What is the purpose of the 'Transform' stage in ETL?

The purpose of the 'Transform' stage in ETL (Extract, Transform, Load) is indeed to clean and format data for analysis. During this stage, raw data obtained from various sources undergoes various processing steps to ensure it is accurate and suitable for analytical purposes. This may include filtering out errors, standardizing formats, aggregating information, and applying business rules to create a consistent dataset.

The transformation process is crucial as it converts the raw data into a structured format that a business intelligence tool or analytical software can utilize effectively. By addressing inconsistencies and enhancing data quality, organizations are better positioned to derive meaningful insights from their data when it is loaded into a final database for analysis and reporting.

Other stages in ETL involve different objectives; data extraction focuses on gathering data from different sources, loading pertains to storing transformed data into a final destination, and visualization involves representing the data graphically for stakeholders, which comes after the ETL process is completed.