Amazon QuickSight — Technology Profile & Architecture

Core Information

#core_information

Name, description, vendor website, logo, technology type and tags.

Overview & Role

Amazon QuickSight is a scalable, serverless, ML-powered business intelligence (BI) service built for the cloud. It allows users to easily create and publish interactive dashboards with a variety of data sources, enabling data-driven decision-making across organizations. QuickSight uses SPICE (Super-fast, Parallel, In-memory Calculation Engine) for fast query performance.

Domain Classification & Tags

BI

Performance Profile

#performance_profile

Typical read and write throughput, payload size and processing latency.

Read Performance Profile

Throughput: Varies greatly with dashboard complexity and data volume, potentially thousands of queries per second across many users

Payload Size: Varies; typically small result sets for individual visualizations, but can involve large underlying datasets (GBs to TBs)

Processing Latency: Sub-second to several seconds for dashboard load and interactive filtering, depending on SPICE vs. direct query and data complexity

Write Performance Profile

Throughput: Varies; SPICE ingest can be hundreds of MB/s to GB/s, direct query depends on source database

Payload Size: Varies; can be GBs to TBs for SPICE dataset refreshes

Processing Latency: Minutes to hours for large SPICE dataset refreshes; real-time for direct query depends on source database

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

Features

#features

Catalogued product capabilities and what each one does.

Empirical data for features is currently being compiled in the global catalog.

Typical Use Cases

#typical_use_cases

Scenarios the technology is commonly chosen for.

Empirical data for typical use cases is currently being compiled in the global catalog.

Known Customers

#known_customers

Publicly referenced organisations using the technology.

Empirical data for known customers is currently being compiled in the global catalog.

Known Integrations

#known_integrations

Other products and services it is documented to work with.

Empirical data for known integrations is currently being compiled in the global catalog.

Connectors

#connectors

Directional data connections to other technologies, with direction and maturity.

Empirical data for connectors is currently being compiled in the global catalog.

Reference Architectures

#reference_architectures

Published architectures where the technology is used or mentioned.

No published reference architecture blueprints currently link to Amazon QuickSight.

Security Features

#security_features

Built-in security and access-control capabilities.

Empirical data for security features is currently being compiled in the global catalog.

Known Issues

#known_issues

Documented limitations, defects and operational pitfalls.

Empirical data for known issues is currently being compiled in the global catalog.

Guidelines

#guidelines

Recommended practices for adopting and operating the technology.

Empirical data for guidelines is currently being compiled in the global catalog.

Standards & Compliance

#standards_and_compliance

Standards, certifications and control requirements it maps to.

Empirical data for standards & compliance is currently being compiled in the global catalog.

Sources

#sources

Documentation and research references behind the recorded information.

Empirical data for sources is currently being compiled in the global catalog.

Expert Validation

#expert_validation

Whether domain experts reviewed and confirmed the content.

Empirical data for expert validation is currently being compiled in the global catalog.