9/26/2024, 12:00:00 AM ~ 9/27/2024, 12:00:00 AM (UTC)
Recent Announcements
Amazon MemoryDB is now available in the AWS Europe (Spain) region
Amazon MemoryDB is a fully managed, Redis OSS-compatible database for in-memory performance and multi-AZ durability. Customers in Europe (Spain) can now use MemoryDB as a primary database for use cases that require ultra-fast performance and durable storage, such as payment card analytics, message streaming between microservices, and IoT events processing. With Amazon MemoryDB, all of your data is stored in memory, which enables you to achieve microsecond read and single-digit millisecond write latency and high throughput. Amazon MemoryDB also stores data durably across multiple Availability Zones (AZs) using a Multi-AZ transactional log to enable fast failover, database recovery, and node restarts. Delivering both in-memory performance and Multi-AZ durability, Amazon MemoryDB can be used as a high-performance primary database for your microservices applications eliminating the need to separately manage both a cache and durable database.\n To get started, you can create an Amazon MemoryDB cluster in minutes through the AWS Management Console, AWS Command Line Interface (CLI), or AWS Software Development Kit (SDK). Read more about MemoryDB in this blog post, and visit the MemoryDB webpage for access to the latest webinars, tutorials, and demos. For pricing and regional availability, please refer to the Amazon MemoryDB pricing page.
Application Discovery Service Agentless Collector now supports Amazon Linux 2023
Today, we are excited to announce that the Application Discovery Service Agentless Collector now runs on Amazon Linux 2023 (AL2023). AL2023 offers long-term support with access to the latest Linux security updates.\n The Agentless Collector is deployed as a virtual appliance within an on-premises data center, allowing one install to monitor hundreds of servers. With the Agentless Collector, configure the discovery tool in a matter of minutes. The data can then be used in AWS Migration Hub to explore recommended Amazon EC2 instances or AWS Database Migration Service to explore recommended Amazon RDS instances. The Agentless Collector on AL2023 (version 2) is now generally available, and can be used in all AWS Regions where AWS Application Discovery Service is available. To learn more, visit our user guide.
Amazon RDS Performance Insights now supports queries run through Data API
Amazon RDS (Relational Database Service) Performance Insights now allows customers to monitor queries run through the RDS Data API for Aurora PostgreSQL clusters. The RDS Data API provides an HTTP endpoint to run SQL statements on an Amazon Aurora DB cluster.\n With this launch, customers are now able to use Performance Insights to monitor the impact of the queries run through the RDS Data API on their database performance. Additionally, customers can identify these queries and their related statistics by slicing the database load metric using the host name dimension, and filtering for ‘RDS Data API’. Amazon RDS Performance Insights is a database performance tuning and monitoring feature of RDS that allows you to visually assess the load on your database and determine when and where to take action. With one click in the Amazon RDS Management Console, you can add a fully-managed performance monitoring solution to your Amazon RDS database. To learn more about RDS Performance Insights, read the Amazon RDS User Guide and visit Performance Insights pricing for pricing details and region availability.
Amazon RDS for Oracle now supports Oracle Management Agent (OMA) version 13.5.0.0.v2 for Oracle Enterprise Manager (OEM) Cloud Control 13c Release 5. OEM 13c offers web-based tools to monitor and manage your Oracle databases. Amazon RDS for Oracle installs OMA, which communicates with your Oracle Management Service (OMS) to provide monitoring information. Customers running OMS version 13.5 update 23 can now manage databases by installing OMA 13.5.0.0.v2\n To enable the version 13.5.0.0.v2 of OMA for OEM 13cR5, navigate to “Option Groups” in the AWS Management Console and add the “OEM_AGENT” option to a new or existing option group and set AGENT_VERSION to “13.5.0.0.v2”. You will also need to configure option settings including OMS hostname (or IP), port and agent registration password to allow OMA on your Amazon RDS for Oracle database instances to communicate with your existing Oracle Management Service (OMS) stack. To learn more, please refer to Amazon RDS for Oracle documentation. Amazon RDS for Oracle makes it easy to set up, operate, and scale Oracle Database deployments in the cloud. See Amazon RDS for Oracle Database Pricing for regional availability.
AWS ParallelCluster 3.11 now available with login node enhancements
AWS ParallelCluster 3.11 is now generally available. Key features of this release include support for NICE DCV and custom action scripts on Login nodes. Use custom action scripts to automate the setup and configuration of Login Nodes to meet your specific organization’s needs such as installing additional software, configuring settings, or custom commands. Add custom action scripts by uploading them to an S3 bucket and specifying their paths in the ParallelCluster YAML configuration file. Other important features in this release include:\n
Support for pyxis and enroot for simplified container image management and the efficient execution of container-based HPC and ML/AI workloads using Slurm.
