Built to bounce back: How Azure resiliency evolved

In this article

Resiliency as a shared responsibility, not a handoffPlatform foundations that reflect reality: zones, regions, and sovereigntyAzure features and capabilities strengthen resiliency outcomesBridging intent to execution through experiences on AzureHow you can build Resilience in AzureAzure Essentials

Resiliency in the cloud is often described in terms of availability, such as how quickly a system fails over, how many replicas exist, or what a service-level agreement guarantees. But for most organizations today, especially those operating in regulated, sovereign, or geopolitically sensitive environments, resiliency is something far more fundamental. It is the ability to continue operating under pressure, protect what matters most, and recover safely when the unexpected happens.

A useful way to think about this is not a system problem, but a city problem. A modern city does not depend on a single power source, a single road, or a single control system. It is designed to withstand disruptions, whether from infrastructure failures, natural events, or security incidents. It has redundancy—but more importantly—it has governance, control, and recovery mechanisms that reflect local realities. Cloud resiliency operates in much the same way. It is not just about avoiding outages; it is about ensuring systems can adapt, recover, and keep functioning within real-world constraints.

Get started with Resiliency in Azure

On Azure, resiliency is not something Microsoft delivers to customers. It is something Microsoft builds with them. The platform provides deeply resilient infrastructure and increasingly intelligent capabilities, but resiliency outcomes only emerge when those are intentionally designed, aligned with sovereignty constraints, and continuously validated against real-world conditions. Last year, we explained how at its core, Azure approaches resiliency across three interconnected pillars: infrastructure resiliency, data resiliency, and cyber recovery.

Infrastructure resiliency: ensuring applications remain available through failure conditions.

Data resiliency: ensuring data remains protected, durable, and recoverable.

Cyber recovery: ensuring organizations can recover safely from compromised states.

Together, they ensure not only that systems remain available, but that they remain recoverable and trustworthy—even when failure modes are unpredictable. These pillars are operationalized through a lifecycle approach that helps organizations design, improve, and continuously validate their resiliency posture.

What differentiates Azure is how these elements come together. Azure provides not just resilient infrastructure, but a unified approach that spans platform capabilities, observability, validation, and intelligent remediation, allowing organizations to move from designing for resiliency to continuously operating and improving it.

Resiliency as a shared responsibility, not a handoff

In any city, infrastructure providers ensure that roads, utilities, and foundational systems are reliable. But how buildings are designed, how emergency plans are executed, and how critical services are protected; those remain the responsibility of the city and its operators.

Azure’s shared responsibility model follows the same principle. Microsoft is responsible for delivering a resilient cloud platform foundation like regions, physical datacenters, networking, isolation boundaries, and engineering systems that reduce blast radius and improve durability at scale. This includes capabilities such as Availability Zones, regional isolation, and services like Azure Backup and Azure Site Recovery. Customers then build on Azure enabled experiences to configure the right capabilities and achieve their desired resiliency outcomes. This includes how applications are architected, how dependencies are managed, how recovery objectives are defined, and how backup and disaster recovery are configured and tested. In sovereign and regulated environments, this responsibility becomes even more critical where customers explicitly define where data resides, how it moves, and how recovery aligns with compliance and jurisdictional requirements.

Platform foundations that reflect reality: zones, regions, and sovereignty

Modern Azure resiliency starts with a zone-first design approach, where applications are built to tolerate the loss of an entire Availability Zone. This significantly reduces the likelihood of localized infrastructure failures impacting application availability.

However, resilience does not stop at zones. Regions themselves are not uniform, and assuming uniformity is one of the most common causes of design fragility.

Some Azure regions are paired, with predefined recovery regions aligned for disaster recovery.

Others are non-paired, often due to sovereignty, regulatory, or geographic constraints.

This distinction fundamentally shapes resiliency architecture.

Paired region scenario (predictable recovery): Azure provides a spectrum of durability options from locally redundant storage (LRS) to zone redundant (ZRS) and geo‑redundant storage (GRS), enabling customers to align data protection strategies with their availability, compliance, and data sovereignty requirements. For example, a financial services application deployed in West Europe can leverage its paired region (North Europe) for disaster recovery. Using Azure Site Recovery (ASR), workloads are continuously replicated and orchestrated to enable application-level continuity during a regional disruption.

The predefined region pairing offers predictable failover behavior, along with well-understood Recovery Point Objective (RPO) and Recovery Time Objective (RTO) trade-offs. However, modern Azure resiliency guidance has evolved beyond strict reliance on region pairs. As outlined in the Modern Azure Resilience with Mark Russinovich blog, customers are increasingly adopting flexible multi-region architectures, including non-paired region strategies based on factors such as service availability, capacity, latency, and data residency requirements. These patterns emphasize that disaster recovery is no longer bound to predefined pairs, but instead is a design choice aligned to workload-specific needs.

