Email Syntax Validation and Domain Extraction Template - n8n Workflow

Use this efficient n8n workflow to automatically validate email syntax and extract the domain name using powerful native n8n expressions. Learn key data manipulation techniques within n8n.

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Who is this best for?

Data Analysts and Scientists needing rapid email list cleaning.
Marketing Operations specialists validating lead data before outreach.
n8n users seeking examples of powerful, native data manipulation without relying on function nodes.
Anyone looking to master advanced data processing techniques within an n8n node.

Overview

Data hygiene is crucial, and a common requirement is ensuring email addresses are correctly formatted and identifying their associated domain. This n8n workflow provides an elegant and fast solution using native n8n expressions, eliminating the need for complex regular expressions or custom code. This specific n8n templates showcases the efficiency of core n8n features—specifically the isEmail() and extractDomain() string methods—to transform and validate input data directly within a Set n8n node. It demonstrates a valuable technique for building robust and reliable automated processes using the base capabilities of n8n.

How it Works

This n8n workflow is designed to be highly modular, starting with a manual n8n trigger for testing purposes.


  1. Trigger and Data Simulation: The process is initiated manually via the 'When clicking "Test workflow"' n8n trigger. For demonstration, the workflow uses the 'Generate random data' n8n node to simulate a batch of emails that need validation.

  2. Data Replacement Warning: A critical step, highlighted by a sticky note, is the requirement for users to replace the mock 'Generate random data' n8n node with their actual data source (e.g., a Google Sheets n8n node, or a database query n8n node).

  3. Validation and Extraction (Core Logic): The 'Set these fields to extract domain' n8n node executes the primary logic. It uses native n8n expressions applied to the input email field:

{{ $json.email.isEmail() }}: Checks if the string adheres to standard email format rules, outputting a boolean (true/false).
{{ $json.email.extractDomain() }}: Parses the email string and returns only the domain portion (e.g., 'yahoo.com' or 'hotmail.com').

  1. Final Output: The final output of the n8n workflow is a refined dataset containing the original email, a clear validation status, and the extracted domain, ready for use in subsequent n8n nodes.

Installation Guide

To implement this useful n8n workflow:


  1. Import: Copy the provided JSON code.

  2. In your n8n instance, click 'New' and select 'Import from JSON'. Paste the code and click 'Import'.

  3. Testing: Click the 'When clicking "Test workflow"' n8n trigger node and then 'Test Workflow' to see the mock data validation in action.

  4. Adaptation: Replace the 'Generate random data' n8n node with your actual data source (e.g., a source n8n node connecting to your CRM, database, or API). Ensure your replacement n8n node outputs the email data under the key name email for the Set node expressions to function correctly.

Node Details

This n8n workflow leverages several fundamental n8n nodes:

When clicking "Test workflow" (Manual Trigger): This n8n trigger serves as the starting point for manual execution and testing. It allows users to run the n8n workflow on demand.

Generate random data (Debug Helper): Used solely to simulate input data for immediate testing of the n8n template. Function: Generates sample email addresses.
Key Configuration: Set to output randomDataType of email.

Set these fields to extract domain (Set n8n node): This is the heart of the transformation. It defines new fields based on expressions.
Function: Performs the email validation and domain extraction using native string methods.
Key Configuration:
Valid Email: ={{ $json.email.isEmail() }} (Checks format validity).
Extract Domain: ={{ $json.email.extractDomain() }} (Pulls the domain name).


  • Sticky Note: Provides essential context for the n8n workflow, reminding the user to replace the mock data source with production data.

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Updated: December 26 2025
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