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Jeya Shri
Jeya Shri

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Visualizing Data with Amazon QuickSight - Quick project

#cloud #quicksight #aws #data

Hi everyone,

This is my first AWS project, and I’m excited to share my learning experience here.

I’m really grateful to be a part of the NextWork community through which I came across this project. Seeing everyone’s projects and progress here motivates me to learn more every day. I’m just starting my journey in cloud computing, and I’m looking forward to making the most out of this community—learning, building, and connecting with like-minded people.

For today’s project, I worked on data visualization using Amazon QuickSight and Amazon S3. I’ll walk you through what I did and what I understood. Please feel free to correct me if I’ve misunderstood anything—learning is the goal here.

Project Overview

Goal: Visualize CSV data using Amazon QuickSight by sourcing the data from Amazon S3.

Services used:

  • Amazon S3
  • Amazon QuickSight

Step-by-Step: What I Did

Step 1: Download the Required Files

I downloaded:

  • One CSV data file
  • One manifest.json file

The manifest file is used by QuickSight to understand where the data is stored in S3.

Step 2: Upload Files to Amazon S3

  • Created a new S3 bucket
  • Uploaded both the CSV file and the manifest.json file using the Add files option

This bucket now acts as the data source for QuickSight.

charts

Step 3: Update the Manifest File

  • Copied the S3 URI of the CSV file
  • Pasted this URI inside the manifest.json file under the uri field
  • Re-uploaded the updated manifest file back into the S3 bucket

This step is crucial because QuickSight reads the manifest file to locate the actual data.

Step 4: Connect S3 to Amazon QuickSight

  • Created a QuickSight account
  • Selected S3 as the data source

  • Provided:

    • S3 bucket name
    • Manifest file URI

Once connected, QuickSight successfully loaded the dataset.

Step 5: Create Visualizations

Inside QuickSight:

  • Fields appeared on the left panel
  • Selected fields by clicking or dragging and dropping them into the analysis area

  • Created visualizations such as:

    • Bar charts
    • Pie charts
    • Tables

QuickSight automatically suggests chart types based on the data, which was helpful as a beginner.

Step 6: Publish the Dashboard

  • Published the analysis as a dashboard

  • Learned that QuickSight also allows:

    • Sharing dashboards
    • Exporting visualizations as PDF reports

This makes it easy to present insights to others.

dashboard

Doubts I Had During the Project

  1. What is the difference between a URI and a URL?
  2. What exactly is SPICE in QuickSight?

Clarifications I Got

URI vs URL

  • URI (Uniform Resource Identifier):
    Helps AWS identify where a resource exists within its ecosystem, such as within S3.

  • URL (Uniform Resource Locator):
    Tells the web how to access that resource over the internet.

In QuickSight, we use S3 URIs instead of URLs because QuickSight works directly with AWS resources.

What is SPICE?

SPICE stands for Super-fast, Parallel, In-memory Calculation Engine.

  • It is a high-performance, in-memory engine
  • Designed for fast queries and high concurrency
  • Improves dashboard performance by avoiding repeated queries to S3

SPICE is especially useful when working with large datasets or when multiple users access dashboards simultaneously.

Final Thoughts

This project helped me understand:

  • How AWS services integrate with each other
  • How data flows from Amazon S3 to Amazon QuickSight
  • The basics of data visualization in AWS

It was a great hands-on introduction to AWS analytics, and I’m excited to explore more services and build more projects.

Thanks to the NextWork community for the constant inspiration. I’m looking forward to learning more and growing together.

quicksight

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