Update: July updates to Listing attribute usage and enumeration values

via Ecommerce Data Analytics

New Amazon EC2 P6e-GB200 UltraServers accelerated by NVIDIA Grace Blackwell GPUs for the highest AI performance

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB200 UltraServers, accelerated by NVIDIA GB200 NVL72 to offer the highest GPU performance for AI training and inference. Amazon EC2 UltraServers connect multiple EC2 instances using a dedicated, high-bandwidth, and low-latency accelerator interconnect across these instances.

The NVIDIA Grace Blackwell Superchips connect two high-performance NVIDIA Blackwell tensor core GPUs and an NVIDIA Grace CPU based on Arm architecture using the NVIDIA NVLink-C2C interconnect. Each Grace Blackwell Superchip delivers 10 petaflops of FP8 compute (without sparsity) and up to 372 GB HBM3e memory. With the superchip architecture, GPU and CPU are colocated within one compute module, increasing bandwidth between GPU and CPU significantly compared to current generation EC2 P5en instances.

With EC2 P6e-GB200 UltraServers, you can access up to 72 NVIDIA Blackwell GPUs within one NVLink domain to use 360 petaflops of FP8 compute (without sparsity) and 13.4 TB of total high bandwidth memory (HBM3e). Powered by the AWS Nitro System, P6e-GB200 UltraServers are deployed in EC2 UltraClusters to securely and reliably scale to tens of thousands of GPUs.

EC2 P6e-GB200 UltraServers deliver up to 28.8 Tbps of total Elastic Fabric Adapter (EFAv4) networking. EFA is also coupled with NVIDIA GPUDirect RDMA to enable low-latency GPU-to-GPU communication between servers with operating system bypass.

EC2 P6e-GB200 UltraServers specifications
EC2 P6e-GB200 UltraServers are available in sizes ranging from 36 to 72 GPUs under NVLink. Here are the specs for EC2 P6e-GB200 UltraServers:

UltraServer type	GPUs	GPU memory (GB)	vCPUs	Instance memory (GiB)	Instance storage (TB)	Aggregate EFA Network Bandwidth (Gbps)	EBS bandwidth (Gbps)
u-p6e-gb200x36	36	6660	1296	8640	202.5	14400	540
u-p6e-gb200x72	72	13320	2592	17280	405	28800	1080

P6e-GB200 UltraServers are ideal for the most compute and memory intensive AI workloads, such as training and inference of frontier models, including mixture of experts models and reasoning models, at the trillion-parameter scale.

You can build agentic and generative AI applications, including question answering, code generation, video and image generation, speech recognition, and more.

P6e-GB200 UltraServers in action
You can use EC2 P6e-GB200 UltraServers in the Dallas Local Zone through EC2 Capacity Blocks for ML. The Dallas Local Zone (us-east-1-dfw-2a) is an extension of the US East (N. Virginia) Region.

To reserve your EC2 Capacity Blocks, choose Capacity Reservations on the Amazon EC2 console. You can select Purchase Capacity Blocks for ML and then choose your total capacity and specify how long you need the EC2 Capacity Block for u-p6e-gb200x36 or u-p6e-gb200x72 UltraServers.

Once Capacity Block is successfully scheduled, it is charged up front and its price doesn’t change after purchase. The payment will be billed to your account within 12 hours after you purchase the EC2 Capacity Blocks. To learn more, visit Capacity Blocks for ML in the Amazon EC2 User Guide.

To run instances within your purchased Capacity Block, you can use AWS Management Console, AWS Command Line Interface (AWS CLI) or AWS SDKs. On the software side, you can start with the AWS Deep Learning AMIs. These images are preconfigured with the frameworks and tools that you probably already know and use: PyTorch, JAX, and a lot more.

You can also integrate EC2 P6e-GB200 UltraServers seamlessly with various AWS managed services. For example:

• Amazon SageMaker Hyperpod provides managed, resilient infrastructure that automatically handles the provisioning and management of P6e-GB200 UltraServers, replacing faulty instances with preconfigured spare capacity within the same NVLink domain to maintain performance.
• Amazon Elastic Kubernetes Services (Amazon EKS) allows one managed node group to span across multiple P6e-GB200 UltraServers as nodes, automating their provisioning and lifecycle management within Kubernetes clusters. You can use EKS topology-aware routing for P6e-GB200 UltraServers, enabling optimal placement of tightly coupled components of distributed workloads within a single UltraServer’s NVLink-connected instances.
• Amazon FSx for Lustre file systems provide data access for P6e-GB200 UltraServers at the hundreds of GB/s of throughput and millions of input/output operations per second (IOPS) required for large-scale HPC and AI workloads. For fast access to large datasets, you can use up to 405 TB of local NVMe SSD storage or virtually unlimited cost-effective storage with Amazon Simple Storage Service (Amazon S3).

Now available
Amazon EC2 P6e-GB200 UltraServers are available today in the Dallas Local Zone (us-east-1-dfw-2a) through EC2 Capacity Blocks for ML. For more information, visit the Amazon EC2 pricing page.

Give Amazon EC2 P6e-GB200 UltraServers a try in the Amazon EC2 console. To learn more, visit the Amazon EC2 P6e instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

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Introducing AWS Builder Center: A new home for the AWS builder community

We really love builders at AWS. We’re constantly thinking of new ways to help technical communities thrive and create spaces like AWS Developer Center and community.aws where people can connect and share their knowledge and experiences.

Today, we’re announcing AWS Builder Center, a new home for builders to access all builder resources, engage with the AWS community, and provide feedback or product suggestions to AWS product teams. This new experience also integrates the previous AWS Developer Center and community.aws.

There are a variety of exciting features so let us discover some of them.

Your voice matters: Introducing Wishlist
One of the most exciting new features, in my opinion, is Wishlist. You can now submit your wishes for new features or improvements you’d like to see in AWS services. Others can discover and vote on these wishes while also creating their own.

You can influence product roadmap collectively as a community and help us shape the future of AWS services. You can share ideas, suggestions, feature proposals, or challenges while operating AWS services, with the ability for the AWS community to upvote ideas and highlight the most sought-after improvements. Our internal teams will keep an eye on these and bring the most popular wishes to the attention of our service teams, making your voice an integral part of our product development process.

