hands-on lab

Visualizing Data in Amazon QuickSight

Up to 1h
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.


Attention WarningWARNING: This lab is currently offered in a read-only format when it comes to QuickSight. Attention WarningWhile we work on a path forward to allow QuickSight access under the new pricing model and security limitations, you can grasp the learning objectives by reading through the provided lab notebook.

Once data is loaded into a database the question next becomes how can I make use of this data? Amazon Quicksight can solve that problem, by allowing you to visualize, embed, and share data quickly. These valuable insights allow users to look at quick summaries of aggregated information to make better business decisions. This lab is aimed at beginners who want to understand how to import and make their first insight.

Learning Objectives

Upon completion of this lab you will be able to:

  • Learn and Understand Amazon QuickSight
  • Learn how to connect to an Amazon Datasource
  • Learn how to Visualize your data after importing a dataset

Intended Audience

This lab is intended for:

  • Individuals starting out with data visualization
  • Anyone interested in using Amazon QuickSight to visualize data


You should possess:

  • A basic understanding of Python
  • A basic understanding of Amazon RDS


April 29th, 2024 - Resolved deployment issue

March 21st, 2024 - Resolved deployment issue

September 6th, 2022 - Resolved deployment error

May 13, 2022 - Updated instructions & screenshots to reflect the latest UI

November 19th, 2021 - Converted the lab to a "read-along" format for the QuickSight parts while we continue to find a path forward to allow QuickSight access under the new pricing model and security limitations

August 24th, 2021 - Provide security guardrails around the allowed QuickSight edition

March 11th, 2021 - Explained that it is OK to select to directly query the dataset if an error stating that there is not enough SPICE capacity available appears when creating the dataset (an AWS bug)

About the author

Learning paths

Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity.  With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing  decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.

Covered topics

Lab steps

Logging In to the Amazon Web Services Console
Opening the Lab's Jupyter Notebook