Create an analysis - Prophecy Documentation

An analysis is an interactive business application built on top of a data pipeline. Analyses let teams expose pipeline logic through forms, charts, and other interactive components so users can run and consume data without writing code. When a user runs an analysis, Prophecy executes the underlying pipeline using the values entered in the analysis UI.

How analyses work

An analysis acts as a UI layer on top of a data pipeline. The general workflow is:

You create an analysis connected to a pipeline.
Interactive components are bound to pipeline parameters.
Users enter values in the analysis UI.
Prophecy executes the pipeline using those values.
Results render in charts, tables, and other components.

Analyses support multiple saved configurations. Each configuration stores a reusable set of runtime values and can be run or scheduled independently. You can enter runtime values in the analysis, which override the pipeline’s default parameter values during execution.

Edit mode and preview mode

Analyses have two primary modes:

Edit mode — Build and configure the analysis layout. Add components, configure charts, and bind inputs to parameters.
Preview mode — Run the analysis as an end user would. Enter runtime values, execute the pipeline, and review the results.

Use the Agent
Create manually

Overview

Use the Agent to automatically generate a pipeline and analysis from a prompt.

Add source data

Add a dataset to your pipeline:

Open the Source/Target gem category.
Click Table.
Open the gem and select + New Table.
Choose Seed as the type.
Name the dataset home_tech_transactions.
Paste your data and click Load Data.
Click Save.

Generate an analysis

In the Chat interface, enter a prompt:

Visualize sales performance per product category in @home_tech_transactions

The Agent will:

Transform the data.
Create a visualization.
Generate a pipeline.
Generate an interactive analysis dashboard.

Continue building

You can continue prompting the Agent to:

Add new visualizations
Modify transformations
Expand the dashboard

Add interactive components

To add interactive components to the analysis, you’ll need to embed a pipeline parameter in the pipeline. Pipeline parameters enable dynamic behavior in pipelines by allowing values to be set at runtime. In this case, pipeline parameter values are set by the end user when they run the analysis.

Create a pipeline parameter

In this section, you’ll define a pipeline parameter called region. The parameter will capture the region that the user selects, allowing the pipeline to filter transactions based on the region the user wants to see.

Open the pipeline tied to the analysis.
Click default in the project header to open the parameter settings.
Open the Pipeline Parameters tab.
In the default parameter set, click + Add Parameter.
Name the parameter region.
Set the parameter type to String.
Click Select expression > Value.
Enter North as the default value to be used during interactive pipeline runs.
Click Save.

Add a filter

Next, add a Filter gem to the pipeline. To make the filter condition dynamic, you’ll use the pipeline parameter in the gem.

Add a Filter gem directly after the Source gem and before the next gem (generated by the Agent).
Open the Filter gem configuration.
For the filter condition:
- Click Select expression > Column and select the Region column.
- Click Select operator and select equals.
- Click Select expression > Configuration Variable and select the region parameter.
This expression is equivalent to Region = {{ var('region') }} in the Code view.
Click Save.

Add a Text Input component

Add a Text Input component to allow the end user to enter the region they want to see.

Open the analysis dashboard that the Agent created.
Click Edit.
Open the Interactive dropdown and select Text Input.
In the Inspect tab, for the Configuration field, select the region parameter.
For the Label, enter Region.
Drag the Text Input component above the bar chart visualization.

For full configuration options, see Analysis components.

Overview

Create an analysis manually to build a dashboard from an existing pipeline.

Add source data

Add a dataset to your pipeline:

Open the Source/Target gem category.
Click Table.
Open the gem and select + New Table.
Choose Seed as the type.
Name the dataset home_tech_transactions.
Paste your data and click Load Data.
Click Save.

Create the analysis

Open the Project Browser.
Hover over a pipeline.
Click the Create analysis icon.

Alternatively:

Click the + icon in the canvas header
Select Create analysis

Configure the analysis

Enter a name for the analysis in Analysis name. Use alphanumeric characters and underscores.
(Optional) Enter a description in Description.
Select a pipeline in Pipeline name.
Choose a parameter set in Parameter set.
(Optional) Update the location in Directory path, or leave the default.
Click Create analysis.

