Data Engineering – ELT vs ETL

Data Engineering involves building and developing systems to collect, store, and analyze data at scale. Specifically, ELT and ETL methodologies play a critical role in data engineering by facilitating the movement and processing of data between source systems and data warehouses . Understanding the differences between these methodologies is instrumental when choosing the best approach that is suitable for the organization’s specific needs.

ETL is a method of transferring a data set initiation with the extraction of data from multiple source systems, after which the data is moved to an intermediate staging area where it undergoes a series of transformation operations, before finally being loaded into the destination system. The ETL process includes three main stages described as follows.

• Extract: Data is harvested from the various source systems- databases, CRM systems, ERP systems, and any other data storage repositories. The acquisition stage is designed to move the data to an intermediate staging area.
• Transform: After acquiring the data, a series of complex transformation operations are performed. This phase includes cleansing , normalization, joining, and aggregation , all of which ensure that the data meets the organization’s analytical needs and quality. This process is typically conducted in a separate processing area, often controlled by ETL servers or platforms.
• Load: The transformed data moves to the destination system, typically a data warehouse. In this system, the data is structured and organized into schemas, which then allow fast and efficient querying and analysis. 

ELT is a more modern approach that has become more popular with cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake, which can handle massive data volume and complex processing.

• Extract: Data is extracted from multiple source systems, as with ETL load.
• Load: However, rather than transforming data first, ELT loads data into the data warehouse in its rawest form and relies on the powerful computational capacity of modern data warehouse. This makes ELT a popular choice in modern data warehouses like Amazon Redshift, Google BigQuery, or Snowflake .
• Transform: Transformation occurs after the data has been loaded into the data warehouse.