This article provides a complete, practical walkthrough of the Http Code Process 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:  
   Converting Parquet, Avro, ORC & Feather to JSON with n8n and ParquetReader API
   
   Meta Description:  
   Learn how to build a no-code data conversion pipeline in n8n to seamlessly convert Parquet, Avro, ORC, and Feather files into JSON using the ParquetReader API.
   
   Keywords:  
   n8n, ParquetReader API, Parquet to JSON, Avro to JSON, ORC to JSON, Feather to JSON, data conversion workflow, ETL automation, no-code data pipeline, API integration, file transformation
   
   Third-Party APIs Used:
   - ParquetReader API (https://api.parquetreader.com/parquet)
   
   Article:
   
   In modern data workflows, interoperability between diverse file formats is crucial. While formats like Parquet, Avro, ORC, and Feather are commonly used in big data and analytics, they aren't always directly usable in web applications or APIs that require JSON. Manually converting these formats can interrupt the pipeline or require heavy development work. Fortunately, tools like n8n—a powerful, open-source workflow automation tool—can help bridge this gap with minimal code.
   
   This article walks you through an n8n workflow that automates the conversion of complex binary data files—including Parquet, Avro, ORC, and Feather—into structured JSON using the ParquetReader API. Whether you're building a data ingestion pipeline or an internal data tool, this solution helps you integrate big data formats into lightweight, everyday usage scenarios.
   
   🎯 Overview of the Workflow
   
   The "Convert Parquet, Avro, ORC & Feather via ParquetReader to JSON" n8n workflow is designed to handle incoming files via webhook and convert them into JSON with minimal manual intervention. It operates in four key steps:
   
   1. Accepts a file upload via an HTTP webhook.
   2. Forwards the uploaded file to the ParquetReader API.
   3. Processes and parses the API's JSON response, including metadata.
   4. Outputs structured JSON for downstream n8n processing or external use.
   
   Let’s break down each component.
   
   📥 Step 1: Webhook Input (Trigger Node)
   
   The workflow begins with a simple Webhook node:
   
   - Method: POST  
   - Path: /convert  
   - Payload Type: multipart/form-data  
   - Binary Property Key: file  
   
   This allows users or systems to send a POST request with a `.parquet`, `.avro`, `.orc`, or `.feather` file attached under the field name "file".
   
   Here's an example using curl:
   
   ```bash
   curl -X POST http://localhost:5678/webhook-test/convert \
     -F "file=@converted.parquet"
   ```
   
   This makes it easy for both technical and no-code users to trigger the pipeline.
   
   🌐 Step 2: Sending File to ParquetReader API
   
   Once the file is received, an HTTP Request node named "Send to Parquet API" takes over:
   
   - Method: POST  
   - URL: https://api.parquetreader.com/parquet?source=n8n  
   - Data Type: multipart/form-data  
   - The binary data is sent using the property name file0  
   
   The ParquetReader API is a third-party web service that parses uploaded Parquet, Avro, ORC, or Feather files and returns structured output. It eliminates the need for local client libraries or command-line tools.
   
   📦 Step 3: Parsing the API Response
   
   The response from the ParquetReader API includes fields like:
   
   - data (a stringified JSON array)
   - meta_data (a stringified JSON object)
   
   To make this information usable in downstream n8n nodes, a Code node called "Parse API Response" is used, running this JavaScript logic:
   
   ```javascript
   const item = items[0];
   
   if (typeof item.json.data === 'string') {
     item.json.data = JSON.parse(item.json.data);
   }
   
   if (typeof item.json.meta_data === 'string') {
     item.json.meta_data = JSON.parse(item.json.meta_data);
   }
   
   return [item];
   ```
   
   This ensures the workflow ends with a clean JSON object, ready for database storage, webhook delivery, visualization, or further transformation.
   
   📒 Sticky Note Documentation Built Into the Flow
   
   A strategically placed StickyNote node within the n8n UI provides built-in documentation for the workflow. It includes:
   
   - Usage instructions with curl  
   - Tips for integration into other n8n workflows  
   - A summary of the workflow logic  
   
   This is a best practice for collaborative no-code automation projects and internal tools.
   
   🔁 Reusability and Use Cases
   
   This conversion flow can be triggered by users, systems, or other n8n workflows. Its modular design makes it adaptable for:
   
   - Data lakes ingesting schema-heavy formats into MongoDB or PostgreSQL  
   - BI reporting setups that need JSON-ready data  
   - API development teams needing uniform input through multiple file types  
   - ETL pipelines that start with uploaded binary files  
   
   💡 Final Thoughts
   
   Using n8n and the ParquetReader API, transforming complex structured data into JSON has never been easier. This no-code/low-code workflow saves valuable time and programming effort, while significantly enhancing your data pipeline's flexibility. Whether you're working with legacy Hadoop data formats or simply need to make big-data files API-ready, this approach provides an automated, scalable solution.
   
   If you're new to n8n or looking to extend your data conversion capabilities, this workflow serves as a customizable template for powerful automation.
   
   ▶️ Ready to Try?
   
   Just install n8n, import the workflow, and run the curl command. Or embed it within your broader automation stack. Happy automating!
   
   —
   
   Looking for more real-world content automation use cases with n8n? Subscribe to our newsletter or check out our n8n templates library!

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.