5/28/2024, 12:00:00 AM ~ 5/29/2024, 12:00:00 AM (UTC)
Recent Announcements
Amazon DynamoDB now supports resource-based policies in the AWS GovCloud (US) Regions
Amazon DynamoDB now supports resource-based policies in the AWS GovCloud (US) Regions. Resource-based policies help you simplify access control for your DynamoDB resources. With resource-based policies, you can specify the Identity and Access Management (IAM) principals that have access to a resource and what actions they can perform on it. You can attach a resource-based policy to a DynamoDB table or a stream. The resource-based policy that you attach to a table can include access permissions to its indexes. The resource-based policy that you attach to a stream can include access permissions to the stream. With resource-based policies, you can also simplify cross-account access control for sharing resources with IAM principals of different AWS accounts.\n Resource-based policies support integrations with IAM Access Analyzer and Block Public Access (BPA) capabilities. IAM Access Analyzer reports cross-account access to external entities specified in resource-based policies, and the findings provide visibility to help you refine permissions and conform to least privilege. BPA helps you prevent public access to your DynamoDB tables, indexes, and streams, and is automatically enabled in the resource-based policies creation and modification workflows.
Amazon Redshift Serverless is now generally available in the AWS China (Ningxia) 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 China (Ningxia) region. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists can now 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.\n With a few clicks in the AWS Management Console, you can get started 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 Simple Storage Service (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, in Amazon S3 data lakes, as well as data in your operational databases, such as Amazon Aurora.
Amazon CloudWatch now offers 30 days of alarm history
Amazon CloudWatch extended the duration during which customers can access their alarm history. Now, customers can view the history of their alarm state changes for up to 30 days prior.\n Previously, CloudWatch provided 2 weeks of alarm history. Customers rely on alarm history to review previous triggering events, alarming trends, and noisiness. This extended history makes it easier to observe past behavior and review incidents over a longer period of time.
New Oracle to PostgreSQL built-in system functions in DMS Schema Conversion
DMS Schema Conversion has released five generative artificial intelligence (AI)-assisted built-in functions to improve Oracle to PostgreSQL conversions. This launch marked the first ever gen AI-assisted conversion improvement in DMS Schema Conversion. \n Customers can use these functions by applying the DMS Schema Conversion extension pack. The extension pack is an add-on module that emulates source database functions that aren’t supported in the target database and can streamline the conversion step.
DMS Schema Conversion is generally available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Europe (Stockholm), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Singapore).
To learn more, visit Converting database schemas using DMS Schema Conversion. For more details on how to apply extension pack, go to Using extension packs in DMS Schema Conversion.
Amazon DynamoDB local supports configurable maximum throughput for on-demand tables
Amazon DynamoDB local now supports configurable maximum throughput for individual on-demand tables and associated secondary indexes. Customers can use the configurable maximum throughput for on-demand tables feature for predictable cost management, protection against accidental surge in consumed resources and excessive use, and safe guarding downstream services with fixed capacities from potential overloading and performance bottlenecks. With DynamoDB local, you can develop and test your application with managing maximum on-demand table throughput, making it easier to validate the use of the supported API actions before releasing code to production.\n DynamoDB local is free to download and available for macOS, Linux, and Windows. DynamoDB local does not require an internet connection and it works with your existing DynamoDB API calls. To get started with the latest version see “Deploying DynamoDB locally on your computer”. To learn more, see Setting Up DynamoDB Local (Downloadable Version).
AWS Network Firewall increases quota for stateful rules
The AWS Network Firewall service quota limit for stateful rules is now adjustable. The default limit is still 30,000 stateful rules per firewall policy in a Region, but you can request an increase up to 50,000. This firewall rule limit increase helps customers strengthen their security posture on AWS and mitigate emerging threats more effectively.\n A higher rule limit provides flexibility to customers with large-scale deployments to define their firewall policy with different combinations of AWS managed and customer defined rules. Starting today, you can implement a broader range of rules to defend against various threats and scale as you grow on AWS.
AWS Blogs
AWS Japan Blog (Japanese)
- Connect and authenticate automotive iOS apps to AWS IoT Core
- Dai-ichi Kosho’s AWS-Generated AI Case Study: Help Desk Work Reduction Verification Using Amazon Bedrock
- Establishing an AI/ML CoE (Center of Excellence)
- AWS Gateway Load Balancer Deployment Best Practices
- Get basic cloud knowledge through AWS Certification
AWS News Blog
AWS Cloud Operations & Migrations Blog
AWS Big Data Blog
- Introducing Amazon EMR on EKS with Apache Flink: A scalable, reliable, and efficient data processing platform
- Build a decentralized semantic search engine on heterogeneous data stores using autonomous agents
- Architectural Patterns for real-time analytics using Amazon Kinesis Data Streams, Part 2: AI Applications
AWS Database Blog
- Use AWS DMS to migrate data from IBM Db2 DPF to an AWS target
- Create a fallback migration plan for your self-managed MySQL database to Amazon Aurora MySQL using native bi-directional binary log replication
AWS HPC Blog
The Internet of Things on AWS – Official Blog
AWS for M&E Blog
AWS Messaging & Targeting Blog
Networking & Content Delivery
AWS Security Blog
- Accelerate incident response with Amazon Security Lake
- Navigating the threat detection and incident response track at re:Inforce 2024
AWS Storage Blog
- How Toyota Connected North America reduced storage costs by optimizing its growing data on Amazon S3