Researchers at the Tokyo University of Science have solved one of the (many) sodium ion battery challenges, and discovered why the associated material works so well. The material is β-NaMnO2, …

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AWS announces support for data processing jobs in Amazon SageMaker. This launch enables you to author, manage, monitor, and troubleshoot data processing workloads across your organization and collaborate in projects to securely build and share data processing jobs and workflows.

Amazon SageMaker Unified Studio is a single data and AI development environment where you can find and access all of the data in your organization and act on it using the best tools across any use case. With this launch, you can now build Apache Spark jobs to process large volumes of data. Jobs can be built using your preferred tool. For example, you can create jobs from Extract, Transform, and Load (ETL) scripts coded in the Unified Studio code editor, or interactively in a Unified Studio Notebooks. You can also create jobs visually using the Unified Studio Visual ETL editor. Once created, data processing jobs can be set to run on demand, scheduled using the built in scheduler, or orchestrated with SageMaker workflows. You can monitor the status of your data processing jobs and view run history showing status, logs, and performance metrics. When jobs fail, you can use generative AI troubleshooting to automatically analyze job metadata and logs, providing detailed insights that identify root causes and actionable recommendations to resolve issues quickly. Together, these capabilities enable you to author, manage, monitor, and troubleshoot data processing workloads across your organization.

See Supported Regions for a list of AWS Regions where SageMaker Unified Studio is generally available. To learn more about SageMaker Unified Studio, see the Amazon SageMaker Unified Studio webpage or documentation . You can start using SageMaker Unified Studio today by selecting “Amazon SageMaker” in the AWS Console .

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M6id instances are available in Europe (Spain) Region. These instances are powered by 3rd generation Intel Xeon Scalable Ice Lake processors with an all-core turbo frequency of 3.5 GHz and up to 7.6 TB of local NVMe-based SSD block-level storage.

M6id instances are built on AWS Nitro System , a combination of dedicated hardware and lightweight hypervisor, which delivers practically all of the compute and memory resources of the host hardware to your instances for better overall performance and security. Customers can take advantage of access to high-speed, low-latency local storage to scale performance of applications such data logging, distributed web-scale in-memory caches, in-memory databases, and real-time big data analytics.

Customers can purchase the new instances via Savings Plans, Reserved, On-Demand, and Spot instances. To get started, visit AWS Command Line Interface (CLI) , and AWS SDKs . To learn more, visit our product page for M6id .

Amazon RDS Custom for SQL Server now supports Change Data Capture (CDC). In Microsoft SQL Server, CDC is a feature that utilizes the SQL Server Agent to log insertions, updates, and deletions occurring in a database table, and makes these data changes accessible to applications in a relational format to provide historical context.

Amazon RDS Custom for SQL Server is a managed database service that allows customers to bring-your-own-license (BYOL) for Microsoft SQL Server. When you configure CDC in Amazon RDS Custom for SQL Server, RDS automatically applies your specified configurations when the underlying hardware needs to be replaced, or when you scale up to use a larger database instance. Thus, RDS Custom for SQL Server removes the operational overhead of setting up and verifying configurations in different operational situations.

CDC is available in all AWS Regions where RDS Custom for SQL Server is available.

Amazon S3 Inventory’s capability to include access control lists (ACLs) as object metadata in inventory reports is now available in AWS GovCloud (US) Regions. This allows you to easily review ACLs on all of your objects to simplify review of access permissions. ACLs were the original way to manage object access when S3 launched in 2006. Now, when migrating to IAM-based bucket policies for access control, you can easily review all of the object ACLs in your buckets before enabling S3 Object Ownership.

S3 Inventory provides a complete list of objects in a bucket and their corresponding metadata. The Object ACLs fields include details about the object owner and the grantee along with their permission granted. You can now activate reporting on object ACLs by editing existing S3 Inventory configurations in the AWS Management Console or API.

By enabling S3 Object Ownership, you can change how S3 performs access control for a bucket so that only IAM policies are used. S3 Object Ownership’s ‘Bucket owner enforced’ setting disables ACLs for your bucket and the objects in it, and updates every object so that each object is owned by the bucket owner. We recommend that you carefully review your use of ACLs with inventory reports, migrate to IAM-based bucket policies, and then disable ACLs with S3 Object Ownership. For more information, see Controlling ownership of objects and disabling ACLs for your bucket .

Amazon S3 Inventory support for Object ACL is generally available at no additional charge in all AWS Commercial and AWS GovCloud (US) Regions, where Amazon S3 Inventory is available. To learn more, please visit Amazon S3 Inventory and Amazon S3 pricing .

Today, Amazon SageMaker announces an integration with Amazon QuickSight, allowing you to build dashboards in Amazon QuickSight using data in your Amazon SageMaker projects. You can now launch Amazon QuickSight directly from within Amazon SageMaker Unified Studio, visualize your project’s data without manual setup, and publish dashboards to the Amazon SageMaker Catalog for broader discovery and reuse.

