Meet the IQ's: How Microsoft is Creating Context-Aware AI
Authors: Lavanya Sreedhar LavanyaSreedhar, Tom Dinh TomDinh, Oviya Soundararajan oviyasound, and Rafia Aqil Rafia_Aqil The AI era demands more than powerful language models. It demands context a deep understanding of what enterprise data means, how it connects, and how AI systems can reason and act on it intelligently. Microsoft has been building the foundational intelligence layer that makes this possible: a family of capabilities collectively known as the IQ Platform. The Microsoft IQ Platform is not a single product but a set of complementary intelligence layers: Work IQ, Fabric IQ, and Foundry IQ each designed to inject rich contextual understanding into a different part of the enterprise technology stack. Together, they represent Microsoft’s strategic vision for how AI can move beyond isolated answers and become a true operating system for organizational intelligence. This article unpacks each IQ, explains the problems they solve, and explores how they work together to power the next generation of AI-driven enterprise workflows. How the IQs Work Together? Work IQ, Fabric IQ, and Foundry IQ are not competing products or overlapping investments. They are complementary intelligence layers designed to operate across different contexts within the enterprise, and they are most powerful when combined. Work IQ brings the intelligence of Microsoft 365 to every agent and Copilot experience- connecting people, conversations, documents, and organizational signals into a semantic layer that understands how work happens. Fabric IQ brings the intelligence of enterprise data and business context- teaching AI not just what the data says, but what it means in the language of your business: entities, relationships, rules, and governed actions. Foundry IQ brings the infrastructure intelligence that enables all of this to scale- eliminating the undifferentiated plumbing of agentic AI and letting teams focus on building the workflows that actually differentiate their business. Together, the IQ platform represents Microsoft’s answer to one of the defining challenges of the AI era: not just making AI more capable, but making AI contextually aware-grounded in the real knowledge, relationships, and intent of your organization. Fabric IQ: Teaching AI the Language of Business Microsoft Fabric is an end-to-end, unified data analytics platform centered on OneLake- a centralized data lake that stores all analytical and operational business data in open Delta format. Because every Fabric compute experience (Data Engineering, Data Warehouse, Data Factory, Power BI, and Real-Time Intelligence) natively reads from OneLake, organizations gain a single source of truth without copying or duplicating data. OneLake also provides mirroring and shortcut capabilities so existing data can be accessed in place, wherever it lives. Most organizations have made significant progress consolidating their data. The harder challenge is giving AI- and the people who use it-the ability to reason about that data in business terms, not technical ones. Outside of data professionals, businesses do not talk about tables or schemas. They talk about entities that matter to them. Fabric organizes data. Fabric IQ teaches AI what that data means. Three Layers of Business Context Fabric IQ introduces three intelligence layers that together create a unified, contextually rich environment for enterprise AI: Unified Data Layer: Delivered through OneLake and the OneLake Catalog, this provides a single source of truth for all structured and unstructured data across the organization. Business Intelligence Layer: Delivered through Power BI Semantic Models, this layer provides curated measures, hierarchies, dimensions, and trusted KPIs- translating raw data into the analytical language of your business. Operational Intelligence Layer: This is where Fabric IQ’s most distinctive capability lives: Ontology. An Ontology is a model of your business- a graph of entities (such as Patient, Provider, Product, or Account), the relationships between them, the business rules that govern them, and the actions AI agents can take. It functions as the brain that enables AI to understand business context and act on it in a governed, explainable way. Together, these three layers create shared context across all business data stored in OneLake-enabling modern businesses, people, and AI to operate as one unified system. A Real-World Example: Healthcare Consider a care management executive asking: “Which diabetic patients discharged in the last 30 days are at high risk of readmission because they missed follow-up appointments, had medication adherence issues, and recently visited the Emergency Department?” Without Fabric IQ, answering this requires analysts to manually join EHR data, appointment systems, pharmacy records, and ED utilization data- writing SQL across multiple datasets and validating business logic with clinicians. It is slow, brittle, and error-prone. Semantic models can curate data for reporting and analysis, but they do not provide enterprise-scale context integration. With Fabric IQ, an Ontology can be created with entities like Patient, Encounter, Provider, Medication, Diagnosis, Appointment, and Care Plan- each bound to Lakehouse tables, Eventhouse tables, or Materialized Views. Relationships describe how patients connect to their diagnoses, medications, appointments, and treating providers. Business rules enforce data quality, identifying missed follow-ups, recent Emergency visits, and medication gaps. The result is a shift from siloed analytics to true system-level intelligence- an organization where data, AI, and people operate from a shared understanding of the business. Foundry IQ: From Infrastructure to Intelligence Building production-grade AI agents has traditionally meant writing a significant amount of undifferentiated plumbing, custom retrieval pipelines, memory systems, ranking logic, and orchestration code just to enable core RAG and agentic capabilities. While powerful, this approach often leads to complex, hard-to-maintain codebases that distract from the real goal: solving domain-specific problems. With Foundry IQ, Microsoft is fundamentally changing that model by turning these underlying capabilities into managed platform services, allowing teams to shift from building infrastructure to focusing on intelligent workflows. Foundry IQ acts as part of Microsoft's managed platform, enabling agents to use agentic reasoning to access, process, and act on knowledge from anywhere. It is Microsoft Foundry’s way of turning the undifferentiated plumbing behind a RAG agent, such as retrieval, ranking, citations, memory, and personalization, into managed, server-side services that you provision once and call through clean interfaces. Foundry IQ allows you to remove the infrastructure you never wanted to own in the first place. What This Means in Practice Instead of stitching together retrieval pipelines, embedding logic, ranking strategies, and memory mechanisms, Foundry IQ centralizes these capabilities into a single, opinionated platform layer that agents can directly consume. Developers no longer design and maintain each component individually. The knowledge base becomes the centerpiece of the workflow. Rather than coordinating multiple services and response handlers, applications make a single call to retrieve grounded context. Vector-semantic-hybrid querying, query planning, semantic ranking, and citation generation are all encapsulated within the provisioned knowledge base-with no retrieval or embedding logic to maintain in the client application. Memory follows the same pattern of abstraction. Instead of multiple classes and helper utilities to manage storage, user profiles, summarization, and context reconstruction, Foundry IQ replaces this entire layer with a single memory provider backed by a service-managed store with built-in capabilities for chat summarization and user-profile extraction. A Real-World Example: Clinical Workflows Consider building an AI-powered clinical workflow application. Previously, features like agent memory, knowledge base retrieval for grounding, and personalization all had to be written as custom logic and wired manually into the application. This resulted in thousands of lines of code, numerous helper functions, and brittle architecture that was difficult to evolve. With Foundry IQ, that same solution can be reimagined. A single provisioning script now stands up all required services and executes the data-plane steps to create a memory store, build the search index, and provision a Foundry IQ knowledge base for agentic retrieval. Because the top-level router agent carries its own memory, it can directly answer recalled context without relying on confidence