Amazon S3 Tables now offer managed migration of your Apache Hive tables to Apache Iceberg. Iceberg adds ACID transactions, schema evolution, and time travel to your data lake, but moving off Hive usually means building a migration pipeline for each table, which can take weeks. You can now let S3 Tables manage your migration, resulting in query-ready Iceberg tables that stay optimized as data grows. S3 Tables validate, convert, and copy your data while production workloads keep running. To lower migration risk, you can review each step, sync destination tables with source changes as often as you need, or roll back to your source data. You pay only for the data that the job copies, with no charge for the Hive-to-Iceberg conversion, and source objects in S3 Intelligent-Tiering stay in their current access tier. If your tables use Snappy compression, S3 Tables recompress them to Zstandard (ZSTD) and compact them, which reduces storage and object count and improves query performance. To get started, visit the AWS Management Console or use the Amazon S3 Tables migration APIs. A https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/analytics-skills/migrating-hive-tables-to-s3-tables is also available in the Agent Toolkit for AWS to help AI agents orchestrate the workflow autonomously. At launch, S3 Tables managed migration supports AWS Glue-registered Hive tables stored in Parquet format within the same AWS account and AWS Region. It is available in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, Stockholm). For pricing information visit https://aws.amazon.com/s3/pricing/. To learn more, see the Amazon S3 Tables https://aws.amazon.com/s3/features/tables/, https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-tables-migrate.html, and https://aws.amazon.com/blogs/aws/amazon-s3-tables-now-offer-managed-migration-of-apache-hive-tables-to-apache-iceberg/.

Amazon S3 Tables now offer managed migration of your Apache Hive tables to Apache Iceberg. Iceberg adds ACID transactions, schema evolution, and time travel to your data lake, but moving off Hive usually means building a migration pipeline for each table, which can take weeks. You can now let S3 Tables manage your migration, resulting in query-ready Iceberg tables that stay optimized as data grows. S3 Tables validate, convert, and copy your data while production workloads keep running. To lower migration risk, you can review each step, sync destination tables with source changes as often as you need, or roll back to your source data. You pay only for the data that the job copies, with no charge for the Hive-to-Iceberg conversion, and source objects in S3 Intelligent-Tiering stay in their current access tier. If your tables use Snappy compression, S3 Tables recompress them to Zstandard (ZSTD) and compact them, which reduces storage and object count and improves query performance. To get started, visit the AWS Management Console or use the Amazon S3 Tables migration APIs. A migration skill is also available in the Agent Toolkit for AWS to help AI agents orchestrate the workflow autonomously. At launch, S3 Tables managed migration supports AWS Glue-registered Hive tables stored in Parquet format within the same AWS account and AWS Region. It is available in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, Stockholm). For pricing information visit Amazon S3 pricing. To learn more, see the Amazon S3 Tables overview page, documentation, and AWS News Blog.

You can now migrate existing Apache Hive datasets in Amazon S3 to fully managed Apache Iceberg tables in Amazon S3 Tables, with no pipelines to build or compute to provision, while production workloads keep running.

Amazon S3, including S3 Tables and S3 Vectors, is now available on the Vercel Marketplace and v0 by Vercel. Developers can create and connect S3 resources in seconds without leaving their development environment, getting the durability, scalability, and performance of S3 through Vercel's streamlined provisioning workflow. Millions of customers rely on S3 for low-cost, high-performance storage to power web and AI applications, but until now provisioning it from Vercel required navigating to the AWS Management Console. Now, S3 integrates directly into the Vercel developer experience, automatically handling resource provisioning and connection configuration. You can choose from S3 general purpose buckets for virtually any use case, S3 Tables for fully managed Apache Iceberg tables optimized for analytics workloads, or S3 Vectors to natively store and query vectors at scale. New AWS accounts created through Vercel include $100 in credits valid for up to six months, usable across Amazon S3, Amazon OpenSearch Serverless, and AWS Database services. Existing AWS customers can link their account to use S3 with their Vercel projects. Amazon S3 on Vercel is now available in all AWS Regions where Vercel is available. To get started, read the http://aws.amazon.com/blogs/aws/amazon-s3-vercel-marketplace-v0, visit https://vercel.com/marketplace/aws, or launch https://v0.app/.

