This repository contains a SQL project focused on analyzing a dataset of famous paintings. The dataset is provided in CSV files. To integrate this data into PostgreSQL, a different approach was adopted instead of manually creating tables and uploading data one by one. A Python script was developed to streamline the process, allowing for the efficient transfer of data from CSV to PostgreSQL.
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Data Loading: Utilized a Python script to facilitate the transfer of data from CSV files to Python, and subsequently to the PostgreSQL database.
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Database Connection: Established a connection to the PostgreSQL database using Python packages to ensure seamless data transfer and interaction.
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SQL Queries: Developed over 20 SQL queries to extract insights from the database. These queries employ various SQL functionalities such as joins, aggregate functions, window functions, common table expressions (CTEs), and subqueries. These queries provide valuable insights into both the museum and the paintings contained within the dataset.
The SQL project aims to uncover significant findings and patterns within the dataset, leveraging the capabilities of SQL to analyze and derive insights from the data.