Connect people in the AWS community
On the Connect page, you’ll find many opportunities to connect directly with AWS Heroes and AWS Community Builders. You can explore and join AWS User Groups and AWS Cloud Clubs near your cities around the world.

On top of that, you can bookmark this page as your centralized hub for finding upcoming community events, making it easy to find opportunities to learn and network in your local area and meet like-minded builders who share your interests.

Speaking of following people, AWS Builder Center makes it really straightforward to connect and engage with others, serving as the central hub for the AWS technical community. It brings together all the different ways that you can connect with fellow builders. For example, the Who to Follow section introduces you to AWS Heroes, Community Builders, and active community members who are sharing their knowledge and expertise in your areas of interest.

Explore our AWS hands-on resources
On the Build page, you’ll discover ways to get familiar with AWS with hands-on experience such as interactive learning resources designed for every skill level such as AWS Tutorials and AWS Workshops. You can explore generative AI and agentic AI services playground and find the AWS Free Tier to try out AWS services free of charge up to specified limits for each service.

Choose the Toolbox page and discover the latest tools, programming language resources, and Open Source projects for AWS. The Toolbox has everything you need to get your project scaffolded and up and running.

To improve the build experience for builders, we plan to expand Builder Center’s built-in offerings such as creating dedicated groups and forums for collaborating on a particular topic, run workshops for hands-on labs, and various service playgrounds where builders can freely experiment with AWS services.

Supporting your builder journey
The new Learn section serves as your gateway to skill development, bringing together everything you need to expand your AWS expertise. Here, you can explore learning and training resources, workshops, gamified experiences, and more to make your journey of building on AWS both educational and engaging.

Choose the Topics page, where you can explore and discover more content. You can explore content by topics and tags. There is a featured and trending topics section that helps you to stay connected with what’s capturing the community’s attention right now.

Built-in localization for your spoken language
AWS Builder Center breaks down language barriers with comprehensive localization support. All content published in the Builder Center is automatically available in 16 languages, and user-generated content, such as posts, comments, or wishes, can be machine-translated on demand using Translate. So, you can collaborate with builders worldwide, sharing knowledge and experiences across language boundaries.

By default, all content will be displayed in based on the language that your browser is set to. But, you can override this by visiting the settings page and choosing the language that you want AWS Builder Center to use by default.

Sign up and build your profile now
AWS Builder Center gives you a more personalized and comprehensive way to showcase your AWS journey. Your unique profile comes with a custom URL and shareable QR code, making it straightforward to connect with others and share your presence in the AWS community.

All your posts, wishes, and meaningful interactions are organized within a centralized view so you can easily check them. In the Manage profile page, you can customize your profile, add specific interests and areas of expertise, helping you connect with builders who share your passions. Profile management is seamless: it synchronizes across all AWS services using AWS Builder ID, ensuring your identity remains consistent wherever you engage with AWS offerings.

Visit builder.aws.com, sign up with AWS Builder ID, and claim your unique alias to access all features, including content creation, Wishlist, and community engagement tools.

AWS Builder Center was designed to help you connect, learn, and build with fellow AWS builders, so enjoy your journey together!

— Channy & Matheus Guimaraes | @codingmatheus

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Introducing Oracle Database@AWS for simplified Oracle Exadata migrations to the AWS Cloud

Today, we’re announcing the general availability of Oracle Database@AWS, a new offering for Oracle Exadata workloads, including Oracle Real Application Clusters (RAC) within AWS.

In the past 14 years, customers had the choice of self-managing Oracle database workloads in the cloud using Amazon Elastic Compute Cloud (Amazon EC2) or using fully managed Amazon Relational Database Service (Amazon RDS) for Oracle. Now, you have an additional option for your workloads that require Oracle RAC or Oracle Exadata for quicker and simpler migrations to the cloud. You also get a single invoice through AWS Marketplace, which counts towards AWS commitments and Oracle license benefits, including Bring Your Own License (BYOL) and discount programs such as Oracle Support Rewards.

With Oracle Database@AWS, you can migrate your Oracle Exadata workloads to Oracle Exadata Database Service on Dedicated Infrastructure or Oracle Autonomous Database on Dedicated Exadata Infrastructure within AWS with minimal changes. You can purchase, provision, and manage your Oracle Database@AWS deployments through familiar AWS tools and interfaces such as AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS APIs for applications running on AWS. The AWS APIs call the corresponding Oracle Cloud Infrastructure (OCI) APIs necessary to provision and manage the resources.

Since its preview last December, we’ve improved or added features to help run production workloads at general availability:

• Regional expansion – You can now use Oracle Database@AWS in the U.S. East (N. Virginia) and U.S. West (Oregon) Regions today. We are also announcing plans to expand to 20 AWS Regions globally. This broader availability supports the diverse needs of our customers across various geographical areas so more enterprises can benefit from this option. You can choose from different Exadata system sizes to match your workload requirements in your AWS Region.
• Zero-ETL and S3 backups – You can now benefit from zero-ETL integration with Amazon Redshift for analytics to remove the need to build and manage data pipelines for extract, transform, and load operations. With zero-ETL, you can unify your data on AWS without incurring cross network data transfer costs. We’re providing Amazon Simple Storage Service (Amazon S3) backups with up to eleven nines of data durability.
• Autonomous VM cluster – You can now provision an Autonomous VM Cluster in addition to an Exadata VM cluster on the Exadata Dedicated Infrastructure. You can run Oracle Autonomous Database on Dedicated Exadata Infrastructure, a fully managed database environment using committed hardware and software resources.

Oracle Database@AWS also integrates with other AWS services such as Amazon Virtual Private Cloud (Amazon VPC) Lattice for configuring network paths to AWS services such as S3 and Redshift directly, AWS Identity and Access Management (IAM) for authentication and authorization, Amazon EventBridge for monitoring database lifecycle events, AWS CloudFormation for infrastructure automation, Amazon CloudWatch for collecting and monitoring metrics, and AWS CloudTrail for logging API operations.

Getting started with Oracle Database@AWS
Oracle Database@AWS supports two key services: Oracle Exadata Database Service on Dedicated Infrastructure and Oracle Autonomous Database on Dedicated Exadata Infrastructure within AWS data centers.

