🧠 ETL Methodologies — Summary and Comparison

🚧 Work in Progress
This repository contains an ongoing theoretical study on ETL (Extract, Transform, Load) and ELT methodologies, developed for the Information Systems II course at the Polytechnic Institute of Coimbra (ISEC).
The final goal is to compare classical and modern approaches to data integration in the context of Data Warehousing and Business Intelligence systems.

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📘 Objectives

• Understand the purpose and stages of ETL and ELT processes
• Compare traditional and cloud-based data integration methodologies
• Identify main tools and technologies used in modern data pipelines
• Analyze advantages, limitations, and performance considerations of each approach

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⚙️ Key Topics Covered

• ETL and ELT concepts and workflows
• Data staging and transformation layers
• Data Warehouse architecture
• Modern cloud ETL platforms (Azure Data Factory, AWS Glue, Google Dataflow)
• Comparison between on-premises and cloud integration models
• Best practices for scalability, automation, and data governance

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💡 Current Status

The project is still under development.
So far, the report includes:

• A conceptual overview of ETL vs. ELT
• Preliminary comparison of tools and architectures
• Notes on data transformation strategies and storage optimization

Upcoming sections will expand the comparative analysis with performance, cost, and real-world use cases.