Google Trend Data Extraction and Summarization with Bright Data & Google Gemini

This workflow is manually triggered and automatically connects to retrieve the latest data from a specified Monday.com board, streamlining the data acquisition process. Users can call the API without writing any code, quickly obtaining structured data, thus addressing the cumbersome issue of manually logging in and reviewing data line by line, thereby enhancing data utilization efficiency. It is suitable for project managers and data analysts, facilitating data analysis and decision support.

This workflow is manually triggered to call SpaceX's publicly available GraphQL API, retrieving detailed information about the five most recent space launches in real time. The content includes the mission name, launch time, launch site, relevant links, rocket and its stages, payload, and information about related vessels. It automates the integration of official data, enhancing the efficiency and accuracy of information retrieval, making it suitable for space enthusiasts, media, educators, and developers to conveniently stay updated on the latest launch activities.

This workflow automatically calls the API of the Taiwan agricultural department to obtain lamb price data for specified markets. It then structures this data and writes it into Google Sheets, achieving automated data collection and organization. The process is efficient and straightforward, significantly reducing the time and error rate associated with manual data collection. It helps users stay updated on market dynamics in real-time, enhancing the accuracy and timeliness of data updates. This workflow is suitable for agricultural product traders, analysts, and relevant departments.

The main function of this workflow is to consolidate the generated simulation data into a unified array of objects, facilitating subsequent processing and transmission. It addresses the issue of merging scattered data entries in automated processes, making the data format more concise and efficient. This is suitable for simulation data testing, interface testing, and batch data processing, particularly for automation developers and data engineers, enhancing the flexibility and efficiency of the workflow.

This workflow can automatically extract the most recently released video data from a specified YouTube channel, filter out short videos, and select high-performing long videos from the past two weeks while calculating the like rate. After organizing the data, the high-quality video information will be stored in a PostgreSQL database, supporting subsequent data analysis and operational decision-making. This will help content creators and data analysts monitor video performance in real-time and optimize content strategies.

This workflow is designed to automatically convert uploaded CSV files or text data into JSON format, supporting multiple input methods and intelligently parsing delimiters to ensure data accuracy. The conversion results are returned via API response, and in the event of an error, detailed notifications are sent to a Slack channel for real-time monitoring. This tool simplifies traditional data processing workflows, enhances response speed and stability, and lowers the technical barrier, making it suitable for software developers, business operations, and data teams to efficiently perform data format conversion and integration.

This workflow automatically retrieves 24-hour trading data for BTC, ETH, and SOL from Binance every hour. It uses a custom analysis function to calculate key market indicators and pushes the results to a designated Telegram group chat in a formatted HTML message. It can summarize cryptocurrency market trends in real-time, eliminating the need for manual queries, and provides multi-dimensional market insights to help traders and investors stay updated on market dynamics, thereby improving decision-making efficiency and information transparency.

This workflow demonstrates how to efficiently merge information from different data sources, similar to various join operations in SQL. By simulating two sets of data, it showcases multiple data merging methods such as inner join, left join, and union, helping users understand the processes of data filtering, enrichment, and integration. It is applicable in scenarios such as supply chain management, data analysis, and team management, assisting users in quickly achieving data integration and analysis to enhance work efficiency.