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Azure NetApp Files solutions for three EDA Cloud-Compute scenarios
Table of Contents Abstract Introduction EDA Cloud-Compute scenarios Scenario 1: Burst to Azure from on-premises Data Center Scenario 2: “24x7 Single Set Workload” Scenario 3: "Data Center Supplement" Summary Abstract Azure NetApp Files (ANF) is transforming Electronic Design Automation (EDA) workflows in the cloud by delivering unparalleled performance, scalability, and efficiency. This blog explores how ANF addresses critical challenges in three cloud compute scenarios: Cloud Bursting, 24x7 All-in-Cloud, and Cloud-based Data Center Supplement. These solutions are tailored to optimize EDA processes, which rely on high-performance NFS file systems to design advanced semiconductor products. With the ability to support clusters exceeding 50,000 cores, ANF enhances productivity, shortens design cycles, and eliminates infrastructure concerns, making it the default choice for EDA workloads in Azure. Additionally, innovations such as increased L3 cache and the transition to DDR5 memory enable performance boosts of up to 60%, further accelerating the pace of chip design and innovation. Co-authors: Andy Chan, Principal Product Manager Azure NetApp Files Arnt de Gier, Technical Marketing Engineer Azure NetApp Files Introduction Azure NetApp Files (ANF) solutions support three major cloud compute scenarios running Electronic Design Automation (EDA) in Azure: Cloud Bursting 24x7 All-in-Cloud Cloud based Data Center Supplement ANF solutions can address the key challenges associated with each scenario. By providing an optimized solution stack for EDA engineers ANF will increase productivity and shorten design cycles, making ANF the de facto standard file system for running EDA workloads in Azure. Electronic Design Automation (EDA) processes are comprised of a suite of software tools and workflows used to design semiconductor products such as advanced computer processors (chips) which are all in need of high performance NFS file system solutions. The increasing demand for chips with superior performance, reduced size, and lower power consumption (PPA) is driven by today's rapid pace of innovation to power workloads such as AI. To meet this growing demand, EDA tools require numerous nodes and multiple CPUs (cores) in a cluster. This is where Azure NetApp Files (ANF) comes into play with its high-performance, scalable file system. ANF ensures that data is efficiently delivered to these compute nodes. This means a single cluster—sometimes encompassing more than 50,000 cores—can function as a unified entity, providing both scale-out performance and consistency which is essential for designing advanced semiconductor products. ANF is the most performance optimized NFS storage in Azure making it the De facto solution for EDA workloads. According to Philip Steinke, AMD's Fellow of CAD Infrastructure and Physical Design, the main priority is to maximize the productivity of chip designers by eliminating infrastructure concerns related to compute and file system expansion typically experienced with on-premises deployments that require long planning cycles and significant capital expenditure. In register-transfer level (RTL) simulations, Microsoft Azure showcased that moving to a CPU with greater amounts of L3 Cache can give EDA users a performance boost of up to 60% for their workloads. This improvement is attributed to increased L3 cache, higher clock speeds (instructions per cycle), and the transition from DDR4 to DDR5 memory. Azure’s commitment to providing high-performing, on-demand HPC (High-Performance Computing) infrastructure is a well-known advantage and has become the primary reason EDA companies are increasingly adopting Azure for their chip design needs. In this paper, three different scenarios of Azure for EDA are explored, namely “Cloud Bursting”, “24x7 Single Set Workload” and “Data Center Supplement” as a reference framework to help guide engineer’s Azure for EDA journey. EDA Cloud-Compute scenarios The following sections delve into three key scenarios that address the computational needs of EDA workflows: “Cloud Bursting,” “24x7 Single Set Workload,” and “Data Center Supplement.” Each scenario highlights how Azure's robust infrastructure, combined with high-performance solutions like Azure NetApp Files, enables engineering teams to overcome traditional limitations, streamline chip design processes, and significantly enhance productivity. Scenario 1: Burst to Azure from on-premises Data Center An EDA workload is made up of a series of workflows where certain steps are bursty which can lead to incidents in semiconductor project cycles where compute demand exceeds the on-premises HPC server cluster capacity. Many EDA customers have been bursting to Azure to speed up their engineering projects. In one example, a total of 120,000 cores were deployed serving in many clusters, all were well supported with the high-performance capabilities of ANF. As design projects approach completion, the design is continuously and incrementally modified to fix bugs, synthesis and timing issues, optimization of area, timing and power, resolving issues associated with manufacturing design rule checks, etc. When design changes are made, many if not all the design steps must be re-run to ensure the change did not break the design. As a result, “design spins” or “large regression” jobs will put a large compute demand on the HPC server cluster. This leads to long job scheduler queues (IBM LSF and Univa Grid Engine are two common schedulers for EDA) where jobs wait to be dispatched to run on an available compute node. Competing project schedules are another reason HPC server cluster demands can exceed on-premises fixed capacity. Most engineering divisions within a company share infrastructure resources across teams and projects which inevitably leads to oversubscription of compute capacity and long job queues resulting in production delays. Bursting EDA jobs into Azure with its available compute capacity, is a way to alleviate these delays. For example, Azure’s latest CPU offering can deliver up to 47% shorter turnaround times for RTL simulation than on-premises. Engineering management tries to increase productivity with effective use of their EDA tool licensing. Utilizing Azure's on-demand compute resources and high-performance storage solutions like Azure NetApp Files, enables engineering teams to accelerate design cycles and reduce Non-recurring Engineering (NRE) costs, enhancing productivity significantly. For “burst to Azure” scenarios that allow engineers quick access to compute resources to finish a job without worrying about the underlying NFS infrastructure and traditional complex management overhead, ANF delivers: High Performance: up to 826,000 IOPS per large volume, serving the data for the most demanding simulations with ease to reduce turn-around-time. Scalability: As EDA projects advance, the data generated can grow exponentially. ANF provides large-capacity single namespaces with volumes up to 2PiB, enabling your storage solution to scale seamlessly, while supporting compute clusters with more than 50,000 cores. Ease of Use: ANF is designed for simplicity, with SaaS-like user experience, allowing deployment and management with a few clicks or API automation. Since storage deployment can be done rapidly, engineering to access their EDA HPC hardware quickly for their jobs. Cost-Effectiveness: ANF offers cool access, which transparently moves ‘cold’ data blocks to lower-cost Azure Storage. Additionally, Reserved Capacity (RC) can provide significant cost savings compared to pay-as-you-go pricing, further reducing the high upfront CapEx costs and long procurement cycle associated with on-premises storage solutions. Use the ANF effective pricing estimator to estimate your savings. Reliability and Security: ANF provides enterprise-grade data management and security features, ensuring that your critical EDA data is protected and available when you need it with key management and encryption built-in. Scenario 2: “24x7 Single Set Workload” As Azure for EDA matured over time and the value of providing engineers with available and faster HPC Infrastructure is becoming more widely shared, more users are now moving a entire sets of workloads into Azure that run 24x7. In addition to SPICE or RTL simulations, one such set of workloads is "digital signoff” with the same goal of increasing productivity. Scenario 1 concerns cloud bursting which involves batch processes with high performance and rapid deployment, whereas Scenario 2 involves operating a set of workloads with additional ANF capabilities for data security and user control needs. QoS support: ANF's QoS function fine-tunes storage utilization by establishing a direct correlation between volume size (quota) and performance, which set storage limit an EDA tool or workload may have access to. Snapshot data protection: As more users are using Azure resources, data protection is crucial. ANF snapshots protect primary data often and efficiently for fast recovery from corruption or loss, by restoring a volume to a snapshot in seconds or by restoring individual files from a snapshot. Enabling snapshots is recommended for user home directories and group shares for this reason as well. Large volume support: A set of workloads generates greater output than a single workload, and as such ANF’s large volume support is a feature that’s being widely adopted by EDA users of this scenario. ANF now supports single volumes up to 2PiB in size, allowing a more fine-tuned management of user’s storage footprint. Cool access: Cool access is an ANF feature that enables better cost control because only data that is being worked on at any given time remains in the hot tier. This functionality enables inactive data blocks from the volume and volume snapshots to be transferred from the hot tier to an Azure storage account (the cool tier), saving cost. Because EDA workloads are known to be metadata heavy, ANF does not relocate metadata to the cool tier, ensuring that metadata operations operate as expected. Dynamic capacity pool resizing: Cloud compute resources can be dynamically allocated. To support this deployment model, Azure NetApp Files (ANF) also offers dynamic pool resizing, which further enhances Azure-for-EDA's value proposition. If the size of the pool remains constant but performance requirements fluctuate, enabling dynamic provisioning and deprovisioning of capacity pools of different types provides just-in-time performance. This approach lowers costs during periods when high performance is not needed. Reserved Capacity: Azure allows compute resources to be reserved as a way to guarantee access to that capacity and allowing you to receive significant cost savings compared to the standard "pay-as-you-go" pricing model. This Azure offering is available to ANF. A reservation in 100-TiB and 1-PiB units per month for a one- or three-year term for a particular service level within a region is now available. Scenario 3: "Data Center Supplement" This scenario builds on Scenarios 1 and 2, while Scenario 3 involves EDA users expanding their workflow into Azure as their data center. In this scenario, a mixed EDA flow is hosted with tools from several EDA ISVs, spanning frontend, backend, and Analog mixed signal are being deployed. EDA Companies such as d-Matrix were able to design an entire AI chip, all in Azure as an example of Scenario 3. In this data center supplement scenario, data mobility and additional data life cycle management solutions are essential. Once again, Azure NetApp Files (ANF) rises to the challenge by offering additional features within its solution stack Backup support: ANF has a policy-based backup feature that uses AES-256-bit encryption during the encoding of the received backup data. Backup frequency is defined by a policy. Cross-region replication: ANF data can be replicated asynchronously between Azure NetApp Files volumes (source and destination) with cross-region replication. The source and destination volumes must be deployed in different Azure regions. The service level for the destination capacity pool might be the same or different, allowing customers to fine-tune their data protection demands as efficiently as possible. Cross-zone replication: Similar to the Azure NetApp Files cross-region replication feature, the cross-zone replication (CZR) capability provides data protection between volumes in different availability zones. You can asynchronously replicate data from an Azure NetApp Files volume (source) in one availability zone to another Azure NetApp Files volume (destination) in another availability zone. This capability enables you to fail over your critical application if a zone-wide outage or disaster happens. BC/DR: Users can construct their own solution based on their own goals by using a variety of BC/DR templates that include snapshots, various replication types, failover capabilities, backup, and support for REST API, Azure CLI, and Terraform. Summary The integration of ANF into the EDA workflow addresses the limitations of traditional on-premises infrastructure. By leveraging the latest CPU generations and Azure's on-demand HPC infrastructure, EDA users can achieve significant performance gains and improve productivity, all while being connected by the most optimized, performant file system that’s simple to deploy and support. The three Azure for EDA scenarios—Cloud Bursting, 24x7 Single Set Workload, and Data Center Supplement—showcase Azure's adaptability and effectiveness in fulfilling the changing needs of the semiconductor industry. As a result, ANF has become the default NFS solution for EDA in Azure, allowing businesses to innovate even faster.
GeertVanTeylingen
May 14, 2025 Place Azure Architecture Blog
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Now in Public Preview: System events for data-plane in API Management gateway
We’re excited to announce the public preview of new data-plane system events in Azure Event Grid for the Azure API Management managed gateway (starting with classic tiers). This new capability provides near-real-time visibility into critical operations within your data-plane, helping you extend your API traffic with monitoring, automate responses, and prevent disruptions. These data-plane events complement the existing control-plane events available in Azure Event Grid system topics, marking the beginning of expanded event-driven capabilities in Azure API Management. What’s New? 1. Circuit Breaker Events: Our managed gateway now publishes circuit breaker status changes to Event Grid, so you can act before issues escalate. Microsoft.ApiManagement.Gateway.CircuitBreakerOpened Triggered when the failure threshold is reached, and traffic to a backend is temporarily blocked. Microsoft.ApiManagement.Gateway.CircuitBreakerClosed Indicates recovery and that traffic has resumed to the previously blocked backend. 2. Self-Hosted Gateway Token Events: Stay informed about authentication token status to ensure deployed gateways do not become disconnected. Microsoft.ApiManagement.Gateway.TokenNearExpiry Published 7 days before a token’s expiration to prompt proactive key rotation. Microsoft.ApiManagement.Gateway.TokenExpired Indicates a failed authentication attempt due to an expired token—preventing synchronization with the cloud instance. (note: API traffic is not disrupted). And this is just the beginning! We're continuously expanding event-driven capabilities in Azure API Management. Stay tuned for more system events coming soon! Why This Matters? With system events for data-plane, managed gateway now offer near-real-time extensibility via Event Grid. This allows customers to: Detect and respond to failures instantly. Automate alerts and workflows for proactive issue resolution. Ensure smooth operations with timely token management. Public Preview Limitations Single-Instance Scope: Events are scoped to the individual gateway instance where they occur. No cross-instance aggregation yet. Available in classic tiers only: This feature is currently supported only on the classic Developer, Basic, Standard, and Premium tiers of API Management. Get Started Today Start monitoring your APIs in real-time with event-driven architecture today. Follow the event schema and samples to build subscribers and handlers. Review integration guidance with Event Grid to wire up your automation pipelines. For a full list of supported Azure API Management system events and integration guidance, visit the Azure Event Grid integration docs.
