Data Warehouse Analysis using PySpark

EDA & ML Pipeline using PySpark

Welcome to the Data Warehouse Analysis using PySpark repository! This repository contains a comprehensive exploration of data warehousing concepts, analysis, and a comparative report on popular data warehouse technologies. Whether you're a data enthusiast, analyst, or developer, this repository aims to provide insights into the world of data warehousing and its associated tools.

Contents

1. Notebooks: This directory contains Jupyter notebooks that walk you through various aspects of data warehousing analysis using PySpark. From data preprocessing to querying, transformation, and visualization, these notebooks offer step-by-step guidance.

2. Data: Here, you'll find the datasets used in the notebooks for demonstration and analysis. These datasets cover a range of industries and scenarios to showcase the versatility of data warehousing.

3. Reports: The reports directory hosts a comparative analysis of popular data warehouse technologies. This report highlights the strengths, weaknesses, features, and use cases of each technology, aiding you in making informed decisions about which solution best fits your needs.

Getting Started

To dive into the world of data warehousing analysis using PySpark, follow these steps:

1. Clone this repository to your local machine using:

   git clone https://github.com/your-username/data-warehouse-analysis.git

2. Install the required dependencies. You can use a virtual environment to manage dependencies:

   cd data-warehouse-analysis
   pip install -r requirements.txt

3. Explore the Notebooks directory and open the Jupyter notebooks to follow the analysis, execute code, and gain insights into data warehousing techniques using PySpark.

4. Check out the Reports directory for the comprehensive report comparing popular data warehouse technologies. This report provides valuable information for making informed decisions about which solution aligns with your specific use case.

Contributions

We welcome contributions from the community! Whether it's improving code in the notebooks, adding new analyses, or enhancing the comparative report, your contributions can help make this repository even more valuable to learners and professionals interested in data warehousing.

To contribute:

1. Fork this repository to your GitHub account.
2. Create a new branch for your contribution.
3. Make your changes and improvements.
4. Submit a pull request, detailing the changes you've made and their significance.

Happy exploring and analyzing data using PySpark and gaining insights into the world of data warehousing! If you have any questions or feedback, feel free to reach out via the repository's Issues section.