D.HubLearning Center

KOEN

D.Hub Learning Center

Role-based Paths, hands-on Workshops, and quick Tutorials for D.Hub.

New here?

Pick a role and start with 5–10 minute lessons.

Analysts start with the Collection · Dashboard flow, engineers with the Pipeline · Code flow — two tracks to begin from.

Concepts

Short reads that explain the idea, with no click-through steps.

Collections and datasets

Explains where data lives and why the collection is the unit that holds it.

Explains what it means to attach meaning to a table and how an ontology differs from a schema.

Entities and relations

Explains why connections are modelled as relations rather than joins, and what entities and relations each hold.

Explains which questions only relations can answer and how graph traversal differs from a table query.

Explains what the semantic layer translates between a natural-language question and the underlying data.

Pipelines and agents

Explains the automation and query flows where the preceding concepts actually run.

Paths

Role-tailored learning journeys built from 5–10 minute lessons.

Essentials Path

A 30-minute full cycle for anyone landing on the portal for the first time — tour the five D.Hub surfaces, make your first collection, dataset, and chart, then branch into your role-specific Path.

6 lessons 30 min

Analyst Path

A 45-minute, six-lesson Path covering Collections, dashboards, charts, and the AI Assistant for a data analyst's end-to-end portal workflow.

6 lessons 45 min

Data Engineer Path

A 55-minute path for data engineers. Connect a public REST API, ingest from it, transform and aggregate the result, put it on a schedule, and hand it to an analyst as a dashboard — all in one pipeline.

6 lessons 55 min

Ontology Modeler

Ontology Modeler Path

Six lessons modeling data as a graph of entities and relations — entity design, pipeline-based instance loading, relations, inspecting instances in Graph Explorer, and handoff to analysts and engineers.

6 lessons 55 min

Admin Path

Learn to operate organizational sign-in, users and groups, and data-access policies, then distinguish Recents and version history from audit logs.

6 lessons 45 min

Agent Builder Path

Build an AI agent that connects Python actions, approval steps, knowledge search, safety rules, deployment, and execution monitoring.

6 lessons 55 min

FDE Path

Follow the complete D.Hub solution-building flow from customer data and semantic structure to automation, AI work, result screens, and validation.

6 lessons 50 min

Workshops

End-to-end builds against real scenario data.

Engineering env required

COVID-19 Regional Analytics — from administrative-region population to hotspot graphs

Act as an analyst in a fictional outbreak situation room and connect region, population, patient, clinic, and hotspot data to explain which area needs attention first.

Engineering env required

Document AI — from PDF claim parsing to rule-based decisions and audit trails

Follow one claim at fictional insurer VeridianLife and set the boundary between automated handling and human review.

Engineering env required

Draft the next campaign from customer segments

Act as a fictional JaffleMart analyst, connect orders to customers, read the actual segment distribution, and plan re-engagement only for groups that exist.

Engineering env required

Asset management

Prioritize maintenance for ten power assets

Act as a fictional electric utility asset manager, distinguish HI from RUL, and prioritize ten assets within a limited budget and daily crew capacity.

Engineering env required

Choose a CNC inspection order from four HIGH alerts

Act as a fictional FabriSense maintenance specialist and use sensor quality signals, health scores, and an ontology that reaches technicians, locations, and shifts to order four CNC inspections.

Engineering env required

Turn natural-language commands into safe chart control and audit history

Act as a fictional maritime operator and complete a flow that executes display commands safely without requiring zone IDs or coordinates while recording why requests were allowed or blocked.

Engineering env required

Review refunds with AI recommendations and human decisions

Act as a support and risk specialist for the fictional electronics retailer QuickReturn, combine order evidence with policy retrieval and risk triage, and keep the final decision human.

Engineering env required

Retail Inventory Intelligence

Act as a fictional inventory operator, connect branch inventory with competitor prices, and explain whether replenishment or price review should come first.

Engineering env required

Explain a sales change from scattered SAP tables

Connect five SAP sources, explain a sales change by month, customer, region, and material, then choose the first order to review.

Tutorials

Bite-sized lessons you can finish in under 10 minutes.

Analyst starter

Create your first collection (5 min)

Create a collection, register a CSV as a dataset, and inspect its data.

Analyst starter

Ask the AI Assistant your first question (5 min)

Find data with the Portal Assistant, review a dashboard proposal, and use AI inside a code editor.

Analyst starter

Import a hands-on scenario in one shot (5 min)

Import a scenario file (ZIP) through Portal, then verify its created resources and uploaded data.

Engineer starter

Quick scenario import (advanced, 10 min)

Import the retail scenario's assets and data through the Manager API with the current dhub2-examples CLI.

Permission model at a glance (7 min)

A one-page admin summary of D.Hub roles, inherited collection permissions, per-resource overrides, and fine-grained policies (FGAC).

SSO and account provisioning in one page (8 min)

Understand OIDC-based SSO login, IdP user and group provisioning, and OIDC clients for automation.

Column and row masking — from FGAC policy authoring to rollout

When collection permissions aren't enough, hide specific columns and rows differently per user or group. The operational flow to author, test, and roll out a single fine-grained policy.