Aws Cli

10 posts

aws2 min readCurated summary

Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode | Amazon Web Services

AWS CloudFormation Express mode speeds deployments by marking them complete once resource configuration is applied, rather than waiting for full stabilization checks. AWS says this can reduce deployment times by up to four times, while resources continue becoming operational in the background. It is intended for rapid infrastructure iteration and scenarios where eventual stabilization is acceptable, not workflows requiring resources to be fully ready before proceeding. ## How Express Mode Works - Standard CloudFormation deployments wait for post-configuration stabilization checks. - Express mode completes earlier, immediately after configuration is applied. - Resources continue stabilizing asynchronously. - CloudFormation retries dependent resources that encounter transient provisioning failures. - The provisioning process itself is unchanged; only the point at which deployment completion is reported changes. ## Performance Improvements - Creating an SQS queue with a dead-letter queue took: - Standard mode: 64 seconds - Express mode: up to 10 seconds - Deleting a Lambda function with attached network interfaces took: - Standard mode: 20–30 minutes - Express mode: up to 10 seconds in AWS’s benchmark ## Best Use Cases - Iteratively building infrastructure one component at a time. - Testing individual application components. - AI-assisted infrastructure development requiring sub-minute feedback. - Production workflows that can tolerate resources stabilizing after deployment completion. ## Enabling Express Mode - In the AWS Console, select **Enable** under stack deployment options. - With the CLI or SDKs, set the deployment configuration mode to `EXPRESS`: ```bash aws cloudformation create-stack \ --stack-name my-app \ --template-body file://template.yaml \ --deployment-config '{"mode": "EXPRESS", "disableRollback": true}' ``` - AWS CDK supports: ```bash cdk deploy --express ``` - No CloudFormation template changes are required. - Express mode supports existing templates, change sets, nested stacks, and IaC or AI tools such as Kiro. - Enabling it on a parent stack also applies it to nested stacks. ## Rollback and Operational Considerations - Rollback is disabled by default in Express mode to maximize iteration speed. - For production use, rollback can be restored with `"disableRollback": false`. - Teams should otherwise provide monitoring and cleanup procedures for failed deployments. - IAM templates should continue following least-privilege principles. ## Availability - Express mode is available at no additional cost in all AWS commercial Regions. - AWS recommends standard deployment behavior when resources must be fully operational before traffic shifting or testing. For fast development and AI-driven infrastructure iteration, Express mode is a useful optimization. Use it selectively, while retaining standard mode—or explicitly enabling rollback—when deployment readiness and failure recovery are critical.

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Amazon ECS introduces new high-resolution metrics for faster service auto scaling | Amazon Web Services

Amazon ECS now supports 20-second high-resolution metrics for faster service auto scaling. AWS reports that scale-out triggers improved from 363 to 86 seconds, while total scaling and task provisioning dropped from 386 to 109 seconds. The update helps applications handle traffic spikes more reliably, reduce excess baseline capacity, and achieve aggressive scaling with simpler target-tracking policies. ## Faster Scaling with High-Resolution Metrics - ECS service auto scaling can use predictive, scheduled, or reactive target-tracking policies. - New 20-second metrics allow ECS to detect workload changes faster than standard 60-second metrics. - AWS benchmarking showed: - 76% faster scale-out triggering, or 4.2× improvement. - 72% faster total scaling and task provisioning, or 3.5× improvement. - Faster scaling can reduce latency and failures during sudden demand increases. ## Lower Costs and Simpler Configuration - Applications may reduce baseline task counts because capacity can be added quickly during spikes. - This can lower compute costs without sacrificing availability. - High-resolution target tracking can provide aggressive scaling behavior that previously required custom step-scaling policies. ## Configuration and Supported Metrics - Enable high-resolution metrics for the ECS service, then configure a target-tracking scaling policy. - New options include: - `ECSServiceAverageCPUUtilizationHighResolution` - `ECSServiceAverageMemoryUtilizationHighResolution` - The feature works with AWS Fargate, ECS Managed Instances, and Amazon EC2. - It can be configured through the ECS console, AWS SDKs, AWS CLI, Application Auto Scaling, or CloudFormation. - Existing services must first enable high-resolution metrics and complete deployment before their scaling policy can be updated. ## Cost and Availability - The ECS feature itself has no additional charge. - High-resolution CloudWatch metrics incur additional CloudWatch costs, unlike standard 60-second metrics. - The capability is available now. For workloads with unpredictable or rapid traffic changes, enabling high-resolution metrics with target tracking can improve responsiveness while reducing the need for overprovisioned ECS tasks.