thresholds, rule-based branching, or forced workflow paths. Conversation history is handled automatically at ingress- no custom thread management system required. What remains is only what was always worth building: domain-specific logic. Citation validation against grounded evidence. Hallucination checking using LLM-as-a-judge patterns. Agent revision loops. Everything else- retrieval, ranking, memory, user profiles, conversation management- is provisioned once and consumed as a platform capability. The result: a dramatically reduced surface area for bugs, significantly less code to maintain, and teams freed to focus entirely on the work that differentiates their product. Work IQ: Making Microsoft 365 Data Meaningful For years, Microsoft has given organizations API access to their Microsoft 365 data through the Microsoft Graph- emails, calendar events, OneDrive files, Teams conversations, and more. While valuable, this access essentially treated M365 as a structured database: query an endpoint, retrieve an artifact, parse the metadata. The problem was volume and context. With thousands of signals generated every day across the organization, customers needed a way to extract not just data but meaning. In the past year, Microsoft introduced a semantic index built on top of that raw M365 data- a layer that understands not just what exists in your ecosystem, but how everything relates to one another. This intelligence layer is Work IQ, and in an increasingly agent-driven world, it fundamentally changes what AI can do for your organization. In an AI-first world, the advantage is not simply in a model’s ability to reason- it’s in the richness of the context it can reason over. The Contrast in Action Consider asking an agent a simple question: “What’s the latest on Customer Contoso?” With the Microsoft Graph API alone, the agent must stitch together multiple endpoint queries- Teams chats, SharePoint documents, email threads and attempt to piece the results into a coherent answer. It lacks any connective tissue. It doesn’t know what’s relevant, what’s meaningful, or how these isolated data sources relate to each other. The burden of reasoning falls entirely on the agent. With Work IQ, that same prompt taps into a semantic layer that has already done the connecting. The agent knows Contoso-related details span a specific SharePoint folder, identifies the active Teams channel for progress tracking, and surfaces the key people involved. The response is grounded in a web of contextual relationships not just retrieved data. Three Core Components Work IQ is enabled by three powerful components: Data: Unifies signals from files, emails, meetings, chats, and other M365 business systems to capture how work actually gets done across your organization. Memory: Enables persistent context about how people and teams work: details inferred from past conversations, explicit memories stored with Copilot, and custom instructions you’ve configured. Each interaction allows Copilot to learn more about your priorities, preferences, and working style. Inference: Brings together skills, models, and tools to move work forward. It goes beyond understanding your work to deciding what should happen next. Data captures and indexes your M365 knowledge. Memory builds a personalized understanding of how you work. Inference translates this into action. Think of Work IQ as a specialized brain trained on who you are at work within the full context of what your organization knows. Get Started Whether you’re exploring how to ground your AI applications in richer organizational context, looking to reduce the infrastructure burden of building intelligent agents, or seeking to make your enterprise data more actionable the Microsoft IQ Platform offers a path forward. We encourage you to explore the Microsoft Fabric documentation, Azure AI Foundry resources, and the Microsoft 365 developer platform to learn more about how each IQ capability can fit into your architecture. We’d love to hear how you’re thinking about context-aware AI in your organization. Share your thoughts and questions in the comments below. Links: Microsoft IQ | Unified Enterprise Intelligence for AI Work IQ overview | Microsoft Learn What is Foundry IQ? - Microsoft Foundry | Microsoft Learn Fabric IQ documentation - Microsoft Fabric | Microsoft LearnStreaming and Batch Data Architectures with Microsoft Fabric to Azure Databricks
Author's: Aladdin Alchalabi aalchalabi, Oscar Alvarado oscaralvarado and Rafia Aqil Rafia_Aqil Note: This article describes a solution idea. Your cloud architect can use this guidance to help visualize the major components for a typical implementation. Use this article as a starting point to design a well-architected solution that aligns with your workload’s specific requirements. As organizations adopt Microsoft Fabric as their unified analytics platform, it has become a leading path for ingesting both streaming and batch data into Azure Databricks. This article covers integration approaches -via Microsoft Fabric- and details the five Fabric-specific paths that connect OneLake/ADLS and Databricks for end-to-end data processing. Medallion Architecture The following data flow corresponds to the architecture diagram: Data is ingested through Microsoft Fabric (via Mirroring, RTI, or Data Factory) lands data into OneLake/ADLS. With the medallion pattern, consisting of Bronze, Silver, and Gold storage layers, organizations have flexible access and extendable data processing: Bronze – Raw data entry point. Data arrives in its source format and is converted to the open, transactional Delta Lake format. Silver – Optimized for BI and data science. ETL and stream processing tasks filter, clean, transform, join, and aggregate Bronze data into curated datasets using SQL, Python, R, or Scala. Gold – Enriched data ready for analytics and reporting. Analysts use Power BI, PySpark, SQL, or Excel for insights and queries. Fabric Integration Paths Note: This architecture establishes a complete loop-back between Microsoft Fabric and Azure Databricks, enabling Gold layer tables to be seamlessly mirrored back to Microsoft Fabric for dashboarding through Azure Databricks Mirroring. The following five paths connect Microsoft Fabric to Azure Databricks: Fabric Mirroring to OneLake – A low-cost, low-latency turnkey solution that creates a replica of data from operational sources (SQL Server, Azure Cosmos DB, Oracle) in OneLake. Handles the initial load and ongoing CDC changes automatically, keeping data continuously up to date. Fabric RTI to OneLake – Fabric Real-Time Intelligence ingests streaming event data into OneLake with sub-second latency, enabling real-time analytics on live event streams. Fabric Data Factory to OneLake – Orchestrates ingestion from diverse sources not covered by Mirroring (such as Sybase or REST APIs) and lands data in OneLake, ensuring complete source coverage. OneLake to Azure Databricks – Unity Catalog connections to OneLake, secured via Managed Identities from Microsoft Entra ID, allow Databricks to query OneLake data items as a native catalog without data duplication. Fabric Data Factory to Azure Databricks (direct) – Orchestrates ingestion from diverse sources directly into Azure Data Lake Storage (ADLS), where Azure Databricks picks up the data for medallion architecture processing. Design Considerations Area Updated guidance Direct RTI-to-Databricks integration There is still no broad GA direct integration where Fabric RTI and Databricks operate as one native real-time runtime. Integration should be positioned through open protocols, Event Hubs/Kafka-style patterns, OneLake, Delta, and federation. OneLake federation in Azure Databricks OneLake federation in Azure Databricks is now the key integration story. It allows Databricks Unity Catalog to query Fabric Lakehouse and Warehouse data in OneLake without copying it. Access is read-only and depends on Fabric tenant settings, workspace permissions, and Databricks Unity Catalog setup. RTI data availability to Databricks Data ingested through Fabric RTI can be made available to Databricks by landing or exposing the data into OneLake-backed items, especially Lakehouse/Warehouse patterns. Eventhouse data can be made available in OneLake in Delta format through OneLake availability, but Databricks OneLake federation should be validated against the specific Fabric item type and access path. Existing Databricks customers Existing Databricks customers do not need to abandon Databricks. They can use Fabric RTI as the event ingestion, real-time detection, operational alerting, and business action layer, while continuing to use Databricks for engineering, ML, advanced analytics, and Unity Catalog-governed access. Activator and business action Fabric Activator is the cleanest business-user action layer. It can monitor