Kategorie: Amazon AWS

Amazon EC2 X8aedz instances are now available in Asia Pacific (Mumbai, Seoul) regions

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) X8aedz instances are available in Asia Pacific (Mumbai) and Asia Pacific (Seoul) regions. These instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin). These instances offer the highest maximum CPU frequency, 5GHz in the cloud. X8aedz instances are built using the latest sixth generation AWS Nitro Cards and are ideal for electronic design automation (EDA) workloads such as physical layout and physical verification jobs, and relational databases that benefit from high single-threaded processor performance and a large memory footprint. The combination of 5 GHz processors and local NVMe storage enables faster processing of memory-intensive backend EDA workloads such as floor planning, logic placement, clock tree synthesis (CTS), routing, and power/signal integrity analysis. X8aedz instances feature a 32:1 ratio of memory to vCPU and are available in 8 sizes ranging from 2 to 96 vCPUs with 64 to 3,072 GiB of memory, including two bare metal variants, and up to 8 TB of local NVMe SSD storage. Customers can purchase X8aedz instances via Savings Plans, On-Demand instances, and Spot instances. To get started, sign in to the AWS Management Console. For more information visit the Amazon EC2 X8aedz instance page.
Quelle: aws.amazon.com

Amazon Neptune Database now supports R7g and R8g instances in 5 additional regions

Amazon Neptune Database now supports Graviton3-based R7g and Graviton4-based R8g instances for Amazon Neptune engine versions 1.4.5 or above, in Asia Pacific (Hong Kong), Asia Pacific (Osaka), Asia Pacific (Singapore), Canada (Central) and US West (N. California). R7g and R8g instances are priced -16% vs R6g. Graviton3-based R7g are the first AWS database instances to feature the latest DDR5 memory, enabling high-speed access to data in memory. R7g database instances offer up to 30Gbps enhanced networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). Graviton4-based R8g instances offer larger instance sizes, up to 48xlarge and features an 8:1 ratio of memory to vCPU, and the latest DDR5 memory. AWS Graviton4 processors are up to 40% faster for databases than AWS Graviton3 processors. You can launch R7g and R8g instances for Neptune using the AWS Management Console or using the AWS CLI. Upgrading a Neptune cluster to R7g or R8g instances requires a simple instance type modification for Neptune engine versions 1.4.5 or higher. For more information on pricing and regional availability, refer to the Amazon Neptune pricing page.
Quelle: aws.amazon.com

Amazon Lex launches configurable voice activity detection sensitivity

Amazon Lex now provides three VAD sensitivity levels that can be configured for each bot locale: Default, High, and Maximum. The Default setting is suitable for most environments with typical background noise levels. High is designed for environments with consistent but moderate noise levels, such as busy offices or retail spaces. Maximum provides the highest tolerance for very noisy environments such as manufacturing floors, construction sites, or outdoor locations with significant ambient noise. You can configure VAD sensitivity when creating or updating a bot locale in the Amazon Connect’s Conversational AI designer.
This feature is available in all AWS commercial regions where Amazon Connect and Lex operate. To learn more, visit the Amazon Lex documentation or explore the Amazon Connect website to learn how Amazon Connect and Amazon Lex deliver seamless end-customer self-service experiences.
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Amazon SageMaker HyperPod now validates service quotas before creating clusters on console

Amazon SageMaker HyperPod console now validates service quotas for your AWS account before initiating cluster creation, enabling you to confirm sufficient quota availability before provisioning begins. SageMaker HyperPod helps you provision resilient clusters for running AI/ML workloads and developing state-of-the-art models such as large language models (LLMs), diffusion models, and foundation models (FMs). When creating large-scale AI/ML clusters, you need to ensure your account has sufficient quotas for instances, storage, and networking resources, but quota validation previously required manual checks across multiple AWS services, often resulting in failed cluster creation attempts and wasted time if you miss requesting quota limit increases. The new quota validation capability in the SageMaker HyperPod console automatically checks your account-level quotas against your cluster configuration, including instance type limits, EBS volume sizes, and VPC-related quotas when creating new resources. The validation displays a clear table showing expected utilization, applied quota values, and compliance status for each quota. When quotas may be exceeded, you receive a warning alert with direct links to the Service Quotas console to request increases. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. For a complete list of service quota validation checks performed, refer to the Amazon SageMaker HyperPod User Guide.
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Amazon Inspector adds Java Gradle support and expands ecosystem coverage

