This article provides a complete, practical walkthrough of the Cron Postgres Automation Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating Sensor Data Logging with n8n and PostgreSQL
   
   Meta Description:  
   Learn how to automate environmental sensor data ingestion into a PostgreSQL database using n8n's no-code workflow automation tools. This guide walks through setting up a scheduled workflow that collects and stores sensor data every minute.
   
   Keywords:  
   n8n workflow automation, PostgreSQL integration, sensor data logging, no-code automation, IoT data ingestion, cron job, n8n tutorial, real-time data capture
   
   Third-Party APIs Used:  
   - PostgreSQL (through n8n's native PostgreSQL node)
   
   Article:
   
   Automating Sensor Data Logging with n8n and PostgreSQL
   
   In today’s data-driven world, being able to automate the collection of sensor data can significantly streamline operations and provide real-time insights. Whether you’re a smart home enthusiast, a factory automation engineer, or simply exploring the Internet of Things (IoT), collecting periodic sensor data into a dynamic database is often an essential requirement. Fortunately, with workflow automation tools like n8n and robust relational databases like PostgreSQL, this task becomes incredibly straightforward.
   
   In this article, we’ll explore a real-world use case implemented using n8n—a powerful, open-source workflow automation tool—to automatically collect simulated humidity sensor data and store it into a PostgreSQL database. The best part? This entire setup requires no manual intervention once deployed.
   
   🛠️ Overview of the Workflow
   
   The workflow, titled “Postgres Data Ingestion,” consists of three main nodes:
   1. Cron Node – Triggers the workflow every minute.
   2. Function Node – Simulates sensor data with timestamp and value.
   3. PostgreSQL Node – Stores the generated data in a dedicated database table.
   
   Let’s break down each component for better understanding.
   
   ⏰ Cron Node – Scheduling the Workflow
   
   The Cron node is the starting point of this workflow and is configured to trigger the flow every minute. This is particularly useful in IoT and monitoring applications where high-frequency data collection is required.
   
   In the node configuration, the setting is as follows:
   - Mode: everyMinute
   
   This means n8n will automatically execute the connected nodes once every 60 seconds without any further input from the user. This forms the heartbeat of our workflow automation.
   
   📐 Function Node – Simulating Sensor Data
   
   The Function node is where we create sensor-like data. Although the current implementation generates dummy data, it mimics what would typically be received from a real-world device.
   
   Here’s what this JavaScript-based function does:
   - Generates a current timestamp,
   - Assigns a constant sensor ID (humidity01),
   - Randomly picks a humidity value between 1 and 100,
   - Sets a 'notification' flag to false.
   
   Sample Function Code:
   ```javascript
   var today = new Date();
   var date = today.getFullYear()+'-'+(today.getMonth()+1)+'-'+today.getDate();
   var time = today.getHours() + ":" + today.getMinutes() + ":" + today.getSeconds();
   var dateTime = date+' '+time;
   
   items[0].json.sensor_id = 'humidity01';
   items[0].json.value = Math.ceil(Math.random()*100);
   items[0].json.time_stamp = dateTime;
   items[0].json.notification = false;
   
   return items;
   ```
   
   This setup allows for realistic simulation during testing or prototyping. In a production environment, this Function node could be swapped out for a webhook or API call to a physical sensor.
   
   🛢️ PostgreSQL Node – Storing the Data
   
   The final step in the workflow is the PostgreSQL node, which takes the generated JSON data from the Function node and writes it into a predefined table called "n8n".
   
   Key settings:
   - Table: n8n
   - Columns: sensor_id, value, time_stamp, notification
   
   This ensures each data point—along with its metadata—is properly logged for future retrieval, visualization, or analytics.
   
   🔌 Integration and Execution
   
   Connecting the nodes is straightforward:
   - The Cron node’s output connects to the Function node’s input.
   - The Function node’s output connects to the PostgreSQL node’s input.
   
   When activated, the workflow runs autonomously every minute, generating and storing new sensor data entries in your database.
   
   🌐 Scalability & Real-World Applications
   
   This basic implementation can be extended in numerous ways:
   - Multiple sensor types (add logic for different sensor_id values)
   - Threshold-based alerts (trigger emails or notifications)
   - Real sensor inputs (from ESP32, Raspberry Pi, etc.)
   - Integration with dashboards like Grafana for live tracking
   
   Because n8n supports a wide array of integrations via its nodes—including HTTP requests, MQTT, Slack, Telegram, and many more—the opportunities for enhancement and customization are virtually limitless.
   
   🧠 Why Use n8n?
   
   n8n allows users to build complex workflows using a no-code/low-code interface. Compared to other automation tools, it offers self-hosting options, excellent extensibility through JavaScript, and a growing community.
   
   With just three nodes and minimal configurations, we now have a robust mini-pipeline that can manage sensor data reliably and in real time.
   
   📦 Conclusion
   
   This simple yet highly practical workflow showcases the power of combining n8n's automation framework with PostgreSQL for sensor data logging. Whether you're monitoring environmental conditions in a greenhouse, tracking server temperatures in a data center, or logging user activity metrics, this setup offers a flexible and scalable foundation.
   
   Start small, scale big—the beauty of automation lies in its adaptability.
   
   Ready to automate your data ingestion pipeline? Try n8n today and see how quickly your workflows can come to life.
   
   —  
   Tags: #n8n #workflowAutomation #PostgreSQL #IoT #SensorData #NoCode

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.