In such scenarios, Azure Site Recovery plays a critical role by providing consistent, application-aware replication and failover orchestration across any chosen region, paired or not. This allows customers to standardize their recovery strategy while retaining the flexibility to meet evolving business, regulatory, and scale considerations.

Non-paired region scenario (sovereign constraint): a government workload operates in a sovereign region with no predefined pair. Cross-region recovery is restricted. The architecture prioritizes zonal high availability and restore-based recovery using backup to region of choice, ensuring data remains within jurisdictional boundaries. Recovery is slower but fully compliant.

Asymmetric recovery scenario (regulated enterprise): a multinational enterprise deploys in a constrained geography where only subsets of data can leave the region. For example, Azure Site Recovery enables failover for critical services, while sensitive data relies on Azure Backup for in-boundary recovery. The result is an intentionally asymmetric resiliency model, balancing compliance with business continuity.

The result is a shift from one-size-fits-all architectures to workload-driven resiliency design, where recovery strategies are intentionally aligned to business, regulatory, and operational constraints.

Azure features and capabilities strengthen resiliency outcomes

Resiliency in Azure is not delivered by a single service, but it is achieved through a set of capabilities and services. These capabilities work together to ensure applications remain available, data remains protected, and systems can recover even under infrastructure failures, regional disruptions, or cyber-attacks. It begins with zone-resilient foundations that reduce exposure to localized failures, and extends through autoscaling, load balancing, and health-aware traffic management that keeps applications responsive under stress.

For broader infrastructure or regional disruptions, Azure Site Recovery enables continuity through replication and failover orchestration. Equally important, Azure Backup addresses a different class of risk like corruption, accidental deletion, compliance retention, and cyber compromise by enabling recovery to a trusted point in time when failover is not enough. These capabilities are most effective when paired with strong observability and rehydration-friendly design, where systems can detect issues early, recover automatically, and rebuild quickly. The result is a more complete view of resiliency: not just maintaining uptime but sustaining trust and recoverability under real-world failure conditions.

Bridging intent to execution through experiences on Azure

Customers had tools but lacked a unified way to measure and improve their resiliency posture. Introduced at Microsoft Build 2026 and available in public preview, Azure Infrastructure Resiliency Manager addresses this challenge. It provides an application-centric and resource-centric view of resiliency, bringing together Resiliency in Azure, Azure Advisor, Azure Chaos Studio, and Azure Monitor into a single, cohesive experience.

A key starting point is zonal resiliency posture. It helps customers understand whether their workloads are truly zone-resilient, identify hidden dependencies, and pinpoint gaps between intended architecture and actual deployment.

It introduces a lifecycle approach to resiliency:

Start resilient: design workloads with the right foundational posture.

Get resilient: identify and close gaps in existing systems.

Stay resilient: continuously validate and improve through drills and monitoring.

At the core of Azure Infrastructure Resiliency Manager is the Resiliency Agent, which brings intelligence and automation into the lifecycle. The agent evaluates workloads holistically and identifies risks, surfaces misconfigurations, and explains trade-offs across cost, availability, and compliance. But its role extends beyond analysis. This represents a shift from reactive guidance to proactive and increasingly autonomous resiliency management.

In addition to guiding remediation, the Resiliency Agent can generate Infrastructure-as-Code (IaC) templates, enabling teams to directly implement recommended changes in their deployment pipelines. This is a fundamental shift: resiliency moves from being advisory to executable. It becomes embedded in DevOps workflows; codified, repeatable, and consistently applied.

In addition to this, with the Azure Backup MCP Server, these capabilities become programmable. Organizations can integrate backup posture validation, recovery readiness checks, and policy-driven restore workflows into automated systems while maintaining full control within sovereignty boundaries.

How you can build Resilience in Azure

On Azure, this evolution reflects a shift from predefined constructs to intentional architectures, from fragmented tools to unified experiences, and from guidance to execution. As organizations navigate increasing complexity, regulatory constraints, and unpredictable failure modes, the path forward is clear: build resilience into the foundation, validate it continuously, and automate it wherever possible. With Azure’s platform capabilities, application-centric experiences, and intelligent agents, resiliency is not just achievable but operationalized to deliver with confidence.

Explore Azure Essentials to get started with a unified resiliency experience across your applications and infrastructure. Azure Essentials, Microsoft Unified, and Azure Accelerate help organizations move from resiliency design to operational execution across every stage of the lifecycle.

