Amazon S3 Tables

Amazon S3 Tables will be a transformative addition to our data architecture, especially with its managed Iceberg support, which effectively creates a materialized view layer for diverse data analysis needs. This offering has the potential to help Genesys simplify complex data workflows by eliminating extra layers of table management, with S3 handling key maintenance tasks like compaction, snapshot management, and unreferenced file cleanup automatically. The ability to read and write Iceberg Tables directly from S3 will help us boost performance and create new possibilities for integrating data seamlessly across our analytics ecosystem. This interoperability, combined with the performance enhancements, positions S3 Tables as a pivotal part of our future strategy to deliver fast, flexible, and reliable data insights.

At Pendulum Intelligence, we analyze data from hundreds of millions of social channels and sources. Amazon S3 Tables has transformed how we manage our data lake, which processes thousands of hours of analyzed video and audio content while extracting context from images and other media in near real-time using our proprietary machine learning tools. By eliminating the burden of table management, including compaction, snapshots, and file cleanup, our team can focus on what matters most: deriving actionable insights from massive datasets. The seamless integration with our analytics stack—Amazon Athena, AWS Glue, and Amazon EMR—has significantly enhanced our ability to process complex data at scale.

Amazon S3 Tables, with built-in Apache Iceberg support and AWS Analytics services integration, help companies optimize their data analytics costs while transforming how they use business data for analytics, compliance, and AI initiatives. By automating complex data management tasks and providing complete audit trails of data changes, teams can instantly analyze historical data, maintain regulatory compliance, and accelerate business insights while significantly reducing their technology costs.

As a healthcare company handling massive amounts of frequently changing patient data, we decided to invest in Apache Iceberg because it solves many pain points with Apache Hive around partitioning and automation, with the added benefit of wider interoperability. One of our biggest challenges with Iceberg has been understanding and managing table optimization. This is why we’re excited about S3 Tables and the managed optimization capabilities. Being able to offload the developer overhead of table maintenance will allow us to focus more on bringing high-quality data and valuable insights to our customers.

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.

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.

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.

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.

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.

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.

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.

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.