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Step 4: Create a QuickSight Dashboard

Amazon QuickSight is a cloud-native business intelligence tool that lets you build interactive dashboards directly from AWS data sources like S3, Athena, or Redshift.

💡 Think of it as: “Excel meets Tableau — but native to AWS and serverless.”

1. Set Up Your QuickSight Account

  1. Go to the QuickSight Console
  2. Click “Sign up for QuickSight”
  3. Choose the Standard or Enterprise edition (you can start with a 30 days free trial)
  4. Enter your preferred notification email
  5. Under QuickSight account name, enter: movie-insights
  6. Select your region (e.g., Asia Pacific (Sydney) / ap-southeast-2)
  7. Choose the QuickSight-managed role (default)
  8. Grant access to your S3 bucket: movie-data-bucket-<account-id>-<region>
  9. Deselect any optional add-ons

⏳ Wait 2–5 minutes for your account to be provisioned.

2. Connect to the Cleaned S3 Data in QuickSight!

⚠️ You can deploy some QuickSight resources via CloudFormation, but support is limited. For now, we'll configure the resources manually.

For the S3 connector, QuickSight uses a manifest file to locate your data and understand how to interpret it. You’ll need to prepare this file first. In the step4-quicksight folder, there is a file named quicksight-movie-manifest.json. Replace with your actual AWS account ID. Once updated, the manifest file will be ready for use.

Let's connect to S3:

  1. In the QuickSight console, go to Datasets
  2. Create a new dataset
  3. Choose S3 as a source
  4. Enter a name: CleanedMoviesData (or any other name you like for your source)
  5. Under Manifest file path, change from URL to Upload option
  6. Select the manifest file we just updated.
  7. Once uploaded - click on Visualize to start building.
Screenshot 2025-07-19 at 2 01 17 pm

3. Build Your Dashboard in QuickSight

Once your dataset is imported and loaded, it's time to explore and visualise your data!

Here are a few ideas for charts and insights you can build:

Popular Genres by Count

  • Visual type: Pie or bar chart
  • X-axis: genre
  • Value: none (picks count of records)

This helps you see which genres appear most frequently in your dataset.

Top Spoken Languages

  • Visual type: Pie or bar chart
  • Dimension: spoken_languages
  • Value: none (picks count of records)
  • Filter: Pick the most common languages

Identify which languages dominate the movie dataset.

Average Popularity by Genre

  • Visual type: Horizontal bar or heatmap
  • Group by: genre
  • Metric: avg(popularity) (be careful, defaults to Sum)

Helps surface which genres trend as the most "popular" overall.

Save Your Dashboard

Once your visuals are created:

  1. Click "Publish"
  2. Name it: Movie Insights Dashboard
  3. After publishing, you can even share dashboards with teammates or export them as PDFs.

The workshop is complete — congratulations! You’ve successfully built a data analytics pipeline! 🚀

Continue to Step 5 - Cleanup