Network Security Perimeter for Azure Event Hubs: Hardening Your Data Streams
What is Network Security Perimeter for Azure Event Hubs? Azure Event Hubs now supports Network Security Perimeter (NSP), a logical network isolation boundary that lets you define a security perimeter around your PaaS resources and control public network access through perimeter-based access rules. In practical terms, this means you can now group Event Hubs resources within a perimeter, apply consistent network access policies across them, and prevent unauthorized inbound traffic at the PaaS boundary level. It's not a firewall replacement, it's a compliance and segmentation tool that works alongside your existing NSGs and private endpoints. Before NSP, managing network access to Event Hubs involved: Private endpoints (which route traffic over private networks) IP firewall rules (which block public access from specific CIDR blocks) Virtual Network Service Endpoints (which restrict traffic to VNets) Network Security Perimeter adds a declarative, organization-wide layer: you define which resources belong inside the perimeter, and then manage access rules once, and those policies apply consistently across all perimeter members. Changes to the perimeter automatically cascade to all enrolled resources. Why ITPros Should Care If you're managing Event Hubs in a regulated industry like healthcare, finance, or government, you know the pressure. Compliance auditors want proof that data pipelines are segmented, isolated, and protected from lateral movement. Network Security Perimeter directly addresses that. Operational Value Network Security Perimeter delivers three immediate operational wins: Single Source of Truth for Access Rules. Instead of managing firewall rules on each Event Hubs namespace independently, you manage rules once at the perimeter level. Reduce configuration drift, reduce the attack surface, reduce human error. Compliance and Audit Readiness. Demonstrate network isolation to auditors with a clear diagram: "All Event Hubs in the perimeter are protected by these rules." That narrative matters for SOC 2, FedRAMP, HIPAA, and PCI-DSS compliance. You can export perimeter configurations and attach them to compliance documentation. Simplified Onboarding. When a new Event Hubs namespace joins the organization, add it to the perimeter and it inherits all access rules automatically. No manual rule-by-rule configuration. No weeks of back-and-forth with security teams. Secondary benefits include: Reduced blast radius during incidents, if an application is compromised, perimeter rules limit what it can access. Simplified network topology diagrams for architecture reviews. Faster mean time to remediation (MTTR) when security issues arise. Real-World Example: Securing a Multi-Tenant Event Hub Deployment Let's walk through a practical scenario. You're an ITPro at a financial services firm. You have three Event Hubs namespaces: hubs-prod-transactions (production trading data) hubs-prod-compliance (regulatory event streams) hubs-staging-dev (development and testing) Your security policy mandates: Production namespaces should only accept traffic from specific applications (IP-restricted). Staging can accept traffic from developer VNets but not from the internet. All outbound access to external services must be logged and monitored. Step 1: Define Your Perimeter First, create a Network Security Perimeter in the Azure Portal or via Azure CLI: az network perimeter create --resource-group rg-security --name nsp-financialservices --location eastus This creates the perimeter container. Think of it as a logical security zone. Step 2: Enroll Event Hubs Resources Add your Event Hubs namespaces to the perimeter: az network perimeter access-rule create --resource-group rg-security --perimeter-name nsp-financialservices --name allow-prod-apps --direction Inbound --access Allow --protocols Tcp --source-address-prefix 10.0.0.0/8 --destination-port-range 5671-5672 Enroll the Event Hubs namespace: az network perimeter resource create --resource-group rg-security --perimeter-name nsp-financialservices --resource-name hubs-prod-transactions --resource-type "Microsoft.EventHub/namespaces" You've now enrolled your production Event Hubs namespace. It inherits the "allow-prod-apps" rule, only traffic from your internal VNET (10.0.0.0/8) is permitted. Step 3: Define Access Rules $ns = "hubs-prod-transactions" $hub = "transactions-hub" $key = (az eventhubs namespace authorization-rule keys list --resource-group rg-prod --namespace-name $ns --name RootManageSharedAccessKey --query primaryConnectionString --output tsv) Create rules that reflect your security policy. Allow internal compliance applications: az network perimeter access-rule create --resource-group rg-security --perimeter-name nsp-financialservices --name allow-compliance-writers --direction Inbound --access Allow --protocols Tcp --source-address-prefix 10.50.0.0/16 --destination-port-range 5671-5672 Deny all other public traffic: az network perimeter access-rule create --resource-group rg-security --perimeter-name nsp-financialservices --name deny-internet --direction Inbound --access Deny --protocols "*" --source-address-prefix "*" --destination-port-range "*" Now your Event Hubs accept traffic only from specific internal subnets. Everything else is rejected at the PaaS boundary. Step 4: Validate Connectivity Test that legitimate applications can still reach Event Hubs: $ns = "hubs-prod-transactions" $hub = "transactions-hub" $key = (az eventhubs namespace authorization-rule keys list --resource-group rg-prod --namespace-name $ns --name RootManageSharedAccessKey --query primaryConnectionString --output tsv) Check logs in Azure Monitor: az monitor log-analytics query --workspace $(az monitor log-analytics workspace list --query "[0].id" -o tsv) --analytics-query "AzureDiagnostics | where ResourceProvider=='MICROSOFT.EVENTHUB' | summarize by NetworkSecurityPerimeter_s" If you see accepted connections logged with your perimeter name, you're good. If you see denied connections from unexpected IPs, you've caught a security issue before it impacts production. Step 5: Monitor and Alert Set up alerts for denied traffic: az monitor metrics alert create --name "NSP-Denied-Connections" --resource-group rg-security --scopes /subscriptions/{subId}/resourceGroups/rg-security/providers/Microsoft.Network/networkSecurityPerimeters/nsp-financialservices --condition "avg ConnectionRejectedCount > 5" --window-size 5m --evaluation-frequency 1m --action email-admin@company.com Now you'll be notified if someone attempts to access Event Hubs from an unauthorized source. Your security posture just went from reactive to proactive. Technical Details: How NSP Works Under the Hood Perimeter Architecture Network Security Perimeter operates at the Azure platform level, not in your VNets. Here's the flow: Connection arrives at Event Hubs public IP. Azure evaluates the source IP/protocol against NSP rules. If allowed, connection is routed to the namespace. If denied, connection is dropped and logged. This happens before TLS handshake, reducing CPU overhead and improving response times. Denied connections generate zero namespace load. Rule Evaluation Order NSP rules are evaluated in this order: Explicit Allow rules (matched first wins) Explicit Deny rules Implicit Deny (default action) Best practice: Create your Allow rules first (be specific about what you permit), then add Deny rules for anything not explicitly allowed. This ensures you don't accidentally block legitimate traffic. Integration with Existing Security Tools NSP works alongside (not instead of): Private Endpoints: NSP adds a policy layer; private endpoints route traffic over Azure backbone. Use both. IP Firewall: NSP provides namespace-level access control; IP firewall is still available for per-namespace rules. VNet Service Endpoints: NSP complements VNet endpoints by adding perimeter-wide policies. Managed Identity + RBAC: NSP is transport-layer security; identity-based access control remains separate. Performance Considerations NSP introduces minimal latency (<1ms typically). Azure evaluates rules in parallel and caches common decisions. For high-throughput Event Hubs: Keep rules simple and specific (avoid wildcard ranges if possible). Use CIDR blocks instead of individual IPs where applicable. Monitor connection acceptance rates in Azure Monitor. Comprehensive Resources Official Microsoft Documentation: Network Security Perimeter Overview Event Hubs Network Security Configuring NSP for Event Hubs Azure CLI: az network perimeter Azure RBAC for Event Hubs Azure Event Hubs Protocol Guide Closing: Perimeter Security for Modern Data Streams Network Security Perimeter for Event Hubs is a quiet but powerful addition to Azure's security toolkit. You get the ability to enforce organization-wide network policies without having to reconfigure every namespace individually. You can demonstrate perimeter-based isolation to auditors. You can catch lateral-movement attacks before they happen. For ITPros managing event-driven architectures, message processors, IoT data streams, financial transactions, this capability directly improves your security posture and reduces operational overhead. I encourage you to: Audit your current Event Hubs deployments. How many namespaces? How many security policies are you managing today? Design your perimeter boundaries. Group namespaces by security zone (prod, staging, dev) or by business unit. Start with one perimeter in a dev environment. Define rules. Validate connectivity. Then expand to staging and production. Document your perimeter architecture and rules. Include it in your security runbook and architecture reviews. Set up monitoring and alerting. Denied connections are a leading indicator of either misconfiguration or attack attempts. The networking challenges in cloud are complex. Network Security Perimeter gives you a declarative, policy-driven way to solve them at scale. Take advantage of it, and let me know how it changes your security workflows. Keep your networks hardened, and your data flowing safe. Cheers! Pierre RomanAz Update - Week 2 of the return editions
