This article provides a complete, practical walkthrough of the Http Timescaledb 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:  
   Tracking the International Space Station Using n8n and TimescaleDB
   
   Meta Description:  
   Learn how to automate the real-time tracking of the International Space Station (ISS) using n8n, the Where the ISS At API, and TimescaleDB—all in a fully automated data pipeline.
   
   Keywords:  
   n8n, TimescaleDB, ISS tracking, automation, real-time data, satellite data, no-code workflow, Where the ISS At API, space data, cron job automation
   
   Third-Party APIs Used:
   
   - Where the ISS At API (https://wheretheiss.at)
   
   Article:
   
   Automate ISS Tracking with n8n and TimescaleDB
   
   The International Space Station (ISS) travels around the Earth approximately every 90 minutes, creating an ever-changing stream of geospatial data. Using modern automation tools like n8n and a time-series database like TimescaleDB, you can build a lightweight workflow that captures this positional data in near real-time.
   
   In this article, we’ll walk through a simple but powerful n8n workflow that fetches current ISS latitude and longitude coordinates from a public API, processes the data, and stores it into a TimescaleDB database for analysis or visualization.
   
   Why Use n8n?
   
   n8n is a powerful open-source workflow automation tool that allows for logic-based data handling—without needing to write code. It connects with hundreds of services and supports custom transformations with low effort, which makes it ideal for setting up a data ingestion pipeline for ongoing tasks like ISS tracking.
   
   Workflow Overview
   
   This workflow performs the following steps every minute:
   
   1. Triggers on a cron schedule (every 60 seconds)
   2. Makes an HTTP request to the Where the ISS At API to retrieve the current satellite position
   3. Extracts key data (latitude, longitude, timestamp) from the returned data
   4. Inserts the new position into a TimescaleDB table named iss
   
   Let’s explore each node in detail.
   
   Step 1: Cron Trigger — Timed Automation
   
   The workflow starts with the Cron node, configured to trigger every minute. This node ensures that the ISS position is checked regularly and new data is continuously collected without manual intervention. Timed triggers like this are crucial in time-series data pipelines where consistent intervals matter.
   
   Configuration:
   - Interval: Every minute
   
   Step 2: HTTP Request — Fetch ISS Location
   
   Next, the HTTP Request node calls the Where the ISS At API, a public service that tracks the ISS location in real time. The API endpoint used is:
   
   GET https://api.wheretheiss.at/v1/satellites/25544/positions
   
   A single timestamp is passed as a query parameter to get the ISS’s current position at that specific time. This ensures fresh, precise location data with each call.
   
   The request includes:
   - URL: https://api.wheretheiss.at/v1/satellites/25544/positions
   - Query Parameter: timestamps={{Date.now()}}
   
   The response contains data like latitude, longitude, altitude, velocity, and timestamp.
   
   Step 3: Set Node — Data Extraction
   
   The JSON response from the API contains a list, so the Set node is used to extract and map only the necessary fields — latitude, longitude, and timestamp — from the JSON object. These fields are critical for placing the ISS on a map or running analyses over time.
   
   This node cleans up the dataset before it is sent to the database, ensuring that only relevant and properly formatted data is retained for storage.
   
   Step 4: TimescaleDB — Storing the Data
   
   Finally, the formatted data is pushed into a TimescaleDB table named iss. TimescaleDB is optimized for time-series data and can easily handle frequent inserts like this.
   
   The database table includes the following columns:
   - latitude (FLOAT)
   - longitude (FLOAT)
   - timestamp (TIMESTAMP)
   
   By inserting data every minute, we maintain a high-resolution log of the ISS’s orbit, ready for querying, visualization on tools like Grafana, or alerts based on geofencing or proximity to a given location.
   
   Putting It All Together
   
   This workflow exemplifies how a minimal setup of open-source tools and public APIs can yield powerful results. Every minute, a new ISS position is recorded in your database—without a single line of hand-coded logic. n8n’s visual interface makes debugging, scaling, and maintenance easy, while TimescaleDB offers high-performance storage and querying capabilities, especially suited for use cases involving time and location.
   
   Use Cases and Next Steps
   
   Here are a few ways you can expand this setup:
   
   - Visualize the ISS orbit on a live dashboard with Grafana
   - Send alerts when the ISS is over specific geographic zones
   - Aggregate velocity and altitude data for research
   - Create a public space-tracking website
   
   Conclusion
   
   With just a few nodes in n8n, you can build an automated pipeline that continuously tracks one of humanity’s most significant space achievements. Whether you're an educator, a space enthusiast, or a data engineer, this solution demonstrates how accessible real-time data pipelines have become thanks to modern tools.
   
   Bonus Tip: Since the ISS completes an orbit roughly every 90 minutes, storing data every minute gives you a granular yet efficient 90 data points per orbit — a useful balance between precision and storage cost.
   
   Ready to build your own space tracker? Start with n8n, hook up TimescaleDB, and let automation take over.
   
   And yes, the sky is no longer the limit. 🚀

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.