What is the purpose of data transformation in ETL?

The purpose of data transformation in the ETL process is crucial for preparing data for analysis. In this context, data transformation involves the conversion of raw data from various sources into a suitable format or structure that is compatible with analytical tools and processes. This typically includes data cleaning, normalization, aggregation, and enrichment, which collectively enhance the quality and usability of the data.

By transforming the data, organizations can ensure that it is consistent, accurate, and meaningful for business intelligence reporting and decision-making. This step is vital as it allows for a unified view of data from disparate sources, making it easier to draw insights and perform analyses effectively.

While managing database queries, storing data in cloud services, and visualizing data are important aspects of data handling and analysis, they do not specifically relate to the transformation of data within the ETL framework. Therefore, the primary purpose of data transformation in ETL is to convert raw data into a suitable format for analysis.