Why is ETL not a component of the Data Engineering learning path? Are the two totally different? And is ETL not the responsibility of the data engineer in the real world of analytics?
ETL definitely is part of many data engineers’ roles.
In computing, extract, transform, load ( ETL ) is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s).
It’s a general procedure. The very definition isn’t particularly technical. ETL breaks down into many concrete tasks, skills and technologies, some of them we teach, some of them we don’t, but definitely are on our radar to include.
Our greatest emphasis at the moment is in the load part of ETL. This is instantianted in the content by our Postgres courses.
I’m grateful for this response and glad to hear that you have the other aspects of ETL, the E & T, on the radar to include in the Data Engineering path. And I would be most grateful if you could fulfill this thought of bringing in all other components of data engineering together to make it a standalone path to pursue a career in data science. This platform is very vibrant and to be able to define a path of study to enable one to take up a challenging career in data engineering would be awesome. The ability to do all this will truly make Dataquest be recognized for what it stands for.