A data warehouse is a system that collects, integrates, and stores data from various sources for analysis and reporting purposes¹. A data warehouse enables an organization to run powerful analytics on large volumes of historical data in ways that a standard database cannot¹. A data warehouse also provides a single source of truth for data, ensuring its consistency and quality².

Some of the main functions of a data warehouse are:

• Data integration: A data warehouse integrates data from multiple heterogeneous sources, such as operational databases, external sources, or unstructured data. The data is transformed and standardized into a common format and structure, using processes such as Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT)¹.

• Data storage: A data warehouse stores data in a centralized repository, usually a relational database system, that is optimized for fast querying and analysis. The data is organized into tables, views, schemas, or multidimensional cubes, depending on the type of data warehouse architecture¹.

• Data analysis: A data warehouse supports various types of data analysis, such as descriptive, diagnostic, predictive, or prescriptive analytics. The data can be accessed and manipulated using tools such as Online Analytical Processing (OLAP), data mining, artificial intelligence (AI), or machine learning¹.

• Data reporting: A data warehouse enables users to create and generate reports and dashboards that present the results of data analysis in a visual and interactive way. The reports and dashboards can be customized and tailored to different business needs and audiences³.

• Data governance: A data warehouse ensures that the data is secure, reliable, accurate, and compliant with the relevant policies and regulations. The data warehouse implements mechanisms such as access control, auditing, backup, recovery, encryption, metadata management, and quality control².