Reverse ETL for Improved Scalability, Observability, and Performance of Modern Operational Analytics - A Comparative Review

We are on the verge of the Fourth Industrial revolution, in which data analytics and industry insights will play a critical role in data-driven, quicker, and better decision-making. A data warehouse (DW) is a store of strategic data accumulated over time using ETL techniques from various sources. Traditional DW operations primarily consist of obtaining data from many sources, converting the data into a suitable format, and then putting the data into a DW schema for further analysis. Reverse ETL fits into the current data stack and is distinct from regular ETL as firms pursue creative data strategies. This paper will discuss the significance and process of reverse ETL, why it is taking center stage in cloud environments to eliminate data silos created by SQL in data engineering, and why modern analytics prefers reverse ETL for scalability, performance, and better visual representations.