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Data Mesh & Data Fabric Architecture Training Course

Compares Data Mesh and Data Fabric approaches and shows how to design domain data ownership, data products and federated governance across the enterprise.

DARC · Data ArchitectureAll Levels#2738ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Data Architecture

Course Overview

This course equips participants with the practical knowledge to design and implement modern enterprise data architectures using Data Mesh and Data Fabric approaches. It explores how organizations can improve data accessibility, interoperability, ownership, and governance while supporting distributed analytics and data-driven decision-making. Participants examine domain-oriented data ownership, data as a product, federated computational governance, metadata-driven integration, and automated data management. The programme focuses on architecture design, implementation strategies, integration patterns, security, scalability, and governance to help organizations build flexible data ecosystems aligned with business objectives.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Explain the principles, components, and business value of Data Mesh and Data Fabric architectures.
  • Compare Data Mesh and Data Fabric approaches and identify suitable use cases for each.
  • Design domain-oriented data ownership models aligned with organizational structures.
  • Apply data-as-a-product principles to improve data quality, usability, and accountability.
  • Develop federated governance frameworks that balance organizational standards with domain autonomy.
  • Design metadata-driven integration and data discovery capabilities.
  • Evaluate data integration, orchestration, and interoperability patterns across distributed environments.
  • Incorporate security, access management, privacy, and compliance requirements into architecture designs.
  • Define operating models, roles, responsibilities, and performance measures for distributed data ecosystems.
  • Develop a practical roadmap for adopting Data Mesh or Data Fabric within an enterprise.

Who Should Attend

This course is designed for enterprise architects, data architects, data engineers, data platform engineers, data governance specialists, analytics professionals, and IT leaders responsible for developing or modernizing enterprise data environments. It is particularly relevant to professionals managing distributed data sources, cross-functional data ownership, enterprise integration, and large-scale analytical platforms. The programme is also suitable for chief data officers, digital transformation managers, business intelligence managers, information management specialists, and technology decision-makers in government entities, banks, financial institutions, oil and gas companies, industrial organizations, and large corporations. Participants should have a basic understanding of data management, databases, enterprise systems, or cloud technologies.

Learning Outcomes

  • Assess organizational readiness for Data Mesh and Data Fabric adoption.
  • Distinguish between domain-oriented decentralization and metadata-driven data integration.
  • Define data domains, ownership boundaries, and accountability structures.
  • Develop data product specifications covering quality, accessibility, documentation, and service expectations.
  • Design federated governance processes and shared data standards.
  • Explain how metadata catalogs, lineage, semantic models, and automation support data discovery and integration.
  • Select appropriate integration and interoperability patterns for distributed data environments.
  • Establish security, privacy, access control, and compliance requirements across data domains.
  • Define operational responsibilities and measures for data product performance and adoption.
  • Produce an architecture proposal and phased implementation roadmap aligned with enterprise priorities.

Course Outline

Day 1

Foundations of Data Mesh and Data Fabric Architecture

  • Understanding the evolution of enterprise data architecture and the limitations of centralized approaches.
  • Examining the principles, objectives, and architectural components of Data Mesh.
  • Exploring Data Fabric concepts, metadata-driven integration, and connected data environments.
  • Comparing Data Mesh and Data Fabric in terms of ownership, integration, governance, and operating models.
  • Understanding distributed data ecosystems and their relationship with data warehouses, data lakes, and lakehouse platforms.
  • Identifying organizational, technical, and operational drivers for architecture modernization.
  • Evaluating business requirements, data complexity, organizational maturity, and integration challenges.
  • Reviewing architecture patterns for analytical and operational data environments.
  • Practical Exercise: Assess an organization's current data architecture and identify opportunities for Data Mesh or Data Fabric adoption.
Day 2

Data Mesh Principles, Domain Ownership, and Data Products

  • Understanding domain-oriented decentralized data ownership and its organizational implications.
  • Identifying business domains, subdomains, data boundaries, and ownership responsibilities.
  • Applying the data-as-a-product concept to enterprise information assets.
  • Defining data product interfaces, documentation, access methods, and service expectations.
  • Establishing data quality, reliability, usability, and lifecycle management requirements.
  • Exploring self-service data infrastructure and reusable platform capabilities.
  • Defining collaboration between domain teams, platform teams, and central data functions.
  • Addressing data product discovery, interoperability, versioning, and dependency management.
  • Practical Exercise: Design a data product for a selected business domain, including ownership, consumers, quality requirements, access methods, and service measures.
Day 3

Data Fabric, Metadata Management, and Intelligent Integration

  • Understanding metadata-driven architecture and its role in connecting distributed data assets.
  • Designing metadata catalogs, business glossaries, and data discovery capabilities.
  • Exploring active metadata management, data lineage, and relationship mapping.
  • Evaluating data integration, virtualization, replication, and synchronization patterns.
  • Understanding semantic layers and consistent interpretation of enterprise data.
  • Applying automation to data discovery, integration workflows, and policy enforcement.
  • Connecting heterogeneous databases, cloud platforms, enterprise applications, and analytical systems.
  • Addressing metadata quality, interoperability, data movement, and integration complexity.
  • Practical Exercise: Develop a Data Fabric architecture that connects multiple data sources through metadata, discovery, integration, and governed access capabilities.
Day 4

Federated Governance, Security, and Operational Management

  • Designing federated computational governance for distributed data environments.
  • Defining enterprise-wide policies alongside domain-level decision-making authority.
  • Establishing common standards for data quality, interoperability, classification, and documentation.
  • Applying identity management, role-based access, encryption, and privacy controls.
  • Integrating regulatory obligations, auditability, retention, and compliance requirements.
  • Managing data lineage, ownership records, policy enforcement, and accountability.
  • Defining service-level objectives and operational measures for data products and shared services.
  • Monitoring reliability, availability, adoption, data quality, and governance effectiveness.
  • Practical Exercise: Develop a federated governance model with enterprise standards, domain responsibilities, security controls, and measurable service expectations.
Day 5

Enterprise Implementation Strategy and Architecture Roadmap

  • Evaluating organizational readiness, technology capabilities, and operating model maturity.
  • Selecting suitable pilot domains and prioritizing data products based on business value.
  • Defining the roles of enterprise architecture, data governance, domain teams, and platform engineering.
  • Planning integration with existing data warehouses, lakes, cloud platforms, and enterprise applications.
  • Assessing technology dependencies, migration requirements, and implementation risks.
  • Establishing success measures for data quality, discoverability, reuse, delivery speed, and adoption.
  • Developing a phased implementation plan covering pilots, scaling, governance, and continuous improvement.
  • Evaluating architectural trade-offs involving decentralization, integration complexity, cost, and operational accountability.
  • Final Workshop: Develop and present an enterprise Data Mesh, Data Fabric, or complementary architecture proposal, including target architecture, operating model, governance framework, implementation phases, risks, and performance indicators.

Upcoming Dates

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Bangkok · 22 – 26 Mar 2027 · Classroom

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