How cloud computing and analytics engineering forced the transition from ETL to ELT

ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) are two terms commonly utilized in the realm of Data Engineering and more specifically within the context of knowledge ingestion and transformation.

While these terms are sometimes used interchangeably, they consult with barely different concepts and have different implications for the design of a knowledge pipeline.

On this post, we’ll make clear the definitions of ETL and ELT processes, outline the differences between the 2, and discuss the benefits and drawbacks each need to offer to engineers and data teams on the whole.

And most significantly, I’m going to explain how the recent changes in modern data teams’ formation has impacted the landscape around ETL vs ELT battle.

The most important stake in terms of comparing ETL and ELT is clearly the sequence the Extract, Load and Transform steps are executed inside a knowledge pipeline.

For now, let’s ignore this execution sequence and let’s give attention to the actual terminology and discuss about what each individual step is purported to do.

Extract: This step refers back to the strategy of pulling data from a persistent source. This data source could possibly be a database, an API endpoint, a file or really anything that accommodates any form of knowledge, including each structured or unstructured.

Transform: On this step, the pipeline is anticipated to perform some changes within the structure or format of the info so as to achieve a certain goal. A change could possibly be an attribute selection, a modification of records (e.g. transform 'United Kingdom' into 'UK'), a knowledge validation, a join to a different source or really anything that changes the format of the input raw data.

Load: The load step refers back to the strategy of copying the info (either the raw or the transformed version) into the goal system…