ACCELERATING DATA INTEGRATION: HARNESSING THE POWER OF A MODEL-DRIVEN FRAMEWORK FOR ETL PROCESS DEVELOPMENT

Аннотация

The field of data integration plays a crucial role in extracting meaningful insights from diverse data sources. Extract, Transform, Load (ETL) processes form the backbone of data integration, enabling organizations to consolidate, clean, and analyze data from various systems. However, the traditional approach to ETL development often suffers from inefficiencies and a lack of scalability. This article proposes a model-driven framework for ETL process development, aiming to accelerate the integration process and improve overall efficiency. By leveraging a model-driven approach, organizations can streamline their ETL workflows, reduce development time, and increase data integration agility. This article delves into the details of the proposed framework, outlining its benefits and discussing its potential applications in the realm of data integration. KEYWORDS Data integration; ETL (Extract, Transform, Load) process development; Model-driven framework; Acceleration; Efficiency; Scalability; Agility; Visual modelling.