Youtube Searcher

YouTube Searcher automates the process of fetching and analyzing video data, allowing users to easily identify and save the best-performing videos from specific channels. It efficiently filters out shorts and retrieves detailed statistics, ensuring that only relevant content is stored in the database. This streamlined workflow enhances content management and optimizes video selection for improved engagement.

7/8/2025
21 nodes
Complex
manualcomplexsplitinbatchesexecuteworkflowtriggerpostgresqlyoutubesticky noteadvancedapiintegrationdatabasedatalogicconditional
Categories:
Business Process AutomationData Processing & AnalysisCreative Content & Video AutomationManual TriggeredComplex Workflow
Integrations:
SplitInBatchesExecuteWorkflowTriggerPostgreSQLYouTubeSticky Note

Target Audience

This workflow is ideal for:
- Content Creators: YouTubers looking to analyze and optimize their video performance.
- Data Analysts: Professionals who need to extract and analyze video statistics from YouTube.
- Marketing Teams: Teams aiming to identify trending content and improve their video marketing strategies.
- Developers: Those interested in automating data retrieval and storage from YouTube to PostgreSQL databases.

Problem Solved

This workflow addresses the challenge of efficiently gathering and analyzing YouTube video statistics. It automates the process of:
- Fetching video data: Retrieves performance metrics like views, likes, and comments for specified channels.
- Filtering out shorts: Excludes short videos to focus on longer content that may have more engagement.
- Storing data: Saves the gathered statistics in a PostgreSQL database for further analysis, ensuring no data is lost.
- Identifying best-performing videos: Helps users discover videos that are currently trending or have high engagement in the last two weeks.

Workflow Steps

  • Manual Trigger: The workflow starts when the user manually initiates it.
    2. Loop Over Items: Iterates through a list of YouTube channels.
    3. Fetch Last Registered Video: Queries the database to get the latest video publish time for each channel.
    4. Get Videos: Retrieves a list of videos from YouTube based on the latest publish time, filtering for relevance and specific criteria.
    5. Check for Empty Results: Determines if the video list is empty, proceeding to fetch video data if not.
    6. Find Video Data: Calls the YouTube API to get detailed statistics for each video.
    7. Remove Shorts: Filters out videos shorter than 3.5 minutes.
    8. Structure Data: Prepares the video data for database insertion.
    9. Create Query: Constructs an SQL insert query to add the video statistics to the PostgreSQL database.
    10. Insert Items: Executes the insert query to store video statistics.
    11. Sanitize Data: Cleans up the data to ensure accuracy before processing.
    12. Identify Best Performing Videos: Analyzes the stored data to find videos that have performed well in the last two weeks.
    13. Output Results: The workflow can output best-performing videos for further review or action.

    • Customization Guide

    To customize this workflow:
    - Change API Keys: Update the YouTube API key in the find_video_data1 node to ensure proper authentication.
    - Modify Filters: Adjust the filters in the get_videos node to change criteria such as regionCode or publishedAfter settings.
    - Database Connection: Ensure the PostgreSQL credentials are correctly set in the Postgres nodes for successful data storage.
    - Adjust SQL Queries: Modify the SQL queries in the create_query and Postgres nodes to fit your data schema or analysis needs.
    - Add More Nodes: Integrate additional nodes for further processing or notifications, such as sending alerts when new best-performing videos are identified.