Temporal vs n8n vs Airflow for Data Pipelines

What You'll Need

n8n Cloud or self-hosted n8n
Hetzner VPS or Contabo VPS for hosting
DigitalOcean as alternative
Python 3.8+ (for Airflow and Temporal)
Docker and Docker Compose
Git for version control

What You'll Need
The Big Picture: Why This Matters
Temporal: The Enterprise-Grade Workflow Engine
n8n: The Visual Automation Platform
Apache Airflow: The Python-Native Orchestrator
Head-to-Head Comparison
Getting Started with Your First Pipeline
Choosing the Right Tool for Your Use Case

The Big Picture: Why This Matters

I've built data pipelines for everything from real-time market data ingestion to batch processing infrastructure. The question I get asked most isn't "which tool is best?" but rather "which tool is best for what I'm trying to do?" These three platforms—Temporal, n8n, and Apache Airflow—solve workflow automation problems, but they approach it from completely different angles.

The choice you make affects deployment complexity, team learning curve, scalability, and your operational overhead. Get it wrong, and you're either over-engineering a simple automation or struggling with a tool that wasn't designed for your scale.

Temporal: The Enterprise-Grade Workflow Engine

Temporal is built for distributed systems that need bulletproof reliability and auditing. It's what you reach for when "the job must complete, no matter what," and you need a complete execution history for compliance.

How Temporal Works:

Temporal separates your business logic (Workflows) from the execution engine. You write code in TypeScript, Go, Python, or Java, and Temporal handles retries, timeouts, and failure recovery automatically. It's built on a novel idea: every decision in your workflow is deterministic and replayed through the same code path.

Here's a minimal TypeScript workflow that processes orders with built-in failure handling:

import { proxyActivities, defineSignal, defineQuery, setHandler } from '@temporalio/workflow';
import type * as activities from './activities';

const { processPayment, notifyUser, logOrder } = proxyActivities<typeof activities>({
  startToCloseTimeout: '5 minutes',
});

export interface OrderInput {
  orderId: string;
  amount: number;
  email: string;
  retryCount: number;
}

export async function orderProcessingWorkflow(input: OrderInput) {
  let attempts = 0;
  const maxRetries = 3;

  while (attempts < maxRetries) {
    try {
      const paymentResult = await processPayment({
        orderId: input.orderId,
        amount: input.amount,
      });

      if (paymentResult.success) {
        await logOrder({
          orderId: input.orderId,
          status: 'completed',
          paidAmount: input.amount,
        });

        await notifyUser({
          email: input.email,
          message: `Your order ${input.orderId} has been processed successfully`,
        });

        return { status: 'success', orderId: input.orderId };
      }
    } catch (error) {
      attempts++;
      if (attempts >= maxRetries) {
        await notifyUser({
          email: input.email,
          message: `Order ${input.orderId} failed after ${maxRetries} attempts`,
        });
        throw new Error(`Payment processing failed for order ${input.orderId}`);
      }
    }
  }
}

And the corresponding activities (the actual work):

export async function processPayment(params: {
  orderId: string;
  amount: number;
}): Promise<{ success: boolean; transactionId: string }> {
  const response = await fetch('https://api.payment-provider.com/charge', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${process.env.PAYMENT_API_KEY}` },
    body: JSON.stringify({
      orderId: params.orderId,
      amount: params.amount,
      currency: 'USD',
    }),
  });

  if (!response.ok) {
    throw new Error(`Payment API returned ${response.status}`);
  }

  const data = await response.json();
  return { success: true, transactionId: data.transaction_id };
}

export async function logOrder(params: {
  orderId: string;
  status: string;
  paidAmount: number;
}): Promise<void> {
  await fetch('https://your-api.com/orders/log', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(params),
  });
}

export async function notifyUser(params: {
  email: string;
  message: string;
}): Promise<void> {
  await fetch('https://your-api.com/notify', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(params),
  });
}

When Temporal Shines:

Long-running processes (anything that takes hours or days)
Distributed transactions requiring exact-once semantics
Complex state machines with strict audit requirements
Financial systems, order processing, payment reconciliation

The Tradeoff:

Temporal requires operational expertise. You're running a database (either Cassandra or PostgreSQL), worker services, and the server itself. It's not a SaaS product you spin up in five minutes.

n8n: The Visual Automation Platform

n8n is the opposite of Temporal. It's designed for people who want to see their workflows and click together integrations without writing code.

