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

Data Mesh Fundamentals and Principles

Module 1: Introduction and Context

• Evolution of data architectures: 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 Data Ownership

• Domain-oriented organization
• Benefits and challenges of decentralized responsibility
• Practical examples: defining domains in a real enterprise

Module 3: Principle 2 – Data as a Product

• What is a “data product”
• Role of the data product owner
• Best practices for designing data products
• Hands-on exercise: designing a data product by team

Platform, Governance, and Operational Design

Module 4: Principle 3 – Self-Serve Data 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 Computational Governance

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

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, Tooling, and Simulation

Module 7: Adoption and Implementation Strategies

• Phased roadmap for implementing Data Mesh
• 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 successes and failures

Module 9: Exam Simulation and Practical Exercises

• Review exercises by module
• Certification-style mock exam
• Results review and discussion