Where to go next — the five-role split

If you made it this far, you've handled D.Hub's four core results — collection · dataset · chart · scenario — by hand. The next step is to pick the role Path that fits you. This lesson is the brief tour of the five branches.

The five-role split

Each Path's short pitch for its first lesson is listed below. Step into whichever pulls you most. This Path's progress is automatically saved, so you can always come back via the Welcome — continue learning panel.

Analyst Path — 40 min, 4 lessons

Find data, see patterns through visualization, share results. First lesson: Analyst workflow overview — re-draws portal's four surfaces from the analyst's vantage and pins down the division of Collection Explorer · Dashboard · Search · AI Assistant.

Pick this if: your day-to-day work is analyzing data to produce answers.

Data Engineer Path — 50 min, 6 lessons

Ingest, transform, and automate. First lesson: Engineer workflow overview — the split between connectors · pipelines · code nodes · scheduling, and the first judgment call for when to write a code node versus when standard nodes suffice.

Pick this if: your day-to-day work is supplying and automating data.

Ontology Modeler Path — 55 min, 6 lessons

Model your data as entities and relations in a graph. First lesson: Ontology overview — the division of entities · relations · graph instances, and backing dataset — a D.Hub-specific core concept.

Pick this if: you design the semantic model of your data, or sit in the modeling role that bridges analysts and engineers.

Admin Path — 45 min, 6 lessons

Permission model · SSO · FGAC · audit trails. First lesson: Permission model at a glance — the collection-ceiling effect of Reader / Writer / Owner roles and the first rule: permission grants always start from a collection.

Pick this if: you operate users · groups · policies, or own permission governance during early adoption.

Agent Builder Path — 55 min, 6 lessons

Design agent workflows that unpack natural-language input into tools and decisions. First lesson: Agent overview — the three axes (tools · actors · intent classification) and HITL (Human-in-the-Loop) — the first safety pattern.

Pick this if: your work integrates LLMs into automation flows, or you're building AI-driven decision scenarios in the early adoption stage.

When you sit across roles

Three common transition orders:

1. Analyst → Engineer — When you started in analytics but want to transform data yourself.
2. Engineer → Ontology Modeler — Once pipelines are second nature and you want to design the semantic model.
3. Admin → Agent Builder — From governance into responsibility for AI adoption.

Progress is tracked independently per Path, so running multiple in parallel works fine.

Wrap-up

This Essentials Path ends here. In 30 minutes, you walked through D.Hub's map + first result + a complete scenario tour. Take the next Path for depth.

Great job.