Sync YouTube Video URLs with Google Sheets

This workflow implements the automated synchronization and export of Shopify customer data, effectively addressing the API pagination limitation issue. It extracts and merges all customer information from Shopify, which can be triggered either on a schedule or manually, and updates it in real-time to Google Sheets for easier management and backup. Additionally, it automatically generates CSV files that meet Squarespace import requirements, significantly reducing the time spent on manual processing and improving the efficiency of multi-platform data management.

This workflow automatically checks the specified Google Sheets every 45 minutes to detect newly added data in real-time. Once new entries are found, the system sends an instant notification via Mattermost, including the ID, name, and email of the new data. This process significantly enhances the efficiency of data monitoring and addresses the cumbersome issue of data personnel manually checking the spreadsheet. It is suitable for teams that require quick responses to customer information updates, such as sales and customer service.

This workflow automates the data scraping from the Google Trends website and performs structured extraction using Bright Data's Web Unlocker. By integrating the Google Gemini language model, it completes information extraction and content summarization, generating trend data and summary reports. It supports real-time result push notifications and email delivery, ensuring users can conveniently access market dynamics, enhancing data analysis and decision-making efficiency. This workflow is applicable in various fields such as market research, content creation, and business intelligence.

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.