Chances are that if you’ve found your way to this blog, you’re somewhat familiar with ETL (Extract-Transform-Load). If you’ve ever been reading a blog post or article and saw “ELT” instead of “ETL” you may have brushed it off as a typo. Perhaps you were listening to a speaker who said “ELT” and you thought they mixed their letters up.
Chances are they probably knew what they were talking about.
There is such a thing as ELT (Extract-Load-Transform) that is different from ETL. Both ETL and ELT are used for data integration, but ELT, with its switched letters, has its own process.
Let’s go over their differences.
ETL (Extract-Transform-Load)
ETL involves taking the data, using a staging area to perform the necessary transformations, and then loading it where it needs to go. This could be a database, a data warehouse, etc. ETL is all about doing the heavy lifting to get the data cleaned up before sending it off to its destination.
ELT (Extract-Load-Transform)
ELT, as the name suggests, loads the data into the destination first and then performs the transformation of data into the desired state. With ELT, the Load and Transform steps are swapped, and there is no staging area needed. It’s easier to have a constant flow of data since there’s no staging area to navigate through.
ETL or ELT?
As far as what to use for your ETL or ELT needs, commonly referenced tools associated with both ETL and ELT are SSIS (SQL Server Integration Services), Azure Data Factory, and Integrate.io. There are plenty of other tools but those are a few that come to my mind.
I would say that in general, if you’re working with a smaller set of data, ETL is enough to get the job done. If you’re working with larger sets of data like in a data warehouse and you need the process to be faster, ELT is the better option.
Thanks for reading!
Chad Callihan 