Amazon Inspector scanning for Lambda functions and Elastic Container Registry (ECR) images now supports Java Gradle inventory and vulnerability scanning. This release also adds coverage for MySQL, MariaDB, PHP, Jenkins-core, 7zip (on Windows), Elasticsearch, and Curl/LibCurl. This update enhances Amazon Inspector’s ability to detect vulnerabilities and misconfigurations across a broader range of applications and environments. Amazon Inspector is an automated vulnerability management service that continually scans AWS workloads for software vulnerabilities and unintended network exposure, helping organizations improve their security posture and meet compliance requirements. The new Java Gradle support allows Inspector to scan Java dependencies based on gradle.lockfile content, providing comprehensive vulnerability assessments for Java applications. When you use Inspector to scan Lambda functions and ECR images, you will now see findings for MySQL, MariaDB, PHP, Jenkins-core, 7zip (on Windows), Elasticsearch, and Curl/LibCurl installations. These enhancements enable more accurate detection of vulnerabilities in packages installed outside of package managers, improving overall security coverage for AWS customers using these technologies. To learn more about Amazon Inspector and how it can help secure your AWS workloads, visit the Amazon Inspector page. For a full list of Amazon Inspector supported operating systems and programming languages, see the user guide. You can start using these new features today in all AWS Regions where Amazon Inspector is available.
Quelle: aws.amazon.com

Amazon Redshift Serverless is now available in the AWS Asia Pacific (New Zealand) region

Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (New Zealand) region. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications. With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, Apache Iceberg in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs. To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.
Quelle: aws.amazon.com

Amazon Connect now provides agent screen recording status tracking

Amazon Connect now offers customers the ability to view status of agent screen recordings in near real time in CloudWatch using Amazon EventBridge. With screen recording, supervisors can identify areas for agent coaching (e.g., non-compliance with business processes) by not only listening to customer calls or reviewing chat transcripts, but also watching agents’ actions while handling a contact (i.e., a voice call, chat and task). Using Amazon EventBridge, customers can see status of each agent screen recording including success/failure, failure codes with description, installed client version, agent web browser version, agent operating system, screen recording start and end times from CloudWatch. Customers can start using Amazon Connect screen recording status tracking by subscribing to Screen Recording Status Changed event type in Amazon EventBridge event bus. Screen recording status tracking is available in all the AWS Regions where Amazon Connect is already available. To learn more about screen recording, please visit the documentation and webpage. For information about screen recording pricing, visit the Amazon Connect pricing page.
Quelle: aws.amazon.com

Amazon EMR Serverless adds support for job run level cost allocation

Amazon EMR Serverless now supports job run-level cost allocation that provides better visibility into charges for individual job runs by allowing you to configure granular billing attribution at the individual job run level. You can get granular cost visibility by filtering and tracking costs in AWS Cost Explorer and Cost and Usage Reports by specific job run IDs and cost allocation tags associated with job runs. Amazon EMR Serverless is a deployment option in Amazon EMR that makes it simple for data engineers and data scientists to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. Previously, you could assign cost allocation tags to EMR Serverless applications, with cost attribution limited to the application level. With job run-level cost allocation, now you can assign cost allocation tags to each job run, enabling fine-grained billing attribution at the individual job run level. Cost allocation tags at the job run level also allow you to track costs by domains within a single application. For example, a single application could support jobs for finance and marketing domains, allowing you to track costs separately for each domain. Tracking costs for individual job runs makes it easier to conduct benchmarks that assess the costs of each job run as well as focus cost optimization efforts more precisely, allowing deeper insights into resource utilization and spending patterns across different jobs and domains. This feature is available in all AWS Regions where Amazon EMR Serverless is available including AWS GovCloud (US) and China regions. To learn more, see Enabling Job Level Cost Allocation in the Amazon EMR Serverless User Guide
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Announcing larger managed database bundles for Amazon Lightsail

Amazon Lightsail now offers two larger database bundles with up to 8 vCPUs, 32GB memory, and 960GB SSD storage. The new database bundles are available in both standard and high-availability plans. You can create MySQL and PostgreSQL databases using the new Lightsail managed database bundles. The new larger database bundles enable you to scale your database workloads and run more data-intensive applications in Lightsail. These higher-performance database bundles are ideal for production workloads that require increased storage capacity and processing power to handle growing datasets and concurrent connections. Using these new bundles, you can run e-commerce platforms, content management systems, business intelligence applications, SaaS products, and more. These new bundles are now available in all AWS Regions where Amazon Lightsail is available. For more information on pricing, or to get started with your free trial, click here.
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Amazon RDS for SQL Server now supports cross-region read replica in additional AWS Regions

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports setting up cross-region read replicas in 16 additional AWS Regions. Cross-region read replicas enable customers to provide a replica database for read-only applications closer to users in a different region, and scale out read-only workloads. Since a read replica can be “promoted” to a standalone production database, cross-region read replicas can also be used for disaster recovery in case of regional failures. Customers can setup up to fifteen read replicas in the same or different region as the primary database instance. This launch adds support for cross-region read replicas in RDS for SQL Server in the following AWS Regions: Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Malaysia), Asia Pacific (Melbourne), Asia Pacific (Taipei), Asia Pacific (Thailand), Canada West (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Israel (Tel Aviv), Mexico (Central), Middle East (Bahrain), and Middle East (UAE). To get started, visit the Amazon RDS SQL Server User Guide.
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