Amazon S3 Tables
Store tabular data at scale with fully managed Apache Iceberg tables in Amazon S3
What are S3 Tables?
Amazon S3 Tables are fully managed Apache Iceberg tables that automate the operational burden of managing data lakes and lakehouses. Through advanced compaction and maintenance strategies, S3 Tables automatically optimize query performance as data volumes grow. S3 Tables work with any Iceberg-compatible engine, including Apache Spark, Trino, Amazon Athena, Amazon Redshift, and other third-party tools, allowing for architectural flexibility and delivering the easiest way to store tabular data at scale.
Benefits
S3 Tables continuously optimize Iceberg tables through compaction, snapshot management, and unreferenced file removal. Automatic replication reduces query latency for distributed teams, and Intelligent-Tiering reduces storage costs by up to 80%. As a result, data teams can focus on building instead of managing infrastructure.
The more workloads grow, the more Iceberg table maintenance and optimization matter, and the harder they become to keep upwith. S3 Tables automatically keep tables performant, so queries remains consistent as your data grows rather than degrading under it. Data is backed by the most durable storage in the cloud, designed to provide 99.999999999% (11 nines) of durability and 99.99% availability by default.
Built on the Apache Iceberg open standard, S3 Tables ensure your data is never locked into a single compute engine or vendor. S3 Tables expose the Iceberg REST Catalog API, so they work with Iceberg-compatible engines including Spark, Trino, Flink, Athena, Redshift, Snowflake, and other third-party tools, preserving investment in existing tools while allowing for long-term flexibility.
Managing Iceberg table governance and security can be complex and fragmented. S3 Tables are first-class AWS resources with table-level access control, encryption, and lifecycle management built in, eliminating the need to manage S3 bucket policies for every table and simplifying governance for complex analytics environments.
S3 Tables deliver storage optimized for analytics, with up to 10x higher transactions per second compared to Iceberg tables stored in general purpose S3 buckets. With MCP support, AI agents and LLMs can interact with S3 Tables, making AI-driven analytics possible. Native integrations with AWS Analytics services and compatibility with third-party tools through the Iceberg REST API mean S3 Tables can power emerging AI-powered workflows.
How S3 Tables work
Use cases
Modernize data lakes by migrating from Parquet, Apache Hive, or Hadoop to Apache Iceberg tables, reducing operational complexity while building scalable AI-ready data lakes that support advanced analytics and AI/ML learning workloads.
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Stream data directly into Iceberg tables from sources like IoT sensors, transaction systems, and application logs using AWS Streaming services, with automatic background optimization that keeps streaming data queryable in near real-time.
S3 Tables deliver up to 10x higher transactions per second compared to storing Iceberg tables in general purpose buckets, making them well-suited for large-scale analytics workloads and operations that require high throughput.
Query data stored in Iceberg tables using natural language through Model Context Protocol (MCP), enabling ad-hoc exploration without SQL expertise. S3 Tables supports concurrent access from multiple users and AI assistants with automatic optimization maintaining query performance.
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Learn about Amazon S3 Tables, why we built it, and how they work
Watch nowPartners and integrations
Daft
"Amazon S3 Tables is the perfect complement to Daft’s support for Apache Iceberg. By leveraging its integrations with AWS Lake Formation and AWS Glue, we were able to effortlessly extend our existing Iceberg read and write capabilities to S3 Tables while taking advantage of its optimized performance. We look forward to the evolution of this new service, and we are excited to provide the best in class S3 Tables support for the Python Data Engineering & ML/AI ecosystem."
Sammy Sidhu, CEO & Co-Founder - Daft
Dremio
"Dremio is pleased to support the general availability of Amazon S3 Tables. By supporting the Apache Iceberg REST Catalog (IRC) specification, S3 Tables ensure seamless interoperability with Dremio, enabling users to benefit from a high-performance SQL engine capable of querying Apache Iceberg tables managed in optimized S3 table buckets. This collaboration reinforces the importance of open standards in the lakehouse ecosystem, eliminating integration complexity and accelerating customer adoption. With Amazon S3 Tables and IRC support, organizations gain the flexibility and choice needed to build a unified lakehouse architecture in the AI era."
Rahim Bhojani, CTO - Dremio
DuckDB Labs
"Amazon S3 Tables aligns perfectly with DuckDB's vision for democratizing data analytics using open file formats. The collaboration between AWS and DuckDB Labs allows us to further extend Iceberg support in DuckDB and develop seamless integration with S3 Tables. We believe the shared batteries-included mentality of DuckDB and S3 Tables combines into a powerful analytics stack that can handle a wide range of workloads while maintaining an incredibly low barrier to entry."
Hannes Mühleisen, Chief Executive Officer - DuckDB Labs
HighByte
"Amazon S3 Tables is a powerful new feature that optimizes the management, performance, and storage of tabular data for analytics workloads. HighByte Intelligence Hub’s direct integration with Amazon S3 Tables makes it easy for global manufacturers to build an open, transactional data lake for their industrial data. S3 Tables enable instant querying of raw Parquet data, allowing customers to send contextualized information from the edge to the cloud for immediate use without additional processing or transformations. This has a major impact on both performance and cost optimization for our mutual customers."
