The EDW Data Architecture – Enterprise Data Model (EDM), 3NF & Data Vault 2.0 Training Course provides a comprehensive foundation for designing and implementing scalable Enterprise Data Warehouse (EDW) architectures.

The course covers the role of the Enterprise Data Model (EDM) in establishing consistent enterprise-wide data structures and explores Third Normal Form (3NF) as a normalized modeling approach for integrated enterprise data. Participants then progress into Data Vault 2.0 (DV 2.0) concepts and learn how Hubs, Links, Satellites, and Bridge structures support scalable, auditable, historically traceable, and adaptable enterprise data platforms.

Throughout the course, participants examine how conceptual, logical, and physical data models relate to EDW architecture and how traditional normalized warehouse approaches compare and integrate with Data Vault architectures. The course also addresses business keys, relationships, historical tracking, loading patterns, information delivery, performance considerations, governance, metadata, and architectural best practices.

The program is suitable for organizations building or modernizing enterprise data warehouses, data integration platforms, analytical environments, and enterprise data architectures.

 

Duration 5 Days – 35 hrs.

 

Objectives

• Explain the purpose and major components of an Enterprise Data Warehouse architecture.
• Understand the role of Enterprise Data Models in enterprise data management.
• Differentiate conceptual, logical, and physical data modeling.
• Apply normalization principles and design data structures using 3NF.
• Identify entities, attributes, business keys, relationships, and cardinalities.
• Understand the strengths and limitations of traditional 3NF EDW architectures.
• Explain the principles and architecture of Data Vault 2.0.
• Design Data Vault structures using Hubs, Links, and Satellites.
• Identify appropriate enterprise business keys for Hub design.
• Model business relationships using Links.
• Manage descriptive and historical information using Satellites.
• Understand different Satellite design and historization patterns.
• Explain the role of Bridge structures in Data Vault information delivery.
• Understand Point-in-Time (PIT) structures and their relationship to query optimization.
• Distinguish Raw Vault, Business Vault, and information delivery layers.
• Map source-system data into enterprise Data Vault structures.
• Understand incremental loading and historical data tracking concepts.
• Compare 3NF, Data Vault 2.0, and dimensional modeling approaches.
• Integrate EDW architecture with downstream analytics and business intelligence environments.
• Apply governance, metadata, lineage, security, and data quality principles to EDW architecture.
• Develop an architectural approach for scalable and maintainable enterprise data platforms.

 

Target Audience

• Enterprise Data Architects
• Data Architects
• Solution Architects
• Enterprise Architects
• Data Warehouse Architects
• Data Modelers
• Data Engineers
• ETL/ELT Developers
• Database Developers
• Database Administrators
• Business Intelligence Developers
• Analytics Engineers
• Data Integration Specialists
• Data Governance Professionals
• Technical Leads
• Data Warehouse Developers
• IT Professionals involved in EDW modernization projects

 

Prerequisites

• Basic understanding of relational databases and database concepts
• Familiarity with tables, primary keys, foreign keys, and relationships
• Basic SQL knowledge
• General understanding of data warehousing concepts
• Familiarity with ETL or ELT processes is beneficial
• Basic knowledge of business intelligence and analytics environments is helpful
• Previous data modeling experience is advantageous but not mandatory

 

 

Course Outline

Day 1 – Enterprise Data Warehouse Architecture and Enterprise Data Modeling

Module 1: Enterprise Data Warehouse (EDW) Fundamentals

• Understanding enterprise data warehousing
• Purpose and business value of an EDW
• Operational systems versus analytical systems
• Enterprise data integration challenges
• Characteristics of enterprise-scale data platforms
• Common EDW architecture components
• Sources, integration layers, storage layers, and consumption layers
• Historical data and enterprise analytics
• EDW architecture patterns
• Traditional versus modern data architecture