anishta
May 13, 2025 Place Azure Integration Services Blog
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🧩 Use Index + Direct Access to pull data across loops in Data Mapper
When working with repeating structures in Logic Apps Data Mapper, you may run into situations where two sibling loops exist under the same parent. What if you need to access data from one loop while you’re inside the other? This is where the Direct Access function, used in combination with Index, can save the day. 🧪 Scenario In this pattern, we’re focusing on the schema nodes shown below: 📸 Source & Destination Schemas (with loops highlighted) In the source schema: Under the parent node VehicleTrips, we have two sibling arrays: Vehicle → contains VehicleRegistration Trips → contains trip-specific values like VehicleID, Distance, and Duration In the destination schema: We're mapping into the repeating node Looping/Trips/Trip It expects each trip’s data along with a flattened VehicleRegistration value that combines both: The current trip’s VehicleID The corresponding vehicle’s VehicleRegistration The challenge? These two pieces of data live in two separate sibling arrays. 🧰 Try it yourself 📎 Download the sample files from GitHub Place them into the following folders in your Logic Apps Standard project: Artifacts → Source, destination and dependency schemas (.xsd) Map Definitions → .lml map file Maps → The .xslt file generated when you save the map Then right-click the .lml file and select “Open with Data Mapper” in VS Code. 🛠️ Step-by-step Breakdown ✅ Step 1: Set up the loop over Trips Start by mapping the repeating Trips array from the source to the destination's Trip node. Within the loop, we map: Distance Duration These are passed through To String functions before mapping, as the destination schema expects them as string values. As you map the child nodes, you will notice a loop automatically added on parent nodes (Trips->Trip) 📸 Mapping Distance and Duration nodes (context: we’re inside Trips loop) 🔍 Step 2: Use Index and Direct Access to bring in sibling loop values Now we want to map the VehicleRegistration node at the destination by combining two values: VehicleID (from the current trip) VehicleRegistration (from the corresponding vehicle) ➡️ Note: Before we add the Index function, delete the auto-generated loop from Trips to Trip To fetch the matching VehicleRegistration: Use the Index function to capture the current position within the Trips loop 📸 Index setup for loop tracking Use the Direct Access function to retrieve VehicleRegistration from the Vehicle array. 📘 Direct Access input breakdown The Direct Access function takes three inputs: Index – from the Index function, tells which item to access Scope – set to Vehicle, the array you're pulling from Target Node – VehicleRegistration, the value you want This setup means: “From the Vehicle array, get the VehicleRegistration at the same index as the current trip.” 📸 Direct Access setup 🔧 Step 3: Concatenate and map the result Use the Concat function to combine: VehicleID (from Trips) VehicleRegistration (from Vehicle, via Direct Access) Map the result to VehicleRegistration in the destination. 📸 Concat result to VehicleRegistration ➡️ Note: Before testing, delete the auto-generated loop from Vehicle to Trip 📸 Final map connections view ✅ Step 4: Test the output Once your map is saved, open the Test panel and paste a sample payload. You should see each Trip in the output contain: The original Distance and Duration values (as strings) A VehicleRegistration field combining the correct VehicleID and VehicleRegistration from the sibling array 📸 Sample Trip showing the combined nodes 💬 Feedback or ideas? Have feedback or want to share a mapping challenge? Open an issue on GitHub
shahparth
May 12, 2025 Place Azure Integration Services Blog
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Logic app - Escaped Characters and Formatting Problems in KQL Run query and list results V2 action
I’m building a Logic App to detect sign-ins from suspicious IP addresses. The logic includes: Retrieving IPs from incident entities in Microsoft Sentinel. Enriching each IP using an external API. Filtering malicious IPs based on their score and risk level. Storing those IPs in an array variable (MaliciousIPs). Creating a dynamic KQL query to check if any of the malicious IPs were used in sign-ins, using the in~ operator. Problem: When I use a Select and Join action to build the list of IPs (e.g., "ip1", "ip2"), the Logic App automatically escapes the quotes. As a result, the KQL query is built like this: IPAddress in~ ([{"body":"{\"\":\"\\\"X.X.X.X\\\"\"}"}]) Instead of the expected format: IPAddress in~ ("X.X.X.X", "another.ip") This causes a parsing error when the Run Query and List Results V2 action is executed against Log Analytics. ------------------------ Here's the For Each action loop who contain the following issue: Dynamic compose to formulate the KQL query in a concat, since it's containing the dynamic value above : concat('SigninLogs | where TimeGenerated > ago(3d) | where UserPrincipalName == \"',variables('CurrentUPN'),'\" | where IPAddress in~ (',outputs('Join_MaliciousIPs_KQL'),') | project TimeGenerated, IPAddress, DeviceDetail, AppDisplayName, Status') The Current UPN is working as expected, using the same format in a Initialize/Set variable above (Array/String(for IP's)). The rest of the loop : Note: Even if i have a "failed to retrieve" error on the picture don't bother with that, it's just about the dynamic value about the Subscription, I've entered it manually, it's working fine. What I’ve tried: Using concat('\"', item()?['ip'], '\"') inside Select (causes extra escaping). Removing quotes and relying on Logic App formatting (resulted in object wrapping). Flattening the array using a secondary Select to extract only values. Using Compose to debug outputs. Despite these attempts, the query string is always malformed due to extra escaping or nested JSON structure. I would like to know if someone has encountered or have the solution to this annoying problem ? Best regards
Solved
Deleted
May 05, 2025 Place Microsoft Sentinel
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Azure API Management Your Auth Gateway For MCP Servers
The Model Context Protocol (MCP) is quickly becoming the standard for integrating Tools 🛠️ with Agents 🤖 and Azure API Management is at the fore-front, ready to support this open-source protocol 🚀. You may have already encountered discussions about MCP, so let's clarify some key concepts: Model Context Protocol (MCP) is a standardized way, (a protocol), for AI models to interact with external tools, (and either read data or perform actions) and to enrich context for ANY language models. AI Agents/Assistants are autonomous LLM-powered applications with the ability to use tools to connect to external services required to accomplish tasks on behalf of users. Tools are components made available to Agents allowing them to interact with external systems, perform computation, and take actions to achieve specific goals. Azure API Management: As a platform-as-a-service, API Management supports the complete API lifecycle, enabling organizations to create, publish, secure, and analyze APIs with built-in governance, security, analytics, and scalability. New Cool Kid in Town - MCP AI Agents are becoming widely adopted due to enhanced Large Language Model (LLM) capabilities. However, even the most advanced models face limitations due to their isolation from external data. Each new data source requires custom implementations to extract, prepare, and make data accessible for any model(s). - A lot of heavy lifting. Anthropic developed an open-source standard - the Model Context Protocol (MCP), to connect your agents to external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet through e.g. APIs). MCP Hosts: LLM applications such as chat apps or AI assistant in your IDEs (like GitHub Copilot in VS Code) that need to access external capabilities MCP Clients: Protocol clients that maintain 1:1 connections with servers, inside the host application MCP Servers: Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients MCP Protocol: Transport layer in the middle At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it is going to connect to the MCP server. The MCP server will then connect to for example a database or an API. MCP hosts and servers will connect with each other through the MCP protocol. You can create your own custom MCP Servers that connect to your or organizational data sources. For a quick start, please visit our GitHub repository to learn how to build a remote MCP server using Azure Functions without authentication: https://aka.ms/mcp-remote Remote vs. Local MCP Servers The MCP standard supports two modes of operation: Remote MCP servers: MCP clients connect to MCP servers over the Internet, establishing a connection using HTTP and Server-Sent Events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth. Local MCP servers: MCP clients connect to MCP servers on the same machine, using stdio as a local transport method. Azure API Management as the AI Auth Gateway Now that we have learned that MCP servers can connect to remote services through an API. The question now rises, how can we expose our remote MCP servers in a secure and scalable way? This is where Azure API Management comes in. A way that we can securely and safely expose tools as MCP servers. Azure API Management provides: Security: AI agents often need to access sensitive data. API Management as a remote MCP proxy safeguards organizational data through authentication and authorization. Scalability: As the number of LLM interactions and external tool integrations grows, API Management ensures the system can handle the load. Security remains to be a critical piece of building MCP servers, as agents will need to securely connect to protected endpoints (tools) to perform certain actions or read protected data. When building remote MCP servers, you need a way to allow users to login (Authenticate) and allow them to grant the MCP client access to resources on their account (Authorization). MCP - Current Authorization Challenges State: 4/10/2025 Recent changes in MCP authorization have sparked significant debate within the community. 🔍 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 with the Authorization Changes: The MCP server is now treated as both a resource server AND an authorization server. This dual role has fundamental implications for MCP server developers and runtime operations. 💡 𝗢𝘂𝗿 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: To address these challenges, we recommend using 𝗔𝘇𝘂𝗿𝗲 𝗔𝗣𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 as your authorization gateway for remote MCP servers. 🔗For an enterprise-ready solution, please check out our azd up sample repo to learn how to build a remote MCP server using Azure API Management as your authentication gateway: https://aka.ms/mcp-remote-apim-auth The Authorization Flow The workflow involves three core components: the MCP client, the APIM Gateway, and the MCP server, with Microsoft Entra managing authentication (AuthN) and authorization (AuthZ). Using the OAuth protocol, the client starts by calling the APIM Gateway, which redirects the user to Entra for login and consent. Once authenticated, Entra provides an access token to the Gateway, which then exchanges a code with the client to generate an MCP server token. This token allows the client to communicate securely with the server via the Gateway, ensuring user validation and scope verification. Finally, the MCP server establishes a session key for ongoing communication through a dedicated message endpoint. Diagram source: https://aka.ms/mcp-remote-apim-auth-diagram Conclusion Azure API Management (APIM) is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). In this blog, we've emphasized the simplicity of connecting AI agents to various data sources through MCP, streamlining previously complex implementations. Given the critical role of secure access to platforms and services for AI agents, APIM offers robust solutions for managing OAuth tokens and ensuring secure access to protected endpoints, making it an invaluable asset for enterprises, despite the challenges of authentication. API Management: An Enterprise Solution for Securing MCP Servers Azure API Management is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). It is designed to help you to securely expose your remote MCP servers. MCP servers are still very new, and as the technology evolves, API Management provides an enterprise-ready solution that will evolve with the latest technology. Stay tuned for further feature announcements soon! Acknowledgments This post and work was made possible thanks to the hard work and dedication of our incredible team. Special thanks to Pranami Jhawar, Julia Kasper, Julia Muiruri, Annaji Sharma Ganti Jack Pa, Chaoyi Yuan and Alex Vieira for their invaluable contributions. Additional Resources MCP Client Server integration with APIM as AI gateway Blog Post: https://aka.ms/remote-mcp-apim-auth-blog Sequence Diagram: https://aka.ms/mcp-remote-apim-auth-diagram APIM lab: https://aka.ms/ai-gateway-lab-mcp-client-auth Python: https://aka.ms/mcp-remote-apim-auth .NET: https://aka.ms/mcp-remote-apim-auth-dotnet On-Behalf-Of Authorization: https://aka.ms/mcp-obo-sample 3rd Party APIs – Backend Auth via Credential Manager: Blog Post: https://aka.ms/remote-mcp-apim-lab-blog APIM lab: https://aka.ms/ai-gateway-lab-mcp YouTube Video: https://aka.ms/ai-gateway-lab-demo
PranamiJ
May 03, 2025 Place Azure Integration Services Blog
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Granting Azure Resources Access to SharePoint Online Sites Using Managed Identity
When integrating Azure resources like Logic Apps, Function Apps, or Azure VMs with SharePoint Online, you often need secure and granular access control. Rather than handling credentials manually, Managed Identity is the recommended approach to securely authenticate to Microsoft Graph and access SharePoint resources. High-level steps: Step 1: Enable Managed Identity (or App Registration) Step 2: Grant Sites.Selected Permission in Microsoft Entra ID Step 3: Assign SharePoint Site-Level Permission Step 1: Enable Managed Identity (or App Registration) For your Azure resource (e.g., Logic App): Navigate to the Azure portal. Go to the resource (e.g., Logic App). Under Identity, enable System-assigned Managed Identity. Note the Object ID and Client ID (you’ll need the Client ID later). Alternatively, use an App Registration if you prefer a multi-tenant or reusable identity. How to register an app in Microsoft Entra ID - Microsoft identity platform | Microsoft Learn Step 2: Grant Sites.Selected Permission in Microsoft Entra Open Microsoft Entra ID > App registrations. Select your Logic App’s managed identity or app registration. Under API permissions, click Add a permission > Microsoft Graph. Select Application permissions and add: Sites.Selected Click Grant admin consent. Note: Sites.Selected ensures least-privilege access — you must explicitly allow site-level access later. Step 3: Assign SharePoint Site-Level Permission SharePoint Online requires site-level consent for apps with Sites.Selected. Use the script below to assign access. Note: You must be a SharePoint Administrator and have the Sites.FullControl.All permission when running this. PowerShell Script: # Replace with your values $application = @{ id = "{ApplicationID}" # Client ID of the Managed Identity displayName = "{DisplayName}" # Display name (optional but recommended) } $appRole = "write" # Can be "read" or "write" $spoTenant = "contoso.sharepoint.com" # Sharepoint site host $spoSite = "{Sitename}" # Sharepoint site name # Site ID format for Graph API $spoSiteId = $spoTenant + ":/sites/" + $spoSite + ":" # Load Microsoft Graph module Import-Module Microsoft.Graph.Sites # Connect with appropriate permissions Connect-MgGraph -Scope Sites.FullControl.All # Grant site-level permission New-MgSitePermission -SiteId $spoSiteId -Roles $appRole -GrantedToIdentities @{ Application = $application } That's it, Your Logic App or Azure resource can now call Microsoft Graph APIs to interact with that specific SharePoint site (e.g., list files, upload documents). You maintain centralized control and least-privilege access, complying with enterprise security standards. By following this approach, you ensure secure, auditable, and scalable access from Azure services to SharePoint Online — no secrets, no user credentials, just managed identity done right.
anammalu
May 02, 2025 Place Azure Architecture Blog
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Streamlining data discovery for AI/ML with OpenMetadata on AKS and Azure NetApp Files
This article contains a step-by-step guide to deploying OpenMetadata on Azure Kubernetes Service (AKS), using Azure NetApp Files for storage. It also covers the deployment and configuration of PostgreSQL and OpenSearch databases to run externally from the Kubernetes cluster, following OpenMetadata best practices, managed by NetApp® Instaclustr®. This comprehensive tutorial aims to assist Microsoft and NetApp customers in overcoming the challenges of identifying and managing their data for AI/ML purposes. By following this guide, users will achieve a fully functional OpenMetadata instance, enabling efficient data discovery, enhanced collaboration, and robust data governance.
