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A managed, elastic analytical database in the cloud, using columnar storage and MPP for scalable SQL analytics.
DESCRIPTION
A cloud data warehouse offloads infrastructure to a provider, offering elastic compute, columnar storage, and distributed SQL engines. It ingests data via ELT and supports BI, dashboards, and ad hoc analysis. Decoupled storage and compute enable cost and performance scaling. Features like time travel, semi-structured support, and secure data sharing are common. It competes with data lakes and lakehouses for flexibility and cost efficiency.
ALTERNATIVE DEFINITIONS
IBM
A cloud data warehouse is a managed data warehouse service hosted in the cloud that is optimized for analytics and scalability.
Stresses managed service, analytics focus, and scalability.
Google Cloud
A cloud data warehouse is a repository that consolidates data for analysis and is delivered as a cloud service.
Defines consolidation for analysis with cloud delivery model.
Snowflake
A cloud data warehouse is a database delivered as a managed service in the cloud, designed for scalable analytics and business intelligence.
Connects to BI explicitly and emphasizes managed service.
RELATED TERMS
Data Warehouse Columnar Storage Massively Parallel Processing ELT Separation of Storage and Compute Materialized View Query Federation Time Travel
COMPETING TERMS
RELATIONS
- Broader: Data Warehouse
KNOWLEDGE AREAS
Data Warehousing & BI Cloud Data Architecture Data Engineering
TARGET AUDIENCE
Data Engineer Analytics Engineer Data Architect BI Specialist Data Analyst Cloud Architect
EXTERNAL SOURCES
- Wikipedia - Data warehouse
- Google BigQuery - BigQuery overview
- Snowflake - Key concepts
- Microsoft Azure - Synapse overview
TOP QUESTIONS
What is a Cloud Data Warehouse?
It’s a managed, elastic analytical database delivered as a cloud service. It scales storage and compute independently for SQL analytics.
Category: Definition
Why choose a Cloud Data Warehouse over on-premises?
To gain elasticity, managed operations, and rapid provisioning. It reduces undifferentiated infrastructure work.
Category: Benefits
What workloads fit best?
BI reporting, ad hoc SQL, ELT transformations, and semi-structured analytics fit well. Real-time needs may require streaming integration.
Category: UseCases
How does ELT relate to Cloud Data Warehouses?
ELT leverages scalable warehouse compute to transform data after loading. It’s common in cloud-native pipelines.
Category: Concept
What are cost pitfalls?
Uncontrolled compute, cross-region egress, and inefficient queries inflate costs. Implement budgets, workload management, and query tuning.
Category: Mistakes
How does it compare to a Data Lakehouse?
Lakehouses unify lake flexibility with warehouse performance. Choice depends on governance needs, cost, and workload mix.
Category: Comparison
How to ensure data governance?
Use catalogs, role-based access, row/column security, and lineage. Apply quality checks and audits.
Category: Governance
What performance practices matter?
Partitioning, clustering, materialized views, and result caching boost performance. Optimize data types and file formats.
Category: Performance
How to start migrating?
Prioritize subject areas, establish ELT, and validate parity. Use phased cutovers with parallel runs and benchmarks.
Category: GettingStarted