Amazon S3, including S3 Tables and S3 Vectors, is now available on the Vercel Marketplace and v0 by Vercel. Developers can create and connect S3 resources in seconds without leaving their development environment, getting the durability, scalability, and performance of S3 through Vercel's streamlined provisioning workflow. Millions of customers rely on S3 for low-cost, high-performance storage to power web and AI applications, but until now provisioning it from Vercel required navigating to the AWS Management Console. Now, S3 integrates directly into the Vercel developer experience, automatically handling resource provisioning and connection configuration. You can choose from S3 general purpose buckets for virtually any use case, S3 Tables for fully managed Apache Iceberg tables optimized for analytics workloads, or S3 Vectors to natively store and query vectors at scale. New AWS accounts created through Vercel include $100 in credits valid for up to six months, usable across Amazon S3, Amazon OpenSearch Serverless, and AWS Database services. Existing AWS customers can link their account to use S3 with their Vercel projects. Amazon S3 on Vercel is now available in all AWS Regions where Vercel is available. To get started, read the AWS News Blog, visit AWS on Vercel Marketplace, or launch v0 by Vercel.

Some numbers are hard to picture until you sit with them for a moment. Last week we published the Prime Day 2026 metrics, and the one that stuck with me is 2.3 quadrillion: the daily requests Amazon ElastiCache peaked at during the event. Prime Day ran back in June, so this post always lands as […]

The Amazon S3 Express One Zone storage class is now available in 4 additional AWS Regions: Europe (London), Asia Pacific (Jakarta), Asia Pacific (Hong Kong), and Africa (Cape Town). Amazon S3 Express One Zone is a high-performance, single-Availability Zone storage class purpose-built to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications. S3 Express One Zone delivers data access speed up to 10x faster and request costs up to 80% lower than S3 Standard. It enables workloads such as machine learning training, interactive analytics, and key-value caching in AI search engines to achieve fast data access with high durability and availability. With this expansion, S3 Express One Zone is now available in 19 AWS Regions. For pricing details, visit the S3 pricing page. To learn more, visit the product page and documentation.

The Amazon S3 Express One Zone storage class is now available in 4 additional AWS Regions: Europe (London), Asia Pacific (Jakarta), Asia Pacific (Hong Kong), and Africa (Cape Town). Amazon S3 Express One Zone is a high-performance, single-Availability Zone storage class purpose-built to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications. S3 Express One Zone delivers data access speed up to 10x faster and request costs up to 80% lower than S3 Standard. It enables workloads such as machine learning training, interactive analytics, and key-value caching in AI search engines to achieve fast data access with high durability and availability. With this expansion, S3 Express One Zone is now available in https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-express-Endpoints.html. For pricing details, visit the https://aws.amazon.com/s3/pricing/. To learn more, visit the https://aws.amazon.com/s3/storage-classes/express-one-zone/ and https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-express-getting-started.html.

Amazon S3 Tables now offer complete support for the Apache Iceberg V3 spec. Upgrade existing V2 tables or create new V3 tables with built-in compaction, maintenance, and engine compatibility.

Amazon S3 Vectors now supports metadata pre filtering, delivering up to 5x higher recall on filtered searches. Filters evaluate before similarity search so scoped queries return more relevant results. Ideal for RAG, agentic apps, and document search.

Amazon Aurora PostgreSQL now lets you directly query Apache Iceberg and Parquet data stored in your data lake alongside live operational data—no ETL pipelines required. Powered by DuckDB embedded within Aurora, this capability enables single queries that join transactional and historical data using familiar PostgreSQL syntax. It supports AWS Glue Data Catalog, S3, and S3 Tables, with optimizations like predicate pushdown and caching for efficient performance.

Amazon S3 Tables add support for geometry, geography, unknown, and nanosecond timestamp data types, along with column default values, as defined in the Apache Iceberg Version 3 (V3) specification. You can now store geospatial coordinates and nanosecond-precision event times natively instead of encoding them in strings or integers. This helps simplify data architecture while improving storage efficiency and performance. With this launch, S3 Tables support all data types introduced in V3, adding to existing support for the Variant data type, deletion vectors, and row lineage. Geometry and geography columns store points, lines, and polygons natively, so fleet tracking and asset mapping workloads can filter on location at query time. Nanosecond timestamps let telemetry and financial workloads record event times at source precision. Column default values populate a newly added column for existing rows, with no backfill required. S3 Tables provide automatic table maintenance and compaction for Apache Iceberg tables, so tables using V3 data types stay performant and cost effective as data scales. Support for these data types in S3 Tables is available in all AWS Regions where S3 Tables are available. To learn more, see Amazon S3 Tables, Apache Iceberg V3 on AWS, and the AWS Prescriptive Guidance for working with Iceberg table format specification version 3.

