Siemens and Microsoft: Beyond Connectivity to Autonomous, Sustainable Manufacturing
Explore how Siemens Industrial Edge and Microsoft Azure IoT Operations enable secure edge-to-cloud integration, contextualized data, and AI-driven insights—transforming factories into adaptive, future-ready operations.
Azure IoT Hub with ADR (preview): Extending Azure capabilities and certificate management to IoT
Operational excellence in every industry begins by linking the physical world to the digital, enabling organizations to turn raw data from connected assets into actionable insights and real-world improvements. Azure IoT Hub and Azure IoT Operations make this possible by seamlessly integrating data from machines whether on a single factory floor or spread across the globe into a unified platform. Together, they serve as the backbone of connected operations, ensuring that assets, sensors this data is then moved to Microsoft Fabric for real-time analytics and further leveraged by AI agents to drive informed decisions. This approach lets organizations scale efficiently, unifying teams, sites, and systems under the Adaptive Cloud Strategy. It enables use of cloud-native and AI technologies across hybrid, multi-cloud, edge, and IoT environments in a single operational model. Azure IoT Hub empowers organizations to securely and reliably manage connected assets across the globe, providing real-time visibility and control over diverse operations. With proven scalability, broad device support, and robust management tools, IoT Hub delivers a unified platform for developing and operating IoT solutions. Organizations in various industries are using Azure IoT Hub to enhance their operations. In mining, sensors provide real-time safety data and support compliance. Fleet managers track equipment health to boost efficiency and prevent failures, while rail operators use GPS and vibration sensors for precise monitoring and issue detection. Ports utilize conveyor and loading system metrics to optimize scheduling and reduce delays. These examples show how Azure IoT Hub delivers actionable insights, greater safety, and operational efficiency through connected devices. As customers evolve, Azure IoT Hub continues to advance, deepening its integration with the Azure ecosystem and enabling AI-driven, connected operations for the next generation of applications. Today, we’re announcing the public preview of Azure IoT Hub integration with Azure Device Registry bringing IoT devices under the purview of Azure management plane via ARM resource representation and securing them with best-in-class Microsoft-backed X.509 certificate management capabilities. From Connected Devices to Connected Operations Ready-to-use AI platforms are enabling organizations to unlock untapped operational data and gain deeper insights. Organizations are leveraging AI to unify machine and enterprise data, extract actionable insights, and translate them into measurable business gains. They are broadly transitioning from connected devices that simply gather and transmit telemetry, to connected operations which empower supervisors and AI agents to interpret events and respond to scenarios in real time. The integration of Azure IoT Hub with ADR enhancements extends the comprehensive capabilities of Azure to IoT devices. With this integration, Azure Device Registry (ADR) acts as the unified control plane for managing both physical assets from Azure IoT Operations and devices from Azure IoT Hub. It provides a centralized registry, ensuring every entity whether an industrial asset or a connected device is uniquely represented and managed throughout its lifecycle. By integrating with Azure IoT Hub, ADR enables consistent device onboarding, certificate management, and operational visibility at scale. This integration simplifies large-scale IoT fleet management and supports compliance and auditability across diverse deployments. What’s New in this Preview We’re excited to announce the public preview of new capabilities that bring IoT devices into the broader Azure ecosystem. This integration allows IoT to be managed at scale through the Azure management plane. It also strengthens security and enables consistent governance across large deployments: Deep integration with Azure: The Azure Device Registry (ADR) now offers a unified control plane, simplifying identity, security, and policy management for millions of devices. New ADR features make it easier to register, classify, and monitor devices, supporting consistent governance and better operational insights. Combined with Device Provisioning Service (DPS), these enhancements help reduce deployment challenges, speed up time-to-value, and lower operational risks. With IoT Hub integration, IoT Hub devices are represented as Azure resources, providing: One unified registry across multiple IoT Hubs and Azure IoT Operations (AIO) instances. ARM-based management for all Azure resources from cloud to edge. A consolidated view of the entire IoT fleet, simplifying large-scale deployments, monitoring and management. Certificate lifecycle management: Now in public preview, this capability enables secure onboarding and automated certificate rotation for IoT devices, directly integrated with ADR and IoT Hub. X.509 certificates are widely recognized for providing a robust security posture by establishing trusted, cryptographically verifiable device identities. Starting today, customers can use a Microsoft-backed PKI to issue X.509 certificates across their IoT fleets. Devices receive operational certificates that authenticate with IoT Hub, chained to Certificate Authorities (CAs). Policy-driven lifecycle management makes certificate renewal simpler and keeps state in sync with your Hubs. This integration sets the stage for Physical AI by connecting digital and physical systems, thus unlocking new possibilities for data and artificial intelligence. Customer feedback from Private Preview This release has received positive feedback from private preview customers. Particularly the Microsoft-supported PKI and certificate management capabilities, highlighting that previous manual processes were inefficient and fragmented. Customers further noted the advantages of grouping devices from multiple IoT Hubs under a unified namespace, which streamlined management. Moreover, the integration of certificate management within ADR has diminished the reliance on custom solutions. “We were genuinely impressed by how seamless it was to implement. With just a few clicks, clear policy definitions, and two calls in firmware, the entire process became automated, frictionless, and reliable with no external dependencies.” – Uriel Kluk, CTO, Mesh Systems Why It Matters These investments make Azure IoT Hub the cornerstone for connected operations at scale, empowering customers to: Reduce manual cert ops with policy‑driven rotation (fewer outages due to expired certs). Consolidate device registry in ADR for cross‑hub fleet governance. Accelerate compliance audits with centralized certificate lineage. Apply advanced AI tooling for predictive insights and automation. Call to Action Explore the new capabilities in public preview today and start building the next generation of connected operations with Azure IoT Hub and ADR. Learn more on Azure IoT Hub documentation
