For the last couple of decades, ETL (extract, transform, load) has been the traditional approach for data warehousing and analytics. The ELT (extract, load, transform) approach changes the old paradigm. But, what’s actually happening when the “T” and “L” are switched?
ETL and ELT solve the same need:
Billions of data and events need to be collected, processed, and analyzed by businesses. The data needs to be clean, manageable, and ready to analyze. It needs to be enriched, molded, and transformed. To make it meaningful.
But, the “how” is what’s different and leads to new possibilities in many modern data projects. There are differences in how raw data is managed when processing is done, and how analysis is performed.
In this article, we’ll demonstrate the ETL and ELT technological differences showing data engineering and analysis examples of the two approaches and summarizing 10 pros and cons.