data warehouse

A data warehouse is a centralized data management system that aggregates and consolidates large amounts of data from multiple sources. Its primary purpose is to enable and support business intelligence (BI) activities, particularly analytics. Data warehouses serve as a “single source of truth” for organizations, providing a consistent and reliable platform for extracting insights from data, monitoring business performance, and supporting decision-making across various departments[1][2][3].

Data warehouses are typically composed of a central database, ETL (Extract, Transform, Load) tools, metadata, and access tools, all engineered for speed to facilitate quick results and on-the-fly analysis[3]. They are built around relational database systems and can be hosted either on-premises or in the cloud. Modern data warehouses have evolved to support real-time analytics and machine learning projects, with cloud-based architectures becoming more common due to their scalability and cost-effectiveness[4].

The architecture of a data warehouse includes a front-end client for presenting results, an analytics engine for accessing and analyzing data, and a database server where data is loaded and stored. Data is organized into tables and columns within databases, and query tools use schemas to determine which data tables to access and analyze[2].

Data warehouses differ from databases, which are built primarily for fast queries and transaction processing, and from data lakes, which store all types of data, including unstructured and semi-structured, without predefined schemas. Data warehouses require data to be organized in a tabular format for efficient querying[2][3].

Citations:

[1] https://www.oracle.com/database/what-is-a-data-warehouse/

[2] https://aws.amazon.com/what-is/data-warehouse/

[3] https://www.ibm.com/topics/data-warehouse

[4] https://www.qlik.com/us/data-warehouse

[5] https://en.wikipedia.org/wiki/Data_warehouse

[6] https://www.investopedia.com/terms/d/data-warehousing.asp