GeertVanTeylingen
May 01, 2025 Place Azure Architecture Blog
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Hybrid deployment model for Logic Apps- Performance Analysis and Optimization recommendations
A few weeks ago, we announced the Public Preview Refresh release of Logic Apps hybrid deployment model that allows customers to run Logic Apps workloads on a customer managed infrastructure. This model provides the flexibility to execute workflows, either on-premises or in any cloud environment, thereby offering enhanced control over the operation of logic apps. By utilizing customer-managed infrastructure, organizations can adhere to regulatory compliance requirements and optimize performance according to their specific needs. As customers consider leveraging hybrid environments, understanding the performance of logic apps under various configurations and scenarios becomes critical. This document offers an in-depth performance evaluation of Azure Logic Apps within a hybrid deployment framework. It examines, several key factors such as CPU and memory allocation and scaling mechanisms, providing valuable insights aimed at maximizing the application’s efficiency and performance. Achieving Optimal Logic Apps Performance in Hybrid Deployments In this section, we will explore the key aspects that affect Logic Apps performance when deployed in a hybrid environment. Factors such as the underlying infrastructure of the Kubernetes environment, SQL configuration and scaling configuration can significantly impact the efficiency of workflows and the overall performance of the applications. The following blog entry provides details of the scaling mechanism of Hybrid deployment model - Scaling mechanism in hybrid deployment model for Azure Logic Apps Standard | Microsoft Community Hub Configure Container Resource allocation: When you create a Logic App, a default value of 0.5 vCPU and 1GiB of memory would be allocated. From the Azure Portal, you can modify this allocation from the Container blade. - Create Standard logic app workflows for hybrid deployment - Azure Logic Apps | Microsoft Learn Currently, the maximum allocation is set to 2vCPU and 4 GiB memory per app. In the future, there would be a provision made to choose higher allocations. For CPU intense/memory intense processing like custom code executions, select a higher value for these parameters. In the next section, we will be comparing the performance with different values of the CPU and memory allocation. This allocation would impact the billing calculation of the Logic App resource. Refer vCPU calculation for more details on the billing impact. Optimize the node count and size in the Kubernetes cluster. Kubernetes runs application workloads by placing containers into Pods to run on Nodes. A node may be a virtual or physical machine, depending on the cluster. A node pool is a group of nodes that share the same configuration (CPU, Memory, Networking, OS, maximum number of pods, etc.). You can choose the capacity (cores and memory), minimum node count and maximum node count for each node pool of the Kubernetes cluster. We recommend allocating a higher capacity for processing CPU intense, or memory intense applications Configure Scale rule settings: For a Logic App resource, we recommend you configure the maximum and minimum replicas which could be scaled out when a scale event occurs. A higher value for the max replicas helps in sudden spikes in the number of application requests. The interval with which the scaler checks for the scaling event and the cooldown period for the scaling event can also be configured from the Scale blade of Logic Apps resource. These parameters impact the scaling pattern. Optimize the SQL server configuration: The hybrid deployment model uses Microsoft SQL for runtime storage. As such, there are lot of SQL operations performed throughout the execution of the workflow and SQL capacity has a significant impact on the performance of the app. Microsoft SQL server could either be a SQL server on Windows, or an Azure SQL database. Few recommendations on the SQL configuration for better performance: If you are using, Azure SQL database, run it on a SQL elastic pool. If you are using SQL server on Windows, run with at least 4vCPU configuration. Scale out the SQL server once the CPU usage of the SQL server hits 60-70% of the total available CPU. Performance analysis: For this performance analysis exercise, we have used a typical enterprise integration scenario which includes the below components. Data transformation: XSLT transformation, validation, and XML parsing actions Data routing: File system connector for storing the transformed content in a file share. Message queuing: RabbitMQ connector for sending the transformation result to Rabbit MQ queue endpoint. Control operations: For-each loop for looping through multiple records, condition execution, Scope, and error handling blocks. Request response: The XML data transmitted via HTTP request, and the status returned as a response. Summary: For these tests, we used the following environment settings: Kubernetes cluster: AKS cluster with Standard D2sV3 (2vCPU, 8GiBmemory) Max replicas: 20 Cooldown period: 300 seconds Polling interval: 30 With the above environment and settings, we have performed multiple application tests with different configuration of SQL server, resource allocation and test durations using Azure load testing tool. In the following table, we have summarized the response time, throughput, and the total vCPU consumption for each of these configurations. You can check each scenario for detailed information. Configuration Results Scenario SQL CPU and Memory allocation per Logic App Test duration Load 90 th Percentile Response time Throughput Total vCPU consumed Scenario 1 SQL general purpose V2 1vCPU/2GiB Memory 10 minutes with 50 users 503 requests 68.62 seconds 0.84/s 3.42 Scenario 2 SQL Elastic pool-4000DTU 1vCPU/2GiB Memory 10 minutes with 50 users 1004 requests 40.74 seconds 1.65/s 3 Scenario 3 SQL Elastic pool-4000DTU 2vCPU/4GiB Memory 10 minutes with 50 users 997 requests 40.63 seconds 1.66/s 4 Scenario 4 SQL Elastic pool-4000DTU 2vCPU/4GiB Memory 30 minutes with 50 users 3421 requests 26.6Seconds 1.9/s 18.6 Scenario 5 SQL Elastic pool-4000DTU 0.5vCPU/1GiB Memory 30 minutes with 50 users 3055 requests 31.38 seconds 1.7/s 12.4 Scenario 6 SQL 2022 Enterprise on Standard D4s V3 VM 0.5vCPU/1GiB Memory 30 minutes with 50 users 4105 requests 27.15 seconds 2.28/s 10 Scenario 1: SQL general purpose V2 with 1vCPU and 2 GiB Memory – 10 minutes test with 50 users In this scenario, we conducted a load test for 10 minutes with 50 users with the Logic App configuration of: 1 vCPU and 2 GiB Memory and Azure SQL database running on General purpose V2 plan. There were 503 requests with multiple records in each payload and it achieved the 68.62 seconds as the 90 th percentile response time and a throughput of 0.84 requests per second. Scaling: The Kubernetes nodes scaled out to 12 nodes and in total 3.42 vCPUs used by the app for the test duration. SQL Metrics: The CPU usage of the SQL server reached 90% of CPU usage quite early and stayed above 90% for the remaining duration of the test. From our backend telemetry as well, we observed that the actions executions were faster, but there was latency between the actions, which indicates SQL bottlenecks. Scenario 2: SQL elastic pool, with 1vCPU and 2 GiB memory- 10 minutes test with 50 users In this scenario, we conducted a load test for 10 minutes with 50 users with the Logic App configuration of: 1 vCPU and 2 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. There were 1004 requests with multiple records in each payload and it achieved the 40.74 seconds as the 90 th percentile response time and a throughput of 1.65 requests per second. Scaling: The Kubernetes nodes scaled out to 15 nodes and in total 3 vCPUs used by the app for the test duration. SQL Metrics: The SQL server’s CPU utilization peaked to 2% of the elastic pool. Scenario 3: SQL elastic pool, with 2vCPU and 4 GiB memory- 10 minutes test with 50 users In this scenario, we conducted a load test for 10 minutes with 50 users with the Logic App configuration of 2 vCPU and 4 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. There were 997 requests with multiple records in each payload and it achieved the 40.63 seconds as the 90 th percentile response time and a throughput of 1.66 requests per second. Scaling: The Kubernetes nodes scaled out to 21 nodes and in total 4 vCPUs used by the app for the test duration. SQL Metrics: The SQL server’s CPU utilization peaked to 5% of the elastic pool. Scenario 4: SQL elastic pool, with 2vCPU and 4 GiB memory- 30 minutes test with 50 users In this scenario, we conducted a load test for 30 minutes with 50 users with the Logic App configuration of: 2 vCPU and 4 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. There were 3421 requests with multiple records in each payload and it achieved the 26.67 seconds as the 90 th percentile response time and a throughput of 1.90 requests per second. Scaling: The Kubernetes nodes scaled out to 20 nodes and in total 18.6 vCPUs used by the app for the test duration. SQL Metrics: The SQL server’s CPU utilization peaked to 4.7% of the elastic pool. Scenario 5: SQL Elastic pool, with 0.5vCPU and 1 GiB memory- 30 minutes test with 50 users In this scenario, we have conducted a load test for 30 minutes with 50 users with the Logic App configuration of 0.5 vCPU and 1 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. There were 3055 requests with multiple records in each payload and it achieved the 31.38 seconds as the 90 th percentile response time and a throughput of 1.70 requests per second. Scaling: The Kubernetes nodes scaled out to 18 nodes and in total 12.4 vCPUs used by the app for the test duration. SQL Metrics: The SQL server’s CPU utilization peaked to 8.6% of the elastic pool CPU. Scenario 6: SQL 2022 Enterprise Gen2 on Windows 2022 on Standard D4s v3 image, with 0.5vCPU and 1 GiB memory- 30 minutes test with 50 users In this scenario, we conducted a load test for 30 minutes with 50 users with the Logic App configuration of: 0.5 vCPU and 1 GiB Memory and Azure SQL database running on an on-premises SQL 2022 Enterprise Gen2 version running on a Windows 2022 OS with Standard D4s v3 image (4 vCPU and 16GIB memory) There were 4105 requests with multiple records in each payload and it achieved the 27.15 seconds as the 90 th percentile response time and a throughput of 2.28 requests per second. Scaling: The Kubernetes nodes scaled out to 8 nodes and in total 10 vCPUs used by the app for the test duration. SQL metrics: The CPU usage of the SQL server went above 90% after few minutes and there was latency on few runs. Findings and recommendations: The following are the findings and recommendations for this performance exercise. Consider that this load test was conducted using unique conditions. If you conduct a similar test, the results and findings might vary, depending on factors such as workflow complexity, configuration, resource allocation and network configuration. The KEDA scaler performs the scale-out and scale-in operations faster, as such, while the total vCPU usage remains quite low, though the nodes scaled out in the range of 1-20 nodes. The SQL configuration plays a crucial role in reducing the latency between the action executions. For a satisfactory load test, we recommend starting with at least 4vCPU configuration on SQL server and scale out once CPU usage of the SQL server hits 60-70% of the total available CPU. For critical applications, we recommend having a dedicated SQL database for better performance. Increasing the dedicated vCPU allocation of the Logic App resource is helpful for the SAP connector, Rules Engine, .NET Framework based custom code operations and for the applications with many complex workflows. As a general recommendation, regularly monitor performance metrics and adjust configurations to meet evolving requirements and follow the coding best practices of Logic Apps standard. Consider reviewing the following article, for recommendations to optimize your Azure Logic Apps workloads: https://techcommunity.microsoft.com/blog/integrationsonazureblog/logic-apps-standard-hosting--performance-tips/3956971
Shree_Divya_M_V
May 01, 2025 Place Azure Integration Services Blog
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Announcement: Azure Logic Apps (Standard) Automated Testing Public Preview
We are excited to announce the public preview of the Azure Logic Apps (Standard) Automated Testing Framework! This new framework is designed to simplify and enhance the testing process for your Logic Apps workflows, ensuring that your integrations are robust, reliable, and ready for production. Starting with version 5.58.8, the Azure Logic Apps (Standard) extension for Visual Studio Code provides the capability to create unit test from a workflow run or a workflow saved definition, that can be edited and executed locally. Learn more about this feature in the April session for Logic App Live: Learn more Create unit tests from Standard workflow definitions in Azure Logic Apps with Visual Studio Code (Preview) Create unit tests from Standard workflow runs in Azure Logic Apps with Visual Studio Code (Preview) Sample Unit Tests (GitHub)
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NetApp Files solutions for three EDA Cloud-Compute scenarios","conversation":{"__ref":"Conversation:conversation:4411918"},"id":"message:4411918","revisionNum":4,"uid":4411918,"depth":0,"board":{"__ref":"Blog:board:AzureArchitectureBlog"},"author":{"__ref":"User:user:222853"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Discover why top industry leaders are turning to Azure NetApp Files as their go-to NFS storage for designing cutting-edge semiconductor products. ","metrics":{"__typename":"MessageMetrics","views":37},"postTime":"2025-05-13T17:04:12.931-07:00","lastPublishTime":"2025-05-13T17:04:12.931-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Table of Contents \n Abstract \n Introduction \n EDA Cloud-Compute scenarios \n Scenario 1: Burst to Azure from on-premises Data Center \n Scenario 2: “24x7 Single Set Workload” \n Scenario 3: \"Data Center Supplement\" \n Summary \n Abstract \n \n Azure NetApp Files (ANF) is transforming Electronic Design Automation (EDA) workflows in the cloud by delivering unparalleled performance, scalability, and efficiency. This blog explores how ANF addresses critical challenges in three cloud compute scenarios: Cloud Bursting, 24x7 All-in-Cloud, and Cloud-based Data Center Supplement. \n These solutions are tailored to optimize EDA processes, which rely on high-performance NFS file systems to design advanced semiconductor products. With the ability to support clusters exceeding 50,000 cores, ANF enhances productivity, shortens design cycles, and eliminates infrastructure concerns, making it the default choice for EDA workloads in Azure. \n Additionally, innovations such as increased L3 cache and the transition to DDR5 memory enable performance boosts of up to 60%, further accelerating the pace of chip design and innovation. \n Co-authors: \n \n Andy Chan, Principal Product Manager Azure NetApp Files \n Arnt de Gier, Technical Marketing Engineer Azure NetApp Files \n \n Introduction \n Azure NetApp Files (ANF) solutions support three major cloud compute scenarios running Electronic Design Automation (EDA) in Azure: \n \n Cloud Bursting \n 24x7 All-in-Cloud \n Cloud based Data Center Supplement \n \n ANF solutions can address the key challenges associated with each scenario. By providing an optimized solution stack for EDA engineers ANF will increase productivity and shorten design cycles, making ANF the de facto standard file system for running EDA workloads in Azure. \n Electronic Design Automation (EDA) processes are comprised of a suite of software tools and workflows used to design semiconductor products such as advanced computer processors (chips) which are all in need of high performance NFS file system solutions. The increasing demand for chips with superior performance, reduced size, and lower power consumption (PPA) is driven by today's rapid pace of innovation to power workloads such as AI. \n To meet this growing demand, EDA tools require numerous nodes and multiple CPUs (cores) in a cluster. This is where Azure NetApp Files (ANF) comes into play with its high-performance, scalable file system. ANF ensures that data is efficiently delivered to these compute nodes. This means a single cluster—sometimes encompassing more than 50,000 cores—can function as a unified entity, providing both scale-out performance and consistency which is essential for designing advanced semiconductor products. ANF is the most performance optimized NFS storage in Azure making it the De facto solution for EDA workloads. \n According to Philip Steinke, AMD's Fellow of CAD Infrastructure and Physical Design, the main priority is to maximize the productivity of chip designers by eliminating infrastructure concerns related to compute