streaming events and trigger Teams messages, email, Power Automate flows, Fabric pipelines, notebooks, Spark jobs, Dataflows, UDFs, and other downstream actions. This is a strong differentiator because it lets business users act on events without waiting for batch analytics. Operations Agents Operations Agents are in preview and should be positioned carefully. They monitor real-time data from Eventhouse or ontology sources, surface insights, recommend actions, and can connect to Activator/Power Automate action paths. They are not simply a pre-ingestion decision engine before data lands anywhere; they work from configured Fabric knowledge/data sources. Before landing in Lakehouse For decisioning before Lakehouse persistence, use Eventstream processing and Activator rules on streams. For AI-assisted operational recommendations, use Operations Agents once the relevant data is available in Eventhouse or ontology. Requirement-Specific Notes Data Ingestion Microsoft Fabric Mirroring currently supports SQL Server, Azure Cosmos DB, and Oracle as source systems. For sources not yet supported by Mirroring—such as Sybase or REST APIs—use Fabric Data Factory pipelines to ensure full coverage across all data systems. Once data is in the landing zone with the correct format, Mirroring’s CDC replication starts automatically and manages the complexity of merging changes (updates, inserts, and deletes) into Delta tables, keeping data in Fabric continuously up to date. Learn more about open mirroring Storage Format and Time Travel OneLake supports Delta tables, enabling schema evolution and time travel across all data stored in the lakehouse. Learn more about OneLake and Delta tables Security Encryption at rest: OneLake automatically encrypts all data at rest using Microsoft-managed keys, compliant with FIPS 140-2 standards. Learn more Encryption in transit: All data in transit is encrypted using TLS 1.2 or higher, securing data movement between Fabric, OneLake, and Azure Databricks. Learn more Data Governance OneLake can be registered and scanned by Microsoft Purview, enabling cataloging of stored metadata and data quality profiling. This protects sensitive information, including PHI and PII, across ingestion and analytics workflows. Learn more about Purview with Fabric Lakehouse Operations and Monitoring Use the Fabric monitor hub to track pipeline health, Spark application performance, and ingestion job status across all Fabric workloads. Learn more about the Fabric monitor hub Scenario Details This architecture applies to any organization that needs to unify streaming and batch data at scale. Common characteristics include: Multiple operational data sources (databases, SaaS applications, event streams) A requirement to process both real-time and historical data in the same platform Governance and compliance requirements for sensitive data (PHI, PII, financial records) Analytics consumers spanning BI (Power BI), data science (Databricks notebooks), and ML workloads Potential Use Cases Healthcare and life sciences – PHI/PII protection via Purview; real-time patient telemetry + batch EHR analytics Financial services – Real-time fraud detection streams + batch regulatory reporting Retail and e-commerce – Streaming clickstream analytics + batch inventory and supply chain processing Energy and utilities – IoT sensor telemetry streaming + batch consumption analytics Next Steps Get started with Microsoft Fabric Mirroring Build an ETL pipeline with Lakeflow Declarative Pipelines Configure Unity Catalog with OneLake shortcuts Monitor Fabric pipelines with the Fabric monitor hubSentinel - Defender XDR KQL Queries Library
Hello all, I’ve been building something over the past few weeks that I think the security community might find useful. GoXDR is a searchable KQL query library for Microsoft Sentinel and Defender XDR. The name comes from a nickname my colleagues gave me (GoX) combined with XDR. I also picked up https://www.linkedin.com/safety/go/?url=http%3A%2F%2Fgoxdr%2Efyi&urlhash=_Woa&mt=tkFQhDwIhUaUuizHXKTf9rOd8eGfZJ97aCPuiTBXuE3RlsAHkvTbqDoxBiyPcq9w-CAe3kkSV0tPW1XMq7JwTYO2YY58GXuiEa2lf_OCBXU5wszWw0wW4LbsuA&isSdui=true as a short and easy to remember domain for it. You can check it out here: https://www.linkedin.com/safety/go/?url=https%3A%2F%2Fgoxdr%2Efyi&urlhash=jgOI&mt=EHrWfnFiS_KrdMAYBvAMhbAIsX0VCZu--Z_9V4ARQOyPk2Pt__C4aH8bxSELw2IS5sbvhfRfrD8rkb6Jttb3-TGOjZ18taXakZEjgYte1Zb_jUui_xylwunC7A&isSdui=true The idea came from my own day to day work as someone working in IAM and SOC operations. I constantly find myself writing and refining KQL queries for threat hunting, detection engineering and incident investigation. Over time I realized I had a growing collection of queries that I kept going back to and I thought why not make these available to others? It currently has 117 queries covering identity security, BEC/AiTM detection, NTLM and LDAP attack hunting, OAuth governance, AI/Copilot security, Sentinel alert trending, SOC performance metrics and more. Some of these queries are ones I wrote from scratch based on real scenarios I encountered in production environments. Others are community queries I tested and validated in my own setup. Only the ones I found genuinely useful and that actually worked against real data made it in. Each query comes with a description explaining what it detects and why it matters, along with severity levels, platform tags (Sentinel, XDR or both) and a copy button so you can paste it directly into Advanced Hunting or use it as the basis for an Analytics Rule. The site is open source, hosted on GitHub Pages and licensed under CC BY 4.0. No sign-up, no paywall, no tracking. The source is available. I’ll keep adding queries as new scenarios come up. If there’s enough interest I’m also considering adding Cortex XQL queries for Palo Alto environments. Suggestions, feedback or ideas for new detections are always welcome. Feel free to reach out. ThanksWhat to Do When You Hit Capacity in Azure Databricks: Engage, Mitigate, Plan!
Microsoft's Cloud Architects: Paul Singh PaulSingh, Aladdin Alchalabi aalchalabi, Eduardo Dos Santos eduardomdossantos, Chris Walk cwalk, Peter Lo PeterLo, Tim Orentlikher tim_orentlikher, Ajmal Hossain ajmalhossain, Chris Haynes Chris_Haynes, and Rafia Aqil Rafia_Aqil Start Here: Engage Microsoft Capacity constraints in Azure Databricks are not an Azure Databricks product issue. Azure Databricks does not own or reserve compute, it dynamically provisions VMs from Azure when clusters are created or scaled. This means cluster creation, autoscaling, or job execution can stall when the underlying VM SKUs are constrained at the regional level. The fastest path to resolution is a structured conversation with your Microsoft account team, who can engage the Azure capacity intake process on your behalf. Create a Quota Support Ticket via Microsoft Support and bring the following to your account team with your Support Ticket Number. Each field maps directly to what capacity intake teams will ask for: missing fields slow the request. What to Prepare Before You Reach Out Your Account Team Field What Capacity Intake Needs Example Subscription IDs The exact Azure subscriptions that will host the workspaces and clusters 7ebee83d-7923-426c-8449-59fd4dff25ab Region(s) Primary region, plus any acceptable alternates East US 2 VM family / SKU Specific series and version requested Eadsv5, ESv4, DSv4, DSv2 Core count / new limit Total vCPU or core count per SKU 10,000 cores for Eadsv5 Workload characteristic CPU-bound vs. memory/shuffle-heavy vs. IO-heavy; batch vs. streaming vs. SQL “Memory-intensive ETL with large joins and shuffles” Scale and timing When you need it, ramp profile, peak vs. steady state “Need by month-end; ramp from 2,000 to 9,650 cores over Q3” Business context Business use case “Migration off AWS” What “Capacity” Really Means: A Layered Mental Model Before diving into fixes, it is important to understand what is actually happening behind the scenes. Capacity constraints can occur at three distinct layers, and solving them requires addressing each one. Layer 1: Azure Infrastructure This is the layer most teams underestimate. Capacity here is governed by: VM SKU availability in the region. D-series and E-series: the two most common Databricks worker families: have repeatedly hit capacity constraints across multiple Azure regions, causing cluster creation failures, autoscale stalls, and provisioning delays. Regional supply constraints, which are dynamic and shared across all Azure tenants. vCPU quotas and limits per subscription, which are separate from regional supply. Quota is your subscription’s limit to deploy resources (like a credit card limit); regional capacity is the underlying infrastructure available. Both must be sufficient. Layer 2: Azure Databricks Platform The