EDGE-Skills Training Materials
Learn how to design, build, and operate real data space services. The EDGE-Skills training program, built on the Prometheus-X open source stack, guides you through the full lifecycle of a data space project — from strategy and governance to data, architecture, and operations.
The program is role-based and practical: choose the path that matches your role, or start with the full curriculum for the complete picture. All three trainings share a common foundation (what a data space is, the EDGE-Skills architecture, and a shared reference case) before going deep into role-specific content.
Full Training Curriculum
The complete curriculum document
It describes the seven training modules — from Foundations and Use Case Design through Governance, Data & Semantics, Technical Architecture, Implementation, and Operations — plus target groups, learning objectives, the didactic model, and a glossary. Start here if you want an overview of the whole program or need to plan training for your team.
Training Project Managers
Who it’s for: Project managers, business and ecosystem leaders, product owners, and anyone responsible for strategy, governance, and stakeholder management in a data space initiative.
What’s inside: The training starts with the shared foundations (what a data space is, the EDGE-Skills architecture, and the common reference case) and then goes deep into the business and governance side:
- Identifying high-value use cases and turning them into actionable projects
- Ecosystem and stakeholder design: roles, incentives, and value propositions
- Designing sustainable business and governance models, including service chains
- Trust frameworks, contracts, and consent management
- Legal and regulatory requirements: GDPR, AI Act, and Data Act
- Leading interdisciplinary teams through implementation, adoption, and scaling
What you’ll be able to do: Design, lead, and scale a real data space initiative from concept to operation — and speak enough of the technical and data language to work effectively with your team.
Training for Data Scientists
Who it’s for: Data scientists, data engineers, data architects, data stewards, semantic modelers, ontology specialists, and ML engineers — everyone who designs and implements the data pipeline of a data space.
What’s inside: After the shared foundations (slides 1–13, same as the Developer training), the training follows the complete data journey within a data space:
- Data ingestion and connectors: how data enters the data space
- Semantic standards and ontologies for interoperability: xAPI, JSON-LD, RDF, ESCO, ROME
- Transformation pipelines, ontology mappings, and normalization
- Data quality and veracity, with provenance and lineage tracking
- Metadata and catalog management
- Analytics and ML pipelines that produce re-offerable data assets
- Publishing, re-offering, and consuming data — plus privacy-preserving processing
- Configuring Dataspace Connectors (PDC), catalog offers, and governance policies from a data perspective
What you’ll be able to do: Design and implement the complete semantic, data, and analytics backbone of a data space, making data interoperable, trustworthy, and AI-ready across organizational boundaries.
Training for Developers
Who it’s for: Software developers, solution architects, system integrators, DevOps engineers, cloud/edge specialists, API designers, and security engineers — everyone who builds and operates the technical infrastructure of a data space.
What’s inside: After the shared foundations (slides 1–13, same as the Data Scientist training), the training covers the Prometheus-X open source stack hands-on:
- The Prometheus-X architecture: building blocks, core components, and the consent-driven PDI approach
- Deploying and configuring Connectors (Control Plane) with DSP catalog, negotiation, and transfer
- Building and registering custom Data Planes (Pull, Push, Stream) with SDKs in Go, Java, Rust, and .NET
- Decentralized identity: Identity Hub, DIDs (did:web), Verifiable Credentials, and DCP proof composition
- Defining and evaluating ODRL policies at runtime with the policy engine
- Multi-tenant deployment with the PDC Tenant Manager
- Validating end-to-end flows: credential presentation → catalog discovery → contract negotiation → data transfer
- DevOps practices: Kubernetes, NATS messaging, PostgreSQL, Prometheus metrics, CI/CD
What you’ll be able to do: Technically build, integrate, and operate a compliant and scalable data space infrastructure using protocol-driven architecture.
EDGE-Skills Guide - Lessons Learned
The EDGE-Skills Guide provides practical insights and lessons learned from developing, implementing, and scaling trusted data spaces for education and skills. It covers use case design, interoperable building blocks, governance, sustainable business models, and ecosystem adoption — supporting organisations from initial planning to operational deployment and long-term market readiness.