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.