At the same time, modern data architectures are evolving toward scalable data lakes built on open table formats such as Apache Iceberg, which offer improved performance, cost efficiency, and governance. However, analyzing large-scale data often requires moving it into data warehouses or OLAP systems, introducing latency, added cost, and operational complexity. Although existing query modes—such as Direct Query and SPICE (Super-fast, Parallel, In-memory Calculation Engine) with data warehouses —address most analytics needs, customers continue to seek a more seamless way to analyze large, real-time datasets directly from their data lakes.

To address this, Amazon Quick introduces Amazon S3 Tables (Apache Iceberg tables) as a new data source. With this feature, customers can directly query and visualize Apache Iceberg tables stored in an Amazon S3 table bucket without the need for intermediate data layers. This approach provides additional architectural choice especially when customers are requiring to reduce data movement, improve performance, and maintain a secure, governed single source of truth.

In this post, we explore how Amazon Quick and S3 Tables work together to enable near real-time analytics and streamline modern data architectures.

Direct Query and SPICE modes for S3 Tables, a new Amazon Quick feature, enables direct consumption of Apache Iceberg tables in Amazon S3 table bucket without requiring intermediate query layers. This feature is beneficial for enterprise looking to implement modern data architecture using Apache Iceberg open table format to treat their data lake as a “central source of truth,” enabling high-performance analytics without complex data pipeline and the overhead of moving data between disparate systems.

With this new launch, Amazon Quick now supports querying data lakes using either SPICE or Direct Query mode. In this post, we focus on Direct Query mode, though you can choose SPICE mode when creating your dataset.