These services physically reside within an Availability Zone in an AWS Region and logically reside in an OCI region, enabling seamless integration with AWS services through high-speed, low-latency connections.

You create an ODB network, a private, isolated network that hosts Oracle Exadata VM Clusters within an Availability Zone. Then, you use ODB peering accessible to EC2 application servers running in a VPC. To learn more, visit How Oracle Database@AWS works in the AWS documentation.

Request a private offer in AWS Marketplace

To begin your journey with Oracle Database@AWS, visit the AWS console or request the AWS Marketplace private offer. Your AWS and Oracle sales team will receive your request, then contact you to find the best option for your workloads, and activate your account.

When you activate and get access to Oracle Database@AWS, you can use the Dashboard to create an ODB network, Exadata infrastructure, and Exadata VM cluster or Autonomous VM cluster, and ODB peering connection.

To learn more, visit the Onboarding to Oracle Database@AWS and AWS Marketplace buyer private offers in the AWS documentation.

Create an ODB network

An ODB network is a private isolated network that hosts OCI infrastructure on AWS. The ODB network maps directly to the network that exists within the OCI child site, thus serving as the means of communication between AWS and OCI.

In the Dashboard, choose Create ODB network, enter a network name, choose the Availability Zone, and specify a CIDR ranges for client connections established by applications and backup connections used for taking automated backups. You can also enter a name to use as a prefix to your domain fixed as oraclevcn.com. For example, if you enter myhost, the fully qualified domain name is myhost.oraclevcn.com.

Optionally, you can configure ODB network access to perform automated backups to Amazon S3 and zero-ETL for near real-time analytics and ML on your Oracle data using Amazon Redshift.

After you create your ODB network, update your VPC route tables of your EC2 application servers with the client connection CIDR in the ODB network. To learn more, visit ODB network, ODB peering, and Configuring VPC route tables for ODB peering in the AWS documentation.

Create Exadata infrastructure

The Oracle Exadata infrastructure is the underlying architecture of your database servers, storage servers, and networking that run your Oracle Exadata databases.

Choose Create Exadata infrastructure, enter a name, and use the default Availability Zone. In the next step, you can choose Exadata.X11M for the Exadata system model. You can also set a default of 2 or up to 32 database servers and 3 or up to 64 storage servers with 80 TB storage capacity per server.

Finally, you can configure system maintenance preferences, such as scheduling, patching mode, and OCI maintenance notification contacts. You can’t modify an infrastructure after you create it from the AWS console. But, you can navigate to the OCI console and modify it.

To delete an Exadata infrastructure, visit Deleting an Oracle Exadata infrastructure in Oracle Database@AWS in the AWS documentation.

Create an Exadata VM cluster or Autonomous VM cluster

You can create VM clusters on Exadata infrastructure and deploy multiple VM clusters with different Oracle Exadata infrastructures in the same ODB network.

Here are two types of VM clusters:

• An Exadata VM cluster is a set of virtual machines that has a complete Oracle database installation that includes all features of Oracle Enterprise Edition.
• An Autonomous VM cluster is a set of fully managed databases that automate key management tasks using AI/ML with no human intervention required.

Choose Create Exadata VM cluster, enter a VM cluster name and a time zone, choose Bring Your Own License (BYOL) or license included for license options. In the next step, you can choose your Exadata infrastructure, grid infrastructure version, and Exadata image version. For database servers, you can choose the CPU core count, memory, and local storage for each VM or accept the defaults.

In the next step, you can configure the connectivity setting by choosing your ODB network and entering a prefix for the VM cluster. You can enter a port number for TCP access to the single client access name (SCAN) listener. The default port is 1521 or you can enter a custom SCAN port in the range 1024–8999. For SSH key pairs, enter the public key portion of one or more key pairs used for SSH access to the VM cluster.

Then, you can choose diagnostics and tags, review your settings, and create a VM cluster. The creation process can take up to 6 hours, depending on the size of the VM cluster.

Create and manage an Oracle database

When the VM cluster is ready, you can create and manage your Oracle Exadata databases in the OCI console. Choose Manage in OCI in the details page of the Exadata VM cluster. You will be redirected to the OCI console.

When you create an Oracle Database in the OCI console, you can select Oracle Database 19c or 23ai. When enabling automatic backups for your provisioned databases, you can use an S3 bucket or OCI Object Storage in the OCI region. To learn more, visit Provision Oracle Exadata Database Service in Oracle Database@AWS in the OCI documentation.

Things to know
Here are a couple of things to know about Oracle Database@AWS:

• Monitoring – You can monitor Oracle Database@AWS using Amazon CloudWatch metrics in the AWS/ODB namespaces for VM clusters, container databases, and pluggable databases. AWS CloudTrail captures all AWS API calls for Oracle Database@AWS as events. Using CloudTrail logs, you can determine the request that was made to Oracle Database@AWS, the IP address from which the request was made, when it was made, and additional details. To learn more, visit Monitoring Oracle Database@AWS.
• Security – You can use IAM to assign permissions that determine who is allowed to manage Oracle Database@AWS resources and SSL/TLS encrypted connections to secure data. You can also use Amazon EventBridge for seamless event-driven database operations—all working together to maintain security standards while enabling efficient cloud operations. To learn more, visit Security in Oracle Database@AWS.
• Compliance – Your compliance responsibility when using Oracle Database@AWS is determined by the sensitivity of your data, your company’s compliance objectives, and applicable laws and regulations. We provides the following compliances with Oracle Database@AWS: SOC 1, SOC 2, SOC 3, HIPAA, C5, CSA STAR Attest, CSA STAR Cert, HDS (France), ISO Series (ISO/IEC 9001, 20000-1, 27001, 27017, 27018, 27701, 22301), PCI DSS, and HITRUST. To learn more, visit Compliance validation for Oracle Database@AWS.
• Support – Your AWS or Oracle sales account team can help you evaluate your current database infrastructure, determine how Oracle Database@AWS can best serve your organization’s requirements, and develop a tailored migration strategy and timeline. You can also get help from AWS Oracle Competency Partners specialized to architect, deploy, and manage Oracle-based workloads running in the AWS Cloud.

