Data Preparation on AWS: Comparing ELT Options to Cleanse and Normalize Data

Comparing the features and performance of different AWS analytics services for Extract, Load, Transform (ELT)

According to Wikipedia, “Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake but stored in its original raw format. This enables faster loading times. However, ELT requires sufficient processing power within the data processing engine to carry out the transformation on demand, to return the results in a timely manner.”

Introduction

As capital investments and customer demand continue to drive near hyper-growth of the cloud-based analytics market, the choice of commercial and open-source tools for data processing and data analysis seems endless. Even the major Cloud Service Providers (CSPs) have reached a point where they now offer multiple analytics services to accomplish similar tasks.

This post will examine the choice of analytics services available on AWS capable of performing ELT. Specifically, this post will compare the features and performance of AWS Glue Studio, Amazon Glue DataBrew, Amazon Athena, and Amazon EMR using multiple ELT use cases and service configurations.