DEV Community

Cover image for 💹Visualize Data with QuickSight: Turn Raw Data into Stunning Visuals [Part 4]
Suvrajeet Banerjee
Suvrajeet Banerjee Subscriber

Posted on • Edited on

💹Visualize Data with QuickSight: Turn Raw Data into Stunning Visuals [Part 4]

#aws #quicksight #analytics #visualization

Transform your Netflix dataset into beautiful, interactive dashboards that tell compelling stories

🎯 What You'll Build Today

Picture this: You have thousands of rows of Netflix data sitting in your S3 bucket, but it's just... numbers. Raw, uninspiring data that tells no story. By the end of this tutorial, you'll have transformed that data into a stunning, interactive dashboard that reveals fascinating insights about Netflix's content strategy.

We're going to create visualizations that answer questions like:

  • 📅 Which year saw the biggest surge in Netflix content?
  • 🎬 Are movies or TV shows dominating the platform?
  • 📈 When does Netflix add the most content to their catalog?
  • 🎭 What genres are most popular?

🚀 Prerequisites

Before we dive into the visual magic, make sure you have:

  • ✅ An AWS account with IAM admin user (from Part 2)
  • ✅ Basic familiarity with AWS console
  • ✅ The Netflix dataset we'll be working with

📋 Step-by-Step Implementation

🗂️ Step 1: Prepare Your Data Foundation

Think of this step as setting up your art studio before creating a masterpiece. We need to get our data files ready and store them in the cloud.

Download the Essential Files

First, grab these two crucial files [Rigth-click ⏩ Save link as] :

The manifest.json file is like a map that guides QuickSight through your data structure. Without it, QuickSight would be like a tourist in a foreign city without directions.

Create Your Data Storage

Navigate to the S3 console and create a bucket with this naming pattern:

quicksight-netflix-project-[your-name]
Enter fullscreen mode Exit fullscreen mode

Why S3? Think of it as your data's permanent home in the cloud. QuickSight will visit this home whenever it needs to refresh your visualizations.

Configure the Manifest File

Here's where many beginners stumble, so pay close attention. Open your manifest.json file in any text editor and update the URL to point to your S3 bucket:

{ "fileLocations": [ { "URIs": [ "s3://quicksight-netflix-project-[your-name]/netflix_titles.csv" ] } ] }
Enter fullscreen mode Exit fullscreen mode

This tells QuickSight exactly where to find your data file.

🎨 Step 2: Set Up QuickSight (The Visual Magic Begins)

Amazon QuickSight is like having a professional data analyst and graphic designer rolled into one tool. It takes your raw data and transforms it into insights that anyone can understand.

Create Your QuickSight Account

Navigate to the QuickSight console and sign up for the free trial of the Enterprise edition. This gives you all the features you need without any cost.

⚠️ Critical Setup Notes:

  • Use the same email as your AWS account
  • Choose the same region as your S3 bucket
  • When prompted, select access to Amazon S3
  • Important: Uncheck any upgrade offers to avoid charges

Connect S3 to QuickSight

During setup, you'll be asked which S3 buckets QuickSight can access. Select your newly created bucket. This creates a secure bridge between your data storage and visualization tool.

🔗 Step 3: Connect Your Data

This is where your data comes alive. You're essentially introducing your dataset to QuickSight and teaching it how to interpret your information.

Create a New Dataset

From the QuickSight dashboard:

  1. Navigate to Datasets in the left panel
  2. Click New dataset
  3. Select S3 as your data source
  4. Name your source: netflix-data-analysis
  5. Enter your manifest.json S3 URL

The system will validate your connection and import your data. You should see a success message confirming that your Netflix dataset is now ready for visualization.

Handle Common Connection Issues

If you encounter the "Not enough SPICE capacity" error, don't panic. This usually means:

  • Your QuickSight account and S3 bucket are in different regions
  • You need to switch regions or recreate resources in the same region

SPICE (Super-fast, Parallel, In-memory Calculation Engine) is QuickSight's way of processing your data lightning-fast.

📊 Step 4: Create Your First Visualization

Now comes the exciting part - turning numbers into stories. We'll start with a simple but powerful visualization that reveals Netflix's content trends over time.

Build a Release Year Analysis

Drag the release_year field from your data panel into the visualization area. QuickSight automatically creates a bar chart showing the distribution of content by year.

What story does this tell? You'll likely see:

  • 📈 A massive surge in content after 2015
  • 🎯 Peak years for Netflix's content expansion
  • 📉 Fewer older titles in the catalog

Experiment with Chart Types

Try converting your bar chart to a donut chart. This circular visualization makes it easier to see proportions and percentages at a glance.

Add Color Coding

Enhance your visualization by dragging the type field to the "Group/Color" section. Now you can see not just when content was released, but whether it was movies or TV shows.

🎭 Step 5: Build a Multi-Visual Dashboard

A single chart tells one story, but a dashboard tells an epic. Let's create multiple visualizations that work together to reveal deeper insights.

Create a Stacked Bar Chart

Build a visualization that shows the proportion of movies vs TV shows for each release year:

  • Set release_year as your Y-axis
  • Add type to the Group/Color section
  • Change chart type to "Horizontal stacked 100% bar chart"

This reveals whether Netflix's strategy has shifted between movies and TV shows over time.

