The acronym ETL stands for “Extract, Transform, Load”. It refers to the data integration process, where raw data is prepared and transferred from a source server to a destination data system. This system can be a database such as PostgreSQL or MySQL, a data warehouse like BigQuery or Snowflake, or even a data lake.
The ETL pipeline is made up of 3 distinct processes:
By cleaning and organizing the data, it enables data teams to address specific BI issues. And since it is an automated and batch-driven process, it proves to be a significant time-saver in the long run!
The ETL process was first introduced in the 1970s, when databases were starting to gain momentum. It quickly became popular, and is now the bedrock of Analytics and machine learning workstreams.
In the ELT process, the phases do not follow the same order as in the ETL one, since the raw data is loaded into its destination system before being transformed.
Why was ELT created? As Cloud storing developed, storage costs have been dropping significantly these last decades – which meant that storing both the raw data and transformed data in the destination system ended up affordable.
Organizations now tend to choose ELT instead of ETL – with reason. With business and Analytics requirements changing rapidly, ELT enables Analysts to alter the data without needing to go through the whole data pipeline – which would require time, hard skills, and data engineering support.
It also means that Analysts will be able to work from that data how they wish to in the future, and that the original raw data will remain accessible whenever they need it.
As a result, the ELT process makes Analysts much more self-reliant and less dependent on Data engineers – and it contributes to the company’s overall ability to make relevant data-driven decisions!