Tag Archives: announcements

Amazon OpenSearch Serverless introduces collection groups to optimize cost for multi-tenant workloads

2026-02-26 Madhusudhan Narayana

Post Syndicated from Madhusudhan Narayana original https://aws.amazon.com/blogs/big-data/amazon-opensearch-serverless-introduces-collection-groups-to-optimize-cost-for-multi-tenant-workloads/

Today, we’re excited to announce the general availability of the collection groups feature for Amazon OpenSearch Serverless. With this feature you can reduce compute costs for multi-tenant workloads while creating secure tenant boundaries through per-tenant encryption, giving you the flexibility to balance cost efficiency with the exact level of isolation and security your applications requires.

Amazon OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service, that eliminates the complexity of infrastructure management for running search and analytics workloads at scale. It automatically provisions and scales resources to deliver fast data ingestion rates and millisecond response times, even as usage patterns change. For organizations that are managing multi-tenant environments, data isolation, where the tenant’s data must be encrypted and protected (often with their own encryption keys), is a compliance requirement.

Previously, OpenSearch Serverless provided maximum security through physical isolation: each AWS Key Management Service key (KMS key) required dedicated OpenSearch Compute Units (OCUs) to maintain complete physical data separation. While this architecture provided the highest level of protection, it created challenges for multi-tenant deployments at scale. For customers managing multiple tenants with shared encryption keys, OCU resources are efficiently pooled, making the economics favorable. However, customers managing large numbers of smaller tenants, each requiring their own KMS key for data isolation, faced a challenge with higher cost. With dedicated OCU resources needed per unique key, the infrastructure costs could become prohibitive when individual tenants required only a fraction of an OCU’s capacity. This particularly impacted service providers wanting to offer bring your own key (BYOK) capabilities to their customers, forcing them to either absorb unsustainable costs or limit their service offerings.

OpenSearch Serverless has always provided flexible capacity management with maximum OCU settings to help you control costs. For most workloads, this model works seamlessly capacity scales up and down in response to demand, so you only pay for what you use. However, some workload patterns are simply better served by having a guaranteed baseline of compute ready to go from the start. Workloads with sudden traffic spikes, high-speed data ingestion pipelines, or load testing scenarios benefit from having capacity pre-allocated, so that the first requests are handled with the same responsiveness as any other. Similarly, multi-tenant architectures and time-sensitive operations often require predictable, consistent performance from the moment a collection becomes active.

Flexible controls with collection groups

Collection groups give you flexible control over security boundaries and resource allocation. Instead of forcing a one-size-fits-all approach, you can now tailor your architecture to match your specific security and cost requirements. Here’s how it works:

Define your security boundary that matches your need: Collection groups is a logical security construct for related collections. Each collection groups maintains strong isolation with physically separated memory, CPU and disk from other collection groups, ensuring robust security boundaries between different security constructs.
Share resources across encryption keys: Allocate collections to your collection groups regardless of whether they share KMS keys or use separate ones. Collections with different encryption keys can now share OCU resources within the same security boundary, dramatically reducing costs while maintaining full encryption protection and logical separation for each tenant.
Deploy with flexible network access: Collection groups support collections with different network access types, allowing you to combine collections with public endpoints and VPC endpoints within the same group. This flexibility lets you match your security and connectivity requirements while benefiting from shared resource management across all collections in the group.
Control cost and performance: Set maximum OCUs to cap spending and minimum OCUs to guarantee baseline performance. This dual control gives you a defined resource envelope for each collection groups, eliminating cost surprises while ensuring consistent performance.
Optimize with insights: Access detailed CloudWatch metrics showing resource consumption, relative usage patterns, and latency across collection groups. These insights help you right-size allocations, identify optimization opportunities, and tune performance based on actual workload behavior.

With collection groups, you now have full control over resource allocation through both minimum and maximum OCU settings

Maximum OCUs: Cost control

Set an upper limit on resources to prevent runaway scaling and control costs per collection groups. This helps ensure you never exceed your budget, even during unexpected traffic spikes. Collection groups capacity limits operate independently from account-level limits. Account-level maximum OCU settings apply only to collections not associated with any collection groups, while collection groups maximum OCU settings apply to collections within that specific group. The sum of (Max OCUs across all your collection groups + Max OCU setting at the account level) should be less than your Service Quota Max OCUs allowed for your account. This separation gives you granular cost control across different security contexts.

Minimum OCUs: Performance guarantees

Define the baseline compute resources that will always be allocated to your collection groups, for consistent performance and resource availability. These OCUs are reserved exclusively for your collection groups and provide:

Instant availability with no cold starts: Your collections benefit from instant availability without scaling delays. Resources are always warm and ready, eliminating scaling delays when traffic arrives.
Guaranteed capacity: Resources are always available, even during periods of low activity or when competing with other collection groups, ensuring predictable performance even during low-traffic periods.
Predictable costs: Minimum OCUs are charged continuously, providing you with reserved capacity in exchange for predictable billing giving you cost certainty in exchange for guaranteed performance. This reserved baseline serves as the foundation for auto-scaling, which expands capacity up to your maximum limit as demand increases.

This combination gives you the flexibility to balance cost optimization with performance guarantees based on your specific requirements.

Multi-tenant cost economics with collection groups

Managing costs in multi-tenant architectures has always required balancing isolation, performance, and efficiency often at the expense of one another. Collection groups change that equation by enabling shared capacity across collections without sacrificing security boundaries. The following details how this plays out when you work with collection groups or without.

Before collection groups: Consider a customer with 10 tenants, each requiring their own KMS key for data isolation. Most of these tenants have modest data requirements typically 10-100GB, with the majority on the smaller end of that range. Managing dedicated resources for each tenant’s encryption key, regardless of their actual capacity needs, created operational complexity and cost challenges at scale.

With collection groups: The same customer can now group their tenants with similar security requirements into the collection groups, sharing OCU resources across collections. Tenants requiring only a small portion of OCU capacity no longer force the allocation of dedicated resources, reducing costs by up to 90% for large number of smaller tenant workloads.

With minimum OCU configuration: Premium tenants can be placed in collection groups with minimum OCUs set to guarantee performance, while standard tenants use collection groups with lower minimum thresholds for cost efficiency.

The following table illustrates how these cost savings play out across different tenant configurations, comparing infrastructure costs with and without collection groups across varying data sizes and query loads.

Number of tenants with unique KMS keys	Data size and query parameters	Cost with complete data isolation (without collection groups)	Cost with collection groups	Additional comments
10	Data size: 60GB or less Query: Not needing more than base OCU (1 for redundant collection) compute	$3,500	$350	10x Savings in cost.
10	Data size: 60GB or less Query: More than base OCU (1 for redundant collection) compute during peak times (For example – 5 additional OCUs per tenant without collection groups & 40 OCUs across all tenants based with collection groups due to benefit of shared infra).	$3500 + Peak time scale out per tenant ($8650)	$350+ Peak time scale out ($6912).	The system will scale up when there is additional query load, additional OCUs are deployed during this time. However when the load scales back, the system will scale-in to base OCU’s.
10	Data size: Sample data size in GB per tenant [3, 5, 7, 8, 10, 15, 18, 25, 28, 150] Query: Can handle queries upto certain level with minimum OCU for the data size and then scales out on load.	For the sample data sizes, minimum OCU requirement will be [2, 2, 2, 2, 2, 2, 2, 2, 2, 8] = 26 OCUs [$4492] + Peak time scale out per tenant	Minimum cost is determine by the number of OCUs required to hold the data across all tenants (120GB per OCU *2) + Peak time scale out.For the sample data sizes, 8 OCUs [$1382] + Peak time scale out per tenant	The system will scale up when there is additional query load, additional OCUs are deployed during this time. However when the load scales back, the system will scale-in to minimum number of OCU required to hold the data.

Note: Above calculations are made with assumption for redundant enabled collections. For non-redundant mode it will be half the above calculations.

Getting started with collection groups

Collection groups and minimum OCU configuration are available in all AWS Regions where OpenSearch Serverless is offered, at no additional charge. Collection groups offers a new organizational feature to create collection groups and add new collections directly to these groups for enhanced management capabilities. While your existing collections will continue to operate unchanged and remain independent of any collection groups, you can immediately start using collection groups for new collections to benefit from improved organization and workflow management.

Currently, only newly created collections can be associated with collection groups, and all collections within a group must be of the same type (search, time series, or vector search). Existing collections continue to operate independently with their current capacity management settings, and you cannot mix different collection types within a single collection groups. You can use the AWS Management Console, AWS CLI, AWS CloudFormation, or AWS CDK to create the collection groups. In the following section we will show you how you can create the collection groups using the OpenSearch Service console.

To create your first collection groups:

Open the OpenSearch Service console.
In the left navigation pane, choose Serverless, then choose Collection groups.
Choose Create collection groups.
For collection groups name, enter a name for your collection groups. The name must be 3-32 characters long, start with a lowercase letter, and contain only lowercase letters, numbers, and hyphens.
(Optional) For Description, enter a description for your collection groups.
In the Capacity management section, configure the OCU limits:
1. Maximum indexing capacity – The maximum number of indexing OCUs that collections in this group can scale up to.
2. Maximum search capacity – The maximum number of search OCUs that collections in this group can scale up to.
3. Minimum indexing capacity – The minimum number of indexing OCUs to maintain for consistent performance.
4. Minimum search capacity – The minimum number of search OCUs to maintain for consistent performance.
(Optional) In the Tags section, add tags to help organize and identify your collection groups.
Choose Create collection groups.

To assign collection to the collection groups

Open the Amazon OpenSearch Service console.
In the left navigation pane, choose Serverless, then choose Collections.
Choose Create collection.
For Collection name, enter a name for your collection. The name must be 3-28 characters long, start with a lowercase letter, and contain only lowercase letters, numbers, and hyphens.
(Optional) For Description, enter a description for your collection.
In the Collection groups section, select the collection groups you want the collection to be assigned to. A collection can only belong to one collection groups at a time.
(Optional) You can also choose to Create a new group. This will navigate you to the Create collection groups workflow. After you finish creating the collection groups, return to the step 1 of this procedure to begin creating your new collection.
Continue through the workflow to create the collection.

Managing collection groups

Once you’ve created your collection groups, you can update their settings as your architecture evolves. The Amazon OpenSearch Serverless documentation provides step-by-step guidance on how to edit and delete collection groups, including updating OCU limits and modifying group configurations using the AWS Management Console, CLI, and CloudFormation.

Conclusion

OpenSearch Serverless collection groups transform how you can architect multi-tenant deployments by offering flexible deployment modes that balance security requirements with operational efficiency. You can now choose the collection groups where you define logical security boundaries that allow collections, regardless of whether they share the same KMS key or use different KMS keys to share OCU resources.

This flexibility directly addresses the cost challenges that previously made multi-tenant deployments prohibitive. By consolidating collections within collection groups, you can reduce infrastructure costs while maintaining robust encryption and tenant isolation. Configuring both minimum and maximum OCUs for each collection groups solves the cold-start and capacity guarantee challenges: minimum OCUs ensure your collections maintain ready compute resources to handle high-speed ingestion, sudden traffic spikes, and load testing without performance degradation. Maximum OCUs provide cost predictability and spending controls. This dual configuration gives you a defined resource envelope that eliminates both the uncertainty of cold starts and the risk of runaway costs.

To dive deeper into the collection groups and minimum OCU configuration, visit the Amazon OpenSearch Serverless documentation.

About the authors

Amazon SageMaker AI now hosts NVIDIA Evo-2 NIM microservices

2026-02-24 Malvika Viswanathan

Post Syndicated from Malvika Viswanathan original https://aws.amazon.com/blogs/compute/amazon-sagemaker-ai-now-hosting-nvidia-evo-2-nim-microservices/

This post is co-written with Neel Patel, Abdullahi Olaoye, Kristopher Kersten, Aniket Deshpande from NVIDIA.

Today, we’re excited to announce that the NVIDIA Evo-2 NVIDIA NIM microservice are now listed in Amazon SageMaker JumpStart. You can use this launch to deploy accelerated and specialized NIM microservices to build, experiment, and responsibly scale your drug discovery workflows on Amazon Web Services (AWS).

In this post, we demonstrate how to get started with these models using Amazon SageMaker Studio.

NVIDIA NIM microservices on AWS

NVIDIA NIM integrates closely with AWS managed services, such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Kubernetes Service (Amazon EKS), and Amazon SageMaker AI, to support deployment of generative AI models at scale. As part of NVIDIA AI Enterprise, which is available in the AWS Marketplace, NVIDIA NIM is a set of microservices designed to accelerate the deployment of generative AI. These prebuilt containers support a broad spectrum of generative AI models, from open source community models, to NVIDIA Nemotron and custom models. NIM microservices are deployed with just a few lines of code, or with a few actions in the SageMaker Studio console. Engineered to facilitate seamless generative AI inferencing at scale, NIM ensures that generative AI applications can be deployed on various AWS services.

NVIDIA BioNeMo Evo 2 overview

NVIDIA BioNeMo is a platform of NIM microservices, developer tools, and AI models that accelerate building, adapting, and deploying biomolecular AI models for drug discovery. It packages curated training recipes, data loaders, and domain-optimized pretrained models for DNA, RNA, and proteins, alongside NVIDIA CUDA-X libraries such as NVIDIA cuEquivariance. These components power tasks such as 3D structure prediction, de novo design, virtual screening, docking, and property prediction with GPU-accelerated performance.

NVIDIA NIM microservices provide optimized, API-first inference that integrates directly into enterprise pipelines across on-premises and the cloud, providing scalable and secure deployment with faster time-to-market and lower Total Cost of Ownership (TCO). The Evo 2 NIM delivers a 40-billion parameter foundation model (FM) trained on a vast dataset of genomes that can be used to predict protein function, identify mutations, and accelerate bioengineering research. Furthermore, the Evo 2 NIM can be chained with other NIM microservices such as ESMFold to create end-to-end, containerized workflows that cut time-to-insight while streamlining deployment through consistent APIs.

SageMaker Studio overview

SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that provides a unified visual interface for all of the tools that you need to complete each step of the ML development lifecycle. SageMaker Studio provides complete access, control, and visibility into each step of the ML workflow, from data preparation to model building, training, and deployment.

The key features of SageMaker Studio include:

Unified interface: Access all SageMaker capabilities through a single, web-based visual interface
Jupyter notebooks: Fully managed Jupyter notebooks with pre-configured kernels for popular ML frameworks
Model management: Browse, deploy, and manage models from AWS Marketplace and other sources through an intuitive interface
Collaboration: Share notebooks, experiments, and models with your team members
Built-in security: Integrated with AWS Identity and Access Management (IAM) for secure access control
Cost management: Monitor and control costs with built-in usage tracking and resource management tools

Amazon SageMaker JumpStart overview

SageMaker JumpStart is a fully managed service that offers state-of-the-art foundation models for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval. It provides a collection of pre-trained models that you can deploy quickly, accelerating the development and deployment of ML applications. One of the key components of SageMaker JumpStart is model hubs, which offer a vast catalog of pre-trained models, such as Mistral, for a variety of tasks. You can now discover and deploy Evo 2 NIM in Amazon SageMaker Studio or programmatically through the SageMaker Python SDK, so you can derive model performance and MLOps controls with Amazon SageMaker AI features such as Amazon SageMaker Pipelines, Amazon SageMaker Debugger, or container logs. The model is deployed in a secure AWS environment and in your VPC, helping to support data security for enterprise security needs.

Prerequisites

Before getting started with deployment, make sure that your IAM service role for SageMaker AI has the SageMakerFullAccess permission policy attached. To deploy the NVIDIA NIM microservices successfully, confirm one of the following:

Make sure that your IAM role has the following permissions, and that you have the authority to make AWS Marketplace subscriptions in the AWS account used:

aws-marketplace:ViewSubscriptions
aws-marketplace:Unsubscribe
aws-marketplace:Subscribe

If your account is already subscribed to the model, then you can skip to the following Deploy section. Otherwise, start by subscribing to the model package and move to the Deploy section after.

Subscribe to the model package

To subscribe to the model package, complete the following steps:

Open the SageMaker Jumpstart portal from the SageMaker AI page.
Search for Evo 2 NIM.
Choose View model, and on the Model details page choose Subscribe. This will take you to the AWS Marketplace listing for the Evo 2 NIM.
On the AWS Marketplace listing page, choose View purchase options, review the purchase terms and choose the Subscribe button if you and your organization agree with EULA, pricing, and support terms.
Choose Continue to with the configuration and choose an AWS Region where you have the service quota for the desired instance type.

A product Amazon Resource Name (ARN) is displayed. This is the model package ARN that you need to specify while creating a deployable model using the SageMaker SDK.

Option 1: Deploy the Evo 2 NIM using SageMaker Studio

The following section outlines how to deploy the EVO 2 NIM using SageMaker Studio.

Getting started with SageMaker Studio

Begin by accessing the AWS Management Console and navigating to the SageMaker AI service. When you’re in the SageMaker AI console, locate Studio in the left navigation panel and choose Open Studio next to your user profile. If you haven’t set up a SageMaker Studio domain yet, then you must create a new domain and user profile first. This launches the web-based SageMaker Studio interface where you can manage all aspects of your ML workflow.

Navigating to model packages

Within SageMaker Studio, look for Models in the left sidebar and choose JumpStart base models tab within the Models interface. This section contains all available model packages in SageMaker JumpStart, including those from the AWS Marketplace

Locating the Evo-2 NIM model

Use the search functionality to find the NVIDIA Evo-2 NIM model by searching for terms such as “Evo-2” or “NVIDIA”. When you locate the model package in the filtered results, choose it to view the Model overview page. This page provides an overview of the model and can have a Notebooks tab that will show a sample notebook that contains an example showing how to use the NIM. You can choose Open in JupyterLab to open the notebook in JupyterLab and use it as a starting point for using the NIM.

Configuring the model deployment

On the model package overview page, choose the Deploy button on the top right to begin the deployment process. You must configure several important settings: provide a unique endpoint name (such as “Evo-2-nim-endpoint”), choose an appropriate instance type (ml.g6e.12xlarge is recommended for optimal performance), set the initial instance count (typically 1 for initial testing), and specify an endpoint configuration name. Review all of these settings carefully before proceeding.

Initiating and monitoring the deployment

After verifying your configuration settings, choose Deploy to start the deployment process for creating a Real-time inferance endpoint. Navigate to the Deployments section and then the Endpoints section in the left sidebar to monitor the deployment progress. The endpoint status initially shows Creating and typically takes 5–10 minutes to complete. You can track the progress and should see the status change to InService once the deployment is successful.

Testing and validation

When your endpoint is deployed and shows the In Service status, you can optionally test it directly through the SageMaker Studio interface. Choose your deployed endpoint from the endpoints list to access the Endpoint summary page. Scroll down and select the Playground tab. If available, you will see two options: Test the sample request and Use Python SDK example code. You can use either option to validate the deployment by using a sample protein sequence. This validates the endpoint is working correctly before integrating it into your applications.

Option 2: Deploy Evo 2 using the SageMaker SDK

In this section we walk through deploying the Evo-2 NIM through the SageMaker SDK. Make sure that you have the account-level service limit for using ml.g6e.12xlarge for endpoint usage as one or more instances. Furthermore, NVIDIA provides a list of supported instance types that support deployment. Refer to the AWS Marketplace listing for the model to see the supported instance types. To request a service quota increase, go to the AWS service quotas.

import sagemaker
import boto3
from sagemaker import ModelPackage, get_execution_role
import json
# Initialize SageMaker session and role
role = get_execution_role()
sagemaker_session = sagemaker.Session()
# Model Package ARN from your AWS Marketplace subscription
# Replace this with your actual Model Package ARN after subscription
model_package_arn = "arn:aws:sagemaker:<region>:<account-id>:model-package/Evo-2-nim-model"
# Create model from AWS Marketplace Model Package
model = ModelPackage(
    role=role, 
    model_package_arn=model_package_arn,
    sagemaker_session=sagemaker_session
)
# Deploy the model to an endpoint
predictor = model.deploy(
    initial_instance_count=1,
    instance_type="ml.g6e.12xlarge",  # Using recommended NVIDIA GPU instance
    endpoint_name="Evo-2-endpoint",
    wait=True
)

Run Inference with Evo 2 SageMaker endpoint

When you have the model, you can use a sample text to do an inference request. NIM on SageMaker supports the OpenAI API inference protocol inference request format. For an explanation of the supported parameters, go to the Evo-2 API documentation.

Real-time inference example

sm_runtime = boto3.client("sagemaker-runtime", region_name=region)

generate_payload = {

 "sequence": "ACGTACGTACGT",

 "num_tokens": 100,

 "temperature": 0.7,

 "top_k": 3,

}

response = sm_runtime.invoke_endpoint(

EndpointName='Evo2-40b-2-1-0',

ContentType="application/json",

Body=json.dumps(generate_payload),

)

result = json.loads(response["Body"].read())

print("Generated DNA:", result["sequence"])
print("Elapsed (ms):", result.get("elapsed_ms"))

Example output:

Generated DNA: ACGTACATATGTTCGTACATTCGCACAGACGCCATTTTGAAAAATGCTTTAAATGGATTCAGAATTGGTCAAAATGCATAAATCCATCAAAATTTTTTTC
Elapsed (ms): 10770

Cleaning up

To avoid unwanted charges, complete the steps in this section to clean up your resources.

