Live Studio (EEDS simulation)

Let me walk you through a safe way two organisations can share data. We will simulate a shared catalog and a simple agreement process on top of the DSI Pilot data plane. This is a simulation on purpose. It teaches the control plane and where the real platform boundaries are.

Choose your participant

You will publish locally, sync from the other operator, and run agreements.

Active: Operator A (producer) • Peer: Operator B

How to use this page (2 minutes)

Step 1: Choose Operator A or Operator B.
Step 2: Publish a data product, then sync so the other side can discover it.
Step 3: Request an agreement, approve it, then simulate delivery.
Step 4: Try accreditation and consent gating to see what a real control plane could feel like.

Rule I use for conflicts: the first owner of an id stays the owner. If the same owner publishes a higher version, the higher version wins.

Safety note (important)

Keep this simulated. Do not paste real screenshots or real customer data into demos. If you are sharing anything, redact it first.

Pick a tool

If you only have five minutes, do Catalog, then Agreements, then Trust and consent. That shows the whole story.

Maps to the real DSI Pilot (reference model)

This simulation is a control plane teaching tool. In the real pilot stack, these steps correspond to concrete components and configuration surfaces.

INFRASTRUCTURE

Azure infrastructure primitives

Provisioning of AKS, Event Hub (Kafka interface), CosmosDB, Storage, networking, ACR, and Log Analytics for a sandbox/pilot environment.

Evidence anchor: energy-dsi/dpn-deployment

GITOPS

GitOps (FluxCD + Kustomize + Helm)

Cluster state managed via Flux Kustomizations and HelmReleases (reproducible cluster setup and workload rollout).

Evidence anchor: energy-dsi/dpn-flux-deployment

PLATFORM

Identity + edge

Keycloak for identity and Istio for ingress/mesh and policy enforcement points (pilot posture).

Evidence anchor: energy-dsi/dpn-flux-deployment

PLATFORM

Core data platform services

Access, query UI, and a graph/cache service stack deployed into the cluster (knowledge-graph and query surfaces).

Evidence anchor: energy-dsi/dpn-flux-deployment

PIPELINES

Data pipelines (Adaptor → Mapper stages)

Composable ingestion and transformation stages: adaptor reads from external source (often blob) and produces to Kafka; mappers validate/label/extract by consuming and producing topics.

Evidence anchor: energy-dsi/dsi-data-pipelines

PIPELINES

Schema assurance (SHACL + ontology validation)

A mapper stage that validates RDF against SHACL/ontology packs and publishes either conformant data or structured validation results downstream.

Evidence anchor: energy-dsi/dsi-data-pipelines

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