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Amazon S3 annotations: attach rich, queryable context directly to your objects | Amazon Web Services

Amazon S3 annotations let organizations attach rich, mutable context directly to objects without rewriting the underlying data. Each object can have up to 1,000 named annotations, with a combined limit of 1 GB, in formats such as JSON, XML, YAML, or plain text. The annotations move with objects during copying and replication, can be queried through S3 Metadata tables, and are intended to support large-scale analytics and AI-driven workflows. ## Richer Metadata at Object Scale - Annotations address limitations of existing S3 metadata features: - System metadata describes properties such as size and storage class. - Object tags support operational tasks but are limited to 10 immutable tags. - User-defined metadata is limited to approximately 2 KB of headers and must be supplied during upload. - Annotations can be modified or deleted independently without rewriting the object. - Each annotation has a unique name and can hold up to 1 MB, with up to 1 GB total per object. - Supported content includes structured formats such as JSON, XML, and YAML, as well as plain text. ## Supporting AI and Industry Workflows - AI-generated transcripts, summaries, classifications, ratings, and technical specifications can remain attached to the relevant data. - Media companies can store transcripts, moderation results, subtitles, licensing information, and media specifications alongside video assets. - Financial institutions can attach investment summaries and sentiment analysis to research documents for autonomous discovery. - Life sciences organizations can add regulatory status, cohort information, and approval chains to clinical-trial data. - Annotations remain available for objects in archival storage classes without restoring the objects or paying retrieval charges. ## Object Lifecycle and Synchronization - Annotations automatically move with objects during copy, replication, and cross-Region transfers. - S3 removes annotations when the associated object is deleted. - This reduces dependence on separate metadata databases or sidecar files and avoids synchronization overhead. - Different teams or enrichment processes can maintain separate annotations on the same object without overwriting one another. ## Working with Annotations - IAM or bucket policies must grant: - `s3:PutObjectAnnotation` - `s3:GetObjectAnnotation` - The main APIs are: - `PutObjectAnnotation` to create or update an annotation - `GetObjectAnnotation` to retrieve one annotation - `ListObjectAnnotations` to view all annotations on an object - `DeleteObjectAnnotation` to remove an annotation - Reusing an annotation name with `PutObjectAnnotation` updates its content. - For multipart uploads, annotations are added after the upload is completed. - The example uses separate `mediainfo` JSON and `ai_summary` text annotations on a video object. ## Querying Through S3 Metadata - When S3 Metadata is enabled, annotations automatically populate managed annotation tables. - These tables can be queried with Amazon Athena and other analytics engines. - The S3 Tables MCP server provides a standardized interface for AI models and agents to query annotation data using natural-language workflows. - This enables discovery across large collections of S3 objects without retrieving the objects themselves. S3 annotations are most useful when object context is large, frequently updated, and needed for cross-object search or AI automation. Organizations should use them to keep business and machine-generated metadata directly associated with the data while using S3 Metadata tables for scalable querying.