With this integration, you can go from exploring data in Amazon SageMaker Unified Studio to visualizing it in Amazon QuickSight. Behind the scenes, Amazon SageMaker creates an Amazon QuickSight dataset and organizes it in a secured folder accessible only to members within the project. Any dashboards you build in Amazon QuickSight stay within this folder and are automatically added as assets to your SageMaker project, where you can publish them to the SageMaker Catalog. From there, you can share them with users or groups in your corporate directory for broader access—all within SageMaker Unified Studio. This keeps your dashboards organized, discoverable, shareable, and governed, making cross-team collaboration and asset reuse much easier. 

The integration is generally available for customers using Amazon QuickSight with AWS IAM Identity Center in all AWS Regions where Amazon SageMaker is available, including: 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 read the Admin guide and the User guide .

Amazon S3 Metadata creates and manages metadata that provides information about datasets stored in S3, so you can more easily discover and use your S3 data. Previously, S3 Metadata supported new and updated objects. Now, S3 Metadata also creates and manages metadata for all your existing S3 data so you can write a SQL query, across metadata for any amount of S3 storage.

S3 Metadata creates and maintains two Apache Iceberg tables that give you a queryable and continuously updated view of metadata associated with an S3 bucket. A journal table records changes made to your data in near real time, helping you to identify new data uploaded to your bucket, track recently deleted objects, monitor lifecycle transitions, and more. A new live inventory table provides and maintains an up-to-date view of all objects in your bucket. Both S3 Metadata tables are stored in an AWS managed Iceberg table bucket in your account, and can be queried using standard SQL through AWS analytics services such as Amazon Athena or Amazon EMR, and open source tooling such as DuckDB and PyIceberg.

S3 Metadata is currently available in US East (N. Virginia), US East (Ohio), and US West (Oregon). You pay a small fee for every change to the underlying dataset that is recorded in S3 Metadata journal tables. As of today, we’re reducing the journal table price by 33% to make real-time change tracking and backfilling more cost-effective for large datasets. Additionally, you pay a small per-object fee to fill your live inventory tables with information about existing S3 datasets. For more details on pricing, visit the Amazon S3 pricing page . To learn more and get started, visit the product page , documentation , and read the AWS News Blog .

Amazon S3 Tables now offer more cost-effective compaction operations for Apache Iceberg tables, with processing fees reduced by up to 90%. S3 Tables provide storage that is optimized for analytics workloads, which automates maintenance operations like compaction to continuously improve query performance and reduce storage costs. With these compaction price reductions, the per-object price is now 50% lower, while per-byte processing prices are 90% lower for binpack compaction and 80% lower for sort and z-order compaction.

S3 Tables provide three automated compaction strategies, designed for varying query patterns. The default binpack strategy combines files for basic optimization. Sort compaction organizes data by specific columns to reduce file scans during queries. Z-order compaction helps when querying multiple columns at once. S3 Tables automatically apply hierarchical sorting during compaction when a sort order is defined in table metadata, with z-order compaction available through the S3 Tables maintenance API.

These reduced S3 Tables compaction prices took effect July 1, 2025 in all AWS Regions where S3 Tables is available and will be automatically reflected in your AWS bill. To learn more, visit the Amazon S3 pricing page and S3 Tables maintenance documentation .

Amazon EventBridge now supports logging to Amazon CloudWatch Logs, Amazon Simple Storage Service (Amazon S3), and Amazon Kinesis Data Firehose, improving observability and simplifying debugging for your event-driven applications. You can now log results from matched rules, errors, and target invocations for your event bus, giving you deeper insights into your applications event processing and helping you root cause issues faster.

The Amazon EventBridge event bus is a serverless event broker that enables you to create scalable event-driven applications by routing events between your own applications, third-party SaaS applications, and other AWS services. With support for enhanced logging, you can now track the lifecycle of events by selecting one of three log levels (error, info, and trace), and optionally choose to include event payloads, giving you control over privacy and cost. EventBridge event bus logs can be enabled through the AWS Management Console, AWS CLI, or SDKs and you can configure log destinations using Amazon CloudWatch Logs vended logs capabilities.

This feature is currently available in the US East and Asia Pacific Regions. Support for logs are provided at no additional cost, however, standard rates for log storage and data transfer apply. For more information on getting started with logging in EventBridge,  please read our blog post or visit our documentation .

Amazon S3 has expanded compaction support to include Apache Avro and ORC formats for Apache Iceberg tables, complementing existing Parquet format capabilities. This enhancement works across both S3 Tables and for general purpose S3 buckets using AWS Glue Data Catalog optimization.

While Parquet is the default format for Iceberg tables, you can also write data in Avro or ORC formats for specific workloads. For example, you can use Avro to improve write performance for data ingestion and streaming use cases like daily purchase transactions, streaming sensor data, or collecting ad impressions. S3 Tables automatically compact small files into larger ones to minimize scanned data, improve query performance, and reduce costs. By default, compaction converts Avro and ORC files to Parquet for optimal read performance, but you can specify your preferred target format in your table properties.

Compaction support for Apache Avro and ORC formats is now available in all AWS Regions where S3 Tables or optimization with the AWS Glue Data Catalog are available. To learn more about S3 Tables compaction, see the S3 Tables maintenance documentation . For general purpose bucket optimization, see the AWS Glue Data Catalog optimization documentation .