Deleting the endpoint from SageMaker Studio

In SageMaker Studio, navigate to the Endpoints section in the left sidebar under Inference to view all your active endpoints. Locate your Evo-2 NIM endpoint in the list and select it to open the endpoint details page. On this page, there is a Delete button. Choose Delete and confirm the deletion when prompted. The endpoint status changes to Deleting and disappears from your endpoints list when the deletion is complete. This process typically takes a few minutes, and when it’s deleted the endpoint stops incurring charges immediately.

Delete the SageMaker endpoint

The SageMaker endpoint that you deployed incurs costs if you leave it running. Use the following code to delete the endpoint if you want to stop incurring charges. For more details, go to Delete endpoints and resources.

# Delete endpoint when done (important for cost management)
predictor.delete_endpoint()

Conclusion

The availability of NVIDIA Evo-2 NIM microservices on Amazon SageMaker Jumpstart represents a significant advancement for researchers and organizations working in drug discovery. This solution provides GPU-accelerated multiple sequence alignments and dramatically speeds up structure prediction pipelines that are critical for protein design and antibody research. Users can implement the flexible deployment options—through SageMaker Studio, or SageMaker SDK—to choose the approach that best fits their workflow and technical expertise. The optimized performance of these NIM microservices, combined with the scalability and security of SageMaker, enables faster time-to-insight while streamlining the deployment of complex biomolecular AI models. We encourage you to try the Evo-2 NIM today and look out for future release of MSA-search and Boltz-2 NIMs to accelerate your drug discovery workflows and use the power of NVIDIA’s specialized microservices on AWS infrastructure.

Amazon Athena adds 1-minute reservations and new capacity control features

2026-02-12 Manan Nayar

Post Syndicated from Manan Nayar original https://aws.amazon.com/blogs/big-data/amazon-athena-adds-1-minute-reservations-and-new-capacity-control-features/

Many of you choose serverless services for your analytics workloads because of its simplicity and elasticity. But many of you running mission-critical queries face a common challenge: ensuring your high-priority workloads run when needed and without interference from other queries in your account.

Amazon Athena is a serverless interactive query service that makes it simple to analyze data using SQL. Capacity Reservations is a feature of Athena that addresses the need to run critical workloads by providing dedicated serverless capacity for the workloads you specify. With Capacity Reservations, you request capacity in the form of Data Processing Units (DPU) and you assign them to your workloads.

In this post, we highlight three new capabilities that make Capacity Reservations more flexible and easier to manage: reduced minimums for fine-grained capacity adjustments, an autoscaling solution for dynamic workloads, and capacity cost and performance controls.

Now available: 1-minute reservations and 4 DPU minimum

Yesterday, we announced a big change for Capacity Reservations: you can now reserve as few as 4 DPU (down from 24 DPU) for as little as 1 minute (down from 60 minutes). This update lets you make frequent, fine-grained capacity adjustments to closely match your workload patterns and hold less capacity, with savings up to 95% for workloads that complete in under an hour.

We’ve optimized Athena for interactive queries that need a quick response, but many of you use Athena for non-interactive queries as well. For example, you may have queries that run on a schedule to prepare data for downstream analysis or perform updates to Apache Iceberg tables. These queries often process larger volumes of data and run for longer than interactive queries. If you’re using Athena’s scan-based pricing option, all your queries count towards your account-level quota. This means that your latency sensitive interactive queries can sometimes end up queued behind non-interactive queries that are running in your account.

Capacity Reservations addresses prioritization problems like this by making it possible to assign dedicated capacity to Athena workgroups. For example, Twilio operates a query platform that serves 1,500+ users who run over 2.5 million queries per month. They use Capacity Reservations for important workloads that need to have dedicated capacity to run optimally and avoid competing with other workloads.

Capacity Reservations have worked well when your workloads have been large and predictable. For example, users accessing dashboards at the start of the workday, or data processing jobs that run continuously 24/7. However, you’ve told us that you wanted more flexibility to update your reservations more frequently, to better match changes in demand.

With Athena’s new 4 DPU and 1-minute minimums, you’re now able to adjust capacity more frequently and match demand more closely than before. We’re excited to see how these updates benefit your mission-critical query workloads.

Autoscaling for dynamic workloads

The reduced minimums enable frequent capacity adjustments, but making those adjustments requires effort. Consider a business intelligence workload that peaks in the morning as executives review dashboards but decreases throughout the day. You want this workload isolated so that high-priority queries aren’t queued behind less important queries.

With 1-minute minimums, you can now adjust capacity to closely track these patterns. However, manually adjusting capacity this frequently is tedious—you need to monitor utilization, decide when to scale, and periodically adjust DPU.

We recently launched an autoscaling solution that uses AWS Step Functions to orchestrate capacity adjustments. It monitors capacity utilization metrics that Athena emits to Amazon CloudWatch at 1-minute granularity, analyzes utilization signal over configurable intervals, then conditionally adds or removes DPU so you can maintain consistent performance even during traffic spikes.

We have made this available as a 1-click deployment from the Athena console: just click Set up autoscaling on the details page for your reservation. When you do, a AWS CloudFormation template sets up all the resources you need. Among the resources set up is the Step Functions state machine, which you can view by opening Athena’s left-side navigation menu and clicking Workflows.

You can also find the template and information on the configurable autoscaling parameters in our documentation. See Automatically adjust capacity in the Athena User Guide.

We chose Step Functions for this solution to enable extensibility and customization. Step Functions integrates tightly with AWS services and allows you to define sophisticated state machines in Amazon States Language, a JSON-based language for serverless workflows. This makes it straightforward to add conditional logic, integrate additional services, or modify the workflow to match your specific requirements.

Control DPU usage at the workgroup and query levels

Part of the ease of use and simplicity of Athena is that it allocates capacity to queries automatically based on their complexity. However, sometimes preventing a single query from using too much capacity or operating at a required level of concurrency is more important than individual query performance. We recently released new DPU cost and performance controls so you can set constraints on Athena’s capacity allocation behavior when you’re using Capacity Reservations.

You can control DPU allocation in two places: workgroup-level controls that apply to all queries in that workgroup, or per query using the StartQueryExecution API. Both approaches set a type of budget that Athena adheres to when planning queries and determining how much capacity to allocate.

You can set minimum and maximum DPU limits from 4 to 124 DPU in increments of 4. Setting a maximum prevents Athena from allocating more DPU than specified. When you set a minimum, you instruct Athena to allocate at least the specified DPU. This can be beneficial when you know that a specific query requires a specific number of DPU to run optimally for your use case. Set both to create a range. For example, a minimum of 4 and maximum of 16 lets Athena start with 4 DPU and scale to 16 if needed. Setting them to the same value forces queries to run on an exact number of DPU.

Controls that you set at the workgroup-level are visible in the workgroup details page and the reservation that the workgroup has been added to.

Last but not least: every query you run on reserved capacity now reports its DPU usage in the Athena console and GetQueryExecution / BatchGetQueryExecution APIs, giving you complete visibility into capacity utilization.

Moving to Capacity Reservations

Getting started with Capacity Reservations involves creating a reservation with your desired DPU count, then assigning workgroups to that reservation. For end users, nothing changes. You continue running queries as usual and no SQL changes are needed. For administrators, you create a Capacity Reservation with your desired DPU count, then assign workgroups to that reservation. Athena automatically routes queries from assigned workgroups to your reserved capacity, isolated from other queries in your account and no impact to your account-level concurrency quota.

Conclusion

These updates to Capacity Reservations give you greater flexibility and control over your Athena workloads. The reduced minimums let you adjust capacity in smaller increments and shorter time windows, allowing you to match your usage patterns more closely than before. Autoscaling eliminates the work of making those adjustments manually. And DPU controls give you fine-grained influence over how individual queries consume capacity. Together, these capabilities help you optimize costs, manage concurrency, and deliver predictable performance for your most critical workloads—all while preserving Athena’s serverless benefits.

To learn more, see Athena Capacity Reservations in the Athena User Guide, Athena pricing page, or create your first Capacity Reservation in the Athena console.

About the authors

AWS Weekly Roundup: Claude Opus 4.6 in Amazon Bedrock, AWS Builder ID Sign in with Apple, and more (February 9, 2026)

2026-02-09 Sébastien Stormacq

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-claude-opus-4-6-in-amazon-bedrock-aws-builder-id-sign-in-with-apple-and-more-february-9-2026/

Here are the notable launches and updates from last week that can help you build, scale, and innovate on AWS.

Last week’s launches
Here are the launches that got my attention this week.

Let’s start with news related to compute and networking infrastructure:

Introducing Amazon EC2 C8id, M8id, and R8id instances: These new Amazon EC2 C8id, M8id, and R8id instances are powered by custom Intel Xeon 6 processors. These instances offer up to 43% higher performance and 3.3x more memory bandwidth compared to previous generation instances.
AWS Network Firewall announces new price reductions: The service has added the hourly and data processing discounts on NAT Gateways that are service-chained with Network Firewall secondary endpoints. Additionally, AWS Network Firewall has removed additional data processing charges for Advanced Inspection, which enables Transport Layer Security (TLS) inspection of encrypted network traffic.
Amazon ECS adds Network Load Balancer support for Linear and Canary deployments: Applications that commonly use NLB, such as those requiring TCP/UDP-based connections, low latency, long-lived connections, or static IP addresses, can take advantage of managed, incremental traffic shifting natively from ECS when rolling out updates.
AWS Config now supports 30 new resource types: These range across key services including Amazon EKS, Amazon Q, and AWS IoT. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources.
Amazon DynamoDB global tables now support replication across multiple AWS accounts: DynamoDB global tables are a fully managed, serverless, multi-Region, and multi-active database. With this new capability, you can replicate tables across AWS accounts and Regions to improve resiliency, isolate workloads at the account level, and apply distinct security and governance controls.
Amazon RDS now provides an enhanced console experience to connect to a database: The new console experience provides ready-made code snippets for Java, Python, Node.js, and other programming languages as well as tools like the psql command line utility. These code snippets are automatically adjusted based on your database’s authentication settings. For example, if your cluster uses IAM authentication, the generated code snippets will use token-based authentication to connect to the database. The console experience also includes integrated CloudShell access, offering the ability to connect to your databases directly from within the RDS console.

Then, I noticed three news items related to security and how you authenticate on AWS:

AWS Builder ID now supports Sign in with Apple: AWS Builder ID, your profile for accessing AWS applications including AWS Builder Center, AWS Training and Certification, AWS re:Post, AWS Startups, and Kiro, now supports sign-in with Apple as a social login provider. This expansion of sign-in options builds on the existing sign-in with Google capability, providing Apple users with a streamlined way to access AWS resources without managing separate credentials on AWS.
AWS STS now supports validation of select identity provider specific claims from Google, GitHub, CircleCI and OCI: You can reference these custom claims as condition keys in IAM role trust policies and resource control policies, expanding your ability to implement fine-grained access control for federated identities and help you establish your data perimeters. This enhancement builds upon IAM’s existing OIDC federation capabilities, which allow you to grant temporary AWS credentials to users authenticated through external OIDC-compatible identity providers.
AWS Management Console now displays Account Name on the Navigation bar for easier account identification: You now have an easy way to identify your accounts at a glance. You can now quickly distinguish between accounts visually using the account name that appears in the navigation bar for all authorized users in that account.
Amazon CloudFront announces mutual TLS support for origins: Now with origin mTLS support, you can implement a standardized, certificate-based authentication approach that eliminates operational burden. This enables organizations to enforce strict authentication for their proprietary content, ensuring that only verified CloudFront distributions can establish connections to backend infrastructure ranging from AWS origins and on-premises servers to third-party cloud providers and external CDNs.

Finally, there is not a single week without news around AI :

Claude Opus 4.6 now available in Amazon Bedrock: Opus 4.6 is Anthropic’s most intelligent model to date and a premier model for coding, enterprise agents, and professional work. Claude Opus 4.6 brings advanced capabilities to Amazon Bedrock customers, including industry-leading performance for agentic tasks, complex coding projects, and enterprise-grade workflows that require deep reasoning and reliability.
Structured outputs now available in Amazon Bedrock: Amazon Bedrock now supports structured outputs, a capability that provides consistent, machine-readable responses from foundation models that adhere to your defined JSON schemas. Instead of prompting for valid JSON and adding extra checks in your application, you can specify the format you want and receive responses that match it—making production workflows more predictable and resilient.

Upcoming AWS events
Check your calendars so that you can sign up for this upcoming event:

AWS Community Day Romania (April 23–24, 2026): This community-led AWS event brings together developers, architects, entrepreneurs, and students for more than 10 professional sessions delivered by AWS Heroes, Solutions Architects, and industry experts. Attendees can expect expert-led technical talks, insights from speakers with global conference experience, and opportunities to connect during dedicated networking breaks, all hosted at a premium venue designed to support collaboration and community engagement.

If you’re looking for more ways to stay connected beyond this event, join the AWS Builder Center to learn, build, and connect with builders in the AWS community.

Check back next Monday for another Weekly Roundup.

— seb

Building fault-tolerant applications with AWS Lambda durable functions

2026-02-06 Rahul Pisal

Post Syndicated from Rahul Pisal original https://aws.amazon.com/blogs/compute/building-fault-tolerant-long-running-application-with-aws-lambda-durable-functions/

Business applications often coordinate multiple steps that need to run reliably or wait for extended periods, such as customer onboarding, payment processing, or orchestrating large language model inference. These critical processes require completion despite temporary disruptions or system failures. Developers currently spend significant time implementing mechanisms to track progress, handle failures, and manage resources when waiting for external events, shifting focus from business logic to undifferentiated tasks.

At re:Invent 2025, Amazon Web Services (AWS) launched AWS Lambda durable functions, a new capability extending Lambda’s event-driven programming model with built-in capabilities to build fault-tolerant multi-step applications and AI workflows using familiar programming languages. At its core, durable functions are regular Lambda functions, so your development and operational processes for Lambda continue to apply. However, when you create a Lambda function you can now enable durable execution, so that you can checkpoint progress, automatically recover from failures, and suspend execution for up to one year when waiting on long-running tasks, such as human-in-the-loop processes.

How Lambda durable functions work

When working with standard Lambda functions, your code runs from start to finish in a single invocation. If a failure occurs at any point during the execution, the entire function must be retried by the invoking event source. Any state that needs to be preserved between executions must be explicitly saved and retrieved. This is typically done by using external storage services such as Amazon DynamoDB or Amazon Simple Storage Service (Amazon S3). Furthermore, you must typically guard against duplicate (concurrent) invocations of the same event and have a strategy to safely deploy updates while continuing to process events.

In contrast, with Lambda durable functions, developers use durable operations such as “Steps” and “Waits” in the event handler to checkpoint progress, handle failures, and suspend execution during wait periods without incurring compute charges for on-demand functions. These durable operations and any optional state returned from them are automatically persisted by Lambda in a fully-managed durable execution backend. If failures occur during the execution, or if your function resumes its execution after being paused, Lambda invokes your function again, restoring (replaying) the previous state by executing the event handler from the start, but skipping over completed durable operations. To streamline this checkpoint/replay mechanism for developers, you can use the Lambda durable execution SDK to wrap or annotate your event handler, which enhances the existing Lambda context with several new methods like context.step() and context.wait(). Furthermore, you can use methods such as context.waitForCallback() to wait on external jobs or asynchronous processes, such as “human-in-the-loop” scenarios. The execution is paused until a SendDurableExecutionCallbackSuccess or SendDurableExecutionCallbackFailure response is sent to the Lambda API.

Getting started

Use the AWS Serverless Application Model (AWS SAM) to create a new durable function with sam init with an AWS Quick Start Template. Lambda durable functions are also supported by the AWS Cloud Development Kit (AWS CDK), AWS Command Line Interface (AWS CLI), AWS CloudFormation and other infrastructure as code (IaC) frameworks such as Terraform.

Consider the following function, which performs user onboarding. First, it creates a user profile based on some data, then it sends out an email for verification and waits until the user either confirms the email address, or a 24-hour timeout is reached. Finally, it sends out a confirmation.

import {
  DurableContext,
  withDurableExecution,
} from '@aws/durable-execution-sdk-js';
export const handler = withDurableExecution(
  async (event: OnboardingEvent, context: DurableContext) => {
    try {    
      // Create user profile
      const profile = await context.step("create-profile", async () =>
        createUserProfile(event.email, event.name)
      );
      // Wait for email verification via callback
      const verification = await context.waitForCallback(
        "wait-for-email-verification",
        async (callbackId) => {
          // Send email to user and pass callbackId
          await sendVerificationEmail(profile, callbackId);
        },
        {
          timeout: { hours: 24 } 
        }
      );
      // Send confirmation and welcome email
      const result = await context.step("complete-onboarding", async () => {
        if (!verification || !verification.verified) 
     return { ...profile, status: 'failed' };
        await sendWelcomeEmail(profile.email, profile.name);
        return { ...profile, status: 'active' };
      });
      return result;
    } catch (error) {
      // omitted 
    }
  }
);

Durable functions have built-in and fully customizable error handling for steps. For example, if the profile was successfully created and verified, but a temporary error occurred when sending out the confirmation, then the step is retried. The retry skips over any previously completed checkpoints, such as the profile creation and callback. Only the code within the send confirmation step is run again.

Next, you update the AWS SAM template to include your durable function. You create a Lambda durable function by including the DurableConfig setting for your function. Note that you currently cannot add a durable configuration to a function that was originally created without it. The ExecutionTimeout defines after which time the durable execution times out to protect against runaway or deadlock application bugs. This setting is separate from the invocation timeout, which defines for how long a single invocation can run. The maximum invocation timeout for a single function invocations remains unchanged at 15 minutes. With Lambda durable functions, you will typically see multiple invocations per durable execution, such as when using the wait capabilities in the SDK or automatic retries. You can set the ExecutionTimeout for up to one year when using asynchronous invocations.

The RetentionPeriodInDays defines how long the execution data of a durable execution is available to you after executions complete.

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
 
Resources:
  UserOnboardingFunction:
    Type: AWS::Serverless::Function
    Properties:
      FunctionName: UserOnboardingFunction
      CodeUri: ./src
      Handler: index.handler
      Runtime: nodejs24.x
      Architectures:
        - x86_64
      MemorySize: 256
      Timeout: 60		   // Timeout for an individual invocation
      DurableConfig:		   // This makes the function a durable function
        ExecutionTimeout: 90000 // 25h timeout for the durable execution overall
        RetentionPeriodInDays: 7 
UserOnboardingFunctionRole:
    Type: AWS::IAM::Role
    // omitted for brevity

You must include the necessary permissions for your function. For example, the AWSLambdaBasicDurableExecutionRole managed policy only allows the minimal AWS Identity and Access Management (IAM) actions to create/retrieve checkpoints and logs to increase security. Therefore, it does not include permissions to invoke other (durable) functions or manage callbacks. Refer to the documentation for more details.

Testing locally

Before deploying your function, you can test it locally using AWS SAM local invoke.

AWS SAM locally invokes your function and runs the event handler until it reaches the context.waitForCallback(). To complete callbacks, AWS SAM offers new commands to interact with your durable functions. In this example, you send a Success response to complete the callback. You can also include relevant data in the response. You can send the response directly using the on-screen guide or using another AWS SAM CLI command from another process.

sam local callback succeed <your-callback-id> --result '<your data>'

To inspect an execution, you can use AWS SAM to retrieve the durable execution history of your function, which includes details about steps, callbacks, and wait durations, as shown in the following example code.

sam local execution history <execution-arn>

Depending on your use case, you can instead send a Failure response to a callback and handle those errors in your code. For example, by performing compensation logic in a subsequent step:

sam local callback fail <your-callback-id> --result '<your data>'

Now that you have verified that your function works as intended, deploy it to AWS using sam deploy command.

Best practices and considerations

Invoking a Lambda durable function requires a qualified Amazon Resource Name (ARN), such as an alias or version. We recommend that you don’t use the $LATEST qualifier except for rapid prototyping or local testing. Using explicit versions ensures that replays always happen with the same code with which the execution was started. This is to ensure deterministic execution and prevent inconsistencies when updating your function code during executions.

We recommend bundling the durable execution SDK with your function code using your preferred package manager. The SDKs are fast-moving, so you can update dependencies as new features become available.

There are other durable operations in the Lambda durable functions SDK that you can use to build your application:

waitForCondition(): Pauses the execution of your function until a condition is met. For example, the status of a job polled with an API. For this to work, you provide the waitStrategy and a check function to poll the status.
parallel(): Runs multiple durable operations in parallel within the same function, with configurable options such as the maximum number of concurrent branches and desired failure behavior. This streamlines managing durability and checkpointing for simultaneous asynchronous actions.
map(): Creates a durable operation and checkpoint for each item of an array, based on the provided mapping function. The items are processed concurrently.
invoke(): Invokes another Lambda function and waits for its result. The SDK creates a checkpoint, invokes the target function, and resumes your function when the invocation completes. This enables function composition and workflow decomposition.

