End-To-End-Data-Engineering-Youtube-Analysis-Pipeline

Project Overview

This project serves two main purposes:

1. Gain Hands-on Experience with ETL Pipelines: We'll build an ETL (Extract, Transform, Load) pipeline to learn the process of collecting, processing, and storing data for analysis. This will provide practical experience with the core stages of data preparation for analytics workflows.
2. Develop a Secure and Scalable YouTube Video Analytics System: By building this pipeline, we'll create a secure and efficient system capable of analyzing massive amounts of YouTube video data. The system will focus on video categories and trending metrics, providing valuable insights into YouTube video performance.

Dataset

We utilize a Kaggle dataset containing daily statistics on trending YouTube videos across various locations for many months. Each region has a dedicated CSV file with details on up to 200 daily trending videos (title, channel, publish time, tags, views, likes/dislikes, descriptions, comments). A linked JSON file provides category IDs for video categorization. This rich dataset, with both structured and semi-structured data, fuels our analysis of YouTube video trends.

Technologies Used

This project leverages a variety of AWS services to build a secure, scalable, and efficient YouTube video analytics system:

• Amazon S3
• AWS IAM
• Amazon QuickSight
• AWS Glue
• AWS Lambda
• AWS Athena

Architecture

Learning from Project

1. ETL Pipeline Mastery: Gained practical experience in building ETL pipelines for data extraction, transformation, and loading.

2. AWS Integration: Leveraged AWS services:

   • S3 for storage.
   • Glue for data transformation and cataloging.
   • Lambda for serverless processing.
   • Athena for querying.
   • QuickSight for visualization.

3. Data Lake Architecture: Structured the data lake into landing, cleansed/enriched, and analytics/reporting areas.

4. Security: Implemented secure access using AWS IAM.

5. Orchestration: Used AWS Step Functions for efficient data processing workflows.

6. Monitoring: Employed AWS CloudWatch for system monitoring and alerts.

7. Scalable Analytics: Enabled quick insights using AWS Athena without data movement.

8. Visualization Tools: Utilized QuickSight and other tools for creating interactive dashboards and visualizations.