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7.4 Data Marts & Enterprise Data Warehouses

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DAMA Certified Data Management Professional CDMP

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1 Chapter 1: Exam Overview & DMBoK Framework

1.1 CDMP Exam Details & Strategy 1.2 The DMBoK Framework 1.3 Data Management Organization & Roles

2 Chapter 2: Data Governance (Part A)

2.1 Data Governance Principles & Drivers 2.2 Operating Models & Structures 2.3 Policies, Standards, and Procedures

3 Chapter 2: Data Governance (Part B)

2.4 Data Stewardship 2.5 Governance Metrics & Implementation

4 Chapter 3: Data Modeling and Design (Part A)

Section 3.1: Conceptual, Logical, and Physical Models Section 3.2: ERD Notations & Relational Modeling Section 3.3: Normalization

5 Chapter 3: Data Modeling and Design (Part B)

3.4 Dimensional Modeling 3.5 Naming Standards & Modeling Best Practices

6 Chapter 4: Data Quality (Part A)

Section 4.1: Data Quality Dimensions Section 4.2: Data Quality Policy & Rules Section 4.3: DMAIC & DQ Improvement Lifecycle

7 Chapter 4: Data Quality (Part B)

4.4 Root-Cause Analysis & Remediation 4.5 DQ Metrics, Tools, and Reporting

8 Chapter 5: Metadata Management (Part A)

5.1 Business, Technical, and Operational Metadata 5.2 Metadata Lineage & Impact Analysis 5.3 Repository Architectures

9 Chapter 5: Metadata Management (Part B)

5.4 Standards and Semantics 5.5 Metadata Governance & Integration

10 Chapter 6: Master and Reference Data Management

6.1 Master vs Reference Data 6.2 MDM Repository Architectures 6.3 Match-Merge & Identity Resolution 6.4 Reference Data Governance & Standards

11 Chapter 7: Data Warehousing and Business Intelligence

7.1 DW Architectures: Inmon vs Kimball 7.2 Data Warehousing Storage & Load Patterns 7.3 BI Delivery & OLAP 7.4 Data Marts & Enterprise Data Warehouses

12 Chapter 8: Data Architecture

8.1 Enterprise Data Architecture & Zachman Framework 8.2 Data Flow Diagrams & Value Chains 8.3 Architecture Frameworks & Standards

13 Chapter 9: Document and Content Management

9.1 Records Management & ECM 9.2 Retention, Disposal, and E-Discovery 9.3 ARMA GARP Principles & Taxonomies

14 Chapter 10: Data Integration and Interoperability

Section 10.1: Integration Patterns & Technologies Section 10.2: Replication & Virtualization Section 10.3: Canonical Models & Integration Standards

15 Chapter 11: Data Security

Section 11.1: Security Principles: CIA Triad & Privacy Section 11.2: Access Control & Encryption Section 11.3: Security Regulations & Standards

16 Chapter 12: Data Storage and Operations

Section 12.1: Database Technologies & DBA Roles Section 12.2: Backup, Recovery, and High Availability Section 12.3: Performance Tuning & Operations

17 Chapter 13: Big Data and Data Science

13.1 Big Data V's & Technologies

18 Chapter 14: Data Ethics

14.1 DMBoK Data Ethics Framework

19 Chapter 15: Data Management Process and Maturity

15.1 Maturity Assessment Frameworks

7.4 Data Marts & Enterprise Data Warehouses

Key Takeaways Explain This Topic AI

Dependent data marts are fed directly from a centralized EDW, ensuring enterprise consistency, whereas independent data marts bypass the EDW and create data silos.

Independent data marts lead to redundant ETL pipelines, conflicting business definitions, high maintenance costs, and severe reconciliation issues.

A Data Hub coordinates operational near-real-time data exchange and transit, whereas an EDW persists historical data for strategic analytical querying.

Virtual Data Warehouses use logical virtualization layers to query sources directly without physical consolidation, providing agility but risking source system performance.

Last updated: July 2026

Data Marts, Enterprise Data Warehouses, and Integration Patterns

In enterprise data management, the configuration and integration of analytical data stores determine how effectively business units can access consistent data. An organization's architecture might feature a centralized Enterprise Data Warehouse ( EDW ), distributed data marts , a unified data hub , or a virtualized logical data layer. Understanding the distinctions, relationships, and governance challenges associated with these different configurations is essential for maintaining data integrity and avoiding toxic data silos.

