Convert Parquet, Avro, ORC & Feather via ParquetReader to JSON

This workflow automates the processing of user research data by importing survey responses from Google Sheets, generating text vectors using OpenAI, and storing them in the Qdrant database. It identifies major groups through the K-means clustering algorithm and utilizes large language models to perform intelligent summarization and sentiment analysis on the group responses. Finally, the insights are automatically exported back to Google Sheets, creating a structured research report. This process enhances analysis efficiency and helps decision-makers quickly gain deep insights.

This workflow is manually triggered to automatically extract all order data with a "Completed" status from the Unleashed Software system, helping users efficiently filter and centrally manage order information. It is suitable for finance, sales, or operations teams, effectively reducing the time spent on manual queries, improving the accuracy and efficiency of order management, and facilitating subsequent data analysis and report generation.

The main function of this workflow is to automate the extraction of content from specified web pages. Users only need to provide the webpage URL, and the system will use the FireCrawl API to retrieve the webpage data and convert it into Markdown format for return. This process lowers the technical barrier and enhances extraction efficiency, making it suitable for various scenarios such as AI intelligent agents, office automation, data collection, and content monitoring, thereby facilitating quick integration of web scraping functionality for both developers and non-technical users.

This workflow automates the collection of customer reviews from Trustpilot and utilizes AI technology to extract key information from the reviews and perform sentiment analysis. By structuring the review data and analyzing sentiment trends, businesses can quickly gain insights into customer feedback, monitor brand reputation, and simultaneously update the results in real-time to Google Sheets. This enhances the efficiency of data collection and analysis, supporting market research, customer service improvement, and decision-making.

This workflow enables real-time monitoring of new or updated data in Google Sheets. When relevant rows are updated, the system automatically extracts key fields such as "Security Code," "Price," and "Quantity," and converts them into a neatly formatted ASCII table, which is then sent to a designated channel via Discord's Webhook. This process significantly enhances the timeliness and accuracy of data synchronization, making it suitable for teams that require quick sharing and collaboration, especially in the fields of finance and project management.

This workflow automatically retrieves website traffic data from the Umami analytics tool on a regular basis. It utilizes AI models for in-depth interpretation and SEO analysis, ultimately saving the results to a Baserow database. By comparing this week's performance with last week's, it generates optimization suggestions, significantly enhancing the efficiency of data insights. It helps website operators and SEO experts quickly identify traffic changes, optimize content strategies, save time, and avoid misjudgments, making it an effective tool for improving website competitiveness.

This workflow is designed to monitor price fluctuations in the cryptocurrency market in real-time. It automatically retrieves data from the Binance exchange at scheduled intervals and filters out cryptocurrencies with price changes exceeding 15%. The organized key information will be pushed to a designated group via Telegram, ensuring that users stay updated on market dynamics and can quickly seize investment opportunities or risks, thereby enhancing decision-making efficiency. It is applicable in various scenarios, including for traders, analysts, and cryptocurrency asset management teams.

This workflow combines advanced data collection services with AI language models to automatically scrape information from personal and company pages on LinkedIn, generating high-quality company stories or personal profiles. Users can efficiently obtain structured data, avoiding the time wasted on manual operations. It also supports saving the scraped results as local files or real-time pushing via Webhook for convenient later use. This is suitable for various scenarios such as market research, recruitment, content creation, and data analysis, significantly enhancing information processing efficiency.