This article provides a complete, practical walkthrough of the Http Kafka Update Webhook 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 Real-Time ISS Tracking: Sending Live Position Updates to Kafka with n8n
   
   Meta Description:  
   Learn how to automate the tracking of the International Space Station (ISS) using n8n by sending live position updates every minute to a Kafka topic. A practical guide using public APIs and event-driven data streaming.
   
   Keywords:  
   n8n workflow, ISS tracking, real-time data, Kafka integration, public APIs, automation, space station monitoring, IoT with Kafka, n8n cron, HTTP Request node, data streaming, satellite position API
   
   Third-Party APIs Used:  
   - Where the ISS API (https://api.wheretheiss.at)
   
   Article:
   
   In today's data-driven world, automation tools like n8n empower users to build complex workflows without writing a single line of code. One fascinating use case is combining real-time space data with event streaming platforms like Apache Kafka. In this article, we’ll explore how an n8n workflow can be used to fetch the live position of the International Space Station (ISS) every minute and publish the information to a Kafka topic for downstream consumption.
   
   Overview of the Workflow
   
   The n8n workflow titled “Send updates about the position of the ISS every minute to a topic in Kafka” consists of four key steps:
   
   1. A Cron node that runs every minute.
   2. An HTTP Request node to fetch the ISS’s position.
   3. A Set node to structure the retrieved data.
   4. A Kafka node to publish the structured data.
   
   Let's break down how each of these nodes contributes to the workflow.
   
   Step 1: Cron Node – Timed Execution
   
   At the core of this automation is the Cron node, which triggers the workflow every minute. The "everyMinute" mode ensures that the ISS’s position is constantly monitored in near real-time, without user intervention. This is ideal for telemetry dashboards, space apps, or IoT systems that require constant updates on orbital data.
   
   Step 2: HTTP Request Node – Fetching ISS Data
   
   Once triggered, the workflow makes a RESTful API call to https://api.wheretheiss.at/v1/satellites/25544/positions using an HTTP Request node. This API provides accurate real-time satellite data about the location of the ISS, designated with the NORAD ID 25544.
   
   The workflow includes a dynamically generated timestamp parameter using JavaScript: {{Date.now();}}, which ensures the API call reflects the current position of the ISS based on time-of-request.
   
   Step 3: Set Node – Structuring the Output
   
   After receiving the response from the ISS API, the Set node extracts and structures specific fields for easier downstream use:
   
   - name: The satellite's name
   - latitude: The ISS’s current latitude
   - longitude: The ISS’s current longitude
   - timestamp: The timestamp of the position data
   
   This method keeps the Kafka message payload clean and ensures only the relevant data is passed forward, reducing bandwidth and improving performance on the Kafka consumer side.
   
   Step 4: Kafka Node – Publishing the Data
   
   The final step in the workflow is handled by a Kafka node configured to publish messages under the topic "iss-position". Kafka, a distributed event streaming platform, ensures seamless transmission of the ISS’s position to any subscribing service in real-time. Developers can then build microservices, dashboards, analytics tools, or alert systems downstream that respond to this continuous stream of spatial data.
   
   Why Use Kafka?
   
   Apache Kafka is widely adopted for building real-time streaming data pipelines. In the context of this workflow, Kafka allows:
   
   - Real-time ISS data sharing across services
   - Scalable data ingestion for analytics or machine learning
   - Subscription-based architecture—multiple services can consume ISS position data without changes to the workflow
   
   Use Cases
   
   This n8n-powered ISS tracking workflow has a broad spectrum of practical applications:
   
   - Educational tools: Schools and science centers can use this real-time data in visual displays or educational apps.
   - Space enthusiast dashboards: Web and mobile apps can display the exact position of the ISS globally.
   - Scientific research: Position logs over time can be used to correlate the ISS’s presence with Earth-based events.
   - IoT Integration: Smart systems like home observatories can respond when the ISS is visible overhead.
   
   Benefits of the n8n + Kafka Combo
   
   - Low-code automation: n8n provides an accessible interface to build even complex automation logic.
   - Modular: Each step is customizable—swap out the ISS API, change the message format, or add logging.
   - Scalable: Kafka ensures that the real-time updates are handled reliably even under heavy load.
   - Extensible: The workflow can be expanded to include storage (e.g., MongoDB or PostgreSQL) or visualization (e.g., Grafana or Next.js dashboards) with additional nodes.
   
   Conclusion
   
   Using n8n to automate the fetching and publishing of ISS position data is a practical example of combining low-code tools with the power of real-time data streaming. Whether you're a hobbyist or a professional developer, this use case shows how easily you can tap into space data via public APIs and integrate them into scalable streaming infrastructure like Kafka.
   
   As space technology becomes more accessible, tools like n8n will play a critical role in democratizing data and enabling meaningful applications—from classrooms to control rooms.
   
   Stay tuned for more automation ideas and detailed workflow guides using n8n!
   
   Author’s Note:  
   The designated API in use—Where the ISS At?—is a publicly available endpoint. Be mindful of API rate limits and appropriate usage in production environments.

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.