This article provides a complete, practical walkthrough of the Http Awssqs 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 ISS Tracking with n8n: A Workflow to Stream ISS Position Data to AWS SQS  
   
   Meta Description:  
   Learn how to build an automated n8n workflow that retrieves the real-time position of the International Space Station (ISS) every minute and streams the data to AWS SQS for further processing or storage.  
   
   Keywords:  
   n8n, ISS tracking, AWS SQS, automation, serverless workflows, satellite data, real-time data, space API, n8n HTTP request, no-code automation, data pipeline  
   
   Third-Party APIs Used:  
   - Where The ISS At API (https://wheretheiss.at/w/developer)
   
   Article:  
   In the age of automation, tracking satellites like the International Space Station (ISS) can serve various use cases—from educational projects to logistics and even space-based signal coordination. If you're looking to capture live ISS positional data and stream it into a message queue system for further processing, this article demonstrates how a no-code or low-code workflow orchestration tool like n8n can make it a breeze.
   
   This tutorial breaks down an n8n workflow that pulls data from the Where The ISS At API every minute and sends it to an AWS SQS (Simple Queue Service) queue. This dynamic workflow can easily be adapted to a variety of real-time data ingestion pipelines.
   
   Overview of the Workflow  
   At a high level, this n8n workflow is composed of four nodes:
   
   1. Cron Node – Triggers the workflow every minute  
   2. HTTP Request Node – Calls the Where The ISS At API to get the current ISS position  
   3. Set Node – Extracts and organizes relevant data fields (latitude, longitude, timestamp, and name)  
   4. AWS SQS Node – Sends the data in a message to an AWS SQS queue  
   
   Let’s break down what each component does.
   
   Step 1: Triggering with Cron  
   The "Cron" node is configured with the `everyMinute` option. This means every minute, like clockwork, it triggers the rest of the workflow. This frequency is ideal for capturing real-time or near-real-time positional data of the ISS without overwhelming your infrastructure.  
   
   This node is excellent for scheduled tasks and time-based automations where consistency is key.
   
   Step 2: Getting ISS Data via API  
   The second node, “HTTP Request,” queries the Where The ISS At API. This public API provides real-time position data for multiple satellites, including the ISS, which is identified by its NORAD ID: 25544.  
   
   In this configuration, the API is called with a query parameter that includes the current timestamp. This effectively fetches the most recent position for the ISS.
   
   URL used:  
   https://api.wheretheiss.at/v1/satellites/25544/positions?timestamps={current_timestamp}
   
   The API returns a JSON response that includes data like latitude, longitude, timestamp, and name—ideal for geospatial applications or real-time monitoring systems.
   
   Step 3: Structuring the Data  
   The “Set” node is where we clean and prepare the data for the final destination. Only a few fields are selected and retained:  
   
   - Name: The name of the satellite (ISS)  
   - Latitude: The latitude of the ISS at the current timestamp  
   - Longitude: The longitude of the ISS  
   - Timestamp: A Unix timestamp indicating the exact time of the ISS coordinates  
   
   We use the `keepOnlySet` option to filter out any other fields from the HTTP Response, ensuring a lean message payload, saving both bandwidth and storage.
   
   Step 4: Sending to AWS SQS  
   The final component is the “AWS SQS” node, which is configured to use AWS credentials stored within n8n. This node takes the structured JSON data output from the Set node and sends it as a message to an AWS SQS queue.  
   
   This allows you to decouple the retrieval of the ISS position from downstream applications that may consume this data asynchronously—like a data pipeline, analytics dashboard, or even a space-themed game.
   
   Why This Workflow Matters  
   This kind of automated workflow can be scaled and modified for numerous applications:
   
   - Educational dashboards showing real-time satellite movement  
   - Research tools analyzing orbital paths and satellite coverage  
   - Integration with IoT systems or alerting mechanisms  
   
   Additionally, using AWS SQS as the target in this architecture makes the data easily consumable by other AWS services such as Lambda, DynamoDB, or Kinesis for further real-time processing, storage, or visualization.
   
   Customizing for Your Use Case  
   Want to go a step further? Here are some ways to level up this workflow:
   
   - Add a conditional node to filter latitudes or longitudes, triggering alerts when the ISS is over specific geographies  
   - Store the position in a database like PostgreSQL or Google BigQuery for historical analysis  
   - Use a Slack or Telegram node to get notifications when the ISS reaches a specific longitude  
   - Include error-handling nodes to retry failed API calls or SQS transmissions  
   
   Conclusion  
   With serverless tools like n8n, integrating real-time space data into your own applications or services is no longer the domain of NASA or private aerospace giants. Anyone with basic automation knowledge can create powerful, reusable workflows to bring space a little closer to home.
   
   The workflow we described automates the fetching and streaming of ISS data every minute, leveraging a public API and Amazon AWS’s robust messaging architecture. As with any modular system, the real magic lies in how you adapt it for your own needs. Happy automating!

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.