BACKGROUND

As the technological capacity for organizations to create, track, and retain information continues to grow, a variety of different technologies for managing and storing the rising tide of information have been developed. Database systems, for example, provide clients with many different specialized or customized configurations of hardware and software to manage stored information. The increasing amount of data that organizations must store and manage often correspondingly increases both the size and complexity of data storage and management technologies, like database systems, which in turn escalate the cost of maintaining the information. New technologies seek to reduce both the complexity and storage requirements of maintaining data by extracting, transforming, and loading data into different data storage or analysis systems. The advent of machine learning techniques has introduced new possibilities for transforming data which can be incorporated to improve the capability of systems to perform these Extract Transform Load (ETL) jobs.

