A New Paradigm—ELT

ETL vs. ELT vs. EtLT

In recent years, there has been growth in generated data and an increasing demand for high real-time data availability. Users worldwide are creating more and more data and demanding instant service and responses from servers and applications. This demand has created a need for transferring data faster from a source to a destination and has given birth to the concept of “big data.” Big data can be defined as very large amounts of structured and unstructured data with high demand for real-time availability. When dealing with big data, delivering data from one location to another will be slightly different.

Different paradigms other than ETL have emerged to deal with the unbelievable load of big data. A popular one is the ELT (extract, load, transform). ELT is another type of data pipeline in which we extract raw data and immediately store it in a data repository without processing it. Usually, a cloud solution like a data lake is suited for storing raw data.

We then transform the data only when we need it. Unlike ETL pipelines, the transformation stage occurs within the destination repository. This process shortens the distance between the source and the destination by leaving the heavy burden of processing the data for later.

As soon as ELT became popular, another sub-paradigm called EtLT emerged. Unlike ELT, where raw data is immediately loaded into a data repository, EtLT involves performing initial transformations and preprocessing before loading the data. These transformations don’t include business logic or data modeling but rather serve to improve the quality of the data.