Data Marts: Dependent vs. Independent

A data mart is a subset of data that is focused on a single subject area, department, or business function. Data marts are designed to make it easier and faster for specific user groups to access the data they need. However, the architectural design of a data mart dictates its long-term value to the enterprise.

Dependent Data Marts

Dependent data marts are populated directly from a centralized, unified Enterprise Data Warehouse (EDW). In this model (which aligns with Bill Inmon’s CIF architecture), all enterprise data is extracted from sources, cleaned, integrated, and loaded into the core 3NF EDW. The dependent data marts are then constructed by pulling data from this single source of truth and restructuring it to meet departmental needs.

Pros : Complete data consistency. Because every data mart draws from the same source, their metrics, business definitions, and values are fully aligned. Reconciliation is straightforward, and enterprise-wide data governance is tightly controlled.

Cons : Long initial implementation cycles. The centralized warehouse must be built first, meaning business departments may wait months before their specific data mart is operational. High upfront development cost.

Independent Data Marts

Independent data marts are built directly from operational source systems, bypassing any centralized data warehouse. Each department (such as Sales, Finance, or Marketing) designs and implements its own ETL pipelines, storage structures, and reporting systems to meet its immediate analytical needs.

Pros : Rapid deployment and low initial cost for a single department. Business units have complete autonomy and do not have to wait for corporate IT to build an enterprise-wide system.

Cons : Creation of highly fragmented data silos . Because there is no central coordination, departments define key metrics differently (for example, Sales and Finance may have different definitions of 'revenue' or 'active customer'). This leads to duplicate ETL pipelines, redundant storage, high maintenance costs, and conflicting reports that make corporate-level decision-making difficult. DAMA DMBoK2 strongly cautions against independent data marts due to these structural governance risks.

Data Hub Model

A data hub (or Data Hub Architecture ) is a centralized model designed to facilitate data exchange, routing, and synchronization between multiple source and target systems. While it shares some physical similarities with a data warehouse (like centralized data storage), its purpose is entirely different.

Data Hub : Focused on operational integration and near-real-time data exchange. It is a transit-oriented store that routes data between applications, services, and operational databases. It typically stores only the current, active state of the data, and data is often transient (purged after delivery).

Data Warehouse (EDW) : Focused on strategic analysis and historical reporting. It is a persistent store that keeps decades of historical data, structured for read-heavy query performance. It is not designed to route data back to operational systems in real-time.

Organizations often implement both. A data hub acts as the operational clearinghouse for app-to-app integration and master data synchronization, while periodically loading data into the EDW for long-term analytical tracking.

Virtual Data Warehouses

A Virtual Data Warehouse ( VDW ) is an architectural pattern that integrates data from disparate source systems using data virtualization technology instead of physically consolidating the data into a central database. The VDW establishes a logical middleware layer that exposes a unified semantic model. When a user runs a query, the virtualization engine translates it on-the-fly into multiple sub-queries, executes them against the physical source systems, compiles the results, and returns them to the user.

Pros : Extreme agility. It allows organizations to prototype reports and access new data sources quickly without waiting for lengthy ETL developments. It avoids the hardware and storage costs of physically duplicating data.

Cons : Performance is highly vulnerable to network latency and source database workloads; complex analytical queries can degrade operational systems. Furthermore, it does not preserve historical data; if a source system deletes records after 90 days, that historical data is permanently lost to the virtual warehouse.

Summary of Analytical Data Stores

  • Attribute Enterprise Data Warehouse (EDW) Dependent Data Mart Data Hub Virtual Data Warehouse
  • Storage Physical (Persistent) Physical (Persistent) Physical (Often Transient) Logical (None)
  • Scope Enterprise-wide Subject / Departmental Enterprise Integration Enterprise or federated

Purpose Historical analysis & strategic BI Departmental BI & reporting Application integration & exchange Agile integration & rapid query

Data Latency High (Batch/Scheduled) High (Lagged from EDW) Low (Near-real-time) Low (Real-time federated query)

  • Silo Risk Low Low Low Low-to-Medium
  • CDMP Exam Tips and Common Pitfalls

Disadvantages of Independent Data Marts : The CDMP exam will frequently test your knowledge of the risks of independent data marts. Look for terms like 'data silos,' 'redundant ETL,' 'reconciliation challenges,' and 'conflicting metrics.'

Data Hub vs. EDW : Understand that a Data Hub is for operational exchange and is transactional/current-state, while an EDW is for analytical reporting and is historical.

Data Virtualization Limits : Remember that data virtualization does not store physical data, making it unsuitable for long-term historical tracking when source systems delete historical records.