What happens during the transform step of the ETL process?

In the context of the ETL (extract, transform, and load) process, the transformation step is essential for preparing raw data for analysis and ensuring it aligns with the schema of the destination data warehouse.

During this phase, the data is cleaned to minimize errors and inconsistencies. Specific actions performed during transformation may include:

• Cleaning: Removing duplicate records to ensure data integrity.
• Formatting: Changing the formats of specific fields to meet analytical requirements.
• Standardization: Renaming fields or adjusting data structures to fit the target schema.

By refining the data in this manner, the transformation step ensures that information from various sources is consistent and ready to be integrated with existing historical data once it is loaded into the warehouse.1