Visualizing Netflix's Catalog Trends Using Amazon QuickSight 📊

Introduction

In this project, I explored Amazon QuickSight, a powerful business analytics service provided by AWS, to visualize and analyze Netflix's catalog trends. This hands-on experience allowed me to delve into data-driven decision-making by creating insightful visualizations.

What is Amazon QuickSight?

Amazon QuickSight is an AWS service that enables users to analyze and visualize data quickly and easily. It’s a tool that equips businesses to gain insights from their data, helping them make faster, data-driven decisions.

Project Overview

1. Setting Up S3 and Uploading Data

• Dataset: I used a dataset of Netflix’s catalog, which includes information about movies, TV shows, genres, release years, etc.
• S3 Bucket: I uploaded the dataset and a manifest.json file to an S3 bucket. The manifest.json file was crucial as it helped QuickSight locate and interpret the data correctly.

2. Creating an Amazon QuickSight Account

• Setting up a QuickSight account was straightforward, taking only a couple of minutes.
• I enabled QuickSight's access to the S3 bucket, allowing it to read the data stored in my S3 bucket.

3. Connecting S3 to QuickSight

• Dataset Connection: I connected my S3 bucket to QuickSight by creating a new dataset, selecting S3 as the data source, and entering the S3 URL for the manifest.json file.
• The manifest.json file acted as a guide for QuickSight, ensuring accurate data interpretation and visualization.

4. Creating Visualizations

• Visualization Process: I started by dragging relevant fields into QuickSight's AutoGraph space to create my first visualizations.
• One of my initial visualizations depicted a breakdown of movies versus TV shows by release year.

5. Using Filters for Specific Insights

• Filters: Filters allowed me to focus on specific subsets of data. For instance, I filtered out movies and TV shows released before 2015, honing in on more recent trends.
• This filtering process helped me create a more targeted visualization focusing on the most recent content.

6. Finalizing the Dashboard

• Dashboard Setup: I polished my visualizations by editing titles to ensure clarity and purpose. I also explored exporting the dashboard as a PDF, which can be useful for sharing insights.

Key Learnings

1. Ease of Use: QuickSight is user-friendly and simplifies the data visualization process.
2. S3 Integration: Connecting S3 to QuickSight was straightforward with the help of a manifest.json file.
3. Visualization Flexibility: QuickSight offers flexible tools for creating a wide range of visualizations.
4. Real-Time Insights: This project has inspired me to explore real-time data visualization further.

Conclusion

This project was an excellent opportunity to familiarize myself with Amazon QuickSight. The entire process, from setting up S3 and QuickSight to creating meaningful visualizations, took around 125 minutes. Moving forward, I aim to deepen my understanding of data visualization, especially in the context of real-time data and storytelling.

Feel free to connect with me or explore the project further on my LinkedIn.

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Additional Notes

I deleted the resources from my AWS console to avoid costs, so this GitHub repository serves as the sole showcase for my work on this project. I have documented all the steps and details to ensure a comprehensive understanding of my approach.