Support for multiple login node pools where the login nodes of each pool can be configured with a specific Amazon EC2 instance type to best fit their specific use case.
For more details on the release, review the AWS ParallelCluster 3.11.0 release notes. AWS ParallelCluster is a fully-supported and maintained open-source cluster management tool that enables R&D customers and their IT administrators to operate high-performance computing (HPC) clusters on AWS. AWS ParallelCluster is designed to automatically and securely provision cloud resources into elastically-scaling HPC clusters capable of running scientific, engineering, and machine-learning (ML/AI) workloads at scale on AWS. AWS ParallelCluster is available at no additional charge in the AWS Regions listed here, and you pay only for the AWS resources needed to run your applications. To learn more about launching HPC clusters on AWS, visit the AWS ParallelCluster User Guide. To start using ParallelCluster, see the installation instructions for ParallelCluster UI and CLI.
Amazon CloudWatch Natural Language Query Generation is now available in 7 additional regions
Amazon CloudWatch announces the general availability of natural language query generation powered by generative AI for Logs Insights and Metrics Insights in 7 additional regions including Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney) Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), US East (Ohio). This feature enables you to quickly generate queries in the context of your logs and metrics data using plain language so that you can accelerate gathering insights from your observability data without needing extensive knowledge of the query language.\n Query Generator simplifies your CloudWatch Logs and Metrics Insights experience through natural language querying. You can ask questions in plain English, such as “Show me the 10 slowest Lambda requests in the last 24 hours” and it will generate the appropriate query or refine any existing query in the query window. The generated queries automatically adjust the time ranges for queries that require data within a specified period. Query Generator is hosted in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Frankfurt) regions. For other supported regions, the feature makes cross-region calls to US regions to generate queries. To learn more, view documentation. To access the feature, click on “Query generator” in the CloudWatch Logs Insights or Metrics Insights console. For more information and examples, click “Info” in the help panel. There is no charge for using Query generator. Any queries executed in Logs Insights or Metrics Insights are subject to standard CloudWatch pricing. To learn more, visit our getting started guide.
AWS Lambda now supports SnapStart for Java functions in the AWS GovCloud (US) Regions
Starting today, AWS Lambda SnapStart for Java functions is generally available in the AWS GovCloud (US) Regions. AWS Lambda SnapStart for Java delivers up to 10x faster function startup performance at no extra cost, making it easier for you to build highly responsive and scalable Java applications using AWS Lambda without having to provision resources or spend time and effort implementing complex performance optimizations.\n For latency sensitive applications where you want to support unpredictable bursts of traffic, high and outlier startup latencies—known as cold starts—can cause delays in your users’ experience. Lambda SnapStart offers improved startup times by initializing the function’s code ahead of time, taking a snapshot of the initialized execution environment, and caching it. When the function is invoked and subsequently scales up, Lambda SnapStart resumes new execution environments from the cached snapshot instead of initializing them from scratch, significantly improving startup latency. Lambda SnapStart is ideal for applications such as synchronous APIs, interactive microservices, or data processing. You can activate SnapStart for new or existing Java-based Lambda functions running on Amazon Corretto 11, 17, and 21 using the AWS Lambda API, AWS Management Console, AWS Command Line Interface (AWS CLI), AWS CloudFormation, AWS Serverless Application Model (AWS SAM), AWS SDK, and AWS Cloud Development Kit (AWS CDK). For more information on Lambda SnapStart, see the documentation and the launch blog post. To learn more about Lambda, see the Lambda developer guide.
PostgreSQL 17.0 is now available in Amazon RDS Database preview environment
Amazon RDS for PostgreSQL 17.0 is now available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 17 on Amazon RDS for PostgreSQL. You can deploy PostgreSQL 17.0 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database.\n PostgreSQL 17 includes updates to vacuuming that reduces memory usage, improves time to finish vacuuming, and shows progress of vacuuming indexes. With PostgreSQL 17, you no longer need to drop logical replication slots when performing a major version upgrade. PostgreSQL 17 continues to build on the SQL/JSON standard, adding support for
JSON_TABLEfeatures that can convert JSON to a standard PostgreSQL table. TheMERGEcommand now supports theRETURNINGclause, letting you further work with modified rows. PostgreSQL 17 also includes general improvements to query performance and adds more flexibility to partition management with the ability to SPLIT/MERGE partitions. Please refer to the PostgreSQL community announcement for more details. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the Preview Environment. You can use the PostgreSQL dump and load functionality to import or export your databases from the Preview Environment. Amazon RDS Database Preview Environment database instances are priced as per the pricing in the US East (Ohio) Region.