Bridging the Digital and Physical Worlds with Azure IoT Hub and Azure IoT Operations
Operational excellence starts with people. Empowering those people with the most up to date insights and recommendations requires bridging the gap between the physical and digital worlds to generate the best possible outcomes for real time decision making. Creating this bridge transforms data into insights, insights into intelligent actions, and actions into real-world results. Digital Operations, integrated with AI insights, help make this possible by combining data from connected assets across a variety of physical locations and deployment topologies, and transforming that data into insights and decisions that scale using AI and Analytics. At Microsoft Ignite, we’re extending this vision with new Azure IoT Hub and Azure IoT Operations capabilities to manage connected assets at scale, unify digital operations, and realize AI-enabled outcomes across your enterprise. Connected Operations in Action Azure IoT Hub and Azure IoT Operations form the backbone of connected operations, where every asset, sensor, and system contributes to a continuous loop of intelligence by moving data to Microsoft Fabric for real-time analytics, and for use with AI agents. This pattern applies to nearly every sector of the economy. In manufacturing, these capabilities allow production engineers to predict and avoid equipment failures by analyzing vibration and temperature data at the edge before costly downtime occurs. In energy and utilities, distributed sensors can provide data to control points that help balance load, optimize grid efficiency, and ensure safe operations even in remote areas. In transportation and logistics, connected fleets use edge AI models to detect safety risks in real time, while cloud-based analytics optimize routing and fuel efficiency across entire regions. Across industries, this edge-to-cloud collaboration enables the ability for intelligent systems to sense, reason, and act in the physical world with speed, safety, and precision. From Data to Intelligent Action Organizations today must capture and act on data from both geographically dispersed and tightly collocated assets. That data needs to be processed close to where it’s generated, at the edge, to enable real-time decision-making, reduce latency, and enhance security. At the same time, the cloud remains vital for contextualizing operational data with enterprise systems, training AI models, and managing a consistent identity and security framework across all assets. AI models trained in the cloud can then be deployed back to the edge, where they act on events in real time. Operators can work with AI agents to reason over this data whether it’s structured or unstructured, organized in silos, or contained in free-text fields, to provide results to a mixed team of human and AI operational assets. We have a portfolio of products uniquely designed to make this continuum, from edge to cloud, more intelligent, secure, and repeatable. Together with our partners, we help bridge Operational Technology (OT) with Information Technology (IT) to deliver better business outcomes. New at Ignite: Accelerating Digital Operations We’re excited to share our latest set of investments at Ignite across our portfolio of services. A few key announcements: Azure IoT Hub New Features (Preview): Simplifying Secure Connectivity at Scale Azure IoT Hub empowers organizations to securely and reliably manage connected assets across the globe, providing real-time visibility and control over diverse operations. With proven scalability, broad device support, and robust management tools, IoT Hub delivers a unified platform for developing and operating IoT solutions. As customers evolve, Azure IoT Hub continues to advance, deepening its integration with the Azure ecosystem and enabling AI-driven, connected operations for the next generation of applications. The next generation of Azure IoT Hub investments makes it easier and more secure than ever to connect and manage distributed assets. At Ignite, we’re previewing: New certificate management capabilities that simplify device onboarding and lifecycle management. Integration with Azure Device Registry (ADR) that brings all devices into a common control plane, enabling unified identity, security, and policy management. ADR enhancements that make it easier to register, classify, and monitor assets, paving the way for consistent governance and operational insight across millions of devices. This deeper Azure integration with ADR standardizes operations, simplifies oversight of edge portfolios including IoT devices, and brings the full power of Azure’s management ecosystem to IoT and Digital Operations workloads. Azure IoT Operations New Features (GA): The Foundation for AI in the Physical World Azure IoT Operations is more than an edge-to-cloud data plane, it’s the foundation for achieving AI in the physical world, enabling intelligent operational systems that can perceive, reason, and act to drive new operational efficiencies. Built on Arc-enabled Kubernetes, Azure IoT Operations unifies operational and business data across distributed environments, eliminating silos and providing a repeatable, scalable foundation for autonomous, adaptive operations. By extending familiar Azure management concepts to physical sites, Azure IoT Operations creates an AI-ready infrastructure that supports autonomous, adaptive operations at scale. Our latest GA release of Azure IoT Operations introduced major enhancements: Wasm-powered data graphs deliver fast, modular analytics helping businesses