21 Feb 2025

Understanding Authors vs. Readers in Amazon QuickSight: ETL Perspective

Amazon QuickSight is a powerful business intelligence (BI) service that enables users to create interactive dashboards and visualizations. Within QuickSight, users are assigned specific roles—Authors and Readers —each with distinct permissions and functionalities. Understanding the difference between these roles is crucial for optimizing ETL (Extract, Transform, Load) workflows, managing data access, and enhancing collaboration.

This article explores the key differences between Authors and Readers in QuickSight, their respective responsibilities in ETL processes, and best practices for leveraging both roles efficiently.

Authors vs. Readers: A Detailed Comparison

Who Are QuickSight Authors?

Authors are power users in QuickSight with full control over data, visualizations, and dashboards. From an ETL perspective, they can:

Extract data from various sources (e.g., Amazon Redshift, Athena, S3, RDS, and third-party sources).
Transform data using calculated fields, filters, and aggregations.
Load data into QuickSight datasets for analysis and visualization.
Design interactive dashboards to present insights.
Set up scheduled refreshes to keep data up to date.

Who Are QuickSight Readers?

Readers are end-users who consume dashboards and reports without modifying them. Their role in the ETL process is minimal but crucial:

They access and interact with pre-loaded, transformed data.
They apply filters and drill down to customize views.
They export data for further offline analysis.
They rely on Authors to ensure data accuracy and freshness.

Key Differences Between Authors and Readers in ETL

Authors are responsible for the entire ETL process. They extract data from multiple sources, transform it through calculated fields, filters, and aggregations, and load it into QuickSight datasets for visualization. They also create and edit dashboards, share reports, and set up scheduled refreshes to maintain real-time insights. In contrast, Readers primarily consume data rather than modify it. They can view and interact with dashboards, apply filters to customize their insights, and export data for offline analysis. However, they rely on Authors to maintain data accuracy, freshness, and security.

ETL Best Practices for QuickSight Authors & Readers

1. Optimize ETL Workflows for Performance

Authors should ensure data is properly transformed before loading it into QuickSight to reduce processing time.
Use Amazon Athena or Redshift to perform complex transformations before importing data into QuickSight.

2. Implement Data Governance & Access Controls

Apply IAM policies and Row-Level Security (RLS) to control data visibility.
Ensure only authorized Authors can modify ETL pipelines and datasets.

3. Automate Data Refreshes

Use scheduled refreshes to keep dashboards updated.
Ensure ETL jobs upstream are running efficiently to prevent stale data.

4. Educate Readers on Data Interpretation

Provide training to help Readers effectively navigate dashboards and apply filters.
Encourage Readers to use exported data responsibly, understanding its ETL limitations.

Understanding the distinction between Authors and Readers in Amazon QuickSight from an ETL perspective helps organizations allocate resources efficiently, maintain data integrity, and enhance decision-making. Authors manage the ETL workflow—extracting, transforming, and loading data—while Readers focus on consuming the insights generated.

By strategically assigning roles, implementing security measures, and optimizing ETL workflows, businesses can fully harness QuickSight’s capabilities for data-driven success.

DataTerrain: Your Amazon QuickSight & ETL Partner

Looking to streamline your ETL processes in Amazon QuickSight? DataTerrain provides expert solutions to help businesses harness the full power of QuickSight with seamless data integration, optimized workflows, and custom dashboard development.

Why Choose DataTerrain?