Schemas

Learn about Embed Workflow Schemas and how they enhance workflow designing.

Schemas

When designing a workflow, it is essential to know what type of data will be provided so you can configure the trigger, insert dynamic placeholders, and set conditions. This is where data schemas come into play.

A schema is a collection of DataType's. A DataType comprises a type, formatting, validation, and permissions.

Types of Schemas

Data comes from from different sources.

  1. For a User named user_data_schema
  2. From a Trigger which we call the data_input_schema
  3. And, the response from an API Request stored as response_data_schema

DataType

A collection of DataType's make a schema. Each DataType contains the following properties (but not limited to):

  • A variable is the name you want your workflow creators to use a reference. It should be human friendly.

  • data_path is how we locate the data from the data source. The path might be deeply nested resulting in a long technical name. Use square brackets to indicate a nesting. For instance: user[address][home][zip] for { user: { address: { home: zip: '33021' }}}.

  • The type can be one of the following:

    • String
    • Date
    • Boolean
    • Integer
    • Float
    • List
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