and file system expansion typically experienced with on-premises deployments that require long planning cycles and significant capital expenditure. \n In register-transfer level (RTL) simulations, Microsoft Azure showcased that moving to a CPU with greater amounts of L3 Cache can give EDA users a performance boost of up to 60% for their workloads. This improvement is attributed to increased L3 cache, higher clock speeds (instructions per cycle), and the transition from DDR4 to DDR5 memory. \n Azure’s commitment to providing high-performing, on-demand HPC (High-Performance Computing) infrastructure is a well-known advantage and has become the primary reason EDA companies are increasingly adopting Azure for their chip design needs. In this paper, three different scenarios of Azure for EDA are explored, namely “Cloud Bursting”, “24x7 Single Set Workload” and “Data Center Supplement” as a reference framework to help guide engineer’s Azure for EDA journey. \n EDA Cloud-Compute scenarios \n The following sections delve into three key scenarios that address the computational needs of EDA workflows: “Cloud Bursting,” “24x7 Single Set Workload,” and “Data Center Supplement.” Each scenario highlights how Azure's robust infrastructure, combined with high-performance solutions like Azure NetApp Files, enables engineering teams to overcome traditional limitations, streamline chip design processes, and significantly enhance productivity. \n Scenario 1: Burst to Azure from on-premises Data Center \n \n An EDA workload is made up of a series of workflows where certain steps are bursty which can lead to incidents in semiconductor project cycles where compute demand exceeds the on-premises HPC server cluster capacity. Many EDA customers have been bursting to Azure to speed up their engineering projects. In one example, a total of 120,000 cores were deployed serving in many clusters, all were well supported with the high-performance capabilities of ANF. \n As design projects approach completion, the design is continuously and incrementally modified to fix bugs, synthesis and timing issues, optimization of area, timing and power, resolving issues associated with manufacturing design rule checks, etc. \n When design changes are made, many if not all the design steps must be re-run to ensure the change did not break the design. As a result, “design spins” or “large regression” jobs will put a large compute demand on the HPC server cluster. This leads to long job scheduler queues (IBM LSF and Univa Grid Engine are two common schedulers for EDA) where jobs wait to be dispatched to run on an available compute node. \n Competing project schedules are another reason HPC server cluster demands can exceed on-premises fixed capacity. Most engineering divisions within a company share infrastructure resources across teams and projects which inevitably leads to oversubscription of compute capacity and long job queues resulting in production delays. Bursting EDA jobs into Azure with its available compute capacity, is a way to alleviate these delays. For example, Azure’s latest CPU offering can deliver up to 47% shorter turnaround times for RTL simulation than on-premises. \n Engineering management tries to increase productivity with effective use of their EDA tool licensing. Utilizing Azure's on-demand compute resources and high-performance storage solutions like Azure NetApp Files, enables engineering teams to accelerate design cycles and reduce Non-recurring Engineering (NRE) costs, enhancing productivity significantly. \n For “burst to Azure” scenarios that allow engineers quick access to compute resources to finish a job without worrying about the underlying NFS infrastructure and traditional complex management overhead, ANF delivers: \n \n High Performance: up to 826,000 IOPS per large volume, serving the data for the most demanding simulations with ease to reduce turn-around-time. \n Scalability: As EDA projects advance, the data generated can grow exponentially. ANF provides large-capacity single namespaces with volumes up to 2PiB, enabling your storage solution to scale seamlessly, while supporting compute clusters with more than 50,000 cores. \n Ease of Use: ANF is designed for simplicity, with SaaS-like user experience, allowing deployment and management with a few clicks or API automation. Since storage deployment can be done rapidly, engineering to access their EDA HPC hardware quickly for their jobs. \n Cost-Effectiveness: ANF offers cool access, which transparently moves ‘cold’ data blocks to lower-cost Azure Storage. Additionally, Reserved Capacity (RC) can provide significant cost savings compared to pay-as-you-go pricing, further reducing the high upfront CapEx costs and long procurement cycle associated with on-premises storage solutions. Use the ANF effective pricing estimator to estimate your savings. \n Reliability and Security: ANF provides enterprise-grade data management and security features, ensuring that your critical EDA data is protected and available when you need it with key management and encryption built-in. \n \n Scenario 2: “24x7 Single Set Workload” \n \n As Azure for EDA matured over time and the value of providing engineers with available and faster HPC Infrastructure is becoming more widely shared, more users are now moving a entire sets of workloads into Azure that run 24x7. \n In addition to SPICE or RTL simulations, one such set of workloads is \"digital signoff” with the same goal of increasing productivity. Scenario 1 concerns cloud bursting which involves batch processes with high performance and rapid deployment, whereas Scenario 2 involves operating a set of workloads with additional ANF capabilities for data security and user control needs. \n \n QoS support: ANF's QoS function fine-tunes storage utilization by establishing a direct correlation between volume size (quota) and performance, which set storage limit an EDA tool or workload may have access to. \n Snapshot data protection: As more users are using Azure resources, data protection is crucial. ANF snapshots protect primary data often and efficiently for fast recovery from corruption or loss, by restoring a volume to a snapshot in seconds or by restoring individual files from a snapshot. Enabling snapshots is recommended for user home directories and group shares for this reason as well. \n Large volume support: A set of workloads generates greater output than a single workload, and as such ANF’s large volume support is a feature that’s being widely adopted by EDA users of this scenario. ANF now supports single volumes up to 2PiB in size, allowing a more fine-tuned management of user’s storage footprint. \n Cool access: Cool access is an ANF feature that enables better cost control because only data that is being worked on at any given time remains in the hot tier. This functionality enables inactive data blocks from the volume and volume snapshots to be transferred from the hot tier to an Azure storage account (the cool tier), saving cost. Because EDA workloads are known to be metadata heavy, ANF does not relocate metadata to the cool tier, ensuring that metadata operations operate as expected. \n Dynamic capacity pool resizing: Cloud compute resources can be dynamically allocated. To support this deployment model, Azure NetApp Files (ANF) also offers dynamic pool resizing, which further enhances Azure-for-EDA's value proposition. If the size of the pool remains constant but performance requirements fluctuate, enabling dynamic provisioning and deprovisioning of capacity pools of different types provides just-in-time performance. This approach lowers costs during periods when high performance is not needed. \n Reserved Capacity: Azure allows compute resources to be reserved as a way to guarantee access to that capacity and allowing you to receive significant cost savings compared to the standard \"pay-as-you-go\" pricing model. This Azure offering is available to ANF. A reservation in 100-TiB and 1-PiB units per month for a one- or three-year term for a particular service level within a region is now available. \n \n \n Scenario 3: \"Data Center Supplement\" \n \n This scenario builds on Scenarios 1 and 2, while Scenario 3 involves EDA users expanding their workflow into Azure as their data center. In this scenario, a mixed EDA flow is hosted with tools from several EDA ISVs, spanning frontend, backend, and Analog mixed signal are being deployed. EDA Companies such as d-Matrix were able to design an entire AI chip, all in Azure as an example of Scenario 3. \n In this data center supplement scenario, data mobility and additional data life cycle management solutions are essential. Once again, Azure NetApp Files (ANF) rises to the challenge by offering additional features within its solution stack \n \n Backup support: ANF has a policy-based backup feature that uses AES-256-bit encryption during the encoding of the received backup data. Backup frequency is defined by a policy. \n Cross-region replication: ANF data can be replicated asynchronously between Azure NetApp Files volumes (source and destination) with cross-region replication. The source and destination volumes must be deployed in different Azure regions. The service level for the destination capacity pool might be the same or different, allowing customers to fine-tune their data protection demands as efficiently as possible. \n Cross-zone replication: Similar to the Azure NetApp Files cross-region replication feature, the cross-zone replication (CZR) capability provides data protection between volumes in different availability zones. You can asynchronously replicate data from an Azure NetApp Files volume (source) in one availability zone to another Azure NetApp Files volume (destination) in another availability zone. This capability enables you to fail over your critical application if a zone-wide outage or disaster happens. \n BC/DR: Users can construct their own solution based on their own goals by using a variety of BC/DR templates that include snapshots, various replication types, failover capabilities, backup, and support for REST API, Azure CLI, and Terraform. \n \n Summary \n The integration of ANF into the EDA workflow addresses the limitations of traditional on-premises infrastructure. By leveraging the latest CPU generations and Azure's on-demand HPC infrastructure, EDA users can achieve significant performance gains and improve productivity, all while being connected by the most optimized, performant file system that’s simple to deploy and support. \n The three Azure for EDA scenarios—Cloud Bursting, 24x7 Single Set Workload, and Data Center Supplement—showcase Azure's adaptability and effectiveness in fulfilling the changing needs of the semiconductor industry. As a result, ANF has become the default NFS solution for EDA in Azure, allowing businesses to innovate even faster. 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This new capability provides near-real-time visibility into critical operations within your data-plane, helping you extend your API traffic with monitoring, automate responses, and prevent disruptions. These data-plane events complement the existing control-plane events available in Azure Event Grid system topics, marking the beginning of expanded event-driven capabilities in Azure API Management. \n \n What’s New? \n 1. Circuit Breaker Events: Our managed gateway now publishes circuit breaker status changes to Event Grid, so you can act before issues escalate. \n \n Microsoft.ApiManagement.Gateway.CircuitBreakerOpened Triggered when the failure threshold is reached, and traffic to a backend is temporarily blocked. \n \n \n Microsoft.ApiManagement.Gateway.CircuitBreakerClosed Indicates recovery and that traffic has resumed to the previously blocked backend. \n \n 2. Self-Hosted Gateway Token Events: Stay informed about authentication token status to ensure deployed gateways do not become disconnected. \n \n Microsoft.ApiManagement.Gateway.TokenNearExpiry Published 7 days before a token’s expiration to prompt proactive key rotation. \n \n \n Microsoft.ApiManagement.Gateway.TokenExpired Indicates a failed authentication attempt due to an expired token—preventing synchronization with the cloud instance. (note: API traffic is not disrupted). \n \n And this is just the beginning! We're continuously expanding event-driven capabilities in Azure API Management. Stay tuned for more system events coming soon! \n Why This Matters? \n With system events for data-plane, managed gateway now offer near-real-time extensibility via Event Grid. This allows customers to: \n \n Detect and respond to failures instantly. \n Automate alerts and workflows for proactive issue resolution. \n Ensure smooth operations with timely token management. \n \n Public Preview Limitations \n \n Single-Instance Scope: Events are scoped to the individual gateway instance where they occur. No cross-instance aggregation yet. \n Available in classic tiers only: This feature is currently supported only on the classic Developer, Basic, Standard, and Premium tiers of API Management. \n \n Get Started Today \n Start monitoring your APIs in real-time with event-driven architecture today. \n \n Follow the event schema and samples to build subscribers and handlers. \n \n \n Review integration guidance with Event Grid to wire up your automation pipelines. \n \n For a full list of supported Azure API Management system events and integration guidance, visit the Azure Event Grid integration docs. 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Use Index + Direct Access to pull data across loops in Data Mapper","conversation":{"__ref":"Conversation:conversation:4410181"},"id":"message:4410181","revisionNum":3,"uid":4410181,"depth":0,"board":{"__ref":"Blog:board:IntegrationsonAzureBlog"},"author":{"__ref":"User:user:1051277"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":214},"postTime":"2025-05-01T16:37:03.612-07:00","lastPublishTime":"2025-05-01T16:37:03.612-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" When working with repeating structures in Logic Apps Data Mapper, you may run into situations where two sibling loops exist under the same parent. What if you need to access data from one loop while you’re inside the other? \n This is where the Direct Access function, used in combination with Index, can save the day. \n 🧪 Scenario \n In this pattern, we’re focusing on the schema nodes shown below: \n 📸 Source & Destination Schemas (with loops highlighted) \n \n In the source schema: \n \n Under the parent node VehicleTrips, we have two sibling arrays:\n \n Vehicle → contains VehicleRegistration \n Trips → contains trip-specific values like VehicleID, Distance, and Duration \n \n \n \n In the destination schema: \n \n We're mapping into the repeating node Looping/Trips/Trip \n It expects each trip’s data along with a flattened VehicleRegistration value that combines both:\n \n The current trip’s VehicleID \n The corresponding vehicle’s VehicleRegistration \n \n \n \n The challenge? \n These two pieces of data live in two separate sibling arrays. \n 🧰 Try it yourself \n 📎 Download the sample files from GitHub \n Place them into the following folders in your Logic Apps Standard project: \n \n Artifacts → Source, destination and dependency schemas (.xsd) \n Map Definitions → .lml map file \n Maps → The .xslt file generated when you save the map \n \n Then right-click the .lml file and select “Open with Data Mapper” in VS Code. \n 🛠️ Step-by-step Breakdown \n ✅ Step 1: Set up the loop over Trips \n Start by mapping the repeating Trips array from the source to the destination's Trip node. \n Within the loop, we map: \n \n Distance \n Duration \n \n These are passed through To String functions before mapping, as the destination schema expects them as string values. As you map the child nodes, you will notice a loop automatically added on parent nodes (Trips->Trip) \n 📸 Mapping Distance and Duration nodes (context: we’re inside Trips loop) \n \n 🔍 Step 2: Use Index and Direct Access to bring in sibling loop values \n Now we want to map the VehicleRegistration node at the destination by combining two values: \n \n VehicleID (from the current trip) \n VehicleRegistration (from the corresponding vehicle) \n \n \n ➡️ Note: Before we add the Index function, delete the auto-generated loop from Trips to Trip \n \n To fetch the matching VehicleRegistration: \n \n Use the Index function to capture the current position within the Trips loop \n \n 📸 Index setup for loop tracking \n \n \n Use the Direct Access function to retrieve VehicleRegistration from the Vehicle array. \n \n 📘 Direct Access input breakdown \n The Direct Access function takes three inputs: \n \n Index – from the Index function, tells which item to access \n Scope – set to Vehicle, the array you're pulling from \n Target Node – VehicleRegistration, the value you want \n \n \n This setup means: “From the Vehicle array, get the VehicleRegistration at the same index as the current trip.” \n \n 📸 Direct Access setup \n \n \n 🔧 Step 3: Concatenate and map the result \n Use the Concat function to combine: \n \n VehicleID (from Trips) \n VehicleRegistration (from Vehicle, via Direct Access) \n \n Map the result to VehicleRegistration in the destination. \n 📸 Concat result to VehicleRegistration \n \n ➡️ Note: Before testing, delete the auto-generated loop from Vehicle to Trip \n \n 📸 Final map connections view \n \n ✅ Step 4: Test the output \n Once your map is saved, open the Test panel and paste a sample payload. \n You should see each Trip in the output contain: \n \n The original Distance and Duration values (as strings) \n A VehicleRegistration field combining the correct VehicleID and VehicleRegistration from the sibling array \n \n 📸 Sample Trip showing the combined nodes \n \n 💬 Feedback or ideas? \n Have feedback or want to share a mapping challenge? Open an issue on GitHub \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3898","kudosSumWeight":0,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLVJidVR1MQ?