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9月19日2026-09-19
AWS ArchitectureAI 评分 45/10005:33

ReadyOn在Amazon EKS上的四层租户隔离架构实践

ReadyOn在Amazon EKS上运行处理高敏感企业数据的多租户平台。该公司提出了“四面墙”(Four Walls)隔离模型,通过结合Kubernetes命名空间、Karpenter节点池、Amazon VPC安全组以及按租户独立的Amazon Aurora数据库,构建了四个相互独立的租户隔离层,实现针对多租户环境的零信任纵深防御体系。

阅读原文 ↗推荐理由:详细介绍了基于AWS EKS与Kubernetes实现多租户零信任隔离的纵深防御架构方案。# 安全# 算力
9月18日2026-09-18
AWS ArchitectureAI 评分 50/10022:35

澳大利亚CSIRO基于AWS构建高扩展基因组变异查询方案sBeacon

澳大利亚国家科学机构CSIRO基于AWS开发了无服务器架构解决方案Serverless Beacon(sBeacon),旨在实现基因组变异数据的安全与高扩展查询。该方案遵循GA4GH Beacon标准,集成了Amazon S3、AWS Lambda、Amazon DynamoDB和Amazon Athena等云服务,有效优化了查询成本,可支持生产级临床和科研应用场景。

阅读原文 ↗推荐理由:展示了基于公有云无服务器架构处理大规模医疗基因组数据的实际落地工程案例。# 医疗AI# 算力# AWS
AWS Architecture✦ 精选AI 评分 65/10002:00

Equinix 基于 Amazon EKS 构建共享服务架构,降低 40% 运维开销

全球数字基础设施巨头 Equinix 在 Amazon EKS 上构建了共享服务架构,以解决此前自建 Kubernetes 环境带来的运维蔓延难题。通过采用多账户 North Star 架构实现集中化治理与服务共享,Equinix 将应用部署速度提升了 4 倍,同时将基础设施运维开销降低了 40%,有效提升了多集群管理的扩展性与运维效率。

阅读原文 ↗推荐理由:展示了全球头部数据中心基础设施运营商 Equinix 通过云原生容器平台优化内部运维架构与降本增效的实践案例。# Equinix# AWS# 数据中心# 算力
9月17日2026-09-17
AWS ArchitectureAI 评分 58/10023:21

基于AWS构建云原生医学影像归档与通信系统(PACS)

本文介绍了在AWS上实现医疗影像现代化的混合云架构模式。该方案阐述了多医院网络如何集中管理PACS档案、实现跨机构互操作性,并借助Amazon S3分层存储在大规模场景下有效优化存储成本与数据保留生命周期。

阅读原文 ↗推荐理由:展示了医疗行业影像系统向公有云存储与混合架构演进的实用工程落地方案。# 医疗AI# AWS# 存储# 云计算
AWS Architecture✦ 精选AI 评分 68/10023:13

DHI Group携手AWS通过黑客松加速生成式AI工作负载投产

DHI Group与AWS合作,通过结构化黑客松模式推动生成式AI工作负载从概念快速走向生产环境。文章详细介绍了其“黑客松加速包”、基于Amazon Bedrock AgentCore构建的ClearanceJobs与AgileATS智能体架构,以及如何将黑客松打造成一套可重复的AI项目落地机制与工程实践原则。

阅读原文 ↗推荐理由:展示了企业利用AWS智能体架构将生成式AI创意快速工程化落地的实践案例与方法论。# 智能体# 大模型# AWS# AI编程
9月11日2026-09-11
AWS Architecture✦ 精选规则精选00:14

Building resilient real-time streaming workers with Amazon DynamoDB leases

Real-time streaming workers that hold hundreds of persistent WebSocket connections lose data when a worker fails. Learn how to build a WebSocket fleet management system on Amazon ECS and AWS Fargate that uses Amazon DynamoDB conditional writes as a distributed lease to track ownership, fail over automatically, and deploy with low downtime.

9月10日2026-09-10
AWS Architecture✦ 精选规则精选05:05

Validating multi-Region DR for Terraform Enterprise with AWS FIS

Learn how AWS, HashiCorp, and Athenahealth designed and chaos-tested a multi-Region disaster recovery strategy for Terraform Enterprise on AWS. This post walks through three-phase AWS Fault Injection Service experiments across Amazon EC2, Aurora, and Amazon S3, the 12-14 minute recovery times achieved, and the state file dependency pitfall to avoid.

9月1日2026-09-01
8月27日2026-08-27
AWS Architecture✦ 精选规则精选01:33

Closing the AI agent trust gap with graduated autonomy

Most teams give AI agents either full access or read-only, leaving value unused or risk unmanaged. This post describes graduated autonomy, an architectural pattern in which agents earn expanded permissions through sustained reliability and lose them when performance degrades, built on Amazon Bedrock AgentCore, Amazon DynamoDB, and AWS CodePipeline.

8月24日2026-08-24
AWS Architecture✦ 精选规则精选23:23

How a global payment processor preserved AWS RAM shares and Lake Formation permissions during an AWS Organizations migration

When AWS accounts move between organizations, organization-bound AWS RAM resource shares break and control-plane access is lost. Learn how a global payment processor used temporary bridge shares to preserve AWS Lake Formation permissions across a 382-account AWS Organizations migration, then restored the original shares as the durable source of truth.

8月22日2026-08-22
AWS Architecture✦ 精选规则精选02:19

Build a unified AI agent architecture with DynamoDB and Bedrock

With native vector search in Amazon DynamoDB, you can store vector embeddings alongside your operational data in a single table. This post shows how to build a unified AI agent architecture where an Amazon Bedrock agent uses one DynamoDB table for both structured lookups and semantic search, with a DynamoDB Streams pipeline that keeps embeddings in sync.

8月21日2026-08-21
8月20日2026-08-20
8月19日2026-08-19
8月18日2026-08-18
AWS Architecture✦ 精选规则精选19:13

Consistency is the new latency: AI at the data layer

As AI agents move from chatbots to taking action, their reliability depends on the consistency of the data layer beneath them. This post examines how replication lag poisons an agent's context and shows how to match Amazon Aurora, Amazon DynamoDB, and Amazon Keyspaces replication models to each task's consistency requirements.