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YouTube Data Harvesting and Warehousing

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YouTube Data Harvesting and Warehousing using SQL, MongoDB, and Streamlit

https://iambitttu-youtube-data-harvesting-and-warehousing-ytub-k90wgj.streamlit.app/

Introduction

YouTube Data Harvesting and Warehousing is a project that aims to allow users to access and analyze data from multiple YouTube channels. The project utilizes SQL, MongoDB, and Streamlit to create a user-friendly application that allows users to retrieve, store, and query YouTube channel and video data.

Project Overview

The YouTube Data Harvesting and Warehousing project consists of the following components:

Streamlit Application: A user-friendly UI built using Streamlit library, allowing users to interact with the application and perform data retrieval and analysis tasks.
YouTube API Integration: Integration with the YouTube API to fetch channel and video data based on the provided channel ID.
MongoDB Data Lake: Storage of the retrieved data in a MongoDB database, providing a flexible and scalable solution for storing unstructured and semi-structured data.
SQL Data Warehouse: Migration of data from the data lake to an SQL database, allowing for efficient querying and analysis using SQL queries.
Data Visualization: Presentation of retrieved data using Streamlit's data visualization features, enabling users to analyze the data through charts and graphs.

Technologies Used

The following technologies are used in this project:

Python: The programming language used for building the application and scripting tasks.
Streamlit: A Python library used for creating interactive web applications and data visualizations.
YouTube API: Google API is used to retrieve channel and video data from YouTube.
MongoDB: A NoSQL database used as a data lake for storing retrieved YouTube data.
SQL (MySQL): A relational database used as a data warehouse for storing migrated YouTube data.
SQLAlchemy: A Python library used for SQL database connectivity and interaction.
Pandas: A data manipulation library used for data processing and analysis.
Matplotlib: A data visualization library used for creating charts and graphs.

Installation and Setup

To run the YouTube Data Harvesting and Warehousing project, follow these steps:

Install Python: Install the Python programming language on your machine.
Install Required Libraries: Install the necessary Python libraries using pip or conda package manager. Required libraries include Streamlit, MongoDB driver, SQLAlchemy, Pandas, and Matplotlib.
Set Up Google API: Set up a Google API project and obtain the necessary API credentials for accessing the YouTube API.
Configure Database: Set up a MongoDB database and SQL database (MySQL) to store the data.
Configure Application: Update the configuration file or environment variables with the necessary API credentials and database connection details.
Run the Application: Launch the Streamlit application using the command-line interface.

Usage

Once the project is setup and running, users can access the Streamlit application through a web browser. The application will provide a user interface where users can perform the following actions:

Enter a YouTube channel ID to retrieve data for that channel.
Store the retrieved data in the MongoDB data lake.
Collect and store data for multiple YouTube channels in the data lake.
Select a channel and migrate its data from the data lake to the SQL data warehouse.
Search and retrieve data from the SQL database using various search options.
Perform data analysis and visualization using the provided features.

Features

The YouTube Data Harvesting and Warehousing application offers the following features:

Retrieval of channel and video data from YouTube using the YouTube API.
Storage of data in a MongoDB database as a data lake.
Migration of data from the data lake to a SQL database for efficient querying and analysis.
Search and retrieval of data from the SQL database using different search options, including joining tables.
Data analysis and visualization through charts and graphs using Streamlit's data visualization capabilities.
Support for handling multiple YouTube channels and managing their data.

Future Enhancements

Here are some potential future enhancements for the YouTube Data Harvesting and Warehousing project:

Authentication and User Management: Implement user authentication and management functionality to secure access to the application.
Scheduled Data Harvesting: Set up automated data harvesting for selected YouTube channels regularly.
Advanced Search and Filtering: Enhance the search functionality to allow for more advanced search criteria and filtering options.
Additional Data Sources: Extend the project to support data retrieval from other social media platforms or streaming services.
Advanced-Data Analysis: Incorporate advanced analytics techniques and machine learning algorithms for deeper insights into YouTube data.
Export and Reporting: Add features to export data and generate reports in various formats for further analysis and sharing.

Conclusion

The YouTube Data Harvesting and Warehousing project provides a powerful tool for retrieving, storing, and analyzing YouTube channel and video data. By leveraging SQL, MongoDB, and Streamlit, users can easily access and manipulate YouTube data in a user-friendly interface. The project offers flexibility, scalability, and data visualization capabilities, empowering users to gain insights from the vast amount of YouTube data available.

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