Now available and coming soon
Oracle Database@AWS is now available in the U.S. East (N. Virginia) and U.S. West (Oregon) Regions through the AWS Marketplace. Oracle Database@AWS pricing and any AWS Marketplace private offers are set by Oracle. You can see specific details around pricing on Oracle’s pricing page for the offering.

Oracle Database@AWS will expand to 20 more AWS Regions across the Americas, Europe, and Asia-Pacific including: US East (Ohio), US West (N. California), Asia Pacific (Hyderabad), Asia Pacific (Melbourne), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Spain), Europe (Stockholm), Europe (Zurich), and South America (São Paulo).

You can get started with Oracle Database@AWS with using AWS console. To learn more, visit the Oracle Database@AWS User Guide and OCI documentation and send feedback through your usual AWS Support contacts or OCI support.

— Channy

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Update: Data Kiosk economics dataset includes more fee types

via Ecommerce Data Analytics

AWS Weekly Roundup: EC2 C8gn instances, Amazon Nova Canvas virtual try-on, and more (July 7, 2025)

Every Monday we tell you about the best releases and blogs that caught our attention last week.

Before continuing with this AWS Weekly Roundup, I’d like to share that last month I moved with my family to San Francisco, California, to start a new role as Developer Advocate/SDE, GenAI.

This excites me because I’ll have the opportunity to connect with new communities in the Bay Area while tackling exciting new challenges. If you’re part of a community focused on building generative AI and agentics applications, or know of one, I’d love to connect. Let’s connect!

Last week’s launches
Here are the launches from last week:

• New Amazon EC2 C8gn instances powered by AWS Graviton4 offering up to 600Gbps network bandwidth – Amazon Elastic Compute Cloud (Amazon EC2) C8gn instances are now generally available, powered by AWS Graviton4 processors and 6th generation AWS Nitro Cards. These network-optimized instances deliver up to 600 Gbps network bandwidth. This represents the highest bandwidth among EC2 network-optimized instances, with up to 192 vCPUs and 384 GiB memory. They provide 30% higher compute performance than C7gn instances and are ideal for network-intensive workloads like virtual appliances, data analytics, and cluster computing jobs.
• Build the highest resilience apps with multi-Region strong consistency in Amazon DynamoDB global tables – Amazon DynamoDB global tables now supports multi-Region strong consistency (MRSC) for applications requiring zero Recovery Point Objective (RPO). This capability ensures applications can read the latest data from any Region during outages, addressing critical needs in payment processing and financial services. MRSC requires three AWS Regions configured as either three full replicas or two replicas plus a witness, providing the highest level of application resilience for mission-critical workloads.
• Amazon Nova Canvas update: Virtual try-on and style options now available – Amazon Nova Canvas introduces virtual try-on capabilities that help you visualize how clothing looks on a person by combining two images, plus eight new pre-trained style options (3D animation, design sketch, vector illustration, graphic novel, etc.) for generating images with improved artistic consistency. Available in three AWS Regions, these features enhance AI-powered image generation capabilities for retailers and content creators seeking realistic product visualizations.
• Amazon Q in Connect now supports 7 languages for proactive recommendations – Amazon Q in Connect, a generative AI-powered assistant for customer service, now provides proactive recommendations in seven languages: English, Spanish, French, Portuguese, Mandarin, Japanese, and Korean. The AI-powered customer service assistant detects customer intent during voice and chat interactions to help agents resolve issues quickly and accurately.
• Amazon Aurora MySQL and Amazon RDS for MySQL integration with Amazon SageMaker is now available – This integration provides near real-time data availability for analytics. It automatically extracts MySQL data into lakehouses with Apache Iceberg compatibility. You can then access this data seamlessly through various analytics engines and machine learning tools.
• Amazon Aurora DSQL is now available in additional AWS Regions – Amazon Aurora DSQL expands to Asia Pacific (Seoul) and now supports multi-Region clusters across Asia Pacific and European regions. This serverless, distributed SQL database offers unlimited scalability, highest availability, and zero infrastructure management with AWS Free Tier access.

Other AWS blog posts

• Optimize RAG in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service – Learn how to optimize Retrieval Augmented Generation (RAG) in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service. This comprehensive guide demonstrates implementing RAG workflows with LangChain, covers OpenSearch optimization strategies, provides setup instructions, and explains benefits of combining these AWS services for scalable, cost-effective generative AI applications.v
• Agentic GenAI App Using Bedrock, MCP servers on EKS – This post shows how to build a scalable AI chat application using Amazon Bedrock, Strands Agent, and Model Context Protocol (MCP) servers deployed on Amazon Elastic Kubernetes Service (Amazon EKS). The architecture combines agentic workflows with containerized microservices for intelligent, auto-scaling conversations with multiple foundation models.
• Enforce table level access control on data lake tables using AWS Glue 5.0 with AWS Lake Formation – AWS Glue 5.0 introduces Full-Table Access (FTA) control for Apache Spark with AWS Lake Formation, providing table-level security without fine-grained access overhead. This feature supports native Spark SQL/DataFrames for Lake Formation tables. It enables read/write operations on Iceberg and Hive tables with improved performance and lower costs.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

• AWS re:Invent – Register now to get a head start on choosing your best learning path, booking travel and accommodations, and bringing your team to learn, connect, and have fun. Early-career professionals can apply for the All Builders Welcome Grant program, designed to remove financial barriers and create diverse pathways into cloud technology. Applications are now open and close on July 15, 2025.
• AWS NY Summit – You can gain insights from Swami’s keynote featuring the latest cutting-edge AWS technologies in compute, storage, and generative AI. My News Blog team is also preparing some exciting news for you. If you’re unable to attend in person, you can still participate by registering for the global live stream. Also, save the date for these upcoming Summits in July and August near your city.
• AWS Builders Online Series – If you’re based in one of the Asia Pacific time zones, join and learn fundamental AWS concepts, architectural best practices, and hands-on demonstrations to help you build, migrate, and deploy your workloads on AWS.
• Join AWS Gen AI Lofts – Experience AWS Gen AI Lofts across San Francisco, Berlin, Dubai, Dublin, Bengaluru, Manchester, Paris, Tel Aviv, and additional locations – hands-on workshops, expert guidance, investor networking, and collaborative spaces designed to accelerate your generative AI startup journey.