Hello Folks! This week's updates all focus on something we hear from IT pros and platform engineers all the time: How do we make our environments more secure, more manageable, and easier to modernize without adding more complexity? Whether you're running PostgreSQL workloads in Azure, securing Kubernetes storage, or planning your next wave of SQL Server migrations, this week's announcements bring practical improvements that can help reduce operational overhead while strengthening your overall platform strategy. We'll look at three newly available capabilities: Update #1 - Generally Available: Microsoft Defender security assessments for Azure Database for PostgreSQL Flexible Server Update #2 - Generally Available: Encryption in Transit for Azure Files NFS Shares in Azure Kubernetes Service (AKS) Update #3 - Generally Available: Expanding Azure Arc SQL Migration with SQL Server on Azure Virtual Machines As always, I'm approaching these updates from an infrastructure and operations perspective. I'll cover why each capability matters, what to watch out for before production deployment, and some practical steps you can take to start evaluating them in your own environment. Let's dig in. Update #1 - Generally Available: Microsoft Defender security assessments for Azure Database for PostgreSQL Flexible Server Why ITPros should care This release brings automated security posture assessment directly into managed PostgreSQL environments. For ITPros, this matters because database security is often treated separately from infrastructure security tooling, creating blind spots and silos. What changed is that Defender now runs native vulnerability scanning and compliance checks against PostgreSQL configurations, patches, and the ways a database could be exposed to security risks or attack opportunities. Instead of relying on external scanners or manual audits, you get platform-native assessments integrated with your existing Defender workflows. The operational impact is significant: you can now enforce security baselines at the database layer with the same consistency you apply to VMs and network resources, reducing the gap between infrastructure and data security accountability. Operational value Operationally, this improves your security baseline enforcement and reduces the need for separate database security assessment tools. It also strengthens how well you can demonstrate and prove that security controls are in place and working for compliance reviews where regulators expect consistent, documented security controls. Before production rollout, validate that Defender cost models fit your budget, that assessment frequency aligns with your change windows, and that remediation guidance maps to your patch and maintenance processes. Prerequisites include enabling Microsoft Defender for Cloud, registering the PostgreSQL Flexible Server provider, and ensuring network connectivity so assessments can reach the database endpoint. Real-world example with step-by-step guidance Enable Microsoft Defender for Cloud if not already active, and ensure PostgreSQL Flexible Server subscription coverage. Register the target PostgreSQL Flexible Server instances and confirm Defender has network visibility to the database endpoints. Run a baseline assessment and review initial findings to understand current security posture and common remediation patterns. Prioritise findings by severity and business impact, then schedule patches and configuration changes in maintenance windows. Monitor ongoing assessments and track remediation progress through Defender dashboards, validating that fixes reduce exposure scores. Technical details including code examples This example validates that Defender is actively assessing your PostgreSQL estate. The sequence checks Defender status, confirms PostgreSQL registration, and retrieves current assessment scores. Run these queries in a pilot subscription first to understand data structure and expected output before scaling to production databases. az account set --subscription <subscriptionId> az security sql-vulnerability-assessment baseline show --resource-group <rg> --server-name <postgresServer> --database-name <databaseName> az security pricing show --subscription <subscriptionId> --query "[?name=='VirtualMachines' || name=='SqlServers' || name=='StorageAccounts'].[name,pricingTier]" -o table az provider show --namespace Microsoft.DBforPostgreSQL --query "registrationState" -o tsv Expected behaviour: Defender status shows active, PostgreSQL instances are registered with the provider, and pricing tier reflects your coverage level. If assessments do not run, check network rules, managed identity permissions, and Defender plan activation. If baseline data is missing, trigger a manual scan and wait for completion. Comprehensive Resources Azure update: Microsoft Defender security assessments for Azure Database for PostgreSQL Flexible Server Microsoft Defender for Cloud overview Azure Database for PostgreSQL security SQL vulnerability assessments in Defender for Cloud Enable Defender for Cloud Update #2 - Generally Available: Encryption in Transit for Azure Files NFS Shares in Azure Kubernetes Service (AKS) Why ITPros should care This release closes a significant gap in data protection for Kubernetes workloads consuming NFS shares from Azure Files. Previously, NFS traffic between AKS nodes and Azure Files was unencrypted, creating compliance and security risks for sensitive workloads. What changed is that you can now enforce encryption for NFS communication at the Azure Files layer, not just at the application layer. This is important because traditional NFS lacks built-in encryption, and relying on network isolation alone is increasingly insufficient. For ITPros managing regulated workloads (healthcare, finance, PII-sensitive data), this removes a control gap. Encryption in transit now becomes a platform-native feature instead of a workaround, reducing architecture complexity and improving auditability. Operational value The operational value is stronger compliance posture and reduced attack surface for data in motion between containers and storage. It also simplifies the security story when auditors ask about data protection controls. Before enabling in production, validate that NFS-over-TLS introduces acceptable latency overhead for your workload patterns, test failover and reconnection behaviour under encryption, and confirm that monitoring and logging still work correctly. Prerequisites include running AKS with Azure CNI or Kubenet networking, having Azure Files with NFS 4.1 enabled, and ensuring the NFS client libraries on container images support TLS. Real-world example with step-by-step guidance Create an Azure Files NFS share with encryption in transit enabled and confirm TLS version alignment with your security standards. Deploy a test AKS workload that mounts the NFS share and validate that pods mount successfully with encrypted traffic. Run performance baselines (throughput, latency, CPU overhead) before and after enabling encryption to document operational expectations. Monitor pod logs and Azure Files metrics during the test to confirm no silent failures or unexpected throttling occurs. Roll out to production workloads in stages, with clear rollback criteria tied to application latency and error rates. Technical details including code examples This example validates that your AKS cluster can successfully mount NFS shares with encryption enabled. The sequence checks cluster networking, confirms NFS connectivity, and tests mount success. Run these commands in a non-production cluster first to validate environment readiness before touching production storage. az aks show --resource-group <rg> --name <clusterName> --query "networkProfile.{networkPlugin:networkPlugin,networkPolicy:networkPolicy,podCidr:podCidr}" -o jsonc az storage account show --resource-group <rg> --name <storageAccount> --query "{name:name,kind:kind,accessTier:accessTier}" -o jsonc kubectl get pvc -A --all-namespaces -o wide kubectl describe pv <pvName> | grep -i nfs Expected behaviour: cluster networking is properly configured, storage account kind supports NFS, and PVC/PV resources show NFS mount points. If mounts fail, check network security group rules, storage account firewall allowances, and subnet delegation. If latency increases, monitor resource utilisation and adjust workload placement if needed. Comprehensive Resources Azure update: Encryption in Transit for Azure Files NFS Shares in Azure Kubernetes Service (AKS) Azure Files NFS support Mount Azure Files with NFS in AKS Azure storage security AKS networking concepts Update #3 - Generally Available: Expanding Azure Arc SQL Migration with SQL Server on Azure Virtual Machines Why ITPros should care This capability brings SQL Server migration into the Azure Arc operational footprint, creating a unified migration and inventory experience. For ITPros, this matters because SQL Server modernisation is often fragmented across multiple tools and teams. What changed is that you can now discover, assess, and execute SQL migrations through Arc-native workflows, using the same permissions and governance model you already have for infrastructure and hybrid resources. The operational gain is consistency: discovery data feeds migration planning, assessments surface blockers early, and rollout can be controlled through the same change and approvals processes you use for other infrastructure migrations. Operational value Operationally, this reduces tooling sprawl and improves coordination between infrastructure and database teams. Arc becomes your single control plane for tracking migration progress, managing runbooks, and collecting audit evidence. Before production use, validate that your SQL Server inventory is complete, that migration blockers are understood and addressed, and that your maintenance windows can accommodate expected cutover timings. Prerequisites include Azure Arc agent deployment on source VMs, Azure Database Migration Service readiness, and network connectivity to target Azure SQL resources. Real-world example with step-by-step guidance Deploy Azure Arc agents to SQL Server VMs and confirm all instances report healthy status with complete inventory data. Run Arc-integrated SQL Server assessments to identify compatibility issues, dependencies, and recommended migration targets. Pilot migration for a non-critical workload to establish runbook patterns, measure cutover time, and validate post-migration validation procedures. Execute validation tests: connectivity, login success, database consistency checks, job execution, and application integration tests. Scale migration in waves using documented runbooks, with gates for monitoring data health and application performance after each cutover. Technical details including code examples This example validates Arc agent health and SQL Server discovery completeness. The sequence ensures your Arc infrastructure is ready for migration workflows. Run these commands as part of your pre-migration checklist to catch configuration gaps before committing to migration timelines. az account show --output table az connectedmachine list --resource-group <rg> --query "[].