What technique involves identifying common fields between two data sources to create a unified schema?

The technique of identifying common fields between two data sources to create a unified schema is known as data mapping. This process involves analyzing the structures of two distinct data sources to determine how their fields correspond to each other. By establishing these relationships, data mapping facilitates the integration of data from different sources into a consistent and cohesive format, making it easier to analyze and report on.

In practice, data mapping is essential for merging datasets, especially when dealing with databases or tables that may have varying naming conventions or formats for similar information. Successful data mapping ensures that the merged data maintains accuracy and integrity, supporting insights derived from comprehensive analysis.

Other techniques listed in the options serve different purposes. Data aggregation focuses on combining data from multiple sources to provide a summary or overview, data transformation involves changing the format or structure of the data, and data filtering refers to the process of excluding certain data points based on specific criteria. These processes do not inherently focus on establishing relationships between fields in different data sources to create a unified schema, making data mapping the most relevant answer in this context.