ELT with Amazon Redshift – An Overview

If you've been in Data Engineering, or what we once referred to as Business Intelligence, for more than a few years you've probably spent time building an ETL process. With the advent of (relatively) cheap storage and processing power in data warehouses, the majority of bulk data processing today is designed as ELT instead. Though this post speaks specifically to Amazon Redshift, most of the content is relevant to other similar data warehouse architectures such as Azure SQL Data Warehouse, Snowflake and Google BigQuery. First, ETL stands for "Extract-Transform-Load", while ELT just switches to order to "Extract-Load-Transform". Both are approaches to batch data processing used to feed data to a data warehouse and make it useful to analysts and reporting tools.