Module 2: Enterprise Data Model (EDM) Fundamentals

• Understanding the Enterprise Data Model
• Role of EDM within enterprise architecture
• Business concepts and enterprise data domains
• Subject areas and domain modeling
• Enterprise-wide data definitions
• Establishing common business terminology
• Identifying entities and attributes
• Business keys and identifiers
• Relationships and cardinality
• Reference and master data considerations

Module 3: Conceptual, Logical, and Physical Data Models

• Conceptual data modeling
• Logical data modeling
• Physical data modeling
• Moving from business concepts to implementation
• Entities, attributes, relationships, and constraints
• Natural versus surrogate keys
• Data types and physical structures
• Modeling standards and conventions
• Model documentation
• Maintaining consistency across modeling layers

Module 4: Developing an Enterprise Data Model

• Gathering enterprise data requirements
• Identifying core business entities
• Establishing enterprise business keys
• Resolving terminology differences between systems
• Modeling cross-functional business relationships
• Handling shared enterprise entities
• Designing extensible enterprise models
• EDM governance and ownership
• Model versioning and lifecycle management

 

Day 2 – 3NF Modeling for Enterprise Data Warehouses

Module 5: Relational Modeling and Normalization

• Relational data modeling principles
• Functional dependencies
• Understanding data redundancy
• Data anomalies and integrity
• First Normal Form (1NF)
• Second Normal Form (2NF)
• Third Normal Form (3NF)
• Higher normalization concepts
• Normalization versus denormalization
• Practical normalization considerations

Module 6: Designing a 3NF Enterprise Data Warehouse

• Role of 3NF in enterprise data warehousing
• Integrated enterprise data structures
• Designing normalized EDW entities
• Business key management
• Surrogate key considerations
• Modeling enterprise relationships
• Reference and lookup structures
• Historical data considerations
• Integrating multiple source systems
• Managing source-system differences

Module 7: 3NF EDW Integration Patterns

• Source-to-target mapping
• Data acquisition and staging
• Data standardization
• Data cleansing and transformation
• Entity resolution
• Master and reference data integration
• Managing inserts and updates
• Historical data preservation
• Audit columns and technical metadata
• Data lineage considerations

Module 8: 3NF Architecture Strengths and Challenges

• Advantages of normalized EDW models
• Data integrity and consistency
• Enterprise integration benefits
• Complexity of large normalized models
• Schema evolution challenges
• Performance considerations
• Query complexity
• Change management
• Comparing normalized EDW and dimensional models
• Determining when 3NF is appropriate

Day 3 – Data Vault 2.0 Architecture: Hubs, Links, and Satellites

Module 9: Introduction to Data Vault 2.0

• Evolution of Data Vault
• Data Vault 2.0 principles
• Business-driven data integration
• Scalability and agility
• Historical traceability
• Auditability and lineage
• Parallel loading concepts
• Insert-only architecture principles
• Separating business keys, relationships, and context
• Data Vault within modern EDW architecture

Module 10: Data Vault 2.0 Architecture Layers

• Staging architecture
• Raw Data Vault
• Business Vault
• Information delivery layer
• Relationship between layers
• Source-system integration
• Business rule placement
• Historical preservation
• Data consumption patterns
• Integration with analytics platforms

Module 11: Designing Hubs

• Purpose of a Hub
• Identifying business keys
• Enterprise business key considerations
• Hub structure
• Hash key concepts
• Load date and record source
• Business key uniqueness
• Multiple-source integration
• Hub loading concepts
• Hub design best practices

Module 12: Designing Links

• Purpose of Links
• Modeling business relationships
• Binary and multi-way Links
• Link keys
• Connecting Hubs
• Relationship historization
• Transactional relationships
• Hierarchical relationships
• Link loading concepts
• Link design considerations

Module 13: Designing Satellites

• Purpose of Satellites
• Descriptive attributes
• Historical tracking
• Satellite parent structures
• Effective dates and load dates
• Record source
• Hash difference concepts
• Satellite splitting strategies
• Rate-of-change considerations
• Source-driven Satellites
• Business-driven Satellites
• Satellite design best practices

