15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
- Data Architects
- Enterprise Architects
- Data Platform Architects
- Data Engineers
- Data Platform Engineers
- Data Governance Professionals
- Data Product Managers
- Data Product Owners
- Data Stewards
- Chief Data Officers
- Data and Analytics Leaders
- IT and Technology Managers
- Cloud Architects
Session Objectives
- Understand the fundamentals of data mesh architecture.
- Master domain-driven data ownership and data as a product.
- Utilize self-serve data infrastructure platforms.
- Implement federated computational governance.
- Design and build distributed data products.
- Optimize data mesh for scalability and interoperability.
- Troubleshoot and address common challenges in data mesh implementation.
- Implement data governance and security in a decentralized environment.
- Integrate data mesh with various data sources and analytics tools.
- Understand how to handle data discovery and metadata management in a mesh.
- Explore advanced data mesh patterns (e.g., data contracts, data APIs).
- Apply real world use cases for data mesh architecture.
- Leverage data mesh tools and frameworks for efficient implementation.
About the Course
This advanced training course equips participants with the knowledge and practical skills required to design, implement, and govern enterprise-scale Data Mesh architectures. It explores the shift from centralized data platforms toward a decentralized operating model where business domains take ownership of their data and deliver it as reliable, discoverable, and reusable data products.Participants will examine the core principles of Data Mesh, including domain-oriented ownership, data as a product, self-service data platforms, and federated computational governance. The course addresses how organizations can structure data domains, define data products, establish ownership and accountability, implement interoperability standards, and maintain enterprise-wide governance while enabling decentralized decision-making.Through architecture exercises and real-world enterprise scenarios, participants will learn how to assess organizational readiness, design Data Mesh operating models, establish domain boundaries, develop data products, implement governance mechanisms, and integrate Data Mesh with modern data lakes, lakehouses, warehouses, cloud platforms, and analytics environments.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of data mesh architecture.
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Workshop 1.2:
Overview of domain-driven ownership, data as a product, and federated governance.
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Workshop 1.3:
Setting up a data mesh development environment.
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Workshop 1.4:
Introduction to data mesh principles and components.
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Workshop 1.5:
Best practices for data mesh implementation.
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Workshop 2.1:
Mastering domain-driven data ownership and data as a product.
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Workshop 2.2:
Utilizing domain-driven design principles for data management.
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Workshop 2.3:
Implementing data ownership models.
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Workshop 2.4:
Designing and building domain-oriented data products.
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Workshop 2.5:
Best practices for data ownership.
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Workshop 3.1:
Utilizing self-serve data infrastructure platforms.
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Workshop 3.2:
Implementing data infrastructure automation.
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Workshop 3.3:
Designing and building self-serve data tools.
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Workshop 3.4:
Optimizing infrastructure for data product development.
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Workshop 3.5:
Best practices for self-serve infrastructure.
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Workshop 4.1:
Implementing federated computational governance.
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Workshop 4.2:
Utilizing policies and standards for data interoperability.
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Workshop 4.3:
•esigning and building governance frameworks.
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Workshop 4.4:
Optimizing governance for decentralized data management.
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Workshop 4.5:
Best practices for governance.
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Workshop 5.1:
Designing and building distributed data products.
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Workshop 5.2:
Utilizing data APIs and data contracts.
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Workshop 5.3:
Implementing data product development workflows.
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Workshop 5.4:
Optimizing data products for consumption.
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Workshop 5.5:
Best practices for data products.
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Workshop 6.1:
Optimizing data mesh for scalability and interoperability.
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Workshop 6.2:
Utilizing data virtualization and federation techniques.
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Workshop 6.3:
Implementing data standardization and harmonization.
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Workshop 6.4:
Designing scalable data mesh architectures.
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Workshop 6.5:
Best practices for scalability.
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Workshop 7.1:
Troubleshooting and addressing common challenges in data mesh implementation.
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Workshop 7.2:
Analyzing data product integration issues.
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Workshop 7.3:
Utilizing problem-solving techniques for resolution.
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Workshop 7.4:
Resolving common data mesh errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data governance and security in a decentralized environment.
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Workshop 8.2:
Utilizing data access control and security policies.
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Workshop 8.3:
Designing and building data governance models.
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Workshop 8.4:
Optimizing security for distributed data.
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Workshop 8.5:
Best practices for governance.
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Workshop 9.1:
Integrating data mesh with various data sources and analytics tools.
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Workshop 9.2:
Utilizing data integration platforms and APIs.
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Workshop 9.3:
Implementing data mesh with cloud-native data platforms.
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Workshop 9.4:
Optimizing integration for data consumption.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle data discovery and metadata management in a mesh.
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Workshop 10.2:
Utilizing data catalogs and metadata repositories.
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Workshop 10.3:
Implementing data lineage tracking.
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Workshop 10.4:
Designing efficient metadata management strategies.
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Workshop 10.5:
Best practices for metadata.
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Workshop 11.1:
Exploring advanced data mesh patterns (data contracts, data APIs).
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Workshop 11.2:
Utilizing data contracts for data product interoperability.
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Workshop 11.3:
Implementing data APIs for data access.
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Workshop 11.4:
Designing and building advanced data mesh solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced patterns.
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Workshop 12.1:
Implementing data mesh for enterprise-wide data management.
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Workshop 12.2:
Utilizing data mesh for real-time data analytics.
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Workshop 12.3:
Implementing data mesh for data-driven product development.
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Workshop 12.4:
Utilizing data mesh for data democratization.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing data mesh tools and frameworks (DataHub, Amundsen).
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Workshop 13.2:
Implementing data mesh with specific tools.
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Workshop 13.3:
Designing and building automated data mesh workflows.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing data product monitoring.
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Workshop 14.2:
Utilizing data product metrics and logs.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in data mesh architecture.
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Workshop 15.2:
Utilizing AI for data product automation.
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Workshop 15.3:
Implementing data mesh in cloud-native environments.
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Workshop 15.4:
Best practices for future applications.
