What Are ETLs And Why We Use Them - For Data Scientists And Data Engineers

The rise in self-service analytics is a significant selling point for data warehousing, automatic data integrations, and drag and drop dashboards. In fact, in 2020, the largest software IPO this year was a data warehousing company called Snowflake.

The question is how do you get your data from external application data sources into a data warehouse like Snowflake?

The answer is ETLs and ELTs.

ETLs (Extract, Transform, Load) are far from new but they remain a vital aspect of Business Intelligence (BI). With ETLs, data from different sources can be grouped into a single place for analytics programs to act on and realize key business insights.

ELTs have the same exact steps referenced by ETLs except in a slightly different order. In particular, the major difference lies in when the transform step occurs. We will discuss in depth what the T stands for shortly.

However, to talk about it abstractly, it references business logic, data pivoting and transformations that often take a lot of time and resources to maintain.

In addition to covering what the E, L, and T stand for, this article will also cover the ETL process and various tools.
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