CDP-P
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CDP P
The CCPP-P certification equips data professionals with practical expertise in advanced governance, enterprise architecture, data modeling, quality strategy, and large‑scale integration, enabling them to design and implement robust data solutions.
90
Minutes
Who Should Take This
It is intended for data analysts, engineers, or managers with two to ten years of experience who are responsible for shaping data policies, architectures, and pipelines. These professionals seek to deepen their strategic capabilities, lead cross‑functional data initiatives, and demonstrate mastery of the DMBoK2 knowledge areas.
What's Covered
1
Domain 1: Advanced Data Governance and Stewardship
2
Domain 2: Enterprise Data Architecture
3
Domain 3: Advanced Data Modeling and Design
4
Domain 4: Data Quality Strategy and Operations
5
Domain 5: Data Integration and Interoperability at Scale
6
Domain 6: Data Security and Privacy Management
7
Domain 7: Metadata Management and Data Cataloging
8
Domain 8: Data Warehousing and Analytics Architecture
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
Domain 1: Advanced Data Governance and Stewardship
3 topics
Governance operating models and strategy
- Implement a data governance operating model selecting from centralized, federated, or hybrid approaches based on organizational size, structure, and data management maturity.
- Develop data governance policies and standards that align with enterprise strategy, defining data ownership, accountability chains, escalation procedures, and compliance monitoring mechanisms.
- Analyze a data governance program to assess maturity levels using the DAMA capability maturity framework and recommend a prioritized improvement roadmap.
- Design a data governance communication and change management strategy to secure executive sponsorship, stakeholder engagement, and organizational adoption.
Regulatory compliance and risk management
- Implement compliance monitoring processes for data regulations including GDPR, CCPA, HIPAA, SOX, and sector-specific mandates with automated detection and reporting.
- Analyze data management risks across the enterprise to identify compliance gaps, data breach exposure, and governance control weaknesses requiring remediation.
- Design a data risk management framework that integrates with enterprise risk management, defining risk appetite, risk assessment methodologies, and mitigation strategies for data assets.
Data ethics and responsible data management
- Implement data ethics review processes including ethical impact assessments, bias detection in data collection, and responsible data use guidelines for enterprise data assets.
- Analyze ethical risks in data management practices by evaluating consent validity, data minimization compliance, and potential for discriminatory outcomes in data-driven decisions.
- Design a responsible data management program that establishes ethical guidelines, review boards, and monitoring processes for data collection, usage, and sharing across the enterprise.
2
Domain 2: Enterprise Data Architecture
2 topics
Data architecture design and modernization
- Implement enterprise data architecture aligned with business capabilities, defining data domains, bounded contexts, data products, and integration contracts.
- Analyze existing data architectures to identify technical debt, scalability constraints, and modernization opportunities for migration to cloud-native or hybrid data platforms.
- Design a data platform modernization strategy incorporating data mesh principles, data fabric technologies, and event-driven architecture patterns for enterprise-scale data delivery.
Cloud data architecture and integration
- Implement cloud data architecture patterns including data lakes, lakehouses, cloud data warehouses, and serverless data processing for scalable analytics workloads.
- Analyze data integration patterns across hybrid cloud environments to evaluate latency, consistency, cost, and governance trade-offs for multi-cloud data strategies.
- Design a data lineage and observability strategy that provides end-to-end visibility across on-premises and cloud data assets for compliance and impact analysis.
3
Domain 3: Advanced Data Modeling and Design
3 topics
Enterprise modeling and schema governance
- Implement enterprise conceptual data models that serve as canonical schemas across business domains, ensuring alignment with business terminology and governance standards.
- Implement schema evolution and version management practices for production databases including backward-compatible changes, migration scripts, and deprecation policies.
- Analyze data model quality by evaluating normalization levels, naming standards compliance, relationship completeness, and alignment with business semantics.
- Design a model governance process that establishes review workflows, approval gates, and reuse standards for shared data models across project teams.
Polyglot persistence and advanced patterns
- Implement polyglot persistence strategies that select appropriate storage engines for different data access patterns including transactional, analytical, search, and graph workloads.
- Analyze trade-offs between data modeling paradigms for complex domains, evaluating consistency, query performance, and developer productivity for each persistence choice.
- Design data modeling standards for event sourcing and CQRS architectures, defining event schema conventions, aggregate boundaries, and read model projection patterns.
Data model governance and standards
- Implement data model review processes including naming convention enforcement, design pattern compliance, and cross-project model reuse standards.
- Analyze data model technical debt by assessing normalization violations, schema bloat, unused entities, and cross-system model divergence requiring reconciliation.
4
Domain 4: Data Quality Strategy and Operations
2 topics
Enterprise data quality programs
- Implement enterprise data quality programs with defined quality dimensions, measurement processes, SLA targets, and organizational accountability structures.
- Implement automated data quality monitoring pipelines that profile incoming data, detect anomalies, trigger alerts, and generate quality scorecards for business stakeholders.
- Analyze data quality root causes using fishbone diagrams, Pareto analysis, and process mapping to identify systemic issues in data collection, transformation, and integration.
- Design a total data quality management strategy that links data quality outcomes to business value, establishes continuous improvement cycles, and integrates with data governance.
Master data quality and matching
- Implement master data matching and survivorship rules using deterministic and probabilistic matching algorithms to create and maintain golden records.
- Analyze master data quality by evaluating match accuracy, false positive rates, data completeness, and cross-system consistency for critical data entities.