make near real-time decisions at the edge. Expanded connectors now include OPC UA, ONVIF, REST/HTTP, Server-Sent Events (SSE), and direct MQTT for richer industrial and IT integrations. OpenTelemetry (OTel) endpoint support enables seamless telemetry pipelines and observability. Asset health monitoring to provide unprecedented visibility and control. These capabilities help bridge Information Technology, Operational Technology, and data domains, empowering customers to discover, collect, process, and send data using open standards while laying the groundwork for self-optimizing environments where AI agents and human supervisors collaborate seamlessly. Integration with Fabric IQ and Digital Twin Builder To fully unlock the value of connected data, organizations need to contextualize it, linking operational signals to business meaning. Fabric IQ, a new offering announced at Ignite, and Digital Twin Builder in Fabric make this possible, transforming raw telemetry into AI-ready context. This integration allows companies to model complex systems, run simulations, and create intelligent feedback loops across manufacturing, logistics, and energy environments. Edge AI: Real-Time Intelligence in the Physical World Azure’s AI capabilities for edge environments bring intelligence closer to where it matters most. And, because these services are Arc-enabled, organizations can develop, manage and scale AI workloads across diverse environments using consistent tooling. Today, we are announcing updates to two of our key services that enable AI at the edge: Live Video Analysis features (Public Preview) in Azure AI Video Indexer enabled by Arc: delivers real-time agentic video intelligence to improve safety, quality, and operations. Edge RAG (Retrieval Augmented Generation) Public Preview Refresh enables local generative AI reasoning with contextual awareness - empowering AI agents to act within industrial constraints securely and efficiently. These innovations accelerate time to insight and help organizations deploy AI where milliseconds matter. Partner Innovation: Scaling Real Business Value Last year, we showcased the breadth of Azure IoT Operations’ industrial ecosystem. This year, we’re celebrating how partners are integrating, co-innovating, and scaling real customer outcomes. Our partners are packaging repeatable, scalable solutions that connect operational data to enterprise systems—enabling AI-driven insights and automation across sites, regions, and industries. At this year’s Ignite, we’re highlighting some great new partner innovations: NVIDIA is working with Microsoft to enable factory digital twins using the OpenUSD standard Siemens is enabling adaptive production through AI- and digital-twin-powered solutions supported by the integration of Siemens Industrial Edge with Azure IoT Operations Litmus Edge integrates with Azure IoT Operations via the Akri framework to automatically discover industrial devices, enable secure data flows, and support Arc-enabled deployment. Rockwell Automation is streamlining edge-to-cloud integration with its FactoryTalk Optix platform by delivering contextualized, AI-ready data seamlessly within Microsoft Azure IoT Operations architectures. Sight Machine is driving advanced analytics for quality and efficiency across multi-site operations. Through initiatives like Akri, Co-Innovate, and Co-Sell Readiness, our ecosystem is developing managed applications, packaged solutions, and marketplace offerings that accelerate deployment and unlock new revenue streams. These collaborations show how Azure IoT Operations is not just a platform, but a growth engine for industrial transformation. The Path Forward With these advancements, we’re helping organizations bring AI to the physical world by turning data into intelligence and intelligence into action. Customers like Chevron and Husqvarna are scaling beyond initial pilots, expanding their deployments from single-site to multi-site rollouts, unlocking new use cases from predictive maintenance to worker safety, and proving how adaptive cloud architectures deliver measurable impact across global operations. By connecting assets, empowering partners, and delivering open, scalable platform solutions, Microsoft is helping industries achieve resilient, adaptive operations that drive measurable business value. The digital and physical worlds are coming together with solutions that are secure, observable, AI-ready, and built to scale from a single site to global operations. Together, we’re creating a smarter, more connected future. Learn More Learn more about Azure IoT Hub and Azure IoT Operations here: Azure IoT – Internet of Things Platform | Microsoft Azure Learn more about new IoT Hub public preview features here: Azure IoT Hub documentation Discover Partner Solutions: Learn how Litmus and Sight Machine are advancing industrial analytics and integration with Azure IoT Operations. Explore Rockwell Automation and Siemens for more on adaptive cloud architectures and shop floor intelligence. Going to Ignite? If you’re at Ignite this week, you can learn more about how Microsoft enables Industrial Transformation at the following sessions: The New Industrial Frontier Reshaping Digital Operations with AI from Cloud and Edge Or come visit us on the show floor at the Azure Arc Expert Meet Up Focus Area in the Cloud and AI Platforms neighborhoodAzure IoT Operations 2510 Now Generally Available
Introduction We’re thrilled to announce the general availability of Azure IoT Operations 2510, the latest evolution of the adaptive cloud approach for AI in industrial and large scale commercial IoT. With this release, organizations can unlock new levels of scalability, security, and interoperability, empowering teams to seamlessly connect, manage, and analyze data from edge to cloud. What is Azure IoT Operations? Azure IoT Operations is more than an edge-to-cloud data plane, it’s the foundation for AI in physical environments, enabling intelligent systems to perceive, reason, and act in the real world. Built on Arc-enabled Kubernetes clusters, Azure IoT Operations unifies operational and business data across distributed environments, eliminating silos and delivering repeatability and scalability. By extending familiar Azure management concepts to physical sites, AIO creates an AI-ready infrastructure that supports autonomous, adaptive operations at scale. This approach bridges information technology (IT), operational