Refer to the developer guide for more details.

Lambda compute charges apply to all invocations, including any replays. When using wait operations, the function suspends execution and, for on-demand functions, doesn’t incur duration charges until execution resumes. You’re also charged for durable operations, data written, and data retention. To learn more about Lambda durable functions pricing, refer to the Lambda pricing page.

For the latest Region availability, visit the AWS Capabilities by Region page.

Conclusion

AWS Lambda durable functions extends the Lambda programming model to streamline building fault-tolerant, long-running applications using familiar programming patterns. You can use Lambda durable functions to write multi-step workflows in your preferred programming language, using built-in methods that automatically handle progress checkpointing and error recovery. This streamlines your architectures so that you can focus on your business logic, and optimize cost by charging only for active compute time.

You can build durable functions for Python or Node.js based Lambda functions using the Lambda API, AWS Management Console, AWS CLI, AWS CloudFormation, AWS SAM, AWS SDK, and AWS CDK.

To get started, visit the Lambda Developer Guide or watch the re:Invent breakout session.

Announcing the AWS Digital Sovereignty Well-Architected Lens

2026-01-30 Swapnonil Mukherjee

Post Syndicated from Swapnonil Mukherjee original https://aws.amazon.com/blogs/architecture/announcing-the-aws-digital-sovereignty-well-architected-lens/

As organizations accelerate cloud adoption, meeting digital sovereignty requirements has become essential to build trust with customers and regulators worldwide. The challenge isn’t whether to adopt the cloud—it’s how to do so while meeting sovereignty requirements, using a multidisciplinary approach.

Even though requirements vary by geography, organizations commonly address them through technical and operational controls applied consistently at scale. Controls address specific needs related to data residency, data protection, data privacy, access control, and resiliency. These controls also map to security and privacy baselines plus industry regulations. Examples include German BSI C5, UK GDPR, EU DORA, and newer regulations such as the EU AI Act.

Beyond technical and operational controls, in some jurisdictions, customers might have to align with interoperability and portability mandates requiring the adoption of specific infrastructure components, technology standards, and locally sourced software components.Partners and customers have said that they understand how AWS is sovereign-by-design, but they want to go further and apply those same design principles and best practices to their own workloads. Today, we’re introducing the AWS Digital Sovereignty Well-Architected Lens, a framework that helps you design, build, and operate workloads that are sovereign, compliance-aligned, and auditable while being survivable, interoperable, and portable across a range of deployment options.

The Digital Sovereignty Lens is available in the form of a whitepaper and as a custom lens file from AWS Well-Architected custom lens GitHub repository.

The AWS Well-Architected Framework

The AWS Well-Architected Framework is a structured assessment tool divided into six pillars. Each pillar is organized into a hierarchy of themes, questions, and best practices. Best practices describe the benefits of adoption. They also list actionable implementation guidance and implementation steps. The Digital Sovereignty Lens follows the same structure and is meant to complement the Well-Architected Framework. It presents additional questions and best practices designed to improve the digital sovereignty posture of your workloads.

How is the lens organized?

The Digital Sovereignty Lens outlines more than 60 best practices spread across the four pillars of Operational Excellence, Security, Reliability, and Performance Efficiency. It does not add new best practices to the Cost Optimization and Sustainability pillars. You should use existing best practices already defined in the Well-Architected Framework under those two pillars.

Each best practice in the Digital Sovereignty Lens maps to a specific question of the form “How do you do X?” For example, for the question “How do you design your workload for continuous auditability?”, the associated best practices include planning and preparing for audits, and automating evidence collection and reporting.

Underpinning the questions and the associated best practices are a set of design principles. The design principles list the key challenges organizations face and document steps required to address those challenges.

Design principles

The Digital Sovereignty Lens outlines five core design principles that engineering teams can adopt to address sovereignty requirements. These design principles build on top of secure by design and privacy by design principles. The principles and the key areas they address include:

Apply standardized enforceable controls – Rather than relying on spreadsheets and manual enforcement, apply standardized compliance-aligned controls using policy as code and compliance as code practices. Automated controls leave no room for interpretation and reduce the risk of inconsistent implementations across teams.
Establish adequate security posture in line with data sensitivity levels – Apply access controls, build data perimeters, and protect data at rest, in transit, and during compute. Calibrate controls to data sovereignty requirements—such as residency and export controls—to maintain business agility without compromising security or compliance.
Design for continuous compliance – Point-in-time certifications are just snapshots. Integrate compliance checks throughout your software development lifecycle and collect evidence required for audits on a continuous basis. When compliance is built in from the start, you reduce compliance violations and maintain a consistently audit-ready posture.
Design for interoperability and portability – Design workloads for interoperability and portability from the start. Build abstractions into your code and configurations, then test across multiple environments to verify consistent functionality.
Design for survivability – Document system dependencies and fault isolation boundaries. Align your recovery objectives with business continuity goals, define what a minimum restorable service looks like, and test your recovery paths regularly.

Best practices

The following diagram provides a snapshot of some of the best practices in the lens.

Trust and transparency are key attributes of a sovereign workload. Trust is achieved through verification, not through claims. The Operational Excellence pillar focuses on best practices that lead to continuous compliance and auditability, improving verifiability. The Security pillar provides best practices that lead to greater visibility of controls and recommends independent Regional operations. The Reliability pillar addresses the need to achieve a balance between sovereignty and survivability by carefully considering how you design workloads for automated recoverability and protect data sovereignty. The Performance Efficiency pillar focuses on adopting standard protocols to optimize networking and compute in alignment with regulatory needs.

Who should use this lens?

The following users can benefit from this lens:

Policy-makers and regulators – Use the sovereignty outcomes and general design principles as described earlier in this post to develop jurisdictional and sectoral digital sovereignty models.
Technical leaders (CxOs and enterprise architects) – Use the lens as an input while outlining enterprise architecture strategies, or towards making objective technology decisions.
Security and compliance consultants – Use the design principles and best practices to develop privacy and security policies that can subsequently be translated into technical and operational controls.
Builders – Use the lens as a key input while designing, developing, and validating sovereign-ready workloads.
Audit professionals – Use the implementation steps described in the best practices to understand possible sources of evidence and artifacts they should seek during security and privacy audits.
Governance risk and compliance professionals – Use the lens to understand and document the overall risk landscape. Develop per-application risk profiles and manage risks over time.

Your path to sovereign-ready workloads

The Digital Sovereignty Lens is part of a wider effort at AWS to equip our customers with comprehensive guidance and tools required to address their sovereignty needs. We recently introduced the AWS European Sovereign Cloud: Sovereign Reference Framework (ESC-SRF). Customers and partners can use the ESC-SRF (available from AWS Artifact) as a foundation upon which they can build their own complementary controls when using the AWS European Sovereign Cloud. This can also be used as supporting documentation as part of audits showing how AWS meets sovereignty requirements across dimensions such as independence, operational control, data residency, and technical isolation.

During and leading up to AWS re:Invent 2025, we announced several new capabilities designed to increase trust, bring more transparency, and provide customers with more control and choice. They include the Nitro Isolation Engine, IAM Policy Autopilot, the Landing Zone Accelerator on AWS Universal Configuration, Controls Dedicated experience in AWS Control Tower, and productivity tools such as the CloudFormation IDE Experience.

We are not stopping here. We look forward to your feedback as we continue to improve the lens content. We will also continue to develop decision guides, reference architectures, prescriptive guidance, and solution accelerators that embed and codify the best practices described in the lens.

About the authors

More room to build: serverless services now support payloads up to 1 MB

2026-01-30 Anton Aleksandrov

Post Syndicated from Anton Aleksandrov original https://aws.amazon.com/blogs/compute/more-room-to-build-serverless-services-now-support-payloads-up-to-1-mb/

To support cloud applications that increasingly depend on rich contextual data, AWS has raised the maximum payload size from 256 KB to 1 MB for asynchronous AWS Lambda function invocations, Amazon Simple Queue Service (Amazon SQS), and Amazon EventBridge. Developers can use this enhancement to build and maintain context-rich event-driven systems and reduce the need for complex workarounds such as data chunking or external large object storage.

Overview

Modern cloud applications rely on context-rich, structured data to drive intelligent behavior. Large language model (LLM) prompts, telemetry signals, personalization data, machine learning (ML) outputs, and user interaction logs are no longer simple strings. Instead, they’re typically complex, nested JSON or YAML objects carrying meaningful context. Previously, developers working with serverless services such as Amazon SQS, Lambda (asynchronous invocations and Amazon SQS event-source mapping), or EventBridge had to carefully manage their data to fit within the 256 KB payload size limit. This commonly meant chunking larger payloads, externalizing payloads to object stores such as Amazon S3, or using data compression. These workarounds added complexity and latency, creating edge cases that were difficult to monitor and debug.

With the recent launches, you can now transmit payloads up to 1 MB, significantly reducing the need for complex data chunking and architectural workarounds. This increased capacity streamlines design patterns, reduces operational overhead, and makes event-driven systems more intuitive to build and maintain. Developers can now include richer data in single payloads—from detailed LLM prompts and full system states to comprehensive context and complete transaction histories.

The new 1 MB payload size limit applies to asynchronous Lambda function invocations, whether you trigger them using either SQS event-source mapping, AWS Command Line Interface (AWS CLI), AWS SDKs, Lambda Invoke API, or AWS services such as EventBridge. The increased limit also extends to all messages and events flowing through Amazon SQS queues and EventBridge Event Buses.

Getting started

There’s nothing you need to do to get started. This enhancement is automatically applied to all new and existing Lambda functions, SQS queues, and EventBridge Event Buses.

If you were previously chunking data at 256KB (or lower) threshold, then you might need to make changes to your service configurations or business logic code to start using the new limit. For example, if you’ve explicitly set Amazon SQS MaximumMessageSize attribute, then you might need to adjust it to a new desired value. Larger payloads might also result in higher costs, as described in the following section.

Real-world example: rich event context in agentic event-driven architectures

Event-driven architectures allow services to operate independently without centralized coordination. In these systems, comprehensive event context is essential. With the increased 1 MB payload limit, events can now carry more comprehensive data—from user profiles and order details to historical interactions. This enables services such as inventory, shipping, and notifications to act autonomously.

Consider the following example. In hospitality and quick-service industries, customer satisfaction depends on timely, thoughtful service recovery. When a guest submits negative feedback through a survey, review, or complaint form, service teams must gather context, interpret the issue, and craft a response. Traditionally, this meant manually piecing together visit logs, loyalty data, and prior complaints. Now, this can be fully automated using an AI agent powered by AWS serverless services and Amazon Bedrock, as shown in the following figure.

Figure 1: Customer feedback processing pipeline

The workflow:

Receive: A new review is submitted through the Review application and emitted as an event to EventBridge Event Bus.
Detect: Event Bus delivers the event to downstream Feedback analysis agent. The agent running in a Lambda function recognizes the review as low-rating or complaint.
Enrich: The agent collects the guest’s visit metadata, booking details, loyalty activity, and complaint history using attached MCP tools into a single structured JSON payload (up to 1 MB).
Queue: The payload is sent to an SQS queue for further asynchronous processing by downstream components.
Generate: A separate Lambda function polls messages from Amazon SQS and invokes an Amazon Bedrock model to analyze the full complaint context, draft a personalized response, suggest a gesture (such as a refund or credit), and classify issue severity.
Deliver: The message is logged and sent to the customer, and to the service team for further analysis.

This use case demonstrates the importance of having a rich context: current and previous visits details, loyalty tier, prior interactions, and feedback history. Previously, teams had to offload pieces of context to Amazon S3 and reference them externally, adding latency and architectural complexity. The new 1 MB payload size means that all this information can be transported together, improving the serverless agentic workflow efficiency and streamlining maintenance.

Best practices when using large payloads

The following sections outline best practices that you should apply when using larger payloads.

Performance considerations

Monitor Lambda function memory usage carefully when working with larger payloads, because parsing and processing complex JSON objects can increase memory usage and execution duration. Test your systems thoroughly under load, especially for high-throughput applications, by benchmarking with realistic payload sizes and traffic patterns. Although the payload limit has increased to 1 MB, the Lambda 15-minute timeout and memory limits remain unchanged. When applicable, you can use compression to process even larger datasets efficiently, but remember to account for the added CPU overhead of compression and decompression in your performance calculations. Read the Monitoring best practices for event delivery with Amazon EventBridge post for more best practices to tune your event-driven architectures performances.

Operational guidelines

Configure dead-letter-queues (DLQ) to make sure that failed messages are retained for inspection and troubleshooting. This becomes especially important with larger payloads, because debugging complex data structures necessitates access to the complete message context. Implement robust error handling and retries to manage transient failures, particularly when processing rich payload content that may contain nested structures or complex relationships.

To further optimize throughput, you can batch similar smaller events together into a single payload. However, avoid mixing unrelated events and maintain clear boundaries between different business domains and processes.

Always make sure that your downstream dependencies are capable of handling larger payloads.

When to use external storage

Even with the increased 1 MB payload limit, there are scenarios where patterns such as claim check remain a sound architectural choice. These patterns involve storing a full payload in an external system, such as Amazon S3, and passing a lightweight reference through your event stream. This approach continues to provide value when payloads exceed the new limit, when data needs to be reused by multiple consumers, or when strict governance, traceability, and security requirements are involved. For example, audit logs, image metadata, or large ML inference inputs may still surpass the 1 MB boundary, even when compressed. Instead of risking truncation or fragmentation, a claim check enables consistent, scalable access to the complete data set.

You can use open source libraries such as the Kafka sink connector for EventBridge and Amazon SQS Extended Client Library (available for Python and Java) that abstract complexities of storing large objects in external storage.

Cost management

Although larger payloads enable richer context in your applications, logging full payloads can increase storage and processing costs. Services such as CloudWatch Logs charge based on data volume, thus implementing selective logging, payload truncation, or sampling becomes crucial for high-volume events. Consider logging only essential fields or implementing smart sampling strategies based on business importance.

For full payload archival and retention, evaluate cost-effective storage solutions such as Amazon S3 with appropriate lifecycle policies. This can include moving older logs to cheaper storage tiers or implementing automated cleanup procedures for non-critical data. Balance your retention needs with cost optimization by defining clear policies for what data needs to be kept and for how long.

Review the pricing pages for AWS Lambda, Amazon EventBridge, and Amazon SQS to learn about the costs of delivering and processing events and messages.

Conclusion

The increase in maximum payload size from 256 KB to 1 MB enables developers to build more efficient distributed architectures. You can use this enhancement to transport richer context in event and message payloads, reducing the need for complex workarounds that previously added architectural complexity and operational overhead. This added room to transmit rich context means that you can streamline your workflows, improve observability, and reduce architectural complexity whether using choreography or orchestration patterns.

Go to the developer guides for AWS Lambda, Amazon EventBridge, and Amazon SQS, to learn more about how to take advantage of this update.

To learn more about serverless architectures, visit Serverless Land.

Get started faster with one-click onboarding, serverless notebooks, and AI agents in Amazon SageMaker Unified Studio

2026-01-27 Siddharth Gupta

Post Syndicated from Siddharth Gupta original https://aws.amazon.com/blogs/big-data/get-started-faster-with-one-click-onboarding-serverless-notebooks-and-ai-agents-in-amazon-sagemaker-unified-studio/

Data teams today struggle with fragmented tools, complex infrastructure provisioning, and hours spent writing boilerplate code to connect to data sources. This forces analysts, data scientists, and engineers to work in separate environments, which slows collaboration and time to insight. Since our launch of Amazon SageMaker Unified Studio in March 2025, leading companies such as Bayer, NatWest, and Carrier have adopted it to bring their data teams into one collaborative workspace with unified tools, straightforward infrastructure provisioning, and fast connections to data sources.

Continuing our mission to provide faster time-to-value for customers, in November 2025, we announced Amazon SageMaker notebooks, a serverless workspace with a built-in AI agent in Amazon SageMaker Unified Studio. You can now launch a notebook in seconds, generate code from natural language prompts, and connect automatically to data across Amazon Simple Storage Service (Amazon S3), Amazon Redshift, third-party databases, and more from a single environment without needing to pre-provision or tune data processing infrastructure. Inside these serverless notebooks, analysts can perform SQL queries, data scientists can execute Python code, and data engineers can process large-scale data jobs in Spark within a single workspace. Together with the new one-click onboarding available for SageMaker Unified Studio, customers can go from their existing AWS data to running analytics and machine learning workloads much faster, spending their time on analysis rather than setup and configuration.

In this post, we walk you through how these new capabilities in SageMaker Unified Studio can help you consolidate your fragmented data tools, reduce time to insight, and collaborate across your data teams. Here’s a short demo of the new capabilities:

One-click onboarding of existing AWS datasets

Get started exploring your data with one-click onboarding that provisions and configures environments in minutes instead of weeks. The new onboarding experience can reuse existing AWS Identity and Access Management (IAM) roles to provide access to SageMaker Unified Studio, automatically connecting to data sources across S3 buckets, S3 Tables, AWS Glue Data Catalog, and AWS Lake Formation policies, removing the need for additional data permission setup. Under the covers, a new IAM-based domain and project are created with default notebook and compute resources preconfigured. When complete, you enter SageMaker Unified Studio with all your tools available in the left-side navigation along with built-in samples to accelerate first use, as seen in the following screenshot.

“New features with Amazon Sagemaker will unlock a new paradigm of innovation, allowing Codex to significantly accelerate time-to-value for our customers, and transform them from aging to agentic in weeks, not months.“

– Abhinav Sharma, Chief Data Officer, Codex

You can start directly from Amazon SageMaker, Amazon Athena, Amazon Redshift, or Amazon S3 Tables, giving them a fast path from their existing tools and data to the unified experience in SageMaker Unified Studio. After you choose Get Started and specify an IAM role, SageMaker automatically creates a project with the existing data permissions intact from Data Catalog, Lake Formation, and Amazon S3. As a result, teams can immediately discover and act on their data using the existing data permissions and infrastructure.

For more information, see New one-click onboarding and notebooks with a built-in AI agent in Amazon SageMaker Unified Studio

Serverless SageMaker notebooks

The fully managed, web-based notebooks in SageMaker Unified Studio support multiple programming languages, letting you write Python, SQL, and Spark code in the same notebook. The infrastructure adjusts automatically based on your workload, while built-in libraries create charts and insights directly in your workflow. When your analysis scales beyond interactive queries to large-scale data processing, Amazon Athena for Apache Spark engine delivers optimized performance, integrating with the serverless notebook experience to execute analytical workloads efficiently. This serverless approach eliminates the need to provision clusters or maintain servers, reducing the time from question to insight.

“The new SageMaker interface brings clarity and speed to the entire ML lifecycle. Its developer-friendly design has made our experimentation and delivery significantly faster,“

– Sachin Mittal, Product Manager at Deloitte.

As shown in the preceding image, the notebook gives data engineers, analysts, and data scientists one place to perform SQL queries, execute Python code, process large-scale data jobs, run machine learning workloads, and create visualizations without having to switch between tools.

AI-assisted development with Data Agent

To accelerate development further, the new SageMaker Data Agent helps create SQL, Python, or Spark code using natural language prompts. Instead of spending hours writing boilerplate code to connect to your data sources and understand schemas, you can describe what you want to accomplish. The agent analyzes data catalog metadata about your available datasets, schemas, and relationships to provide context-aware assistance.

In the preceding example image, if you prompt Build and analyze a complete sales forecast based on the sample retail data, the agent helps identify the relevant tables and suggests the appropriate joins and analysis approach, transforming what might take hours into minutes. To try this yourself, navigate to the Overview tab in your SageMaker Studio environment and look for the Retail Sales Forecasting with SageMaker XGBoost notebook in the sample notebooks collection—these examples are automatically available when you first set up SageMaker Studio. The agent breaks down complex analytical workflows into manageable, executable steps, so you can move from question to insight faster.

Learn more about SageMaker

In this post, we focused on three new SageMaker Unified Studio capabilities recently made available, but they’re a fraction of the more than 40 launches last year. Here’s a list of videos of re:Invent sessions and the measurable results from leading organizations adopting SageMaker Unified Studio, including:

Summary of 2025 launches: What’s new with Amazon SageMaker in the era of unified data and AI (ANT216)
NatWest Group plans to scale to 72,000 employees having federated data access using SageMaker Unified Studio. Watch their presentation.
Commonwealth Bank of Australia migrated 10 petabytes and 61,000 pipelines into AWS and has setup SageMaker Unified Studio to provide unified access to 40 different lines of business in their ongoing data transformation journey. Watch their presentation.
Carrier Global Corporation improved natural language to SQL agent accuracy by 38% through the SageMaker Catalog’s governed metadata and business glossary. Watch their presentation.
Bayer is now positioned to onboard over 300 TB of biomarker data and integrate siloed omics, clinical, and chemistry data repositories into a cohesive environment built on Amazon SageMaker. Read their story.