Amazon S3 Tables add support for geometry, geography, unknown, and nanosecond timestamp data types, along with column default values, as defined in the Apache Iceberg Version 3 (V3) specification. You can now store geospatial coordinates and nanosecond-precision event times natively instead of encoding them in strings or integers. This helps simplify data architecture while improving storage efficiency and performance. With this launch, S3 Tables support all data types introduced in V3, adding to existing support for the Variant data type, deletion vectors, and row lineage. Geometry and geography columns store points, lines, and polygons natively, so fleet tracking and asset mapping workloads can filter on location at query time. Nanosecond timestamps let telemetry and financial workloads record event times at source precision. Column default values populate a newly added column for existing rows, with no backfill required. S3 Tables provide automatic table maintenance and compaction for Apache Iceberg tables, so tables using V3 data types stay performant and cost effective as data scales. Support for these data types in S3 Tables is available in all AWS Regions where S3 Tables are available. To learn more, see https://aws.amazon.com/s3/features/tables/, https://docs.aws.amazon.com/AmazonS3/latest/userguide/working-with-apache-iceberg-v3.html, and the https://docs.aws.amazon.com/prescriptive-guidance/latest/apache-iceberg-on-aws/table-spec-v3.html for working with Iceberg table format specification version 3.

Mountpoint for Amazon S3 can now limit memory usage, either automatically based on the environment it runs in or with a limit that you define. This lets you run Mountpoint alongside memory-intensive applications, for example, in machine learning training or analytics workloads where applications share a memory budget. Previously, Mountpoint’s memory usage could expand over time based on usage patterns, potentially causing performance or stability issues when competing with other memory-intensive applications. With this launch, you can define a memory target for Mountpoint to reserve memory for your applications. Alternatively, Mountpoint can automatically determine a safe default based on the environment it runs in. For example, when running in Amazon EKS, Mountpoint can automatically detect a container's assigned memory budget. Under memory pressure, Mountpoint slows down operations to stay within this budget, enabling you to run Mountpoint in containers with strict memory allocations.   Mountpoint is available in all AWS Regions. To upgrade to the latest version, visit the https://aws.amazon.com/s3/features/mountpoint/. To learn more about Mountpoint, see the https://github.com/awslabs/mountpoint-s3/blob/main/doc/CONFIGURATION.md and the https://github.com/awslabs/mountpoint-s3/blob/main/doc/CONFIGURATION.md in the Mountpoint GitHub repository.

Mountpoint for Amazon S3 can now limit memory usage, either automatically based on the environment it runs in or with a limit that you define. This lets you run Mountpoint alongside memory-intensive applications, for example, in machine learning training or analytics workloads where applications share a memory budget. Previously, Mountpoint’s memory usage could expand over time based on usage patterns, potentially causing performance or stability issues when competing with other memory-intensive applications. With this launch, you can define a memory target for Mountpoint to reserve memory for your applications. Alternatively, Mountpoint can automatically determine a safe default based on the environment it runs in. For example, when running in Amazon EKS, Mountpoint can automatically detect a container's assigned memory budget. Under memory pressure, Mountpoint slows down operations to stay within this budget, enabling you to run Mountpoint in containers with strict memory allocations.   Mountpoint is available in all AWS Regions. To upgrade to the latest version, visit the Mountpoint GitHub repository. To learn more about Mountpoint, see the overview page and the configuration guide in the Mountpoint GitHub repository.

Amazon S3 Files makes S3 buckets accessible as high-performance file systems on AWS compute resources, eliminating the tradeoff between object storage benefits and interactive file capabilities while enabling seamless data sharing with ~1ms latencies.

In my last Week in Review post, I mentioned how much time I’ve been spending on AI-Driven Development Lifecycle (AI-DLC) workshops with customers this year. A common theme in those sessions is the need for better cost visibility. Teams are moving fast with AI, but as they go from experimenting to full production, finance and […]

Amazon S3 Files makes S3 buckets accessible as high-performance file systems on AWS compute resources, eliminating the tradeoff between object storage benefits and interactive file capabilities while enabling seamless data sharing with ~1ms latencies.