technology (OT), and data domains, empowering customers to discover, collect, process, and send data using open standards while laying the groundwork for self-optimizing environments where AI agents and human supervisors collaborate seamlessly. We've put together a quick demo video showcasing the key features of this 2510 release. Watch below to discover how Azure IoT Operations' modular and scalable data services empowers IT, OT and developers. What’s New in Azure IoT Operations 2510? Management actions: Powerful management actions put you in control of processes and asset configurations, making operations simpler and smarter. Web Assembly (Wasm) data graphs: Wasm-powered data graphs for advanced edge processing, delivering fast, modular analytics and business logic right where your data lives. New connectors: Expanded connector options now include OPC UA, ONVIF, Media, REST/HTTP, and Server-Sent Events (SSE), opening the door to richer integrations across diverse industrial and IT systems. OpenTelemetry (OTel) endpoints: Data flows now support sending data directly to OpenTelemetry collectors, integrating device and system telemetry into your existing observability infrastructure. Improved observability: Real-time health status for assets gives you unmatched visibility and confidence in your IoT ecosystem. Reusable Connector templates: Streamline connector configuration and deployment across clusters. Device support in Azure Device Registry: Azure Device Registry (ADR) now treats devices as first‑class resources within ADR namespaces, enabling logical isolation and role‑based access control at scale. Automatic device and asset discovery and onboarding: Akri‑powered discovery continuously detects devices and industrial assets on the network, then automatically provisions and onboards them (including creating the right connector instances) so telemetry starts flowing with minimal manual setup. MQTT Data Persistence: Data can now be persisted to disk, ensuring durability across broker restarts. X.509 Auth in MQTT broker: The broker now supports X.509 authentication backed by Azure's Device Registry. Flexible RBAC: Built-in roles and custom role definitions to simplify and secure access management for AIO resources. Customers and partners Chevron, through its Facilities and Operations of the Future initiative, deployed Azure IoT Operations with Azure Arc to manage edge-to-cloud workloads across remote oil and gas sites. With a single management plane, the strategy unifies control over thousands of distributed sensors, cameras, robots, and drones. Real-time monitoring and AI enabled anomaly detection not only to enhance operational efficiency but also significantly improve worker safety by reducing routine inspections and enabling remote issue mitigation. This reuse of a global, AI-ready architecture positions Chevron to deliver more reliable, cleaner energy. [microsoft.com] Husqvarna implemented Azure IoT Operations across its global manufacturing network as part of a comprehensive strategy. This adaptive cloud approach integrates cloud, on-premises, and edge systems, preserves legacy investments, and enables real-time edge analytics. The result: data operationalization is 98% faster, imaging costs were slashed by half, productivity was improved, and downtime was reduced. Additionally, AI-driven capabilities like the Factory Companion powered by Azure AI empower technicians with instant, data-informed troubleshooting, shifting maintenance from reactive to predictive across sites. [microsoft.com] Together, these success stories show how Azure IoT Operations, combined with capabilities like Azure Arc, can empower industrial leaders to advance from siloed operations to unified, intelligent systems that boost efficiency, safety, and innovation. Additionally, this year we are celebrating how our partners are integrating, co-innovating, and scaling real customer outcomes. You can learn more about our partner successes at https://aka.ms/Ignite25/DigitalOperationsBlog. Learn more at our launch event Join us at Microsoft Ignite to dive deeper into the latest innovations in Azure IoT Operations 2510. Our sessions will showcase real-world demos plus expert insights on how new capabilities accelerate industrial transformation. Don’t miss the chance to connect with product engineers, explore solution blueprints, and see how Azure IoT Operations lays the foundation for building and scaling physical AI. Get Started Ready to experience the new capabilities in Azure IoT Operations 2510? Explore the latest documentation and quickstart guides at https://aka.ms/AzureIoTOperations Connect with the Azure IoT Tech Community to share feedback and learn from peers.Solving the Data Challenge for Manufacturers with Sight Machine & Azure IoT Operations
Delivering Industrial AI: From Data to Results As manufacturers accelerate their digital transformation, the ability to unify and leverage operational data is the difference between incremental improvement and competitive advantage. Today, we’re launching a joint solution with Sight Machine, purpose-built to solve the OT data challenge and deliver the full Industrial AI stack in weeks, not months: Sight Machine and Microsoft Integrated Industrial AI Stack on Azure This offering is proven in the field, already driving measurable productivity gains for customers in automotive, food, and other sectors with rapid POC cycles and commercial-scale deployments. By integrating Sight Machine’s industrial AI platform with Azure IoT Operations and Microsoft Fabric, we standardize and contextualize machine data at scale, enabling analytics, automation, and actionable insights across the enterprise. What Sets This Solution Apart Fast Deployment: Get the full Industrial AI stack up and running in weeks, not months. End-to-End Integration: Sight Machine’s industrial AI platform works seamlessly with Azure IoT Operations and Microsoft Fabric, standardizing OT data for enterprise-wide use. Real Results: Customers in automotive, food, and other industries are already seeing measurable productivity gains and faster decision cycles. Scalable & Secure: Built on Azure’s adaptive cloud and zero-trust security, with SI partners ready to support commercial scale. Delivering a unified Industrial AI stack Today marks a pivotal moment for manufacturers: the launch of a fully integrated Industrial AI solution, jointly delivered by Microsoft and Sight Machine. This offering brings