Data Mesh for Enterprise Environments: Design, GoGovernance and Operation Training Cursus

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, Goernance, 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 – Goernance Federated
   • Goernance 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