Which process combines multiple data sources with the same structure into a new dataset?

The process that combines multiple data sources with the same structure into a new dataset is known as Data Append. This technique is commonly used in data management and analytics when organizations want to enrich their existing datasets with additional data from other sources while maintaining a consistent format.

Data Append involves adding columns or rows from one dataset to another, ensuring that the datasets being combined share the same schema or structure. This allows for a comprehensive dataset that consolidates relevant information from different sources, making it easier to perform analysis, reporting, or further data processing.

In data workflows, this approach is crucial for enhancing the depth of analysis and gaining insights that might not be available from a single data source alone. It helps in building a more robust dataset that supports better decision-making by integrating broader information.

Other options like Data Profiling, Imputation, and Aggregation have different functions in data processing. Data Profiling refers to examining data to understand its structure, content, and quality. Imputation deals with filling in missing values within a dataset. Aggregation, on the other hand, involves summarizing data, such as calculating averages or totals across groups. Thus, these processes do not fit the definition of combining multiple data sources into a new dataset as thoroughly as Data Append does.