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Eli

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Amazon Nova Canvas update: Virtual try-on and style options now available

Have you ever wished you could quickly visualize how a new outfit might look on you before making a purchase? Or how a piece of furniture would look in your living room? Today, we’re excited to introduce a new virtual try-on capability in Amazon Nova Canvas that makes this possible. In addition, we are adding eight new style options for improved style consistency for text-to-image based style prompting. These features expand Nova Canvas AI-powered image generation capabilities making it easier than ever to create realistic product visualizations and stylized images that can enhance the experience of your customers.

Let’s take a quick look at how you can start using these today.

Getting started
The first thing is to make sure that you have access to the Nova Canvas model through the usual means. Head to the Amazon Bedrock console, choose Model access and enable Amazon Nova Canvas for your account making sure that you select the appropriate regions for your workloads. If you already have access and have been using Nova Canvas, you can start using the new features immediately as they’re automatically available to you.

Virtual try-on
The first exciting new feature is virtual try-on. With this, you can upload two pictures and ask Amazon Nova Canvas to put them together with realistic results. These could be pictures of apparel, accessories, home furnishings, and any other products including clothing. For example, you can provide the picture of a human as the source image and the picture of a garment as the reference image, and Amazon Nova Canvas will create a new image with that same person wearing the garment. Let’s try this out!

My starting point is to select two images. I picked one of myself in a pose that I think would work well for a clothes swap and a picture of an AWS-branded hoodie.

Note that Nova Canvas accepts images containing a maximum of 4.1M pixels – the equivalent of 2,048 x 2,048 – so be sure to scale your images to fit these constraints if necessary. Also, if you’d like to run the Python code featured in this article, ensure you have Python 3.9 or later installed as well as the Python packages boto3 and pillow.

To apply the hoodie to my photo, I use the Amazon Bedrock Runtime invoke API. You can find full details on the request and response structures for this API in the Amazon Nova User Guide. The code is straightforward, requiring only a few inference parameters. I use the new taskType of "VIRTUAL_TRY_ON". I then specify the desired settings, including both the source image and reference image, using the virtualTryOnParams object to set a few required parameters. Note that both images must be converted to Base64 strings.

import base64


def load_image_as_base64(image_path): 
   """Helper function for preparing image data."""
   with open(image_path, "rb") as image_file:
      return base64.b64encode(image_file.read()).decode("utf-8")


inference_params = {
   "taskType": "VIRTUAL_TRY_ON",
   "virtualTryOnParams": {
      "sourceImage": load_image_as_base64("person.png"),
      "referenceImage": load_image_as_base64("aws-hoodie.jpg"),
      "maskType": "GARMENT",
      "garmentBasedMask": {"garmentClass": "UPPER_BODY"}
   }
}

Nova Canvas uses masking to manipulate images. This is a technique that allows AI image generation to focus on specific areas or regions of an image while preserving others, similar to using painter’s tape to protect areas you don’t want to paint.

You can use three different masking modes, which you can choose by setting maskType to the correct value. In this case, I’m using "GARMENT", which requires me to specify which part of the body I want to be masked. I’m using "UPPER_BODY" , but you can use others such as "LOWER_BODY", "FULL_BODY", or "FOOTWEAR" if you want to specifically target the feet. Refer to the documentation for a full list of options.

I then call the invoke API, passing in these inference arguments and saving the generated image to disk.

# Note: The inference_params variable from above is referenced below.

import base64
import io
import json

import boto3
from PIL import Image

# Create the Bedrock Runtime client.
bedrock = boto3.client(service_name="bedrock-runtime", region_name="us-east-1")

# Prepare the invocation payload.
body_json = json.dumps(inference_params, indent=2)

# Invoke Nova Canvas.
response = bedrock.invoke_model(
   body=body_json,
   modelId="amazon.nova-canvas-v1:0",
   accept="application/json",
   contentType="application/json"
)

# Extract the images from the response.
response_body_json = json.loads(response.get("body").read())
images = response_body_json.get("images", [])

# Check for errors.
if response_body_json.get("error"):
   print(response_body_json.get("error"))

# Decode each image from Base64 and save as a PNG file.
for index, image_base64 in enumerate(images):
   image_bytes = base64.b64decode(image_base64)
   image_buffer = io.BytesIO(image_bytes)
   image = Image.open(image_buffer)
   image.save(f"image_{index}.png")

I get a very exciting result!

And just like that, I’m the proud wearer of an AWS-branded hoodie!

In addition to the "GARMENT" mask type, you can also use the "PROMPT" or "IMAGE" masks. With "PROMPT", you also provide the source and reference images, however, you provide a natural language prompt to specify which part of the source image you’d like to be replaced. This is similar to how the "INPAINTING" and "OUTPAINTING" tasks work in Nova Canvas. If you want to use your own image mask, then you choose the "IMAGE" mask type and provide a black-and-white image to be used as mask, where black indicates the pixels that you want to be replaced on the source image, and white the ones you want to preserve.

This capability is specifically useful for retailers. They can use it to help their customers make better purchasing decisions by seeing how products look before buying.

Using style options
I’ve always wondered what I would look like as an anime superhero. Previously, I could use Nova Canvas to manipulate an image of myself, but I would have to rely on my good prompt engineering skills to get it right. Now, Nova Canvas comes with pre-trained styles that you can apply to your images to get high-quality results that follow the artistic style of your choice. There are eight available styles including 3D animated family film, design sketch, flat vector illustration, graphic novel, maximalism, midcentury retro, photorealism, and soft digital painting.

Applying them is as straightforward as passing in an extra parameter to the Nova Canvas API. Let’s try an example.

I want to generate an image of an AWS superhero using the 3D animated family film style. To do this, I specify a taskType of "TEXT_IMAGE" and a textToImageParams object containing two parameters: text and style. The text parameter contains the prompt describing the image I want to create which in this case is “a superhero in a yellow outfit with a big AWS logo and a cape.” The style parameter specifies one of the predefined style values. I’m using "3D_ANIMATED_FAMILY_FILM" here, but you can find the full list in the Nova Canvas User Guide.

inference_params = {
   "taskType": "TEXT_IMAGE",
   "textToImageParams": {
      "text": "a superhero in a yellow outfit with a big AWS logo and a cape.",
      "style": "3D_ANIMATED_FAMILY_FILM",
   },
   "imageGenerationConfig": {
      "width": 1280,
      "height": 720,
      "seed": 321
   }
}

Then, I call the invoke API just as I did in the previous example. (The code has been omitted here for brevity.) And the result? Well, I’ll let you judge for yourself, but I have to say I’m quite pleased with the AWS superhero wearing my favorite color following the 3D animated family film style exactly as I envisioned.

