What is the process of combining and preparing data from multiple sources called?

The process of combining and preparing data from multiple sources is best described as Data Integration. This process is essential in data science as it allows for the unification of data from different systems, formats, or databases into a cohesive dataset that can be analyzed.

Data integration involves various steps, which often include extracting data from various sources, transforming it into a suitable format, and loading it into a target system. While ETL is a common framework used in data integration, it is more specific to the methodology of moving and transforming data. Data integration encompasses the broader concept of consolidating data into a single view, regardless of the specific processes used.

In contrast, Data Quality refers to the accuracy, completeness, and reliability of data rather than the process of combining it. Data Aggregation involves summarizing data from multiple sources to provide a consolidated view but does not necessarily imply the preparation or unification of data from various formats or systems. Therefore, the most fitting term for the process in question is Data Integration, as it represents the overall goal of combining and preparing data effectively from diverse sources.