Conclusion

Using Amazon SageMaker Unified Studio serverless notebooks, AI-assisted development, and unified governance, you can speed up your data and AI workflows across data team functions while maintaining security and compliance. To learn more visit the SageMaker product page or get started in the SageMaker console.

About the authors

IAM Identity Center now supports IPv6

2026-01-26 Suchintya Dandapat

Post Syndicated from Suchintya Dandapat original https://aws.amazon.com/blogs/security/iam-identity-center-now-supports-ipv6/

Amazon Web Services (AWS) recommends using AWS IAM Identity Center to provide your workforce access to AWS managed applications—such as Amazon Q Developer—and AWS accounts. Today, we announced IAM Identity Center support for IPv6. To learn more about the advantages of IPv6, visit the IPv6 product page.

When you enable IAM Identity center, it provides an access portal for workforce users to access their AWS applications and accounts either by signing in to the access portal using a URL or by using a bookmark for the application URL. In either case, the access portal handles user authentication before granting access to applications and accounts. Supporting both IPv4 and IPv6 connectivity to the access portal helps facilitate seamless access for clients, such as browsers and applications, regardless of their network configuration.

The launch of IPv6 support in IAM Identity Center introduces new dual-stack endpoints that support both IPv4 and IPv6, so that users can connect using IPv4, IPv6, or dual-stack clients. Current IPv4 endpoints continue to function with no action required. The dual stack capability offered by Identity Center extends to managed applications. When users access the application dual-stack endpoint, the application automatically routes to the Identity Center dual-stack endpoint for authentication. To use Identity Center from IPv6 clients, you must direct your workforce to use the new dual-stack endpoints, and update configurations on your external identity provider (IdP), if you use one.

In this post, we show you how to update your configuration to allow IPv6 clients to connect directly to IAM Identity Center endpoints without requiring network address translation services. We also show you how to monitor which endpoint users are connecting to. Before diving into the implementation details, let’s review the key phases of the transition process.

Transition overview

To use IAM Identity Center from an IPv6 network and client, you need to use the new dual-stack endpoints. Figure 1 shows what the transition from IPv4 to IPv6 over dual-stack endpoints looks like when using Identity Center. The figure shows:

A before state where clients use the IPv4 endpoints.
The transition phase, when your clients use a combination of IPv4 and dual-stack endpoints.
After the transition is complete, your clients will connect to dual-stack endpoints using their IPv4 or IPv6, depending on their preferences.

Figure 1: Transition from IPv4-only to dual-stack endpoints

Prerequisites

You must have the following prerequisites in place to enable IPv6 access for your workforce users and administrators:

An existing IAM Identity Center instance
Updated firewalls or gateways to include the new dual-stack endpoints
IPv6 capable clients and networks

Work with your network administrators to update the configuration of your firewalls and gateways and to verify that your clients, such as laptops or desktops, are ready to accept IPv6 connectivity. If you have already enabled IPv6 connectivity for other AWS services, you might be familiar with these changes. Next, implement the two steps that follow.

Step 1: Update your IdP configuration

You can skip this step If you don’t use an external IdP as your identity source.

In this step, you update the Assertion Consumer Service (ACS) URL from your IAM Identity Center instance into your IdP’s configuration for single sign-on and the SCIM configuration for user provisioning. Your IdP’s capability determines how you update the ACS URLs. If your IdP supports multiple ACS URLs, configure both IPv4 and dual-stack URLs to enable a flexible transition. With that configuration, some users can continue using IPv4-only endpoints while others use dual-stack endpoints for IPv6. If your IdP supports only one ACS URL, to use IPv6 you must update the new dual-stack ACS URL in your IdP and transition all users to using dual-stack endpoints. If you don’t use an external IdP, you can skip this step and go to the next step.

Update both the SAML single sign-on and the SCIM provisioning configurations:

Update the single sign-on settings in your IdP to use the new dual-stack URLs. First, locate the URLs in the AWS Management Console for IAM Identity Center.
1. Choose Settings in the navigation pane and then select Identity source.
2. Choose Actions and select Manage authentication.
3. in Under Manage SAML 2.0 authentication, you will find the following URLs under Service provider metadata:
  - AWS access portal sign-in URL
  - IAM Identity Center Assertion Consumer Service (ACS) URL
  - IAM Identity Center issuer URL
If your IdP supports multiple ACS URLs, then add the dual-stack URL to your IdP configuration alongside existing IPv4 one. With this setting, you and your users can decide when to start using the dual-stack endpoints, without all users in your organization having to switch together.

Figure 2: Dual-stack single sign-on URLs
If your IdP does not support multiple ACS URLs, replace the existing IPv4 URL with the new dual-stack URL, and switch your workforce to use only the dual-stack endpoints.
Update the provisioning endpoint in your IdP. Choose Settings in the navigation pane and under Identity source, choose Actions and select Manage provisioning. Under Automatic provisioning, copy the new SCIM endpoint that ends in api.aws. Update this new URL in your external IdP.

Figure 3: Dual-stack SCIM endpoint URL

Step 2: Locate and share the new dual-stack endpoints

Your organization needs two kinds of URLs for IPv6 connectivity. The first is the new dual-stack access portal URL that your workforce users use to access their assigned AWS applications and accounts. The dual-stack access portal URL is available in the IAM Identity Center console, listed as the Dual-stack in the Settings summary (you might need to expand the Access portal URLs section, shown in Figure 4).

Figure 4: Locate dual-stack access portal endpoints

This dual-stack URL ends with app.aws as its top-level domain (TLD). Share this URL with your workforce and ask them to use this dual-stack URL to connect over IPv6. As an example, if your workforce uses the access portal to access AWS accounts, they will need to sign in through the new dual-stack access portal URL when using IPv6 connectivity. Alternately, if your workforce accesses the application URL, you need to enable the dual-stack application URL following application-specific instructions. For more information, see AWS services that support IPv6.

The URLs that administrators use to manage IAM Identity Center are the second kind of URL your organization needs. The new dual-stack service endpoints end in api.aws as their TLD and are listed in the Identity Center service endpoints. Administrators can use these service endpoints to manage users and groups in Identity Center, update their access to applications and resources, and perform other management operations. As an example, if your administrator uses identitystore.{region}.amazonaws.com to manage users and groups in Identity Center, they should now use the dual-stack version of the same service endpoint which is identitystore.{region}.api.aws, so they can connect to service endpoints using IPv6 clients and networks.

If your users or administrators use an AWS SDK to access AWS applications and accounts or manage services, follow Dual-stack and FIPS endpoints to enable connectivity to the dual-stack endpoints.

After completing these two steps, your workforce and administrators can connect to IAM Identity Center using IPv6. Remember, these endpoints also support IPv4, so clients not yet IPv6-capable can continue to connect using IPv4.

Monitoring dual-stack endpoint usage

You can optionally monitor AWS CloudTrail logs to track usage of dual-stack endpoints. The key difference between IPv4-only and dual-stack endpoint usage is the TLD and appears in the clientProvidedHostHeader field. The following example shows the difference between these CloudTrail events for the CreateTokenWithIAM API call.

IPv4-only endpoints Dual-stack endpoints

"CloudTrailEvent": {
  "eventName": "CreateToken",
  "tlsDetails": {
     "tlsVersion": "TLSv1.3",
     "cipherSuite": "TLS_AES_128_GCM_SHA256",
     "clientProvidedHostHeader": "oidc.us-east-1.amazonaws.com"
  }
}

"CloudTrailEvent": {
  "eventName": "CreateToken",
  "tlsDetails": {
     "tlsVersion": "TLSv1.3",
     "cipherSuite": "TLS_AES_128_GCM_SHA256",
     "clientProvidedHostHeader": "oidc.us-east-1.api.aws"
  }
}

Conclusion

IAM Identity Center now allows clients to connect over IPv6 natively with no network address translation infrastructure. This post showed you how to transition your organization to use IPv6 with Identity Center and its integrated applications. Remember that existing IPv4 endpoints will continue to function, so you can transition at your own pace. Also, no immediate action is required by you. However, we recommend planning your transition to take advantage of IPv6 benefits and meet compliance requirements. If you have questions, comments, or concerns, contact AWS Support, or start a new thread in the IAM Identity Center re:Post channel.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Updated PCI PIN compliance package for AWS CloudHSM now available

2026-01-26 Tushar Jain

Post Syndicated from Tushar Jain original https://aws.amazon.com/blogs/security/updated-pci-pin-compliance-package-for-aws-cloudhsm-now-available/

Amazon Web Services (AWS) is pleased to announce the successful completion of Payment Card Industry Personal Identification Number (PCI PIN) audit for the AWS CloudHSM service.

With CloudHSM, you can manage and access your keys on FIPS 140-3 Level 3 validated hardware, protected with customer-owned, single-tenant hardware security module (HSM) instances that run in your own virtual private cloud (VPC). This PCI PIN attestation gives you the flexibility to deploy your regulated workloads with reduced compliance overhead. CloudHSM might be suitable when operations supported by the service are integrated into a broader solution that requires PCI-PIN compliance. For payment operations, such as PIN translation, we encourage you to consider AWS Payment Cryptography as a fully managed alternative for PCI-PIN compliance.

The PCI PIN compliance report package for AWS CloudHSM includes two key components:

PCI PIN Attestation of Compliance (AOC) – demonstrating that AWS CloudHSM was successfully validated against the PCI PIN standard with zero findings
PCI PIN Responsibility Summary – provides guidance to help AWS customers understand their responsibilities in developing and operating a highly secure environment for handling PIN-based transactions

AWS was evaluated by Coalfire, a third-party Qualified Security Assessor (QSA). Customers can access the PCI PIN Attestation of Compliance (AOC) and PCI PIN Responsibility Summary reports through AWS Artifact.

To learn more about our PCI program and other compliance and security programs, see the AWS Compliance Programs page. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Contact Us page.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Apache Spark 4.0.1 preview now available on Amazon EMR Serverless

2026-01-26 Al MS

Post Syndicated from Al MS original https://aws.amazon.com/blogs/big-data/apache-spark-4-0-1-preview-now-available-on-amazon-emr-serverless/

Amazon EMR Serverless now supports Apache Spark 4.0.1 in preview, making analytics accessible to more users, simplifying data engineering workflows, and strengthening governance capabilities. The release introduces ANSI SQL compliance, VARIANT data types support for JSON handling, Apache Iceberg v3 table format support, and enhanced streaming capabilities. This preview is available in all regions where EMR Serverless is available.

In this post, we explore key benefits, technical capabilities, and considerations for getting started with Spark 4.0.1 on Amazon EMR Serverless—a serverless deployment option that simplifies running open-source big data frameworks, without requiring managing clusters. With the emr-spark-8.0-preview release label, you can evaluate new SQL capabilities, Python API improvements, and streaming enhancements in your existing EMR Serverless environment.

Benefits

Spark 4.0.1 helps you solve data engineering problems with specific improvements. This section shows how new capabilities help with real-world scenarios.

Make analytics accessible to more users

Simplify Extract Transform Load (ETL) development with SQL scripting. Data engineers often switch between SQL and Python to build complex ETL logic with control flow. SQL scripting in Spark 4.0.1 enables loops, conditionals, and session variables directly in SQL, reducing context-switching and simplifying pipeline development. Use pipe syntax (|>) to chain operations for more readable, maintainable queries.

Improve data quality with ANSI SQL mode. Silent type conversion failures can introduce data quality issues. ANSI SQL mode (now default) enforces standard SQL behavior, raising errors for invalid operations instead of producing unexpected results. Important: ANSI SQL mode is now enabled by default. Test your queries thoroughly during this preview evaluation.

Simplify data engineering workflows

Process JSON data efficiently with VARIANT. Teams working with semi-structured data often see slow performance from repeated JSON parsing. The VARIANT data type stores JSON in an optimized binary format, eliminating parsing overhead. You can efficiently store and query JSON data in data lakes without schema rigidity.

Build Python data sources without Scala. Integrating custom data sources previously required Scala expertise. The Python data Source API lets you build connectors entirely in Python, using existing Python skills and libraries without learning a new language.

Debug streaming applications with queryable state. Troubleshooting stateful streaming applications has historically required indirect methods. The new state data source reader shows streaming state as queryable DataFrames. You can inspect state during debugging, test state values in unit tests, and diagnose production incidents.

Strengthen governance capabilities

Establish comprehensive audit trails with Apache Iceberg v3. The Apache Iceberg v3 table format provides transaction guarantees and tracks data changes over time, giving you the audit trails needed for regulatory compliance. When combined with VARIANT data type support, you can maintain governance controls while handling semi-structured data efficiently in data lakes.

Key capabilities

Spark 4.0.1 Preview on EMR Serverless introduces four major capability areas:

SQL enhancements – ANSI mode, pipe syntax, VARIANT type, SQL scripting, user-defined functions (UDFs)
Python API advances – custom data sources, UDF profiling
Streaming improvements – stateful processing API v2, queryable state
Table format support – Amazon S3 Tables, AWS Lake Formation integration

The following sections provide technical details and code examples for each capability.

SQL enhancements

Spark 4.0.1 introduces new SQL capabilities including ANSI mode compliance, SQL UDFs, pipe syntax for readable queries, VARIANT type for JSON handling, and SQL scripting with control flow.

ANSI SQL mode by default

ANSI SQL mode is now enabled by default, enforcing standard SQL behavior for data integrity. Silent casting of out-of-range values now raises errors rather than producing unexpected results. Existing queries may behave differently, particularly around null handling, string casting, and timestamp operations. Use spark.sql.ansi.enabled=false if you need legacy behavior during migration.

SQL pipe syntax

You can now chain SQL operations using the |> operator for improved readability. The following example shows how you can replace nested subqueries with a more maintainable pipeline:

FROM customer
|> LEFT OUTER JOIN orders ON c_custkey = o_custkey
|> AGGREGATE COUNT(o_orderkey) c_count GROUP BY c_custkey
|> AGGREGATE COUNT(*) AS custdist GROUP BY c_count
|> ORDER BY custdist DESC

This replaces nested subqueries, making complex transformations easier to understand and maintain.

VARIANT data type

The VARIANT type handles semi-structured JSON/XML data efficiently without repeated parsing. It uses an optimized binary representation internally while maintaining schema-less flexibility. Previously, JSON expressions required repeated parsing, degrading performance. VARIANT eliminates this overhead. The following snippet shows how to parse JSON into the VARIANT type:

df = spark.sql("SELECT parse_json('{\"name\":\"Alice\",\"age\":30}') as data")

Spark 4.0.1 on EMR Serverless supports Apache Iceberg v3, enabling the VARIANT data type with Iceberg tables. This combination provides efficient storage and querying of semi-structured JSON data in your data lake. Store VARIANT columns in Iceberg tables and use Iceberg’s schema evolution and time travel capabilities alongside Spark’s optimized JSON processing. The following example shows how to create an Iceberg table with a VARIANT column:

CREATE TABLE catalog.db.events (
  event_id BIGINT,
  event_data VARIANT,
  timestamp TIMESTAMP
) USING iceberg;

INSERT INTO catalog.db.events SELECT 1, parse_json('{"user":"alice","action":"login"}'), current_timestamp();

SQL scripting with session variables

Manage state and control flow directly in SQL using session variables, and IF/WHILE/FOR statements. The following example demonstrates a loop that populates a results table:

BEGIN
  DECLARE counter INT = 10;
  WHILE counter > 0 DO
    INSERT INTO results VALUES (counter);
    SET counter = counter - 1;
  END WHILE;
END

This enables complex ETL logic entirely in SQL without switching to Python.

SQL user-defined functions

Define custom functions directly in SQL. Functions can be temporary (session-scoped) or permanent (catalog-stored). The following example shows how to register and use a simple UDF:

CREATE FUNCTION plusOne(x INT) RETURNS INT RETURN x + 1;
SELECT plusOne(5);

Python API advances

This section covers new Python capabilities including custom data sources and UDF profiling tools.

Python data source API

You can now build custom data sources in Python without Scala knowledge. The following example shows how to create a simple data source that returns sample data:

from pyspark.sql.datasource import DataSource, DataSourceReader
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

class SampleDataSource(DataSource):
    def schema(self):
        return StructType([
            StructField("name", StringType()),
            StructField("age", IntegerType())
        ])
    
    def reader(self, schema):
        return SampleReader()

class SampleReader(DataSourceReader):
    def read(self, partition):
        yield ("Alice", 30)
        yield ("Bob", 25)

# Register and use
spark.dataSource.register(SampleDataSource)
spark.read.format("SampleDataSource").load().show()

Unified UDF profiling

Profile Python and Pandas UDFs for performance and memory insights. The following code enables performance profiling:spark.conf.set("spark.sql.pyspark.udf.profiler", "perf") # or "memory"

Structured streaming enhancements

This section covers improvements to stateful stream processing, including queryable state and enhanced state management APIs.

Arbitrary stateful processing API v2

The transformWithState operator provides robust state management with timer and TTL support for automatic cleanup, schema evolution capabilities, and initial state support for pre-populating state from batch DataFrames.

State data source reader

Query streaming state as a DataFrame for debugging and monitoring. Previously, state data was internal to streaming queries. Now you can verify state values in unit tests, diagnose production incidents, detect state corruption, and optimize performance. Note: This feature is experimental. Source options and behavior may change in future releases.

State store improvements

Upgraded changelog checkpointing for RocksDB removes performance bottlenecks. Enhanced checkpoint coordination and improved sorted string table (SST) file reuse management optimizes streaming operations.

Table format support

This section covers support for AWS S3 Tables and full table access (FTA) with AWS Lake Formation.

AWS S3 Tables

Use Spark 4.0.1 with AWS S3 Tables, a storage solution that provides managed Apache Iceberg tables with automatic optimization and maintenance. S3 Tables simplify data lake operations by handling compaction, snapshot management, and metadata cleanup automatically.

Full table access with Lake Formation

FTA is supported for Apache Iceberg, Delta Lake, and Apache Hive tables when using AWS Lake Formation, a managed service that simplifies data access control. FTA provides coarse-grained access control at the table level. Note that fine-grained access control (FGAC) with column-level or row-level permissions is not available in this preview.

Getting started

Follow these steps to create an EMR Serverless application, run sample code to test new features, and provide feedback on the preview.

Prerequisites

Before you begin, confirm you have the following:

AWS Command Line Interface (CLI): Install and configure the AWS CLI with credentials that have permissions to create and manage EMR Serverless applications
EMR Serverless access: Your Identity and Access Management (IAM) role must have permissions for EMR Serverless operations (emr-serverless:CreateApplication,emr-serverless:StartJobRun). See the EMR Serverless IAM policy examples for minimum required permissions
S3 bucket: An S3 bucket to store your job scripts and data
Runtime role: An IAM execution role for EMR Serverless with access to your S3 resources

Note: EMR Studio Notebooks and SageMaker Unified Studio are not supported during this preview. Use the AWS CLI or AWS SDK to submit jobs.

Step 1: Create your EMR Serverless application

Create or update your application with the emr-spark-8.0-preview release label. The following command creates a new application:

aws emr-serverless create-application --type spark \
  --release-label emr-spark-8.0-preview \
  --region us-east-1 --name spark4-test

Step 2: Test sample code

Run this PySpark job to verify setup and test Spark 4.0.1 features:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Spark 4.0.1 Test").getOrCreate()
print(f"Spark Version: {spark.version}")

# Create sample data
data = [("Alice", 34, "Engineering"), ("Bob", 45, "Sales"),
        ("Charlie", 28, "Engineering"), ("Diana", 52, "Marketing")]
df = spark.createDataFrame(data, ["name", "age", "department"])
df.createOrReplaceTempView("employees")

# Test SQL PIPE syntax
try:
    result = spark.sql("""
        FROM employees
        |> WHERE age > 30
        |> SELECT name, age, department
        |> ORDER BY age DESC
    """)
    result.show()
    print("✓ SQL pipe syntax test passed")
except Exception as e:
    print(f"✗ SQL pipe syntax test failed: {e}")

# Test VARIANT data type
try:
    json_data = spark.sql("""
        SELECT parse_json('{"name":"Alice","skills":["Python","Spark","SQL"]}') as data
    """)
    json_data.show(truncate=False)
    print("✓ VARIANT data type test passed")
except Exception as e:
    print(f"✗ VARIANT data type test failed: {e}")

Submit the job with the following command:

aws emr-serverless start-job-run \
    --application-id <your-application-id> \
    --execution-role-arn <your-execution-role-arn> \
    --job-driver '{
        "sparkSubmit": {
            "entryPoint": "s3://<your-bucket>/spark_4_test.py",
            "sparkSubmitParameters": "--conf spark.executor.cores=4 --conf spark.executor.memory=16g"
        }
    }'

Step 3: Test your workloads

Review the Spark SQL Migration Guide and PySpark Migration Guide, then test production workloads in non-production environments. Focus on queries affected by ANSI SQL mode and benchmark performance.

Step 4: Clean up resources

After testing, delete all resources created during this evaluation to avoid ongoing charges:

# Delete the EMR Serverless application
aws emr-serverless delete-application \
    --application-id spark4-test \
    --region us-east-1
# Remove the test script from S3
aws s3 rm s3://<your-bucket>/spark_4_test.py

Migration considerations

Before evaluating Spark 4.0.1, review the updated runtime requirements and behavioral changes that may affect your existing code.

