Aggregate and Transform Big Data Using Mapping Data Flows

Abstract

The process of cleansing and transforming big datasets in the data lake has become an increasingly popular and critical step in a modern enterprise’s data architecture. Microsoft has introduced several big data analytics and orchestration tools to serve the need for big data lake Extract-Load-Transform (ELT). Customers are seeking cloud-based services that can cleanse, transform, and aggregate extremely big datasets with ease, coupled with a low learning curve. They are seeking to understand what tools and technologies could potentially fit the bill for big data lake cleansing and transformations.