Can you explain what metadata types are in dataweave? In MuleSoft 14

 Can you explain what metadata types are in dataweave? In MuleSoft

DataWeave in MuleSoft 4 offers various metadata types that provide essential information about data structures, formats, and processing requirements within your scripts. These metadata types enhance data understanding, improve error handling, and facilitate validation during data processing.

Here's a breakdown of key metadata types and their uses:

1. Input/Output Metadata:

• input and output: Define expected input and desired output data formats (e.g., JSON, XML, CSV).

• format: Specifies the specific sub-format within the main format (e.g., JSON Schema, CSV delimiter).

• type: Indicates the expected data type of elements and arrays (e.g., string, number, date).

• required: Flags mandatory elements that must be present in the input data.

• description: Provides human-readable descriptions for elements and types, enhancing clarity.

Example:

Code snippet

%dw 2.0
input payload = {
  name: "John Doe" as required string,
  age: 30 as number
}

output application/json with format={ schema: "person.json" }

... your transformation logic ...

2. Custom Metadata:

• User-defined properties: Attach additional attributes to elements with key-value pairs for specific needs.

• Useful for storing processing instructions, flags, or other relevant information not covered by built-in metadata.

Example:

Code snippet

%dw 2.0

input payload = {
  name: "Alice Smith",
  processed: false as boolean
}

output application/json with {
  "custom": {
    "priority": "high"
  }
}

... your transformation logic ...

payload updated with {
  processed: true,
  "custom": {
    "status": "completed"
  }
}

3. Format-Specific Metadata:

• Certain formats like JSON and XML support further details like schemas, namespaces, and data type annotations.

• Provide additional validation and guidance for processing those formats.

Remember:

• DataWeave automatically infers some metadata types based on data analysis.

• Explicitly defining metadata improves clarity, validation, and error handling.

• Refer to the official DataWeave documentation for a comprehensive list of available metadata types: [invalid URL removed]

By effectively utilizing metadata types in your DataWeave scripts, you can build data transformations that are robust, well-documented, and easier to maintain within your MuleSoft 4 applications.