This article provides a complete, practical walkthrough of the Http Googlebigquery 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 Position Tracking with n8n and Google BigQuery
   
   Meta Description:  
   Learn how to automate the real-time tracking of the International Space Station (ISS) using n8n, the WhereTheISS.at API, and Google BigQuery. Discover how data pipelines can efficiently log ISS positions every minute for analysis.
   
   Keywords:  
   n8n automation, Google BigQuery, ISS tracking, WhereTheISS.at API, satellite tracker, data pipeline, automation workflow, serverless integration, real-time data, satellite data storage
   
   Third-Party APIs Used:
   
   - WhereTheISS.at API
   - Google BigQuery API (via OAuth2 authentication)
   
   Article:
   
   In an era dominated by automation and data analytics, tracking real-time events from space no longer requires sophisticated hardware or astronomical budgets. Thanks to modern, low-code platforms like n8n and APIs like WhereTheISS.at, almost anyone can create a dynamic data pipeline to track the International Space Station (ISS) with ease. In this article, we walk through a powerful n8n workflow that fetches ISS positional data every minute and automatically stores it into Google BigQuery for long-term analysis and visualization.
   
   📡 The Use Case: Tracking the ISS
   
   The International Space Station (ISS), traveling at over 28,000 kilometers per hour, completes an orbit around the Earth approximately every 90 minutes. Keeping tabs on its location can provide valuable insights for educational, scientific, and commercial applications. This n8n workflow is designed to systematically log the ISS's position (latitude, longitude, and timestamp) into a Google BigQuery table named “position” within the “iss” dataset.
   
   🚀 The Tools Involved
   
   This solution leverages the following technologies:
   
   - n8n: An open-source workflow automation tool.
   - WhereTheISS.at API: A public API offering real-time and historical data about the ISS’s location.
   - Google BigQuery: A powerful, serverless data warehouse for storing and querying large volumes of data.
   
   🧠 How the Workflow Works
   
   Let’s break down each component of this n8n workflow and see how they integrate into a seamless pipeline:
   
   1. Cron Node (Scheduler)
   
   Purpose: Acts as the trigger to start the workflow.
   
   Configured to run every minute, the Cron node ensures that data is pulled from the ISS location API regularly. This granularity allows for nearly live location monitoring and offers a rich dataset for time series analysis.
   
   2. HTTP Request Node
   
   Purpose: Fetches the current position of the ISS.
   
   The HTTP Request node makes a GET request to:
   https://api.wheretheiss.at/v1/satellites/25544/positions
   
   The satellite ID 25544 corresponds to the ISS. The only query parameter used is a JavaScript expression — Date.now() — to fetch data for the current timestamp. The API returns a JSON payload that includes essential details such as latitude, longitude, and timestamp for the ISS's position at that moment.
   
   3. Set Node
   
   Purpose: Formats and prepares the data for insertion.
   
   The Set node extracts and transforms the necessary fields from the HTTP response:
   - name (satellite name),
   - latitude (numerical),
   - longitude (numerical),
   - timestamp (Unix epoch).
   
   This step ensures the output data structure matches the schema required by the BigQuery table.
   
   4. Google BigQuery Node
   
   Purpose: Inserts the formatted data into Google BigQuery.
   
   With the proper OAuth2 credentials, this node inserts the processed data into the “position” table of the “iss” dataset within the Google Cloud project “supple-cabinet-289219.” This real-time push into BigQuery allows users to immediately query or visualize the ISS’s historical and current paths using tools like Looker Studio or Tableau.
   
   💡 Benefits of the Workflow
   
   - Seamless Automation: With a single configuration, the ISS’s data is fetched and stored automatically every minute—no manual intervention required.
   - Scalable Storage: BigQuery’s serverless architecture ensures the system scales as the frequency or complexity of data grows.
   - Real-Time Analytics: The near-instantaneous nature of data collection leads to timely insights and richer analytics.
   - Easily Customizable: The n8n workflow structure can be extended to include alerts, integrations with mapping tools, or even machine learning tasks.
   
   🛠️ Potential Enhancements
   
   Although functional as-is, this solution can be upgraded in numerous ways:
   - Add Geolocation Visualization: Connect to Google Maps APIs for a live dashboard.
   - Set Up Alerts: Send notifications when the ISS is passing over a specific location.
   - Historical Comparison: Plot trends using BigQuery’s advanced SQL-based analytics engine.
   
   🎯 Conclusion
   
   This n8n workflow showcases the power of automation when paired with open APIs and cloud-grade infrastructure. From educational enthusiasts to data scientists, anyone can use this pipeline to continually log and analyze the movement of the ISS. Such integrations not only conserve development time but also empower users to focus on what matters most—extracting insights from an ocean of data.
   
   Whether you're space-curious, teaching STEM, or building data products, this no-code approach to satellite telemetry has a place in your toolkit.
   
   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.