{name:name,status:status,osName:osName}" -o table az resource list --resource-type Microsoft.AzureArcData/sqlServerInstances --query "[].{name:name,resourceGroup:resourceGroup,location:location}" -o table az connectedmachine machine extension list --resource-group <rg> --machine-name <vmName> --query "[].{name:name,provisioningState:provisioningState}" -o table Expected behaviour: Arc agents report healthy status, SQL Server instances are fully discovered with accurate inventory, and required extensions are provisioned successfully. If discovery is incomplete, check Arc agent connectivity, extension deployment, and SQL service running status on source VMs. If migration pre-checks fail, verify SQL Server version compatibility and review Defender logs for blocking issues. Comprehensive Resources Azure update: Expanding Azure Arc SQL Migration with SQL Server on Azure Virtual Machines Azure Arc SQL Server Overview Azure Arc-enabled servers SQL Server on Azure Virtual Machines Azure Database Migration Service For any new capability this week, if they map to your operational roadmap, run a controlled pilot, measure the impact, and then scale with confidence. That is how you move the needle on modernisation while managing risk. Cheers! Pierre RomanAzure Elastic SAN: Pooled, Cloud-Native Block Storage That Actually Acts Like a SAN
Hello Folks! If you have ever lived through a Friday night SAN expansion, racking new shelves and praying the zoning held together, the idea of getting that same shared block storage model in Azure (without owning a single fibre channel cable) sounds almost too good to be true. In his session at the Microsoft Azure Infra Summit 2026, Kiran Cherukuwada, Principal PM in Azure Storage, walked us through exactly how Azure Elastic SAN does that, and where it fits next to the other block storage options on Azure. 📺 Watch the session: Why IT Pros Should Care Most of us were taught a simple rule. One workload, one disk, size it for peak, move on. That rule has been kind to managed disks, but it gets expensive fast when you have dozens or hundreds of workloads that all peak at different times. Elastic SAN flips the model. You provision a pool of capacity and performance once, then carve volumes out of it for many workloads. Here is why that matters for IT pros: You stop over-provisioning each workload to its own peak; the SAN absorbs the bursts. You get a SAN-style resource hierarchy (SAN, volume groups, volumes) that looks and behaves like the on-prem model you already know. iSCSI connectivity means a wide compute footprint, including Azure Virtual Machines, Azure Kubernetes Service, Azure Container Instances, Azure VMware Solution, and Nutanix Cloud Clusters. You can drive storage throughput over VM network bandwidth, which often lets you keep a smaller (and cheaper) VM SKU. In short, if you have many IO-intensive workloads sharing one region, Elastic SAN is the lever that turns “buy peak for every workload” into “buy combined peak for the group.” What Azure Elastic SAN Is, Technical Overview Azure Elastic SAN is the industry’s first fully managed SAN storage service in the cloud. It brings the on-prem SAN consumption model to Azure as a single managed pool of block storage, shared across many workloads, accessed over the industry-standard iSCSI protocol. Inside the service you get three resources, matching the on-prem mental model: The Elastic SAN itself. Top-level resource. This is where you provision overall capacity and performance, and where billing happens. Volume groups. Where you set network rules (service or private endpoints) and security policies. Any policy you apply here is inherited by every volume in the group, so a volume group is effectively your workload boundary. Volumes. The LUNs that you mount on compute. They show up as raw block devices on a VM, as iSCSI targets to a Kubernetes node, or as VMware data stores on AVS. A single SAN can scale to a petabyte of capacity, 2 million IOPS, and 80 GB/s of throughput. It is locally redundant by default, with a zone-redundant option, and shared volume support is there for clustered solutions like SQL Server Failover Cluster Instances and Azure VMware Solution. Network isolation is delivered via service endpoints and private endpoints, and data is encrypted at rest. Incremental snapshots are supported for fast point-in-time restore, and snapshots can be exported to managed disk snapshots when you need a hardened copy for backup or DR purposes. Where does it land in the block storage portfolio? Kiran framed it simply. Premium SSD v2 is the best price/performance for dedicated per-workload performance. Ultra Disk is for the mission-critical, every-microsecond-matters workloads. Elastic SAN is the best price/performance option at scale, when you have many workloads that can share a storage pool. How It Works, Under the Hood The economics live in the provisioning model. You buy two types of units: Base unit. Each base unit gives you 1 TiB of capacity plus 5,000 IOPS and 200 MB/s. Roughly 8 cents per GiB per month in East US. Capacity-only unit. Each capacity-only unit gives you 1 TiB of capacity but no extra performance. About 25 percent cheaper, around 6 cents per GiB per month in East US. The pattern Kiran showed is “size for performance first, then top up capacity.” A 250 TiB SAN delivering 1 million IOPS and 40 GB/s came out to roughly 200 base units plus 50 capacity-only units, landing around 20 grand per month for the whole pool. The magic ingredient is dynamic performance sharing. With traditional disks you provision each workload to its own peak. With Elastic SAN, you provision the combined peak. So a SQL Server needing 60,000 IOPS, an AVS cluster needing 40,000, and an Oracle workload needing 100,000 IOPS look like 200,000 IOPS of dedicated disk. But if they never peak simultaneously, you can land a 150,000 IOPS SAN and let each workload hit its peak on demand. That is real money back. The second lever is throughput over network bandwidth. Because Elastic SAN connects over iSCSI, storage I/O flows through the VM’s network pipe, not the VM’s disk throughput cap. Most VMs have far more network bandwidth than disk bandwidth, so you can drive higher storage throughput from a smaller VM SKU. That smaller SKU is cheaper to run, and (this is the quiet win) it can also cut per-core database licensing costs. As one attendee asked in the live Q&A, “Why is it possible to go beyond the VM disk throughput limit with SAN?” The answer: iSCSI traffic uses VM network bandwidth like any other VM-to-VM traffic, so the disk throttle does not apply. One honest tradeoff: that same network bandwidth is also used by your app-tier-to-database traffic. So if you are planning to push storage hard, size the VM with both flows in mind. Real-World Value Where does this actually pay off? Mixed enterprise workloads on Azure VMs. SQL Server, Oracle, custom OLTP, sharing one SAN. Kiran’s demo ran SQL TPCC, an AVS cluster benchmark, and an Oracle OLTP load simultaneously off a single 30-base-unit SAN, and the metrics blade showed exactly how each volume group consumed performance. Extending Azure VMware Solution storage. Instead of buying expensive vSAN nodes just to grow storage, you connect AVS to an Elastic SAN datastore. Gen2 AVS private clouds skip the ExpressRoute gateway requirement and let you use a single private endpoint on the volume group. Container Storage. Azure Container Storage v2 with Elastic SAN backing is generally available. The fast attach and detach behavior means that even if a node or cluster goes down, the data sits on the SAN and persists. Lift and shift from on-prem SAN. Kiran shared one migration example: a workload with 100-plus vCPUs running off a mid-tier all-flash SAN array landed on Elastic SAN with roughly 64 percent TCO savings and performance that exceeded the original array. In short, this is a “many workloads, one pool” story. If you have one heavy workload, premium SSD v2 may be a better fit. Getting Started Here is a practical order of operations: Size the SAN. Add up the combined peak IOPS and throughput for the workloads you plan to consolidate, then pick base units to cover performance and capacity-only units to top up storage. Lock down the network. Access is closed by default. Choose service endpoints or private endpoints per volume group, and open them only to the right subnets. Place compute in the same zone. For best latency, deploy your VMs (or AVS cluster) in the same region and availability zone as the SAN. Tune the client. Use Gen 5 (D, E, or M series) VMs with Accelerated Networking on, configure the iSCSI initiator, set up native MPIO on Windows or Linux, and use the Connect scripts from the portal which default to 32 sessions per volume. Watch the metrics. The SAN’s Metrics tab shows transactions, ingress, and egress at the SAN, volume group, and individual volume level. Drop the granularity to one minute when you are troubleshooting. Plan snapshots. Use Elastic SAN volume snapshots for fast dev/test restores. Export to managed disk snapshots when you need hardened backup or cross-region DR. If you are coming from on-prem, the partnership with Cirrus Data (free in the Azure Marketplace) is the recommended path to migrate storage at the block level. Resources Azure Elastic SAN documentation hub What is Azure Elastic SAN (introduction) Plan for an Azure Elastic SAN deployment Azure Elastic SAN configuration best practices Snapshot Azure Elastic SAN volumes Keep Learning at the Summit Catch the full Microsoft Azure Infra Summit 2026 session playlist here Cheers! Pierre RomanPlanning the Monitoring of my hybrid environment.
