Collects images from web search results and send to Google Sheets
This workflow collects images from web search results on a specific query, analyzes the image for labels, formats the text, and adds the information in Google Sheets. HTTP Request node gets images from the web. AWS Rekognition node analyzes the images (in this case, it detects text in an image). Set node sets the values necessary for the data set. Function node transforms the text detected in the image to lower case. Google Sheets node adds the image information to a sheet that serves as data set.
By LorenaConvert parquet, feather, ORC & Avro files with ParquetReader
๐ 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 โ including rows, schema, and metadata โ without needing to write any custom code. --- โ 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 --- โ๏ธ 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 --- ๐ How to Use This Workflow ๐ฅ 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. ๐ง Example (via curl): bash 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. ๐ 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. ๐ 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 --- ๐งช Example JSON Response from this flow json { "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": [ { "columnname": "fullname", "column_type": "string" }, { "columnname": "email", "columntype": "string" }, { "columnname": "age", "columntype": "int64" }, { "columnname": "active", "columntype": "bool" }, { "columnname": "latitude", "columntype": "double" }, { "columnname": "longitude", "columntype": "double" }, { "columnname": "company", "columntype": "string" }, { "columnname": "country", "columntype": "string" } ] } --- ๐ API Info Authentication: None required Supported formats: .parquet, .avro, .orc, .feather Free usage: No signup needed; API is currently open to the public Limits: Usage and file size limits may apply in the future (TBD)
By ParquetReader