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Subject-area focused analytical repository, typically a subset of an enterprise data warehouse tailored to departmental needs.
DESCRIPTION
A data mart narrows enterprise data into a domain-specific, curated store designed for focused analytics. It simplifies modeling and access for teams like sales or finance. Data marts often use dimensional schemas, support OLAP, and accelerate dashboards. They can be dependent on an EDW or independent, sourced directly from operational systems. Proper governance and integration prevent siloed metrics and ensure consistency with enterprise definitions.
ALTERNATIVE DEFINITIONS
IBM
A data mart is a subset of an enterprise data warehouse (EDW), focused on a particular subject or department.
Stresses subset of EDW and subject focus.
Microsoft Learn
A data mart is a subject-oriented data repository, often a subset of a data warehouse.
Emphasizes subject orientation and relationship to a warehouse.
Oracle
A data mart is a subset of a data warehouse designed to serve the needs of a specific business unit.
Highlights alignment with unit-specific analytics needs.
RELATED TERMS
Kimball Methodology Star Schema OLAP Extract-Transform-Load Data Lake BI Dashboard Analytics Engineer Governance Data Discovery Platform
COMPETING TERMS
KNOWLEDGE AREAS
Data Warehousing & BI Data Architecture Data Management
TARGET AUDIENCE
BI Specialist Analytics Engineer Data Engineer Data Architect Data Analyst
EXTERNAL SOURCES
- Wikipedia - Data mart
- IBM - IBM overview of data marts
- Microsoft - Azure architecture pattern
- Snowflake - Implementing data marts in Snowflake
TOP QUESTIONS
Why use a Data Mart instead of querying the Data Warehouse directly?
Data marts simplify models, improve performance, and tailor metrics for a team’s needs. They reduce complexity for focused analytics while inheriting governance from the warehouse.
Category: Relevancy
What is a dependent Data Mart?
A dependent data mart sources its data exclusively from an enterprise data warehouse, ensuring consistency with enterprise definitions and avoiding direct operational system extracts.
Category: Clarification
What are common mistakes when building a Data Mart?
Duplicating logic from the warehouse creates conflicting metrics. Weak change management and poor documentation lead to misinterpretation and trust issues.
Category: Mistakes
How does a Data Mart relate to OLAP?
Data marts often use dimensional models optimized for OLAP, enabling fast aggregations, slice-and-dice analysis, and time-based comparisons.
Category: Overview
What is a hot topic around Data Marts today?
Virtual data marts on cloud platforms provide logical segmentation without duplicating data, leveraging views, security, and compute isolation.
Category: Topic
How should Data Mart governance be handled?
Establish standardized dimensions, conformed metrics, and stewardship. Automate lineage, quality checks, and access controls to align with enterprise policies.
Category: Management
How do Data Marts compare to Data Lakehouses?
Lakehouses can host dimensional marts but also support unstructured data and ML workloads. Data marts focus on curated analytics for specific domains.
Category: Comparison
What performance practices help Data Marts?
Use star schemas, partitioning, materialized views, summarization tables, and columnar storage to speed aggregations and BI queries.
Category: BestPractice
Where to start building a Data Mart?
Select a clear subject area, define conformed dimensions, model a star schema, automate ETL with dbt or ELT, and integrate with BI tools.
Category: Learning