A recap of the top announcements from AWS's New York Summit 2026

AWS Glue Data Catalog now supports AWS IAM-based authorization for Amazon S3 Tables and Apache Iceberg materialized views. With IAM-based authorization, you can define all necessary permissions across storage, catalog, and query engines in a single IAM policy. This capability simplifies the integration of S3 Tables or materialized views with any AWS Analytics service, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. You can also opt in to AWS Lake Formation at any time to manage fine-grained access controls using the AWS Management Console, AWS CLI, API, and AWS CloudFormation. This feature is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. To learn more, visit the https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-tables-integration-overview.html and the https://docs.aws.amazon.com/glue/latest/dg/glue-federation-s3tables.html.

Amazon S3 Tables now support the Variant data type as defined in the Apache Iceberg Version 3 (V3) specification. You can now write semi-structured data like JSON directly to S3 Tables without defining a fixed schema in advance, allowing you to land data faster while still getting efficient analytical query performance. With the Variant data type, Apache Iceberg V3-compatible engines shred your semi-structured data into hidden columns as you write it, generating Parquet column statistics that query engines use for optimizations like file pruning, which reduces the data your analytical queries scan. S3 Tables also provide ongoing table maintenance, including compaction, for Variant columns, so you can consolidate data from small files into large files that Iceberg engines can read. Variant support in S3 Tables is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), and South America (São Paulo). To learn more, see https://aws.amazon.com/s3/features/tables/ and the https://docs.aws.amazon.com/prescriptive-guidance/latest/apache-iceberg-on-aws/table-spec-v3.html.

Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.

Amazon S3 Event Notifications now include system-generated tags in events delivered to all destinations including Amazon EventBridge, Amazon SQS, Amazon SNS, and AWS Lambda. System-generated tags are metadata labels attached to your bucket by AWS services. You can use these tags to filter events from thousands of buckets with a single EventBridge rule, instead of listing each bucket name individually. To get started, enable S3 Event Notifications on your general purpose buckets through the AWS Management Console, AWS SDK, or AWS CLI. If AWS services like AWS CloudFormation have already applied system-generated tags to your buckets, S3 automatically includes them in new event notifications. System-generated tags in S3 Event Notifications are available at no additional cost in all AWS Regions and require no changes to existing configurations. To learn more, visit the https://docs.aws.amazon.com/AmazonS3/latest/userguide/notification-content-structure.html.

Amazon S3 now supports delivering server access logs to Amazon CloudWatch Logs, giving you instant querying, alarms, cross-account and cross-Region aggregation, and AWS Key Management Service (KMS) encryption for your access log data. You can also mirror your logs to Amazon S3 Tables in Apache Iceberg format at no additional storage cost. These new delivery paths complement the existing free delivery of server access logs to S3 general purpose buckets, giving you more flexibility in how you monitor and analyze access to your data. With delivery to CloudWatch Logs, you can set alarms on error rates, monitor traffic patterns, investigate access incidents across accounts and Regions, and correlate S3 access activity with the rest of your operational data. Logs mirrored to S3 Tables are immediately queryable with standard SQL in Amazon Athena, Amazon Redshift, and other Iceberg-compatible query engines, so you can audit access patterns, analyze usage trends, and identify cost drivers across buckets over time. S3 server access logs delivery to CloudWatch Logs is available today in all AWS Regions, except for AWS China Regions and AWS GovCloud (US) Regions. To learn more, see the https://aws.amazon.com/s3, https://docs.aws.amazon.com/AmazonS3/latest/userguide/ServerLogs.html in the Amazon S3 User Guide, and the https://aws.amazon.com/blogs/storage/query-amazon-s3-access-logs-instantly-with-cloudwatch-and-s3-tables/.

Amazon S3 Vectors has reduced data processed charges for queries on vector indexes with over 10 million vectors by up to 80%. This reduction lowers costs for customers running similarity search across large-scale AI, RAG, and semantic search workloads. The new pricing applies automatically with no application changes required. While this change reduces costs for large indexes, we continue to recommend distributing vectors across multiple indexes for improved query performance. S3 Vectors query pricing reductions are effective today in all https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-regions-quotas.html where S3 Vectors is available. For updated pricing information, visit the https://aws.amazon.com/s3/pricing/ To learn more about S3 Vectors, visit the https://aws.amazon.com/s3/features/vectors/ and https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html.