Azure Databricks control plane has its own published ceilings that your architecture must proactively respect. Key limits from the official Azure Databricks resource limits documentation: Resource Limit Scope Jobs created per hour 10,000 Workspace Tasks running simultaneously 2,000 Workspace (Run Job and For Each parent tasks excluded) Parent tasks running simultaneously (Run Job / For Each) 750 Workspace SQL warehouses 1,000 Workspace Attached notebooks or execution contexts 145 Cluster Virtual machines 25,000 Per subscription per region Note: For limits marked as non-fixed in the official documentation, you can request an increase through your Azure Databricks account team. Reference: https://learn.microsoft.com/en-us/azure/databricks/resources/limits Layer 3: Workload (Spark Execution) Even when both lower layers cooperate, Spark’s own execution model can produce capacity-like symptoms: Parallelism and task distribution, which dictate how many cores a job can usefully consume. Memory pressure from joins, shuffles, and skewed keys. IO demand and caching behavior, including Delta cache effectiveness and Spark cache misuse. Understanding these layers is critical. Retries sometimes succeed because capacity is dynamic: as other workloads complete, nodes are released back to Azure and briefly become available. Recognizing When You’ve Hit Capacity Capacity issues rarely present as a single clean error. Instead, they appear as inconsistent behaviors: Clusters stuck in Pending state Autoscaling fails or never reaches the desired size Jobs intermittently fail to start Retry attempts sometimes succeed These inconsistencies occur because capacity is shared across Azure tenants and fluctuates throughout the day. Running workloads outside peak business hours in the impacted region’s time zone is one of the most effective short-term mitigations. Immediate Actions: How to Unblock Your Workloads When you are actively hitting capacity constraints, speed matters. Please reach out to your Microsoft Account team and try these mitigations that are ordered from quickest to most involved. Retry and Run During Off-Peak Hours Capacity availability changes throughout the day as workloads complete and release VMs. Running outside peak business hours for the impacted region significantly improves success rates. Switch VM SKU or Family If a specific VM SKU is constrained, switching to another can immediately unblock provisioning. Move within the same family (for example, DSv4 → DSv5) Or switch families entirely (for example, D-series → F-series or L-series) This is one of the most effective but often underused approaches. Also, Choosing the Right VM Family Most Databricks environments default to D-series (general purpose) and E-series (memory optimized). These are also the most heavily used and most capacity-constrained VM families. Consider alternatives based on your workload: VM Family Best For When to Use Trade-off D-series General workloads Default choice Often constrained in high-demand regions E-series Memory-heavy Spark jobs Joins, shuffles, analytics High demand; higher cost F-series CPU-intensive jobs Parsing, transformations Lower memory per core L-series IO-heavy workloads Delta caching, large datasets Higher cost; large local NVMe Practical decision framework: Memory-bound workloads (joins, shuffles): Move from E-series to L-series. Similar memory per core, plus large local NVMe for Delta caching. CPU-bound workloads: Move from D-series to F-series. Higher CPU performance at lower cost. IO-heavy or cache-sensitive workloads: L-series can significantly improve performance and reduce shuffle pressure. Designing a single VM family is one of the biggest production risks in Azure Databricks environments. Implement Regional Diversity in your Databricks workload As Azure capacity constraints are region- and SKU-specific, it is important to build architectural flexibility into your Databricks deployments. For critical or large-scale workloads, consider deploying multiple Databricks workspaces across different Azure regions to reduce dependency on any single region’s capacity. This approach enables: improved resilience to regional capacity constraints greater flexibility in workload placement Important: Multi-region deployment requires deliberate architecture, including deploying separate workspaces and replicating data and configurations across regions—it is not automatic. Why Adding More Nodes Is Not Always the Answer When jobs slow down, the instinct is to scale compute. With Spark, more nodes do not always solve the problem. Common workload issues that masquerade as capacity problems: Data skew Excessive shuffle operations Inefficient partitioning Overuse of UDFs In some workloads, shuffle operations can grow significantly larger than the original input data, placing substantial pressure on compute, memory, disk I/O, and network resources. Because shuffle workloads are distributed across the cluster, adding nodes can improve performance by increasing parallelism. However, that benefit reaches a limit when the bottleneck is caused by data skew, oversized shuffle partitions, network-intensive data movement, or data explosion from joins and aggregations. In these scenarios, the workload becomes constrained by the shuffle pattern itself, and simply adding more nodes does not address the root cause. Instead, the shuffle strategy, partitioning approach, or query design should be optimized. Smarter optimization strategies: Reduce shuffle through repartitioning and query optimization Enable Photon for faster execution Optimize Delta tables using Z-ordering and compaction Leverage caching strategically (not just Spark cache: use the Delta/disk cache) These optimizations can reduce your dependency on scarce VM capacity altogether. What to Do When Your Capacity Is Approved Once Azure approves your capacity request, retaining it requires active steps. Because Azure capacity is dynamic and shared, approved capacity is held only while compute remains actively deployed and running. This is especially important in highly constrained regions. Microsoft recommends the following: Configure an Instance Pool For workloads that cannot yet use serverless compute, configure an Azure Databricks Instance Pool with a minimum number of idle nodes aligned to your production requirements. An instance pool pre-allocates and maintains a set of idle, ready-to-use VM instances. When a cluster is created from the pool, it draws from these warm nodes: eliminating the need to request new VMs from the regional Azure capacity pool between job runs. Key behaviors: The pool holds a minimum number of nodes continuously, keeping them warm and immediately available. Clusters attached to the pool pull from warm nodes, avoiding re-acquisition from Azure between runs. No DBU charges apply while nodes are idle in the pool. Azure VM infrastructure costs do apply for all minimum idle instances. Size the pool conservatively: aligned to production need only: to balance capacity retention against ongoing cost. Important: Instance pools hold idle nodes on a best-effort basis. Periodic platform events can recycle pool nodes, briefly causing the pool to fall below its configured minimum idle count while Azure re-acquires replacement nodes. Pools significantly improve availability and startup latency, but they do not change the fact that the underlying VMs are still requested from Azure on demand. They are not a hard reservation. Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/pools Designing for Resilience: Long-Term Best Practices To avoid repeated capacity issues, your architecture needs to evolve beyond reactive mitigations. Plan for Capacity Early Understand VM quotas and limits before you need them: not after a constraint occurs. Avoid designing a single SKU. Build flexibility into cluster configurations so you can switch families without re-engineering jobs. Standardize Compute Configurations Consistent, policy-driven environments make it easier to adapt when capacity constraints occur. Use Databricks Cluster Policies to constrain cluster creation to approved, available VM families: this prevents teams from inadvertently requesting constrained SKUs. Move Toward Serverless Where Possible Serverless compute abstracts capacity management away from the customer. As the Databricks platform expands serverless support, migrating eligible workloads is the most durable long-term strategy. Azure continues to expand infrastructure capacity, but there are no guaranteed timelines for relief in constrained regions. Note: If your workload supports serverless compute, Databricks recommends using serverless compute instead of pools or classic VM-backed clusters. Serverless removes dependency on specific VM SKUs and regional capacity: scaling is managed by the platform with