15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Architects
- Data Engineers
- Data Strategists
- Data Product Managers
- Cloud Architects
- Business Intelligence Developers
- Anyone needing data mesh architecture skills
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
Transform your data strategy with our Data Mesh Architecture Training Course. This program is designed to equip you with the essential skills to implement a decentralized data architecture, enabling you to build scalable, agile, and domain-oriented data platforms. In today's data-driven world, mastering data mesh architecture is crucial for organizations seeking to break down data silos and empower domain teams. Our data mesh architecture training course offers hands-on experience and expert guidance, empowering you to design and implement a modern data infrastructure.
This decentralize your data training delves into the core concepts of data mesh, covering topics such as domain-driven data ownership, data as a product, and self-serve data infrastructure. You'll gain expertise in using industry-standard techniques to implement a decentralized data architecture, meeting the demands of modern data-intensive organizations. Whether you're a data architect, data engineer, or data strategist, this Data Mesh Architecture course will empower you to build and manage a distributed and efficient data ecosystem.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of data mesh architecture.
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Subtopic 1.2:
Overview of domain-driven ownership, data as a product, and federated governance.
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Subtopic 1.3:
Setting up a data mesh development environment.
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Subtopic 1.4:
Introduction to data mesh principles and components.
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Subtopic 1.5:
Best practices for data mesh implementation.
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Subtopic 2.1:
Mastering domain-driven data ownership and data as a product.
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Subtopic 2.2:
Utilizing domain-driven design principles for data management.
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Subtopic 2.3:
Implementing data ownership models.
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Subtopic 2.4:
Designing and building domain-oriented data products.
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Subtopic 2.5:
Best practices for data ownership.
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Subtopic 3.1:
Utilizing self-serve data infrastructure platforms.
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Subtopic 3.2:
Implementing data infrastructure automation.
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Subtopic 3.3:
Designing and building self-serve data tools.
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Subtopic 3.4:
Optimizing infrastructure for data product development.
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Subtopic 3.5:
Best practices for self-serve infrastructure.
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Subtopic 4.1:
Implementing federated computational governance.
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Subtopic 4.2:
Utilizing policies and standards for data interoperability.
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Subtopic 4.3:
•esigning and building governance frameworks.
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Subtopic 4.4:
Optimizing governance for decentralized data management.
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Subtopic 4.5:
Best practices for governance.
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Subtopic 5.1:
Designing and building distributed data products.
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Subtopic 5.2:
Utilizing data APIs and data contracts.
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Subtopic 5.3:
Implementing data product development workflows.
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Subtopic 5.4:
Optimizing data products for consumption.
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Subtopic 5.5:
Best practices for data products.
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Subtopic 6.1:
Optimizing data mesh for scalability and interoperability.
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Subtopic 6.2:
Utilizing data virtualization and federation techniques.
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Subtopic 6.3:
Implementing data standardization and harmonization.
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Subtopic 6.4:
Designing scalable data mesh architectures.
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Subtopic 6.5:
Best practices for scalability.
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Subtopic 7.1:
Troubleshooting and addressing common challenges in data mesh implementation.
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Subtopic 7.2:
Analyzing data product integration issues.
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Subtopic 7.3:
Utilizing problem-solving techniques for resolution.
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Subtopic 7.4:
Resolving common data mesh errors.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing data governance and security in a decentralized environment.
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Subtopic 8.2:
Utilizing data access control and security policies.
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Subtopic 8.3:
Designing and building data governance models.
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Subtopic 8.4:
Optimizing security for distributed data.
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Subtopic 8.5:
Best practices for governance.
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Subtopic 9.1:
Integrating data mesh with various data sources and analytics tools.
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Subtopic 9.2:
Utilizing data integration platforms and APIs.
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Subtopic 9.3:
Implementing data mesh with cloud-native data platforms.
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Subtopic 9.4:
Optimizing integration for data consumption.
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Subtopic 9.5:
Best practices for integration.
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Subtopic 10.1:
Understanding how to handle data discovery and metadata management in a mesh.
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Subtopic 10.2:
Utilizing data catalogs and metadata repositories.
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Subtopic 10.3:
Implementing data lineage tracking.
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Subtopic 10.4:
Designing efficient metadata management strategies.
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Subtopic 10.5:
Best practices for metadata.
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Subtopic 11.1:
Exploring advanced data mesh patterns (data contracts, data APIs).
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Subtopic 11.2:
Utilizing data contracts for data product interoperability.
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Subtopic 11.3:
Implementing data APIs for data access.
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Subtopic 11.4:
Designing and building advanced data mesh solutions.
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Subtopic 11.5:
Optimizing advanced techniques for specific applications.
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Subtopic 11.6:
Best practices for advanced patterns.
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Subtopic 12.1:
Implementing data mesh for enterprise-wide data management.
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Subtopic 12.2:
Utilizing data mesh for real-time data analytics.
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Subtopic 12.3:
Implementing data mesh for data-driven product development.
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Subtopic 12.4:
Utilizing data mesh for data democratization.
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Subtopic 12.5:
Best practices for real-world applications.
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Subtopic 13.1:
Utilizing data mesh tools and frameworks (DataHub, Amundsen).
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Subtopic 13.2:
Implementing data mesh with specific tools.
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Subtopic 13.3:
Designing and building automated data mesh workflows.
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Subtopic 13.4:
Optimizing tool usage for efficient development.
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Subtopic 13.5:
Best practices for tool implementation.
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Subtopic 14.1:
Implementing data product monitoring.
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Subtopic 14.2:
Utilizing data product metrics and logs.
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Subtopic 14.3:
Designing and building performance dashboards.
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Subtopic 14.4:
Best practices for monitoring.
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Subtopic 15.1:
Emerging trends in data mesh architecture.
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Subtopic 15.2:
Utilizing AI for data product automation.
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Subtopic 15.3:
Implementing data mesh in cloud-native environments.
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Subtopic 15.4:
Best practices for future applications.