together the entire Industrial AI stack spanning cloud, edge, and on-premises, enabling organizations to unlock transformative business value. The integrated solution enables customers to transform data into business value by seamlessly contextualizing and moving data from the Edge using Sight Machine and Azure IoT Operations to Microsoft Fabric. Within Microsoft Fabric, the data can be further contextualized and enriched to support AI agents and can be extended to visualize 3D digital twins using NVIDIA Omniverse. The integrated solution has following key components: Azure IoT Operations Streams secure, real-time telemetry from industrial assets to the cloud, enabling visibility and control across edge and enterprise environments. Microsoft Fabric Provides a single analytics and governance platform, merging IT and OT data for enterprise-wide insights. Sight Machine Industrial AI Platform Refines data into “gold-level” quality, fully contextualized and structured for AI, predictive maintenance, and process optimization. M365 Copilot & Agentic Intelligence Surfaces actionable insights directly in familiar tools like Teams and Excel, empowering operators and managers to make informed decisions instantly. NVIDIA Omniverse Integration Extends capabilities into immersive 3D digital twins and physics-based simulations, enabling manufacturers to visualize live operations and test changes virtually before implementing them. Customer Impact Manufacturing is the world’s largest sector, generating twice as much data as any other industry. Yet, the complexity and fragmentation of OT (Operational Technology) data have long limited the adoption of AI at scale. Sight Machine solves this challenge by integrating with every level of the Azure stack, structuring raw OT data into high-quality, contextualized “gold” data, ready for advanced analytics and AI. This integrated offering removes barriers to AI adoption. Manufacturers can connect assets, contextualize data, and deliver actionable insights directly to teams, whether in Teams, Excel, or immersive 3D digital twins. The result: higher productivity, smarter operations, and continuous improvement. Take the Next Step Ready to accelerate your digital transformation? Explore the Sight Machine + Azure IoT Operations solution in the Marketplace. Start your journey to smarter manufacturing today: Sight Machine on AzureMicrosoft and Rockwell Automation: Transforming Industrial AI Together
Unlocking the Future of Connected Operations In today’s rapidly evolving industrial landscape, manufacturers face mounting pressure to increase agility, optimize operations, and harness data-driven insights across every level of production. The collaboration between Microsoft and Rockwell Automation represents a pivotal step toward achieving these goals. By combining Rockwell’s deep expertise in operational technology (OT) with Microsoft’s adaptive cloud approach, this partnership bridges the gap between OT and IT, creating a unified, intelligent ecosystem that empowers manufacturers to innovate at scale. Together, we enable seamless connectivity, advanced analytics, and AI-driven optimization across the factory floor from edge and cloud environments. Connected Operations powered by Microsoft and Rockwell Rockwell Automation’s FactoryTalk Optix and Microsoft’s Azure IoT Operations together deliver a powerful foundation for industrial transformation. FactoryTalk Optix provides a modern, flexible visualization platform for real-time monitoring and control of OT systems. FactoryTalk Optix supports numerous industrial protocols for secure interoperability and “smart-object” data modeling to provide analytics-ready data. Paired with Azure IoT Operations, a unified, adaptive cloud solution built on open standards and powered by Azure Arc, manufacturers gain seamless connectivity across the factory floor enabling edge to cloud orchestration. With support for protocols like OPC UA and MQTT, camera and third-party integration through Akri and WASM connectors, and Copilot-driven automation for observability and deployment, this partnership bridges OT and IT to unlock advanced analytics, AI-driven optimization, and predictive maintenance at scale. A Partnership That Delivers Scalable Innovation Customers can start utilizing FactoryTalk Optix with Azure IoT Operations as a scalable physical to digital foundation for transforming how they manufacture, design, and operate going forward. In partnership with Rockwell, there is a published GitHub sample that demonstrates how FactoryTalk Optix native IIoT connectivity protocols unlock contextualized data from industrial assets into Azure IoT Operations. With the 2510 Azure IoT Operations release , OPC Write capability is now available as well, creating a true read/write path for richer interoperability. The synergy between these technologies is a game-changer for manufacturers, unlocking advanced analytics, and AI-driven use cases. This collaboration delivers: Improved efficiency and reduced downtime through real-time connectivity and predictive maintenance Scalable edge-to-cloud architecture leveraging OPC UA and MQTT standards for unified OT/IT data Highly replicable, scalable deployments across hybrid and multicloud environments Proactive optimization with AI-driven design and analytics Democratized automation via Copilot capabilities for observability and deployment Unified IT management and centralized monitoring for streamlined operations Robust security and reduced integration complexity for faster time-to-value From the Shop Floor to the Boardroom By combining Rockwell’s industrial expertise with Microsoft’s cloud innovation, manufacturers can break down data silos, unify operations, and drive continuous optimization. AI-powered insights become accessible at every level, helping organizations anticipate change, improve safety and efficiency, and maintain a competitive edge in the digital era. Join Us at Rockwell Automation Fair Visit the Microsoft booth at Automation Fair to experience end-to-end demonstrations, explore customer stories, and see firsthand how the Rockwell–Microsoft ecosystem accelerates your digital transformation journey. Join live sessions at the Discovery Theatre – o Tuesday Nov 18th, 11:15am – 11:45am → The new industrial frontier - Using AI to scale faster, work smarter and unlock new value o Tuesday Nov 18 th 2pm – 3pm, and Thursday Nov 20 th at 10:00am – 11:00am → Bringing AI to the Factory Floor o Wednesday Nov 19 th , 1:45pm – 2:15pm → Start with Secure Solutions From Edge to Cloud Visit us at the Expo at Booth #1931 – For demos and conversations to see what we have to offer. Explore the products Learn more about Azure IoT Operations → https://azure.microsoft.com/en-us/products/iot-operations Explore FactoryTalk Optix → https://www.rockwellautomation.com/en-us/products/software/factorytalk/optix.html Hear more about our integration story at Microsoft Ignite → The new industrial frontierFirmware Analysis now Generally Available
