How does data blending differ from ETL processes?

The distinction between data blending and ETL (Extract, Transform, Load) processes primarily relates to how these methodologies handle data over time. In an ETL process, the data is extracted from various sources, transformed to fit a specific format or structure, and then loaded into a data warehouse. This process often results in the creation of new datasets that can be saved and retained over time, facilitating historical data analysis and reporting.

On the other hand, data blending typically refers to the practice of combining data from multiple sources within a tool or application on-the-fly, often for immediate analysis. This means that when blending data, the focus is usually on integrating datasets for a specific analysis session without necessarily creating and storing new datasets. Data blending is most commonly employed for real-time analytics, where immediate insights are required and historical data storage is not the primary goal.

Therefore, the aspect that distinguishes the correct choice is that ETL processes involve the accumulation and retention of new datasets through structured processes, while data blending does not create or save those new datasets in the same manner. This approach can have implications for data management and the ability to conduct thorough historical analytics, making it essential to understand the fundamental differences between these two data integration methodologies.