9/8/2026, 12:00:00 AM ~ 9/9/2026, 12:00:00 AM (UTC)
Recent Announcements
Amazon Timestream for InfluxDB 3 now supports custom plugins
Amazon Timestream for InfluxDB now lets you run your own custom Python plugins on the managed versions of InfluxDB 3 Core and Enterprise editions. You host your plugin code in public or private repositories you control and the engine fetches and runs it in response to triggers, letting you implement logic specific to your workload without standing up separate external infrastructure.\n Plugins run on the trigger types the processing engine already supports and with them, you can build custom data transformations, alerting, aggregation, and integrations with your own services, all running close to your data. Plugins execute in a managed Python environment that includes the standard library and Amazon-vetted packages , so you can move workload-specific processing into the database instead of operating a separate pipeline to do it.
To get started, set a plugin repository on a DB parameter group, apply that parameter group to your cluster, and create triggers that reference your plugin using the influxdb3 CLI or HTTP API; private repositories are authenticated with a token stored in AWS Secrets Manager. Custom plugins are available in all AWS Regions where Amazon Timestream for InfluxDB is available. To get started with Amazon Timestream for InfluxDB 3, visit the Amazon Timestream for InfluxDB console. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.
Amazon SageMaker Feature Store now supports individual feature updates to lower write latency
Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature Store now supports feature-level writes, a new capability for updating individual features in a record. Data scientists can now update one or more feature values in a single request, without rewriting the entire record.\n Data scientists can use a single update call to replace the read-modify-write pattern their pipelines run today. Each write updates only the features in the request and leaves every other feature in the record unchanged, which lowers write latency and cost. When multiple pipelines write to the same feature group, each pipeline updates only the features it computes, so a streaming job and a nightly batch job can update the same record independently. This capability enables data scientists to update a single feature at high processing volumes, without building merge logic in their data ingestion pipelines.
This capability is now available in all AWS Regions where Amazon SageMaker Feature Store is available. For more information, see Amazon Feature Store Runtime, Standard V2 documentation and launch blog.
AWS Transform is now available in AWS GovCloud (US-West)
AWS Transform is now available in the AWS GovCloud (US-West) Region, enabling government agencies and regulated organizations to plan and execute large-scale migrations to AWS. With this launch, customers operating in AWS GovCloud (US) can use the migration capabilities of AWS Transform to automate server migrations within an isolated environment designed to host sensitive data and regulated workloads. AWS Transform in this Region supports migrating servers to both AWS GovCloud (US-East) and AWS GovCloud (US-West) as target Regions.\n Organizations migrating VMware, bare metal, Hyper-V, or database workloads can use AWS Transform to automate server replication and cutover, reducing the manual effort and risk involved in large-scale moves. This is particularly valuable for federal agencies, defense contractors, and regulated industries that must operate within the AWS GovCloud (US) boundary. Modernization, custom transformation, and assessment capabilities are not included in this regional launch and remain available in supported commercial Regions.
To get started, see the AWS Transform documentation or visit the AWS Transform product page.
Starting today, Amazon Relational Database Service (Amazon RDS) for MariaDB now supports MariaDB minor versions 10.6.28, 10.11.19, 11.4.13, 11.8.9, and 12.3.3, the latest minors released by community MariaDB. In addition to operational improvements, these minor versions introduce support for post-quantum TLS (PQ-TLS) key exchange, providing you with post-quantum cryptography options for encrypting your data in-transit. We recommend upgrading to the newer minor versions to accept fixes for Common Vulnerabilities and Exposures (CVEs) in prior versions of MariaDB and to benefit from bug fixes, performance improvements, and new functionality added by the MariaDB community. Learn more about the enhancements in RDS for MariaDB in the RDS MariaDB release notes.\n You can upgrade your database using Amazon RDS Blue/Green Deployments, in-place upgrade, or restore from a snapshot. To simplify operations at scale, enable automatic minor version upgrades and use the AWS Organizations Upgrade Rollout Policy to orchestrate upgrades across your clusters in phases. Learn more about performing version upgrades in the Amazon RDS User Guide. You can also migrate to RDS for MariaDB from external MariaDB sources using AWS Database Migration Service. Learn more about pricing details and regional availability at Amazon RDS for MariaDB.
Amazon RDS for MariaDB makes it simple to set up, operate, and scale MariaDB deployments in the cloud. Create or update a fully managed Amazon RDS for MariaDB database in the Amazon RDS Management Console.
