rafabelalcazar / data-quality-backend

Data Quality Framework for Knowledge Discovery Task in a web service

Data Quality Back

This project aims to ensure the quality of data in our system by implementing various data validation and cleansing techniques.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Contributing
• License

Introduction

Data quality is crucial for any organization to make informed decisions and maintain accurate records. This project focuses on the backend implementation of data quality checks and processes.

Features

• Data validation: Implementing validation rules to ensure data integrity.
• Data cleansing: Removing or correcting inaccurate or inconsistent data.
• Data profiling: Analyzing data to identify patterns, anomalies, and data quality issues.
• Data monitoring: Continuously monitoring data quality to detect and resolve issues in real-time.

Installation

1. Clone the repository: git clone https://github.com/your-username/data-quality-back.git
2. Run the local environment: source .venv/Scripts/activate
3. Install the required dependencies: pip install -r requirements.txt

Usage

1. Run the server in dev mode fastapi dev main.py.
2. Make post to test the server : http://127.0.0.1:8000/test/
3. Make post, and upload a file http://127.0.0.1:8000/upload-dataset/

Contributing

Contributions are welcome! Please follow the contribution guidelines when making changes to this project.

License

This project is licensed under the MIT License.