significantly improved availability. Reference: https://learn.microsoft.com/en-us/azure/databricks/serverless-compute. For eligible workloads: including Databricks Jobs (automated workflows), Databricks SQL Warehouses, and Delta Live Tables: serverless compute eliminates VM SKU dependency entirely. Configuration guidance is available in the Azure Databricks deployment guide, Development Section, Step 9. Multi-Region Strategy for Critical Workloads For the most critical workloads, evaluate a multi-region deployment as part of your business's continuity planning. This is a significant architectural investment: see the FAQ for the full scope: but it is the only approach that provides true regional redundancy. Coordinate this with your Microsoft account team. Reference: Azure Databricks & Microsoft Fabric Disaster Recovery: The Complete Better‑Together Strategy for Cloud Architects Final Takeaways Capacity issues are infrastructure-level constraints, not Databricks product failures VM family selection is critical: do not rely solely on D-series and E-series Workload optimization can reduce dependency on scarce resources before requesting more capacity Serverless compute is Microsoft’s preferred long-term recommendation for eligible workloads Architectural flexibility: multi-SKU, multi-region awareness is your best defense against future constraints FAQ Why do retries work? Capacity in Azure regions is shared across all tenants and fluctuates throughout the day as workloads complete and release VMs. A retry succeeds when capacity temporarily frees up. Retrying during off-peak hours improves success rates significantly. Why does capacity fluctuate during the day? Capacity is a function of regional supply and concurrent demand. As workloads complete, nodes are released back to Azure. Peak business hours in the impacted region’s time zone tend to be the tightest windows. Why are instance pools not a hard reservation? Pools hold a minimum number of nodes on a best-effort basis. Periodic platform events recycle pool nodes, so a pool can briefly fall below its configured minimum idle count while Azure re-acquires replacement nodes. Setting minimum idle to 0 avoids paying for idle VMs at the cost of slower acquisition time. Pools significantly improve availability and startup latency but do not guarantee capacity at the Azure infrastructure level. Why does serverless behave differently from classic clusters? Serverless compute removes customer control over individual VM SKUs. Databricks manages the underlying capacity across a shared pool. SKU-swap and pool-based mitigations do not apply. Customer-side levers reduce to retry and off-peak scheduling. The trade-off is that serverless is the simplest and most reliable option when the workload supports it. Why is changing regions a last resort? Region changes require redeployment of the Azure Databricks workspace and migration of all dependent artifacts: jobs, clusters, libraries, networking (private endpoints, VNet injection), Unity Catalog assignments, identities, and source data. The destination region must be validated for the same SKU and zonal configuration. For these reasons, region change should always be coordinated with the Microsoft account team and attempted only after preferred mitigations have been exhausted. Why does VM family selection matter so much for capacity? Different VM families have different supply curves. D-series and E-series are the most requested Databricks worker families and the ones most frequently constrained. Choosing a SKU based on whether the workload is memory/shuffle-heavy, CPU-bound, or IO-heavy improves both performance and the probability that capacity is available. The capacity team often steers customers toward newer-generation alternatives when supply differs by generation version. What does the Microsoft account team actually do? They route the request into the Azure capacity intake process, advise alternate SKUs and regions, surface zonal vs. regional considerations, and provide forward visibility into known constraints. The customer’s job is to bring a complete, accurate workload profile so the account team can advocate effectively. It is also recommended to open an Azure Support ticket. This will save time later, as the capacity planning teams would like to track issues and requests via a support ticket. Once an Azure Support ticket is opened, the ticket number should be shared to the Microsoft Account Team, at a minimum to the Customer Success Account Manager (CSAM), if one is assigned to your organization.Microsoft Fabric Operations Agent Step by Step Walkthrough
Fabric Capacity and Workspace You need a Microsoft Fabric workspace backed by a paid capacity. Trial capacities are not supported for Operations Agent. Your capacity must be provisioned in a supported region. As of April 2026, Operations Agent is available in all Microsoft Fabric regions except South Central US and East US. If your capacity is outside the US or EU, you will also need to enable cross geo processing and storage for AI through the tenant settings. Your workspace must contain an Eventhouse with at least one KQL database. The Eventhouse is the telemetry backbone, and the KQL database holds the tables the agent will monitor. In the screenshot below, you can see a workspace named OperationAgent-WS that contains an Eventhouse (ops_eventhouse), two KQL databases (ops_db and ops_eventhouse), and a Lakehouse (ops_lakehouse). This is the environment used throughout this guide. Figure 1. Workspace contents showing the Eventhouse, KQL databases, and Lakehouse ready for the Operations Agent. Enabling the Operations Agent in the Admin Portal A Fabric administrator must enable the Operations Agent preview toggle in the Admin Portal before anyone in the organization can create an agent. Navigate to the Admin Portal, locate the section for Real Time Intelligence, and find the setting labeled Enable Operations Agents (Preview). Toggle it to Enabled for the entire organization or for specific security groups depending on your governance requirements. In addition to this toggle, ensure that Microsoft Copilot and Azure OpenAI Service are also enabled at the tenant level. The Operations Agent relies on Azure OpenAI to generate its playbook and to reason about data when conditions are met. Figure 2. The Admin Portal showing the Enable Operations Agents (Preview) toggle set to Enabled for the entire organization. Note that messages sent to Operations Agents are processed through the Azure AI Bot Service. If your capacity is outside the EU Data Boundary, data may be processed outside your geographic or national cloud boundary. Be sure to communicate this to your compliance stakeholders before enabling the feature in production tenants. Microsoft Teams Account Every person who will receive recommendations from the agent must have a Microsoft Teams account. The Operations Agent delivers its findings and action suggestions through a dedicated Teams app called Fabric Operations Agent. You can install this app from the Teams app store by searching for its name. Once installed, the agent will be able to send messages containing data summaries and recommended actions directly to the designated recipients. Creating and Configuring the Operations Agent With your prerequisites in place, you are ready to create the Operations Agent. The following steps walk you through the entire configuration process using the Fabric portal. Step 1: Create a New Operations Agent Open the Microsoft Fabric portal and navigate to your workspace. On the Fabric home page, select the ellipsis icon and then select Create. In the Create pane, scroll to the Real Time Intelligence section and select Operations Agent. A dialog will appear asking you to name your agent and select the target workspace. Choose a descriptive name that reflects the agent’s purpose. In this guide, the agent is named OperationsAgent_1 and is deployed to the OperationAgent-WS workspace. Step 2: Define Business Goals and Agent Instructions Once the agent is created, you are taken to the Agent Setup page. This page is divided into two halves. On the left side, you configure the agent’s behavior. On the right side, you see the generated Agent Playbook after saving. The first field is Business Goals, where you describe the high level objective the agent should accomplish. Write this in clear, outcome oriented language. In this demo, the business goal is set to: “Monitor data pipeline execution and alert on failures.” The second field is Agent Instructions, where you provide more specific guidance on how the agent should reason about the data. Think of this as a brief you would hand to an analyst who will be watching your systems overnight. Be explicit about the table name, the column to watch, and the condition that constitutes an