What’s really cool is that I can keep my code and prompt exactly the same and only change the value of the style attribute to generate an image in a completely different style. Let’s try this out. I set style to PHOTOREALISM.

inference_params = { 
   "taskType": "TEXT_IMAGE", 
   "textToImageParams": { 
      "text": "a superhero in a yellow outfit with a big AWS logo and a cape.",
      "style": "PHOTOREALISM",
   },
   "imageGenerationConfig": {
      "width": 1280,
      "height": 720,
      "seed": 7
   }
}

And the result is impressive! A photorealistic superhero exactly as I described, which is a far departure from the previous generated cartoon and all it took was changing one line of code.

Things to know
Availability – Virtual try-on and style options are available in Amazon Nova Canvas in the US East (N. Virginia), Asia Pacific (Tokyo), and Europe (Ireland). Current users of Amazon Nova Canvas can immediately use these capabilities without migrating to a new model.

Pricing – See the Amazon Bedrock pricing page for details on costs.

For a preview of virtual try-on of garments, you can visit nova.amazon.com where you can upload an image of a person and a garment to visualize different clothing combinations.

If you are ready to get started, please check out the Nova Canvas User Guide or visit the AWS Console.

Matheus Guimaraes | @codingmatheus

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Update: Increased rate limits for Listings Items API relationship operations

via Ecommerce Data Analytics

Build the highest resilience apps with multi-Region strong consistency in Amazon DynamoDB global tables

While tens of thousands of customers are successfully using Amazon DynamoDB global tables with eventual consistency, we’re seeing emerging needs for even stronger resilience. Many organizations find that the DynamoDB multi-Availability Zone architecture and eventually consistent global tables meet their requirements, but critical applications like payment processing systems and financial services demand more.

For these applications, customers require a zero Recovery Point Objective (RPO) during rare Region-wide events, meaning you can direct your app to read the latest data from any Region. Your multi-Region applications always need to access the same data regardless of location.

Starting today, you can use a new Amazon DynamoDB global tables capability that provides multi-Region strong consistency (MRSC), enabling zero RPO. This capability, first announced as a preview at AWS re:Invent 2024, simplifies building highly resilient global applications.

Here’s how you can enable MRSC starting with an existing empty DynamoDB table:

With MRSC, DynamoDB now offers you the highest level of resilience for your applications. If application processing is interrupted in a Region, you can redirect traffic to another Region containing a MRSC replica and know that you’ll be processing the latest data.

Getting started
Let me walk you through how you can use this new capability.

To start using MRSC, I need to create a global table from an existing DynamoDB table that doesn’t contain any data. I navigate to my existing table, select the Global tables tab and select Create replica.

The MRSC availability architecture requires three AWS Regions. I can configure MRSC with either three full replicas or with two replicas and a witness. A witness contains only replicated change data to provide the required availability without maintaining a full copy of my table data.

The following screenshot shows how I configure MRSC with two replicas and a witness. To configure MRSC with three full replicas instead, I can clear Configure Region 2 as Witness.

If I need to update my existing table programmatically, I can use the Amazon Q CLI to generate the required command with the following prompt:

> Hey Q! Update my existing DynamoDB table called "demo-mrsc" in us-east-1 with multi-Region strong consistency across us-east-2 with witness in us-west-2 Regions

Shortly after, Q CLI will respond back with following command:

> To update your DynamoDB table with multi-Region strong consistency, you'll need to use the update-table command with the appropriate parameters. Here's how you would do it:

aws dynamodb update-table \ 
    --table-name demo-mrsc \ 
    --replica-updates '[{"Create": {"RegionName": "us-east-2"}}]' \ 
    --global-table-witness-updates '[{"Create": {"RegionName": "us-west-2"}}]' \ 
    --multi-region-consistency STRONG \ 
    --region us-east-1

After it’s finished processing, I can check the status of my MRSC global table. I can see I have a witness configured for my DynamoDB global table. A witness reduces costs while still providing the resilience benefits of multi-Region strong consistency.

Then, in my application, I can use ConsistentRead to read data with strong consistency. Here’s a Python example:

import boto3

# Configure the DynamoDB client for your region
dynamodb = boto3.resource('dynamodb', region_name='us-east-2')
table = dynamodb.Table('demo-mrsc')

pk_id = "demo#test123"

# Read with strong consistency across regions
response = table.get_item(
    Key={
        'PK': pk_id
    },
    ConsistentRead=True
)

print(response)

For operations that require the strongest resilience, I can use ConsistentRead=True. For less critical operations where eventual consistency is acceptable, I can omit this parameter to improve performance and reduce costs.

Additional things to know
Here are a couple of things to note:

• Availability – The Amazon DynamoDB multi-Region strong consistency capability is available in following AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Osaka, Seoul, Tokyo), and Europe (Frankfurt, Ireland, London, Paris)
• Pricing – Multi-Region strong consistency pricing follows the existing global tables pricing structure. DynamoDB recently reduced global tables pricing by up to 67 percent, making this highly resilient architecture more affordable than ever. Visit Amazon DynamoDB lowers pricing for on-demand throughput and global tables in the AWS Database Blog to learn more.

Learn more about how you can achieve the highest level of application resilience, enable your applications to be always available and always read the latest data regardless of the Region by visiting Amazon DynamoDB global tables.

Happy building!

— Donnie

 

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New Amazon EC2 C8gn instances powered by AWS Graviton4 offering up to 600Gbps network bandwidth

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) C8gn network optimized instances powered by AWS Graviton4 processors and the latest 6th generation AWS Nitro Card. EC2 C8gn instances deliver up to 600Gbps network bandwidth, the highest bandwidth among EC2 network optimized instances.

You can use C8gn instances to run the most demanding network intensive workloads, such as security and network virtual appliances (virtual firewalls, routers, load balancers, proxy servers, DDoS appliances), data analytics, and tightly-coupled cluster computing jobs.

