How to create a pipeline

Learn how to create, configure, and build a pipeline on the Digibee Integration Platform.

Pipelines are created within the Canvas environment. Use the following steps to learn how to create, configure, and build your pipeline.

Creating the pipeline

Follow these steps to create a pipeline:

1. On the Build page, click on the Pipeline tab.

2. Click the project where you want to create the pipeline.

Pipelines can’t be created from the All projects view. First, select or create a project, then create the pipeline. It will be automatically associated with the chosen project.

1. Select the Pipeline option.

Configuring the pipeline

1. Click the gear icon in the upper-right corner of the screen (next to the Save button) to access the pipeline settings.

1. A configuration panel will open on the right side of the screen. Fill in the fields as described below:

• Description: Enter a brief description of the pipeline.

• Is it multi-instance?: Enable this option if the pipeline should run as a multi-instance pipeline. For more information, see the Multi-Instance documentation.

• Sensitive field: Define the data fields that should be obfuscated in the pipeline logs, which will appear as ***. The hyphen character (-) is allowed in the field name, but other special characters, accents, and the letter ç are not supported.

Sensitive fields defined in this configuration apply only to this specific pipeline. If you need to apply sensitive fields to all pipelines in your realm, refer to the Sensitive Fields Policy.

• InSpec & OutSpec (optional): Use these configurations to define structural validation rules for the pipeline’s input and output in JSON format.

InSpec & OutSpec: Validating the pipeline structure

The InSpec and OutSpec settings allow you to validate the structure of incoming and outgoing data in JSON format. They help ensure that your pipeline only processes payloads that match the expected schema.

• InSpec: Validates the pipeline input payload structure.

• OutSpec: Validates the final output payload structure.

Both use JSON validators (similar to JSON Schema) to define the expected data format.

Benefits

• Prevents execution errors caused by unexpected data structures.

• Reduces rework due to system incompatibilities.

• Improves data flow security and predictability.

• Simplifies debugging via automatic schema validation.

• Documents input/output contracts clearly.

How it works

• Both InSpec and OutSpec contain a JSON Validator schema.

• If the payload violates the defined structure during execution, the pipeline is interrupted.

Example: InSpec

{
  "type": "object",
  "required": ["id", "name", "email"],
  "properties": {
    "id":    { "type": "string" },
    "name":  { "type": "string" },
    "email": { "type": "string", "format": "email" }
  }
}

This example ensures that the input payload includes id, name, and email, with a valid email format.

Important notes

• These fields are optional. Pipelines work normally without InSpec and OutSpec.

• They are recommended for high-criticality pipelines, multi-consumer flows, B2B integrations, and internal contracts between teams.

• They help improve integration reliability and data consistency.

Building the flow in the pipeline

Follow these steps to create a flow in a pipeline:

1. Move the mouse pointer over the trigger already available in the Canvas and click on the Configuration button (represented by a gear icon).

2. Select a trigger from the available options. The trigger starts the execution of the pipeline, such as an event or an email.

3. Configure the selected trigger and click on Confirm to save your changes.

4. After configuring the trigger, click on the + button to begin creating your flow. Refer to the Smart Connector User Experience documentation for more details about available connection options.

5. While building your flow, use the Execution panel for testing and the Inspect mode for troubleshooting.

Pipelines are not saved automatically. You need to click the Save button each time you want to save your changes. The first time you save, provide the Name, Description, and Project to which the pipeline belongs.

The Canvas, the environment where pipelines are built, offers several navigation options. To learn more, read the Canvas documentation.

Pipeline size restriction

Each pipeline has a maximum size limit of 1 MB, which includes all its components: connectors, triggers, accounts, globals, and more. If the total size exceeds this limit, deployment will not be possible. Before deploying it, ensure that the pipeline size is within the allowed limit.

How to identify the size of your pipeline?

Step 1: Copy all the pipeline connectors using one of the following methods:

• Press Ctrl/Cmd + A and then Ctrl/Cmd + C.

Or

• Hold Ctrl/Cmd, click on each connector, and then press Ctrl/Cmd + C.

Step 2: Paste the connectors on JSON Size Analyzer pressing Ctrl/Cmd + V and execute a query to check the approximate size of the pipeline.

If the pipeline is smaller than 1 MB, it can be deployed. If it exceeds this limit, reduce its size before deployment.