alert. In this demo, the instruction reads: “Monitor pipeline_runs table. Alert when status is failed.” Together, the business goals and instructions give the underlying large language model enough context to generate an accurate playbook. The more specific your instructions, the more reliable the agent’s behavior will be. Figure 3. The Agent Setup page showing business goals, agent instructions, and the generated playbook on the right. On the right side of the screen, you can see the Agent Playbook that was generated after saving. The playbook includes a Business Term Glossary, which shows the business objects the agent inferred from your goals and data. In this case, it identified an object called PipelineRun, mapped to the pipeline_runs table, with two properties: status (the pipeline run status from the status column) and runId (the unique identifier from the run_id column). It also displays the Rules section, which contains the conditions the agent will evaluate. Review the playbook carefully. Since it is generated by an AI model, there may be occasional misinterpretations. Verify that every property maps to the correct column and that the rules reflect your intended thresholds. If something is off, update your goals or instructions and save again to regenerate the playbook. Step 3: Add a Knowledge Source Scroll down on the Agent Setup page to find the Knowledge section. This is where you connect the agent to the data it will monitor. When you first open this section, it will display a message indicating that no knowledge source has been added yet. Figure 4. The Knowledge section before any data source has been added. Select the Add Data button to browse the available data sources. A panel will appear listing the KQL databases and Eventhouses accessible within your Fabric environment. In this demo, three sources are available: ops_db in the OperationAgent-WS workspace, wms_eventhouse in the WMS-CDC-Demo workspace, and ops_eventhouse in the OperationAgent-WS workspace. Select the database that contains the table you want the agent to monitor. For this guide, select ops_db, which holds the pipeline_runs table referenced in the agent instructions. Figure 5. Selecting the knowledge source from available KQL databases and Eventhouses. Once the knowledge source is connected, the agent will be able to query this database at regular intervals (approximately every five minutes) to evaluate its rules. Make sure the table in your selected database is actively receiving data, especially if you plan to demonstrate the agent detecting a condition in real time. Step 4: Define Actions Actions are the responses the agent can recommend when it detects a condition that matches its rules. Scroll further down the Agent Setup page to find the Actions section. Select the Add Action button to define a new custom action. A dialog titled New Custom Action will appear. It has three fields. The Action Name is a short, descriptive label for the action. The Action Description explains the purpose of the action and gives the agent context about when to use it. The Parameters section allows you to define input fields that pass dynamic values (such as names, dates, or identifiers) into the Power Automate flow that will be triggered. Figure 6. The New Custom Action dialog where you define the action name, description, and optional parameters. In this demo, the action is named Send Email Alert with a description indicating that it should send an email notification when a pipeline failure is detected. Once created, you can see the action listed in the Actions section with a green status indicator showing that the action is successfully connected. Figure 7. The Actions section showing the Send Email Alert action with a connected status. Step 5: Configure the Custom Action with Power Automate After creating the action, you need to configure it by linking it to an activator item and a Power Automate flow. Select the action you just created to open the Configure Custom Action pane. In this pane, you will see several fields. First, select the Workspace where the activator item resides. In this demo, the workspace is OperationAgent-WS. Next, select the Activator, which is the Fabric item that bridges the Operations Agent and Power Automate. Here, the activator is named Email_Alert_Activator. Once the connection is created, a Connection String is generated. This string is a unique identifier that links the Operations Agent to the Power Automate flow. Select the Copy button to copy this connection string to your clipboard. You will need it in the next step. Below the connection string, you will find the Open Flow Builder button. Select this to launch the Power Automate flow designer where you will build the email notification flow. Figure 8. The Configure Custom Action pane showing the workspace, activator, connection string, and the button to open the flow builder. Step 6: Build the Power Automate Flow When you select Open Flow Builder, a new browser tab opens with the Power Automate designer. The flow is pre-configured with a trigger called When an Activator Rule is Triggered. This trigger fires whenever the Operations Agent approves an action. In the Parameters tab of the trigger, you will see a field labeled Connection String. Paste the connection string you copied from the previous step into this field. This is the critical link that connects the Power Automate flow back to your Operations Agent. If this string is incorrect or missing, the flow will not fire when the agent recommends the action. Figure 9. The Power Automate flow builder with the activator trigger and the Connection String field. Below the trigger, you can add any actions your workflow requires. For an email alert scenario, add an Office 365 Outlook action to send an email to the operations team. You can use dynamic content from the trigger to include details such as the pipeline run ID, the failure status, and any parameters passed through from the Operations Agent. Save the flow and return to the Fabric portal. Your action is now fully configured and ready to be triggered by the agent. Step 7: Generate the Playbook and Start the Agent With all configuration complete (business goals, instructions, knowledge source, and actions), select Save on the Agent Setup page. Fabric will use the underlying large language model to generate the agent’s playbook. The playbook is a structured summary of everything the agent knows: its goals, the properties it monitors, and the rules it evaluates. You can also select Generate Playbook at the top of the page to regenerate the playbook if you have made changes. Review the playbook one final time to confirm that properties map correctly to your table columns and that rules reflect the exact conditions you want to monitor. When you are satisfied, select Start in the toolbar at the top of the page. The agent will begin actively monitoring your data. It queries the knowledge source approximately every five minutes, evaluating the playbook rules against the latest data. If a condition is met, the agent uses the LLM to summarize the data, generate a recommendation, and send a message to the designated recipients through Microsoft Teams. To pause the agent at any time, select Stop. This is useful during demos when you want to control the timing of the demonstration. How the Agent Operates at Runtime Once started, the Operations Agent follows a continuous loop. Every five minutes, it queries the connected KQL database to evaluate the rules defined in the playbook. If no conditions are met, it continues silently. If a condition is matched (for example, a pipeline run with a status of "failed" appears in the pipeline_runs table), the agent proceeds through the following sequence. First, the agent uses the large language model to analyze the data that triggered the condition. It summarizes the context, identifies the relevant business object (such as a specific pipeline run), and determines which action to recommend. Second, the agent sends a message to the designated recipients through Microsoft Teams. This message contains a summary of the detected insight, the data context that triggered it, and a suggested action. Recipients can approve the action by selecting Yes or reject it by selecting No. If parameters are included (such as a run ID or a severity level), they can be reviewed and adjusted before final approval. Third, if the recipient approves the action, the agent executes it on behalf of the creator using the creator’s credentials. In this demo, approving the action would trigger the Power Automate flow that sends an email alert. It is important to note that if a recommendation is not responded to within three days, the operation is automatically canceled. After cancellation, the action can no longer be approved or interacted with.
