Analytics and Search

Building BI Dashboards with Amazon QuickSight - Serverless Analytics and Embedded Visualization

Achieve fast queries with the SPICE engine and integrate BI capabilities into your own applications using the embedding API. This article introduces Q's natural language queries and Reader session-based pricing.

約 3 分で読めます

Overview of QuickSight

QuickSight is a serverless BI service that scales to tens of thousands of users, providing dashboard creation, sharing, and embedding. Unlike traditional BI tools (Tableau, Power BI), it requires no server provisioning and uses a session-based pay-per-use pricing model. The SPICE engine caches data in memory, delivering fast query responses even with large datasets.

Dashboards and Embedding

You create datasets by connecting to data sources, then build dashboards by placing visuals such as charts, tables, and KPI cards on the analysis screen. The Q feature lets you ask questions about your data in natural language, with ML interpreting the question and automatically generating the appropriate visual. The embedding API allows you to embed QuickSight dashboards as iframes in your own applications, providing BI capabilities to end users. Row-level security (RLS) restricts the data displayed per user, supporting multi-tenant environments.

SPICE Engine and Q Feature

SPICE (Super-fast, Parallel, In-memory Calculation Engine) is an in-memory engine that enables fast dashboard rendering without direct queries to the data source. Once data is imported into SPICE, dashboard display speed no longer depends on data source performance, providing stable response times even with a large number of concurrent users. SPICE incremental refresh updates only changed data, optimizing refresh time and cost. The Q feature automatically generates visuals in response to natural language questions like "What were the top 10 sales last month?" Defining Q topics and assigning business meanings to dataset columns improves the accuracy of natural language queries. To learn QuickSight design patterns comprehensively, refer to technical books on Amazon.

QuickSight Pricing Model

QuickSight uses user-based pricing. Authors (dashboard creators) cost approximately $24 per month, while Readers (viewers) are charged per session at approximately $0.30 per session (with a monthly cap of $5). SPICE storage includes 10 GB per Author, with additional storage at approximately $0.25 per GB per month. Session-based pricing for Readers is significantly cheaper than fixed-rate BI tools for users who only view dashboards a few times per month. Embedded dashboards with anonymous access are charged at approximately $0.30 per session. The Q feature is available with Reader Pro ($10 per month), and you can choose a plan based on how frequently natural language queries are used.

Summary

QuickSight is a serverless BI service that provides fast queries through the SPICE engine and integration into your own applications via the embedding API. The Q feature automatically generates visuals from natural language queries, and Reader session-based pricing minimizes costs for users who only view dashboards occasionally. SPICE incremental refresh optimizes the cost and time of data updates.

Related Services

Amazon QuickAn agentic digital workspace that handles everything from answering questions to executing actionsAmazon AthenaA serverless query service that lets you analyze data in S3 using standard SQLAmazon RedshiftA cloud data warehouse service for high-speed analytics on petabyte-scale data

Related Articles

Building a Cloud Data Warehouse with Amazon Redshift - Choosing Between Serverless and RA3Learn the criteria for choosing between Serverless and RA3 provisioned clusters, and how to prevent data silos using data sharing and Spectrum for data lake integration.Detailed Cost Analysis with AWS Cost and Usage Report - Athena Queries and Visualization PipelinesAnalyze resource-level detailed cost data with SQL in Athena and visualize it with QuickSight. This article covers CUR 2.0 data exports and how to build an analytics foundation.BI Dashboard Visualization - Building a Data-Driven Decision Platform with Amazon QuickSightExplains how to build interactive BI dashboards with Amazon QuickSight and a serverless data analytics platform with Athena integration. Covers high-speed visualization with the SPICE engine and practical methods for sharing insights across the organization.

More on This Topic

Practical Use Cases for Amazon Quick - Department-Specific Scenarios and Workflow Automation Design PatternsExplore concrete use cases for sales, IT, and finance departments, along with design patterns for notifications, approvals, and multi-step workflows using Quick Flows.BI Dashboard Visualization - Building a Data-Driven Decision Platform with Amazon QuickSightExplains how to build interactive BI dashboards with Amazon QuickSight and a serverless data analytics platform with Athena integration. Covers high-speed visualization with the SPICE engine and practical methods for sharing insights across the organization.Building Blockchain Networks - Leveraging Distributed Ledgers with Amazon Managed Blockchain and QLDBExplains how to build blockchain networks with Amazon Managed Blockchain and use Amazon QLDB as a verifiable ledger database. Covers practical use cases such as supply chain management and ensuring transparency in financial transactions.Privacy-Preserving Data Collaboration with AWS Clean RoomsRun joint analysis across multiple companies without sharing or copying data. Learn about aggregation rules for preventing individual identification and Cryptographic Computing for encrypted analysis.Customer Identity Unification - Resolving Scattered Customer Data with AWS Entity ResolutionLearn how to perform entity resolution (record matching) on customer data using AWS Entity Resolution. This article covers ML-based matching, rule-based matching, privacy protection, and integration with Clean Rooms.Leveraging Third-Party Data with AWS Data Exchange - Data Procurement and Subscription ManagementProcure third-party data products via Marketplace and build automated delivery pipelines to S3. This article also covers how to productize and monetize your own data.Building a Data Lake with Amazon S3 and Lake Formation - Design Patterns and GovernanceExplore data lake design patterns using S3 as the storage foundation and Lake Formation for fine-grained access control. This article also covers ETL pipelines and cost optimization.Data Lake Governance - Centralized Access Control with AWS Lake FormationLearn about building, access control, and governance for data lakes using AWS Lake Formation. This article covers fine-grained column-level and row-level permission management for S3-based data lakes, along with Glue and Athena integration.

Similar Articles and Services

BI Dashboard Visualization - Building a Data-Driven Decision Platform with Amazon QuickSightExplains how to build interactive BI dashboards with Amazon QuickSight and a serverless data analytics platform with Athena integration. Covers high-speed visualization with the SPICE engine and practical methods for sharing insights across the organization.Amazon QuickSightA serverless BI service featuring the SPICE engine for high-speed in-memory analytics and dashboard embedding capabilities.Detailed Cost Analysis with AWS Cost and Usage Report - Athena Queries and Visualization PipelinesAnalyze resource-level detailed cost data with SQL in Athena and visualize it with QuickSight. This article covers CUR 2.0 data exports and how to build an analytics foundation.Building an Enterprise AI Assistant with Amazon Q Business - Internal Knowledge Search and Workflow AutomationConnect data sources like internal documents, Confluence, and SharePoint to enable RAG-based enterprise search and question answering. Learn how to build a secure generative AI platform with guardrails and access controls.