Import and tour a complete hands-on scenario

In Lessons 2–4, you built a collection · a dataset · a chart one at a time. This lesson goes the other direction — receive a fully assembled collection in a single shot and tour it. Touching both ends — what you built in 5 steps and what a complete scenario loaded at once looks like — is this Path's central insight.

Get the IoT Smart Factory scenario

This lesson starts with the IoT Smart Factory scenario — a small 25 KB zip that downloads instantly.

The zip contains 19 assets in total.

The maintenance_manual knowledge asset contains the maintenance procedure to follow when an anomaly is detected.

Open portal's Import dialog

Select Collections in the left sidebar. In the upper-right of the Collections page, open More options (⋯), then select Import to open the Import dialog.

Pick the iot.zip you just downloaded and upload. Progress shows inside the dialog; on a zip this small, it usually finishes in a few seconds.

Tour the loaded collection

When the upload finishes, the left tree shows the raw and processed collections side by side.

Open these items one at a time.

1. The machine_sensors dataset in processed
2. The anomaly_detections dataset in the same collection
3. The anomaly_detection pipeline in processed
4. The left sidebar Ontology area — three entities (iot_machine · iot_sensor · iot_maintenance_event) appear.
5. The maintenance_manual knowledge in processed
6. The equipment_health dashboard in processed — five widgets fill one screen.

Notice how these resources are interconnected. A dataset can feed a pipeline and serve as a dashboard widget's analytics source, while the pipeline's Entity and Relation outputs load ontology instances. Resources working together across multiple surfaces is a core value of D.Hub.

In the current scenario, raw is an empty collection reserved for future raw assets. All imported assets are scoped to processed.

Connect the dashboard's analytics sources

In the current version, you must select each dashboard widget's analytics source after importing a scenario. When you first open equipment_health, the following message does not mean that the import failed:

Select an analytics source before running this query.

Select Edit Dashboard in the upper-right, select each widget, and use Analytics Source in the right configuration panel to make these connections.

Save the dashboard after connecting all five widgets. Widgets that use machine_sensors can display the imported Parquet data immediately. The anomaly_detections and equipment_health datasets are empty immediately after import, so their widgets can show empty results until you run the related pipelines. The connection is valid when the analytics-source prompt disappears.

Self-check

• Both raw and processed collections appear in the left tree.
• The import created 19 assets in total, including the maintenance_manual knowledge.
• You confirmed one resource (e.g. machine_sensors) appears in two places — as a dataset and as a pipeline node input.
• The dashboard contains five widgets, and the analytics-source prompt disappears after you connect each widget.

What you should be able to do after this lesson

• The entry path to portal's Import dialog
• The shape of a fully assembled collection — datasets, pipelines, ontology, dashboard, all together
• D.Hub's integration model where one resource lives across multiple surfaces

Next lesson

The final lesson — pick the role Path that matches your work.