Data Mesh for Enterprise Environments: Design, Governance and Operation Training Course

Data Mesh Fundamentals and Principles

Module 1: Introduction and context

• Evolution of data architecture: DW, Data Lake and the emergence of Data Mesh
• Common problems in centralised architectures
• Guiding principles of the Data Mesh approach

Module 2: Principle 1 – Domain ownership of data

• Domain-driven organisation
• Benefits and challenges of decentralising responsibility
• Practical cases: defining domains in a real company

Module 3: Principle 2 – Data as a product

• What is a “data product”
• Roles of the data product owner
• Best practices for designing data products
• Practical exercise: designing a data product per team

Platform, Governance and Operational Design

Module 4: Principle 3 – Self-serve platform

• Components of a modern data platform
• Common tools in a Data Mesh ecosystem (Kafka, dbt, Snowflake, etc.)
• Exercise: designing a self-serve platform architecture

Module 5: Principle 4 – Federated governance

• Governance in distributed environments
• Policies, standards and automation
• Implementing data quality, security and privacy policies

Module 6: Organisational design and cultural change

• New roles in Data Mesh: data product owner, platform team, domain teams
• How to align incentives across domains
• Cultural transformation and change management

Implementation, Tools and Simulation

Module 7: Adoption and implementation strategies

• Roadmap for implementing Data Mesh in phases
• Criteria for selecting pilot domains
• Lessons learned from real implementations

Module 8: Tools, technologies and case studies

• Technology stack compatible with Data Mesh
• Implementation examples (Netflix, Zalando, etc.)
• Analysis of success and failure cases

Module 9: Exam simulation and practical cases

• Revision exercises per module
• Mock certification exam
• Review of results and discussion