- Design a master data quality remediation strategy that prioritizes entity resolution, defines stewardship workflows for exception handling, and measures business impact of improvements.
5
Domain 5: Data Integration and Interoperability at Scale
3 topics
Enterprise data integration architecture
- Implement enterprise data integration solutions using batch ETL, real-time streaming, change data capture, and API-based microservices for heterogeneous data ecosystems.
- Implement data pipeline orchestration using workflow engines, dependency management, error handling, retry strategies, and observability instrumentation.
- Analyze data integration performance bottlenecks by profiling pipeline throughput, latency, resource utilization, and data freshness against business SLA requirements.
- Design a data integration strategy for enterprise-wide data consolidation that addresses data sovereignty, cross-border transfer requirements, and multi-cloud portability.
Data contracts and semantic interoperability
- Implement data contracts between producers and consumers that define schema expectations, quality guarantees, SLAs, and backward compatibility requirements.
- Analyze semantic interoperability challenges across data domains, evaluating vocabulary alignment, ontology mapping, and business glossary consistency.
Real-time integration and streaming
- Implement event-driven data integration using message brokers, event streaming platforms, and change data capture for real-time data synchronization across enterprise systems.
- Analyze real-time integration architecture by evaluating event ordering guarantees, exactly-once delivery semantics, and backpressure handling for high-volume data streams.
- Design a hybrid integration strategy that combines batch, micro-batch, and real-time processing patterns based on data freshness requirements and cost constraints.
6
Domain 6: Data Security and Privacy Management
3 topics
Data security architecture and operations
- Implement data security architectures including defense-in-depth controls, encryption key management, data loss prevention, and security monitoring for structured and unstructured data.
- Analyze data security posture by assessing access control effectiveness, encryption coverage, audit trail completeness, and vulnerability exposure across data assets.
- Design a data privacy program that implements privacy-by-design principles, data protection impact assessments, consent management, and individual rights fulfillment processes.
Data classification and access governance
- Implement automated data classification using pattern matching, machine learning classifiers, and metadata tagging to discover and label sensitive data across the enterprise.
- Analyze data access governance by reviewing entitlements, segregation of duties, access recertification processes, and privileged data access controls.
Data breach prevention and response
- Implement data breach prevention controls including data loss prevention, database activity monitoring, and sensitive data discovery for proactive threat mitigation.
- Design a data breach response plan that defines detection triggers, containment procedures, notification requirements, forensic preservation, and post-breach remediation.
7
Domain 7: Metadata Management and Data Cataloging
2 topics
Enterprise metadata and catalog strategy
- Implement an enterprise data catalog with automated metadata harvesting, business glossary integration, data lineage visualization, and usage analytics.
- Analyze metadata management maturity by assessing catalog coverage, lineage completeness, glossary adoption rates, and metadata quality across the enterprise.
- Design a metadata management strategy that establishes metadata standards, automated collection pipelines, and self-service discovery capabilities for data consumers.
Data lineage and impact analysis
- Implement automated data lineage tracking from source systems through transformation layers to consumption endpoints using metadata harvesting and pipeline instrumentation.
- Analyze data lineage completeness and accuracy by comparing automated lineage with manual documentation, identifying blind spots, and measuring lineage coverage across critical data assets.
8
Domain 8: Data Warehousing and Analytics Architecture
2 topics
Modern analytics architecture
- Implement modern data warehouse and lakehouse architectures that support both operational and analytical workloads with appropriate partitioning, caching, and query optimization.
- Implement data virtualization and semantic layers that abstract physical data locations and provide unified query interfaces for self-service analytics consumers.
- Analyze analytics architecture performance by evaluating query response times, data freshness, concurrency support, and cost efficiency against business reporting requirements.
- Design an analytics platform strategy that balances real-time and batch processing needs, optimizes cost-performance trade-offs, and scales to meet enterprise reporting demands.
Self-service analytics governance
- Implement self-service analytics platforms with governed data access, certified datasets, approved metrics, and user-created content management for business user empowerment.
- Analyze self-service analytics adoption by measuring user engagement, content quality, data trust scores, and query performance to optimize platform utilization.
- Design a semantic layer strategy that provides consistent business definitions, calculated metrics, and governed data access through a universal query interface for all analytics tools.
Scope
Included Topics
- Applied data management knowledge across all DMBoK2 knowledge areas at the Practitioner level, requiring minimum 70% passing score and demonstrated expertise in at least two specialist domains.
- Advanced data governance implementation including operating model design, data governance maturity assessment, policy lifecycle management, regulatory compliance program management, and cross-functional governance coordination.
- Practitioner-level data architecture including enterprise data architecture design, data mesh and data fabric paradigms, data platform modernization, cloud data architecture, and integration architecture patterns at scale.
- Advanced data quality management including data quality strategy development, organizational data quality programs, SLA-driven quality monitoring, and root cause analysis methodologies for systemic data issues.
- Strategic data modeling and design including enterprise-scale conceptual modeling, model governance, schema evolution management, and polyglot persistence strategies across relational, dimensional, and NoSQL paradigms.
Not Covered
- Principal-level strategic advisory and executive data management leadership competencies tested at the 80% threshold.
- Deep data science algorithm development, neural network architecture design, and advanced statistical modeling covered by the CDS certification.
- Big data distributed systems engineering including cluster management, performance tuning, and infrastructure scaling covered by the CBDP certification.
- Vendor-specific certification content for specific data management platforms beyond what is needed for DMBoK2 practitioner competency.
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