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLXd6RndJMw?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLW9kenBxag?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLXZLOXBuMg?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLXU3MHlZdw?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLTY5ZXRQRA?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDc","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLUFEQjBTUg?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDg","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLWo2cnJCbA?revision=3\"}"}}],"totalCount":8,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDEwMTgxLVJidVR1MQ?revision=3"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4400256":{"__typename":"Conversation","id":"conversation:4400256","topic":{"__typename":"ForumTopicMessage","uid":4400256},"lastPostingActivityTime":"2025-05-05T06:46:44.459-07:00","solved":true},"Category:category:microsoft-sentinel":{"__typename":"Category","id":"category:microsoft-sentinel","displayId":"microsoft-sentinel"},"Forum:board:MicrosoftSentinel":{"__typename":"Forum","id":"board:MicrosoftSentinel","displayId":"MicrosoftSentinel","nodeType":"board","conversationStyle":"FORUM","title":"Microsoft Sentinel","shortTitle":"Microsoft Sentinel","parent":{"__ref":"Category:category:microsoft-sentinel"}},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjU2LWUwZWxLdQ?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjU2LWUwZWxLdQ?revision=1","title":"image.png","associationType":"BODY","width":275,"height":429,"altText":""},"ForumTopicMessage:message:4400256":{"__typename":"ForumTopicMessage","subject":"Logic app - Escaped Characters and Formatting Problems in KQL Run query and list results V2 action","conversation":{"__ref":"Conversation:conversation:4400256"},"id":"message:4400256","revisionNum":1,"uid":4400256,"depth":0,"board":{"__ref":"Forum:board:MicrosoftSentinel"},"author":{"__ref":"User:user:-1"},"metrics":{"__typename":"MessageMetrics","views":59},"postTime":"2025-04-02T13:00:51.883-07:00","lastPublishTime":"2025-04-02T13:00:51.883-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" I’m building a Logic App to detect sign-ins from suspicious IP addresses. The logic includes: Retrieving IPs from incident entities in Microsoft Sentinel. Enriching each IP using an external API. Filtering malicious IPs based on their score and risk level. Storing those IPs in an array variable (MaliciousIPs). Creating a dynamic KQL query to check if any of the malicious IPs were used in sign-ins, using the in~ operator. Problem: When I use a Select and Join action to build the list of IPs (e.g., \"ip1\", \"ip2\"), the Logic App automatically escapes the quotes. As a result, the KQL query is built like this: IPAddress in~ ([{\"body\":\"{\\\"\\\":\\\"\\\\\\\"X.X.X.X\\\\\\\"\\\"}\"}]) Instead of the expected format: IPAddress in~ (\"X.X.X.X\", \"another.ip\") This causes a parsing error when the Run Query and List Results V2 action is executed against Log Analytics. ------------------------ Here's the For Each action loop who contain the following issue: Dynamic compose to formulate the KQL query in a concat, since it's containing the dynamic value above : concat('SigninLogs | where TimeGenerated > ago(3d) | where UserPrincipalName == \\\"',variables('CurrentUPN'),'\\\" | where IPAddress in~ (',outputs('Join_MaliciousIPs_KQL'),') | project TimeGenerated, IPAddress, DeviceDetail, AppDisplayName, Status') The Current UPN is working as expected, using the same format in a Initialize/Set variable above (Array/String(for IP's)). The rest of the loop : Note: Even if i have a \"failed to retrieve\" error on the picture don't bother with that, it's just about the dynamic value about the Subscription, I've entered it manually, it's working fine. What I’ve tried: Using concat('\\\"', item()?['ip'], '\\\"') inside Select (causes extra escaping). Removing quotes and relying on Logic App formatting (resulted in object wrapping). Flattening the array using a secondary Select to extract only values. Using Compose to debug outputs. Despite these attempts, the query string is always malformed due to extra escaping or nested JSON structure. I would like to know if someone has encountered or have the solution to this annoying problem ? Best regards ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2180","kudosSumWeight":0,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjU2LWUwZWxLdQ?revision=1\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:4402690":{"__typename":"Conversation","id":"conversation:4402690","topic":{"__typename":"BlogTopicMessage","uid":4402690},"lastPostingActivityTime":"2025-05-02T17:38:27.706-07:00","solved":false},"User:user:2988490":{"__typename":"User","uid":2988490,"login":"PranamiJ","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yOTg4NDkwLTAxT3pKbA?image-coordinates=0%2C200%2C1292%2C1492"},"id":"user:2988490"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLUxqSng4aw?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLUxqSng4aw?revision=12","title":"Group 1010106917.png","associationType":"COVER","width":5555,"height":2729,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLWlFRmFXaw?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLWlFRmFXaw?revision=12","title":"Group 1010106915 (1).png","associationType":"BODY","width":3747,"height":2140,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLVVubmRmbQ?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLVVubmRmbQ?revision=12","title":"Group 1010106916.png","associationType":"BODY","width":4000,"height":1710,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLW13TmNQMw?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLW13TmNQMw?revision=12","title":"mcp-client-auth.png","associationType":"BODY","width":3840,"height":2810,"altText":""},"BlogTopicMessage:message:4402690":{"__typename":"BlogTopicMessage","subject":"Azure API Management Your Auth Gateway For MCP Servers","conversation":{"__ref":"Conversation:conversation:4402690"},"id":"message:4402690","revisionNum":12,"uid":4402690,"depth":0,"board":{"__ref":"Blog:board:IntegrationsonAzureBlog"},"author":{"__ref":"User:user:2988490"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Azure API Management is at the forefront, ready to support the open-source Model Context Protocol (MCP). APIM provides an enterprise-ready solution that helps you securely expose your MCP servers while evolving with the latest technology.","metrics":{"__typename":"MessageMetrics","views":5275},"postTime":"2025-04-10T16:10:59.999-07:00","lastPublishTime":"2025-05-02T17:38:27.706-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" The Model Context Protocol (MCP) is quickly becoming the standard for integrating Tools 🛠️ with Agents 🤖 and Azure API Management is at the fore-front, ready to support this open-source protocol 🚀. You may have already encountered discussions about MCP, so let's clarify some key concepts: \n \n Model Context Protocol (MCP) is a standardized way, (a protocol), for AI models to interact with external tools, (and either read data or perform actions) and to enrich context for ANY language models. \n AI Agents/Assistants are autonomous LLM-powered applications with the ability to use tools to connect to external services required to accomplish tasks on behalf of users. \n Tools are components made available to Agents allowing them to interact with external systems, perform computation, and take actions to achieve specific goals. \n Azure API Management: As a platform-as-a-service, API Management supports the complete API lifecycle, enabling organizations to create, publish, secure, and analyze APIs with built-in governance, security, analytics, and scalability. \n \n New Cool Kid in Town - MCP \n AI Agents are becoming widely adopted due to enhanced Large Language Model (LLM) capabilities. However, even the most advanced models face limitations due to their isolation from external data. Each new data source requires custom implementations to extract, prepare, and make data accessible for any model(s). - A lot of heavy lifting. \n Anthropic developed an open-source standard - the Model Context Protocol (MCP), to connect your agents to external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet through e.g. APIs). \n \n \n \n MCP Hosts: LLM applications such as chat apps or AI assistant in your IDEs (like GitHub Copilot in VS Code) that need to access external capabilities \n MCP Clients: Protocol clients that maintain 1:1 connections with servers, inside the host application \n MCP Servers: Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients \n MCP Protocol: Transport layer in the middle \n \n At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it is going to connect to the MCP server. The MCP server will then connect to for example a database or an API. MCP hosts and servers will connect with each other through the MCP protocol. \n \n You can create your own custom MCP Servers that connect to your or organizational data sources. \n For a quick start, please visit our GitHub repository to learn how to build a remote MCP server using Azure Functions without authentication: https://aka.ms/mcp-remote \n \n Remote vs. Local MCP Servers \n The MCP standard supports two modes of operation: \n \n Remote MCP servers: MCP clients connect to MCP servers over the Internet, establishing a connection using HTTP and Server-Sent Events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth. \n Local MCP servers: MCP clients connect to MCP servers on the same machine, using stdio as a local transport method. \n \n Azure API Management as the AI Auth Gateway \n Now that we have learned that MCP servers can connect to remote services through an API. The question now rises, how can we expose our remote MCP servers in a secure and scalable way? This is where Azure API Management comes in. A way that we can securely and safely expose tools as MCP servers. Azure API Management provides: \n \n Security: AI agents often need to access sensitive data. API Management as a remote MCP proxy safeguards organizational data through authentication and authorization. \n Scalability: As the number of LLM interactions and external tool integrations grows, API Management ensures the system can handle the load. \n \n Security remains to be a critical piece of building MCP servers, as agents will need to securely connect to protected endpoints (tools) to perform certain actions or read protected data. When building remote MCP servers, you need a way to allow users to login (Authenticate) and allow them to grant the MCP client access to resources on their account (Authorization). \n MCP - Current Authorization Challenges \n State: 4/10/2025 Recent changes in MCP authorization have sparked significant debate within the community. \n 🔍 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 with the Authorization Changes: \n \n The MCP server is now treated as both a resource server AND an authorization server. \n This dual role has fundamental implications for MCP server developers and runtime operations. \n \n 💡 𝗢𝘂𝗿 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: To address these challenges, we recommend using 𝗔𝘇𝘂𝗿𝗲 𝗔𝗣𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 as your authorization gateway for remote MCP servers. \n \n \n \n 🔗For an enterprise-ready solution, please check out our azd up sample repo to learn how to build a remote MCP server using Azure API Management as your authentication gateway: https://aka.ms/mcp-remote-apim-auth \n \n The Authorization Flow \n \n The workflow involves three core components: the MCP client, the APIM Gateway, and the MCP server, with Microsoft Entra managing authentication (AuthN) and authorization (AuthZ). \n Using the OAuth protocol, the client starts by calling the APIM Gateway, which redirects the user to Entra for login and consent. \n Once authenticated, Entra provides an access token to the Gateway, which then exchanges a code with the client to generate an MCP server token. \n This token allows the client to communicate securely with the server via the Gateway, ensuring user validation and scope verification. \n Finally, the MCP server establishes a session key for ongoing communication through a dedicated message endpoint. \n \n \n Diagram source: https://aka.ms/mcp-remote-apim-auth-diagram \n Conclusion \n Azure API Management (APIM) is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). In this blog, we've emphasized the simplicity of connecting AI agents to various data sources through MCP, streamlining previously complex implementations. Given the critical role of secure access to platforms and services for AI agents, APIM offers robust solutions for managing OAuth tokens and ensuring secure access to protected endpoints, making it an invaluable asset for enterprises, despite the challenges of authentication. \n API Management: An Enterprise Solution for Securing MCP Servers \n Azure API Management is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). It is designed to help you to securely expose your remote MCP servers. \n \n MCP servers are still very new, and as the technology evolves, API Management provides an enterprise-ready solution that will evolve with the latest technology. Stay tuned for further feature announcements soon! \n \n \n Acknowledgments \n This post and work was made possible thanks to the hard work and dedication of our incredible team. Special thanks to Pranami Jhawar, Julia Kasper, Julia Muiruri, Annaji Sharma Ganti Jack Pa, Chaoyi Yuan and Alex Vieira for their invaluable contributions. \n \n Additional Resources \n \n \n MCP Client Server integration with APIM as AI gateway \n \n Blog Post: https://aka.ms/remote-mcp-apim-auth-blog \n Sequence Diagram: https://aka.ms/mcp-remote-apim-auth-diagram \n APIM lab: https://aka.ms/ai-gateway-lab-mcp-client-auth \n Python: https://aka.ms/mcp-remote-apim-auth \n .NET: https://aka.ms/mcp-remote-apim-auth-dotnet \n On-Behalf-Of Authorization: https://aka.ms/mcp-obo-sample \n \n \n \n \n 3rd Party APIs – Backend Auth via Credential Manager:\n \n Blog Post: https://aka.ms/remote-mcp-apim-lab-blog \n APIM lab: https://aka.ms/ai-gateway-lab-mcp \n YouTube Video: https://aka.ms/ai-gateway-lab-demo \n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"8019","kudosSumWeight":7,"repliesCount":2,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLUxqSng4aw?revision=12\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLWlFRmFXaw?revision=12\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLVVubmRmbQ?revision=12\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLW13TmNQMw?revision=12\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAyNjkwLUxqSng4aw?revision=12"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4409633":{"__typename":"Conversation","id":"conversation:4409633","topic":{"__typename":"BlogTopicMessage","uid":4409633},"lastPostingActivityTime":"2025-05-02T16:26:32.044-07:00","solved":false},"User:user:692609":{"__typename":"User","uid":692609,"login":"anammalu","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS02OTI2MDktNDkyMTc5aTBDMjZGRjE5QzVBODlCN0M"},"id":"user:692609"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjMzLUh4MVhPWQ?