Add a Data Table

Some stakeholders prefer numbers to charts. Create a table view:

  • Duplicate your previous visual
  • Change type to "Table"
  • Add release_year as Group By
  • Add title as Value metric
  • Include type as a dimension

Analyze Addition Patterns

Create a chart showing when Netflix adds content to their platform:

  • Use date_added for both Y-axis and Value
  • Sort in descending order to find peak addition dates
  • This reveals Netflix's content strategy timing

🎯 Step 6: Advanced Filtering and Analysis

Real data analysis involves asking specific questions and filtering data to find answers. Let's tackle some advanced scenarios.

Genre Analysis with Filters

Create a focused analysis of specific genres:

  1. Add a filter for the listed_in field
  2. Select only "Action & Adventure", "TV Comedies", and "Thrillers"
  3. Create a donut chart showing the distribution

This filtering technique lets you answer targeted business questions like "What genres are most popular in our catalog?"

Time-Based Filtering

Combine multiple filters to answer complex questions:

  • Keep your genre filter active
  • Add a release_year filter for 2015 and later
  • Count the results to see recent trends in specific genres

🎨 Step 7: Polish Your Dashboard

A professional dashboard isn't just functional - it's beautiful and tells a clear story.

Add Descriptive Titles

Replace generic titles with descriptive ones:
Double click on the titles of all the charts one-by-one you see in front of you, which will pop-up the Edit Panel

  • "Netflix Content by Release Year"
  • "Movies vs TV Shows Distribution"
  • "Popular Genres Analysis"
  • "Content Addition Timeline"

Organize Your Layout

Arrange your visualizations logically:

  • Put the most important insights at the top
  • Group related visualizations together
  • Leave white space for visual breathing room

Publish and Share

Click "Publish" to make your dashboard available to others. This creates a clean, professional view that you can share with stakeholders or include in presentations.

📤 Step 8: Export Your Work

Professional data analysts always document their work. Export your dashboard as a PDF to create a permanent record of your analysis.

The PDF export maintains all your visualizations and can be shared with anyone, regardless of whether they have QuickSight access.

🧠 Understanding the Bigger Picture

What you've built isn't just a collection of pretty charts - it's a powerful business intelligence tool. You've learned to:

Transform raw data into actionable insights - Your Netflix dataset went from meaningless rows to compelling stories about content strategy.

Apply filters to answer specific questions - You can now slice and dice data to answer targeted business questions.

Create professional dashboards - Your work is presentation-ready and tells a coherent story.

Use cloud-based analytics tools - You've mastered the workflow of modern data analysis using AWS services.

🎯 Key Takeaways for Developers

Understanding data visualization is crucial for modern developers because:

APIs generate data that needs interpretation - Your backend services produce logs, metrics, and user data that stakeholders need to understand.

Business decisions require data backing - Your technical recommendations carry more weight when supported by clear visualizations.

Monitoring and debugging benefit from visual tools - Charts and dashboards help you spot patterns and anomalies in your systems.

Cross-functional communication improves - You can explain technical concepts to non-technical stakeholders using visual stories.

🧹 Critical: Clean Up Your Resources

This step prevents unexpected charges and is essential for responsible cloud usage:

  1. Delete QuickSight Account:

    • Go to user icon → Manage QuickSight
    • Account Settings → Manage
    • Switch off account termination
    • Type "confirm" and delete

  2. Delete S3 Bucket:

    • Empty the bucket first (delete all objects)
    • Then delete the bucket itself

🚀 What's Next?

You've just completed a comprehensive journey from raw data to stunning visualizations. You now have the skills to:

  • Connect any dataset to QuickSight
  • Create multiple types of visualizations
  • Build professional dashboards
  • Answer business questions with data

In our next part, we'll explore how to automate these processes and integrate them into larger data pipelines.

🤝 Connect and Share

Built something amazing? Share your dashboard and connect with other developers exploring AWS data services. Your Netflix analysis might inspire someone else's next project.

Remember: Every data story starts with curiosity and ends with insights. You've just proven that you can bridge that gap with the right tools and techniques.

📚 What’s Next in the Series?

Concluding Part 4 of the AWS Beginners Learning Journey !
Upcoming:

  • [Part 5] 🔐 Cloud Security with AWS IAM

🚀 Don't miss out!
Each part builds upon the previous, creating your complete AWS toolkit.

🙏 Acknowledgments

This learning journey was powered & supported by NextWork's structured approach to cloud education, which made breaking down complex concepts into digestable-byte-sized-hands-on practice accessible through systematic skill building & clear-actionable steps.

This blog is based on - NextWork's ⏩ Visualize data with QuickSight!

📖 Additional Resources

Ready to turn your data into visual stories? The tools are in your hands, and the possibilities are endless.

✨ Let’s build epic cloud projects together & transform our CLoud Computing Skills!

  • Questions? Hit the comments!

Connect with me:- https://www.linkedin.com/in/suvrajeet
E-mail for improvements:- banerjee@suvrajeet.me

See you in the cloud! ☁️

Top comments (0)