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AWS Weekly Roundup: AWS FinOps Agent in preview, Gemma 4 on Bedrock, Kiro Pro Max, and more (June 15, 2026) | Amazon Web Services

AWS’s June 15, 2026 roundup highlights rapid adoption of AI-native development, a preview of the AWS FinOps Agent, and major infrastructure and model releases. AWS reports significant productivity gains from agent-assisted engineering, while new services target cost optimization, observability, security, and developer workflows. The overall direction is toward more autonomous, context-aware tools integrated throughout the AWS ecosystem. ## AI-Native Development at AWS - Amazon engineering teams are restructuring development around AI agents. - A six-engineer team rebuilt the Amazon Bedrock inference engine in 76 days, compared with an original estimate of 30 developers over 12–18 months. - Structured Amazon Stores pilots achieved a median 4.5× improvement in normalized deployment velocity, with some teams exceeding 10×. - Recommended practices for “frontier teams” include: - Building steering files, coding standards, and structured repositories to provide agent context. - Expecting an initial productivity slowdown while workflows change. - Maintaining a backlog of well-scoped tasks that agents can execute in parallel. - Writing structured specifications before generating code. - Moving testing earlier so agents can detect and correct issues. - AWS cautions that commit velocity is only one measure; future work will address release management, operations, security, and end-of-life upgrades. ## AWS FinOps Agent Preview - AWS FinOps Agent helps finance and engineering teams: - Answer AWS cost questions and generate reports. - Identify rightsizing, idle-resource, and Savings Plans opportunities. - Investigate cost anomalies automatically. - Schedule recurring FinOps workflows. - Recommendations come from AWS Cost Optimization Hub and AWS Compute Optimizer. - The agent can open Jira tickets and post anomaly-investigation findings to Slack. ## New EC2 M9g and M9gd Instances - M9g and M9gd instances use AWS Graviton5 processors and the sixth-generation Nitro System. - They provide: - Up to 25% better compute performance than Graviton4. - Up to 35% faster web applications and ML inference. - Up to 30% faster database performance. - Up to 15% more network bandwidth and 20% more EBS bandwidth than M8g. - Graviton5 adds PCIe Gen6, DDR5-8800 memory, and a 5× larger L3 cache. - The Nitro Isolation Engine uses formal verification to provide mathematically proven VM isolation. - M9gd adds up to 11.4 TB of local NVMe SSD storage and 30% higher IOPS than M8gd. - Instance Bandwidth Configuration can shift bandwidth between EBS and VPC networking by up to 25%. ## Bedrock Model Updates - Anthropic Claude Fable 5 launched on Bedrock with: - Extended asynchronous task execution. - Advanced vision for diagrams, charts, and PDFs. - Proactive self-verification. - Access required Data Retention API opt-in and 30-day retention for inputs and outputs. - Anthropic later asked AWS to revoke access to Claude Fable 5 and Claude Mythos 5 in response to a U.S. government export-control directive. - Google DeepMind’s Gemma 4 models are available in three versions: - Gemma 4 31B for reasoning and coding, with a 256K-token context window. - Gemma 4 26B-A4B, a mixture-of-experts model for cost- and latency-sensitive use. - Gemma 4 E2B for low-latency interactive applications. - All variants support function calling, structured output, reasoning, streaming, multimodal inputs, and more than 35 languages. ## OpenSearch MCP Apps for Agentic Observability - OpenSearch MCP Apps bring logs, traces, metrics, and alerts into compatible agentic environments such as Claude Desktop and VS Code. - Agents can investigate incidents across OpenSearch and Amazon Managed Service for Prometheus. - Each tool call produces both: - A text summary for the agent. - An interactive visualization displayed in the conversation. - Available tools cover log, metric, and trace analysis; service performance; topology; agent health; cluster health; dynamic visualizations; and instrumentation scoring. ## Additional AWS Updates - AWS CLI v1 is entering maintenance mode: - Dependencies such as `botocore` and `s3transfer` will be vendored into the CLI. - Future releases will focus on critical bugs and security fixes. - AWS recommends migrating to CLI v2. - AWS Workload Credentials Provider provides short-term credentials for applications running outside AWS, including on-premises and third-party environments, without requiring long-term access keys. - Kiro Pro Max adds higher usage limits, access to newer frontier models, and additional agentic development capabilities for professional teams. AWS’s latest releases suggest that teams should prepare for AI agents to handle not only code generation, but also cost management, observability, testing, and operational workflows. Migrating to AWS CLI v2, evaluating Bedrock’s newest models, and establishing strong repository context and specifications are practical next steps.

