Visualization using Amazon QuickSight

Once data is collected, stored, and processed, the next step is to visualize insights to support data-driven decisions. Amazon QuickSight is AWS’s cloud-native business intelligence (BI) tool that enables users to create interactive dashboards, reports, and data stories—all within the AWS ecosystem.

What is Amazon QuickSight?

Amazon QuickSight is a serverless BI service that allows users to:

• Connect to various AWS data sources (like S3, Redshift, Athena, RDS)
• Create interactive charts and dashboards
• Perform ad-hoc analysis with little or no coding
• Share insights with teams securely via the cloud

Key Features

1. Multiple Data Source Connectivity

QuickSight can connect directly to:

• Amazon S3 (through Athena or Glue Catalog)
• Amazon Redshift
• Amazon RDS (MySQL, PostgreSQL)
• AWS Athena (query data from S3)
• Excel/CSV files and on-prem databases

2. Rich Visualization Options

• Bar, Line, Pie, Scatter, Combo charts
• Geographical maps
• KPIs and Pivot Tables
• Forecasting and anomaly detection (built-in ML)

3. SPICE Engine

SPICE (Super-fast, Parallel, In-memory Calculation Engine) allows:

• High-speed query performance
• Scalability without managing infrastructure
• Better performance on large datasets

4. Interactive Dashboards

• Filter controls (drop-downs, sliders)
• Drill-down into metrics (e.g., from total sales to city-level data)
• Dashboard sharing via web links or AWS IAM controls

Real-World Example: Food Delivery App

Let’s say a food delivery company stores order data in Amazon Redshift. A data analyst can use QuickSight to:

• Visualize average delivery time per city
• Build a map chart showing order density
• Create bar charts of popular cuisines by region
• Add interactive filters for dates, restaurants, or order value ranges

The dashboard is shared with operations managers so they can take quick decisions to improve delivery performance.

Steps to Create a QuickSight Dashboard

1. Connect to a Data Source: Choose S3, Athena, Redshift, etc.
2. Prepare Data: Clean and transform data if needed (can use Glue or SQL)
3. Build Analyses: Add visualizations to answer key questions
4. Customize & Filter: Add filters, parameters, controls
5. Publish as Dashboard: Share with users across the organization