datawarehouse

A data warehouse is a centralized repository designed for query and analysis, consolidating data from multiple sources to support business intelligence. It stores current and historical data in a single place to enable informed decision making.

Core characteristics include being subject-oriented, integrated, non-volatile, and time-variant. Data is prepared for analytical use, often modeled with dimensional schemas such as star or snowflake, though normalized designs are also used in some cases.

Architecturally, a data warehouse typically includes sources, a staging area, an ETL or ELT process, the core warehouse, and data marts or analytic sandboxes. Metadata, data lineage, and governance are essential to ensure data quality, security, and discoverability. Users access data via SQL, BI tools, or analytics applications.

Implementation spans on-premises deployments and cloud-based services that separate storage and compute. Cloud data warehouses offer scalable resources, support for semi-structured data, and pay-as-you-go pricing. Popular platforms include Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse.

History and context: data warehouses emerged to support decision making in enterprises by providing a consolidated, time-variant view of data. Over time, architectures have evolved to integrate with data lakes and big data technologies, while maintaining optimized schemas for reporting and analysis.