End-to-End Data Analysis Studio

A full workflow application for raw data ingestion, automated cleaning/processing, exploratory analysis, report generation, and optional cloud export.

Features

• Multi-format ingestion: CSV, Excel, JSON, Parquet, PDF tables, TXT/TSV.
• Automated data cleaning: deduplication, missing-value handling, datetime parsing, outlier capping.
• EDA dashboard: numeric summary, distributions, categorical exploration, correlation heatmap, time trends.
• Deliverables: downloadable cleaned CSV + markdown report.
• Cloud connectivity: optional AWS S3 upload for cleaned dataset and generated report.

Project structure

.
├── app.py
├── data_analyzer
│   ├── cloud.py
│   ├── eda.py
│   ├── ingestion.py
│   ├── processing.py
│   └── reporting.py
└── requirements.txt

Run locally

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
streamlit run app.py

Open the Streamlit URL (typically http://localhost:8501).

Run with Docker

docker build -t end-to-end-data-analysis-studio .
docker run --rm -p 8501:8501 end-to-end-data-analysis-studio

Then open http://localhost:8501.

Recommended production extensions

• Add user authentication/roles.
• Persist metadata/history in PostgreSQL.
• Add scheduled pipelines (Airflow/Prefect).
• Add richer PDF extraction and OCR for scanned docs.
• Deploy with Docker + reverse proxy.