Convert Parquet, Feather, ORC & Avro Files with ParquetReader

Nodes

📄 Convert Parquet, Feather, ORC & Avro Files with ParquetReader

This workflow allows you to upload and inspect Parquet, Feather, ORC, or Avro files via the ParquetReader API. It instantly returns a structured JSON preview of your data — including rows, schema, and metadata — without needing to write any custom code.

✅ Perfect For

Validating schema and structure before syncing or transformation
Previewing raw columnar files on the fly
Automating QA, ETL, or CI/CD workflows
Converting Parquet, Avro, Feather, or ORC to JSON

⚙️ Use Cases

Catch schema mismatches before pipeline runs
Automate column audits in incoming data files
Enrich metadata catalogs with real-time schema detection
Integrate file validation into automated workflows

🚀 How to Use This Workflow

📥 Trigger via File Upload

You can trigger this flow by sending a POST request with a file using curl, Postman, or from another n8n flow.

🔧 Example (via `curl`):

curl -X POST http://localhost:5678/webhook-test/convert \
-F "file=@converted.parquet"

Replace converted.parquet with your local file path. You can also send Avro, ORC or Feather files.

🔁 Reuse from Other Flows

You can reuse this flow by calling the webhook from another n8n workflow using an HTTP Request node.
Make sure to send the file as form-data with the field name file.

🔍 What This Flow Does:

Receives the uploaded file via webhook (file)
Sends it to https://api.parquetreader.com/parquet as multipart/form-data (field name: file)
Receives parsed data (rows), schema, and metadata in JSON format

🧪 Example JSON Response from this flow

{
  "data": [
    {
      "full_name": "Pamela Cabrera",
      "email": "bobbyharrison@example.net",
      "age": "24",
      "active": "True",
      "latitude": "-36.1577385",
      "longitude": "63.014954",
      "company": "Carter, Shaw and Parks",
      "country": "Honduras"
    }
  ],
  "meta_data": {
    "created_by": "pyarrow",
    "num_columns": 21,
    "num_rows": 10,
    "serialized_size": 7598,
    "format_version": "0.12"
  },
  "schema": [
    { "column_name": "full_name", "column_type": "string" },
    { "column_name": "email", "column_type": "string" },
    { "column_name": "age", "column_type": "int64" },
    { "column_name": "active", "column_type": "bool" },
    { "column_name": "latitude", "column_type": "double" },
    { "column_name": "longitude", "column_type": "double" },
    { "column_name": "company", "column_type": "string" },
    { "column_name": "country", "column_type": "string" }
  ]
}