Build your analysis

The analysis opens in Edit mode on the analysis canvas, where you can design the runtime experience for users.You can add three types of components:

Interactive — Capture user input
Data Integration — Display pipeline data
Content — Add text and images

For full configuration options, see Analysis components.

Add a chart

To add a visualization:

Select a chart component.
Choose a data table.
Enter a label.
Select a chart type.
Configure:
- Category column
- Y-axis column

You can further customize chart appearance, including labels and formatting.

Add interactive components

Create a pipeline parameter

Open the pipeline tied to the analysis.
Click default in the project header to open the parameter settings.
Open the Pipeline Parameters tab.
In the default parameter set, click + Add Parameter.
Name the parameter region.
Set the parameter type to String.
Click Select expression > Value.
Enter North as the default value to be used during interactive pipeline runs.
Click Save.

Add a filter

Next, add a Filter gem to the pipeline. To make the filter condition dynamic, you’ll use the pipeline parameter in the gem.

Add a Filter gem directly after the Source gem and before the next gem (generated by the Agent).
Open the Filter gem configuration.
For the filter condition:
- Click Select expression > Column and select the Region column.
- Click Select operator and select equals.
- Click Select expression > Configuration Variable and select the region parameter.
This expression is equivalent to Region = {{ var('region') }} in the Code view.
Click Save.

Add a Text Input component

Add a Text Input component to allow the end user to enter the region they want to see.

Open the analysis dashboard that the Agent created.
Click Edit.
Open the Interactive dropdown and select Text Input.
In the Inspect tab, for the Configuration field, select the region parameter.
For the Label, enter Region.
Drag the Text Input component above the bar chart visualization.

Configurations

Analyses support multiple saved configurations. Configurations appear under the analysis in the App Browser. Each analysis can contain multiple configurations, which can be selected, renamed, scheduled, or run independently. A configuration is a named set of runtime values for the analysis. Configurations allow teams to create reusable presets for different environments, users, or operational scenarios. For example, an analysis might include:

A production configuration.
A staging configuration.
Configurations for different regions or business units.

Each configuration can be run and scheduled independently.

Run the analysis

Let’s return to the analysis preview so we can test the new component.

From the analysis, click Back to Preview.
In the Region field, enter West.
Click the Run button.
Review the bar chart to see the total sales by product category for the West region.

When the analysis runs, Prophecy always executes the entire underlying pipeline. This means any additional transformations, tables, or other components included in the pipeline will also run, even if they’re not exposed in the dashboard. Be mindful of how you design the pipeline to ensure your dashboard triggers only the intended logic.

You can share published analyses with other teams for interactive consumption and execution. Access to analyses is controlled by Prophecy’s team-based permission model. If your team owns a project, you have full edit access. This means that you can build, edit, and delete analyses in the project. If a project is shared with your team, you cannot edit any pipeline’s or analysis’s structure. However, you can run analyses from the shared project. This ensures that your data engineering team can share pipelines they developed without exposing them to changes.

To learn more, reference the documentation on team-based access.

What’s next

To address your specific business requirements, leverage more complex components to construct robust analysis dashboards. To learn about the end user experience, see consume analyses.

​How analyses work

​Edit mode and preview mode

​Overview

​Add source data

​Generate an analysis

​Continue building

​Add interactive components

​Create a pipeline parameter

​Add a filter

​Add a Text Input component

​Overview

​Add source data

​Create the analysis

​Configure the analysis

​Build your analysis

​Add a chart

​Add interactive components

​Create a pipeline parameter

​Add a filter

​Add a Text Input component

​Configurations

​Run the analysis

​Share the analysis

​What’s next

How analyses work

Edit mode and preview mode

Overview

Add source data

Generate an analysis

Continue building

Add interactive components

Create a pipeline parameter

Add a filter

Add a Text Input component

Overview

Add source data

Create the analysis

Configure the analysis

Build your analysis

Add a chart

Add interactive components

Create a pipeline parameter

Add a filter

Add a Text Input component

Configurations

Run the analysis

Share the analysis

What’s next