Amazon Bedrock AgentCore Memory now supports direct ingestion to long-term memory
Amazon Bedrock AgentCore Memory now lets developers submit content directly for long-term memory extraction without persisting it as a short-term memory event. The new IngestData API accepts content, fans it out to the memory’s configured long-term memory strategies, and makes the resulting memory records available through the same retrieval operations used for any other long-term memory records, all without creating a short-term event.\n Until now, all content had to be stored as a short-term memory event before extraction strategies could process it into long-term memory records. IngestData removes this requirement, enabling developers to adopt long-term memory independently of short-term memory. IngestData supports both conversational payloads (messages with USER/ASSISTANT roles) and JSON payloads (behavioral events, activity logs, system events), and accepts optional metadata that feeds the same extraction pipeline as CreateEvent. After processing, developers can verify extraction results with ListMemoryRecords or RetrieveMemoryRecords, stream real-time notifications via Kinesis, and redrive failed extractions with ListMemoryExtractionJobs. To get started, see Direct ingestion to long-term memory in the Amazon Bedrock AgentCore Developer Guide. IngestData is available in all AWS Regions where Amazon Bedrock AgentCore Memory is supported.
AWS HealthOmics introduces resource fallback order for WDL workflows
Today, AWS HealthOmics introduces the resource fallback directive, enabling you to define an ordered list of preferred accelerator types, including an option to fallback to CPU instances, for tasks in your Workflow Description Language (WDL) workflows. Researchers and bioinformaticians can use this directive to reduce time spent diagnosing and resubmitting runs due to accelerator constraints and keep production workflows running. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows. \n With resource fallback, you can prioritize your preferred accelerator for your task. When your preferred accelerator is unavailable, HealthOmics automatically moves through your specified alternatives in the fallback without resubmission. Each accelerator profile has a configurable timeout, giving you control over how long HealthOmics searches for that accelerator before moving to the next. Shorter timeouts help you move quickly through the fallback order, while longer timeouts increase the probability of reserving your preferred accelerator. You can also include a CPU profile as a final fallback to increase the likelihood of your task reserving an instance.
You can now use resource fallback for WDL workflows in all AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Seoul, Singapore, Tokyo). To learn more, visit the advanced resource configuration documentation.
Dynamic Image Transformation for Amazon CloudFront adds four new features
Today, AWS announced four new features for Dynamic Image Transformation for Amazon CloudFront (DIT). Customers can now use enhanced smart cropping with custom label detection and advanced composition controls that preserve products, text, logos, and custom objects within cropped images. In addition, customers benefit from enhanced automatic image optimization that delivers appropriately sized images across every browser and device type, from phones and tablets to smart TVs, using CloudFront’s multi-tier device detection to maximize optimization reach regardless of how users access content. DIT also introduces an interactive image transformation playground for testing and validating transformations, and achieves full feature parity between it’s ECS and Lambda architectures.\n DIT’s expanded smart cropping enables customers to combine multiple detection methods including faces, labels, text, logos, and custom Amazon Rekognition models, in a single request with configurable aspect ratios, padding, and gravity constraints prioritized by business need. Enhanced automatic optimization now uses a tiered detection approach, layering CloudFront’s device classification headers and configurable fallbacks behind Client Hints, to eliminate the browser-support gap that left ~30% of traffic previously served unoptimized and extend right-sized image delivery beyond browsers to all device types. The image transformation playground displays transformed images with extended metrics including original and output dimensions, format, file size, compression ratios, and processing time, enabling customers to validate the performance of their transformation policies.
AWS Builder ID adds recovery options and multi-factor authentication for third-party logins
AWS Builder ID, your personal profile for accessing AWS applications including AWS Builder Center, AWS Training and Certification, Amazon Quick and Kiro, now offers new ways to protect and recover your profile. You can add a recovery email and use new self-service options to regain access if you’re locked out. You can also register multi-factor authentication (MFA) devices for any sign-in method, including third-party logins such as Google or Apple.\n With these enhancements, you have more self-service options to recover your AWS Builder ID. Adding a recovery email provides a second verification factor that makes self-service recovery possible for more scenarios without the need to contact AWS Support. You can reset a forgotten password using a link sent to your primary or recovery email. If you lose access to your MFA device, you can regain access by verifying both your primary and recovery emails. If you use Google, Apple, GitHub or Amazon to sign in to Builder ID, you can now register MFA devices directly in AWS Builder ID, bringing the same strong account protection previously available to email and password users, and you can permanently switch your sign-in method to an email address and password if you lose access to the third-party account.
To learn more about AWS Builder ID and how to set up account recovery and MFA, visit the AWS Builder ID documentation.