SentinelHealth: Scheduled Rule Retry Logging Does Not Match Docs
## Objective I am working on a health checks architecture for Microsoft Sentinel analytic rules. The goal is to build a set of monitoring queries/approaches that cover rule execution failures, configuration issues (entity mapping, partial success), rule audit tracking, and auto-disabled rule detection. ## My Current Approach So far I have built monitoring for the following areas using the SentinelHealth and SentinelAudit tables: - Scheduled rule window failures (retry exhaustion) - NRT rule execution delays (cumulative delay over 25 minutes) - Partial success and configuration issues (entity mapping drops, alert size limits, semantic errors) with transient error codes filtered out - Auto-disabled rules detection - Rule disable/delete audit tracking via SentinelAudit + AzActivity ## The Issue: Scheduled Rule Retry Logging The documentation at https://learn.microsoft.com/en-us/azure/sentinel/monitor-analytics-rule-integrity#scheduled-rules states that when a scheduled rule fails, it is retried 5 more times on the same window (6 total attempts). It also provides this query to detect completely skipped windows: ```kql _SentinelHealth() | where SentinelResourceType == @"Analytics Rule" | where SentinelResourceKind == "Scheduled" | where Status != "Success" | extend startTime = tostring(ExtendedProperties["QueryStartTimeUTC"]) | summarize failuresByStartTime = count() by startTime, SentinelResourceId | where failuresByStartTime == 6 | summarize count() by SentinelResourceId ``` This query assumes that each retry attempt is logged as a separate event in SentinelHealth, all sharing the same QueryStartTimeUTC. You would then count 6 failure records per startTime to identify a fully skipped window. However, in practice I am seeing different behavior. I ran a diagnostic query with a 90-day lookback (480 non-success events total, 73 unique rules). Every single event had a count of 1 per unique (SentinelResourceName, startTime) combination. No grouping of retries was observed at all. I then found an actual failed-window event that confirms this. Here is the record: - Rule: Port scan detected (ASIM Network Session schema) - Status: Failure - Description: "Rule's scheduled run at 06/01/2026 10:43:55 failed after numerous attempts. It will be re-executed over the next scheduled time." - Issue Code: SemanticErrorInQuery - Only 1 SentinelHealth record exists for this failed window The Description field says "failed after numerous attempts" which indicates the retries happened internally, but only one consolidated Failure event was written to SentinelHealth after all retries were exhausted. The individual retry attempts do not appear as separate records. This means the failuresByStartTime == 6 query from the documentation would never match this pattern, because there is only 1 record per failed window, not 6. ## Why This Matters Yes, completely skipped windows are rare. In my 90-day dataset most failures were permanent types (SemanticErrorInQuery, QueryGeneralError) that would not benefit from retries anyway. But they still happen, and if a tenant experiences a transient issue that causes a higher rate of failed windows, the documented query would silently return nothing. For my health checks I have rewritten the detection to simply look for Status == "Failure" with Description containing "failed after numerous attempts" which matches the actual consolidated event Sentinel writes. ## Questions Is the documented failuresByStartTime == 6 query still accurate? Or has the retry logging behavior changed to write a single consolidated event per failed window? Are there specific failure types or conditions where individual retries are logged as separate events? Perhaps transient failures behave differently from permanent ones in this regard? For anyone else building health monitoring on SentinelHealth - am I missing any important use cases beyond what I described above? Any clarification would be appreciated.
Legacy SSRS reports after upgrading Azure DevOps Server 2020 to 2022 or 25H2
We are currently planning an upgrade from Azure DevOps Server 2020 to Azure DevOps Server 2022 or 25H2, and one of our biggest concerns is reporting. We understand that Microsoft’s recommended direction is to move to Power BI based on Analytics / OData. However, for on-prem environments with a large number of existing SSRS reports, rebuilding everything from scratch would require significant time and effort. Since Warehouse and Analysis Services are no longer available in newer versions, we would like to understand how other on-prem teams are handling legacy SSRS reporting during and after the upgrade. Have you rebuilt your reports in Power BI, moved to another reporting approach, or found a practical way to keep existing SSRS reports available during the transition? Any real-world experience, lessons learned, or recommended approaches would be greatly appreciated.Applying DevOps Principles on Lean Infrastructure. Lessons From Scaling to 102K Users.
Hi Azure Community, I'm a Microsoft Certified DevOps Engineer, and I want to share an unusual journey. I have been applying DevOps principles on traditional VPS infrastructure to scale to 102,000 users with 99.2% uptime. Why am I posting this in an Azure community? Because I'm planning migration to Azure in 2026, and I want to understand: What mistakes am I already making that will bite me during migration? THE CURRENT SETUP Platform: Social commerce (West Africa) Users: 102,000 active Monthly events: 2 million Uptime: 99.2% Infrastructure: Single VPS Stack: PHP/Laravel, MySQL, Redis Yes - one VPS. No cloud. No Kubernetes. No microservices. WHY I HAVEN'T USED AZURE YET Honest answer: Budget constraints in emerging market startup ecosystem. At our current scale, fully managed Azure services would significantly increase monthly burn before product-market expansion. The funding we raised needs to last through growth milestones. The trade: I manually optimize what Azure would auto-scale. I debug what Application Insights would catch. I do by hand what Azure Functions would automate. DEVOPS PRACTICES THAT KEPT US RUNNING Even on single-server infrastructure, core DevOps principles still apply: CI/CD Pipeline (GitHub Actions) • 3-5 deployments weekly • Zero-downtime deploys • Automated rollback on health check failures • Feature flags for gradual rollouts Monitoring & Observability • Custom monitoring (would love Application Insights) • Real-time alerting • Performance tracking and slow query detection • Resource usage monitoring Automation • Automated backups • Automated database optimization • Automated image compression • Automated security updates Infrastructure as Code • Configs in Git • Deployment scripts • Environment variables • Documented procedures Testing & Quality • Automated test suite • Pre-deployment health checks • Staging environment • Post-deployment verification KEY OPTIMIZATIONS Async Job Processing • Upload endpoint: 8 seconds → 340ms • 4x capacity increase Database Optimization • Feed loading: 6.4 seconds → 280ms • Strategic caching • Batch processing Image Compression • 3-8MB → 180KB (94% reduction) • Critical for mobile users Caching Strategy • Redis for hot data • Query result caching • Smart invalidation Progressive Enhancement • Server-rendered pages • 2-3 second loads on 4G WHAT I'M WORRIED ABOUT FOR AZURE MIGRATION This is where I need your help: Architecture Decisions • App Service vs Functions + managed services? • MySQL vs Azure SQL? • When does cost/benefit flip for managed services? Cost Management • How do startups manage Azure costs during growth? • Reserved instances vs pay-as-you-go? • Which Azure services are worth the premium? Migration Strategy • Lift-and-shift first, or re-architect immediately? • Zero-downtime migration with 102K active users? • Validation approach before full cutover? Monitoring & DevOps • Application Insights - worth it from day one? • Azure DevOps vs GitHub Actions for Azure deployments? • Operational burden reduction with managed services? Development Workflow • Local development against Azure services? • Cost-effective staging environments? • Testing Azure features without constant bills? MY PLANNED MIGRATION PATH Phase 1: Hybrid (Q1 2026) • Azure CDN for static assets • Azure Blob Storage for images • Application Insights trial • Keep compute on VPS Phase 2: Compute Migration (Q2 2026) • App Service for API • Azure Database for MySQL • Azure Cache for Redis • VPS for background jobs Phase 3: Full Azure (Q3 2026) • Azure Functions for processing • Full managed services • Retire VPS QUESTIONS FOR THIS COMMUNITY Question 1: Am I making migration harder by waiting? Should I have started with Azure at higher cost to avoid technical debt? Question 2: What will break when I migrate? What works on VPS but fails in cloud? What assumptions won't hold? Question 3: How do I validate before cutting over? Parallel infrastructure? Gradual traffic shift? Safe patterns? Question 4: Cost optimization from day one? What to optimize immediately vs later? Common cost mistakes? Question 5: DevOps practices that transfer? What stays the same? What needs rethinking for cloud-native? THE BIGGER QUESTION Have you migrated from self-hosted to Azure? What surprised you? I know my setup isn't best practice by Azure standards. But it's working, and I've learned optimization, monitoring, and DevOps fundamentals in practice. Will those lessons transfer? Or am I building habits that cloud will expose as problematic? Looking forward to insights from folks who've made similar migrations. --- About the Author: Microsoft Certified DevOps Engineer and Azure Developer. CTO at social commerce platform scaling in West Africa. Preparing for phased Azure migration in 2026. P.S. I got the Azure certifications to prepare for this migration. Now I need real-world wisdom from people who've actually done it!