Back in June, we announced the public preview of firmware analysis, a new capability available through Azure Arc to help organizations gain visibility into the security of their Internet of Things (IoT), Operational Technology (OT), and network devices. Today, we are excited to announce that firmware analysis is generally available (GA) for all Azure customers. In modern industrial environments, firmware security is a foundational requirement. IoT sensors and smart devices collect the data fueling AI-driven insights; if those devices aren’t secure, your data and operational continuity are at risk. During the preview, we heard from many customers who used firmware analysis to shine a light into their device software and address hidden vulnerabilities before attackers or downtime could strike. With general availability, firmware analysis is ready to help organizations fortify the “blind spots” in their infrastructure – from factory-floor sensors to branch office routers – by analyzing the software that runs on those devices. What Firmware Analysis Does for You Firmware analysis examines the low-level software (firmware) that powers IoT, OT and network devices, with no agent required on the device. You can upload a firmware image (for example, an extracted embedded Linux image), and the cloud service performs an automated security inspection. Key features include: Software inventory & vulnerability scanning: The service builds a Software Bill of Materials (SBOM) of components within the firmware and checks each component against known CVEs (Common Vulnerabilities and Exposures). This quickly surfaces any known vulnerabilities in your device’s software stack so you can prioritize patching those issues. Security configuration and hardening check: Firmware analysis evaluates how the firmware binaries are built, looking for security hardening measures (e.g. stack protections, ASLR) or dangerous configurations. If certain best practices are missing, the firmware might be easier to exploit – the tool flags this to inform the device manufacturer or your security team. Credential and secrets discovery: The analysis finds any hard-coded credentials (user accounts/password hashes) present in the firmware, as well as embedded cryptographic material like SSL/TLS certificates or keys. These could pose serious risks – for instance, default passwords that attackers could exploit (recall the Mirai botnet using factory-default creds) are identified so you can mitigate them. Any discovered certificates or keys can indicate potentially insecure design if left in production firmware. Comprehensive report: All security findings – from the Software Bill of Materials (SBOM), list of vulnerabilities to hardening recommendations and exposed secrets – are provided in a detailed report for each firmware image analyzed. This gives device makers and operators actionable intelligence to improve their device security posture. In short, firmware analysis provides deep insights into the contents and security quality of device firmware. It turns opaque firmware into transparent data, helping you answer, “What’s really inside my device software?” so you can address weaknesses proactively. What’s New and Licensing We’ve been hard at work making firmware analysis even better as we move to GA. Based on preview feedback, we’ve addressed bugs, implemented usability suggestions and improved the firmware analysis SDKs, CLI and PowerShell extensions. A new Azure resource called “firmware workspace” now stores analyzed firmware images. Firmware analysis workspaces are currently available as a Free Firmware Analysis Workspace SKU with capacity limits. Getting Started If you have IoT, OT and network devices in your environment, use firmware analysis to test just how secure your devices are. Getting started is easy: access firmware analysis by searching “firmware analysis” in the Azure portal, or access using this link. Onboard your subscription and then upload firmware images for analysis. For a step-by-step tutorial, visit our official documentation. The service currently supports embedded Linux-based images up to 1GB in size. We want to thank all the preview participants who tested firmware analysis and provided feedback. You helped us refine the service for GA and we’re thrilled to make this powerful tool broadly available to help secure IoT, OT and network devices around the world. We can’t wait to see how you put it to work. As always, we value your feedback, so please let us know what you think.
Regarding Existing Microsoft Applications for End-to-End Operational Management
I would like to inquire whether Microsoft offers any pre-built, production-ready applications—preferably within the Dynamics 365 ecosystem—that are currently in use by customers and proven to be stable, which support the following functionalities: Work Order Management Operational Management Production Planning and Control Resource Management Asset Management Quality Management Inventory Management Barcode Scanning for real-time job tracking (start/finish) Profitability and Financial Reporting Hours Variation Analysis( Planned Vs Actual) Cost Variation Analysis( Planned Vs Actual) We are seeking a solution that integrates these capabilities into a unified platform, ideally with real-time data capture and reporting features. If such a solution exists, we would appreciate details regarding its availability, deployment options, licensing, and customer success stories. Looking forward to your guidanceModel Mondays S2:E7 · AI-Assisted Azure Development
Welcome to Episode 7! This week, we explore how AI is transforming Azure development. We’ll break down two key tools—Azure MCP Server and GitHub Copilot for Azure—and see how they make working with Azure resources easier for everyone. We’ll also look at a real customer story from SightMachine, showing how AI streamlines manufacturing operations.