EC2 C8gn instances specifications
C8gn instances provide up to 192 vCPUs and 384 GiB memory, and offer up to 30 percent higher compute performance compared Graviton3-based EC2 C7gn instances.

Here are the specs for C8gn instances:

Instance Name	vCPUs	Memory (GiB)	Network Bandwidth (Gbps)	EBS Bandwidth (Gbps)
c8gn.medium	1	2	Up to 25	Up to 10
c8gn.large	2	4	Up to 30	Up to 10
c8gn.xlarge	4	8	Up to 40	Up to 10
c8gn.2xlarge	8	16	Up to 50	Up to 10
c8gn.4xlarge	16	32	50	10
c8gn.8xlarge	32	64	100	20
c8gn.12xlarge	48	96	150	30
c8gn.16xlarge	64	128	200	40
c8gn.24xlarge	96	192	300	60
c8gn.metal-24xl	96	192	300	60
c8gn.48xlarge	192	384	600	60
c8gn.metal-48xl	192	384	600	60

You can launch C8gn instances through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs.

If you’re using C7gn instances now, you will have straightforward experience migrating network intensive workloads to C8gn instances because the new instances offer similar vCPU and memory ratios. To learn more, check out the collection of Graviton resources to help you start migrating your applications to Graviton instance types.

You can also visit the Level up your compute with AWS Graviton page to begin your Graviton adoption journey.

Now available
Amazon EC2 C8gn instances are available today in US East (N. Virginia) and US West (Oregon) Regions. Two metal instance sizes are only available in US East (N. Virginia) Region. These instances can be purchased as On-Demand, Savings Plan, Spot instances, or as Dedicated instances and Dedicated hosts.

Give C8gn instances a try in the Amazon EC2 console. To learn more, refer to the Amazon EC2 C8g instance page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

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AWS Weekly Roundup: Project Rainier, Amazon CloudWatch investigations, AWS MCP servers, and more (June 30, 2025)

Every time I visit Seattle, the first thing that greets me at the airport is Mount Rainier. Did you know that the most innovative project at Amazon Web Services (AWS) is named after this mountain?

Project Rainier is a new project to create what is expected to be the world’s most powerful computer for training AI models across multiple data centers in the United Stages. Anthropic will develop the advanced versions of its Claude models with five times more computing power than its current largest training cluster.

The key technology powering Project Rainier is AWS custom-designed Trainium2 chips, which are specialized for the immense data processing required to train complex AI models. Thousands of these Trainium2 chips will be connected in a new type of Amazon EC2 UltraServer and EC2 UltraCluster architecture that allows ultra-fast communication and data sharing across the massive system.

Learn about the AWS vertical integration of Project Rainer, where it designs every component of the technology stack from chips to software, allows it to optimize the entire system for maximum efficiency and reliability.

Last week’s launches
Here are some launches that got my attention:

• Amazon S3 access for Amazon FSx for OpenZFS – You can access and analyze your FSx for OpenZFS file data through Amazon S3 Access Points, enabling seamless integration with AWS AI/ML, and analytics services without moving your data out of the file system. You can treat your FSx for OpenZFS data as if it were stored in S3, making it accessible through the S3 API for various applications including Amazon Bedrock, Amazon SageMaker, AWS Glue, and other S3 based cloud-native applications.
• Amazon S3 with sort and z-order compaction for Apache Iceberg tables – You can optimize query performance and reduce costs with new sort and z-order compaction. With S3 Tables, sort compaction automatically organizes data files based on defined column orders, while z-order compaction can be enabled through the maintenance API for efficient multicolumn queries.
• Amazon CloudWatch investigations – You can accelerate your operational troubleshooting in AWS environments using the Amazon CloudWatch AI-powered investigation feature, which helps identify anomalies, surface related signals, and suggest remediation steps. This capability can be initiated through CloudWatch data widgets, multiple AWS consoles, CloudWatch alarm actions, or Amazon Q chat and enables team collaboration and integration with Slack and Microsoft Teams.
• Amazon Bedrock Guardrails Standard tier – You can enhance your AI content safety measures using the new Standard tier. It offers improved content filtering and topic denial capabilities across up to 60 languages, better detection of variations including typos, and stronger protection against prompt attacks. This feature lets you configure safeguards to block harmful content, prevent model hallucinations, redact personally identifiable information (PII), and verify factual claims through automated reasoning checks.
• Amazon Route 53 Resolver endpoints for private hosted zone – You can simplify DNS management across AWS and on-premises infrastructure using the new Route 53 DNS delegation feature for private hosted zone subdomains, which works with both inbound and outbound Resolver endpoints. You can delegate subdomain authority between your on-premises infrastructure and Route 53 Resolver cloud service using name server records, eliminating the need for complex conditional forwarding rules.
• Amazon Q Developer CLI for Java transformation – You can automate and scale Java application upgrades using the new Amazon Q Developer Java transformation command line interface (CLI). This feature perform upgrades from Java versions 8, 11, 17, or 21 to versions 17 or 21 directly from the command line. This tool offers selective transformation options so you can choose specific steps from transformation plans and customize library upgrades.
• New AWS IoT Device Management managed integrations – You can simplify Internet of Things (IoT) device management across multiple manufacturers and protocols using the new managed integrations feature, which provides a unified interface for controlling devices whether they connect directly, through hubs or third-party clouds. The feature includes pre-built cloud-to-cloud (C2C) connectors, device data model templates, and SDKs that support ZigBee, Z-Wave, and Wi-Fi protocols, while you can still create custom connectors and data models.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS? page.