Hello folks, By now you may have read that I’ve rebuilt my demo environment to look like what a typical hybrid environment would look like. I did it slowly without having to rip and replace everything in my on-prem environment. Started out with establishing a site-to-site VPN, then a solution to remote into all the servers in my environment, configured a resilient way of resolving the names of all servers in my hybrid deployment, and lastly, configuring an Azure Arc Private Link Scope so that all my on-prem machines could connect to Azure using the VPN and not the open internet. Now as I look at all the operational tasks I need to implement (monitoring/insights, patch management, change management, etc...) To support all these operational requirements, I need the common underpinning provided by the Azure Log Analytics workspace.Modernize VDI with Azure Files and Entra Cloud-Native Identities
Hello Folks! If you have ever run a Virtual Desktop Infrastructure (VDI) estate, you know the recurring riddle. The session hosts are designed to be stateless and pooled, yet every user expects a persistent Outlook profile, their OneDrive cache, their pinned apps, and a sub-ten-second logon. In this session at the Microsoft Azure Infrastructure Summit 2026, Adam Groves and Priyanka Gangal from the Azure Files team showed how Azure Files plus Microsoft Entra ID finally let you deliver that experience without dragging domain controllers along for the ride. 📺 Watch the session: Why IT Pros Should Care VDI has always been a balancing act between elasticity and continuity. The compute layer wants to be ephemeral. The user wants to be at home. Bridging those two worlds used to mean a stack of identity plumbing that quietly grew until it became its own platform. This session changes that math. Here is what jumped out for me: Cloud-only identity for SMB. Azure Files now authenticates pure Microsoft Entra ID users and groups, including B2B guests, directly over SMB Kerberos. No on-premises Active Directory required, no Entra Connect required, no line of sight to a domain controller. NTFS ACLs and Kerberos preserved. You keep the security model your apps already understand. Permissions still live on the file system, tickets still come over SMB, and FSLogix does not care that the identity stack underneath is different. Performance built for the spike. Metadata caching is generally available and rolling out by default. Concurrent file handles per share are moving from 2,000 today to 10,000, with a roadmap toward 30,000 to 50,000. That means fewer storage accounts to shard across when 9 AM hits. Zonal placement and smarter alerts. Premium LRS now lets you co-locate the share with its session hosts inside the same availability zone, and new percentage-based metrics finally make alert thresholds portable across shares of any size. In short, the boring identity and storage plumbing that propped up VDI for a decade is being collapsed into something you can actually run as a cloud-native service. What This Is, A Technical Overview Let’s set the table. VDI on Azure (whether you run Azure Virtual Desktop, Citrix on Azure, or Omnissa Horizon) uses pooled session hosts. Those hosts are intentionally stateless so they can be patched, scaled, and recycled without ceremony. The user’s identity is “Connie Cloud” today, and on a different host tomorrow. FSLogix solves the continuity half of the puzzle. It packages the user’s profile and Office data containers (the profile container and the ODFC, the Office Data Folder Container) as VHDX files that get dynamically attached when Connie logs on and detached when she signs out. Those VHDX files need to live somewhere durable, fast, and reachable over SMB from any host in the pool. That is precisely what Azure Files delivers. It is a fully managed SMB file share service that integrates cleanly with FSLogix profile containers and App Attach image stores for AVD. The reference architecture and sizing guidance are documented on Microsoft Learn for anyone who wants the official map. The historic friction was identity. Until recently, SMB authentication to Azure Files required either on-premises AD DS joined to the storage account or hybrid identities synced through Entra Connect. That meant keeping domain controllers (and the network paths to reach them) alive purely to satisfy storage authentication. As of this year, Azure Files supports pure Microsoft Entra ID identities for SMB Kerberos, which closes that loop. How It Works, Under the Hood Here is the simplified flow Adam and Priyanka walked through during the demo. The user (an Entra-only account, no on-prem footprint) signs into an Entra-joined AVD session host with single sign-on. The session host needs to mount the user’s FSLogix profile container from an Azure Files share. The host requests a Kerberos service ticket. Because the share has Microsoft Entra Kerberos authentication enabled, Entra ID issues that ticket directly, no on-prem KDC involved. The SMB connection is established, the share-level RBAC role (for example Storage File Data SMB Share Contributor) is checked, and then the directory and file ACLs (standard NTFS) are evaluated. FSLogix attaches the VHDX, the profile loads, Outlook is happy, OneDrive is happy, and Connie’s pinned taskbar shows up exactly the way she left it. A few details worth filing away: Two layers of authorization. Share-level access uses Azure RBAC roles. Item-level access uses NTFS ACLs. Both still apply, which is why your existing permissions model carries over cleanly. B2B guest support. Vendor and contractor accounts that come in as guests in your tenant can be granted access to file shares without needing a synced shadow account. Metadata caching is the unlock. VDI is metadata-heavy: directory enumerations, file opens, renames, and closes hammer the share at logon. Metadata caching reduces P50 latency on those operations by roughly 80 to 90% and roughly doubles metadata transaction throughput, which is what makes the higher concurrent handle limits realistic. The full SMB performance reference on Microsoft Learn lays out the knobs. Zonal placement. Premium LRS lets you pin the share to the same availability zone as your session host pool, so the SMB traffic does not bounce across zones. Real-World Value Where does this show up in your operations review? Retire orphan domain controllers. Plenty of shops have a couple of DCs in Azure that exist only so Azure Files can authenticate. Cloud-native Entra ID lets you turn those off and shrink the identity attack surface. Simpler M&A and vendor onboarding. Adding a partner organization or a new acquisition no longer requires forest trusts or a sync project. Invite guests, assign them to a group, grant the group access to the share. Fewer storage accounts and shares to manage. Higher concurrent handle limits mean you can consolidate users that you previously had to spread across many accounts just to dodge the 2,000-handle ceiling. Less sprawl, less monitoring, fewer naming conventions to remember. Predictable logon times at scale. Metadata caching is the kind of feature you only notice when it is missing. With it on by default, large host pools see flatter logon latency curves during the morning rush. Operational consistency. Percentage-based metrics let you set a single rule like “alert at 10% remaining capacity” and apply it cleanly to a 5 TiB share and a 100 TiB share without bespoke thresholds. In short, the ROI conversation moves from “how do we keep VDI running” to “how much of the supporting cast can we delete.” Getting Started If you want to kick the tires this week, here is a practical starting path. Inventory your VDI identity story. Are you running hybrid because the apps need it, or because Azure Files used to need it? If it’s the second one, you have a candidate workload for cloud-only identity. Spin up a pilot Premium SSD Azure Files share in the same region (and ideally the same availability zone) as a small AVD host pool. Enable Microsoft Entra Kerberos authentication on the storage account. The configuration is now in the standard Azure portal, no more side trips to the fileperms portal. Assign Azure RBAC roles at the share level (Storage File Data SMB Share Reader, Contributor, or Elevated Contributor as appropriate) to your Entra groups. Set NTFS ACLs on the directories that will host FSLogix containers, and point FSLogix at the share’s UNC path. Test with a cloud-only user (no on-prem identity at all) to confirm the end-to-end flow. Turn on metadata caching and the new metrics and set percentage-based alerts so you find the limits before your users do. Resources Azure Files documentation Use Azure Files for virtual desktop workloads Enable Microsoft Entra Kerberos authentication for hybrid and cloud-only identities on Azure Files Improve performance for SMB Azure file shares (metadata caching, multichannel, handle limits) Data redundancy for Premium file shares Keep Learning at the Summit Catch the full Microsoft Azure Infra Summit 2026 session playlist here: https://www.youtube.com/playlist?list=PLjt5SKzX1iI8con7FJDB56G6hHqxGm7ki Cheers! Pierre RomanMoving Petabytes Without the Panic: At-Scale Storage Assessments and Migrations to Azure
