By addressing key challenges such as inconsistent quality control, inadequate documentation, and resource-intensive testing, ...

And before that data is ready for analysis, it needs to be combined, cleaned, and normalized—a process otherwise known as extract, transform, load (ETL)—which can be laborious and error-prone.

As enterprises generate and process vast amounts of data, the need for scalable, cost-efficient, and high-performance data ...

The landscape of data analytics is evolving, and metadata-driven Extract, Transform, Load (ETL) frameworks are at the ...