What term describes a centralized repository that stores large volumes of raw data until it is needed for analysis?

A centralized repository that stores large volumes of raw data until it is needed for analysis is termed a data lake. This concept is integral in handling big data, as it allows for the storage of information in its native format and facilitates a flexible, scalable model for analytics. Unlike other storage solutions, data lakes are designed to accommodate unstructured, semi-structured, and structured data. This means that organizations can house everything from text files and images to complex data sets without requiring predefined schemas.

In a data lake, the data is not processed or filtered before storage, retaining its raw form. This enables data scientists and analysts to access the data as needed, applying specific analytics or processing techniques depending on their research requirements. Performance and efficiency are enhanced since the data is stored in a variety of formats that can be efficiently queried when required.

In contrast, a data warehouse is optimized for structured data and pre-defined queries, a data mart serves a specific business line or department with tailored data, and a data archive typically refers to data that is no longer actively used but retained for compliance or historical reference.