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjMzLUh4MVhPWQ?revision=2","title":"aad-managed-identity-graph-spo-thumbnail.png","associationType":"COVER","width":370,"height":174,"altText":""},"BlogTopicMessage:message:4409633":{"__typename":"BlogTopicMessage","subject":"Granting Azure Resources Access to SharePoint Online Sites Using Managed Identity","conversation":{"__ref":"Conversation:conversation:4409633"},"id":"message:4409633","revisionNum":2,"uid":4409633,"depth":0,"board":{"__ref":"Blog:board:AzureArchitectureBlog"},"author":{"__ref":"User:user:692609"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"This blog walks you through the end-to-end process of granting a managed identity read/write access to a specific SharePoint Online site using Microsoft Graph and PowerShell.","metrics":{"__typename":"MessageMetrics","views":463},"postTime":"2025-05-02T16:26:32.044-07:00","lastPublishTime":"2025-05-02T16:26:32.044-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" When integrating Azure resources like Logic Apps, Function Apps, or Azure VMs with SharePoint Online, you often need secure and granular access control. Rather than handling credentials manually, Managed Identity is the recommended approach to securely authenticate to Microsoft Graph and access SharePoint resources. \n High-level steps: \n Step 1: Enable Managed Identity (or App Registration) \n Step 2: Grant Sites.Selected Permission in Microsoft Entra ID \n Step 3: Assign SharePoint Site-Level Permission \n Step 1: Enable Managed Identity (or App Registration) \n For your Azure resource (e.g., Logic App): \n \n Navigate to the Azure portal. \n Go to the resource (e.g., Logic App). \n Under Identity, enable System-assigned Managed Identity. \n Note the Object ID and Client ID (you’ll need the Client ID later). \n \n Alternatively, use an App Registration if you prefer a multi-tenant or reusable identity. How to register an app in Microsoft Entra ID - Microsoft identity platform | Microsoft Learn \n Step 2: Grant Sites.Selected Permission in Microsoft Entra \n \n Open Microsoft Entra ID > App registrations. \n Select your Logic App’s managed identity or app registration. \n Under API permissions, click Add a permission > Microsoft Graph. \n Select Application permissions and add:\n \n Sites.Selected \n \n \n Click Grant admin consent. \n \n Note: Sites.Selected ensures least-privilege access — you must explicitly allow site-level access later. \n Step 3: Assign SharePoint Site-Level Permission \n SharePoint Online requires site-level consent for apps with Sites.Selected. Use the script below to assign access. \n Note: You must be a SharePoint Administrator and have the Sites.FullControl.All permission when running this. \n PowerShell Script: \n # Replace with your values\n$application = @{\n id = \"{ApplicationID}\" # Client ID of the Managed Identity\n displayName = \"{DisplayName}\" # Display name (optional but recommended)\n}\n\n$appRole = \"write\" # Can be \"read\" or \"write\"\n$spoTenant = \"contoso.sharepoint.com\" # Sharepoint site host\n$spoSite = \"{Sitename}\" # Sharepoint site name\n\n# Site ID format for Graph API\n$spoSiteId = $spoTenant + \":/sites/\" + $spoSite + \":\"\n\n# Load Microsoft Graph module\nImport-Module Microsoft.Graph.Sites\n\n# Connect with appropriate permissions\nConnect-MgGraph -Scope Sites.FullControl.All\n\n# Grant site-level permission\nNew-MgSitePermission -SiteId $spoSiteId -Roles $appRole -GrantedToIdentities @{ Application = $application }\n \n That's it, \n \n Your Logic App or Azure resource can now call Microsoft Graph APIs to interact with that specific SharePoint site (e.g., list files, upload documents). \n You maintain centralized control and least-privilege access, complying with enterprise security standards. \n \n By following this approach, you ensure secure, auditable, and scalable access from Azure services to SharePoint Online — no secrets, no user credentials, just managed identity done right. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2999","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjMzLUh4MVhPWQ?revision=2\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjMzLUh4MVhPWQ?revision=2"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4404467":{"__typename":"Conversation","id":"conversation:4404467","topic":{"__typename":"BlogTopicMessage","uid":4404467},"lastPostingActivityTime":"2025-05-01T10:08:10.762-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTIwa2NMUA?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTIwa2NMUA?revision=11","title":"2025-04-15_16-10-59.png","associationType":"COVER","width":752,"height":410,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWZVaFFmSQ?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWZVaFFmSQ?revision=11","title":"image.png","associationType":"BODY","width":940,"height":528,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTU3QWxjZA?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTU3QWxjZA?revision=11","title":"image.png","associationType":"BODY","width":940,"height":528,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUlIcW5vdw?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUlIcW5vdw?revision=11","title":"image.png","associationType":"BODY","width":940,"height":528,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LXk4WlRLMQ?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LXk4WlRLMQ?revision=11","title":"image.png","associationType":"BODY","width":940,"height":528,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWNScFFxeQ?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWNScFFxeQ?revision=11","title":"image.png","associationType":"BODY","width":940,"height":528,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LVpiTExyWQ?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LVpiTExyWQ?revision=11","title":"image.png","associationType":"BODY","width":940,"height":528,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LVRZWlBadg?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LVRZWlBadg?revision=11","title":"image.png","associationType":"BODY","width":940,"height":573,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWM4SjNSaw?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWM4SjNSaw?revision=11","title":"image.png","associationType":"BODY","width":940,"height":563,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTFvU3h6cQ?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTFvU3h6cQ?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTlSbklBQw?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTlSbklBQw?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LXZTNXhVTA?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LXZTNXhVTA?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWpBSThUWA?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWpBSThUWA?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTY3bWJiOA?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTY3bWJiOA?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWdqREl5cA?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LWdqREl5cA?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LXhicjBqSg?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LXhicjBqSg?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTJnTkN1VQ?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LTJnTkN1VQ?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUhaVFZKMg?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUhaVFZKMg?revision=11","title":"image.png","associationType":"BODY","width":940,"height":504,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUZqalJmUg?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUZqalJmUg?revision=11","title":"image.png","associationType":"BODY","width":940,"height":460,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUwyZHlveA?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LUwyZHlveA?revision=11","title":"image.png","associationType":"BODY","width":940,"height":585,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LVN2bGdNbQ?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NDY3LVN2bGdNbQ?revision=11","title":"image.png","associationType":"BODY","width":940,"height":585,"altText":""},"BlogTopicMessage:message:4404467":{"__typename":"BlogTopicMessage","subject":"Streamlining data discovery for AI/ML with OpenMetadata on AKS and Azure NetApp Files","conversation":{"__ref":"Conversation:conversation:4404467"},"id":"message:4404467","revisionNum":11,"uid":4404467,"depth":0,"board":{"__ref":"Blog:board:AzureArchitectureBlog"},"author":{"__ref":"User:user:222853"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This article contains a step-by-step guide to deploying OpenMetadata on Azure Kubernetes Service (AKS), using Azure NetApp Files for storage. It also covers the deployment and configuration of PostgreSQL and OpenSearch databases to run externally from the Kubernetes cluster, following OpenMetadata best practices, managed by NetApp® Instaclustr®. This comprehensive tutorial aims to assist Microsoft and NetApp customers in overcoming the challenges of identifying and managing their data for AI/ML purposes. By following this guide, users will achieve a fully functional OpenMetadata instance, enabling efficient data discovery, enhanced collaboration, and robust data governance. ","introduction":"Unlock the full potential of your data for AI/ML applications with this comprehensive guide to deploying OpenMetadata on Azure Kubernetes Service, backed by Azure NetApp Files and NetApp® Instaclustr®. Learn how to set up a robust metadata management solution that enhances data discovery, collaboration, and governance.","metrics":{"__typename":"MessageMetrics","views":240},"postTime":"2025-05-01T10:08:10.762-07:00","lastPublishTime":"2025-05-01T10:08:10.762-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Table of Contents \n Abstract \n Introduction \n Prerequisites \n Workstation setup \n Repository directory contents \n Terraform variables file \n Credentials \n Azure settings \n Instaclustr settings \n VNet settings \n AKS cluster settings \n Azure NetApp Files settings \n PostgreSQL settings \n OpenSearch settings \n Authorized networks \n Infrastructure deployment \n Application deployment \n Using OpenMetadata \n Adding a service \n Adding an ingestion \n Cleanup \n Summary \n Additional Information \n Abstract \n This article contains a step-by-step guide to deploying OpenMetadata on Azure Kubernetes Service (AKS), using Azure NetApp Files for storage. It also covers the deployment and configuration of PostgreSQL and OpenSearch databases to run externally from the Kubernetes cluster, following OpenMetadata best practices, managed by NetApp® Instaclustr®. This comprehensive tutorial aims to assist Microsoft and NetApp customers in overcoming the challenges of identifying and managing their data for AI/ML purposes. By following this guide, users will achieve a fully functional OpenMetadata instance, enabling efficient data discovery, enhanced collaboration, and robust data governance. \n Co-authors: \n \n Kyle Radder, Azure NetApp Files Technical Marketing Engineer \n Michael Haigh, Senior Technical Marketing Engineer (NetApp) \n \n Introduction \n In this age of data-driven decision making, organizations are increasingly leveraging artificial intelligence and machine learning (AI/ML) to gain insights and drive innovation. However, enterprises face the challenge of identifying and managing their diverse data assets spread across various systems and platforms. Without a comprehensive understanding of available data, it’s difficult to use the full potential of data for AI/ML initiatives. \n OpenMetadata, an open-source metadata management platform, offers a comprehensive solution to these challenges. By providing a centralized metadata catalog, OpenMetadata enables organizations to capture, organize, and discover metadata across various data sources. This ability enhances data visibility and also facilitates better data governance, collaboration, and lineage tracking—key components for successful AI/ML initiatives. \n This article guides you through the process of deploying OpenMetadata on Azure Kubernetes Service (AKS), backed by Azure NetApp Files for storage. OpenMetadata’s PostgreSQL and OpenSearch databases run external to the AKS cluster with NetApp Instaclustr for optimal performance and reliability. By following this article, you will have a fully functional OpenMetadata instance that empowers you to discover and manage your data assets, facilitating your AI/ML initiatives. \n Whether you're a data engineer, data scientist, or IT administrator, this step-by-step guide will help you overcome the challenges of data discovery and management, enabling you to unlock the potential of your data for AI/ML purposes. \n Let's get started building a robust metadata management solution with OpenMetadata on Azure. \n Prerequisites \n To follow along step by step, make sure that the following items are available: \n \n An Azure Account with the ability to create Azure Kubernetes Service (AKS) and Azure NetApp Files resources \n A NetApp Instaclustr account with the ability to create databases \n A local workstation with Git, Azure CLI, kubectl, helm, and Terraform installed \n \n Workstation setup \n This GitHub repository contains the Terraform code to create the required infrastructure and helm values to deploy OpenMetadata. Run the following command to clone the repository to the local workstation (or alternatively fork the repo if you expect to make changes) and change into the new directory: \n git clone https://github.com/MichaelHaigh/netapp-1p-data-catalogs.git \ncd netapp-1p-data-catalogs/OpenMetadata/Azure \n Install the necessary Terraform providers: \n terraform init \n Your workstation is now ready to deploy the necessary infrastructure, but let’s first inspect the repository contents. \n Repository directory contents \n The OpenMetadata/Azure directory contains several Terraform files for deploying the infrastructure and several markup files for deploying the application: \n \n aks.tf: Terraform code that deploys a basic managed Kubernetes cluster \n anf.tf: Terraform code that deploys Azure NetApp Files, which is used as the storage back end for the Kubernetes cluster \n default.tfvars: Terraform variable values that enable customization of the OpenMetadata deployment with the default Terraform workspace \n dependencies-values.yaml: helm values for OpenMetadata’s dependencies, primarily Apache® Airflow® \n instaclustr.tf: Terraform code that deploys an Instaclustr managed PostgreSQL cluster running on Azure NetApp Files and an OpenSearch cluster \n logs_dags_pvc.yaml: Kubernetes manifest that defines two ReadWriteMany persistent volume claims, which are provided by the default Azure NetApp Files storage class \n main.tf: Terraform code that sets the required provider versions, including credential configuration \n openmetadata-values.yaml: helm values for OpenMetadata, primarily referencing the external database and search configurations \n outputs.tf: Terraform code that defines output variables of the to-be-deployed infrastructure, which is used when deploying the OpenMetadata application \n scripts/:\n \n opensearch_setup.sh: shell script invoked by instaclustr.tf, which configures the OpenSearch cluster after deployment by creating an OpenMetadata user with necessary privileges \n postgresql_setup.sh: a shell script invoked by instaclustr.tf, which configures the PostgreSQL cluster after deployment by creating Airflow and OpenMetadata users and databases \n storage_class_setup.sh: a shell script invoked by aks.tf, which installs NetApp Trident™ on the AKS cluster and creates an Azure NetApp Files back end and storage class \n \n \n variables.tf: Terraform code that defines input variables and correlates with tfvar files \n vnet.tf: Terraform code that deploys Azure virtual networking \n \n If you’re familiar with Terraform or Kubernetes and helm concepts, you can inspect these files to better understand the environment. However, most of the complexity and decision making are abstracted by Terraform variables, next. \n Terraform variables file \n As mentioned in the previous section, default.tfvars specifies the values that allow customization of each OpenMetadata deployment. This variable file (and the default workspace) are acceptable to use if you’re deploying a single OpenMetadata environment. \n However, if your use case requires multiple OpenMetadata deployments (for example, prod and dr, or eastus and westus), you can create any number of new workspaces and copy the default variable file (be sure to match your workspace name): \n terraform workspace new <workspace-name> \ncp default.tfvars <workspace-name>.tfvars \n Each deployment can then be configured independently by using its corresponding variable file. Most of the variables can be left as–is; however, a handful must be changed for your environment. \n Credentials \n The credentials section of the variables file has two variables, both of which must be updated for your environment: \n \n # Credentials \nsp_creds = \"~/.azure/azure-sp-tme-demo2-terraform.json\" \nic_creds = \"~/.instaclustr/instaclustr-creds.json\" \n The sp_creds variable points to the local file path of a credential file of a service principal, which has the following format: \n { \n \"subscriptionId\": \"aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee\", \n \"appId\": \"aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee\", \n \"displayName\": \"azure-sp-terraform\", \n \"password\": \"aaaaaaaaaabbbbbbbbbbccccccccccdddddddddd\", \n \"tenant\": \"aaaaaaaaaabbbbbbbbbbccccccccccdddddd\" \n} \n If you need to create a new service principal, follow the Azure Terraform provider documentation (the contributor role will have all the necessary privileges): \n \n The subscriptionIdfield can be gathered from the subscription blade. \n The appId(also known as the client ID), displayName, and tenant (also known as the directory ID) fields can be gathered from the app registration