Amazon Aurora MySQL now supports RDS Data API
Amazon Aurora MySQL-Compatible Edition now supports a redesigned RDS Data API for Aurora Serverless v2 and Aurora provisioned database instances. You can now access these Aurora clusters via a secure HTTP endpoint and run SQL statements without the use of database drivers and without managing connections. This follows the launch of Data API for Amazon Aurora PostgreSQL-Compatible Edition for Aurora Serverless v2 and Aurora provisioned database instances last year.\n Data API was originally only available for single instance Aurora Serverless v1 clusters with a 1,000 request per second (RPS) rate limit. Based on customer feedback, Data API has now been redesigned for increased scalability. Data API will not impose a rate limit on requests made to Aurora Serverless v2 and Aurora provisioned clusters. Data API eliminates the use of drivers and improves application scalability by automatically pooling and sharing database connections (connection pooling) rather than requiring customers to manage connections. Customers can call Data API via AWS SDK and CLI. Data API also enables access to Aurora databases via AWS AppSync GraphQL APIs. API commands supported in the redesigned Data API are backwards compatible with Data API for Aurora Serverless v1 for easy customer application migrations. Data API supports Aurora MySQL 3.07 and higher versions in 14 regions. Customers currently using Data API for Aurora Serverless v1 are encouraged to migrate to Aurora Serverless v2 to take advantage of the redesigned Data API. To learn more, read the documentation.
Amazon MWAA now supports Apache Airflow version 2.10
You can now create Apache Airflow version 2.10 environments on Amazon Managed Workflows for Apache Airflow (MWAA). Apache Airflow 2.10 is the latest minor release of the popular open-source tool that helps customers author, schedule, and monitor workflows.\n Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines in the cloud. Apache Airflow 2.10 introduces several notable enhancements, such as a new Dark Mode for improved user experience, especially in low-light environments; dynamic dataset scheduling for flexible workflow management; and new task-level metrics for enhanced visibility into resource utilization. This update also includes important security updates and bug fixes that enhance the security and reliability of your workflows. You can launch a new Apache Airflow 2.10 environment on Amazon MWAA with just a few clicks in the AWS Management Console in all currently supported Amazon MWAA regions. To learn more about Apache Airflow 2.10 visit the Amazon MWAA documentation, and the Apache Airflow 2.10 change log in the Apache Airflow documentation.
Amazon EKS and Amazon EKS Distro now supports Kubernetes version 1.31
Kubernetes version 1.31 introduced several new features and bug fixes, and AWS is excited to announce that you can now use Amazon Elastic Kubernetes Service (EKS) and Amazon EKS Distro to run Kubernetes version 1.31. Starting today, you can create new EKS clusters using version 1.31 and upgrade existing clusters to version 1.31 using the EKS console, the eksctl command line interface, or through an infrastructure-as-code tool.\n Kubernetes version 1.31 introduces several key improvements, including stable support for AppArmor security modules, storing timestamps for persistent volume phase transitions, and the beta VolumeAttributeClass API for modifying mutable properties of persistent volumes managed by compatible drivers like Amazon EBS CSI driver starting v1.35.0. To learn more about the changes in Kubernetes version 1.31, see our documentation and the Kubernetes project release notes. EKS now supports Kubernetes version 1.31 in all the AWS Regions where EKS is available, including the AWS GovCloud (US) Regions. You can learn more about the Kubernetes versions available on EKS and instructions to update your cluster to version 1.31 by visiting EKS documentation. EKS Distro builds of Kubernetes version 1.31 are available through ECR Public Gallery and GitHub. Learn more about the EKS version lifecycle policies in the documentation.
AWS Blogs
AWS Architecture Blog
AWS Cloud Operations Blog
- Accelerating migrations and IT Tasks for DKB using AWS Systems Manager
- Centrally detect and investigate security findings with AWS Organizations integrations
AWS Big Data Blog
- Apply enterprise data governance and management using AWS Lake Formation and AWS IAM Identity Center
- Enrich your serverless data lake with Amazon Bedrock
AWS Database Blog
AWS HPC Blog
The Internet of Things on AWS – Official Blog
AWS Machine Learning Blog
- GenAI for Aerospace: Empowering the workforce with expert knowledge on Amazon Q and Amazon Bedrock
- Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study
- Control data access to Amazon S3 from Amazon SageMaker Studio with Amazon S3 Access Grants