Data Architecture:

 

•        Fundamentals of data architecture design
•        Enterprise architecture and data alignment
•        Reference architecture models and frameworks
•        Logical vs. physical architecture
•        Data modeling standards and methodologies
•        Architecture governance and scalability
•        Role of cloud computing in modern data architecture

 

 

Data Modeling and Design:

 

•         Conceptual, logical, and physical data models
•        Entities, attributes, relationships, and constraints
•        Normalization and denormalization theory
•        Modeling for operational vs. analytical systems
•        Use of metadata in design documentation
•        Data modeling tools and notations
•        Business rules and their integration with design

 

Data Storage and Operations:

 

•        Database types: relational, NoSQL, object-based
•        Concepts of indexing, partitioning, and storage optimization
•        Data backup and recovery strategies
•        High availability and disaster recovery
•        Lifecycle management for data retention
•        Data replication and storage compliance
•        Operational metadata and monitoring

 

 

Data Security and Privacy:

 

•        Theoretical foundations of data security
•        Confidentiality, integrity, and availability (CIA triad)
•        Access controls and encryption techniques
•        Data masking, anonymization, and pseudonymization
•        Regulatory requirements: GDPR, HIPAA, etc.
•        Risk management and incident response
•        Security policies and training programs

 

Data Integration and Interoperability:

 

•         The role of integration in enterprise architecture
•        Batch vs. real-time data processing
•        Data pipelines and middleware concepts
•        API-based integration frameworks
•        Data virtualization and federation
•        Challenges in achieving interoperability

 

 

Master and Reference Data Management (MDM/RDM):

 

•         Definitions and distinctions between master and reference data
•        Business drivers for MDM implementation
•        Hierarchies and data domains
•        Governance models for MDM
•        Reference data lifecycle and versioning
•        MDM architecture: registry, consolidated, and co-existence
•        Matching, merging, and survivorship rules

 

Data Quality Management:

 

•        Dimensions of data quality: accuracy, completeness, timeliness
•        Root causes of data quality issues
•        Quality assessment frameworks
•        Data profiling and data cleansing techniques
•        Metrics and KPIs for quality management
•        Embedding quality into data lifecycle
•        Building a data quality culture

 

 

Metadata and Final Exam Preparation:

 

•        Types of metadata: business, technical, operational
•        Metadata management strategies
•        Metadata repositories and catalogs
•        Role of metadata in discovery and governance
•        Final exam review: CDMP exam structure and sample questions
•        Tips for successful CDMP exam completion