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

Fundamentals and Principles of Data Mesh

Module 1: Introduction and Context

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

Module 2: Principle 1 – Domain-Oriented Ownership

• Domain-driven organization
• Benefits and challenges of decentralizing responsibility
• Practical case studies: defining domains in a real enterprise

Module 3: Principle 2 – Data as a Product

• What constitutes a 'data product'
• Roles of the data product owner
• Best practices for designing data products
• Hands-on 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 (e.g., Kafka, dbt, Snowflake)
• Exercise: designing a self-serve platform architecture

Module 5: Principle 4 – Federated Governance

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

Module 6: Organizational Design and Cultural Change

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

Implementation, Tools, and Simulation

Module 7: Adoption and Implementation Strategies

• Roadmap for phased Data Mesh implementation
• Criteria for selecting pilot domains
• Lessons learned from real-world implementations

Module 8: Tools, Technologies, and Case Studies

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

Module 9: Exam Simulation and Practical Cases

• Review exercises for each module
• Mock certification-style exam
• Review of results and discussion