Visualizing Data in Amazon QuickSight
Lab Steps
- Stay within resource usage requirements.
- Do not engage in or encourage activity that is illegal.
- Do not engage in cryptocurrency mining.
- View the detailed terms of use here
The hands-on lab is part of these learning paths
Ready for the real environment experience?
Description
This lab is currently offered in a read-only format when it comes to QuickSight. While 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
Prerequisites
You should possess:
- A basic understanding of Python
- A basic understanding of Amazon RDS
Updates
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)
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.