May 1, 2024
Updated July 6, 2025
13 minute read
Amazon QuickSight is a powerful business intelligence (BI) tool that enables users to quickly and easily visualize and analyze data. It provides a wide range of features and functionalities that make it an ideal choice for businesses of all sizes.
Benefits of Using Amazon QuickSight
There are many benefits to using Amazon QuickSight, including:
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Find a path to becoming a Amazon QuickSight. Learn more at:
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Reading list
We've selected 25 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Amazon QuickSight.
Is specifically focused on Amazon QuickSight, offering a practical guide to its functionalities. It covers connecting data sources, building dashboards, and leveraging ML insights within QuickSight. This is an excellent resource for gaining a broad understanding and practical skills directly related to the topic. It is particularly useful for those who want to learn QuickSight in depth using practical, up-to-date examples.
Provides a comprehensive guide to using Amazon QuickSight for data analysis. It covers topics such as data preparation, data visualization, and data analysis. It is an excellent resource for data analysts who want to use Amazon QuickSight to gain insights from their data.
Provides a comprehensive guide to using the Amazon QuickSight API. It covers topics such as creating visualizations, analyzing data, and generating reports. It is an excellent resource for developers who want to use the Amazon QuickSight API to integrate Amazon QuickSight into their applications.
Provides a comprehensive guide to using Amazon QuickSight for business intelligence. It covers topics such as data visualization, data analysis, and data reporting. It is an excellent resource for business users who want to use Amazon QuickSight to gain insights from their data.
Focuses on the principles of effective dashboard design, which is directly applicable to creating insightful visualizations in Amazon QuickSight. It covers common design problems and best practices rooted in visual perception. This valuable resource for anyone creating dashboards, from beginners to experienced designers.
Considered a classic in the data warehousing field, this book provides a comprehensive guide to dimensional modeling, a foundational concept for effective business intelligence and data analysis. While not specific to QuickSight, the principles are essential for structuring data that will be analyzed and visualized in BI tools. It must-read for anyone serious about data analytics and BI, often used as a reference by industry professionals.
Emphasizes the importance of communicating data effectively through storytelling and visualization. It provides practical guidance on creating compelling visuals and narratives, a crucial skill for anyone using BI tools like QuickSight to share insights. It's an excellent starting point for understanding the 'why' behind data visualization.
Another excellent resource from Stephen Few, this book provides detailed guidance on designing tables and graphs for clear communication. It complements his dashboard design book and offers practical techniques applicable to creating effective visuals within QuickSight.
Offers a wide collection of dashboard examples across various industries, providing practical inspiration and illustrating best practices in action. It's a great resource for seeing how data can be visualized effectively in different business contexts, which can be applied when building dashboards in QuickSight.
Provides a blueprint for building a data analytics platform on AWS, covering services relevant to QuickSight such as S3, Athena, and Glue. Understanding the surrounding AWS ecosystem is crucial for effectively using QuickSight with various data sources. This book helps solidify the understanding of how QuickSight integrates with other AWS services.
Building on data visualization, this book focuses on the narrative aspect of presenting data insights. It's highly relevant for QuickSight users who need to communicate their findings effectively to influence decisions.
Covers the process of preparing and transforming data on AWS using various services, including a chapter on working with QuickSight. Understanding data wrangling is crucial for ensuring data is in the right format and quality for analysis and visualization in QuickSight. It provides practical examples within the AWS environment.
A foundational text in data visualization, this book explores the principles of effective graphical display of quantitative data. While published before the advent of modern BI tools, its principles are timeless and essential for creating clear and accurate visualizations in QuickSight. Considered a classic and a must-read for anyone serious about data visualization.
Amazon Athena is frequently used as a data source for QuickSight, especially with data lakes on S3. provides a deep dive into using Athena for serverless analytics, which is highly relevant for users connecting QuickSight to S3-based data.
Data lakes are increasingly common data sources for QuickSight. provides a comprehensive guide to building and managing enterprise data lakes, offering valuable knowledge for those working with QuickSight on data lake architectures.
Another foundational book by the Kimball Group, this resource covers the entire lifecycle of data warehousing and BI projects. It provides context on how BI tools like QuickSight fit into a larger data strategy and implementation. Useful for understanding the broader picture beyond just using QuickSight.
Presents a collection of essays from various experts on creating effective and aesthetically pleasing visualizations. It offers diverse perspectives and examples of how visualization can reveal insights and understanding. While not a how-to guide for QuickSight, it provides valuable context and inspiration for creating impactful visuals.
This guidebook covers the entire spectrum of business intelligence, from data integration to analytics. It provides a holistic view of BI, helping users understand where QuickSight fits within a broader BI strategy and implementation.
Offers a balanced view of both relational and dimensional modeling techniques for data warehouses. It provides a solid understanding of the different approaches to organizing data for analytical purposes, which is beneficial background for anyone working with BI tools like QuickSight.
Focuses on the Extract, Transform, Load (ETL) process, a critical step before data can be effectively analyzed and visualized in QuickSight. Understanding ETL best practices is essential for ensuring data quality and usability in any BI tool.
Discusses the design of data platforms in the cloud, which provides essential context for understanding the infrastructure that supports services like QuickSight. It is relevant for those looking to understand the broader cloud data landscape.
Considered one of the foundational texts on data warehousing, Inmon's book presents the corporate information factory approach. While the Kimball methodology is often more directly applicable to BI visualization, understanding Inmon's perspective provides valuable historical context and a different approach to data warehousing architecture. It's a classic for a deeper academic understanding.
Offers a high-level, accessible introduction to business intelligence, data mining, and business analytics. It's a good starting point for beginners to grasp the fundamental concepts and understand the value of BI tools like QuickSight in a business context.
Offers a high-level, accessible introduction to data warehousing concepts. It's suitable for beginners who need to understand the basics of where the data used in QuickSight often originates from and how it's structured.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/u5dro7/amazon
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