YouTube Data Harvesting and Warehousing: A Complete Project Walkthrough

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Introduction

 Ever wondered how much data YouTube holds beyond just your favorite videos? Imagine building an app that lets you peek behind the curtain and unlock valuable insights hidden in every channel. Dive in with us as we turn raw YouTube data into meaningful stories!

In the age of digital media, data from platforms like YouTube can offer a wealth of insights, from understanding viewer preferences to analyzing engagement metrics. To tap into this potential, I embarked on a project to create a Streamlit application that allows users to harvest, store, and analyze YouTube channel data. This project involves several exciting technologies, including Python, MongoDB, SQL, Streamlit, and the YouTube API.

The aim was to build a user-friendly application where data from multiple YouTube channels could be easily accessed, stored in a MongoDB database as a data lake and then migrated to a SQL database for further analysis.

Project Objective

The primary objective of this project was to develop a Streamlit application that enables users to:

  • Access and analyze data from multiple YouTube channels.
  • Store the data in a MongoDB database as a data lake.
  • Migrate the data from MongoDB to a SQL database for structured querying.
  • Perform searches and execute queries on the stored data using various SQL search options.

The project’s goal was to create a tool that could provide meaningful insights from YouTube data, such as identifying the top-performing videos, understanding viewer engagement, and analyzing content trends.

Approach and Implementation

To achieve the project objectives, I followed a structured approach involving six main steps:

  1. Set up a Streamlit app: A user interface where users can enter a YouTube channel ID, view channel details, and choose channels for data migration to the data warehouse.

  2. Connect to the YouTube API: Retrieve channel and video data using the YouTube API with Python’s Google API client library.

  3. Store data in a MongoDB data lake: Use MongoDB to handle the semi-structured and unstructured data retrieved from the API.

  4. Migrate data to a SQL data warehouse: Store the cleaned data in a SQL database for more structured querying.

  5. Query the SQL data warehouse: Perform SQL queries to analyze data and extract meaningful insights.

  6. Display data in the Streamlit app: Use Streamlit’s data visualization features to create a user-friendly interface for data analysis.

Technical Walkthrough

Let’s dive deeper into each step to understand the technical implementation.

  1. Setting up the Streamlit app:
    I started by setting up a basic Streamlit application. Streamlit is a powerful tool for building data visualization and analysis tools quickly. In this app, users can enter a YouTube channel ID and view relevant details like channel name, subscriber count, total video count, playlist IDs, and video statistics.

  2. Connecting to the YouTube API:
    Using the YouTube API, I was able to retrieve detailed data from multiple YouTube channels. The Google API client library for Python was a great choice for making API requests. By inputting the channel ID, the app pulls data like video IDs, likes, dislikes, comments, etc., providing a comprehensive view of each channel’s activity.

  3. Storing Data in a MongoDB Data Lake:
    After retrieving the data, I stored it in a MongoDB database, which serves as a data lake. MongoDB is excellent for handling unstructured and semi-structured data, making it ideal for storing a wide range of YouTube data, from video descriptions to viewer comments.

  4. Migrating Data to a SQL Data Warehouse:
    Once the data was stored in MongoDB, I provided an option in the app to migrate the data to a SQL database like MySQL or PostgreSQL. This step allowed for more efficient and structured querying of the data, which is essential for deeper analysis.

  5. Querying the SQL Data Warehouse:
    To enable meaningful insights, I created several SQL queries. Some of the queries I used include:

    • Retrieve all video names and their corresponding channels.
    • Identify channels with the most videos.
    • Determine the top 10 most viewed videos.
    • Find videos with the highest number of likes and comments.

  6. Displaying Data in the Streamlit App:
    The final step was to display the data in a user-friendly format. Streamlit’s data visualization features allowed me to create interactive charts, graphs, and tables, making it easy for users to explore and analyze the data.

Results and Analysis

By the end of the project, I was able to create a fully functional Streamlit application that seamlessly integrates with the YouTube API, MongoDB, and SQL. Some of the key results from the data analysis include:

  • Top Channels and Videos: I was able to identify which channels have the most videos and which videos have the highest views and likes.
  • Viewer Engagement Insights: Analysis of comments and likes provided valuable insights into viewer engagement.
  • Content Trends: Data on publishing frequency and video duration helped in identifying content trends and patterns.

Key Takeaways

This project was a fantastic learning experience that helped me gain several new skills and deepen my understanding of data harvesting and warehousing. Some key takeaways include:

  • API Integration: Learned how to effectively use the YouTube API for data retrieval.
  • Data Management: Gained hands-on experience with MongoDB for handling semi-structured data and SQL for structured data analysis.
  • Building Interactive Applications: Streamlit proved to be an excellent tool for creating data-driven applications.
  • Data Analysis: Enhanced my SQL skills to perform complex data queries and analysis.

Future improvements could include adding more interactive data visualization options, supporting more channels, and automating the data migration process.

Conclusion

This project was a deep dive into the world of data harvesting and warehousing using real-world YouTube data. By combining tools like Streamlit, MongoDB, and SQL, I was able to create a robust application that provides valuable insights into YouTube channels and videos. This project showcases the potential of using modern data tools to analyze and understand social media content.

Call to Action

I hope you enjoyed reading about my project! If you're interested in trying it out, feel free to visit my GitHub repository where I have shared the complete code. Also, check out my other data science projects on my portfolio website.

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