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Data mocking#

Data mocking is simulating or faking data. It's useful when developing a workflow. By mocking data, you can:

  • Avoid making repeated calls to your data source. This saves time and costs.
  • Work with a small, predictable dataset during initial development.
  • Avoid the risk of overwriting live data: in the early stages of building your workflow, you don't need to connect your real data source.

Mocking with real data using data pinning#

Using data pinning, you load real data into your workflow, then pin it in the output panel of a node. Using this approach you have realistic data, with only one call to your data source. You can edit pinned data.

Use this approach when you need to configure your workflow to handle the exact data structure and parameters provided by your data source.

To pin data in a node:

  1. Run the node to load data.
  2. In the OUTPUT view, select Pin data Pin data icon. When data pinning is active, the button is disabled and a "This data is pinned" banner is displayed in the OUTPUT view.

Nodes that output binary data

You can't pin data if the output data includes binary data.

Generate custom data using the Code or Edit Fields nodes#

You can create a custom dataset in your workflow using either the Code node or the Edit Fields (Set) node.

In the Code node, you can create any data set you want, and return it as the node output. In the Edit Fields node, select Add fields to add your custom data.

The Edit Fields node is a good choice for small tests. To create more complex datasets, use the Code node.

Output a sample data set from the Customer Datastore node#

The Customer Datastore node provides a fake dataset to work with. Add and execute the node to explore the data.

Use this approach if you need some test data when exploring n8n, and you don't have a real use-case to work with.