Runtime requirements

Scala: Version 2.13.16 required (2.12 support dropped)
Java: JDK 17 or higher required (JDK 8 and 11 support removed)
Python: Version 3.9+ required, continued support for 3.11 and newly added 3.12 (3.8 support removed)
Pandas: Minimum version 2.0.0 (previously 1.0.5)
SparkR: Deprecated; migrate to PySpark

Behavioral changes

With ANSI SQL mode enforcement, you may see different behavior in:

Null handling: Stricter null propagation in expressions
String casting: Invalid casts now raise errors instead of returning null
Map key operations: Duplicate keys now raise errors
Timestamp conversions: Overflow returns null instead of wrapped values
CREATE TABLE statements: Now respect the spark.sql.sources.defaultconfiguration instead of defaulting to Hive format when USING or STORED AS clauses are omitted

You can control many of these behaviors via legacy configuration flags. Consult the official migration guides for details refer: Spark SQL Migration Guide: 3.5 to 4.0 and PySpark Migration Guide: 3.5 to 4.0.

Preview limitations

The following capabilities are not available in this preview:

Fine-grained access control: Fine-grained access control (FGAC) with row-level or column-level filtering is not supported in this preview. Jobs with spark.emr-serverless.lakeformation.enabled=true will fail.
Spark Connect: Not supported in this preview. Use standard Spark job submission with the StartJobRun API.
Open Table Format limitations: Hudi is not supported in this preview. Delta 4.0.0 does not support Flink connectors (deprecated in Delta 4.0.0). Delta Universal Format is not supported in this preview.
Connectors: spark-sql-kinesis, emr-dynamodb, and spark-redshift are unavailable.
Interactive applications: Livy and JupyterEnterpriseGateway are not included. Also, SageMaker Unified Studio and EMR Studio are not supported.
EMR features: Serverless Storage and Materialized Views are not supported.

This preview lets you evaluate Spark 4.0.1’s core capabilities on EMR Serverless, including SQL enhancements, Python API improvements, and streaming state management. Test your migration path, assess performance improvements, and provide feedback to shape the general availability release.

Conclusion

This post showed you how to get started with the Apache Spark 4.0.1 preview release on Amazon EMR Serverless. You explored how the VARIANT data type works with Iceberg v3 to process JSON data efficiently, how SQL scripting and pipe syntax eliminate context-switching for ETL development, and how queryable streaming state simplifies debugging stateful applications. You also learned about the preview limitations, runtime requirements, and behavioral changes to consider during evaluation.

Test the Spark 4.0.1 preview on EMR Serverless and provide feedback through AWS Support to help shape the general availability release.

To learn more about Apache Spark 4.0.1 features, see the Spark 4.0.1 Release Notes. For EMR Serverless documentation, see the EMR Release Guide.

Resources

Apache Spark Documentation

Amazon EMR Resources

Amazon EMR Spark Features

About the authors

Updated PCI PIN compliance package for AWS Payment Cryptography now available

2026-01-24 Tushar Jain

Post Syndicated from Tushar Jain original https://aws.amazon.com/blogs/security/updated-pci-pin-compliance-package-for-aws-payment-cryptography-now-available/

Amazon Web Services (AWS) is pleased to announce the successful completion of Payment Card Industry Personal Identification Number (PCI PIN) audit for the AWS Payment Cryptography service.

With AWS Payment Cryptography, your payment processing applications can use payment hardware security modules (HSMs) that are PCI PIN Transaction Security (PTS) HSM certified and fully managed by AWS, with PCI PIN-compliant key management. This attestation gives you the flexibility to deploy your regulated workloads with reduced compliance overhead.

The PCI PIN compliance report package for AWS Payment Cryptography includes two key components:

PCI PIN Attestation of Compliance (AOC) – demonstrating that AWS Payment Cryptography was successfully validated against the PCI PIN standard with zero findings
PCI PIN Responsibility Summary – provides guidance to help AWS customers understand their responsibilities in developing and operating a highly secure environment for handling PIN-based transactions

To learn more about our PCI programs and other compliance and security programs, visit the AWS Compliance Programs page. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Compliance Support page.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

AWS achieves 2025 C5 Type 2 attestation report with 183 services in scope

2026-01-23 Tea Jioshvili

Post Syndicated from Tea Jioshvili original https://aws.amazon.com/blogs/security/aws-achieves-2025-c5-type-2-attestation-report-with-183-services-in-scope/

Amazon Web Services (AWS) is pleased to announce a successful completion of the 2025 Cloud Computing Compliance Criteria Catalogue (C5) attestation cycle with 183 services in scope. This alignment with C5 requirements demonstrates our ongoing commitment to adhere to the heightened expectations for cloud service providers. AWS customers in Germany and across Europe can run their applications in the AWS Regions that are in scope of the C5 report with the assurance that AWS aligns with C5 criteria.

The C5 attestation scheme is backed by the German government and was introduced by the Federal Office for Information Security (BSI) in 2016. AWS has adhered to the C5 requirements since their inception. C5 helps organizations demonstrate operational security against common cybersecurity threats when using cloud services.

Independent third-party auditors evaluated AWS for the period of October 1, 2024, through September 30, 2025. The C5 report illustrates the compliance status of AWS for both the basic and additional criteria of C5. Customers can download the C5 report through AWS Artifact, a self-service portal for on-demand access to AWS compliance reports. Sign in to AWS Artifact in the AWS Management Console or learn more at Getting Started with AWS Artifact.

AWS has added the following five services to the current C5 scope:

The following AWS Regions are in scope of the 2025 C5 attestation: Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Stockholm), Europe (Spain), Europe (Zurich), and Asia Pacific (Singapore). For up-to-date information, see the C5 page of our AWS Services in Scope by Compliance Program.

Security and compliance is a shared responsibility between AWS and the customer. When customers move their computer systems and data to the cloud, security responsibilities are shared between the customer and the cloud service provider. For more information, see the AWS Shared Security Responsibility Model.

To learn more about our compliance and security programs, see AWS Compliance Programs. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Contact Us page.

Reach out to your AWS account team if you have questions or feedback about the C5 report.
If you have feedback about this post, submit comments in the Comments section below.

AWS renews the GSMA SAS-SM certification for two AWS Regions and expands to cover four new Regions

2026-01-23 Michael Murphy

Post Syndicated from Michael Murphy original https://aws.amazon.com/blogs/security/aws-renews-the-gsma-sas-sm-certification-for-two-aws-regions-and-expands-to-cover-four-new-regions/

Amazon Web Services (AWS) is pleased to announce the expansion of GSMA Security Accreditation Scheme for Subscription Management (SAS-SM) certification to four new AWS Regions: US West (Oregon), Europe (Frankfurt), Asia Pacific (Tokyo), and Asia Pacific (Singapore). Additionally, the AWS US East (Ohio) and Europe (Paris) Regions have been recertified. All certifications are under the GSM Association (GSMA) SAS-SM with scope Data Centre Operations and Management (DCOM). AWS was evaluated by GSMA-selected independent third-party auditors, and all Region certifications are valid through October 2026. The Certificate of Compliance that shows AWS achieved GSMA compliance status is available on both the GSMA and AWS websites.

The US East (Ohio) Region first obtained GSMA certification in September 2021, and the Europe (Paris) Region first obtained GSMA certification in October 2021. Since then, multiple independent software vendors (ISVs) have inherited the controls of our SAS-SM DCOM certification to build GSMA compliant subscription management or eSIM (embedded subscriber identity module) services on AWS. For established market leaders, this reduces technical debt while meeting the scalability and performance needs of their customers. Startups innovating with eSIM solutions can accelerate their time to market by many months, compared to on-premises deployments.

Until 2023, the shift from physical subscriber identity modules (SIMs) to eSIMs was primarily driven by automotives, cellular connected wearables, and companion devices such as tablets. GSMA is promoting the SGP.31 and SGP.32 specifications, which standardize protocols and guarantee compatibility and consistent user experience for all eSIM devices spanning smartphones, IoT, smart home, industrial Internet of Things (IoT), and so on. As more device manufacturers launch eSIM only models, our customers are demanding robust, cloud-centered eSIM solutions. Over 400 telecom operators around the world now support eSIM services for their subscribers. Hosting eSIM platforms in the cloud allows them to integrate efficiently with their next generation cloud-based operations support systems (OSS) and business support systems (BSS).

The AWS expansion to certify four new Regions into scope in November 2025 demonstrates our continuous commitment to adhere to the heightened expectations for cloud service providers and extends our global coverage for GSMA-certified infrastructure. With two GSMA-certified Regions in the US, EU, and Asia respectively, customers can now build geo-redundant eSIM solutions to improve their disaster recovery and resiliency posture.

For up-to-date information related to the certification, see the AWS GSMA Compliance Program page.

Fall 2025 SOC 1, 2, and 3 reports are now available with 185 services in scope

2026-01-20 Tushar Jain

Post Syndicated from Tushar Jain original https://aws.amazon.com/blogs/security/fall-2025-soc-1-2-and-3-reports-are-now-available-with-185-services-in-scope/

Amazon Web Services (AWS) is pleased to announce that the Fall 2025 System and Organization Controls (SOC) 1, 2, and 3 reports are now available. The reports cover 185 services over the 12-month period from October 1, 2024–September 30, 2025, giving customers a full year of assurance. These reports demonstrate our continuous commitment to adhering to the heightened expectations of cloud service providers.

Customers can download the Fall 2025 SOC 1 and 2 reports through AWS Artifact, a self-service portal for on-demand access to AWS compliance reports. Sign in to AWS Artifact in the AWS Management Console, or learn more at Getting Started with AWS Artifact. The SOC 3 report can be found on the AWS SOC Compliance Page.

AWS strives to continuously bring services into the scope of its compliance programs to help customers meet their architectural and regulatory needs. You can view the current list of services in scope on our Services in Scope page. As an AWS customer, you can reach out to your AWS account team if you have any questions or feedback about SOC compliance.

To learn more about AWS compliance and security programs, see AWS Compliance Programs. As always, we value feedback and questions; reach out to the AWS Compliance team through the Contact Us page.

If you have feedback about this post, submit comments in the Comments section below.

Simplify network segmentation for AWS Outposts racks with multiple local gateway routing domains

2026-01-16 Brianna Rosentrater

Post Syndicated from Brianna Rosentrater original https://aws.amazon.com/blogs/compute/simplify-network-segmentation-for-aws-outposts-racks-with-multiple-local-gateway-routing-domains/

AWS now supports multiple local gateway (LGW) routing domains on AWS Outposts racks to simplify network segmentation. Network segmentation is the practice of splitting a computer network into isolated subnetworks, or network segments. This reduces the attack surface so that if a host on one network segment is compromised, the hosts on the other network segments are not affected. Many customers in regulated industries such as manufacturing, health care and life sciences, banking, and others implement network segmentation as part of their on-premises network security standards to reduce the impact of a breach and help address compliance requirements. Some AWS services also have network requirements that specify certain IP ranges to be used for endpoints, and may or may not support customers bringing their own IP pool (also called CoIP routing, see How to choose between CoIP and Direct VPC routing (DVR) modes on AWS Outposts rack for more information). Customers want the flexibility to use both routing modes (CoIP and DVR) on the same logical Outpost. With this new feature, AWS Outposts racks now support multiple LGW routing domains to meet subnetwork isolation and cloud service network requirements in an on-premises environment. For example, a leading automotive company deploys latency-sensitive manufacturing workloads on Outposts racks in a multi-AZ architecture for resiliency. This feature provides traffic separation between routing domains and enables both customer-owned IP (CoIP) and direct VPC routing (DVR) modes on the same logical Outpost.

In this post you will learn how to use multiple LGW routing domains on Outposts racks and considerations for implementation.

Overview

With the introduction of multiple LGW routing domains on Outposts, you can now create multiple routing domains and associate one or more VLANs with each routing domain. This allows you to integrate your Outposts rack into your existing on-premises network schema. Each LGW routing domain will have a unique LGW Virtual Interface (VIF) Group and an LGW Route Table, enabling logical network traffic isolation. You can have a mix of up to 10 active routing domains with route tables using either DVR or CoIP routing mode, and you can make changes to these routing domains as needed in a self-service fashion allowing for network flexibility as architectures are updated over time. These settings can be found in the AWS Outposts console under the Networking tab in the menu.

The following diagram shows an example of 3 VPCs, each with at least 1 subnet on the Outpost rack, and each VPC corresponds to its own routing domain. Each routing domain can then be associated with one or more VLANs, and one or more VPCs. A VPC can be associated with one or more LGW routing domain.

Architecture diagram showing 3 routing domains uplinking to an on-premises network.

Figure 1 – Architecture diagram showing 3 routing domains

Walkthrough

Before creating a LGW routing domain, first you’ll need to create an LGW VIF group and an LGW route table. A local gateway routing domain is the association of a local gateway route table and local gateway VIF group. Each VIF group can be associated with one or more VLANs, but a route table can only be associated with one VIF group.

To create a LGW VIF Group, navigate to the AWS Outposts console, go to LGW virtual interfaces groups, and select Create VIF group. Enter your VIF details which include BGP and VLAN routing information, you must create 4 LGW VIFs per VIF group.

Creating VIF group for RD1 routing domain

Figure 2 – Creating VIF group for RD1 routing domain

After creating your VIF group, create a LGW route table. You’ll have the option to use Direct VPC Routing (DVR) or Customer-owned IP address pool (CoIP) routing. If CoIP routing is selected, you’ll have the option to enter your CIDR before creating. A LGW route table’s routing mode cannot be changed after creating. However, you can disassociate a LGW route table from a VIF group and attach a new route table if you need to change the routing mode of a VIF group.

Figure 3 – Creating LGW route table for RD1 routing domain

After you’ve created your LGW route table and VIF group, you can proceed to the final step which is to create your LGW routing domain where you will associate the LGW route table and VIF group.

Create LGW routing domain form for RD1 example

Figure 4 – Creating LGW routing domain for RD1

You can view and create up to 10 active routing domains through the AWS Outposts console under the Networking tab.

Figure 5 – Local Gateway (LGW) routing domains

Considerations

Multiple LGW routing domains feature is only available on second-generation Outposts racks.
Avoid overlapping IP addresses across subnetworks and local routing domains as those can create IP routing conflicts.
A VIF group can only be associated to one LGW route table/routing domain at a time. A routing domain is the association of a VIF group and LGW route table.
LGW routing domain will allow for logical local network traffic isolation, however all traffic will still travel across your local gateway Link Aggregation Control Protocol (LACP) Link Aggregation Group (LAG) to uplink into your on-premises network.
Additional network isolation can be achieved through Virtual Routing and Forwarding (VRF) on Cisco platforms or Routing Instances on Juniper equipment, providing logical separation of routing tables and enabling secure multi-tenancy within the same physical infrastructure.
You can associate a VPC to one or more LGW routing domains. You can self-serve to change VPC association as needed. Multiple on-premises VLANs can be connected to a single routing domain.

Conclusion

This post demonstrated how to configure multiple local routing domains on Outposts racks to integrate into your on-premises network. For more information see LGW routing domains section in the AWS Outposts user guide. Reach out to your AWS account team to learn more about Outposts racks network configuration options.

In addition to multiple LGW routing domains, we have also announced several updates to Outposts in the past week to help you meet digital sovereignty and local data processing needs. To learn more, read the following announcements:

To discuss Outposts with an expert on any of these topics, submit this form.

Unlock granular resource control with queue-based QMR in Amazon Redshift Serverless

2026-01-15 Srini Ponnada

Post Syndicated from Srini Ponnada original https://aws.amazon.com/blogs/big-data/unlock-granular-resource-control-with-queue-based-qmr-in-amazon-redshift-serverless/

Amazon Redshift Serverless removes infrastructure management and manual scaling requirements from data warehousing operations. Amazon Redshift Serverless queue-based query resource management, helps you protect critical workloads and control costs by isolating queries into dedicated queues with automated rules that prevent runaway queries from impacting other users. You can create dedicated query queues with customized monitoring rules for different workloads, providing granular control over resource usage. Queues let you define metrics-based predicates and automated responses, such as automatically aborting queries that exceed time limits or consume excessive resources.

Different analytical workloads have distinct requirements. Marketing dashboards need consistent, fast response times. Data science workloads might run complex, resource-intensive queries. Extract, transform, and load (ETL) processes might execute lengthy transformations during off-hours.

As organizations scale analytics usage across more users, teams, and workloads, ensuring consistent performance and cost control becomes increasingly challenging in a shared environment. A single poorly optimized query can consume disproportionate resources, degrading performance for business-critical dashboards, ETL jobs, and executive reporting. With Amazon Redshift Serverless queue-based Query Monitoring Rules (QMR), administrators can define workload-aware thresholds and automated actions at the queue level—a significant improvement over previous workgroup-level monitoring. You can create dedicated queues for distinct workloads such as BI reporting, ad hoc analysis, or data engineering, then apply queue-specific rules to automatically abort, log, or restrict queries that exceed execution-time or resource-consumption limits. By isolating workloads and enforcing targeted controls, this approach protects mission-critical queries, improves performance predictability, and prevents resource monopolization—all while maintaining the flexibility of a serverless experience.

In this post, we discuss how you can implement your workloads with query queues in Redshift Serverless.

Queue-based vs. workgroup-level monitoring

Before query queues, Redshift Serverless offered query monitoring rules (QMRs) only at the workgroup level. This meant the queries, regardless of purpose or user, were subject to the same monitoring rules.

Queue-based monitoring represents a significant advancement:

Granular control – You can create dedicated queues for different workload types
Role-based assignment – You can direct queries to specific queues based on user roles and query groups
Independent operation – Each queue maintains its own monitoring rules

Solution overview

In the following sections, we examine how a typical organization might implement query queues in Redshift Serverless.

Architecture Components

Workgroup Configuration

The foundational unit where query queues are defined
Contains the queue definitions, user role mappings, and monitoring rules

Queue Structure

Multiple independent queues operating within a single workgroup
Each queue has its own resource allocation parameters and monitoring rules

User/Role Mapping

Directs queries to appropriate queues based on:
User roles (e.g., analyst, etl_role, admin)
Query groups (e.g., reporting, group_etl_inbound)
Query group wildcards for flexible matching

Query Monitoring Rules (QMRs)

Define thresholds for metrics like execution time and resource usage
Specify automated actions (abort, log) when thresholds are exceeded

Prerequisites

To implement query queues in Amazon Redshift Serverless, you need to have the following prerequisites:

Redshift Serverless environment:

Active Amazon Redshift Serverless workgroup
Associated namespace

Access requirements:

AWS Management Console access with Redshift Serverless permissions
AWS CLI access (optional for command-line implementation)
Administrative database credentials for your workgroup

Required permissions:

IAM permissions for Redshift Serverless operations (CreateWorkgroup, UpdateWorkgroup)
Ability to create and manage database users and roles

Identify workload types

Begin by categorizing your workloads. Common patterns include:

Interactive analytics – Dashboards and reports requiring fast response times
Data science – Complex, resource-intensive exploratory analysis
ETL/ELT – Batch processing with longer runtimes
Administrative – Maintenance operations requiring special privileges

Define queue configuration

For each workload type, define appropriate parameters and rules. For a practical example, let’s assume we want to implement three queues:

Dashboard queue – Used by analyst and viewer user roles, with a strict runtime limit set to stop queries longer than 60 seconds
ETL queue – Used by etl_role user roles, with a limit of 100,000 blocks on disk spilling (query_temp_blocks_to_disk) to control resource usage during data processing operations
Admin queue – Used by admin user roles, without a query monitoring limit enforced

To implement this using the AWS Management Console, complete the following steps:

On the Redshift Serverless console, go to your workgroup.
On the Limits tab, under Query queues, choose Enable queues.
Configure each queue with appropriate parameters, as shown in the following screenshot.

Each queue (dashboard, ETL, admin_queue) is mapped to specific user roles and query groups, creating clear boundaries between query rules. The query monitoring rules implement automated resource governance—for example, the dashboard queue automatically stops queries exceeding 60 seconds (short_timeout) while allowing ETL processes longer runtimes with different thresholds. This configuration helps prevent resource monopolization by establishing separate processing lanes with appropriate guardrails, so critical business processes can maintain necessary computational resources while limiting the impact of resource-intensive operations.

Alternatively, you can implement the solution using the AWS Command Line Interface (AWS CLI).

