Amazon Quick now supports S3 tables bucket as a data source
Query Apache Iceberg lakehouse data directly in Amazon Quick—no warehouse needed—with real-time SaaS and streaming data via Zero-ETL.
View original announcement →Visual Summary
graph TD
A{{Amazon Quick S3 Tables Support}}:::announced
B(Amazon S3 Table Buckets):::storage
C([Dataset Q&A]):::feature
D([Zero-ETL Ingestion]):::feature
E((Business Users)):::external
F(Amazon Kinesis Firehose):::compute
G((Salesforce / SAP)):::external
H([Apache Iceberg Tables]):::feature
I([Agentic AI & BI]):::feature
E ==>|"asks questions"| A
A ==>|"queries directly"| B
B -->|"stores"| H
A -->|"powers"| C
A -->|"enables"| I
G -.->|"Zero-ETL"| D
F -.->|"streams"| D
D -->|"ingests into"| B
C -->|"grounded in"| B
classDef announced fill:#ff9900,stroke:#ec7211,color:#fff,font-weight:bold
classDef compute fill:#e3f2fd,stroke:#1565c0,color:#1565c0
classDef storage fill:#e8f5e9,stroke:#2e7d32,color:#2e7d32
classDef feature fill:#fff3e0,stroke:#e65100,color:#e65100
classDef external fill:#f5f5f5,stroke:#616161,color:#616161
What's New
Amazon Quick now supports Amazon S3 table buckets as a native data source, allowing users to directly query Apache Iceberg tables stored in S3 table buckets without requiring an intermediate data warehouse or OLAP layer. This integration enables both traditional BI dashboard authoring and conversational, agentic AI analytics through Amazon Quick's Dataset Q&A feature. The announcement also highlights Zero-ETL compatibility with sources like Salesforce, SAP, and Amazon Kinesis Data Firehose, enabling near real-time lakehouse analytics with minimal pipeline complexity.
How It Works
- Direct Iceberg Query: Amazon Quick connects directly to Apache Iceberg tables stored in S3 table buckets, eliminating the need to copy or transform data into a separate analytical store before querying.
- One-Time Permission Setup: Administrators configure S3 table bucket IAM permissions once, after which dataset authors can immediately discover and create datasets from those buckets without additional infrastructure work.
- Dataset Q&A Integration: S3 table bucket datasets are fully integrated with Amazon Quick's natural language Q&A interface, allowing users to ask plain-language questions that are answered using the data lake as the authoritative source.
- Zero-ETL Ingestion: Data from external SaaS systems (Salesforce, SAP) and streaming sources (Kinesis Data Firehose) can flow directly into S3 table buckets via Zero-ETL pipelines, making fresh data immediately available for querying in Quick.
- Agentic AI and BI Workloads: The same S3 table bucket dataset can serve both structured dashboard/report authoring and agentic AI workflows, unifying analytical and AI use cases on a single data architecture.
- No OLAP Intermediary: Queries are executed directly against the lakehouse layer, bypassing the need to provision or maintain a separate OLAP engine or data warehouse cluster.
Why It's Important
- Simplified Data Architecture: Organizations can eliminate redundant data movement pipelines that previously copied lakehouse data into warehouses or OLAP cubes solely to support BI tooling, reducing cost and operational overhead.
- Near Real-Time Insights: Combined with Zero-ETL ingestion from SaaS and streaming sources, analysts can query data that is minutes old rather than hours or days old, improving decision-making timeliness.
- Unified Lakehouse BI: This positions S3 table buckets as a true analytical hub, supporting both AI-driven and traditional BI consumption patterns from a single, governed data store.
- Lower Total Cost of Ownership: Removing intermediate warehousing layers reduces storage duplication, compute costs, and the engineering effort required to maintain synchronization pipelines.
- Governed Natural Language Access: Dataset Q&A grounded in the data lake means business users can self-serve answers without requiring SQL expertise, while IT retains governance through standard S3 and IAM controls.
How It's Different
- No Warehouse Required: Unlike traditional QuickSight (or Quick) setups that relied on Redshift, Athena, or SPICE imports as intermediaries, this integration queries Iceberg tables in S3 directly, removing an entire architectural tier.
- Iceberg-Native: S3 table buckets use Apache Iceberg as the table format, providing ACID transactions, schema evolution, and time-travel capabilities that standard S3 data sources in Quick did not natively support.
- Zero-ETL First-Class Support: The integration is explicitly designed to pair with Zero-ETL pipelines, making it distinct from generic S3 or Glue Data Catalog connections that still required manual ETL orchestration.
- Agentic AI Readiness: The feature is positioned for both agentic AI workloads and BI, whereas most BI tool data source integrations are optimized solely for structured reporting use cases.
- Single Permission Model: A one-time admin configuration covers all authors and datasets from that bucket, contrasting with per-dataset or per-connection credential management common in other connectors.
When to Prefer It
- Lakehouse-Centric Organizations: Teams that have standardized on S3 table buckets and Apache Iceberg as their primary data store and want to avoid duplicating data into a warehouse just for dashboarding.
- Real-Time SaaS Analytics: Use cases where Salesforce CRM, SAP ERP, or streaming Kinesis data needs to be visualized in near real-time without building and maintaining custom ETL pipelines.
- Cost-Sensitive Workloads: Scenarios where provisioning and running a dedicated Redshift cluster or other OLAP engine is cost-prohibitive relative to the query volume or business value.
- Conversational / Self-Service Analytics: Business users who need to ask ad hoc natural language questions against large datasets without waiting for a data engineer to build a report or load data into SPICE.
- Agentic AI Applications: Developers building AI agents that need to ground responses in authoritative, up-to-date lakehouse data rather than a potentially stale cached dataset.
- Unified Governance Requirements: Organizations that want a single IAM/S3 permission boundary to control both data lake access and BI tool access, simplifying compliance and audit trails.
Availability
- GA Status: Generally available as of May 4, 2026; this is not a preview or beta release.
- Regional Availability: Available in all AWS Regions where Amazon Quick is currently supported; no region-specific restrictions were noted.
- Pricing: Specific pricing details were not disclosed in the announcement; costs are expected to follow Amazon Quick's standard dataset and query pricing, plus standard S3 storage and request charges.
- Prerequisites: Requires S3 table buckets with Apache Iceberg tables; administrators must configure appropriate S3 and IAM permissions before authors can create datasets.
- Zero-ETL Dependency: Near real-time ingestion scenarios require separately configured Zero-ETL pipelines from supported sources (Salesforce, SAP, Kinesis Data Firehose); these are not automatically provisioned.
