DataTerminology.comNEW
Also known as: DW, EDW
A centralized analytical repository that integrates and harmonizes data from multiple sources for reporting and decision-making.
DESCRIPTION
A data warehouse consolidates structured and semi-structured data into consistent models optimized for analytical queries. It supports BI, dashboards, and OLAP, typically via ETL or ELT pipelines. Dimensional modeling, partitioning, and metadata enhance performance and usability. Cloud-native warehouses offer elastic scaling, separation of storage and compute, and governance features. They serve as a trusted source for enterprise analytics.
ALTERNATIVE DEFINITIONS
Microsoft Learn
A data warehouse is a centralized repository that stores data from multiple sources for reporting and analysis.
Emphasizes centralized design for analytics and reporting.
Google Cloud
A data warehouse, also called an enterprise data warehouse (EDW), is an enterprise data platform used for the analysis and reporting of structured and semi-structured data from multiple data sources.
Notes support for structured and semi-structured workloads.
SAP
A data warehouse (DW) is a digital storage system that connects and harmonizes large amounts of data from many different sources. Its purpose is to feed business intelligence (BI), reporting, and analytics.
Highlights harmonization with BI and reporting use cases.
IBM
A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI), and machine learning.
Includes AI/ML support and centralized consistency.
RELATED TERMS
Extract-Transform-Load ELT Data Mart OLAP Kimball Methodology Star Schema Semantic Layer BI
COMPETING TERMS
RELATIONS
KNOWLEDGE AREAS
Data Management Data Architecture Data Warehousing & BI Data Storage & Operations
TARGET AUDIENCE
Data Architect BI Specialist Data Engineer Data Analyst Analytics Engineer Data Modeler Chief Data Officer
EXTERNAL SOURCES
- Wikipedia - Data warehouse
- Google Cloud - Google Cloud overview and guidance
- Microsoft - Azure architectural data warehouse guide
- Snowflake - Cloud warehouse platform docs
TOP QUESTIONS
Why use a Data Warehouse?
It provides integrated, consistent data optimized for analytics. Centralization improves performance, governance, and trust.
Category: Relevancy
How does a Data Warehouse differ from a Data Lake?
Warehouses enforce schema-on-write for analytics; lakes store raw data with schema-on-read flexibility.
Category: Comparison
What modeling approaches fit a Data Warehouse?
Dimensional modeling (star/snowflake), data vault, and semantic layers are common.
Category: BestPractice
What are common mistakes in Data Warehouse projects?
Over-engineering, poor source integration, weak quality controls, and ignoring governance lead to unreliable data.
Category: Mistakes
Is ELT preferable to ETL for Data Warehouses?
Cloud warehouses often favor ELT for scalability, but ETL suits constrained or compliance-heavy environments.
Category: Opinion
How is a Data Warehouse managed?
Define SLAs, implement ingestion and transformation pipelines, monitor performance, and enforce access and quality controls.
Category: Management
What is the risk of ignoring Data Warehouse maintenance?
Query performance degrades, costs rise, and data becomes stale, undermining decisions.
Category: Risk
What is currently hot around Data Warehouse?
Lakehouse integration, semantic layers, and cost-aware compute scaling are trending.
Category: Topic
Where to start with a Data Warehouse?
Align business metrics, design a dimensional model, pick a platform, and build incremental pipelines with tests.
Category: GettingStarted