A More Refined Advancement to the Low-Code Framework for End-to-End Big Data Management

The massive amount of data constantly growing in our digital world requires a well-defined plan to handle and analyze it effectively. Traditional methods struggle with the vast size and variety of big data, and current frameworks often necessitate specialized coding skills. Florus, a big data framework built on the Lakehouse architecture, was created to address these issues. Florus allows users to easily create data pipelines through a user-friendly interface. However, its use revealed limitations, particularly during the data ingestion and preprocessing stages. This paper introduces a framework, iFlorus, which significantly improves upon Florus by addressing its weaknesses. Through the lens of a real-world use case, such as air quality prediction, this paper illustrates how iFlorus empowers individuals without extensive coding knowledge to extract invaluable insights from diverse big data sources, thereby revolutionizing the landscape of data-driven decision-making.