Build a Data Transformation Pipeline

Real-world data rarely arrives in the format you need. The Transform Builder lets you visually build multi-step transformation pipelines that rename, reshape, and enrich your data — all without code.

🚩 The Scenario

You receive user data from a third-party CRM and need to transform it for your internal system:

Source (CRM export):

[
  {
    "FIRST_NAME": "alice",
    "LAST_NAME": "johnson",
    "EMAIL_ADDRESS": "ALICE.JOHNSON@GMAIL.COM",
    "SIGNUP_DATE": "1706832000",
    "SUBSCRIPTION_LEVEL": "premium",
    "PHONE": "5551234567"
  }
]

Target (your internal format):

[
  {
    "fullName": "Alice Johnson",
    "email": "alice.johnson@gmail.com",
    "signupDate": "2024-02-02",
    "tier": "Premium",
    "phone": "(555) 123-4567",
    "id": "a1b2c3d4-..."
  }
]

🛠️ Step-by-Step

1. Open the Transform Builder.
2. Paste or upload your source JSON.
3. Click Magic Map to auto-detect all source fields.
4. Configure each mapping:

Source Field	Target Key	Operations Applied
FIRST_NAME + LAST_NAME	fullName	Template: {{FIRST_NAME}} {{LAST_NAME}} → Title Case
EMAIL_ADDRESS	email	Lowercase
SIGNUP_DATE	signupDate	Date Format (unix → YYYY-MM-DD)
SUBSCRIPTION_LEVEL	tier	Capitalize
PHONE	phone	Custom Script (format as phone number)
(generated)	id	UUID v4

5. For the phone field, add a Custom Script:

return value.replace(/(\d{3})(\d{3})(\d{4})/, '($1) $2-$3');

6. For the fullName field, use the Template operation:

{{FIRST_NAME}} {{LAST_NAME}}

Then chain a Title Case operation.

7. Preview the result in real-time in the right panel.
8. Export the transformed JSON or download the mapping config for reuse.

⛓️ Chaining Operations

Operations execute in sequence (left to right). For example:

FIRST_NAME → Trim → Capitalize → Template → Title Case → fullName

This ensures whitespace is removed first, then the name is properly cased.

💾 Saving & Reusing Configs

• Export Config: Save your mapping rules as a .json file.
• Import Config: Load a previously saved config to apply the same transformation to new data.
• Workspace Persistence: When used in a workspace, your mappings are auto-saved.

💡 Pro Tips

• Test with small data first: Build your pipeline on a small sample, then apply it to the full dataset.
• Use the Code Export: Generate the equivalent JavaScript code to run your transformation programmatically in a Node.js script.
• Combine with Convert: After transforming, switch to the Convert Tool to export in your target format (CSV, SQL, etc.).

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🚀 Try it now

Open the Transform Builder and click Magic Map to auto-generate your first transformation pipeline.