AWS announces Nx Plugin for AWS for scaffolding full-stack applications
Version 1.0 of the Nx Plugin for AWS, an open source toolkit for scaffolding full-stack applications on AWS, is now available. AI assistants can stand up an application on AWS in minutes, but rarely get security, observability, and type-safety right in one pass. The plugin extends Nx, an open source, language-agnostic build system for monorepos, with generators that each build one part of an application on request, alongside the infrastructure to run it.\n Generators cover AI agents and Model Context Protocol servers on Amazon Bedrock AgentCore, plus APIs, websites, and databases, built on proven open source frameworks in TypeScript and Python. Each writes out a working, deployable piece of your application, defining infrastructure as either AWS Cloud Development Kit (AWS CDK) constructs or Terraform modules, with recommended practices in place such as AWS WAF protection, access logging to Amazon CloudWatch, and AWS X-Ray tracing. The connection generator wires projects together with type-safe clients, so a breaking API change becomes a build error instead of a failed request in production.
Each generator produces the same result every time, making it a dependable foundation for an AI assistant to build on. The generated code belongs to you, with no runtime dependency on the plugin, and Nx migrations bring later improvements to workspaces you have customized.
The plugin is available under the Apache 2.0 license at no additional charge; you pay only for the AWS resources your applications use. To learn more, see the launch blog post and documentation.
Amazon RDS now supports the latest CU and GDR updates for Microsoft SQL Server
Amazon Relational Database Service (Amazon RDS) for SQL Server now supports the latest Cumulative Updates (CU) and General Distribution Release (GDR) updates for Microsoft SQL Server. This release includes support for Microsoft SQL Server 2016 SP3+GDR KB5102340 (RDS version 13.00.6500.1.v1), SQL Server 2017 CU31+GDR KB5102337 (RDS version 14.00.3540.1.v1), SQL Server 2019 CU32+GDR KB5102335 (RDS version 15.09.4480.2.v1), SQL Server 2022 CU26 KB5093420 (RDS version 16.00.4265.3.v1) and SQL Server 2025 CU7 KB5096981 (RDS version 17.00.4065.4.v1).\n The GDR updates address vulnerabilities described in CVE-2026-47295, CVE-2026-47296, CVE-2026-54118, CVE-2026-55002. For additional information on the improvements and fixes included in these updates, see Microsoft documentation for KB5102340, KB5102337, KB5102335, KB5093420 and KB5096981. We recommend that you upgrade your Amazon RDS for SQL Server instances to apply these updates using Amazon RDS Management Console, or by using the AWS SDK or CLI. You can learn more about upgrading your database instance in the Amazon RDS SQL Server User Guide for upgrading your RDS Microsoft SQL Server DB engine.
Amazon S3 Object Lock now supports variable retention with event holds
Amazon S3 Object Lock now supports variable retention, allowing you to apply write-once-read-many (WORM) protection to objects whose required retention period starts with a future event, such as a contract closing or an audit completing. You place an event hold with a retention duration on an object and S3 protects the object while the hold is in place. When you release the hold, S3 retains the object for the duration you specified. Unlike legal holds, which end protection immediately upon removal, event holds provide WORM compliance for the required retention period after the triggering event, so you can meet event-based retention requirements without retaining data longer than your policy requires.\n You can apply event holds to individual objects, configure them as a bucket default, or apply them at scale with S3 Batch Operations. New AWS IAM and bucket policy condition keys let you control who can set or release holds and enforce minimum or maximum hold durations. AWS CloudTrail logs all hold operations and S3 Inventory reports hold status across your buckets. Event holds for S3 Object Lock are available in all AWS Regions, including China Regions, at no additional charge. To learn more, read the AWS Storage Blog post, the S3 Object Lock overview page, and the S3 documentation. This capability has been assessed by Cohasset Associates for use in environments subject to SEC Rule 17a-4(f), FINRA Rule 4511, and CFTC Regulation 1.31.
AWS Blogs
AWS Japan Blog (Japanese)
- AWS Weekly Roundup: AWS version Claude Fable 5.1, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, etc. (2026/9/7)
- Migrating from Kubernetes and Agones to Amazon GameLift Servers
AWS Japan Startup Blog (Japanese)
AWS Open Source Blog
AWS Big Data Blog
- AWS recognized as a Leader in the 2026 Gartner Magic Quadrant for Strategic Cloud Platform Services for the 16th consecutive year
- How Sony LIV built real-time video streaming analytics with AWS
AWS Compute Blog
AWS Database Blog
AWS Developer Tools Blog
Artificial Intelligence
- Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock
- Pathway’s brain-inspired architecture development on Amazon SageMaker HyperPod
- Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes
- Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2
- Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 1
- Automated agent evaluation with Amazon Bedrock AgentCore and GitHub Actions
- Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6
- How HPE Zerto built an agentic troubleshooting system with Amazon Bedrock
- How DiDi built intelligent contact center QA with Amazon Bedrock