Other AWS news
Various Model Context Protocol (MCP) servers for AWS services have been released. Here are some tutorials about MCP servers that you might find interesting:

• AWS costs estimation using Amazon Q CLI and AWS Cost Analysis MCP – You can use Amazon Q CLI with the AWS Cost Analysis MCP server to perform sophisticated cost analysis that follows AWS best practices. We discuss basic setup and advanced techniques, with detailed examples and step-by-step instructions.
• Supercharging AWS database development with AWS MCP servers – You can learn the core concepts behind MCP and demonstrate how new AWS MCP servers can accelerate your database development through natural language prompts.
• Create your own MCP server with C# and .NET with Amazob Q CLI – You’ll use Amazon Q CLI as an MCP client to configure your local development environment for building and testing C# MCP servers, enhancing your ability to create, iterate, and refine AI-powered applications.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

• AWS re:Invent – Register now to get a head start on choosing your best learning path, booking travel and accommodations, and bringing your team to learn, connect, and have fun. If you’re an early-career professional, you can apply to the All Builders Welcome Grant program, which is designed to remove financial barriers and create diverse pathways into cloud technology.
• AWS NY Summits – You can gain insights from Swami’s keynote featuring the latest cutting-edge AWS technologies in compute, storage, and generative AI. My News Blog team is also preparing some exciting news for you. If you’re unable to attend in person, you can still participate by registering for the global live stream. Also, save the date for these upcoming Summits in July and August near your city.
• AWS Builders Online Series – If you’re based in one of the Asia Pacific time zones, join and learn fundamental AWS concepts, architectural best practices, and hands-on demonstrations to help you build, migrate, and deploy your workloads on AWS.

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

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Amazon FSx for OpenZFS now supports Amazon S3 access without any data movement

Starting today, you can attach Amazon S3 Access Points to your Amazon FSx for OpenZFS file systems to access your file data as if it were in Amazon Simple Storage Service (Amazon S3). With this new capability, your data in FSx for OpenZFS is accessible for use with a broad range of Amazon Web Services (AWS) services and applications for artificial intelligence, machine learning (ML), and analytics that work with S3. Your file data continues to reside in your FSx for OpenZFS file system.

Organizations store hundreds of exabytes of file data on premises and want to move this data to AWS for greater agility, reliability, security, scalability, and reduced costs. Once their file data is in AWS, organizations often want to do even more with it. For example, they want to use their enterprise data to augment generative AI applications and build and train machine learning models with the broad spectrum of AWS generative AI and machine learning services. They also want the flexibility to use their file data with new AWS applications. However, many AWS data analytics services and applications are built to work with data stored in Amazon S3 as data lakes. After migration, they can use tools that work with Amazon S3 as their data source. Previously, this required data pipelines to copy data between Amazon FSx for OpenZFS file systems and Amazon S3 buckets.

Amazon S3 Access Points attached to FSx for OpenZFS file systems remove data movement and copying requirements by maintaining unified access through both file protocols and Amazon S3 API operations. You can read and write file data using S3 object operations including GetObject, PutObject, and ListObjectsV2. You can attach hundreds of access points to a file system, with each S3 access point configured with application-specific permissions. These access points support the same granular permissions controls as S3 access points that attach to S3 buckets, including AWS Identity and Access Management (IAM) access point policies, Block Public Access, and network origin controls such as restricting access to your Virtual Private Cloud (VPC). Because your data continues to reside in your FSx for OpenZFS file system, you continue to access your data using Network File System (NFS) and benefit from existing data management capabilities.

You can use your file data in Amazon FSx for OpenZFS file systems to power generative AI applications with Amazon Bedrock for Retrieval Augmented Generation (RAG) workflows, train ML models with Amazon SageMaker, and run analytics or business intelligence (BI) with Amazon Athena and AWS Glue as if the data were in S3, using the S3 API. You can also generate insights using open source tools such as Apache Spark and Apache Hive, without moving or refactoring your data.

To get started
You can create and attach an S3 Access Point to your Amazon FSx for OpenZFS file system using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS SDK.

To start, you can follow the steps in the Amazon FSx for OpenZFS file system documentation page to create the file system, then, using the Amazon FSx console, go to Actions and select Create S3 access point. Leave the standard configuration and then create.

To monitor the creation progress, you can go to the Amazon FSx console.

Once available, choose the name of the new S3 access point and review the access point summary. This summary includes an automatically generated alias that works anywhere you would normally use S3 bucket names.

Using the bucket-style alias, you can access the FSx data directly through S3 API operations.

• List objects using the ListObjectsV2 API

• Get files using the GetObject API

• Write data using the PutObject API

The data continues to be accessible via NFS.

Beyond accessing your FSx data through the S3 API, you can work with your data using the broad range of AI, ML, and analytics services that work with data in S3. For example, I built an Amazon Bedrock Knowledge Base using PDFs containing airline customer service information from my travel support application repository, WhatsApp-Powered RAG Travel Support Agent: Elevating Customer Experience with PostgreSQL Knowledge Retrieval, as the data source.

To create the Amazon Bedrock Knowledge Base, I followed the connection steps in Connect to Amazon S3 for your knowledge base user guide. I chose Amazon S3 as the data source, entered my S3 access point alias as the S3 source, then configured and created the knowledge base.

Once the knowledge base is synchronized, I can see all documents and the Document source as S3.

Finally, I ran queries against the knowledge base and verified that it successfully used the file data from my Amazon FSx for OpenZFS file system to provide contextual answers, demonstrating seamless integration without data movement.

Things to know
Integration and access control – Amazon S3 Access Points for Amazon FSx for OpenZFS file systems support standard S3 API operations (such as GetObject, ListObjectsV2, PutObject) through the S3 endpoint, with granular access controls through AWS Identity and Access Management (IAM) permissions and file system user authentication. Your S3 Access Point includes an automatically generated access point alias for data access using S3 bucket names, and public access is blocked by default for Amazon FSx resources.

Data management – Your data stays in your Amazon FSx for OpenZFS file system while becoming accessible as if it were in Amazon S3, eliminating the need for data movement or copies, with file data remaining accessible through NFS file protocols.

Performance – Amazon S3 Access Points for Amazon FSx for OpenZFS file systems deliver first-byte latency in the tens of milliseconds range, consistent with S3 bucket access. Performance scales with your Amazon FSx file system’s provisioned throughput, with maximum throughput determined by your underlying FSx file system configuration.

Pricing – You’re billed by Amazon S3 for the requests and data transfer costs through your S3 Access Point, in addition to your standard Amazon FSx charges. Learn more on the Amazon FSx for OpenZFS pricing page.

You can get started today using the Amazon FSx console, AWS CLI, or AWS SDK to attach Amazon S3 Access Points to your Amazon FSx for OpenZFS file systems. The feature is available in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, Stockholm), and Asia Pacific (Hong Kong, Singapore, Sydney, Tokyo).

— Eli

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