50000.00),
      (3, 'Headphones', 'Accessories', 1500.00),
      (4, 'Smartwatch', 'Electronics', 10000.00),
      (5, 'Charger', 'Accessories', 500.00);

-- Insert data into transactions table
INSERT INTO transactions (transaction_id, transaction_date)
VALUES (1, '2025-05-10'),
      (2, '2025-05-11'),
      (3, '2025-05-12');

-- Insert data into transaction_items table
INSERT INTO transaction_items (transaction_item_id, transaction_id, product_id, quantity)
VALUES (1, 1, 1, 1),
      (2, 1, 3, 1),
      (3, 2, 2, 1),
      (4, 2, 4, 1),
      (5, 3, 1, 2),
      (6, 3, 5, 1);

Output Explanation:

The products table stores details about each product, including product name, category, and price. The transactions table records each transaction with a unique transaction ID and date.

Also read: Top 25 DBMS Projects [With Source Code] for Students in 2025

Advanced SQL data analysis projects GitHub will help you learn data modeling and query optimization for large datasets, which will be useful in practical applications. However, to master the subject and ensure your project stands out, you need to adopt certain strategies.

Proven Strategies to Make Your SQL Projects Shine on GitHub

When showcasing your SQL projects on GitHub, the goal is to demonstrate technical proficiency through complex problem-solving and optimized query performance. Rather than focusing on basic tutorials, focus on tackling real-world challenges, optimizing your queries, and leveraging advanced SQL features to make your projects stand out.

• Solve Real-World Problems: Focus on solving unique problems such as customer behavior analysis or demand prediction, which show your ability to tackle complex scenarios beyond basic queries.
• Optimize Query Performance: Demonstrate your ability to handle large datasets by optimizing queries for speed and resource consumption. When working with substantial data, use techniques like indexed views and materialized views to improve query performance.
• Apply Advanced SQL Features: Use features like recursive queries, CTEs, and stored procedures to provide scalable and efficient solutions for complex tasks. These features show that you can handle hierarchical data and advanced business logic.
• External Tools and Technologies: Integrate tools like Apache Kafka for streaming data, C++ or C# for backend integration to expand the scope of your SQL projects. This demonstrates your ability to work with multiple technologies within an end-to-end solution.
• Create Comprehensive Documentation: Write clear documentation explaining your SQL queries, logic, and assumptions. Document how complex queries interact with the rest of the system, making it easier for others to understand and extend.

Example Scenario:

Imagine developing a recommendation system for an e-commerce site. You’ll use RFM analysis to segment customers based on purchasing patterns and design a model to suggest products. Implementing indexed views to optimize queries ensures scalability even as the product catalog size grows. You’ll also integrate Apache Kafka to update recommendations in real-time as new data flows in.

Also read: Top 10 Real-Time SQL Project Ideas: For Beginners & Advanced

Now that you’ve explored the strategies that can make your SQL projects GitHub stand out, let’s look at ways to increase your knowledge of SQL.

How upGrad Can Help You Master SQL Projects for GitHub Success?

Exploring SQL projects like Market Basket Analysis and Customer Churn Prediction will allow you to demonstrate proficiency in queries, data analysis, and machine learning integration. By utilizing tools such as PostgreSQL, Apache Kafka, and advanced SQL features like CTEs and indexed views, you can optimize performance and enhance scalability. 

To make an impact, focus on tackling real-world challenges, optimizing query performance, and ensuring thorough documentation for clarity and future scalability. upGrad’s programs equip you with the tools and skills to integrate machine learning models with SQL data. 

If you want to learn industry-relevant SQL skills for enterprise-grade applications. These are some of the additional courses for successful SQL projects.

• Master of Design in User Experience
• Advanced SQL: Functions and Formulas

Curious which courses can help you gain expertise in SQL? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center. 

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References:

1. https://www.index.dev/blog/most-popular-programming-languages-