Hello Folks! If you have ever been asked to move all our file shares to the cloud ASAP. You already know that storage migration is one of those projects that looks easy on a slide and gets ugly in reality. In this session at the Microsoft Azure Infra Summit 2026, Anusha Subramanian and Madhuri Narayana Rao (both Product Managers on the Azure Storage team) walked through a guided roadmap for discovering, assessing, and moving large-scale storage to Azure without the homegrown scripts and the late-night reruns. 📺 Watch the session: Why IT Pros Should Care In short, this session matters because data migration is consistently underestimated. Anusha put it well: customers focus on migrating applications and workloads, and the big chunk of storage those apps depend on tends to be an afterthought. That afterthought is where projects go sideways. Wrong target tier, wrong tool, wrong sizing, and suddenly you are unwinding an architecture six months in. Here is what you get out of the new tooling Anusha and Madhuri covered: A first-party, end-to-end path from discovery to assessment to cutover, all inside services you already pay for. File share discovery and assessment now generally available in Azure Migrate, so you stop guessing about your on-premises estate. A fully managed online migration service (Azure Storage Mover) that handles retries, logging, bandwidth, and scheduling for you. An offline option (Azure Data Box) for when your network is the bottleneck and you are staring down hundreds of terabytes. A partner program (the Storage Migration Program) that covers the edge cases first-party tools do not yet cover, with the ISV software cost picked up by Azure. If you manage file servers, NAS, or large object stores and you have a migration on your roadmap, this is your toolkit. What This Toolkit Does: Technical Overview The session framed large-scale storage migration as a guided roadmap with clear phases. You discover what you have on premises, assess how it is used, pick the right cloud target, decide on a migration strategy, execute in phases, and then run post-migration checks before you cut over. The point of the new tooling is to make each of those steps repeatable instead of bespoke. Three services do most of the heavy lifting: Azure Migrate file share assessment (generally available). Azure Migrate has been Microsoft’s first-party migration platform for a while, but until recently it was very compute-focused (think VMware, Hyper-V, and physical server lift-and-shift). The new capability extends that same discovery appliance to the file shares hosted on those servers. You get share inventory, OS type, protocol, capacity, and basic performance metrics like IOPS and throughput, all flowing back into your Azure Migrate project automatically. Azure Storage Mover. This is the fully managed online migration service. It moves files and folders to Azure without custom scripts or migration infrastructure that you have to babysit. It supports on-premises SMB and NFS sources, cloud-to-cloud moves within Azure (for example, Blob container to Blob container), and AWS S3 to Azure Blob today (with more clouds on the roadmap). Azure Data Box. The offline path. Ruggedized, encrypted, shipped to your datacenter, copied locally, shipped back, and ingested into Azure Storage. The current SKUs include 7 TB disks, 120 TB devices, and 525 TB devices, with 256-bit AES encryption end to end. How It Works Under the Hood For Azure Migrate file share assessment, you download (or update) the Migrate appliance and deploy it on your VMware, Hyper-V, or physical server estate. Grant the required permissions, and the appliance starts collecting share metadata and performance telemetry. That data flows back into your Azure Migrate project, and you see file shares appear as first-class entities in the Infrastructure tab right alongside servers. From there you can tag shares, scope them into groups, and generate assessments that map each share to a recommended Azure Files SKU, give you a TCO estimate, surface readiness blockers, and recommend a migration tool. You can export the whole thing to Excel or PowerPoint, which is exactly what you need when finance asks for the business case. For Storage Mover, the key architectural detail is that the data path is separate from the management path. You deploy a Storage Mover agent close to your source (on premises, in another cloud, or wherever the data lives). The Storage Mover resource in Azure can sit in any region. The agent pulls data from SMB or NFS, then pushes it via REST API directly to the target storage account. Only logs and metadata flow through the service itself. That means migration velocity is governed by the proximity between the agent and the target storage account, not by the region of the management resource. SMB credentials are stored in Azure Key Vault, the agent fetches them at runtime, and one central Storage Mover resource can manage agents deployed globally. Data Box is conceptually simpler. Order the device through the Azure portal, receive it, copy locally over your LAN at LAN speeds, ship it back, and Azure ingests the data into the storage account you specified. The Data Box family is documented at the Microsoft Learn link in the Resources section below. Real-World Value Where does this actually pay off? A few scenarios came up in the session. Lift-and-shift of file servers. Discover with Azure Migrate, assess, get target SKU recommendations and TCO, then move with Storage Mover. Permissions, metadata, and folder structure are preserved during the copy. Cloud-to-cloud (AWS S3 to Azure Blob). The session demoed the multi-cloud connector workflow: deploy a Storage Mover resource, add an AWS connector with an Inventory and Storage Data Management solution, run the AWS CloudFormation template, then create a project, a job definition, and start the job. It is portal-driven from beginning to end. Petabyte-scale offline lift. When you cannot saturate your production WAN for weeks, Data Box gets your seed data to Azure. Then Storage Mover handles the automated delta sync so the cutover window stays small. Recurring incremental sync. Storage Mover now supports recurring schedules (one-time, daily, weekly, or monthly) combined with bandwidth management for peak and off-peak windows. That is useful when data is being collected continuously on premises and you want predictable, throttled transfers. Sovereign cloud. Storage Mover is now available in Azure US Government, so federal and public sector customers can run the same workflow inside their sovereign environment. Specialized scenarios. For source-target pairs the first-party services do not cover yet (say, on-premises NetApp to Azure NetApp Files, which Anusha confirmed in the live Q&A is not yet in Azure Migrate’s scope), the Storage Migration Program brings in partners like Atempo, Data Dynamics, Cirrus Data, and Cirrata. The ISV software cost is covered by Azure. The honest tradeoff: Storage Mover assumes a reasonable network connection between the agent and the target. If the pipe is tiny and the dataset is huge, the math does not work and you should be ordering Data Box hardware. The session was clear about this, and that kind of “use the right tool” guidance is exactly what saves projects. Getting Started If you are kicking off a storage migration, here is the practical sequence. Stand up (or update) an Azure Migrate project and deploy the Migrate appliance on premises. If you already have one, just update to the latest version so file share discovery lights up automatically. Let discovery run, then create an Azure Files assessment scoped to the shares you care about. Pick your region, redundancy, performance look-back window, and percentile utilization. Export the results to Excel or PowerPoint and use it to build your business case. Decide online vs offline based on your dataset size and available bandwidth. Most projects can use Storage Mover. The biggest ones, or the ones with constrained WAN, start with Data Box seed data and then incremental sync with Storage Mover. For Storage Mover, create the resource, deploy the agent close to your data, register it, define endpoints, create a project and job definition, and start the job. Configure bandwidth schedules so your production traffic does not suffer. For specialized source-target pairs, reach out via the Storage Migration Program contact (azstoragemigration at microsoft.com) and engage a listed partner. Resources Azure Migrate file share assessment overview Azure Storage Mover documentation Azure Data Box documentation Azure Data Box overview (SKUs and capacities) Keep Learning at the Summit Catch the full Microsoft Azure Infra Summit 2026 session playlist here Cheers! Pierre RomanBuild and Optimize a Data Lakehouse for Unified Data Intelligence
Hello Folks! Welcome back to the ITOpsTalk Blog and the Microsoft Azure Infrastructure Summit 2026 series. In this session James Baker and Sai Runtham, both from the Azure Data Lake Storage product team, take us through what a modern Lakehouse actually is, how to design one on Azure, and then they roll up their sleeves and build one end to end. If you have been hearing “Lakehouse” thrown around in architecture reviews and were not 100% sure what it changes for you as an IT Pro, this one is for you. 📺 Watch the session: Why IT Pros Should Care You might be thinking, “I run infrastructure, not analytics.” Fair point. But here is the thing. The lakehouse is increasingly the platform your business will run BI dashboards, AI agents, and decision support systems on, and you are the one who has to keep the data safe, governed, and reachable. Here is what is in it for you: It is a platform conversation. James spends a big chunk of the session on horizontal platform capabilities (storage, catalog, identity, secrets, network, policy) versus vertical pipeline concerns. That is squarely an IT Pro problem. Data is the asset. Workspaces, query engines, and dashboards are transient. The data lives forever, and protecting it is on your plate. Governance is what stops your data lake from rotting into a data swamp. Scale is a virtuous cycle. More data drives more insight, which drives more data. Your platform cannot become the ceiling. AI agents are the new consumers. They do not just read dashboards, they query gold tables directly. Your network, identity, and access controls have to keep up. What is a data lakehouse A data lakehouse is exactly what it sounds like. You take the cheap, flexible, schema-light scale of a data lake, and you fuse it with the low-latency query performance, update semantics, and governance of a data warehouse. One copy of the data. One place to govern it. No more forking from the lake into a warehouse just to make BI tools happy. Quick contrast: Data lake. Big, cheap, flexible. No schema enforced on write. Historically prone to becoming a swamp. Data warehouse. Low-latency queries, updates, strong governance, structured. Hits a scale ceiling and costs more. Data lakehouse. Lake-scale storage, with a high-performance query layer and warehouse-grade governance sitting over the top. No data fork. The big shift is that the data does not move. Your BI dashboards, your AI agents, your serverless SQL queries, they all hit the same governed tables in the lake. That keeps lineage clean and your security model sane. Building it on Azure James and Sai are clear that the architecture is less a fixed diagram and more a list of platform capabilities you compose. Here is the shape of it on Azure. Storage layer (the asset). Azure Data Lake Storage Gen2 (ADLS) with hierarchical namespace turned on. That