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Anthropic Claude Fable 5 on AWS: Mythos-class capabilities with built-in safeguards now available | Amazon Web Services

Claude Fable 5 is now available through Amazon Bedrock and Claude Platform on AWS, offering Mythos-level performance with safeguards for broader access. Anthropic highlights its ability to perform long-running tasks, analyze complex visual documents, and verify or improve its own work. Access requires specific data-sharing consent, and higher-risk requests may be routed to Claude Opus 4.8. ## Capabilities and Safeguards - Supports extended, asynchronous coding and knowledge-work tasks with minimal intervention. - Interprets diagrams, charts, tables, files, and PDFs for research, finance, legal, analytics, architecture, gaming, and software development. - Uses vision to compare implemented designs with intended goals. - Can update skills, create evaluation harnesses, and perform proactive self-verification. - Cybersecurity, biology, chemistry, and health prompts with elevated misuse risk may be handled by Opus 4.8 instead. - The unrestricted Claude Mythos 5 is limited to a small group of vetted customers. ## Accessing Fable 5 on Amazon Bedrock - Available through: - Anthropic’s Messages API using `bedrock-mantle` or `bedrock-runtime`. - AWS Invoke and Converse APIs through `bedrock-runtime`. - The Amazon Bedrock console Playground. - Model access is being expanded gradually across AWS accounts; customers can contact AWS Support for expedited access. ## Required Data Sharing - Users must opt into data sharing through the Data Retention API by setting `provider_data_share`. - No console interface is available for this setting at launch. - Anthropic requires: - 30-day retention of inputs and outputs. - Human review. - Data retention enables abuse detection across multiple interactions rather than isolated requests. - Example endpoints are provided for both `bedrock-mantle` and `bedrock-runtime`. ## SDK and API Usage - Install the Anthropic Python SDK with `pip install anthropic`. - The Messages API can be called through the Bedrock Mantle endpoint using model ID `anthropic.claude-fable-5`. - Boto3’s Converse API supports unified multi-model access through model ID `global.anthropic.claude-fable-5`. - Users can configure token limits and submit tasks such as designing a multi-region AWS architecture supporting 100,000 requests per second. ## Pricing and Routing - Requests routed to Opus 4.8 because of harmful content are charged at Opus rates. - If a conversation is blocked mid-request, initial tokens are charged at Fable rates and later tokens at Opus rates. - Pricing details are available on the Amazon Bedrock pricing page. Claude Fable 5 is best suited to ambitious, long-running workloads that benefit from advanced reasoning and document or visual understanding. Before using it, organizations should confirm account access, configure the required data-sharing settings, and evaluate whether the 30-day retention and human-review requirements fit their compliance policies.

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Introducing the next generation of Amazon OpenSearch Serverless for building your agentic AI applications | Amazon Web Services

