Pipeline build and dashboard visualization

This lesson closes the loop from data processing to user-facing operations. You load data into datasets and ontology entities, then wire the output into dashboard widgets.

Review the pipeline list

From Explorer or the pipeline list page, review the pipelines already registered and their recent update history.

Create a pipeline and map it to a collection

When you create a new pipeline, map it to the target collection so the workflow stays inside the right isolated scope.

Reuse resources in the pipeline builder

When the Workflow editor opens, you can create datasets or code resources from the left sidebar on the spot, or search for existing datasets, code, and entity resources and place them on the canvas.

Build the workflow

Arrange and connect the nodes into a working pipeline flow.

Run the pipeline

Click Run from the top menu bar to execute the pipeline and start the transformation and load process.

Validate dataset results

After the run finishes, open Datasets inside the collection and confirm that the data was stored correctly in the expected table structure.

Validate entity load results

Check the ontology entity data as well. Entity data is stored in a transaction-safe area to prevent loss, and it is synchronized in parallel to a query-friendly dataset path for fast access.

Configure the batch scheduler

In the pipeline settings, define a cron schedule or interval so cleansing and loading can run automatically on a repeated basis.

Connect dashboard widgets

Once the load result is confirmed, open the dashboard editor and connect the data source. Use datasets and ontology entity data to place map, scatter, bar or line chart, and table widgets on the operational screen.

What you should be able to do after this lesson

• Search for and reuse datasets, code, and entity resources in the Workflow editor
• Validate that dataset and entity data was stored correctly after a pipeline run
• Configure a batch scheduler for recurring loads
• Turn loaded data into a dashboard view with operational widgets

Next lesson

In the next lesson, you register an LLM model and assemble an agent on top of the resources you created.