In the following example, we create a new workgroup named test-workgroup within an existing namespace called test-namespace. This makes it possible to create queues and establish associated monitoring rules for each queue using the following command:

aws redshift-serverless create-workgroup \
  --workgroup-name test-workgroup \
  --namespace-name test-namespace \
  --config-parameters '[{"parameterKey": "wlm_json_configuration", "parameterValue": "[{\"name\":\"dashboard\",\"user_role\":[\"analyst\",\"viewer\"],\"query_group\":[\"reporting\"],\"query_group_wild_card\":1,\"rules\":[{\"rule_name\":\"short_timeout\",\"predicate\":[{\"metric_name\":\"query_execution_time\",\"operator\":\">\",\"value\":60}],\"action\":\"abort\"}]},{\"name\":\"ETL\",\"user_role\":[\"etl_role\"],\"query_group\":[\"group_etl_inbound\",\"group_etl_outbound\"],\"rules\":[{\"rule_name\":\"long_timeout\",\"predicate\":[{\"metric_name\":\"query_execution_time\",\"operator\":\">\",\"value\":3600}],\"action\":\"log\"},{\"rule_name\":\"memory_limit\",\"predicate\":[{\"metric_name\":\"query_temp_blocks_to_disk\",\"operator\":\">\",\"value\":100000}],\"action\":\"abort\"}]},{\"name\":\"admin_queue\",\"user_role\":[\"admin\"],\"query_group\":[\"admin\"]}]"}]'

You can also modify an existing workgroup using update-workgroup using the following command:

aws redshift-serverless update-workgroup \
  --workgroup-name test-workgroup \
  --config-parameters '[{"parameterKey": "wlm_json_configuration", "parameterValue": "[{\"name\":\"dashboard\",\"user_role\":[\"analyst\",\"viewer\"],\"query_group\":[\"reporting\"],\"query_group_wild_card\":1,\"rules\":[{\"rule_name\":\"short_timeout\",\"predicate\":[{\"metric_name\":\"query_execution_time\",\"operator\":\">\",\"value\":60}],\"action\":\"abort\"}]},{\"name\":\"ETL\",\"user_role\":[\"etl_role\"],\"query_group\":[\"group_etl_load\",\"group_etl_replication\"],\"rules\":[{\"rule_name\":\"long_timeout\",\"predicate\":[{\"metric_name\":\"query_execution_time\",\"operator\":\">\",\"value\":3600}],\"action\":\"log\"},{\"rule_name\":\"memory_limit\",\"predicate\":[{\"metric_name\":\"query_temp_blocks_to_disk\",\"operator\":\">\",\"value\":100000}],\"action\":\"abort\"}]},{\"name\":\"admin_queue\",\"user_role\":[\"admin\"],\"query_group\":[\"admin\"]}]"}]'

Best practices for queue management

Consider the following best practices:

Start simple – Begin with a minimal set of queues and rules
Align with business priorities – Configure queues to reflect critical business processes
Monitor and adjust – Regularly review queue performance and adjust thresholds
Test before production – Validate query metrics behavior in a test environment before applying to production

Clean up

To clean up your resources, delete the Amazon Redshift Serverless workgroups and namespaces. For instructions, see Deleting a workgroup.

Conclusion

Query queues in Amazon Redshift Serverless bridge the gap between serverless simplicity and fine-grained workload control by enabling queue-specific Query Monitoring Rules tailored to different analytical workloads. By isolating workloads and enforcing targeted resource thresholds, you can protect business-critical queries, improve performance predictability, and limit runaway queries, helping minimize unexpected resource consumption and better control costs, while still benefiting from the automatic scaling and operational simplicity of Redshift Serverless.

Get started with Amazon Redshift Serverless today.

About the authors

Opening the AWS European Sovereign Cloud

2026-01-15 Sébastien Stormacq

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/opening-the-aws-european-sovereign-cloud/

Deutsch | English | Español | Français | Italiano

As a European citizen, I understand first-hand the importance of digital sovereignty, especially for our public sector organisations and highly regulated industries. Today, I’m delighted to share that the AWS European Sovereign Cloud is now generally available to all customers. We first announced our plans to build this new independent cloud infrastructure in 2023, and today it’s ready to meet the most stringent sovereignty requirements of European customers with a comprehensive set of AWS services.

Berlin, Brandenburg Gate at sunset

Meeting European sovereignty requirements
Organizations across Europe face increasingly complex regulatory requirements around data residency, operational control, and governance independence. Too often today, European organisations with the highest sovereignty requirements are stuck in legacy on-premises environments or offerings with reduced services and functionality. In response to this critical need, the AWS European Sovereign Cloud is the only fully featured and independently operated sovereign cloud backed by strong technical controls, sovereign assurances, and legal protections. Public sector entities and businesses in highly regulated industries need cloud infrastructure that provides enhanced sovereignty controls that maintain the innovation, security, and reliability they expect from modern cloud services. These organisations require assurance that their data and operations remain under European jurisdiction, with clear governance structures and operational autonomy within the European Union (EU).

A new independent cloud infrastructure for Europe
The AWS European Sovereign Cloud represents a physically and logically separate cloud infrastructure, with all components located entirely within the EU. The first AWS Region in the AWS European Sovereign Cloud is located in the state of Brandenburg, Germany, and is generally available today. This Region operates independently from existing AWS Regions. The infrastructure features multiple Availability Zones with redundant power and networking, designed to operate continuously even if connectivity with the rest of the world is interrupted.

We plan to extend the AWS European Sovereign Cloud footprint from Germany across the EU to support stringent isolation, in-country data residency, and low latency requirements. This will start with new sovereign Local Zones located in Belgium, the Netherlands, and Portugal. In addition, you will be able to extend the AWS European Sovereign Cloud infrastructure with AWS Dedicated Local Zones, AWS AI Factories, or AWS Outposts in locations you select, including your own on-premises data centres.

The AWS European Sovereign Cloud and its Local Zones provide enhanced sovereign controls through its unique operational model. The AWS European Sovereign Cloud will be operated exclusively by EU residents located in the EU. This covers activities such as day-to-day operations, technical support, and customer service. We’re gradually transitioning the AWS European Sovereign Cloud to be operated exclusively by EU citizens located in the EU. During this transition period, we will continue to work with a blended team of EU residents and EU citizens located in the EU.

The infrastructure is managed through dedicated European legal entities established under German law. In October 2025, AWS appointed Stéphane Israël, an EU citizen residing in the EU, as managing director. Stéphane will be responsible for the management and operations of the AWS European Sovereign Cloud, including infrastructure, technology, and services, as well as leading AWS broader digital sovereignty efforts. In January 2026, AWS also appointed Stefan Hoechbauer (Vice President, Germany and Central Europe, AWS) as a managing director of the AWS European Sovereign Cloud. He will work alongside Stéphane Israel to lead the AWS European Sovereign Cloud.

An advisory board comprised exclusively of EU citizens, and including two independent third-party representatives, provides additional oversight and expertise on sovereignty matters.

Enhanced data residency and control
The AWS European Sovereign Cloud provides comprehensive data residency assurances so you can meet the most stringent data residency requirements. As with our existing AWS Regions around the world, all your content remains within the Region you select unless you choose otherwise. Beyond content, customer-created metadata including roles, permissions, resource labels, and configurations also stays within the EU. The infrastructure features its own dedicated AWS Identity and Access Management (IAM) and billing system, all operating independently within European borders.

Technical controls built into the infrastructure prevent access to the AWS European Sovereign Cloud from outside the EU. The infrastructure includes a dedicated European trust service provider for certificate authority operations and uses dedicated Amazon Route 53 name servers. These servers will only use European Top-Level Domains (TLDs) for their own names. The AWS European Sovereign Cloud has no critical dependencies on non-EU personnel or infrastructure.

Security and compliance framework
The AWS European Sovereign Cloud maintains the same core security capabilities you expect from AWS, including encryption, key management, access governance, and the AWS Nitro System for compute isolation. This means your EC2 instances benefit from cryptographically verified platform integrity and hardware-enforced boundaries that prevent unauthorized access to your data without compromising on performance, giving you both the sovereignty controls and the computational power your workloads require. The infrastructure undergoes independent third-party audits, with compliance programs including ISO/IEC 27001:2013, SOC 1/2/3 reports, and Federal Office for Information Security (BSI) C5 attestation.

The AWS European Sovereign Cloud: Sovereign Reference Framework defines the specific sovereignty controls across governance independence, operational control, data residency, and technical isolation. This framework is available in AWS Artifact and provides end-to-end visibility through SOC 2 attestation.

Comprehensive service availability
You can access a broad range of AWS services in the AWS European Sovereign Cloud from launch, including Amazon SageMaker and Amazon Bedrock for artificial intelligence and machine learning (AI/ML) workloads. For compute, you can use Amazon Elastic Compute Cloud (Amazon EC2) and AWS Lambda. Container orchestration is available through Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon Elastic Container Service (Amazon ECS). Database services include Amazon Aurora, Amazon DynamoDB, and Amazon Relational Database Service (Amazon RDS). Storage options include Amazon Simple Storage Service (Amazon S3) and Amazon Elastic Block Store (Amazon EBS), with networking through Amazon Virtual Private Cloud (Amazon VPC) and security services including AWS Key Management Service (AWS KMS) and AWS Private Certificate Authority. For an up-to-date list of services, refer to the AWS Capabilities matrix recently published on the AWS Builder Center.

The AWS European Sovereign Cloud is supported by AWS Partners who are committed to helping you meet your sovereignty requirements. Partners including Adobe, Cisco, Cloudera, Dedalus, Esri, Genesys, GitLab, Mendix, Pega, SAP, SnowFlake, Trend Micro, and Wiz are making their solutions available in the AWS European Sovereign Cloud, providing you with the tools and services you need across security, data analytics, application development, and industry-specific workloads. This broad partner support helps you build sovereign solutions that combine AWS services with trusted partner technologies.

Significant investment in European infrastructure
The AWS European Sovereign Cloud is backed by a €7.8 billion investment in infrastructure, jobs creation, and skills development. This investment is expected to contribute €17.2 billion to the European economy through 2040 and support roughly 2,800 full-time equivalent jobs annually in local businesses.

Some technical details
The AWS European Sovereign Cloud is available to all customers, regardless of where they are located. You can access the infrastructure using the partition name aws-eusc and the Region name eusc-de-east-1. A partition is a group of AWS Regions. Each AWS account is scoped to one partition.

The infrastructure supports all standard AWS access methods including the AWS Management Console, AWS SDKs, and the AWS Command Line Interface (AWS CLI), making it straightforward to integrate into your existing workflows and automation. After having created a new root account for the AWS European Sovereign Cloud partition, you start by creating new IAM identities and roles specific to this infrastructure, giving you complete control over access management within the European sovereign environment.

Getting started
The AWS European Sovereign Cloud provides European organisations with enhanced sovereignty controls whilst maintaining access to AWS innovation and capabilities. You can contract for services through Amazon Web Services EMEA SARL, with pricing in EUR and billing in any of the eight currencies we support today. The infrastructure uses familiar AWS architecture, service portfolio, and APIs, making it straightforward to build and migrate applications.

The AWS European Sovereign Cloud addendum contains the additional contractual commitments for the AWS European Sovereign Cloud.

For me as a European, this launch represents the AWS commitment to meeting the specific needs of our continent and providing the cloud capabilities that drive innovation across industries. I invite you to find out more about the AWS European Sovereign Cloud and how it can help your organisation meet its sovereignty requirements. Read Overview of the AWS European Sovereign Cloud to learn more about the design goals and approach, sign up for a new account, and plan for the deployment of your first workload today.

— seb

German version

Start der AWS European Sovereign Cloud

Als Bürger Europas weiß ich aus eigener Erfahrung, wie wichtig digitale Souveränität ist, insbesondere für unsere öffentlichen Einrichtungen und stark regulierten Branchen. Ich freue mich, Ihnen heute mitteilen zu können, dass die AWS European Sovereign Cloud nun für alle Kunden allgemein verfügbar ist. Wir haben unsere Pläne zum Aufbau dieser neuen unabhängigen Cloud-Infrastruktur erstmals im Jahr 2023 vorgestellt. Heute ist diese Infrastruktur bereit, mit einem umfassenden Angebot an AWS-Services die strengsten Souveränitätsanforderungen europäischer Kunden zu erfüllen.

Berlin, Brandenburger Tor bei Sonnenuntergang

Erfüllung europäischer Souveränitätsanforderungen
Organisationen in ganz Europa sehen sich mit zunehmend komplexen regulatorischen Anforderungen in Bezug auf Datenresidenz, operative Kontrolle und Unabhängigkeit der Governance konfrontiert. Europäische Organisationen mit höchsten Souveränitätsanforderungen sind heutzutage allzu oft in veralteten lokalen Umgebungen oder Angeboten mit eingeschränkten Services und Funktionen gefangen. Die AWS European Sovereign Cloud ist die Antwort auf diesen dringenden Bedarf. Sie ist die einzige unabhängig betriebene souveräne Cloud mit vollem Funktionsumfang, die durch strenge technische Kontrollen, Souveränitätszusicherungen und rechtlichen Schutz abgesichert ist. Einrichtungen des öffentlichen Sektors und Unternehmen in stark regulierten Branchen benötigen eine Cloud-Infrastruktur, die erweiterte Souveränitätskontrollen bietet und gleichzeitig die von modernen Cloud-Services erwartete Innovation, Sicherheit und Zuverlässigkeit gewährleistet. Diese Organisationen benötigen die Zusicherung, dass ihre Daten und Aktivitäten unter europäischer Zuständigkeit bleiben, mit klaren Governance-Strukturen und operativer Autonomie innerhalb der Europäischen Union (EU).

Eine neue unabhängige Cloud-Infrastruktur für Europa
Die AWS European Sovereign Cloud ist eine physisch und logisch getrennte Cloud-Infrastruktur, deren Komponenten sich vollständig innerhalb der EU befinden. Die erste AWS-Region in der AWS European Sovereign Cloud befindet sich im deutschen Bundesland Brandenburg und ist ab heute allgemein verfügbar. Diese Region arbeitet unabhängig von bestehenden AWS-Regionen. Die Infrastruktur umfasst mehrere Availability Zones mit redundanter Stromversorgung und Netzwerkverbindung, die auch bei einer Unterbrechung der Verbindung zum Rest der Welt einen kontinuierlichen Betrieb gewährleisten.

Wir beabsichtigen, die Präsenz der AWS European Sovereign Cloud von Deutschland aus EU-weit auszuweiten, um strenge Anforderungen hinsichtlich Isolierung, Datenresidenz innerhalb einzelner Länder und geringer Latenz zu erfüllen. Dies beginnt mit neuen souveränen Local Zones in Belgien, den Niederlanden und Portugal. Darüber hinaus können Sie die Infrastruktur der AWS European Sovereign Cloud mit dedizierten AWS Local Zones, AWS AI Factories oder AWS Outposts an Standorten Ihrer Wahl, einschließlich Ihrer eigenen lokalen Rechenzentren, erweitern.

Dank ihres einzigartigen Betriebsmodells bieten die AWS European Sovereign Cloud und ihre Local Zones erweiterte Souveränitätskontrollen. Der Betrieb der AWS European Sovereign Cloud wird ausschließlich von EU-Bürgern mit Wohnsitz in der EU sichergestellt. Dies umfasst Aktivitäten wie den täglichen Betrieb, den technischen Support und den Kundenservice. Wir stellen die AWS European Sovereign Cloud schrittweise so um, dass als Betriebspersonal ausschließlich EU-Bürger mit Wohnsitz in der EU zum Einsatz kommen. Während dieser Übergangsphase werden wir weiterhin mit einem gemischten Team aus in der EU ansässigen Personen und in der EU lebenden EU-Bürgern arbeiten.

Die Infrastruktur wird durch spezielle europäische juristische Personen nach deutschem Recht verwaltet. Im Oktober 2025 berief AWS Stéphane Israël, einen in der EU ansässigen EU-Bürger, zum Geschäftsführer. Stéphane wird für das Management und den Betrieb der AWS European Sovereign Cloud verantwortlich zeichnen. Dies umfasst die Bereiche Infrastruktur, Technologie und Services sowie die Federführung bei den breit angelegten Initiativen von AWS auf dem Gebiet der digitalen Souveränität. Im Januar 2026 ernannte AWS zudem Stefan Hoechbauer (Vice President, Germany and Central Europe, AWS) zum Geschäftsführer der AWS European Sovereign Cloud. Er wird gemeinsam mit Stéphane Israel die Leitung der AWS European Sovereign Cloud innehaben.

Ein Beirat, dem ausschließlich EU-Bürger, einschließlich zwei unabhängigen externen Vertretern, angehören, fungiert als zusätzliche Kontrollinstanz und bringt Fachwissen in Fragen der Souveränität ein.

Verbesserte Datenresidenz und -kontrolle
Die AWS European Sovereign Cloud bietet umfassende Garantien hinsichtlich der Datenresidenz, sodass Sie selbst die strengsten Anforderungen in diesem Bereich erfüllen können. Wie auch bei unseren bestehenden AWS-Regionen weltweit verbleiben alle Ihre Inhalte in der von Ihnen ausgewählten Region, sofern Sie keine anderen Einstellungen vornehmen. Neben den Inhalten verbleiben auch die vom Kunden erstellten Metadaten, einschließlich Rollen, Berechtigungen, Ressourcenbezeichnungen und Konfigurationen, innerhalb der EU. Die Infrastruktur verfügt über ein eigenes AWS Identity and Access Management (IAM) und ein eigenes Abrechnungssystem – beides wird innerhalb der europäischen Grenzen unabhängig betrieben.

In die Infrastruktur integrierte technische Kontrollen verhindern den Zugriff auf die AWS European Sovereign Cloud von außerhalb der EU. Die Infrastruktur umfasst einen dedizierten europäischen Trust Service Provider für Zertifizierungsstellen und nutzt dedizierte Amazon-Route-53-Namenserver. Diese Server verwenden ausschließlich europäische Top-Level-Domains (TLDs) für ihre eigenen Namen. Die AWS European Sovereign Cloud unterliegt keinen kritischen Abhängigkeiten hinsichtlich Personal oder Infrastruktur außerhalb der EU.

Sicherheits- und Compliance-Framework
Die AWS European Sovereign Cloud bietet dieselben zentralen Sicherheitsfunktionen, die Sie von AWS erwarten. Dazu gehören Verschlüsselung, Schlüsselverwaltung, Zugriffskontrolle und das AWS Nitro System für die Isolierung von Rechenressourcen. Dies bedeutet, dass Ihre EC2-Instanzen von einer kryptografisch verifizierten Plattformintegrität und hardwaregestützten Grenzen profitieren, die unbefugten Zugriff auf Ihre Daten verhindern, ohne die Leistung zu beeinträchtigen. So erhalten Sie sowohl die Souveränitätskontrollen als auch die Rechenleistung, die Ihre Workloads erfordern. Die Infrastruktur wird unabhängigen Audits durch Dritte unterzogen. Die Compliance-Programme umfassen ISO/IEC 27001:2013, SOC-1/2/3-Berichte und das C5-Zertifikat des Bundesamtes für Sicherheit in der Informationstechnik (BSI).

Das AWS European Sovereign Cloud: Sovereign Reference Framework definiert spezifische Souveränitätskontrollen in den Bereichen Governance-Unabhängigkeit, operative Kontrolle, Datenresidenz und technische Isolierung. Dieses Framework ist in AWS Artifact verfügbar und bietet durch SOC-2-Zertifizierung durchgängige Transparenz.

Umfassende Serviceverfügbarkeit
Von Beginn an steht Ihnen in der AWS European Sovereign Cloud eine breite Palette von AWS-Services zur Verfügung – darunter Amazon SageMaker und Amazon Bedrock für Workloads im Bereich Künstliche Intelligenz und Machine Learning (KI/ML). Für die Rechenleistung stehen Ihnen Amazon Elastic Compute Cloud (Amazon EC2) und AWS Lambda zur Verfügung. Die Container-Orchestrierung ist über den Amazon Elastic Kubernetes Service (Amazon EKS) und den Amazon Elastic Container Service (Amazon ECS) verfügbar. Zu den Datenbank-Services gehören Amazon Aurora, Amazon DynamoDB und Amazon Relational Database Service (Amazon RDS). Die Speicheroptionen umfassen Amazon Simple Storage Service (Amazon S3) und Amazon Elastic Block Store (Amazon EBS) mit Vernetzung über Amazon Virtual Private Cloud (Amazon VPC) und Sicherheits-Services wie AWS Key Management Service (AWS KMS) und AWS Private Certificate Authority. Eine aktuelle Liste der Services finden Sie in der AWS-Funktionsmatrix, die kürzlich im AWS Builder Center veröffentlicht wurde.

Die AWS European Sovereign Cloud wird von AWS-Partnern unterstützt, die es sich zur Aufgabe gemacht haben, Ihnen bei der Erfüllung Ihrer Souveränitätsanforderungen zur Seite zu stehen. Partner wie Adobe, Cisco, Cloudera, Dedalus, Esri, Genesys, GitLab, Mendix, Pega, SAP, SnowFlake, Trend Micro und Wiz stellen ihre Lösungen in der AWS European Sovereign Cloud zur Verfügung und bieten Ihnen die Tools und Services, die Sie in den Bereichen Sicherheit, Datenanalyse, Entwicklung von Anwendungen und branchenspezifische Workloads benötigen. Dank dieser umfassenden Unterstützung durch Partner können Sie eigenständige Lösungen, die AWS-Services mit bewährten Partnertechnologien kombinieren, entwickeln.