 

Day 4 – Advanced Data Vault 2.0 Modeling, Bridge and Business Vault

Module 14: Advanced Hub, Link, and Satellite Patterns

• Multiple-source Hubs
• Same-as relationships
• Hierarchical Links
• Transaction Links
• Non-historized Links
• Multi-active Satellites
• Effectivity Satellites
• Record-tracking Satellites
• Reference data patterns
• Handling changing business relationships
• Managing complex enterprise structures

Module 15: Business Vault Architecture

• Raw Vault versus Business Vault
• Purpose of the Business Vault
• Applying business rules
• Derived data structures
• Business calculations
• Standardization and enrichment
• Reusable enterprise business logic
• Managing business-rule changes
• Traceability from Raw Vault to Business Vault

Module 16: Point-in-Time (PIT) Structures

• Purpose of PIT structures
• Historical querying challenges
• Satellite alignment
• Snapshot concepts
• PIT table construction principles
• Query performance considerations
• PIT refresh strategies
• PIT usage patterns

Module 17: Bridge Structures

• Purpose of Bridge structures
• Understanding relationship traversal
• Simplifying complex Link navigation
• Hierarchical Bridge structures
• Business relationship Bridges
• Historical Bridge considerations
• Bridge construction concepts
• Refresh and maintenance considerations
• Bridge structures for information delivery
• Performance considerations

Module 18: Loading Data Vault Structures

• Source ingestion and staging
• Loading sequence
• Hub loading
• Link loading
• Satellite loading
• Incremental processing
• Change detection
• Hash keys and hash differences
• Parallelization concepts
• Restartability and recoverability
• Auditability and reconciliation
• Handling late-arriving data

 

Day 5 – EDW Integration, Architecture Comparison and Enterprise Design

Module 19: 3NF versus Data Vault 2.0

• Architectural differences
• Modeling philosophy
• Data integration approaches
• Historical tracking
• Auditability
• Scalability
• Schema evolution
• Development agility
• Performance considerations
• Operational complexity
• Selecting the appropriate modeling approach
• Hybrid architecture considerations

Module 20: Data Vault and Dimensional Information Delivery

• Role of dimensional models
• Facts and dimensions
• Star schema fundamentals
• Transforming Vault data for analytics
• Building information marts
• Business Vault as an information delivery source
• Handling historical dimensions
• Analytical consumption patterns
• Supporting Power BI and other BI platforms
• Balancing integration and reporting requirements

Module 21: Enterprise Data Governance and Data Quality

• Data ownership and stewardship
• Enterprise data standards
• Metadata management
• Business metadata and technical metadata
• Data lineage
• Data quality rules
• Master and reference data
• Data classification
• Security and access considerations
• Audit and compliance requirements
• Governance across EDW layers

Module 22: Designing an End-to-End EDW Architecture

• Identifying source systems
• Establishing enterprise data domains
• Developing the Enterprise Data Model
• Selecting 3NF, Data Vault, or hybrid approaches
• Defining Raw Vault structures
• Designing Hubs, Links, and Satellites
• Defining Business Vault requirements
• Designing PIT and Bridge structures
• Planning information delivery
• Supporting BI and analytics requirements
• Scalability and performance considerations
• Data lineage and governance integration
• Architecture documentation
• EDW design best practices

Module 23: EDW Architecture Case Study

• Reviewing enterprise business requirements
• Identifying business entities and business keys
• Developing a high-level EDM
• Modeling normalized 3NF structures
• Translating requirements into Data Vault structures
• Designing Hubs
• Designing Links
• Designing Satellites
• Identifying PIT and Bridge requirements
• Defining Raw Vault and Business Vault boundaries
• Designing downstream information delivery
• Reviewing the complete enterprise data architecture