Amazon’s next-generation OpenSearch Serverless is a managed search and vector engine optimized for agentic AI applications. It scales from zero to thousands of requests per second, creates resources in seconds, and can reduce costs by up to 60% compared with clusters provisioned for peak demand. The release is generally available across supported AWS commercial Regions and integrates with tools such as Vercel, Kiro, Claude Code, and Cursor. ## Elastic Scaling and Cost Optimization - Scales capacity up to 20 times faster than the previous generation. - Supports scale-to-zero when idle, minimizing compute costs. - Charges separately for compute through OpenSearch Compute Units (OCUs), storage in GB-month, and GPU acceleration where applicable. - Supports capacity limits for indexing and search, with minimum capacity set to zero and configurable maximums. ## Creating Next-Generation Collections - Collections can be created through the Amazon OpenSearch Service console, AWS CLI, or SDKs. - The console’s **Express create** option automatically applies default settings and matching security policies. - At launch, supported collection types are: - Full-text search (`SEARCH`) - Vector search (`VECTORSEARCH`) - Users who need the existing infrastructure can select the classic OpenSearch Serverless generation. - Collections inherit their generation from a parent collection group. Example CLI workflows create a next-generation collection group with standby replicas and then create a search collection within it. ## Integrations for Agent Development - Vercel users can create or connect OpenSearch Serverless collections directly from the Vercel console. - OpenSearch Agent Skills bring search-specific knowledge, best practices, and multi-step workflows into agents using Claude Code, Cursor, and Kiro. - Kiro’s OpenSearch Launchpad provides guided architecture planning for building search applications. ## Availability - The next generation is generally available in all AWS commercial Regions where OpenSearch Serverless is currently offered. - AWS recommends consulting the OpenSearch Serverless documentation and pricing information for configuration and cost details. The release is intended to let developers deploy production-ready search and vector backends quickly, while avoiding the infrastructure management and peak-capacity costs associated with provisioned OpenSearch clusters.

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Launching S3 Files, making S3 buckets accessible as file systems | Amazon Web Services

Amazon S3 Files makes general-purpose S3 buckets accessible through a native NFS-based file system. It combines S3’s durability, cost, and broad service integration with interactive file operations, shared access, and low-latency performance. The post concludes that this reduces the need to choose between object storage and traditional file systems for many AWS workloads. ## Bridging Object Storage and File Systems - S3 Files presents S3 objects as files and directories. - Applications can use standard NFS v4.1+ operations, including creating, reading, updating, and deleting files. - Changes made through the file system are synchronized back to S3 as new objects or object versions. - Changes made directly in S3 generally appear in the file system within seconds, though synchronization can sometimes take longer. - Multiple compute resources can mount the same file system and share data without duplicating it. ## Performance and Data Access - S3 Files uses Amazon EFS underneath and provides approximately 1 ms latency for active data. - Frequently accessed metadata and file contents are placed on high-performance storage. - Large sequential reads can be served directly from S3 to maximize throughput. - Byte-range reads transfer only the requested portion of a file, reducing data movement and cost. - Intelligent prefetching anticipates access patterns. - Administrators can choose whether to cache complete files or metadata only. - NFS close-to-open consistency supports concurrent, interactive workloads such as ML pipelines and collaborative AI agents. ## Supported AWS Compute Services S3 Files can expose buckets to: - Amazon EC2 instances - Amazon ECS and EKS containers - AWS Fargate workloads - AWS Lambda functions This allows production applications, machine-learning systems, and agentic AI tools to access shared S3 data using ordinary file-system interfaces. ## Creating and Mounting an S3 File System The demonstration uses an EC2 instance and a general-purpose S3 bucket: - Create an S3 file system from the S3 console, AWS CLI, or infrastructure-as-code tools. - Configure or discover a mount target inside the relevant VPC. - Mount the file system on EC2 with commands such as: ```bash sudo mkdir /home/ec2-user/s3files sudo mount -t s3files fs-...:/ /home/ec2-user/s3files ``` - Files created in the mounted directory become visible in the S3 bucket after synchronization. - Standard commands such as `ls`, `echo`, and AWS CLI operations can verify that file contents are consistent between the mount and S3. ## Security, Permissions, and Monitoring - IAM identity and resource policies control access at both the file-system and object levels. - Data is encrypted in transit with TLS 1.3. - Data at rest uses SSE-S3 or customer-managed AWS KMS keys. - POSIX permissions rely on user IDs and group IDs stored as object metadata. - CloudWatch provides performance and update metrics. - CloudTrail records management events. - EC2 instances should use the latest `amazon-efs-utils` package, which is included in AWS-provided AMIs. S3 Files is best suited to workloads requiring shared, interactive file access while retaining data in S3. Teams should still evaluate access patterns and latency requirements, but the service offers a practical way to use familiar file operations without giving up S3’s centralized, durable storage model.

