DataWarehousing

Data warehousing is a data management discipline and technology that supports business intelligence by consolidating data from multiple sources into a single, centralized repository designed for analysis and reporting. A data warehouse stores historical data in a consistent, integrated format, enabling users to perform trend analysis, performance measurement, and decision support across the organization. The core characteristics are subject orientation, integration, time-variance, and non-volatility.

Architecturally, data warehousing typically involves a data pipeline that extracts data from operational systems, stages and cleanses it, and loads it into the warehouse or data marts. Data models often use dimensional design, with star or snowflake schemas that optimize read-heavy analytical queries. Data marts are subsets targeted to specific functions or departments. Modern deployments may employ ETL (extract, transform, load) or ELT (load-then-transform) pipelines and can reside on-premises, in the cloud, or in hybrid configurations. Cloud-native data warehouses offer scalable storage and compute with separated resources and on-demand pricing.

In practice, data warehouses support reporting, dashboards, ad hoc analysis, and data mining. Metadata management, data quality, data lineage, security, and governance are integral to effective operation. The concept originated in the late 20th century, with foundational approaches described by researchers such as Bill Inmon and Ralph Kimball, and has evolved toward integration with data lakes and lakehouse architectures in recent years.