is non-negotiable for analytics workloads. It gives you atomic directory operations, POSIX-style ACLs, and the performance Delta Lake relies on. OneLake in Microsoft Fabric if you want a tenant-wide logical lake that is built on ADLS Gen2 and stores everything in open Delta Parquet by default. Table format and pipelines. Open table formats: Delta Lake (and Apache Iceberg as it converges) give you ACID transactions, time travel, schema evolution, and streaming on cheap object storage. Azure Databricks Lakeflow Declarative Pipelines with Autoloader for incremental ingestion of both batch and streaming sources straight into Delta tables. Autoloader handles new file discovery, schema inference, and evolution for you. The medallion architecture for stamping out repeatable pipelines: o Bronze. Raw, append-only landing zone. Source of truth. o Silver. Cleansed, deduplicated, conformed, enriched. o Gold. Business-ready, aggregated, performance-optimized for consumption. Governance and identity. Unity Catalog as the single source of truth for catalog, lineage, and fine-grained access control across bronze, silver, and gold. Entra ID for identity. Managed identities for compute. Key Vault for secrets. Network protection around the perimeter. The data is the crown jewel, so private endpoints, firewalls, and VNet-attached compute are baseline. Consumption layer. Power BI Direct Query against a serverless SQL warehouse on the gold tables. No data copies, governance flows through. AI agents like Databricks Genie pointed at gold tables. Natural-language questions, live lineage, no data movement. The demo that ties it together. Sai walked through a real pipeline: NYC TLC taxi trips enriched with NOAA weather and ESPN/MLB sports events, ingested by Autoloader into bronze, transformed through silver, aggregated into gold. A parallel streaming pipeline handled synthetic live events for a real-time demand view. Power BI dashboards hit gold via Direct Query. And Genie answered questions like “which zones are most sensitive to sport events” by mapping demand around Madison Square Garden, with the query and the chart generated for you. All against the same lakehouse, no data movement, full lineage. Optimizing for cost and performance This is where a lot of lakehouses go sideways. A few things from the session and from the official guidance worth pinning to your wall: Get hierarchical namespace right. It is the difference between atomic directory operations and “copy then delete,” which is slow and expensive at scale. Use storage tiers and lifecycle policies. Hot for working data, Cool or Cold for older partitions, Archive for compliance retention. Lifecycle rules on ADLS do this automatically. Partition and file-size matter. Lots of tiny files kill query performance. Use OPTIMIZE, Z-Order, or liquid clustering on Delta tables, and partition on the columns your queries actually filter on. Lean on vectorized reads. ADLS plus Delta plus modern query engines push a lot of work down to columnar Parquet, which keeps your compute bill in check. Use serverless SQL warehouses where it fits. Direct Query against a serverless endpoint scales compute to demand and lets you keep dashboards fresh without import refreshes. Observe data, not just systems. “Is Databricks up” is necessary but not sufficient. Watch data freshness, row counts, pipeline blockages, and SLAs on the data itself. Govern everything. A well-governed lakehouse drives trust, which drives use, which drives value. Skipping governance early always costs more later. Getting Started If you want to put hands on a keyboard this week: Spin up an Azure Storage account with hierarchical namespace enabled. That is your ADLS Gen2 foundation. Stand up an Azure Databricks workspace, enable Unity Catalog, and point it at your ADLS account. Create a Lakeflow Declarative Pipeline. Use Autoloader to ingest a sample dataset (the NYC taxi data is a classic starting point) into a bronze Delta table. Add silver and gold notebooks or pipelines that clean and aggregate the data. Connect Power BI to a serverless SQL warehouse on your gold tables with Direct Query. If you are a Fabric tenant, mirror or shortcut data into OneLake and try the same pattern there, no infra to manage. Read the Hitchhiker’s Guide to ADLS before you scale up. It will save you future you a lot of grief. Resources Introduction to Azure Data Lake Storage The Hitchhiker’s Guide to the Data Lake Microsoft OneLake documentation Azure Databricks documentation Delta Lake on Azure Databricks Design Delta Lake architecture and medallion patterns Implement medallion lakehouse architecture in Microsoft Fabric Watch the rest of the Summit This session is one stop on a big tour. The full Microsoft Azure Infrastructure Summit 2026 playlist covers everything from sovereign cloud and AKS networking to backup, storage, and AI-assisted operations. If your job touches Azure, there is something in here for you. Head over to the full playlist and binge what is useful: https://www.youtube.com/playlist?list=PLjt5SKzX1iI8con7FJDB56G6hHqxGm7ki Cheers! Pierre RomanInstall and run Azure Foundry Local LLM server & Open WebUI on Windows Server 2025
Foundry Local is an on-device AI inference solution offering performance, privacy, customization, and cost advantages. It integrates seamlessly into your existing workflows and applications through an intuitive CLI, SDK, and REST API. Foundry Local has the following benefits: On-Device Inference: Run models locally on your own hardware, reducing your costs while keeping all your data on your device. Model Customization: Select from preset models or use your own to meet specific requirements and use cases. Cost Efficiency: Eliminate recurring cloud service costs by using your existing hardware, making AI more accessible. Seamless Integration: Connect with your applications through an SDK, API endpoints, or the CLI, with easy scaling to Azure AI Foundry as your needs grow. Foundry Local is ideal for scenarios where: You want to keep sensitive data on your device. You need to operate in environments with limited or no internet connectivity. You want to reduce cloud inference costs. You need low-latency AI responses for real-time applications. You want to experiment with AI models before deploying to a cloud environment. You can install Foundry Local by running the following command: winget install Microsoft.FoundryLocal Once Foundry Local is installed, you download and interact with a model from the command line by using a command like: foundry model run phi-4 This will download the phi-4 model and provide a text based chat interface. If you want to interact with Foundry Local through a web chat interface, you can use the open source Open WebUI project. You can install Open WebUI on Windows Server by performing the following steps: Download OpenWebUIInstaller.exe from https://github.com/BrainDriveAI/OpenWebUI_CondaInstaller/releases. You'll get warning messages from Windows Defender SmartScreen. Copy OpenWebUIInstaller.exe into C:\Temp. In an elevated command prompt, run the following commands winget install -e --id Anaconda.Miniconda3 --scope machine $env:Path = 'C:\ProgramData\miniconda3;' + $env:Path $env:Path = 'C:\ProgramData\miniconda3\Scripts;' + $env:Path $env:Path = 'C:\ProgramData\miniconda3\Library\bin;' + $env:Path conda.exe tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main conda.exe tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r conda.exe tos accept --override-channels --channel https://repo.anaconda.com/pkgs/msys2 C:\Temp\OpenWebUIInstaller.exe Then from the dialog choose to install and run Open WebUI. You then need to take several extra steps to configure Open WebUI to connect to the Foundry Local endpoint. Enable Direct Connections in Open WebUI Select Settings and Admin Settings in the profile menu. Select Connections in the navigation menu. Enable Direct Connections by turning on the toggle. This allows users to connect to their own OpenAI compatible API endpoints. Connect Open WebUI to Foundry Local: Select Settings in the profile menu. Select Connections in the navigation menu. Select + by Manage Direct Connections. For the URL, enter http://localhost:PORT/v1 where PORT is the Foundry Local endpoint port (use the CLI command foundry service status to find it). Note that Foundry Local dynamically assigns a port, so it isn't always the same. For the Auth, select None. Select Save ➡️ What is Foundry Local https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-local/what-is-foundry-local ➡️ Edge AI for Beginners https://aka.ms/edgeai-for-beginners ➡️ Open WebUI: https://docs.openwebui.com/Welcome Back to AZ Update
Hello Folks! Welcome Back to AZ Update A few years ago, Antony Bartolo and I launched a simple idea called AZ Update. The goal was to provide a place where IT professionals could quickly understand what was changing in Azure, why it mattered, and what they should pay attention to next. The show became a weekly conversation focused on Azure news, infrastructure, operations, security, and the real-world impact of Microsoft's latest cloud updates. Today, Azure is moving faster than ever. Every week brings new services, platform capabilities, operational improvements, AI innovations, and architectural guidance. Keeping up is a full-time job. Most of us don't have time to read every blog post, release note, announcement, and documentation update. That's why I'm bringing AZ Update back. This time, as a weekly LinkedIn newsletter and this blog. To be completely transparent I am using an AI Agent to parse the update list for any in the last 7 days, filter for Infra/Ops content and research product docs and help with the draft. I do review content and write the post myself. Each edition will cut through the noise and focus on what matters most for cloud architects, platform engineers, infrastructure teams, SREs, security professionals, and IT operators. I'll share the Azure announcements worth your attention, explain why they're important, highlight practical implications, and point you to the resources that can help you go deeper. Just a concise weekly briefing from one ITPro to another. If your day-to-day involves building, operating, securing, or modernizing infrastructure in Azure, Azure Arc, AKS, hybrid environments, or the growing world of AI-powered operations, this newsletter is for you. Welcome to the next chapter of AZ Update. Here is week 