With Amazon QuickSight, everyone in your organization can better understand your data by asking questions in natural language and exploring interactive dashboards. QuickSight can also automatically look for patterns and outliers (a feature that’s powered by machine learning).
QuickSight powers millions of dashboard views weekly for customers, which means that their end users can make better, data-driven decisions. Here are some key features and benefits of QuickSight:
- Connect and scale all your data
- Connect to all your data in AWS, in third-party cloud service providers, or on premises
- Use SPICE in-memory storage to scale data exploration to thousands of users
- Combine data from multiple sources and create complex data models for governed data sharing - Build customizable dashboards
- Use dashboard design to create customized, use-case-specific dashboards
- Deliver personalized email reports and alerts to end users
- Access information from virtually anywhere by using QuickSight access for iOS, Android, or mobile web applications - Use machine learning (ML) integrations for insights
- Use Anomaly Detection to continuously analyze all your data for anomalies and variations
- Forecast business metrics and perform interactive what-if analyses
- Customize Auto-Narratives and weave them into dashboards to provide deeper context for users - Enable self-service business intelligence (BI) for everyone
- Dive deep into data through simple questions, without BI training
- Create visual analyses of data by using a web-based authoring interface
- Embed QuickSight capabilities in applications for data-driven user experiences - Use native integration with AWS
- Use private virtual private cloud (VPC) connectivity for secure AWS access to Amazon Redshift, Snowflake, Exasol, Amazon Relational Database Service (Amazon RDS), and more
- Use native AWS Identity and Access Management (IAM) permissions for Amazon Simple Storage Service (Amazon S3) and Amazon Athena, with fine-grained access control for serverless data exploration
- Use Amazon SageMaker integration to incorporate sophisticated ML models without complex data pipelines - Take advantage of no servers to manage, and paying by usage
- Automatically scale serverless architecture to hundreds of thousands of users with high availability, with no need to overprovision for peak usage
- Provide consistent, fast response times for end users and analysts through the auto-scaling capabilities of the SPICE in-memory engine, with no need to scale databases for high workloads
- Optimize costs through the pay-per-session model by paying only for actual usage, with no need to buy thousands of end-user licenses for large-scale BI or embedded analytics - Take advantage of built-in security, governance, and compliance
- Use end-to-end data encryption for data, and use encryption at rest for data in SPICE
- Use row-level and column-level security with API support for control at the user or group level.
- Take advantage of rapid deployment for regulated workloads with compliance support for the Health Insurance Portability and Accountability Act (HIPAA), HITRUST CSF, General Data Protection Regulation (GDPR), System and Organizational Controls (SOC), Payment Card Industry (PCI), International Organization for Standardization (ISO) 27001, Federal Risk and Authorization Management Program (FedRAMP) High, and more