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Announcing the AWS Sustainability console: Programmatic access, configurable CSV reports, and Scope 1–3 reporting in one place | Amazon Web Services

The AWS Sustainability console is a standalone service that centralizes AWS emissions reporting and sustainability resources. It builds on the Customer Carbon Footprint Tool while adding independent permissions, customizable reports, fiscal-year support, and programmatic access. The underlying emissions data and methodology remain unchanged, but organizations now have more flexible ways to analyze and automate sustainability reporting. ## Independent Sustainability Access - Sustainability professionals can access emissions data without receiving AWS Billing permissions. - The console uses a permissions model separate from the Billing console. - Historical emissions data is available back to January 2022 at no additional cost. ## Scope 1–3 Emissions Reporting - Reports AWS-related emissions in metric tons of carbon dioxide equivalent (MTCO2e). - Covers: - **Scope 1:** Direct emissions from controlled sources, such as data center fuel use. - **Scope 2:** Indirect emissions from purchased energy. - **Scope 3:** Value-chain emissions, including server manufacturing and data center construction. - Data can be viewed by AWS Region and service, including Amazon EC2, Amazon S3, and CloudFront. - Both market-based method (MBM) and location-based method (LBM) calculations are supported. - The methodology is unchanged from the Customer Carbon Footprint Tool and has been independently verified by Apex. ## Configurable Reports and Fiscal Years - The Reports page provides preset monthly and annual emissions reports. - Users can create custom CSV reports by selecting: - Fields - Time granularity - Date ranges - Services, Regions, and other filters - Organizations can configure fiscal years that differ from the calendar year. - Once configured, data views and exports use the organization’s fiscal quarters and reporting periods. ## API and AWS CLI Access - A new API and AWS SDK support integration with: - Internal reporting pipelines - Sustainability dashboards - Compliance workflows - Teams can retrieve emissions for specific periods across many accounts without creating a data export. - Custom account groupings can be used even when they do not match the AWS Organizations hierarchy. - The AWS CLI command `get-estimated-carbon-emissions` returns emissions values, time periods, units, and model versions for MBM and LBM data. ## Availability and Future Development - The console is accessible through the AWS Management Console. - It complements existing Data Exports, allowing users to investigate emissions visually and automate stakeholder reporting. - AWS plans to expand the console with additional capabilities and publishes feature and methodology updates through its Release notes page. Organizations can begin using the free AWS Sustainability console immediately to explore emissions trends, create tailored reports, and connect AWS carbon data to existing sustainability processes.

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Announcing Amazon Aurora PostgreSQL serverless database creation in seconds | Amazon Web Services