Azure IoT Operations MQTT Broker: Performance Benchmarking on Throughput and Latency
1. Introduction When deploying an MQTT broker in a production environment, understanding its performance characteristics is crucial. Whether you're handling IoT sensor data, real-time event streams, or enterprise messaging, knowing how the broker performs under load helps in optimizing deployments. In this post, we evaluate the performance of the Azure IoT Operations MQTT Broker (subsequently referred to as Broker for brevity), focusing on: Throughput – How many messages per second the broker can handle. Latency – The time taken for messages to travel from publishers to subscribers. All tests were conducted using MQTT QoS 1 to ensure consistent balance between reliability and throughput. By following a structured performance testing approach, we aim to provide insights into how the Broker scales and where potential bottlenecks may arise. 👉 If you're looking for a quick summary, jump to the Key Takeaways section below. 2. Test Setup For accurate benchmarking, we set up Standard_D4s_v5 virtual machines (VMs) to ensure consistent and efficient message handling. To replicate our performance results, use the same VM SKU and test configuration. 2. 1 Infrastructure configuration Hardware configuration VM Architecture: x64 VM Image: Ubuntu Server 22.04 LTS - x64 Gen2 VM SKU: Standard_D4s_v5 vCPUs: 4 Memory: 16 GiB RAM Networking: All VMs are within the same virtual network (VNet) to minimize latency and reduce external network delays Software configuration OS Flavor: Ubuntu Server 22.04 LTS Version: 22.04 LTS Kubernetes distribution: K3s Kubernetes version: v1.28.5 2.2 Azure IoT Operation configuration The Azure IoT Operations configuration defined below is optimized for performance testing and MUST NOT be used in production as TLS encryption, authentication, and diagnostics pods are disabled to reduce variability. The Broker consists of frontend and backend partitions for optimized message handling: This setup is optimized for a 5-node cluster, ensuring scalability, and redundancy. Broker Configuration: Frontend: 5 replicas Backend: 5 partitions Redundancy factor of 2 2 workers Note: Increased redundancy doubles CPU usage, and therefore it also reduces the total available CPU for performing the same workload, potentially impacting overall efficiency. Broker Listener: Configured with a Load Balancer port 1883 Broker Nodes: 5 x Azure D4s_v5 VMs (4 vCPUs, 16 GiB memory, Ubuntu 22.04) Client Node: 1 x Azure D16s v5 VM (16 vCPUs, 64 GiB memory for load testing) Note: A more powerful 8-core VM is recommended to prevent the client from becoming a bottleneck, as EMQTT-bench by EMQX has high CPU consumption. The broker configuration is available in Azure IoT Mqtt Optimization. Json 3. Methodology To evaluate the performance of IoT Operations MQTT broker we used emqtt-bench, an open source MQTT v5.0 benchmark tool designed by EMQX. For optimal performance during testing, the inflight queue should be configured to a minimum of 100. 3.1 Client Configuration For 5-node cluster testing, a dedicated high-performance VM is required to act as the client. This VM must be separate from the cluster to prevent resource contention, ensuring that benchmarking reflects the broker's actual optimal performance. 3.2 Understanding the Performance Metrics Maximum Throughput – Measures the highest number of messages per second the broker can process. Note: Optimal performance requires finding a balance—publishers should send messages fast enough to fully utilize subscribers without overwhelming them. Average Latency– The time, in milliseconds, it takes for a message to travel from a publisher to a subscriber. Message Size – Tested with 16 Bytes, 8 KB, and 255 KB payloads to understand size impact. Evaluates how different payload sizes impact throughput and latency. Data Throughput – Measures the total volume of data transmitted per second, expected in megabytes per second (MB/sec). 3.3 Test Scenarios We tested the broker under different conditions to observe how it handles increasing workloads: Varying Publisher Rates – Analyzing throughput changes with increasing message rates. Different Payload Sizes – Measuring the impact of small (16 B), medium (8 KB), and large (255 KB) payloads. Fan-In / Balanced / Fan-Out – Comparing multiple publishers to one subscriber (fan-in) vs. one publisher to many subscribers (fan-out) vs an equal number of publishers and subscribers (balanced). Publisher / Subscriber Configuration – Vary number of publishers and subscribers across the three scenarios. QoS - All tests were performed using MQTT QoS 1, which ensures at least once message delivery. This strikes a balance between reliability and performance, making it more representative of real-world production scenarios where message loss is unacceptable, but the overhead of QoS 2 is not justified. We measured broker efficiency using different payload sizes across different publisher-to-subscriber ratios. The Fan-In test evaluated performance with a high number of publishers sending messages to a single subscriber. The Fan-Out stress test analyzed message distribution from a limited number of publishers to many subscribers under high throughput conditions. The Balanced test simulated a mixed workload with equal publishers and subscribers. 