Data Mart Architecture

The architecture of a data mart can vary depending on the specific needs of the business unit or department it serves. However, some common elements are typically found in a data mart architecture:

•     Data sources: The data sources for a data mart can come from various systems and applications, such as transactional systems, operational databases, or other data warehouses.

•     ETL process: The ETL process is used to extract data from the source systems, transform it to meet the needs of the data mart, and load it into the data mart.

•     Data mart database: The data mart database is the repository that stores the data for the specific business unit or department. It is typically designed to be optimized for the types of queries and analyses that are performed by the end users. (In modern day, in some cases this may be a temporarily transformed datastore refreshed periodically with no history.)

•     Business intelligence tools: Business intelligence (BI) tools are used to analyze the data in the data mart and provide reports, dashboards, and other visualizations to end users.

Advantages of Data Marts

Data marts are a crucial component of modern data management and analytics strategies, offering several advantages that organizations can leverage to drive informed decision-making and enhance their competitive edge. These streamlined subsets of data warehouses are designed to cater to specific business units or departments, providing a focused and efficient approach to data access and analysis. Some of the key advantages of a data mart, with examples, are as follows:

•     Targeted data: Data marts provide a subset of the larger data warehouse that is specifically designed to meet the needs of a particular business unit or department. This makes it easier for end users to find the data they need and make well-informed decisions.

•     Improved performance: Data marts are designed to be optimized for the types of queries and analyses that are performed by the end users. This can improve query performance and reduce the time it takes to access and analyze data.

•     Reduced complexity: By focusing on a specific business area or function, data marts can simplify the data architecture and make it easier to manage and maintain.