datawarehouseläggningar

A data warehouse is a centralized repository that consolidates data from multiple source systems to support reporting, analysis, and decision making. It stores current and historical data in a single, consistent format and is designed for read-heavy workloads, complex queries, and long-term trend analysis. Data warehouses are typically organised around subject areas such as customers, sales, or products, enabling cross-functional analysis and business intelligence across the organization.

Architecture and concepts

A data warehouse typically involves data from various sources entering through an extract, transform, and load (ETL) or extract, load, and transform (ELT) process, moving into a staging area before being integrated into the warehouse. Data is often arranged using schemas such as star or snowflake, with fact tables containing measurable metrics and dimension tables providing context. Modern implementations may be cloud-based, offering scalable storage and compute and supporting modular components like data marts for department-specific needs. Data governance, quality management, metadata, and security controls are important to ensure accuracy and compliance.

Types and evolution

Common forms include enterprise data warehouses (EDWs), data marts for targeted functions, and virtual or federated data warehouses that query across sources without centralized storage. The field has evolved to include data lake and lakehouse integrations, enabling semi-structured data and analytics that blend warehouse reliability with lake-scale storage. This evolution reflects shifting needs for real-time access, advanced analytics, and flexible deployment.

Applications

Data warehouses underpin business intelligence, reporting, and analytics, including machine learning workflows that rely on stable, historical data. They emphasize consistency and durability over transactional speed, serving as a foundation for strategic insights and data-driven decision making.