1! This week’s Azure infrastructure updates bring practical operational gains for security, platform reliability, disaster recovery, and identity-driven access control. Here is a detailed ITPro breakdown with implementation guidance you can use in production planning. Update #1 - Generally Available: Network Security Perimeter support for Azure Event Hubs Update #2 - Generally Available: Confidential Computing support for Azure Event Hubs Dedicated Update #3 - Generally Available: Support 5x churn in Azure Site Recovery Update #4 - Generally Available: Microsoft Entra ID-based access for Azure Blob Storage SFTP Update #1 - Generally Available: Network Security Perimeter support for Azure Event Hubs Why ITPros should care Network Security Perimeter for Event Hubs changes how ITPros enforce connectivity boundaries around mission-critical event pipelines. Instead of depending only on isolated firewall rules per namespace, you can apply perimeter-aware controls that are easier to govern consistently across multiple services. From an operations perspective, this is a service-level hardening improvement. It helps reduce accidental exposure and supports better audit conversations when security teams ask for clear evidence of allowed and denied paths. Operational value The operational value is stronger day-two control. You can standardise network access policy patterns for producer and consumer applications, reduce policy drift, and simplify incident investigations when unexpected traffic appears. For production rollout, validate all dependencies first: private endpoints, DNS resolution, trusted service exceptions, managed identities, and cross-subscription network paths. Real-world example with step-by-step guidance Inventory current producer and consumer traffic flows, including private endpoints, DNS zones, and any trusted service allowances. Deploy a pilot Event Hubs namespace with perimeter controls in non-production and mirror realistic ingestion and consumption traffic. Apply least-privilege inbound and outbound perimeter rules, then execute end-to-end send/receive tests with representative message volume. Review diagnostic logs for denies, refine exceptions only where business-justified, and capture evidence for change management. Promote to production in stages with a rollback plan that restores previous network policy if message flow health degrades. Technical details including code examples Use the following sequence when validating that perimeter onboarding did not break data plane operations. The first command confirms your active Azure context, the second verifies endpoint reachability, and the third validates Event Hub metadata retrieval. Run this safely in a test window before production enforcement. If connectivity and control-plane checks pass in test, repeat with production namespace read-only checks before enabling stricter policies. az account show --output table Test-NetConnection <namespace>.servicebus.windows.net -Port 5671 az eventhubs eventhub show --resource-group <rg> --namespace-name <namespace> --name <eventhub> --output table Expected outcome: TCP probe to port 5671 succeeds, and Event Hub metadata query returns without auth or network timeout errors. If probe fails, check DNS, NSGs, route tables, private endpoint linkage, and perimeter rule assignment scope. Comprehensive Resources Azure update: Network Security Perimeter support for Azure Event Hubs Network Security Perimeter concepts Azure Event Hubs documentation Event Hubs networking and security Update #2 - Generally Available: Confidential Computing support for Azure Event Hubs Dedicated Why ITPros should care Confidential Computing support for Event Hubs Dedicated matters when ITPros operate regulated or high-sensitivity event streams. It extends protection expectations beyond encryption at rest and in transit, into stronger assurances during processing. Compared with older architectures, this reduces the need for some compensating controls and helps security and operations teams align on platform-native protections for streaming workloads. Operational value Operationally, this strengthens trust boundaries for event ingestion platforms that feed analytics, SIEM, and business-critical automation. It also improves evidence posture for compliance reviews where data handling controls must be demonstrated end to end. Before rollout, validate throughput impact, partition behaviour, client compatibility, and observability baselines so confidentiality controls do not create unexpected SLO regressions. Real-world example with step-by-step guidance Classify Event Hubs namespaces by sensitivity and select the first dedicated environment where enhanced confidentiality requirements apply. Enable and validate in non-production with representative producer and consumer load, including peak and burst patterns. Measure latency, throughput, and throttling trends before and after enablement to confirm workload behaviour remains acceptable. Capture attestation and configuration evidence required by internal security governance or external auditors. Roll out in waves by workload criticality, with rollback criteria tied to message latency, error rates, and throttling thresholds. Technical details including code examples This validation example confirms namespace details and metrics health so you can compare baseline vs post-change behaviour. The metrics query focuses on ingestion, egress, and throttling signals that commonly surface operational risk first. Run with a least-privileged operations identity that can read namespace configuration and metrics. Avoid making unrelated changes while collecting baseline evidence. az eventhubs namespace show --resource-group <rg> --name <namespace> --output jsonc az monitor metrics list --resource /subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.EventHub/namespaces/<namespace> --metric IncomingMessages OutgoingMessages ThrottledRequests --interval PT5M az account show --query user.name -o tsv Expected outcome: namespace query succeeds, metrics return consistently, and no abnormal throttling spike appears after control changes. If results diverge, review dedicated capacity planning, partition strategy, RBAC scope, and workload profile fidelity. Comprehensive Resources Azure update: Confidential Computing support for Azure Event Hubs Dedicated Event Hubs Dedicated overview Azure Confidential Computing overview Monitor Azure Event Hubs Update #3 - Generally Available: Support 5x churn in Azure Site Recovery Why ITPros should care Higher churn support in Azure Site Recovery is directly relevant for ITPros protecting write-intensive systems. It expands what can be replicated reliably, reducing DR exceptions for fast-changing workloads. Compared with the previous operational envelope, this gives more room for modern transactional applications while still requiring disciplined capacity and replication health management. Operational value Operational value is improved DR coverage and better alignment between production write behaviour and recovery plans. Teams can protect more workloads without bespoke workaround architecture. For production rollout, validate process server sizing, bandwidth headroom, cache storage performance, and sustained replication lag during peak change windows. Real-world example with step-by-step guidance Baseline current churn and replication lag for candidate workloads to identify which systems benefit most from the increased support. Enable replication in a pilot for one high-churn workload and observe initial seeding and steady-state health. Run test failover and reprotect to verify recovery objectives and operational runbook completeness. Tune bandwidth and cache settings if lag increases during peak write intervals or backup overlap windows. Onboard additional workloads incrementally and use replication health gates before each expansion wave. Technical details including code examples These commands are relevant for validating actual recovery readiness instead of configuration-only status. They expose protected item health and support controlled failover rehearsal. Use a non-production network for test failover and document outputs so operations and business continuity stakeholders share the same readiness evidence. az site-recovery fabric list --resource-group <rg> --vault-name <vault> -o table az site-recovery protected-item list --resource-group <rg> --vault-name <vault> --fabric-name <fabric> --protection-container <container> -o table az site-recovery recovery-plan test-failover --resource-group <rg> --vault-name <vault> --name <recoveryPlan> --network-id <testNetworkId> Expected outcome: protected items remain healthy, lag remains within target, and test failover completes without consistency errors. If failures occur, inspect connectivity, process server capacity, cache throughput, and policy mappings. Comprehensive Resources Azure update: Support 5x churn in Azure Site Recovery Azure Site Recovery documentation Monitor and troubleshoot Site Recovery Site Recovery capacity planning Update #4 - Generally Available: Microsoft Entra ID-based access for Azure Blob Storage SFTP Why ITPros should care This launch modernises SFTP access for Azure Blob Storage by bringing identity control closer to Microsoft Entra. ITPros gain stronger governance options than local-account-only models for many enterprise scenarios. Operationally, the key change is identity lifecycle alignment: provisioning, review, and revocation can be managed with central identity processes instead of fragmented local credentials. Operational value The value is reduced credential sprawl, better auditability, and clearer access accountability across teams and external partners exchanging files over SFTP. Before production, validate client compatibility, RBAC scope, network restrictions, access review cadence, and emergency break-glass procedures. Real-world example with step-by-step guidance Confirm SFTP is enabled on the storage account and validate networking model (public endpoint restrictions or private access path) matches policy. Assign Entra-based permissions with least privilege and validate scope at storage account and container boundaries. Test SFTP authentication and file operations using approved clients while collecting diagnostic logs for audit evidence. Validate joiner-mover-leaver scenarios by changing membership and role assignments, then confirming access updates propagate