Transforming Data: ELT Or ETL?

Kevin Feasel

2018-04-09

ETL

Artyom Keydunov argues that Extract-Load-Transform is a better model than Extract-Transform-Load:

ETL arose to solve a problem of providing businesses with clean and ready-to-analyze data. We remove dirty and irrelevant data and transform, enrich, and reshape the rest. The example of this could be sessionization: the process of creating sessions out of raw pageviews and users’ events.

ETL is complicated, especially the transformation part. It requires at least several months for a small-sized (less than 500 employees) company to get up and running. Once you have the initial transform jobs implemented, never-ending changes and updates will begin because data always evolves with business.

The other problem of ETL is that during the transformation, we reshape data into some specific form. This form usually lacks some data’s resolution and does not include data that is useless for that time or for that particular task. Often, “useless” data becomes “useful.” For example, if business users request daily data instead of weekly, then you will have to fix your transformation process, reshape data, and reload it. That would take a few weeks more.

Read on for more, including his argument for why ELT is better.

Related Posts

Contrasting Integration Services And Pentaho Data Integration

Koen Verbeeck contrasts SQL Server Integration Services with Pentaho Data Integration: For generating SSIS packages, you need to rely on Biml (much about that can be found on this blog or on the net), or older frameworks such as ezApi. Or you need 3rd party tools such as BimlStudio or TimeXtender. Using Biml means writing […]

Read More

Azure Data Factory Or Integration Services?

Teo Lachev contrasts use cases for Integration Services vesus Azure Data Factory V2: So, ADF was incorrectly positioned as “SSIS for the Cloud” and unfortunately once that message made it out there was a messaging problem that Microsoft has been fighting ever since. Like Azure ML, on the glory road to the cloud things that […]

Read More

Categories

April 2018
MTWTFSS
« Mar May »
 1
2345678
9101112131415
16171819202122
23242526272829
30