7/30/2025, 12:00:00 AM ~ 7/31/2025, 12:00:00 AM (UTC)
Recent Announcements
Amazon RDS for Oracle now supports M7i, R7i and X2idn instances in AWS GovCloud (US) Regions.
Amazon Relational Database Service (Amazon RDS) for Oracle has expanded its instance offerings in AWS GovCloud (US) Regions. The M7i and R7i instances, which support a maximum instance size of 48xlarge, are Intel-based offerings delivering 50% more vCPU and memory compared to M6i and R6i instance types. The X2idn instances are optimized for memory-intensive workloads with up to 2TiB of memory.\n M7i, R7i and X2idn instances are available for Amazon RDS for Oracle in Bring Your Own License model for both Oracle Database Enterprise Edition (EE) and Oracle Database Standard Edition 2 (SE2). You can launch the new instance in the Amazon RDS Management Console or using the AWS CLI or SDK. Refer to the Amazon RDS for Oracle Pricing page to see pricing details and regional availability.
Amazon Managed Service for Prometheus increases default active series limit to 50M per workspace
Amazon Managed Service for Prometheus now offers a higher default limit of 50M active time series per workspace, up from 10M. This increase eliminates the need for limit increase requests up to 50M series. Customers can still request limit increases for up to 1 billion active series per workspace. An Amazon Managed Service for Prometheus workspace is a logical space dedicated to the storage and querying of Prometheus metrics.\n The new limit increase is already applied to your current workspace, and is available in all AWS regions where Amazon Managed Service for Prometheus is generally available. Check out the Amazon Managed Service for Prometheus user guide for detailed documentation. To learn more about Amazon Managed Service for Prometheus, visit the product page and pricing page.
AWS Entity Resolution launches advanced matching using Levenshtein, Cosine, and Soundex
Today, AWS Entity Resolution announces advanced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help organizations resolve consumer records across fragmented, inconsistent, and often incomplete datasets. This feature introduces tolerance for variations and typos, enabling potentially more accurate and flexible entity resolution without requiring the manual pre-processing of records. Advanced rule-based fuzzy matching in AWS Entity Resolution helps customers improve match rates, enhance personalization, and unify consumer views, critical for effective cross-channel targeting, retargeting, and measurement.\n AWS Entity Resolution advanced rule-based fuzzy matching bridges the gap between traditional rule-based and machine learning-based matching techniques. Customers can use fuzzy algorithms to set similarity, distance, and phonetic thresholds on string fields to match records, offering the configurability of deterministic matching with the flexibility of probabilistic matching. This feature can be applied across multiple industries including advertising and marketing, retail and consumer goods, or financial services, where resolving consumer records are critical for verifying customers, fraud detection, or marketing purposes. AWS Entity Resolution helps organizations match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can get started in minutes using matching workflows that are flexible, scalable, and can seamlessly connect to your existing applications, without requiring any expertise in entity resolution or ML. AWS Entity Resolution is generally available in these AWS Regions. To learn more, visit AWS Entity Resolution.
Database Insights adds support for fleets of Aurora Limitless databases
CloudWatch Database Insights announces support of fleet monitoring for Amazon Aurora PostgreSQL Limitless databases. Database Insights is a database observability solution that provides a curated experience designed for DevOps engineers, application developers, and database administrators (DBAs) to expedite database troubleshooting and gain a holistic view into their database fleet health.\n Database Insights consolidates logs and metrics from your applications, your databases, and the operating systems on which they run into a unified view in the console. Using its pre-built dashboards, and automated telemetry collection, you can monitor fleet health across all your database types in one place, and drill down seamlessly from fleet overview to individual instance analysis. Database Insights offers two curated monitoring views: a fleet health dashboard for estate-wide visibility and an instance dashboard for detailed performance analysis. Aurora Limitless PostgreSQL databases were previously supported through instance-level monitoring — enabling you to track load distribution across shard groups. We’re now extending this capability to include fleet-level monitoring, which allows you to view the overall health of your entire database fleets, including Aurora clusters, RDS instances, and Aurora Limitless PostgreSQL databases, all from a single unified dashboard. You can get started with Database Insights for Aurora Limitless by enabling it on your Limitless databases using the Aurora service console, AWS APIs, and SDKs. Database Insights for Aurora Limitless is available in all regions where Aurora Limitless is available and applies a new ACU-based pricing – see pricing page for details. For further information, visit the Database Insights documentation.
Amazon CloudFront introduces new origin response timeout controls
Amazon CloudFront now offers two capabilities to enhance origin timeout controls: a response completion timeout and support for custom response timeout values for Amazon S3 origins. These enhancements provide more granular control over origin response timeouts, allowing you to deliver consistent and reliable user experiences regardless of variation in network conditions or origin performance.\n Previously, you could configure a response timeout to control the amount of time CloudFront waits for your origin to send the first packet, as well as the amount of time CloudFront waits for subsequent packets. If your origin times out, CloudFront resets the response timeout and tries again based on the configured number of retries. With the new response completion timeout, you can now additionally configure the maximum amount of time CloudFront should wait for a complete response from your origin across all packets and retries. This allows you to control the cumulative response time for latency sensitive workloads such as media streaming or API calls. When using Amazon S3 as your origin, you can now also set custom response timeout values instead of using the default value of 30 seconds. These capabilities provide you with more control over how CloudFront handles slow or unresponsive origins. CloudFront supports response completion timeout, and custom response timeout values for Amazon S3 origins, across all CloudFront edge locations excluding the AWS China (Beijing) region. You can configure origin timeouts using the CloudFront console, API, and AWS CloudFormation at no additional charge. To learn more, visit the CloudFront Developer Guide.
AWS announces 100G expansion in Chennai, India.
Today, AWS announced the expansion of 100 Gbps dedicated connections at the AWS Direct Connect location in the STT data center near Chennai, India. You can now establish private, direct network access to all public AWS Regions (except those in China), AWS GovCloud Regions, and AWS Local Zones from this location. This is the fourth AWS Direct Connect location in India to provide 100 Gbps connections with MACsec encryption capabilities.\n The Direct Connect service enables you to establish a private, physical network connection between AWS and your data center, office, or colocation environment. These private connections can provide a more consistent network experience than those made over the public internet. For more information on the over 142 Direct Connect locations worldwide, visit the locations section of the Direct Connect product detail pages. Or, visit our getting started page to learn more about how to purchase and deploy Direct Connect.
AWS Blogs
AWS Japan Blog (Japanese)
- Example of using AWS-generated AI by BTM Co., Ltd.: By automating system investigations using Amazon Bedrock and Investigative Agents, investigation time is drastically shortened and operational work efficiency is improved
- Accelerate AUTOSAR development for SDVs (software-defined vehicles) with Amazon Q Developer
- Improving Ramen Yamaoka Family’s Kitchen Operation Efficiency with AWS Serverless Service
AWS Japan Startup Blog (Japanese)
AWS Big Data Blog
- Optimize traffic costs of Amazon MSK consumers on Amazon EKS with rack awareness
- Automate data lineage in Amazon SageMaker using AWS Glue Crawlers supported data sources
AWS Database Blog
AWS for Industries
Artificial Intelligence
- Automate the creation of handout notes using Amazon Bedrock Data Automation
- Streamline GitHub workflows with generative AI using Amazon Bedrock and MCP