DataWarehouses

Data warehouses are centralized repositories designed for analytic querying and reporting rather than everyday transaction processing. They consolidate data from multiple source systems—ERP, CRM, files, and external feeds—after cleansing and integration, and store it in a schema optimized for analysis.

Common architectures include sources, ETL or ELT pipelines, staging, the warehouse itself, and optional data marts. Data are typically modeled with dimensional designs such as star or snowflake schemas to support fast aggregations and drill-downs. A warehouse is intended to be subject-oriented, integrated, non-volatile, and time-variant, preserving historical data for trend analysis.

Data warehouses support business intelligence activities, including dashboards, reports, ad hoc queries, and forecasting. They differ from operational databases that handle day-to-day transactions. In many cases, warehouses run in the cloud, offering scalable storage and compute with independent scaling. Popular cloud options include Snowflake, Redshift, BigQuery, and Azure Synapse. Hybrid approaches combine on-premises and cloud components.

Governance and quality are essential: metadata management, data lineage, access controls, and data quality checks help ensure reliability and compliance. Design choices—ETL versus ELT, scheduling, partitioning or clustering, and materialized views—affect performance and maintenance.

Data warehouses are part of the broader data landscape, alongside data lakes and data lakehouse concepts, which aim to unify storage of structured and semi-structured data. The warehouse remains the curated, query-optimized layer for business-ready insights.