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This SQL project uses the classic Microsoft Pubs sample database. It includes queries and scripts that explore core SQL techniques such as data retrieval, filtering, joins, aggregations, and subqueries. The aim is to provide clear, hands-on examples of SQL in action using a structured, well-documented dataset.

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SQL Project with Pubs Database

This SQL project explores the classic Microsoft Pubs sample database. It features a collection of queries and scripts designed to demonstrate core SQL skills through real-world scenarios.

πŸ“Œ Overview

The project focuses on essential SQL concepts, including:

  • Data retrieval using SELECT statements
  • Filtering with WHERE, LIKE, IN, and BETWEEN
  • Sorting and ordering data
  • Table joins (INNER, LEFT, RIGHT)
  • Aggregation using GROUP BY and HAVING
  • Subqueries and nested queries

The aim is to provide clear, practical examples of how SQL can be used to explore and analyze relational data in a structured and meaningful way.

πŸ“˜ Pubs Database Relational Diagram

The diagram below shows the relationships between key tables in the classic Pubs database, including authors, titles, publishers, and their junction tables.

Relational Diagram

🧰 Tech Stack

  • Microsoft SQL Server
  • SQL Server Management Studio (SSMS)
  • Git + GitHub

βœ… Use Cases

This project is suitable for:

  • Practicing intermediate SQL skills

  • Demonstrating SQL knowledge to potential employers

  • Understanding cross-server querying via Linked Servers

  • Exploring a relational dataset with well-defined entities

πŸ“¬ Contact

Feel free to connect with me on LinkedIn https://www.linkedin.com/in/shiva-bajelan-4443722a5/ if you have questions, suggestions, or just want to chat about SQL or data analytics.

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This SQL project uses the classic Microsoft Pubs sample database. It includes queries and scripts that explore core SQL techniques such as data retrieval, filtering, joins, aggregations, and subqueries. The aim is to provide clear, hands-on examples of SQL in action using a structured, well-documented dataset.

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