blade (step 1 in the Terraform provider documentation). \n The passwordfield can be gathered (only at time of creation) from the Certificates & secrets: client secrets page of your app registration (step 2 in the Terraform provider documentation). \n \n The ic_creds variable points to the local file path of an Instaclustr API key file with the following format: \n { \n \"username\": \"johndoe@domain.com\", \n \"api_key\": \"aaaaaaaaaabbbbbbbbbbccccccccccdd\" \n} \n Both of these values can be gathered from the Gear Icon > Account Settings > API Keys section of the Instaclustr console (detailed instructions can be found here). \n Azure settings \n The Azure settings section contains to variables: \n # Azure Settings \nazr_region = \"eastus\" \ncreator_tag = \"johndoe\" \n The azr_region variable specifies which region to deploy the non Instaclustr resources into; it can be changed. The creator_tag variable is used as an Azure tag to help identity and organize resources; it should be updated. \n Instaclustr settings \n The Instaclustr settings section contains three variables: \n \n # Instaclustr Settings \nic_region = \"EAST_US\" \nic_provider_account = \"riyoa-name\" # Only needed for RIYOA, set to \"\" for Instaclustr's cloud \nic_resource_group = \"instaclustreastus\" \n \n ic_region: Should be the same region as specified in azr_region; however, the formatting is slightly different (see the Instaclustr-specific Azure terminology here). \n ic_provider_account: Must be changed, to either your provider account name (if running in your own account (RIYOA)), or to an empty string (\"\") if running in the Instaclustr account. \n ic_resource_group: Must be changed, to either the name of the resource group you want to deploy into for RIYOA environments, or to an empty string (\"\") if running in the Instaclustr account. \n \n VNet settings \n The VNet settings section contains the network and subnet addresses for all infrastructure: \n # VNet Settings \nom_vnet_cidr = \"10.20.0.0/22\" \nom_vnet_dns_ip = \"10.20.3.254\" # must be w/in om_vnet_cidr \nom_anf_cidr = \"10.20.2.0/24\" # must be w/in om_vnet_cidr \nom_aks_nodepool_cidr = \"10.20.0.0/23\" # must be w/in \nom_vnet_cidr om_aks_services_cidr = \"172.16.0.0/16\" # must not be w/in om_vnet_cidr \nom_aks_services_dns_ip = \"172.16.0.10\" # must be w/in om_aks_services_cidr \nom_aks_pods_cidr = \"172.18.0.0/16\" # must not be w/in om_vnet_cidr \npostgresql_network = \"10.0.0.0/16\" # must not be w/in om_vnet_cidr \npostgresql_storage_net = \"10.1.0.0/22\" # must not be w/in om_vnet_cidr nor postgresql_network \nopensearch_network = \"10.2.0.0/16\" # must not be w/in om_vnet_cidr nor postgresql_network \n These addresses can be left as is or configured for your environment. Be sure to take note of the comments, because certain addresses must or must not be within other address spaces. \n AKS cluster settings \n The AKS cluster section has four variables: \n # AKS Cluster Settings \naks_kubernetes_version = \"1.30.7\" \naks_trident_version = \"24.10.0\" \naks_node_count = 2 \naks_image_size = \"Standard_D4s_v3\" \n At the time of publishing, these values can be left as–is. However, refer to the AKS Kubernetes release calendar to make sure that aks_kubernetes_version is still under support. Depending on the Kubernetes version deployed, Trident may need to be updated, but at the time of publishing, 24.10.0 is current. \n The other two variables can be left as the default, or they can be updated according to use-case requirements. \n Azure NetApp Files settings \n The Azure NetApp Files section has two variables: \n # ANF Settings \nanf_service_level = \"Standard\" \nanf_pool_size = 2 \n These variables specify the service level and capacity pool size. They can be left as is or customized for your environment. \n PostgreSQL settings \n The PostgreSQL section specifies the Instaclustr PostgreSQL cluster settings: \n # PostgreSQL Settings \npostgresql_sla_tier = \"NON_PRODUCTION\" \npostgresql_version = \"16.6.0\" \npostgresql_replication = \"ASYNCHRONOUS\" \npostgresql_node_size = \"PGS-PRD-Standard_E16s_v4-ANF-2048\" \npostgresql_node_count = 2 \n These settings can all be left at their existing values. However, for production workloads, NetApp recommends modifying the SLA tier and replication to PRODUCTION and SYNCHRONOUS, respectively. Also note that the PostgreSQL node size specifies that it uses Azure NetApp Files as the storage provider. \n OpenSearch settings \n The OpenSearch section specifies the Instaclustr OpenSearch cluster settings: \n # OpenSearch Settings \nopensearch_sla_tier = \"NON_PRODUCTION\" \nopensearch_version = \"2.18.0\" \nopensearch_data_node_count = 3 \nopensearch_data_node_size = \"SRH-PRD-D2s_v5-120-an\" \nopensearch_dashboard_node_size = \"SRH-PRD-D2s_v5-120-an\" \nopensearch_manager_node_size = \"SRH-DM-PRD-D2as_v4-16-an\" \n These settings can all be left at their existing values. However, for production workloads, NetApp recommends modifying the SLA tier to PRODUCTION. \n Authorized networks \n The authorized network section specifies a list of maps which enable the following: \n # Authorized Networks \nauthorized_networks = [ \n { \n cidr_block = \"198.51.100.0/24\" \n display_name = \"company_range\" \n }, \n { \n cidr_block = \"203.0.113.30/32\" \n display_name = \"home_address\" \n }, \n] \n These values must be updated, at a minimum, to your current workstation’s IP address. (Run curl -4 ifconfig.me to get your IP address.) If you only need to specify a single address or subnet, remove the second map. Or, optionally, you can add any number of maps to specify additional networks, depending on your use case. \n Infrastructure deployment \n With the variable files properly configured, it’s time to deploy the infrastructure that OpenMetadata runs on: \n terraform apply -var-file=\"$(terraform workspace show).tfvars\" \n Review the prompt that Terraform displays and enter yes if the plan is acceptable. It should take 15 to 20 minutes for everything to deploy, at which point you’ll see the following output: \n Apply complete! Resources: 25 added, 0 changed, 0 destroyed. \n\nOutputs: \n\naz_kubeconfig_cmd = \"az aks get-credentials --resource-group openmetadata-default-rg --name openmetadata-default-cluster\" \nkubeconfig = <sensitive> \nopenmetadata_airflow_password = <sensitive> \nopensearch_openmetadata_password = <sensitive> \nopensearch_private_address = \"private-search.6c7736c38687463da6f3a0eac1c9fa44.cnodes.io\" \npostgresql_airflow_password = <sensitive> \npostgresql_icpostgresql_password = <sensitive> \npostgresql_openmetadata_password = <sensitive> \npostgresql_private_address = \"10.0.0.5\" \n These output variables will be used programmatically when the application is deployed, which is up next. \n Application deployment \n Now that the infrastructure has been created and configured, it’s time to deploy OpenMetadata. First, add the official OpenMetadata helm repository: \n helm repo add open-metadata https://helm.open-metadata.org/ \nhelm repo update \n Due to the storage_class_setup.sh script, which was invoked as part of the Terraform apply, your kubectl configuration context will already be pointed at the deployed AKS cluster: \n $ kubectl get storageclasses\nkubectl get storageclasses\nNAME PROVISIONER RECLAIMPOLICY VOLUMEBINDINGMODE ALLOWVOLUMEEXPANSION AGE\nazure-netapp-files-standard (default) csi.trident.netapp.io Delete Immediate true 14m\nazurefile file.csi.azure.com Delete Immediate true 18m\nazurefile-csi file.csi.azure.com Delete Immediate true 18m\nazurefile-csi-premium file.csi.azure.com Delete Immediate true 18m\nazurefile-premium file.csi.azure.com Delete Immediate true 18m\ndefault disk.csi.azure.com Delete WaitForFirstConsumer true 18m\nmanaged disk.csi.azure.com Delete WaitForFirstConsumer true 18m\nmanaged-csi disk.csi.azure.com Delete WaitForFirstConsumer true 18m\nmanaged-csi-premium disk.csi.azure.com Delete WaitForFirstConsumer true 18m\nmanaged-premium disk.csi.azure.com Delete WaitForFirstConsumer true 18m \n Run the following commands to create the Kubernetes namespace, secrets, and persistent volumes (note that the secrets are created by referencing the Terraform output variables shown earlier): \n kubectl create namespace openmetadata\nkubectl create secret -n openmetadata generic sql-secrets \\\n --from-literal=airflow-sql-password=$(terraform output -raw postgresql_airflow_password) \\\n --from-literal=openmetadata-sql-password=$(terraform output -raw postgresql_openmetadata_password)\nkubectl create secret -n openmetadata generic elasticsearch-secrets \\\n --from-literal=openmetadata-elasticsearch-password=$(terraform output -raw opensearch_openmetadata_password)\nkubectl create secret -n openmetadata generic airflow-secrets \\\n --from-literal=openmetadata-airflow-password=$(terraform output -raw openmetadata_airflow_password)\nkubectl apply -f logs_dags_pvc.yaml \n OpenMetadata has a few dependencies, primarily Apache Airflow, that must be installed next. As mentioned earlier, dependencies-values.yaml contains the nonsensitive helm values (sensitive values are set through the CLI), optionally modified for your environment. For more information on all possible helm value options, see the official repository. \n helm install -n openmetadata openmetadata-dependencies open-metadata/openmetadata-dependencies \\\n --set airflow.externalDatabase.host=$(terraform output -raw postgresql_private_address) \\\n --values dependencies-values.yaml \n After 5 to 10 minutes, the persistent volumes should be bound, and all pods should be in a ready state: \n $ kubectl -n openmetadata get pods,pvc\nNAME READY STATUS RESTARTS AGE\npod/openmetadata-dependencies-db-migrations-797fd8b77d-cppfx 1/1 Running 0 11m\npod/openmetadata-dependencies-pgbouncer-68d7bbc96f-2mm65 1/1 Running 0 11m\npod/openmetadata-dependencies-scheduler-88c55846-9l7pc 1/1 Running 0 11m\npod/openmetadata-dependencies-sync-users-6484f6896d-2mj57 1/1 Running 0 11m\npod/openmetadata-dependencies-triggerer-75f8584fc9-vvrfx 1/1 Running 0 11m\npod/openmetadata-dependencies-web-6bd7b5698b-sqrwr 1/1 Running 0 11m\npod/opensearch-0 1/1 Running 0 11m\n\nNAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLASS VOLUMEATTRIBUTESCLASS AGE\npersistentvolumeclaim/openmetadata-dependencies-dags-pvc Bound pvc-0508e8e4-5633-46cb-b36e-2bbe1eac3315 50Gi RWX azure-netapp-files-standard <unset> 12m\npersistentvolumeclaim/openmetadata-dependencies-logs-pvc Bound pvc-bd367907-c36d-40ec-a9c7-a1f251fed1db 50Gi RWX azure-netapp-files-standard <unset> 12m\npersistentvolumeclaim/opensearch-opensearch-0 Bound pvc-77d50368-650f-4771-afa7-c343062f4769 50Gi RWO azure-netapp-files-standard <unset> 11m \n Finally, the openmetadata-values.yaml file specifies the OpenMetadata helm values. Optionally modify any values based on the official repository and deploy OpenMetadata: \n helm install -n openmetadata openmetadata open-metadata/openmetadata \\\n --set openmetadata.config.database.host=$(terraform output -raw postgresql_private_address) \\\n --set openmetadata.config.elasticsearch.host=$(terraform output -raw opensearch_private_address) \\\n --values openmetadata-values.yaml \n When the openmetadata-0 pod is in a ready state, deployment of OpenMetadata is complete: \n \n $ kubectl -n openmetadata get pod -l app.kubernetes.io/name=openmetadata\nNAME READY STATUS RESTARTS AGE\nopenmetadata-c4d4f88bc-9l7z8 1/1 Running 0 2m2s \n Using OpenMetadata \n Now that OpenMetadata has been deployed, you can access it through your favorite browser at the following URL: \n echo http://$(kubectl -n openmetadata get service/openmetadata -o jsonpath='{.status.loadBalancer.ingress[0].ip}'):8585 \n \n (i) Note \n For production use cases, you should enable SSL through an AKS Ingress Controller, external Nginx, or directly within the OpenMetadata server, according to usual business practices. \n \n Log in with the default credentials of admin@open-metadata.org / admin to access the OpenMetadata homepage: \n \n Adding a service \n Adding services and ingestions to OpenMetadata enables the extraction of service-specific metadata into the central metadata catalog. This connectivity ensures that the metadata catalog remains up to date and reflects the current state of data assets. There are many supported services (like NetApp® ONTAP®), but as an example we’ll add the Instaclustr PostgreSQL database we just deployed. \n In the OpenMetadata UI, click Settings in the lower left corner. \n \n Select the Services tile. \n \n Select the Databases tile. \n \n Click the Add New Service button. \n \n Scroll down, or search for Postgres, select the Postgres Service, and click Next. \n \n Give the service a descriptive name, such as instaclustr-openmetadata-default-postgresql, optionally provide a description, and click Next. \n \n Specify icpostgresql as the Username and leave Basic Auth selected in the dropdown. Copy the output from the following command and paste it into the Password field. \n terraform output -raw postgresql_icpostgresql_password \n For the Host and Port field, copy and paste the output from this command (the virtual networks of the AKS cluster and Instaclustr resources are peered, enabling internal IP addresses): \n terraform output -raw postgresql_private_address && echo -n :5432 \n Enter postgres as the Database, select Ingest All Databases, and scroll down. \n \n Expand the Postgres Connection Advanced Config section, select require in the SSL Mode dropdown, collapse the Postgres Connection Advanced Config section, and scroll down. \n \n Click Test Connection. \n \n After 10 to 15 seconds, verify that each check and query returns Success. Click OK. \n \n Click Save to finish adding the PostgreSQL service. \n \n Adding an ingestion \n Now that OpenMetadata has the credentials to communicate with PostgreSQL, it’s time to create an ingestion, which uses Airflow to perform the actual metadata collection. Click Add Ingestion. \n \n Optionally specify a database for inclusion or exclusion. In this example, the instaclustr database is specified under Excludes, which results in all other databases being included in metadata extraction. \n \n Scroll down, optionally modifying any default fields based on your use case, and click Next. \n \n Metadata ingestion has the option to run on a regular schedule or on demand. A schedule is preferable for most production use cases. To have the ingestion happen quickly for demo purposes, Every Hour at 55 minutes past the hour is selected. Click Add and Deploy. \n \n Click View Service to be redirected to the PostgreSQL service page. \n \n Head back into your terminal to view the Airflow pod that was created for the ingestion. \n $ kubectl -n openmetadata get pods -l task_id=ingestion_task NAME READY STATUS RESTARTS AGE 09e49cb9-4db8-4a92-bc54-0be48772b6c3-ingestion-task-hbqcoo5c 1/1 Running 0 3m48s \n After a few minutes the ingestion is complete: \n \n Navigate to the Databases tab to view the databases that were discovered during ingestion. \n \n Search for one of the databases in the search bar at the top of the page and view the results in the dropdown. This search is powered by the Instaclustr OpenSearch cluster. \n \n Cleanup \n If you deployed OpenMetadata only for testing or demo purposes, you can destroy OpenMetadata and its infrastructure with the following commands: \n \n helm -n openmetadata uninstall openmetadata\nhelm -n openmetadata uninstall openmetadata-dependencies\nkubectl delete namespace openmetadata\nterraform destroy -var-file=\"$(terraform workspace show).tfvars\" --auto-approve \n Summary \n Deploying OpenMetadata on Azure Kubernetes Service (AKS) with Azure NetApp Files offers a robust solution for metadata management and data discovery, essential for AI/ML initiatives. By following this guide, you can establish a scalable and efficient metadata platform, enhancing collaboration and governance. \n With OpenMetadata, your data teams gain a centralized view of data assets, improving their ability to identify and use data effectively. Leveraging Azure NetApp Files means high performance and reliability, and using external PostgreSQL and OpenSearch databases managed by NetApp Instaclustr optimizes database management. \n This deployment helps NetApp customers overcome data silos, improve data quality, and fully leverage their data for AI/ML purposes. We hope that this article has been valuable in setting up your OpenMetadata instance, empowering your organization to make data-driven decisions and accelerate your AI/ML journey. \n Additional Information \n \n Building scalable and persistent AI applications with LangChain, Instaclustr, and Azure NetApp Files \n What is Azure NetApp Files | Microsoft Learn \n NetApp Instaclustr | Build and Scale Applications Faster \n Catalog Your Data in ONTAP \n 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deployment model for Logic Apps- Performance Analysis and Optimization