Amazon’s new Aurora PostgreSQL express configuration lets developers create a serverless database in seconds with two console clicks or a single CLI/API call. It uses preconfigured defaults, IAM authentication, and an internet access gateway to simplify secure connections without requiring a VPC, VPN, or Direct Connect. The feature is designed to accelerate prototyping and application development while preserving Aurora capabilities such as read replicas and automated failover. ## Express Configuration for Aurora PostgreSQL - Creates an Aurora PostgreSQL serverless cluster and instance within seconds. - Uses preconfigured defaults to reduce setup complexity. - Allows customization of: - Cluster identifier - Serverless capacity range during creation - Read replicas and parameter groups after creation - Express-configured clusters do not require an Amazon VPC. - An internet access gateway is enabled by default for secure connections from development tools worldwide. - The gateway is distributed across multiple Availability Zones for high availability. - IAM authentication is configured for the administrator, enabling passwordless database authentication. ## Creating a Database - In the Aurora and RDS console: - Open the Dashboard. - Choose **Create** with the rocket icon. - Review or adjust the express configuration. - Choose **Create database**. - The AWS CLI and SDKs support the `--with-express-configuration` parameter. - A single `create-db-cluster` call creates both the cluster and its instance: ```bash aws rds create-db-cluster \ --db-cluster-identifier channy-express-db \ --engine aurora-postgresql \ --with-express-configuration ``` - The database becomes ready when its status changes to **Available**. ## Connecting to the Database The **Connectivity & security** tab provides several connection methods: - **Code snippets** - Generates connection examples for .NET, Go, JDBC, Node.js, PHP, PostgreSQL, Python, and TypeScript. - Python examples use `boto3` to generate an IAM authentication token and `psycopg2` to connect over SSL. - **AWS CloudShell** - Launches a shell with a preconfigured `psql` connection command. - Developers can immediately run SQL commands at the PostgreSQL prompt. - **Endpoints** - Supports tools such as pgAdmin that use username-and-password fields. - The password is an IAM authentication token valid for 15 minutes. - A new token must be generated if the connection ends or the token expires. ## Application Development Integrations - Aurora is now included among eligible AWS Free Tier database services. - AWS’s enhanced Free Tier offers up to $200 in credits: - $100 upon signup - Up to another $100 through usage of services such as RDS, Lambda, and Bedrock - Integrations with Vercel and v0 allow developers to create or connect to AWS databases quickly. - v0 can use natural-language prompts to generate full-stack applications backed by Aurora PostgreSQL, Aurora DSQL, or DynamoDB. - Existing Aurora databases created with express configuration can also be connected to Vercel. The express configuration is best suited for quickly starting development, experimentation, and prototypes. Developers can begin with minimal networking and authentication setup, then add capacity, replicas, and other Aurora features as their application grows.

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Introducing account regional namespaces for Amazon S3 general purpose buckets | Amazon Web Services

Amazon S3 now lets customers create general purpose buckets in an account regional namespace, making bucket names predictable and reusable across AWS Regions. Names combine a customer-selected prefix with an account-, Region-, and namespace-specific suffix, preventing other accounts from claiming them. The feature preserves existing general purpose bucket capabilities while improving governance and automation. ## Account Regional Bucket Namespaces - Bucket names use a format such as `mybucket-123456789012-us-east-1-an`. - The suffix identifies the AWS account and Region, ensuring that other accounts cannot create buckets using it. - The combined prefix and suffix must be between 3 and 63 characters. - Buckets support the same features as general purpose buckets in the global namespace. ## Governance and Policy Controls - IAM policies and AWS Organizations service control policies can enforce namespace usage. - The new `s3:x-amz-bucket-namespace` condition key allows organizations to require account regional bucket creation. ## Creating Buckets - In the S3 console, select **Account regional namespace** when creating a bucket. - AWS CLI requests use the `--bucket-namespace account-regional` option. - SDKs can pass `BucketNamespace: "account-regional"` to the `CreateBucket` API. - Applications can use STS to retrieve the account ID and the SDK’s Region to construct compliant names. ## Infrastructure as Code - CloudFormation templates can use `AWS::AccountId` and `AWS::Region` to construct bucket names. - The `BucketNamespace: "account-regional"` property enables the feature. - `BucketNamePrefix` can be used when only the customer-defined prefix should appear in the template; AWS adds the account regional suffix automatically. ## Limitations and Availability - Existing global-namespace buckets cannot be renamed into the account regional namespace; new buckets must be created. - The feature applies only to S3 general purpose buckets. - S3 table and vector buckets use account-level namespaces, while directory buckets use zonal namespaces. - It is available in 37 AWS Regions, including AWS China and GovCloud Regions, with no additional cost. Organizations can adopt account regional namespaces to simplify bucket provisioning, prevent naming conflicts, and enforce consistent naming through IAM, Organizations policies, and infrastructure-as-code tools.

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