Aron Semle, Chief Technology Officer - HighByte
PuppyGraph
"Amazon S3 has long been the foundation of modern data infrastructure, and the launch of S3 Tables marks a major milestone—bringing Apache Iceberg closer to becoming the universal standard for data and AI. This innovation allows organizations to leverage high-performance, open table formats on S3, enabling multi-engine analytics without data duplication. For PuppyGraph customers, it means they can now run real-time graph queries directly on their S3 data, maintaining fresh, scalable insights without the overhead of complex ETL. We’re excited to be part of this evolution, making graph analytics as seamless as the data itself."
Weimo Liu, Co-founder & CEO - PuppyGraph
RisingWave
"RisingWave’s integration with Amazon S3 Tables empowers organizations to seamlessly leverage Apache Iceberg tables in Amazon S3, enhancing their streaming data pipeline capabilities. Whether you’re ingesting raw data, transforming it in real time, or writing results back to S3, RisingWave makes it easy to work with Iceberg tables as a natural extension of your workflow. This integration simplifies data management, reduces operational complexity, and enables smooth interoperability for teams working with streaming analytics."
Rayees Pasha, CPO - RisingWave Labs
Ryft
"Ryft’s integration with Amazon S3 Tables enables teams to operate Apache Iceberg tables as a fully autonomous lakehouse. Customers get workload-aware optimization and governance, automated file layout optimization and compaction, managed snapshot retention and recovery, automated compliance for Apache Iceberg tables and full visibility on their lakehouse, all on Iceberg-native storage. Together, Ryft and S3 Tables deliver consistently fast queries, lower storage costs, and reliable operations without manual tuning or cron-based maintenance."
Yossi Reitblat, CEO & Co-Founder - Ryft
Snowflake
"We are excited to bring the magic of Snowflake to Amazon S3 Tables. This collaboration enables Snowflake customers to seamlessly read and process data stored in S3 Tables using their existing Snowflake setups, eliminating the need for complex data migrations or duplications. By combining Snowflake’s world-class performance analytics capabilities with Amazon S3 Tables’ efficient storage of Apache Iceberg tables, organizations can easily query and analyze tabular data stored in Amazon S3."
Rithesh Makkena, Global Director of Partner Solutions Engineering - Snowflake
Starburst
"We’re thrilled to see Amazon S3 introduce built-in support for Apache Iceberg with S3 Tables, advancing the Iceberg Open Data Lakehouse ecosystem. With S3 table buckets, we look forward to collaborating with AWS to help our joint customers bring the power of an Open Lakehouse, powered by optimized Trino– a leading open source MPP SQL engine, across diverse analytics and AI use cases to data in Amazon S3."
Matt Fuller, Vice President, Product - Starburst
StreamNative
"Our integration with Amazon S3 Tables makes real-time, AI-ready data more open and accessible than ever. Ursa’s leaderless architecture on S3 already reduces storage costs, and direct integration with S3 Tables further improves performance and efficiency. In an AI-driven world, data governance is crucial. At StreamNative, we’re committed to helping businesses reduce TCO by 90% while making it effortless and affordable to build AI-powered applications with governed, real-time data."
Sijie Guo, CEO & Co-Founder - StreamNative
Frequently asked questions
You should use S3 Tables for a simple, performant, and cost-effective way to store tabular data in Amazon S3. S3 Tables give you the ability to organize your structured data into tables, and then to query that data using standard SQL statements, with virtually no setup. Additionally, S3 Tables deliver the same durability, availability, scalability, and performance characteristics as S3 itself, and automatically optimize your storage to maximize query performance and to minimize cost. With the Intelligent-Tiering storage class, S3 Tables automatically optimizes costs based on access patterns, without performance impact or operational overhead.
S3 Tables deliver up to 10x higher transactions per second (TPS) compared to storing Iceberg tables in general purpose Amazon S3 buckets. S3 Tables automatically perform compaction on the underlying data to continually optimize your tables for optimal query performance. Depending on your workload and query patterns, you can also choose from advanced compaction strategies such as sort and z-order compaction to further optimize your tables. Sort compaction organizes data based on specified columns to improve query performance for filtered operations, while z-order compaction optimizes data organization across multiple dimensions, making it ideal when you need to query data across multiple columns simultaneously.
You can get started with S3 Tables in just a few simple steps without having to stand up any infrastructure outside of S3. First, create a table bucket in the S3 console. As part of creating your first table bucket through the console, the integration with AWS Analytics services happens automatically, which enables S3 to automatically populate all table buckets and tables in your account and Region in the AWS Glue Data Catalog. After this, S3 Tables is now accessible to AWS query engines such as Amazon Athena, EMR, and Redshift. Next, you can click to create a table using Amazon Athena from the S3 console. Once in Athena, you can quickly start populating new tables and querying them.
Alternatively, you can access S3 Tables using the Iceberg REST Catalog endpoint through the AWS Glue Data Catalog, which enables you to discover your entire data estate, including all table resources. You can also connect directly to an individual table bucket endpoint to discover all S3 Tables resources within that bucket. This enables you to use S3 Tables with any application or query engine that supports the Apache Iceberg REST Catalog specification.