recommendations","conversation":{"__ref":"Conversation:conversation:4401529"},"id":"message:4401529","revisionNum":4,"uid":4401529,"depth":0,"board":{"__ref":"Blog:board:IntegrationsonAzureBlog"},"author":{"__ref":"User:user:916924"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":437},"postTime":"2025-04-07T02:58:24.069-07:00","lastPublishTime":"2025-04-30T17:35:59.681-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" A few weeks ago, we announced the Public Preview Refresh release of Logic Apps hybrid deployment model that allows customers to run Logic Apps workloads on a customer managed infrastructure. \n This model provides the flexibility to execute workflows, either on-premises or in any cloud environment, thereby offering enhanced control over the operation of logic apps. By utilizing customer-managed infrastructure, organizations can adhere to regulatory compliance requirements and optimize performance according to their specific needs. \n As customers consider leveraging hybrid environments, understanding the performance of logic apps under various configurations and scenarios becomes critical. \n This document offers an in-depth performance evaluation of Azure Logic Apps within a hybrid deployment framework. It examines, several key factors such as CPU and memory allocation and scaling mechanisms, providing valuable insights aimed at maximizing the application’s efficiency and performance. \n Achieving Optimal Logic Apps Performance in Hybrid Deployments \n In this section, we will explore the key aspects that affect Logic Apps performance when deployed in a hybrid environment. Factors such as the underlying infrastructure of the Kubernetes environment, SQL configuration and scaling configuration can significantly impact the efficiency of workflows and the overall performance of the applications. \n The following blog entry provides details of the scaling mechanism of Hybrid deployment model - Scaling mechanism in hybrid deployment model for Azure Logic Apps Standard | Microsoft Community Hub \n Configure Container Resource allocation: \n When you create a Logic App, a default value of 0.5 vCPU and 1GiB of memory would be allocated. From the Azure Portal, you can modify this allocation from the Container blade. - Create Standard logic app workflows for hybrid deployment - Azure Logic Apps | Microsoft Learn \n Currently, the maximum allocation is set to 2vCPU and 4 GiB memory per app. In the future, there would be a provision made to choose higher allocations. \n For CPU intense/memory intense processing like custom code executions, select a higher value for these parameters. In the next section, we will be comparing the performance with different values of the CPU and memory allocation. \n This allocation would impact the billing calculation of the Logic App resource. Refer vCPU calculation for more details on the billing impact. \n \n Optimize the node count and size in the Kubernetes cluster. \n Kubernetes runs application workloads by placing containers into Pods to run on Nodes. A node may be a virtual or physical machine, depending on the cluster. \n A node pool is a group of nodes that share the same configuration (CPU, Memory, Networking, OS, maximum number of pods, etc.). You can choose the capacity (cores and memory), minimum node count and maximum node count for each node pool of the Kubernetes cluster. We recommend allocating a higher capacity for processing CPU intense, or memory intense applications \n \n Configure Scale rule settings: \n For a Logic App resource, we recommend you configure the maximum and minimum replicas which could be scaled out when a scale event occurs. A higher value for the max replicas helps in sudden spikes in the number of application requests. \n The interval with which the scaler checks for the scaling event and the cooldown period for the scaling event can also be configured from the Scale blade of Logic Apps resource. These parameters impact the scaling pattern. \n \n Optimize the SQL server configuration: \n The hybrid deployment model uses Microsoft SQL for runtime storage. As such, there are lot of SQL operations performed throughout the execution of the workflow and SQL capacity has a significant impact on the performance of the app. \n Microsoft SQL server could either be a SQL server on Windows, or an Azure SQL database. \n Few recommendations on the SQL configuration for better performance: \n \n If you are using, Azure SQL database, run it on a SQL elastic pool. \n If you are using SQL server on Windows, run with at least 4vCPU configuration. \n Scale out the SQL server once the CPU usage of the SQL server hits 60-70% of the total available CPU. \n \n Performance analysis: \n For this performance analysis exercise, we have used a typical enterprise integration scenario which includes the below components. \n \n Data transformation: XSLT transformation, validation, and XML parsing actions \n Data routing: File system connector for storing the transformed content in a file share. \n Message queuing: RabbitMQ connector for sending the transformation result to Rabbit MQ queue endpoint. \n Control operations: For-each loop for looping through multiple records, condition execution, Scope, and error handling blocks. \n Request response: The XML data transmitted via HTTP request, and the status returned as a response. \n \n \n Summary: \n For these tests, we used the following environment settings: \n Kubernetes cluster: AKS cluster with Standard D2sV3 (2vCPU, 8GiBmemory) \n Max replicas: 20 \n Cooldown period: 300 seconds \n Polling interval: 30 \n With the above environment and settings, we have performed multiple application tests with different configuration of SQL server, resource allocation and test durations using Azure load testing tool. \n In the following table, we have summarized the response time, throughput, and the total vCPU consumption for each of these configurations. You can check each scenario for detailed information. \n \n \n Configuration \n \n Results \n \n Scenario \n \n SQL \n \n CPU and Memory allocation per Logic App \n \n Test duration \n \n Load \n \n 90 th Percentile Response time \n \n Throughput \n \n Total vCPU consumed \n \n Scenario 1 \n \n SQL general purpose V2 \n \n 1vCPU/2GiB Memory \n \n 10 minutes with 50 users \n \n 503 requests \n \n 68.62 seconds \n \n 0.84/s \n \n 3.42 \n \n Scenario 2 \n \n SQL Elastic pool-4000DTU \n \n 1vCPU/2GiB Memory \n \n 10 minutes with 50 users \n \n 1004 requests \n \n 40.74 seconds \n \n 1.65/s \n \n 3 \n \n Scenario 3 \n \n SQL Elastic pool-4000DTU \n \n 2vCPU/4GiB Memory \n \n 10 minutes with 50 users \n \n 997 requests \n \n 40.63 seconds \n \n 1.66/s \n \n 4 \n \n Scenario 4 \n \n SQL Elastic pool-4000DTU \n \n 2vCPU/4GiB Memory \n \n 30 minutes with 50 users \n \n 3421 requests \n \n 26.6Seconds \n \n 1.9/s \n \n 18.6 \n \n Scenario 5 \n \n SQL Elastic pool-4000DTU \n \n 0.5vCPU/1GiB Memory \n \n 30 minutes with 50 users \n \n 3055 requests \n \n 31.38 seconds \n \n 1.7/s \n \n 12.4 \n \n Scenario 6 \n \n SQL 2022 Enterprise on Standard D4s V3 VM \n \n 0.5vCPU/1GiB Memory \n \n 30 minutes with 50 users \n \n 4105 requests \n \n 27.15 seconds \n \n 2.28/s \n \n 10 \n \n Scenario 1: SQL general purpose V2 with 1vCPU and 2 GiB Memory – 10 minutes test with 50 users \n In this scenario, we conducted a load test for 10 minutes with 50 users with the Logic App configuration of: 1 vCPU and 2 GiB Memory and Azure SQL database running on General purpose V2 plan. \n There were 503 requests with multiple records in each payload and it achieved the 68.62 seconds as the 90 th percentile response time and a throughput of 0.84 requests per second. \n \n \n \n Scaling: \n The Kubernetes nodes scaled out to 12 nodes and in total 3.42 vCPUs used by the app for the test duration. \n \n \n SQL Metrics: \n The CPU usage of the SQL server reached 90% of CPU usage quite early and stayed above 90% for the remaining duration of the test. From our backend telemetry as well, we observed that the actions executions were faster, but there was latency between the actions, which indicates SQL bottlenecks. \n \n Scenario 2: SQL elastic pool, with 1vCPU and 2 GiB memory- 10 minutes test with 50 users \n In this scenario, we conducted a load test for 10 minutes with 50 users with the Logic App configuration of: 1 vCPU and 2 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. \n There were 1004 requests with multiple records in each payload and it achieved the 40.74 seconds as the 90 th percentile response time and a throughput of 1.65 requests per second. \n \n Scaling: \n The Kubernetes nodes scaled out to 15 nodes and in total 3 vCPUs used by the app for the test duration. \n \n SQL Metrics: \n The SQL server’s CPU utilization peaked to 2% of the elastic pool. \n \n Scenario 3: SQL elastic pool, with 2vCPU and 4 GiB memory- 10 minutes test with 50 users \n In this scenario, we conducted a load test for 10 minutes with 50 users with the Logic App configuration of 2 vCPU and 4 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. \n There were 997 requests with multiple records in each payload and it achieved the 40.63 seconds as the 90 th percentile response time and a throughput of 1.66 requests per second. \n \n Scaling: \n The Kubernetes nodes scaled out to 21 nodes and in total 4 vCPUs used by the app for the test duration. \n \n \n SQL Metrics: \n The SQL server’s CPU utilization peaked to 5% of the elastic pool. \n \n \n Scenario 4: SQL elastic pool, with 2vCPU and 4 GiB memory- 30 minutes test with 50 users \n In this scenario, we conducted a load test for 30 minutes with 50 users with the Logic App configuration of: 2 vCPU and 4 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. \n There were 3421 requests with multiple records in each payload and it achieved the 26.67 seconds as the 90 th percentile response time and a throughput of 1.90 requests per second. \n \n \n Scaling: \n The Kubernetes nodes scaled out to 20 nodes and in total 18.6 vCPUs used by the app for the test duration. \n \n \n \n SQL Metrics: \n The SQL server’s CPU utilization peaked to 4.7% of the elastic pool. \n \n Scenario 5: SQL Elastic pool, with 0.5vCPU and 1 GiB memory- 30 minutes test with 50 users \n In this scenario, we have conducted a load test for 30 minutes with 50 users with the Logic App configuration of 0.5 vCPU and 1 GiB Memory and Azure SQL database running on a SQL elastic pool with 4000 DTU. \n There were 3055 requests with multiple records in each payload and it achieved the 31.38 seconds as the 90 th percentile response time and a throughput of 1.70 requests per second. \n \n \n Scaling: \n The Kubernetes nodes scaled out to 18 nodes and in total 12.4 vCPUs used by the app for the test duration. \n \n \n SQL Metrics: \n The SQL server’s CPU utilization peaked to 8.6% of the elastic pool CPU. \n \n Scenario 6: SQL 2022 Enterprise Gen2 on Windows 2022 on Standard D4s v3 image, with 0.5vCPU and 1 GiB memory- 30 minutes test with 50 users \n In this scenario, we conducted a load test for 30 minutes with 50 users with the Logic App configuration of: 0.5 vCPU and 1 GiB Memory and Azure SQL database running on an on-premises SQL 2022 Enterprise Gen2 version running on a Windows 2022 OS with Standard D4s v3 image (4 vCPU and 16GIB memory) \n There were 4105 requests with multiple records in each payload and it achieved the 27.15 seconds as the 90 th percentile response time and a throughput of 2.28 requests per second. \n \n Scaling: \n The Kubernetes nodes scaled out to 8 nodes and in total 10 vCPUs used by the app for the test duration. \n \n SQL metrics: \n The CPU usage of the SQL server went above 90% after few minutes and there was latency on few runs. \n \n Findings and recommendations: \n The following are the findings and recommendations for this performance exercise. Consider that this load test was conducted using unique conditions. If you conduct a similar test, the results and findings might vary, depending on factors such as workflow complexity, configuration, resource allocation and network configuration. \n \n The KEDA scaler performs the scale-out and scale-in operations faster, as such, while the total vCPU usage remains quite low, though the nodes scaled out in the range of 1-20 nodes. \n The SQL configuration plays a crucial role in reducing the latency between the action executions. For a satisfactory load test, we recommend starting with at least 4vCPU configuration on SQL server and scale out once CPU usage of the SQL server hits 60-70% of the total available CPU. \n For critical applications, we recommend having a dedicated SQL database for better performance. \n Increasing the dedicated vCPU allocation of the Logic App resource is helpful for the SAP connector, Rules Engine, .NET Framework based custom code operations and for the applications with many complex workflows. \n As a general recommendation, regularly monitor performance metrics and adjust configurations to meet evolving requirements and follow the coding best practices of Logic Apps standard. Consider reviewing the following article, for recommendations to optimize your Azure Logic Apps workloads: https://techcommunity.microsoft.com/blog/integrationsonazureblog/logic-apps-standard-hosting--performance-tips/3956971 \n \n \n \n \n \n \n \n \n \n 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How to Setup Copilot Chat in VS Code","conversation":{"__ref":"Conversation:conversation:3849227"},"id":"message:3849227","revisionNum":23,"uid":3849227,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:102838"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Copilot Chat is an AI-powered chatbot leveraging OpenAI's GPT-4, designed to enhance your coding workflow. Learn how to set up Copilot Chat step by step in Visual Studio Code (VS Code). Benefit from personalized and flexible coding environments, code analysis, automated unit test generation, and bug fixes. Prerequisites include an active GitHub account and the latest version of VS Code. Elevate your coding efficiency to new heights with Copilot Chat. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":98875},"postTime":"2023-06-22T00:04:00.033-07:00","lastPublishTime":"2023-11-28T17:58:43.854-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Before we dive into the step-by-step instructions for setting up Copilot Chat, it's important to understand the full potential of this powerful tool. Copilot Chat is an innovative AI-powered paired programing solution, leveraging the capabilities of OpenAI's GPT-4, specifically designed to serve as a conversational user interface. With its natural language processing abilities, Copilot Chat enhances your coding experience by providing a personalized and flexible environment. By streamlining your workflow, this tool enables you to develop faster, shift focus to business logic instead of boilerplate code, and concentrate on building awesome apps. Moreover, Copilot Chat offers comprehensive analysis, explanations of code blocks, automated unit test generation, and proposed bug fixes, elevating your coding efficiency to new heights. \n \n Prerequisites for setting up Copilot Chat in VS Code \n Before setting up Copilot Chat, there are a few prerequisites that need to be in place. \n \n \n An active GitHub Account.\n \n GitHub Account Signup: https://github.com/signup \n If you are an exisiting academic student, Lee_Stott has provided a detailed step-by-step on verifiying your student status with GitHub which can be found here: How to verify your student status on GitHub \n \n \n The latest version of VS Code: https://code.visualstudio.com/download \n \n If you already have VS Code installed make sure it is upgraded to the latest version by navigating to the following:\n \n Windows: Help > Check for updates... \n Mac: Code > Check for updates... \n \n \n \n \n \n Setting up Copilot Chat in VS Code \n Once you have received confirmation from GitHub via email that you have been included into GitHub Copilot Chat preview, you can begin your installation via the following steps: \n \n \n Run VS Code and click on the extension icon. \n Type Copilot in the search bar located in the top left-hand side of the Extensions: Marketplace window. \n Select GitHub Copilot Chat. \n Click Install. \n Sign-in using the GitHub account you received confirmation from GitHub via email that you have been included into GitHub Copilot Chat preview when prompted. \n Click Allow when asked to allow the GitHub Copilot extension to sign-in using GitHub. \n A browser window should now open to GitHub's authentication page to ask for your authorization. Click Continue. \n An Open Visual Studio Code.app? pop-up will now appear. Click on Open Visual Studio Code.app to continue. \n Back on VS Code, a pop-up will appear asking to allow the Copilot Extension to open the URI. Click Open to continue. \n Close / Exit VS Code then run VS Code again. \n Click on the GitHub Copilot Chat Icon to get started using GitHub Copilot Chat. \n \n \n Allison also has a great video that shares latest features of Copilot Chat and how they can supercharge your coding experience. \n \n \n \n Please also feel free to share your comments and/or inquires in the comment section below. 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