A data repository, also known as a data library or data archive, refers to a database infrastructure—composed of several databases—that collects, manages, and stores various datasets for analysis, distribution, and reporting. The term is generally used to describe a destination assigned as data storage.
Below are some examples of data repositories that vary in collecting and storing data:
Data Warehouses: These pertain to large central depositories that aggregate data from multiple sources or business segments. The data stored are not necessarily related but essential in helping end-users make critical decisions.
Data Lakes: Like data warehouses, data lakes are large repositories but store structured, semi-structured, and unstructured data. Collected raw data is used for tasks other than reporting, such as advanced analytics, visualizations, and machine learning.
Metadata Repositories: These contain detailed information about the data and databases. Metadata describes where the data originated, how it was captured, and what it means, all of which come in handy for people who want to know more about the data.
Data Cubes: Like a spreadsheet, data cubes are lists of data with multiple dimensions stored in tabular format. They are utilized for describing time sequence and aid in evaluating collected data from different points, which is ideal for identifying trends and business performance.
Data Marts: Unlike other data repositories, data marts are subject-oriented and in a segregated section of a data warehouse. They limit access to isolated datasets and contain only data relevant to a specific business department, making them secure and easy to access.
With a data repository, businesses can use stored data and analyze available information to make better, more strategic decisions faster. In addition, since most data repositories are segmented, users can quickly access critical data for reporting and analysis while simplifying troubleshooting and identifying errors.
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