How to Fix Azure Event Grid Entra Authentication issue for ACS and Dynamics 365 integrated Webhooks
Introduction: Azure Event Grid is a powerful event routing service that enables event-driven architectures in Azure. When delivering events to webhook endpoints, security becomes paramount. Microsoft provides a secure webhook delivery mechanism using Microsoft Entra ID (formerly Azure Active Directory) authentication through the AzureEventGridSecureWebhookSubscriber role. Problem Statement: When integrating Azure Communication Services with Dynamics 365 Contact Center using Microsoft Entra ID-authenticated Event Grid webhooks, the Event Grid subscription deployment fails with an error: "HTTP POST request failed with unknown error code" with empty HTTP status and code. For example: Important Note: Before moving forward, please verify that you have the Owner role assigned on app to create event subscription. Refer to the Microsoft guidelines below to validate the required prerequisites before proceeding: Set up incoming calls, call recording, and SMS services | Microsoft Learn Why This Happens: This happens because AzureEventGridSecureWebhookSubscriber role is NOT properly configured on Microsoft EventGrid SP (Service Principal) and event subscription entra ID or application who is trying to create event grid subscription. What is AzureEventGridSecureWebhookSubscriber Role: The AzureEventGridSecureWebhookSubscriber is an Azure Entra application role that: Enables your application to verify the identity of event senders Allows specific users/applications to create event subscriptions Authorizes Event Grid to deliver events to your webhook How It Works: Role Creation: You create this app role in your destination webhook application's Azure Entra registration Role Assignment: You assign this role to: Microsoft Event Grid service principal (so it can deliver events) Either Entra ID / Entra User or Event subscription creator applications (so they can create event grid subscriptions) Token Validation: When Event Grid delivers events, it includes an Azure Entra token with this role claim Authorization Check: Your webhook validates the token and checks for the role Key Participants: Webhook Application (Your App) Purpose: Receives and processes events App Registration: Created in Azure Entra Contains: The AzureEventGridSecureWebhookSubscriber app role Validates: Incoming tokens from Event Grid Microsoft Event Grid Service Principal Purpose: Delivers events to webhooks App ID: Different per Azure cloud (Public, Government, etc.) Public Azure: 4962773b-9cdb-44cf-a8bf-237846a00ab7 Needs: AzureEventGridSecureWebhookSubscriber role assigned Event Subscription Creator Entra or Application Purpose: Creates event subscriptions Could be: You, Your deployment pipeline, admin tool, or another application Needs: AzureEventGridSecureWebhookSubscriber role assigned Although the full PowerShell script is documented in the below Event Grid documentation, it may be complex to interpret and troubleshoot. Azure PowerShell - Secure WebHook delivery with Microsoft Entra Application in Azure Event Grid - Azure Event Grid | Microsoft Learn To improve accessibility, the following section provides a simplified step-by-step tested solution along with verification steps suitable for all users including non-technical: Steps: STEP 1: Verify/Create Microsoft.EventGrid Service Principal Azure Portal → Microsoft Entra ID → Enterprise applications Change filter to Application type: Microsoft Applications Search for: Microsoft.EventGrid Ideally, your Azure subscription should include this application ID, which is common across all Azure subscriptions: 4962773b-9cdb-44cf-a8bf-237846a00ab7. If this application ID is not present, please contact your Azure Cloud Administrator. STEP 2: Create the App Role "AzureEventGridSecureWebhookSubscriber" Using Azure Portal: Navigate to your Webhook App Registration: Azure Portal → Microsoft Entra ID → App registrations Click All applications Find your app by searching OR use the Object ID you have Click on your app Create the App Role: Display name: AzureEventGridSecureWebhookSubscriber Allowed member types: Both (Users/Groups + Applications) Value: AzureEventGridSecureWebhookSubscriber Description: Azure Event Grid Role Do you want to enable this app role?: Yes In left menu, click App roles Click + Create app role Fill in the form: Click Apply STEP 3: Assign YOUR USER to the Role Using Azure Portal: Switch to Enterprise Application view: Azure Portal → Microsoft Entra ID → Enterprise applications Search for your webhook app (by name) Click on it Assign yourself: In left menu, click Users and groups Click + Add user/group Under Users, click None Selected Search for your user account (use your email) Select yourself Click Select Under Select a role, click None Selected Select AzureEventGridSecureWebhookSubscriber Click Select Click Assign STEP 4: Assign Microsoft.EventGrid Service Principal to the Role This step MUST be done via PowerShell or Azure CLI (Portal doesn't support this directly as we have seen) so PowerShell is recommended You will need to execute this step with the help of your Entra admin. # Connect to Microsoft Graph Connect-MgGraph -Scopes "AppRoleAssignment.ReadWrite.All" # Replace this with your webhook app's Application (client) ID $webhookAppId = "YOUR-WEBHOOK-APP-ID-HERE" #starting with c5 # Get your webhook app's service principal $webhookSP = Get-MgServicePrincipal -Filter "appId eq '$webhookAppId'" Write-Host " Found webhook app: $($webhookSP.DisplayName)" # Get Event Grid service principal $eventGridSP = Get-MgServicePrincipal -Filter "appId eq '4962773b-9cdb-44cf-a8bf-237846a00ab7'" Write-Host " Found Event Grid service principal" # Get the app role $appRole = $webhookSP.AppRoles | Where-Object {$_.Value -eq "AzureEventGridSecureWebhookSubscriber"} Write-Host " Found app role: $($appRole.DisplayName)" # Create the assignment New-MgServicePrincipalAppRoleAssignment ` -ServicePrincipalId $eventGridSP.Id ` -PrincipalId $eventGridSP.Id ` -ResourceId $webhookSP.Id ` -AppRoleId $appRole.Id Write-Host "Successfully assigned Event Grid to your webhook app!" Verification Steps: Verify the App Role was created: Your App Registration → App roles You should see: AzureEventGridSecureWebhookSubscriber Verify your user assignment: Enterprise application (your webhook app) → Users and groups You should see your user with role AzureEventGridSecureWebhookSubscriber Verify Event Grid assignment: Same location → Users and groups You should see Microsoft.EventGrid with role AzureEventGridSecureWebhookSubscriber Sample Flow: Analogy For Simplification: Lets think it similar to the construction site bulding where you are the owner of the building. Building = Azure Entra app (webhook app) Building (Azure Entra App Registration for Webhook) ├─ Building Name: "MyWebhook-App" ├─ Building Address: Application ID ├─ Building Owner: You ├─ Security System: App Roles (the security badges you create) └─ Security Team: Azure Entra and your actual webhook auth code (which validates tokens) like doorman Step 1: Creat the badge (App role) You (the building owner) create a special badge: - Badge name: "AzureEventGridSecureWebhookSubscriber" - Badge color: Let's say it's GOLD - Who can have it: Companies (Applications) and People (Users) This badge is stored in your building's system (Webhook App Registration) Step 2: Give badge to the Event Grid Service: Event Grid: "Hey, I need to deliver messages to your building" You: "Okay, here's a GOLD badge for your SP" Event Grid: *wears the badge* Now Event Grid can: - Show the badge to Azure Entra - Get tokens that say "I have the GOLD badge" - Deliver messages to your webhook Step 3: Give badge to yourself (or your deployment tool) You also need a GOLD badge because: - You want to create event grid event subscriptions - Entra checks: "Does this person have a GOLD badge?" - If yes: You can create subscriptions - If no: "Access denied" Your deployment pipeline also gets a GOLD badge: - So it can automatically set up event subscriptions during CI/CD deployments Disclaimer: The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you found it helpful and informative for this specific integration use case 😀
