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.

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

Six lessons covering Collection Explorer, dashboards, charts, and the AI Assistant to walk a data analyst end-to-end through their portal workflow.

6 lessons 40 min

Data Engineer Path

From connectors to pipelines, code nodes, scheduling, and debugging — follow one full cycle of ingesting and automating data in portal across six lessons.

6 lessons 50 min

Ontology Modeler

Ontology Modeler Path

Six lessons modeling data as a graph of entities and relations — entity design, backing-dataset mapping, relations, inspecting instances in the Graph explorer, and handoff to analysts and engineers.

6 lessons 55 min

Admin Path

Six lessons covering the permission model, SSO/provisioning, user/group management, FGAC policies, policy operations, and audit trails — the full cycle of safely operating D.Hub portal.

6 lessons 45 min

Agent Builder Path

Six lessons covering tool definition · actors + HITL · RAG · intent gating · debugging — the full cycle of designing agent workflows that safely unpack natural-language input into tools and decisions.

6 lessons 55 min

FDE Path

Follow the full D.Hub resource-building flow, from data collection and ontology modeling to pipelines, AI agents, dashboards, and final 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

Walk one cycle of integrating administrative-region (sido / sigungu / dong) × population × patients × clinics × hotspot data into a public-health regional surveillance graph.

Engineering env required

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

Walk one cycle of the VeridianLife scenario — structuring PDF claim forms via OCR + LLM extraction, applying a 6-rule decision engine to auto-approve / escalate / reject, and leaving an auditable reasoning trail.

Engineering env required

E-Commerce Analytics — from order cleansing to RFM segmentation and ontology

Walk one cycle of the JaffleMart scenario — from raw ERP/CRM ingestion through cleansing → RFM segmentation → ontology → dashboard.

Engineering env required

Asset management

Asset Management — from HI evaluation to Weibull RUL and maintenance scheduling for power equipment

Walk one cycle of the asset management scenario — thermal anomaly detection, Health Index (HI), Weibull-based Remaining Useful Life (RUL), and maintenance schedule optimization for 10 transformers, GIS, and underground cables.

Engineering env required

IoT Smart Factory — from anomaly detection to daily rollup and ontology

Connect 30-second telemetry from 12 CNC machines to 3-sigma anomaly detection, a daily health score, and ontology materialization in one scenario.

Engineering env required

Map Control — from natural-language commands to nautical-chart coordinate resolution

Walk one cycle of routing operator natural-language commands through intent classification → tactical zone lookup → coordinate resolution, driving automated pan / zoom / overlay on an electronic nautical chart.

Engineering env required

Refund Approval — from data enrichment to risk scoring and a HITL agent

Walk one cycle of the QuickReturn Electronics scenario — joining orders/customers/refund history, risk scoring, policy RAG, and a Human-in-the-Loop refund-approval agent.

Engineering env required

Retail Inventory Intelligence

Gather per-branch inventory and competitor pricing data, then build a pricing competitiveness dashboard.

Engineering env required

SAP ERP Sales Analytics — from raw SD/MM integration to multidimensional aggregates and ontology

Integrate and transform 5 SAP ERP SD/MM raw tables into multi-dimensional time × customer × material × region aggregates, then materialize the SAP_* ontology for graph exploration.

Tutorials

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

Analyst starter

Ask the AI Assistant your first question (5 min)

Call portal's AI Assistant in natural language and run one cycle each of finding data, building a chart, and generating code.

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.

Analyst starter

Create your first collection (5 min)

Create an empty collection and register a CSV as a dataset, all the way through in five minutes.

Import a scenario zip in one shot (5 min)

Download one scenario zip and load it through portal's collection Import dialog. No terminal, Python, or git required.

Permission model at a glance (7 min)

A one-page admin summary of D.Hub's three permission layers — roles (Reader/Writer/Owner), the collection ceiling, and fine-grained policies (FGAC). Built to fit the first 30 minutes of a security and legal review.

Engineer starter

Quick scenario import (advanced, 10 min)

Load a single dhub2-examples scenario into portal with the Python automation tool. The contributor / engineer view of the scenario import flow.

SSO and account provisioning in one page (8 min)

D.Hub's OIDC-based SSO login, IdP user and group registration and sync, and OIDC clients for automation — captured end to end on a single page. Built for the review just after the adoption decision.