Erhebliche Investitionen in die europäische Infrastruktur
Hinter der AWS European Sovereign Cloud steht eine Investition in Höhe von 7,8 Milliarden Euro in Infrastruktur, die Schaffung von Arbeitsplätzen und die Entwicklung von Kompetenzen. Diese Investition wird bis 2040 voraussichtlich 17,2 Milliarden Euro zur europäischen Wirtschaftsleistung beitragen und jährlich rund 2 800 Vollzeitstellen in lokalen Unternehmen sichern.

Einige technische Details
Die AWS European Sovereign Cloud steht allen Kunden zur Verfügung, unabhängig davon, wo sie sich befinden. Sie können mit dem Partitionsnamen aws-eusc und dem Regionsnamen eusc-de-east-1 auf die Infrastruktur zugreifen. Eine Partition ist eine Gruppe von AWS-Regionen. Jedes AWS-Konto ist auf eine Partition beschränkt.

Die Infrastruktur unterstützt alle gängigen AWS-Zugriffsmethoden, einschließlich der AWS Management Console, AWS SDKs und der AWS Command Line Interface (AWS CLI), sodass sie sich problemlos in Ihre bestehenden Workflows und Automatisierungsprozesse integrieren lässt. Nachdem Sie ein neues Root-Konto für die Partition der AWS European Sovereign Cloud erstellt haben, beginnen Sie mit der Erstellung neuer IAM-Identitäten und -Rollen, die speziell für diese Infrastruktur vorgesehen sind. Dadurch erhalten Sie die vollständige Kontrolle über die Zugriffsverwaltung innerhalb der europäischen souveränen Umgebung.

Erste Schritte
Die AWS European Sovereign Cloud bietet europäischen Organisationen erweiterte Souveränitätskontrollen und gewährleistet gleichzeitig den Zugriff auf die Innovationen und Funktionen von AWS. Sie können Services über Amazon Web Services EMEA SARL in Auftrag geben. Die Preise werden in Euro angegeben und die Abrechnung erfolgt in einer der acht Währungen, die wir derzeit unterstützen. Die Infrastruktur basiert auf der bekannten AWS-Architektur, dem AWS-Serviceportfolio und den AWS-APIs, wodurch die Entwicklung und Migration von Anwendungen vereinfacht wird.

Der AWS European Sovereign Cloud Addendum enthält die zusätzlichen vertraglichen Verpflichtungen für die AWS European Sovereign Cloud.

Für mich als Europäer symbolisiert dieser Launch das Engagement von AWS, den spezifischen Anforderungen unseres Kontinents gerecht zu werden und Cloud-Funktionen bereitzustellen, die Innovationen in allen Branchen vorantreiben. Ich lade Sie ein, mehr über die AWS European Sovereign Cloud zu erfahren und zu entdecken, wie sie Ihrer Organisation dabei helfen kann, ihre Souveränitätsanforderungen zu erfüllen. Lesen Sie die Übersicht über die AWS European Sovereign Cloud und erfahren Sie mehr über die Designziele und den Ansatz. Registrieren Sie sich für ein neues Konto und planen Sie noch heute die Bereitstellung Ihrer ersten Workload.

— seb

French version

Ouverture de l’AWS European Souvereign Cloud

En tant que citoyen européen, je mesure personnellement l’importance de la souveraineté numérique, en particulier pour nos organisations du secteur public et les industries fortement réglementées. Aujourd’hui, j’ai le plaisir d’annoncer que l’AWS European Sovereign Cloud est désormais disponible pour l’ensemble de nos clients. Nous avions annoncé pour la première fois notre projet de construction de cette nouvelle infrastructure cloud indépendante en 2023, et elle est aujourd’hui prête à répondre aux exigences de souveraineté les plus strictes des clients européens, avec un large ensemble de services AWS.

Berlin, porte de Brandebourg au coucher du soleil

Répondre aux exigences européennes en matière de souveraineté
Partout en Europe, les organisations sont confrontées à des exigences réglementaires de plus en plus complexes en matière de résidence des données, de contrôle opérationnel et d’indépendance de la gouvernance. Trop souvent aujourd’hui, les organisations européennes ayant les besoins de souveraineté les plus élevés se retrouvent contraintes de rester sur des environnements sur site, ou de recourir à des offres cloud aux services et fonctionnalités limités. Pour répondre à cet enjeu critique, l’AWS European Sovereign Cloud est le seul cloud souverain entièrement fonctionnel, exploité de manière indépendante, et reposant sur des contrôles techniques robustes, des garanties de souveraineté et des protections juridiques solides. Les acteurs du secteur public et les entreprises des secteurs fortement réglementés ont besoin d’une infrastructure cloud offrant des contrôles de souveraineté renforcés, sans renoncer à l’innovation, à la sécurité et à la fiabilité attendues des services cloud modernes. Ces organisations doivent avoir l’assurance que leurs données et leurs opérations restent sous juridiction européenne, avec des structures de gouvernance claires et une autonomie opérationnelle au sein de l’Union européenne (UE).

Une nouvelle infrastructure cloud indépendante pour l’Europe
L’AWS European Sovereign Cloud repose sur une infrastructure cloud physiquement et logiquement distincte, dont l’ensemble des composants est situé exclusivement au sein de l’UE. La première région AWS de l’AWS European Sovereign Cloud est implantée dans le Land de Brandebourg, en Allemagne, et est disponible dès aujourd’hui. Cette région fonctionne de façon indépendante par rapport aux autres régions AWS existantes. L’infrastructure comprend plusieurs zones de disponibilité, avec des systèmes redondants d’alimentation et de réseau, conçus pour fonctionner en continu même en cas d’interruption de la connectivité avec le reste du monde.

Nous prévoyons d’étendre l’empreinte de l’AWS European Sovereign Cloud depuis l’Allemagne à l’ensemble de l’UE, afin de répondre aux exigences strictes d’isolation, de résidence des données dans certains pays et de faible latence. Cette extension débutera avec de nouvelles Local Zones souveraines situées en Belgique, aux Pays-Bas et au Portugal. En complément, vous pourrez étendre l’infrastructure de l’AWS European Sovereign Cloud à l’aide des AWS Dedicated Local Zones, des AWS AI Factories ou d’AWS Outposts, dans les sites de votre choix, y compris au sein de vos propres centres de données sur site.

L’AWS European Sovereign Cloud et ses Local Zones offrent des contrôles de souveraineté renforcés grâce à un modèle opérationnel unique. L’AWS European Sovereign Cloud est exploité exclusivement par des résidents de l’UE basés dans l’UE. Cela couvre notamment les opérations quotidiennes, le support technique et le service client. Nous sommes en train d’opérer une transition progressive afin que l’AWS European Sovereign Cloud soit exploité exclusivement par des citoyens de l’UE résidant dans l’UE. Durant cette période de transition, nous continuons de travailler avec une équipe mixte composée de résidents de l’UE et de citoyens de l’UE basés dans l’UE.

L’infrastructure est gérée par des entités juridiques européennes dédiées, établies conformément au droit allemand. En octobre 2025, AWS a nommé Stéphane Israël, citoyen de l’UE résidant dans l’UE, au poste de directeur général. Stéphane est responsable de la gestion et de l’exploitation de l’AWS European Sovereign Cloud, couvrant l’infrastructure, la technologie et les services, ainsi que de la direction des initiatives plus larges d’AWS en matière de souveraineté numérique. En janvier 2026, AWS a également nommé Stefan Hoechbauer (Vice-président, Allemagne et Europe centrale, AWS) comme directeur général de l’AWS European Sovereign Cloud. Il travaillera aux côtés de Stéphane Israël pour piloter l’AWS European Sovereign Cloud.

Un conseil consultatif, composé exclusivement de citoyens de l’UE et incluant deux représentants indépendants, apporte un niveau supplémentaire de supervision et d’expertise sur les questions de souveraineté.

Résidence des données et contrôle renforcés
L’AWS European Sovereign Cloud fournit des garanties complètes en matière de résidence des données afin de répondre aux exigences les plus strictes. Comme dans les régions AWS existantes à travers le monde, l’ensemble de vos contenus reste dans la région que vous sélectionnez, sauf indication contraire de votre part. Au-delà des contenus, les métadonnées créées par les clients — telles que les rôles, les autorisations, les étiquettes de ressources et les configurations — restent également au sein de l’UE. L’infrastructure dispose de son propre système dédié de AWS Identity and Access Management (IAM) et de facturation, opérant de manière totalement indépendante à l’intérieur des frontières européennes.

Des contrôles techniques intégrés à l’infrastructure empêchent tout accès à l’AWS European Sovereign Cloud depuis l’extérieur de l’UE. L’infrastructure comprend un prestataire européen de services de confiance dédié pour les opérations d’autorité de certification et utilise des serveurs de noms Amazon Route 53 dédiés. Ces serveurs n’utilisent que des domaines de premier niveau (TLD) européens pour leurs propres noms. L’AWS European Sovereign Cloud ne présente aucune dépendance critique vis-à-vis de personnels ou d’infrastructures situés en dehors de l’UE.

Cadre de sécurité et de conformité
L’AWS European Sovereign Cloud conserve les capacités de sécurité fondamentales attendues d’AWS, notamment le chiffrement, la gestion des clés, la gouvernance des accès et le système AWS Nitro pour l’isolation des charges de calcul. Concrètement, vos instances EC2 bénéficient d’une intégrité de plateforme vérifiée cryptographiquement et de frontières matérielles, empêchant tout accès non autorisé à vos données sans compromis sur les performances. Vous bénéficiez ainsi à la fois des contrôles de souveraineté et de la puissance de calcul nécessaires à vos charges de travail. L’infrastructure fait l’objet d’audits indépendants réalisés par des tiers, et s’inscrit dans des programmes de conformité incluant ISO/IEC 27001:2013, les rapports SOC 1/2/3, ainsi que l’attestation C5 de l’Office fédéral allemand pour la sécurité de l’information (BSI).

Le AWS European Sovereign Cloud: Sovereign Reference Framework définit précisément les contrôles de souveraineté couvrant l’indépendance de la gouvernance, le contrôle opérationnel, la résidence des données et l’isolation technique. Ce cadre est disponible dans AWS Artifact et offre une visibilité de bout en bout via une attestation SOC 2.

Disponibilité étendue des services
Dès son lancement, l’AWS European Sovereign Cloud donne accès à un large éventail de services AWS, notamment Amazon SageMaker et Amazon Bedrock pour les charges de travail d’intelligence artificielle et de machine learning (IA/ML). Pour le calcul, vous pouvez utiliser Amazon Elastic Compute Cloud (Amazon EC2) et AWS Lambda. L’orchestration de conteneurs est disponible via Amazon Elastic Kubernetes Service (Amazon EKS) et Amazon Elastic Container Service (Amazon ECS). Les services de bases de données incluent Amazon Aurora, Amazon DynamoDB et Amazon Relational Database Service (Amazon RDS). Les options de stockage comprennent Amazon Simple Storage Service (Amazon S3) et Amazon Elastic Block Store (Amazon EBS), avec des capacités réseau via Amazon Virtual Private Cloud (Amazon VPC) et des services de sécurité tels que AWS Key Management Service (AWS KMS) et AWS Private Certificate Authority. Pour obtenir la liste la plus à jour des services disponibles, consultez la matrice des capacités AWS récemment publiée sur l’AWS Builder Center.

L’AWS European Sovereign Cloud est supporté par de nombreux partenaires AWS engagés à vous aider à répondre à vos exigences de souveraineté. Des partenaires tels qu’Adobe, Cisco, Cloudera, Dedalus, Esri, Genesys, GitLab, Mendix, Pega, SAP, SnowFlake, Trend Micro et Wiz rendent leurs solutions disponibles sur l’AWS European Sovereign Cloud, vous offrant ainsi les outils et services nécessaires dans les domaines de la sécurité, de l’analyse de données, du développement applicatif et des charges de travail spécifiques à certains secteurs industriels. Cet ensemble de partenaires vous permet de construire des solutions souveraines combinant les services AWS et des technologies de partenaires de confiance.

Un investissement majeur dans l’infrastructure européenne
L’AWS European Sovereign Cloud s’appuie sur un investissement de 7,8 milliards d’euros dans l’infrastructure, la création d’emplois et le développement des compétences. Cet investissement devrait contribuer à hauteur de 17,2 milliards d’euros à l’économie européenne d’ici 2040 et soutenir environ 2.800 emplois équivalent temps plein par an au sein des entreprises locales.

Quelques détails techniques
L’AWS European Sovereign Cloud est accessible à tous les clients, quel que soit leur lieu d’implantation. Vous pouvez accéder à l’infrastructure en utilisant le nom de partition aws-eusc et le nom de région eusc-de-east-1. Une partition correspond à un ensemble de régions AWS. Chaque compte AWS est rattaché à une seule partition.

L’infrastructure prend en charge toutes les méthodes d’accès AWS standards, y compris la console de gestion AWS, les AWS SDKs et la AWS Command Line Interface (AWS CLI), ce qui facilite son intégration dans vos flux de travail et vos automatisations existants. Après avoir créé un nouveau compte racine pour la partition AWS European Sovereign Cloud, vous commencez par définir de nouvelles identités et rôles IAM spécifiques à cette infrastructure, vous donnant un contrôle total sur la gestion des accès au sein de l’environnement souverain européen.

Pour commencer
L’AWS European Sovereign Cloud offre aux organisations européennes des contrôles de souveraineté renforcés tout en leur permettant de continuer à bénéficier de l’innovation et des capacités d’AWS. Vous pouvez contractualiser les services via Amazon Web Services EMEA SARL, avec une tarification en euros et une facturation possible dans l’une des huit devises que nous prenons en charge aujourd’hui. L’infrastructure repose sur une architecture, un portefeuille de services et des API AWS familiers, ce qui simplifie le développement et la migration des applications.

L’avenant AWS European Sovereign Cloud précise les engagements contractuels supplémentaires propres à l’AWS European Sovereign Cloud.

En tant qu’Européen, ce lancement illustre l’engagement d’AWS à répondre aux besoins spécifiques de notre continent et à fournir les capacités cloud qui stimulent l’innovation dans tous les secteurs. Je vous invite à découvrir l’AWS European Sovereign Cloud et à comprendre comment il peut aider votre organisation à satisfaire ses exigences de souveraineté. Consultez Overview of the AWS European Sovereign Cloud (en anglais) pour en savoir plus sur les objectifs de conception et l’approche retenue, créez un nouveau compte et planifiez dès aujourd’hui le déploiement de votre première charge de travail.

— seb

Italian version

Lancio di AWS European Sovereign Cloud

Come cittadino europeo, conosco benissimo l’importanza della sovranità digitale, in particolare per le nostre organizzazioni del settore pubblico e dei settori altamente regolamentati. Oggi sono lieto di annunciare che AWS European Sovereign Cloud è ora generalmente disponibile per tutti i clienti. Abbiamo annunciato per la prima volta i nostri piani per la creazione di questa nuova infrastruttura cloud indipendente nel 2023. Finalmente oggi è pronta a soddisfare i più rigorosi requisiti di sovranità dei clienti europei con un set completo di servizi AWS.

Berlino, Porta di Brandeburgo al tramonto

Soddisfare i requisiti di sovranità europea
Le organizzazioni di tutta Europa devono far fronte a requisiti normativi sempre più complessi in materia di residenza dei dati, controllo operativo e indipendenza della governance. Troppo spesso, le organizzazioni europee con i più elevati requisiti di sovranità sono bloccate a causa di offerte o ambienti on-premises legacy con funzionalità e servizi ridotti. In risposta a questa esigenza fondamentale, l’AWS European Sovereign Cloud rappresenta l’unico cloud sovrano con funzionalità complete e a gestione autonoma. supportato da solidi controlli tecnici, garanzie di sovranità e protezioni legali. Gli enti pubblici e le aziende di settori altamente regolamentati necessitano di un’infrastruttura cloud che fornisca controlli di sovranità avanzati che garantiscano l’innovazione, la sicurezza e l’affidabilità che si aspettano dai moderni servizi cloud. Queste organizzazioni devono essere certe che i propri dati e le proprie operazioni restino sotto la giurisdizione europea, con chiare strutture di governance e autonomia operativa nell’ambito dell’Unione europea (UE).

Una nuova infrastruttura cloud indipendente per l’Europa
L’AWS European Sovereign Cloud rappresenta un’infrastruttura cloud separata fisicamente e logicamente, con tutti i componenti situati interamente all’interno dell’UE. La prima Regione AWS nell’AWS European Sovereign Cloud, situata nello stato di Brandeburgo (Germania) e ora disponibile a livello generale, opera indipendentemente dalle Regioni AWS esistenti. L’infrastruttura presenta diverse zone di disponibilità con risorse di alimentazione e rete ridondanti, progettate per funzionare continuamente anche in caso di interruzione della connettività con il resto del mondo.

Abbiamo intenzione di estendere la presenza dell’AWS European Sovereign Cloud dalla Germania all’intera UE per supportare i rigorosi requisiti di isolamento, residenza dei dati all’interno di un determinato Paese e bassa latenza. Inizieremo con nuove zone locali sovrane situate in Belgio, nei Paesi Bassi e in Portogallo. Inoltre, sarà possibile estendere l’infrastruttura AWS European Sovereign Cloud con Zone locali AWS dedicate, AWS AI Factories o AWS Outposts in posizioni selezionate, inclusi i data center on-premises.

L’AWS European Sovereign Cloud e le relative zone locali forniscono controlli sovrani avanzati tramite un modello operativo esclusivo. L’AWS European Sovereign Cloud sarà gestito esclusivamente da residenti UE che si trovano nell’UE. Ciò copre attività come operazioni quotidiane, supporto tecnico e servizio clienti. Stiamo gradualmente trasformando l’AWS European Sovereign Cloud in modo che sia gestito esclusivamente da cittadini UE che si trovano nell’UE. Durante questo periodo di transizione, continueremo a lavorare con un team misto di residenti e cittadini comunitari che si trovano nell’UE.

L’infrastruttura è gestita da entità giuridiche europee dedicate, costituite secondo il diritto tedesco. Nell’ottobre 2025, AWS ha assegnato l’incarico di managing director a Stéphane Israël, cittadino comunitario residente nell’UE. Sarà responsabile della gestione e delle operazioni dell’AWS European Sovereign Cloud, inclusi infrastruttura, tecnologia e servizi, oltre a guidare le più ampie iniziative di sovranità digitale di AWS. Nel gennaio 2026, AWS ha inoltre nominato managing director dell’AWS European Sovereign Cloud Stefan Höchbauer (vicepresidente, Germania ed Europa centrale, AWS). Collaborerà con Stéphane Israël per guidare l’AWS European Sovereign Cloud.

Un comitato consultivo composto esclusivamente da cittadini comunitari, inclusi due rappresentanti terzi indipendenti, fornirà ulteriore supervisione e competenza in materia di sovranità.

Ottimizzazione del controllo e della residenza dei dati
L’AWS European Sovereign Cloud offre garanzie complete sulla residenza dei dati in modo da poter soddisfare i requisiti più rigorosi in materia. Come per le nostre Regioni AWS esistenti a livello mondiale, tutti i contenuti restano all’interno della Regione selezionata, a meno che non si scelga diversamente. Oltre ai contenuti, anche i metadati creati dai clienti, tra cui ruoli, autorizzazioni, etichette delle risorse e configurazioni, restano nell’ambito dell’UE. L’infrastruttura è dotata di un proprio sistema AWS Identity and Access Management (AWS IAM) e di fatturazione dedicato, completamente gestito in modo indipendente all’interno dei confini europei.

I controlli tecnici integrati nell’infrastruttura impediscono l’accesso all’AWS European Sovereign Cloud dall’esterno dell’UE. L’infrastruttura include un provider di servizi fiduciari europeo dedicato per le operazioni delle autorità di certificazione e utilizza nameserver Amazon Route 53 dedicati. Questi server utilizzeranno solo domini di primo livello (TLD) europei per i propri nomi. L’AWS European Sovereign Cloud non ha dipendenze fondamentali da personale o infrastrutture non UE.

Framework di sicurezza e conformità
L’AWS European Sovereign Cloud mantiene le stesse funzionalità di sicurezza di base di AWS, tra cui crittografia, gestione delle chiavi, governance degli accessi e AWS Nitro System per l’isolamento computazionale. Ciò significa che le istanze EC2 beneficiano dell’integrità della piattaforma verificata a livello di crittografia e dei limiti imposti dall’hardware che impediscono l’accesso non autorizzato ai dati senza compromettere le prestazioni, offrendo i controlli di sovranità e la potenza di calcolo richiesti dai carichi di lavoro. L’infrastruttura è sottoposta ad audit di terze parti indipendenti, con programmi di conformità che includono ISO/IEC 27001:2013, report SOC 1/2/3 e attestazione C5 dell’Ufficio Federale per la Sicurezza Informatica (BSI).

L’AWS European Sovereign Cloud: Sovereign Reference Framework definisce i controlli di sovranità specifici in termini di indipendenza della governance, controllo operativo, residenza dei dati e isolamento tecnico. Questo framework è disponibile in AWS Artifact e fornisce visibilità end-to-end tramite l’attestazione SOC 2.

