Building an AWS QuickSight Dashboard for Amazon Bestsellers Analysis

Project Overview

This project demonstrates a cloud-native approach to data visualization using AWS services. I analyzed a dataset of 50,000 Amazon best-selling products stored in S3, creating interactive dashboards with QuickSight to explore pricing trends, brand popularity, and category performance.

Note: the results visualized only analyzes a subset of the Entire Dataset

Technical Workflow

Data Preparation

• Dataset: Utilized a CSV file containing product metadata (price, category, brand, etc.). The data was scraped from Amazon’s website by BrightData

• Manifest File: Created a JSON manifest to map S3 data to QuickSight (example structure from search results):

    {
      "fileLocations": [{
        "URIs": ["s3://your-bucket-name/Amazon-Bestseller-Dataset.csv"]
      }],
      "globalUploadSettings": {
        "format": "CSV",
        "delimiter": ",",
        "textqualifier": "\"",
        "containsHeader": "true"
      }
    }
  

  (Replace your-bucket-name with your actual S3 bucket name)

S3 Configuration

• Bucket Setup: Created an S3 bucket with ACLs enabled and public access blocked for security1.

• Upload: Stored both the dataset and manifest file in the bucket.

QuickSight Integration

1. Dataset Creation:

   • Linked the S3 bucket to QuickSight via the manifest file.

   • Selected CSV as the format and mapped columns to QuickSight fields.

2. Dashboard Design:

   • Built interactive visualizations (bar charts) to compare product categories, pricing ranges, and brand performance.

   • Added filters for dynamic exploration (e.g., filtering by product category).

   • Made a Visualization of the comparison between the 2500 top brands and their average prices(k)

Key Insights

My dashboard revealed:

• Top-performing categories (e.g., electronics vs. home goods).

• Price distribution across brands and categories.

• Brand dominance in specific markets.

Tech Stack

Lessons Learned

1. S3 Manifest Files: Critical for structuring data ingestion into QuickSight1.

2. QuickSight Flexibility: Easily switch between visualization types (e.g., pivot tables to heatmaps).

3. Security: Ensure S3 bucket permissions align with QuickSight access requirements.

Explore the Dashboard

View the live Dashboard here:

https://us-east-1.quicksight.aws.amazon.com/sn/dashboards/07129b3e-5034-4582-a779-152957456083

Why This Matters

Data visualization tools like QuickSight democratize insights for non-technical stakeholders while maintaining cloud scalability. This project aligns with AWS’s serverless philosophy, minimizing infrastructure overhead