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 problems in centralized architectures
• Guiding principles of the Data Mesh approach

Module 2: Principle 1 – Domain-based Data Ownership
• Domain-oriented organization
• Benefits and challenges of decentralizing 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 by team

Platform, Governance, and Operational Design

Module 4: Principle 3 – Self-Service Platform
• Components of a modern data platform
• Common tools in a Data Mesh ecosystem (Kafka, dbt, Snowflake, etc.)
• Exercise: designing self-service 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: Organizational 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 phased implementation of 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 success and failure

Module 9: Exam Simulation and Practical Cases
• Review exercises by module
• Certification-style exam simulation
• Result review and discussion