Skip to content

Kindoli/End-to-End-JSON-Data-Analysis-and-Visualization-Project

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 

Repository files navigation

End-to-End Data Analysis & Visualization Project

Technologies Used: JSON | Python | Amazon S3 | Snowflake | PowerBI


📊 Project Overview

This project demonstrates a complete data pipeline and analysis workflow, starting from raw JSON data to generating interactive dashboards. The pipeline showcases the integration of modern cloud-based data storage, processing, and visualization tools.

It reflects real-world scenarios of handling unstructured data, transforming it, storing it securely in the cloud, and deriving actionable insights through business intelligence tools.


🚀 Tools & Technologies

Tool Purpose
JSON Raw data source format
Python Data preprocessing, cleaning & transformation
Amazon S3 Cloud storage for structured & raw data
Snowflake Cloud data warehousing & querying
PowerBI Data visualization & dashboarding

🔄 Workflow Architecture

  1. Extract JSON Data (Simulated Yelp Dataset)
  2. Clean & Transform Data using Python
  3. Upload Clean Data to Amazon S3
  4. Load Data from S3 into Snowflake
  5. Query & Analyze Data within Snowflake
  6. Visualize Insights using PowerBI Dashboards

📝 Project Structure

├── data/
│   └── raw/                      # Original JSON data
│   └── processed/                # Cleaned and transformed data
│
├── scripts/
│   ├── extract_data.py          # Load & Explore JSON data
│   ├── transform_data.py        # Clean & structure data
│   ├── upload_s3.py             # Upload to Amazon S3
│   ├── snowflake_loader.py      # Load into Snowflake
│
├── powerbi/                     # PowerBI dashboard files (.pbix)
│
├── requirements.txt             # Python dependencies
├── README.md                    # Project documentation
└── .gitignore

Project Architecture

Data Diagram


📈 PowerBI Visualization

The PowerBI dashboard showcases:

  • Rating distribution
  • Customer sentiment trends
  • Top categories by review count
  • Geographic distribution of reviews
  • Time-series trends analysis

✍️ Author

Name: Kindoli Edward Role: Data Analyst | Data Engineer | BI Developer
GitHub: https://github.com/Kindoli LinkedIn: https://www.linkedin.com/in/kindoli-edward-5058544a/


About

End to End data analysis and visualization project using JSON, Python, Amazon S3, Snowflake and PowerBI

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages