This repository provides Python code for integrating data from various sources using the Pandas library for data manipulation and PySpark for distributed processing.

Data integration is a critical process in data analysis and machine learning projects. It involves combining data from different sources, such as CSV files and JSON files, to create a unified dataset for analysis.

This project demonstrates how to perform data integration using a combination of Pandas and PySpark. Pandas is used for data manipulation on a single machine, while PySpark enables distributed processing across multiple machines.

Before running the code in this repository, ensure that you have the following installed:

• Python (version 3.6 or higher)
• Required libraries listed in the requirements.txt file

You can install the required libraries using the following command:

pip install -r requirements.txt

The data integration process consists of the following steps:

1. Reading Data: Data is read from multiple sources using Pandas and PySpark.

2. Data Transformation: Data may undergo transformation steps, such as cleaning and preprocessing, to prepare it for integration.

3. Merging Data: Data from different sources is merged based on common keys or columns to create a unified dataset.

4. Persisting Data: The integrated data is stored in a database or file system for further analysis or use.

1. Data Integration Script: The cleanup_analysis.ipynb script demonstrates how to integrate data from various sources using Pandas and PySpark. Follow the instructions in the script to customize it for your specific data sources and requirements.

2. Jupyter Notebook: The cleanup_analysis.ipynb Jupyter notebook provides a step-by-step guide to cleaning, transforming, and analyzing the integrated data. Execute the cells sequentially to perform data analysis tasks.

3. Ingestion Process: The main.py script demonstrates how to ingest the integrated data into a PostgreSQL database using PySpark. Adjust the database connection parameters and table name according to your environment.

Contributions to this project are welcome! If you have any ideas for improvement or new features, please feel free to submit a pull request or open an issue.

This project is licensed under the MIT License.