4. Results Detailed Performance Metrics: Fan-In, Fan-Out, and Balanced Scenarios Scenario Configuration Payload Size Max Throughput (msg/sec) Data Throughput (MB/sec) Average Latency (ms) Workload Description Fan-In 1000 pub 1 sub 16 B 41,352 0.63 124 High-Load Fan-In 1000 pub 1 sub 8 KB 14,439 112.67 26 High-Load Fan-In 1000 pub 1 sub 255 KB 992 246.8 520 High-Load Fan-In Balanced 1 pub 1 sub 16 B 50,739 6.49 2 Balanced Mixed-Load 1 pub 1 sub 8 KB 9,500 77.8 10 Balanced Mixed-Load 1 pub 1 sub 255 KB 1,314 327.08 540 Balanced Mixed-Load 100 pub 100 sub 16 B 279,949 4.27 350 Balanced Mixed-Load 100 pub 100 sub 8 KB 34,193 266.95 139 Balanced Mixed-Load 100 pub 100 sub 255 KB 2,871 715.42 2,800 Balanced Mixed-Load Fan-Out 1 pub 1000 sub 16 B 42,000 0.64 4 Large-scale Broadcast Fan-Out 1 pub 1000 sub 8 KB 15,003 117.25 6 Large-scale Broadcast Fan-Out 1 pub 1000 sub 255 KB 1,000 249.86 130 Large-scale Broadcast Fan-Out 5. Key Takeaways Takeaway 1: Data Throughput Scales with Payload Size. Even though the number of messages per second drops with larger payloads, data throughput (MB/sec) increases significantly. For example: Fan-In at 255 KB: 246.8 MB/sec Balanced at 255 KB: 715.4 MB/sec Takeaway 2: Performs Best in Low-Latency Use Cases. When message sizes are small (e.g. 16 B, 8 KB) and the topology is lightweight (e.g. 1 pub to 1 sub), the broker achieves: Avg latency as low as 1-2 ms Throughput over 270,000 msg/sec (Balanced scenario at 16 B) Ideal low-latency use cases: Real-time control systems (e.g. robotic arm commands, PLC feedback loops) Smart home device synchronization Autonomous vehicle telemetry coordination Industrial automation events (e.g. triggers from sensors to actuators) For time-sensitive operations, our broker provides sub-10 ms latencies and massive message fanout capability, even under constrained payload sizes. Takeaway 3: Fan-In Saturates Faster Than Fan-Out In QoS 1 tests, we observed Fan-In topologies (1000 devices → 1 endpoint) hit latency walls earlier than Fan-Out topologies (1 device → 1000 endpoints), even with similar message throughput. Fan-In (8 KB): 14,439 msg/sec @ 26 ms latency Fan-Out (8 KB): 15,003 msg/sec @ only 6 ms latency What this shows: In Fan-In, the broker handles thousands of simultaneous inbound QoS 1 acknowledgments — creating coordination pressure. In Fan-Out, a single publisher sends at a controlled rate, making it easier for the broker to fan out efficiently. We designed our broker to sustain intelligent traffic shaping and are continuing to enhance its performance under Fan-In workloads where coordination pressure is highest. 6. Optimization Strategies The Azure IoT Operations MQTT broker is built to support scalable, high-throughput, and low-latency messaging. To harness its full potential across diverse workload patterns, optimization should focus on balanced resource utilization and minimizing message delivery bottlenecks. Maximize Throughput without overload Fan-Out scenarios achieved strong throughput with consistently low latency, even under high subscriber counts. While they didn’t reach the raw message rate of Balanced workloads, their efficiency under broadcast pressure makes them ideal for scenarios requiring timely delivery to many endpoints. Recommended Actions: Batch and Compress Messages: Reduces overhead, improving payload transmission rates. Balance Publish Load: Distribute publishers evenly across broker nodes to avoid overloading a single point of ingestion. Maintain Low Latency for Real-Time Use Cases The broker excels in low-latency performance for small payloads and tightly coupled pub-sub pairs — as seen in 1:1 scenarios with 16 B payloads achieving 2–6 ms latency. These characteristics are crucial for real-time, control-plane workloads. Recommended Actions: Use Smaller Payloads for Time-Sensitive Ops: Critical for scenarios like robotics, actuator control, or telemetry alerting. Load Balance Across Nodes: Adjust broker cardinality, including frontend replicas (for client connection distribution) and backend partitions (for message throughput scaling) to ensure even load distribution across nodes and optimal performance. Enable MQTT Persistent Sessions: Minimizes reconnection overhead for frequently offline clients, learn how to enable persistent sessions with Mosquitto CLI by setting -c and --session-expiry-interval Optimize Deployment Scale for Workload Demands Scalability depends on configuring the right cardinality — that is, the number of frontend replicas, backend partitions, and compute resources — to match your connection and throughput requirements. Recommended Actions: High Connection Load: Scale frontend replicas to match node count (e.g., 3 replicas for 3 nodes) to distribute client connections evenly. High Message Throughput: Increase backend partitions to parallelize message processing (e.g., start with 1 partition per node and scale as needed). Heavy Payload Scenarios: Allocate more memory and CPU to worker pods to avoid slowdowns from large payload serialization and transmission. Backend Resiliency: Ensure redundancyFactor remains at the default of 2 (or more) so that each partition has at least two replicas, enabling failover protection without additional configuration For detailed guidance on these optimizations, visit our documentation: Learn more → 7. Conclusion The Azure IoT Operations MQTT broker is engineered for high performance, scalability, and efficiency, as demonstrated through rigorous benchmarking. In high-throughput balanced configurations, it sustained up to 279,949 messages/sec with 16 B payloads, showcasing best-in-class throughput for high-volume, symmetric pub-sub workloads. For bandwidth-heavy use cases, the broker handled up to 715 MB/sec (255 KB payloads), proving its scalability for large data transfers. Balanced 1:1 scenario also delivered predictable low-latency performance, with average latencies as low as 2 ms, making them ideal for real-time messaging. Meanwhile, Fan-In configurations remain optimal for centralized data aggregation tasks like telemetry logging, handling tens of thousands of messages/secs with acceptable latency. To maximize performance, we recommend key optimization strategies including load balancing, latency reduction, and workload-specific tuning. These approaches ensure efficiency at scale—whether you're managing high connection loads, scaling throughput, or handling large payloads in real-world deployments. For in-depth configuration guidance, visit our documentation.