Disponibilità completa del servizio
Dal momento del lancio, sarà possibile accedere a un’ampia gamma di servizi AWS nell’AWS European Sovereign Cloud, tra cui Amazon SageMaker e Amazon Bedrock per carichi di lavoro di intelligenza artificiale e machine learning (IA/ML). Per i calcoli, è possibile utilizzare Amazon Elastic Compute Cloud (Amazon EC2) e AWS Lambda. L’orchestrazione di container è disponibile tramite Amazon Elastic Kubernetes Service (Amazon EKS) e Amazon Elastic Container Service (Amazon ECS). I servizi di database includono Amazon Aurora, Amazon DynamoDB e Amazon Relational Database Service (Amazon RDS). Le opzioni di storage includono Amazon Simple Storage Service (Amazon S3) e Amazon Elastic Block Store (Amazon EBS), con connessione in rete tramite Amazon Virtual Private Cloud (Amazon VPC) e servizi di sicurezza tra cui Servizio AWS di gestione delle chiavi (AWS KMS) e Autorità di certificazione privata AWS (AWS Private CA). Per un elenco aggiornato dei servizi, è possibile consultare l’elenco delle funzionalità AWS pubblicato di recente su AWS Builder Center.

L’AWS European Sovereign Cloud è supportato dai partner AWS, che aiutano a soddisfare i propri requisiti di sovranità. Partner come Adobe, Cisco, Cloudera, Dedalus, Esri, Genesys, GitLab, Mendix, Pega, SAP, SnowFlake, Trend Micro e Wiz stanno rendendo disponibili le loro soluzioni nell’AWS European Sovereign Cloud, fornendo gli strumenti e i servizi necessari per la sicurezza, l’analisi dei dati, lo sviluppo di applicazioni e i carichi di lavoro specifici del settore. Questo ampio supporto dei partner aiuta a creare soluzioni sovrane che combinano i servizi AWS con tecnologie di partner affidabili.

Investimenti significativi nelle infrastrutture europee
L’AWS European Sovereign Cloud è sostenuto da un investimento di 7,8 miliardi di euro destinati a infrastrutture, creazione di posti di lavoro e sviluppo delle competenze. Si prevede che questo investimento contribuirà con 17,2 miliardi di euro all’economia europea fino al 2040 e sosterrà circa 2.800 posti di lavoro equivalenti a tempo pieno all’anno nelle aziende locali.

Alcuni dettagli tecnici
L’AWS European Sovereign Cloud è disponibile per tutti i clienti, indipendentemente da dove si trovino. È possibile accedere all’infrastruttura utilizzando il nome della partizione aws-eusc e il nome della Regione eusc-de-east-1. Una partizione è un gruppo di Regioni AWS. Ogni account AWS è associato a una partizione.

L’infrastruttura supporta tutti i metodi di accesso AWS standard, tra cui la Console di gestione AWS, gli SDK AWS e l’interfaccia della linea di comando AWS (AWS CLI), semplificando l’integrazione nell’automazione e nei flussi di lavoro esistenti. Dopo aver creato un nuovo account root per la partizione Di AWS European Sovereign Cloud, si inizia creando nuove identità e ruoli IAM specifici per questa infrastruttura, che consentiranno di avere il controllo completo sulla gestione degli accessi all’interno dell’ambiente sovrano europeo.

Nozioni di base
L’AWS European Sovereign Cloud fornisce alle organizzazioni europee controlli di sovranità avanzati pur mantenendo l’accesso all’innovazione e alle funzionalità di AWS. È possibile contrattare servizi tramite Amazon Web Services EMEA SARL, con prezzi in EUR e fatturazione in una delle otto valute supportate oggi. L’infrastruttura utilizza l’architettura AWS, il portafoglio di servizi e le API tradizionali, semplificando la creazione e la migrazione delle applicazioni.

L’addendum di AWS European Sovereign Cloud include gli impegni contrattuali aggiuntivi per l’AWS European Sovereign Cloud.

Per me come europeo, questo lancio rappresenta l’impegno di AWS per soddisfare le esigenze specifiche del nostro continente e fornire le funzionalità cloud che guidano l’innovazione in tutti i settori. Invito tutti a scoprire di più sull’AWS European Sovereign Cloud e su come può aiutare le organizzazioni a soddisfare i requisiti di sovranità. Nella Panoramica di AWS European Sovereign Cloud sono disponibili maggiori informazioni sugli obiettivi e sull’approccio di progettazione, creare un nuovo account e pianificare l’implementazione del primo carico di lavoro oggi stesso.

— seb

Spanish version

Apertura de AWS European Sovereign Cloud

Como ciudadano europeo, comprendo de primera mano la importancia de la soberanía digital, especialmente para las organizaciones de nuestro sector público y las industrias altamente reguladas. Hoy me complace anunciar que la AWS European Sovereign Cloud ya está disponible de forma generalizada para todos los clientes. Anunciamos por primera vez nuestros planes de crear esta nueva infraestructura de nube independiente en 2023, y hoy está lista para cumplir los requisitos de soberanía más estrictos de los clientes europeos con un exhaustivo conjunto de servicios de AWS.

Berlín, Puerta de Brandeburgo al atardecer

Cumplimiento de los requisitos de soberanía europeos
Las organizaciones de toda Europa se enfrentan a unos requisitos normativos cada vez más complejos en relación con la residencia de los datos, el control operativo y la independencia de la gobernanza. Hoy en día, con demasiada frecuencia, las organizaciones europeas con los requisitos de soberanía más estrictos se ven atrapadas en entornos locales heredados u ofertas con servicios y funcionalidades reducidos. En respuesta a esta necesidad crítica, la AWS European Sovereign Cloud es la única nube soberana independiente con todas las características que está respaldada por sólidos controles técnicos, garantías de soberanía y protecciones legales. Las entidades del sector público y las empresas de industrias altamente reguladas necesitan una infraestructura en la nube que proporcione controles de soberanía mejorados que mantengan la innovación, la seguridad y la fiabilidad que se esperan de los servicios modernos en la nube. Estas organizaciones necesitan la garantía de que sus datos y operaciones permanecen bajo la jurisdicción europea, con estructuras de gobernanza claras y autonomía operativa dentro de la Unión Europea (UE).

Una nueva infraestructura de nube independiente para Europa
La AWS European Sovereign Cloud es una infraestructura de nube separada de manera física y lógica, en la que todos los componentes están ubicados íntegramente dentro de la UE. La primera región de AWS de la AWS European Sovereign Cloud se encuentra en el estado de Brandeburgo (Alemania) y ya está disponible para el público en general. Esta región opera de forma independiente de las regiones de AWS existentes. La infraestructura cuenta con varias zonas de disponibilidad con fuentes de alimenacion y redes redundantes, diseñadas para funcionar de forma continua incluso si se interrumpe la conectividad con el resto del mundo.

Tenemos previsto ampliar la presencia de la AWS European Sovereign Cloud de Alemania a toda la UE para cumplir los requisitos de aismlamiento estrictos, residencia de los datos dentro del país y baja latencia. Esto comenzará con nuevas zonas locales soberanas ubicadas en Bélgica, los Países Bajos y Portugal. Además, podrá ampliar la infraestructura de la AWS European Sovereign Cloud con zonas locales dedicadas de AWS, AWS AI Factories o AWS Outposts en las ubicaciones que elija, incluidos sus propios centros de datos locales.

La AWS European Sovereign Cloud y sus zonas locales proporcionan controles soberanos mejorados a través de su modelo operativo único. La AWS European Sovereign Cloud será operada exclusivamente por residentes de la UE ubicados en la UE. Abarca actividades como las operaciones diarias, la asistencia técnica y el servicio de atención al cliente. Estamos realizando una transición gradual de la AWS European Sovereign Cloud para que se opere exclusivamente por ciudadanos de la UE ubicados en la UE. Durante este período de transición, seguiremos trabajando con un equipo mixto de residentes de la UE y ciudadanos de la UE ubicados en la UE.

La infraestructura se administra a través de entidades jurídicas europeas especializadas constituidas en el marco de la legislación alemana. En octubre de 2025, AWS nombró director general a Stéphane Israël, ciudadano de la UE que reside en la UE. Stéphane será responsable de la administración y las operaciones de la AWS European Sovereign Cloud, lo que incluye la infraestructura, la tecnología y los servicios, además de liderar los esfuerzos más amplios de AWS en materia de soberanía digital. En enero de 2026, AWS también nombró a Stefan Hoechbauer (vicepresidente de AWS para Alemania y Europa Central) director general de la AWS European Sovereign Cloud. Stefan dirigirá la AWS European Sovereign Cloud junto con Stéphane Israël.

Un consejo consultivo compuesto exclusivamente por ciudadanos de la UE, que incluye a dos representantes independientes externos, proporciona supervisión y experiencia adicional en materia de soberanía.

Mejor control y residencia de datos
La AWS European Sovereign Cloud ofrece amplias garantías de residencia de datos para que pueda cumplir los requisitos más estrictos en materia de residencia de datos. Al igual que ocurre con nuestras regiones de AWS existentes en todo el mundo, todo el contenido permanece dentro la región que elija, a menos que decida lo contrario. Además del contenido, los metadatos creados por los clientes, incluidos los roles, los permisos, las etiquetas de recursos y las configuraciones, también permanecen dentro de la UE. La infraestructura cuenta con su propio sistema dedicado de AWS Identity and Access Management (AWS IAM) y facturación, que funciona de forma independiente dentro de las fronteras europeas.

Los controles técnicos integrados en la infraestructura impiden el acceso a la AWS European Sovereign Cloud desde fuera de la UE. La infraestructura incluye un proveedor de servicios de confianza europeo dedicado para las operaciones de las autoridades de certificación y utiliza servidores de nombres de Amazon Route 53 dedicados. Estos servidores solo usarán dominios de nivel superior europeos para sus propios nombres. La AWS European Sovereign Cloud no tiene dependencias críticas de personal o infraestructura fuera de la UE.

Marco de seguridad y cumplimiento
La AWS European Sovereign Cloud mantiene las mismas capacidades de seguridad básicas que cabe esperar de AWS, como el cifrado, la administración de claves, la gobernanza del acceso y AWS Nitro System para el aislamiento de la computación. Esto significa que sus instancias de EC2 se benefician de la integridad de la plataforma verificada criptográficamente y de los límites impuestos por el hardware que impiden el acceso no autorizado a sus datos sin comprometer el rendimiento, lo que le proporciona tanto los controles de soberanía como la potencia computacional que requieren sus cargas de trabajo. La infraestructura se somete a auditorías independientes externas, con programas de cumplimiento que incluyen la norma ISO/IEC 27001:2013, informes SOC 1/2/3 y la certificación C5 de la Oficina Federal de Seguridad de la Información.

El marco de referencia de soberanía de la AWS European Sovereign Cloud define los controles de soberanía específicos en relación con la independencia de la gobernanza, el control operativo, la residencia de datos y el aislamiento técnico. Este marco está disponible en AWS Artifact y proporciona visibilidad total a través de la certificación SOC 2.

Disponibilidad total del servicio
Puede acceder a una amplia variedad de servicios de AWS en la AWS European Sovereign Cloud desde su lanzamiento, incluidos Amazon SageMaker y Amazon Bedrock para cargas de trabajo de inteligencia artificial y machine learning. Para el procesamiento, puede usar Amazon Elastic Compute Cloud (Amazon EC2) y AWS Lambda. La orquestación de contenedores está disponible a través de Amazon Elastic Kubernetes Service (Amazon EKS) y Amazon Elastic Container Service (Amazon ECS). Los servicios de bases de datos incluyen Amazon Aurora, Amazon DynamoDB y Amazon Relational Database Service (Amazon RDS). Dispone de opciones de almacenamiento como Amazon Simple Storage Service (Amazon S3) y Amazon Elastic Block Store (Amazon EBS), con redes a través de Amazon Virtual Private Cloud (Amazon VPC) y servicios de seguridad como AWS Key Management Service (AWS KMS) y AWS Private Certificate Authority. Para obtener una lista actualizada de los servicios, consulte la matriz de capacidades de AWS que se ha publicado recientemente en AWS Builder Center.

La AWS European Sovereign Cloud cuenta con el respaldo de los socios de AWS, que se comprometen a ayudarle a cumplir sus requisitos de soberanía. Socios como Adobe, Cisco, Cloudera, Dedalus, Esri, Genesys, GitLab, Mendix, Pega, SAP, SnowFlake, Trend Micro y Wiz ofrecen sus soluciones en la AWS European Sovereign Cloud, lo que le proporciona las herramientas y los servicios que necesita en materia de seguridad, análisis de datos, desarrollo de aplicaciones y cargas de trabajo específicas del sector. Este amplio apoyo de los socios le ayuda a crear soluciones soberanas que combinan los servicios de AWS con tecnologías de socios de confianza.

Inversión importante. en la infraestructura europea
La AWS European Sovereign Cloud está respaldada por una inversión de 7.800 millones de EUR en infraestructura, creación de empleo y desarrollo de habilidades. Se espera que esta inversión aporte 17.200 millones de EUR a la economía europea para 2040 y ayude a crear el equivalente a aproximadamente 2.800 puestos de trabajo a tiempo completo por año en empresas locales.

Algunos detalles técnicos
La AWS European Sovereign Cloud está disponible para todos los clientes, independientemente de dónde se encuentren. Puede acceder a la infraestructura utilizando el nombre de partición aws-eusc y el nombre de región eusc-de-east-1. Una partición es un grupo de regiones de AWS. Cada cuenta de AWS tiene limitado su alcance a una sola partición.

La infraestructura admite todos los métodos de acceso estándar de AWS, como la Consola de administración de AWS, los AWS SDK y la Interfaz de la línea de comandos de AWS (AWS CLI), lo que facilita la integración en sus flujos de trabajo y automatización existentes. Tras crear una nueva cuenta raíz para la partición de la AWS European Sovereign Cloud, primero deberá crear nuevas identidades y roles de IAM específicos para esta infraestructura, lo que le permitirá tener un control total sobre la administración del acceso en el entorno soberano europeo.

Cómo empezar
La AWS European Sovereign Cloud proporciona a las organizaciones europeas controles de soberanía mejorados, al tiempo que mantiene el acceso a la innovación y las capacidades de AWS. Puede contratar los servicios a través de Amazon Web Services EMEA SARL, con precios en EUR y facturación en cualquiera de las ocho divisas que admitimos actualmente. La infraestructura utiliza la arquitectura, la cartera de servicios y las API habituales de AWS, lo que facilita la creación y la migración de aplicaciones.

La adenda de la AWS European Sovereign Cloud contiene los compromisos contractuales adicionales para la AWS European Sovereign Cloud.

Para mí, como europeo, este lanzamiento representa el compromiso de AWS de satisfacer las necesidades específicas de nuestro continente y proporcionar las capacidades en la nube que impulsan la innovación en todos los sectores. Le invito a descubrir más sobre la AWS European Sovereign Cloud y cómo puede ayudar a su organización a cumplir sus requisitos de soberanía. Lea la descripción general de la AWS European Sovereign Cloud para obtener más información sobre los objetivos y el enfoque del diseño, regístrese para obtener una nueva cuenta y planifique hoy mismo el despliegue de su primera carga de trabajo.

— seb

Fall 2025 PCI DSS compliance package available now

2026-01-13 Tushar Jain

Post Syndicated from Tushar Jain original https://aws.amazon.com/blogs/security/fall-2025-pci-dss-compliance-package-available-now/

Amazon Web Services (AWS) is pleased to announce that two additional AWS services and one additional AWS Region have been added to the scope of our Payment Card Industry Data Security Standard (PCI DSS) certification:

Newly added services:

Newly added AWS Region:

Asia Pacific (Taipei)

This certification allows customers to use these services while maintaining PCI DSS compliance, enabling innovation without compromising security. The full list of services can be found on the AWS Services in Scope by Compliance Program. The PCI DSS compliance package includes two key components:

Attestation of Compliance (AOC) demonstrating that AWS was successfully validated against the PCI DSS standard.
AWS Responsibility Summary provides guidance to help AWS customers understand their responsibility in developing and operating a highly secure environment on AWS for handling payment card data.

AWS was evaluated by Coalfire, a third-party Qualified Security Assessor (QSA).

This refreshed PCI certification offers customers greater flexibility in deploying regulated workloads while reducing compliance overhead. Customers can access the PCI DSS certification through AWS Artifact. This self-service portal provides on-demand access to AWS compliance reports, streamlining audit processes.

AWS is excited to be the first cloud service provider to offer compliance reports to customers in NIST’s Open Security Controls Assessment Language (OSCAL), an open source, machine-readable (JSON) format for security information. The PCI DSS report package (which includes both the PCI DSS AOC and the AWS Responsibility Summary) in OSCAL format is now available separately in AWS Artifact, marking a milestone towards open, standards-based compliance automation. This machine-readable version of the PCI DSS report package enables workflow automation to reduce manual processing time and modernize security and compliance processes. Your use cases for this content are innovative and we want to hear about them through the contact information found in the OSCAL report package.

To learn more about our PCI programs and other compliance and security programs, see the AWS Compliance Programs page. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Compliance Support page.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

AWS Weekly Roundup: AWS Lambda for .NET 10, AWS Client VPN quickstart, Best of AWS re:Invent, and more (January 12, 2026)

2026-01-12 Danilo Poccia

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-lambda-for-net-10-aws-client-vpn-quickstart-best-of-aws-reinvent-and-more-january-12-2026/

At the beginning of January, I tend to set my top resolutions for the year, a way to focus on what I want to achieve. If AI and cloud computing are on your resolution list, consider creating an AWS Free Tier account to receive up to $200 in credits and have 6 months of risk-free experimentation with AWS services.

During this period, you can explore essential services across compute, storage, databases, and AI/ML, plus access to over 30 always-free services with monthly usage limits. After 6 months, you can decide whether to upgrade to a standard AWS account.

Whether you’re a student exploring career options, a developer expanding your skill set, or a professional building with cloud technologies, this hands-on approach lets you focus on what matters most: developing real expertise in the areas you’re passionate about.

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

AWS Lambda – Now supports creating serverless applications using .NET 10 both as a managed runtime and a container base image. AWS will automatically apply updates to the managed runtime and base image as they become available. More info in this blog post.
Amazon ECS – Adds support for tmpfs mounts to Linux tasks running on AWS Fargate and Amazon ECS Managed Instances in addition to the EC2 launch type. With tmpfs, you can create memory-backed file systems for your containerized workloads without writing data to task storage.
AWS Config – Can now discover, assess, audit, and remediate additional AWS resource types across key services including Amazon EC2, Amazon SageMaker, and Amazon S3 Tables.
Amazon MQ – Introduces HTTP based authentication for RabbitMQ brokers. You can configure this plugin on brokers by making changes to the associated configuration file. It now also supports certificate based authentication with mutual TLS for RabbitMQ brokers.
Amazon MWAA – You can now create Apache Airflow version 2.11 environments with Amazon Managed Workflows for Apache Airflow. This version of Apache Airflow introduces changes that help you prepare for upgrading to Apache Airflow 3.
Amazon EC2 – M8i, C8i and C8i-flex, R8i and R8i-flex, and I7ie instances are now available in additional AWS Regions.
AWS Client VPN – A new quickstart reduces the number of steps required to set up a Client VPN endpoint.
Amazon Quick Suite – Added integrations for AI agents and to its built-in actions library. For example, these now include GitHub, Notion, Canva, Box, Linear, Hugging Face, Monday.com, HubSpot, Intercom, and more.

Additional updates
Here are some additional projects, blog posts, and news items that I found interesting:

Automating AWS SDK for Java v1 to v2 Upgrades with AWS Transform – To help you modernize Java applications efficiently while minimizing manual intervention and potential errors.
Unlock Amazon Aurora’s Advanced Features with Standard JDBC Driver using AWS Advanced JDBC Wrapper – Enhance an existing application that uses the open source standard JDBC driver and unlock the capabilities of Aurora and the AWS Cloud with minimal code changes.
Implement multi-Region endpoint routing for Amazon Aurora DSQL – An automated solution for redirecting database traffic to alternate regional endpoints without manual configuration changes.
Crossmodal search with Amazon Nova Multimodal Embeddings – How to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance with working code examples and tips to add these capabilities to your applications.

Upcoming AWS events
Join us January 28 or 29 (depending on your time zone) for Best of AWS re:Invent, a free virtual event where we bring you the most impactful announcements and top sessions from AWS re:Invent. Jeff Barr, AWS VP and Chief Evangelist, will share his highlights during the opening session.

There is still time until January 21 to compete for $250,000 in prizes and AWS credits in the Global 10,000 AIdeas Competition (yes, the second letter is an I as in Idea, not an L as in like). No code required yet: simply submit your idea, and if you’re selected as a semifinalist, you’ll build your app using Kiro within AWS Free Tier limits. Beyond the cash prizes and potential featured placement at AWS re:Invent 2026, you’ll gain hands-on experience with next-generation AI tools and connect with innovators globally.

If you’re interested in these opportunities, join the AWS Builder Center to learn with builders in the AWS community.

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

– Danilo