fevunge/etl-lar

An ETL pipeline aimed at automating and visualization of hospital statistical data, taking as its case study the National Ophthalmology Institute of Angola. # etl-lar ### *An ETL pipeline aimed at automating and visualization of hospital statistical data, taking as its case study the National Ophthalmology Institute of Angola.* **Report Bug**  ·  **Request Feature** --- ## 📋 Table of Contents - Overview - Features - Tech Stack - Getting Started - Prerequisites - Installation - Environment Variables - Usage - Project Structure - API Reference - Screenshots - Roadmap - Contributing - License - Authors - Acknowledgements --- ## Overview > **ETL-lar** is a comprehensive ETL (Extract, Transform, Load) pipeline designed to automate the processing and visualization of hospital statistical data. The project focuses on the National Ophthalmology Institute of Angola, aiming to streamline data management and provide actionable insights through interactive dashboards. By leveraging modern technologies and best practices in data engineering, ETL-lar enables healthcare professionals to make informed decisions based on accurate and timely data. --- ## Features - **Data Extraction**: Implement robust data extraction mechanisms to pull data from various sources, including databases, APIs, and flat files. - **Data Transformation**: Develop a flexible transformation layer to clean, normalize, and enrich the extracted data, ensuring it is in the correct format for analysis and visualization. - **Data Loading**: Set up efficient loading processes to store the transformed data in a centralized data warehouse, optimizing for query performance and scalability. - **Visualization**: Create interactive dashboards and reports that allow users to explore the data, identify trends, and derive insights relevant to ophthalmology care and hospital management. - **Automation**: Schedule and automate the entire ETL process to ensure that the data is always up-to-date, reducing manual intervention and minimizing errors. - **Extract to CSV, XLSX, SQL**: Implement functionality to export the processed data into various formats such …