Azure Essentials

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Related resources

Announcing Azure Infrastructure Resiliency Manager Public Preview

Resiliency documentation

Modern Azure Resilience with Mark Russinovich

Reliability guides for Azure services

Cloud Resiliency

Proving application resilience on Azure with Chaos Studio

The post Built to bounce back: How Azure resiliency evolved appeared first on Microsoft Azure Blog.
Quelle: Azure

Amazon EC2 I7ie instances now available in AWS Asia Pacific (Hyderabad) region

AWS is announcing starting today, Amazon EC2 I7ie instances are now available in AWS Asia Pacific (Hyderabad) region. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit the I7ie instances page.
Quelle: aws.amazon.com

AWS DMS Schema Conversion now supports offline SQL Server conversion

AWS Database Migration Service (DMS) Schema Conversion now supports offline source conversion for Microsoft SQL Server, enabling you to convert SQL Server schemas and code without direct connectivity to your source databases. You extract metadata using standard database commands in your own environment, then upload it to DMS Schema Conversion for processing. This eliminates security reviews, firewall changes, and VPN setup that delay migration projects, while delivering the same conversion results as the connected approach.
Offline Source is ideal for organizations with security policies that restrict external tool access to production SQL Server databases. Database administrators generate human-readable metadata files within their existing environment, and security teams can review the commands and output before uploading, making approval straightforward. By removing the connectivity requirement, Offline Source transforms weeks of security reviews into a simple command-and-upload workflow.
Offline Source supports all DMS Schema Conversion targets at no additional conversion charge. For regional availability, see the Supported AWS Regions page. To get started, see Using Offline Source in the DMS Schema Conversion documentation.
Quelle: aws.amazon.com

Amazon EC2 G7 instances are now available in the AWS US East (N. Virginia) Region

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in US East (N. Virginia) Region. G7 instances deliver up to 4.6x AI inference performance and up to 2.1 graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated data analytics workloads.
Customers can use G7 instances for deploying AI models for language translation, video and image analysis, and speech recognition. They also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics and game streaming. Additionally, G7 instances support video transcoding, spatial computing, and data analytics workloads such as recommender systems, Retrieval Augmented Generation (RAG) inference, and real-time data pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU and custom Intel Xeon 6 processors. They support up to 192 virtual CPUs (vCPUs) and up to 700 Gbps of Elastic Fabric Adapter networking bandwidth. They also support up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.
You can start using Amazon EC2 G7 instances today in three AWS Regions: US East (N. Virginia and Ohio), and US West (Oregon). You can purchase G7 instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the G7 instance page.
Quelle: aws.amazon.com

Amazon Location Service enhances Places APIs with new address and search options

Today, Amazon Location Service announced new enhancements to its Places APIs that give developers greater control over address name formatting, multilingual address, travel-optimized POI search, and drive-through data. These capabilities span the Geocode, ReverseGeocode, GetPlace, Suggest, Autocomplete, SearchNearby, and SearchText APIs. Amazon Location Service is a mapping service that offers geospatial data and location functionality such as maps, places search and geocoding, route planning, device tracking, and geofencing. Developers can now control how address component names are returned using the new AddressNamesMode parameter — choosing between matched (echoing input), normalized (canonical names), or administrative (government hierarchy names) — and override behavior per component with AddressNamesVariant. The new AddressTranslations parameter returns place name translations in 50+ languages, making it easier to build multilingual applications. A TravelMode parameter optimizes Suggest and SearchText results for users on the move, improving relevance for navigation and in-vehicle scenarios. Additionally, GetPlace, Suggest, SearchNearby, and SearchText now return a DriveThrough attribute indicating whether a place offers drive-through service — useful for logistics, food delivery, and navigation applications. The Geocode API also now supports a new Parsing.AdditionalInfo response field with additional detail about how the input address was interpreted. Amazon Location Service is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Spain), Europe (Stockholm), South America (São Paulo), and AWS GovCloud (US-West). To get started, see the Amazon Location Service Places API reference, or learn more about Places in the Amazon Location Service Developer Guide.
Quelle: aws.amazon.com

Amazon EMR on EKS now supports Apache Spark troubleshooting agent

Amazon EMR on EKS now supports the Apache Spark troubleshooting agent. Data engineers can now diagnose EMR on EKS job failures through natural language, receiving automated root cause analysis and PySpark code recommendations without manually navigating distributed logs and Spark History Server data. The agent analyzes Spark History Server data, distributed executor logs, and cluster configurations to identify issues such as memory errors, data skew, resource contention, and connectivity failures. With this launch, the Spark troubleshooting agent now covers all EMR deployment options: EMR on EC2, EMR Serverless, and EMR on EKS. The agent is accessible directly from the EMR on EKS console through a “Troubleshoot with AI” option on failed jobs. Additionally, the agent is available through MCP (Model Context Protocol) using any compatible AI coding agent, including Kiro, Claude Code, and Cursor. All operations are read-only, authenticated with IAM roles, and logged in AWS CloudTrail. The Spark troubleshooting agent for Amazon EMR on EKS is available in all AWS Regions where the SageMaker Unified Studio is available. To get started, go to EMR on EKS console, or set up the MCP server in your preferred AI coding agent. For detailed guidance, see the EMR troubleshooting agent documentation.
Quelle: aws.amazon.com