SQL-Joins-in-Action-Lab

Developed at devCodeCamp

This lab provides hands-on exercises for writing SQL queries that utilize various types of JOINs to extract, analyze, and combine data from multiple related tables. Through practical challenges, you’ll become proficient with INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, and other key join techniques essential for real-world data analysis and reporting.

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Description

Practice writing SQL JOIN queries to:

• Combine data from multiple tables with different relationships using INNER, LEFT, RIGHT, and FULL JOINs.
• Use entity-relationship diagrams (ERDs) to understand table connections and enhance query accuracy.
• Extract, compare, and ensure consistency in results across joined tables.

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Features

• Console-based SQL querying and result inspection
• Focus on JOIN operations: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN
• Query consistency— validating results across multiple tables
• Exercises referencing ERDs for schema visualization
• Real-world scenarios for robust database querying

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Technologies Used

• SQL (Structured Query Language)
• MySQL - Relational Database Management System (RDBMS)

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Installation

1. Clone the repository:

   git clone https://github.com/thompsonmikej/SQL-Joins-in-Action-Lab.git
   cd SQL-Joins-in-Action-Lab
   

2. Set up your SQL environment:

   • Install and configure your preferred relational database (e.g., MySQL, PostgreSQL).
   • Use the provided .sql files to create tables and load sample data based on the provided ERDs.

3. Practice running join queries:

   • Use your SQL client or command-line interface to execute and analyze JOIN operations.

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Usage

• Study provided ERDs to understand table relationships.
• Complete JOIN challenges by writing and executing queries for combining, filtering, and comparing data from multiple tables.
• Experiment with different JOIN types to observe their effect on query results.

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Challenges & Lessons Learned

• Developed advanced SQL skills, focusing on relational JOINs and multi-table queries.
• Learned to interpret ERDs for better query construction and data consistency.
• Practiced troubleshooting and optimizing SQL queries for real-world applications.

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Future Improvements

• Add more advanced JOIN exercises, including self-joins and cross joins.
• Integrate large-scale sample datasets for more realistic scenarios.
• Include automated scripts for result validation and performance measurement.

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Author

Feel free to reach out or connect:

Michael Thompson
https://www.linkedin.com/in/thompsonmikej