correctly. Roll out in stages by partner or workload segment with clear support ownership and incident response runbooks. Technical details including code examples This sequence verifies account capability and role assignment posture before user acceptance testing. It is useful for catching scope mistakes that often cause authentication-success/data-access-failure patterns. Run safely by using a dedicated test identity and non-production storage account first; then repeat read-only validation in production before broad enablement. az storage account show --name <storageAccount> --resource-group <rg> --query "{name:name,isSftpEnabled:isSftpEnabled,allowBlobPublicAccess:allowBlobPublicAccess}" -o jsonc az role assignment list --assignee <principalObjectId> --scope /subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.Storage/storageAccounts/<storageAccount> -o table az account show --query user.name -o tsv Expected outcome: SFTP capability is enabled, expected role assignments are present, and test identity can perform allowed operations only. If sign-in works but file actions fail, inspect RBAC propagation delay, ACL/permission scope, and storage network restrictions. Comprehensive Resources Azure update: Microsoft Entra ID-based access for Azure Blob Storage SFTP SFTP support for Azure Blob Storage Authorize blob data with Microsoft Entra ID Azure Storage security baseline If you are planning adoption, start with one workload per update area, collect operational evidence, and standardise the validated pattern in your runbooks and IaC modules. That approach keeps change safe while accelerating delivery. Cheers! PierreStop Hand-Building VMs at 2 AM: Automated Image Pipelines with Azure Image Builder and Compute Gallery
Hello Folks! If you have ever stood up a marketplace Ubuntu VM, SSH’d in, layered on your monitoring agent, security tooling, a couple of CA certs, and a hardening script, then captured the result and called it your “golden image,” I have bad news. That image was already drifting from the next one your coworker built before you finished naming the snapshot. At the Microsoft Azure Infra Summit 2026, Sandeep Raichura (PM for Azure Compute Gallery) and Kofi Forsen (PM for Azure VM Image Builder) rebuilt the whole workflow the right way. Source, customize, validate, distribute, deploy. No clicks. No tribal knowledge. No 2 AM heroics. 📺 Watch the session: Why IT Pros Should Care You carry the pager when a bad image rolls into ten regions. You explain why three teams have three different Ubuntu 22.04 baselines with three different agents. You find out at 2 AM that someone deleted “the old image” and the old image was the one production VMSS was still pulling. This session is in your lane. It covers: Why hand-rolled images stop working the moment a second team needs one. How Azure VM Image Builder (AIB) turns image creation into declarative pipeline code. How Azure Compute Gallery handles versioning, replication, sharing, and accidental-deletion protection. How automatic image creation triggers chain a marketplace update through your golden image, into every downstream image, with zero manual steps. How VM Scale Sets close the loop with rolling upgrades and automatic OS upgrade. In short, this is the practitioner version of “do VM image management properly,” from the PMs who own both services. What is Azure Image Builder and Azure Compute Gallery The two services do different jobs and you really do need both. Azure VM Image Builder is the build engine. You hand it a JSON template that declares: A source (marketplace image, managed image, VHD, or existing gallery version). Customizers (shell, PowerShell, Windows updates, file copies, restart steps). One or more distribute targets (usually a Compute Gallery image definition). AIB spins up a temporary build VM, runs your customizers in order, validates, generalizes, captures, and publishes. Every build runs the exact same way. No SSH, no RDP, no “I forgot to install the monitoring agent this time.” Azure Compute Gallery is the management layer for the resulting artifacts. Formerly Shared Image Gallery, it has three levels: Gallery. The top-level container. Sharing policy lives here: RBAC, Direct Shared Gallery, or Community Gallery. Image definition. The metadata. OS type, generation, security type, publisher / offer / SKU. The SKU of an image family. Image version. The actual replicated artifact. Controls regions, replica counts, storage type (ZRS by default), end-of-life date, and the safety flags. AIB writes the artifact. Compute Gallery stores, versions, replicates, and shares it. Building an automated image pipeline The session walked through the five steps a real pipeline needs, with no manual intervention in the critical path: Source. A marketplace image or any other base. Customize. Scripts that install agents, harden, configure, and validate. Stored in a storage account so AIB can pull them with the right managed identity. Validate. Built-in validation hooks plus your own smoke tests baked into the customizer. Fail fast. Do not silently continue. Distribute. Push the captured image to a Compute Gallery image definition. Pick your regions and replica counts here. Version. Compute Gallery handles semantic versioning, replication, and safety flags. The trick that makes this a real pipeline is the two-template pattern Kofi demoed: A source template builds the org-wide golden image from the marketplace base. Its source reference is set to latest for the marketplace SKU (for example, Canonical Ubuntu 22.04 latest). A distro template layers user-group-specific tooling on top of the golden image. Its source reference is the golden image gallery version, also set to latest. Both templates get an automatic image creation trigger attached. Triggers only fire when the template references latest. From that point on: Canonical publishes a new Ubuntu 22.04. The source template’s trigger fires, AIB rebuilds your golden image, and a new version lands in the source gallery. That new golden image version fires the distro template’s trigger. AIB rebuilds every downstream distro image automatically. VM Scale Sets configured for automatic OS upgrade pick up the new image version and roll it out in batches, pausing if the Application Health probe goes red. You set it up once. After that you only come back when you want to change something on purpose. Safety by design in Compute Gallery A bad image at the top of this chain takes out thousands of VMs at the bottom. Sandeep was clear: safety is not optional, it is built in. The four features worth turning on every time: ZRS storage by default. Image versions stored on zone-redundant storage so a zonal failure does not take the image down. Exclude from latest. Stage an image into a region without making it the default for new deployments. Flip the flag when you are ready to roll. You can set this globally on the version or per region. Block deletion before end-of-life. The image cannot be deleted until its end-of-life date. This is the flag that stops the 2 AM accidental delete. Soft delete. If everything else fails, soft delete gives you a recovery window to restore an image version that should not have been removed. Combine those four with a sane end-of-life date on every version and your blast radius drops dramatically. Real-world scenarios A few patterns that came up in the session and the Q&A: Multi-region fleets. Define your target regions in the AIB template. AIB hands the artifact to Compute Gallery and Compute Gallery does the replication. Your scale sets in every region pull a local replica, not a cross-region copy. Open-source publisher. Use a Community Gallery so anyone in Azure can deploy your image. You provide a contact URL and email at the gallery level so consumers know where to file issues. Partner sharing. Use Direct Shared Gallery to grant specific subscriptions or tenants access without making the image public. VM Scale Sets with rolling upgrade. Reference the image definition (not a specific version) when you create the scale set. The scale set tracks latest. Pair it with a rolling upgrade policy and the Application Health extension. AIB publishes, Compute Gallery replicates, the scale set rolls, and the rollout pauses itself if the Application Health probe goes red. Getting Started Pick the highest-pain item and start there. You do not have to do this all at once. Stand up a Compute Gallery in one region. Create one image definition with proper publisher / offer / SKU metadata. Turn on soft delete at the gallery. Wrap an existing build script in an AIB image template. Use a marketplace image as the source. Distribute to your new gallery. Add excludeFromLatest, endOfLifeDate, and the block-deletion flags to your image version. Default to ZRS storage. Register the Microsoft.VirtualMachineImages and the triggers feature. Attach an automatic image creation trigger to the template. Set the source reference to latest. Build a second template that takes your golden image as its source. Attach a trigger to that one too. Create a VM Scale Set that references the image definition and enable automatic OS upgrade with rolling upgrades and the Application Health extension. That is the loop. Source updates flow through automatically. Bad images do not delete each other. Fleets roll forward in batches. Resources Azure VM Image Builder overview. The service concepts, supported OS, regions, and capabilities. Azure Compute Gallery overview. Gallery, definition, version, replication, and sharing. Azure VM Image Builder best practices. Identity, networking, customizers, and operational guidance from the product team. Automatic Image Creation with Image Builder triggers. Step-by-step to wire up source-image triggers. Create an image definition and image version. Portal, CLI, PowerShell, and REST flows for publishing artifacts. Automatic OS image upgrades for VM Scale Sets. The closing leg of the pipeline. Share images using Community Gallery. Public, non-commercial sharing for open-source publishers. Azure Image Builder samples on GitHub. Reference templates, customization scripts, and end-to-end examples. Watch the rest of the Summit This session was one of many at the Microsoft Azure Infrastructure Summit 2026. If you want the keynotes, the IaC deep dives, the AKS sessions, and the rest of the infra track, the full playlist is here: Microsoft Azure Infra Summit 2026 playlist Cheers! Pierre Roman