Using n8n Cloud, I can build data pipelines by connecting nodes. Here's what a typical data enrichment workflow looks like:

{
  "nodes": [
    {
      "parameters": {
        "url": "https://api.example.com/users",
        "authentication": "oAuth2",
        "requestMethod": "GET",
        "options": {}
      },
      "name": "Fetch Users",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.1,
      "position": [250, 300]
    },
    {
      "parameters": {
        "mode": "runOnceForAllItems",
        "expression": "={\n  \"id\": $json.user_id,\n  \"email\": $json.email,\n  \"processed_at\": $now.toIso()\n}"
      },
      "name": "Transform Data",
      "type": "n8n-nodes-base.set",
      "typeVersion": 3.2,
      "position": [500, 300],
      "onError": "continueRegularOutput"
    },
    {
      "parameters": {
        "method": "POST",
        "url": "https://your-database.com/insert",
        "authentication": "basicAuth",
        "sendBody": true,
        "bodyParameters": {
          "parameters": [
            {
              "name": "data",
              "value": "={{ $json }}"
            }
          ]
        }
      },
      "name": "Save to Database",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.1,
      "position": [750, 300]
    }
  ],
  "connections": {
    "Fetch Users": {
      "main": [
        [
          {
            "node": "Transform Data",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Transform Data": {
      "main": [
        [
          {
            "node": "Save to Database",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

This three-node workflow:

Fetches users from an API
Transforms each record to include metadata
Saves them to your database

You can host n8n on your own infrastructure for full control. Here's a Docker Compose setup:

version: '3.8'
services:
  n8n:
    image: n8nio/n8n:latest
    container_name: n8n
    ports:
      - "5678:5678"
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=change_me_securely
      - N8N_HOST=localhost
      - N8N_PORT=5678
      - N8N_PROTOCOL=http
      - NODE_ENV=production
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_PORT=5432
      - DB_POSTGRESDB_DATABASE=n8n
      - DB_POSTGRESDB_USER=n8n
      - DB_POSTGRESDB_PASSWORD=n8n_db_password
    volumes:
      - n8n_data:/home/node/.n8n
    depends_on:
      - postgres
    restart: unless-stopped

  postgres:
    image: postgres:15-alpine
    container_name: n8n_postgres
    environment:
      - POSTGRES_DB=n8n
      - POSTGRES_USER=n8n
      - POSTGRES_PASSWORD=n8n_db_password
    volumes:
      - postgres_data:/var/lib/postgresql/data
    restart: unless-stopped

volumes:
  n8n_data:
  postgres_data:

Deploy this on Hetzner VPS or DigitalOcean, and you have a production n8n instance. For more details on the self-hosted vs cloud debate, check out my guide on self-hosted workflow automation vs cloud SaaS platforms.

💡 Fast-Track Your Project: Don't want to configure this yourself? I build custom n8n pipelines and bots. Message me with code SYS3-DEVTO.

When n8n Wins:

Quick integrations between SaaS tools
Visual workflow design for non-technical users
Rapid prototyping and testing
Small to medium-scale data operations
Teams that need ease-of-use over raw power

The Tradeoff:

While n8n is flexible, it's not ideal for complex business logic that requires significant code. You can write custom code in nodes, but you're fighting the platform's paradigm if you need heavy computation.

Apache Airflow: The Python-Native Orchestrator

Airflow lets you define workflows as Python DAGs (Directed Acyclic Graphs). If you're comfortable with Python and need fine-grained control, Airflow is your platform.

Here's a data pipeline that downloads crypto price data, processes it, and uploads results:

from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.operators.bash import BashOperator
from airflow