Resilient Azure Platforms: Durable Functions, Cosmos DB, and DR by Design
Hello Folks! Operating at Azure scale means managing change across multiple interconnected systems. As applications, services, and dependencies evolve, resilience becomes a foundational design principle rather than an afterthought. In this Microsoft Azure Infra Summit 2026 session, Bhavana Konchada, Principal Software Engineer at Microsoft and lead architect of the Resilience Control Platform, takes us behind the scenes of a production-grade resilience platform built on Azure and explains the engineering choices that helped bring it to life. Why IT Pros Should Care Most of us have shipped a system that worked beautifully on day one and then quietly fell apart the first time something downstream blinked. Bhavana’s session is a brutally honest tour of the decisions you make early that determine whether your platform survives reality. Here’s what you walk away with: A pragmatic blueprint for service boundaries that you can actually operate at 2 a.m. Concrete Durable Functions patterns (Monitor, continue-as-new, idempotency) that keep long-running workflows healthy. A Cosmos DB partitioning strategy grounded in real access patterns, not gut feel. A multi-region, fail-and-continue mindset (instead of fail-and-recover) that holds up when a region disappears. Real lessons from production, including the “non-deterministic orchestration” outages nobody warns you about. In short, if you build, run, or modernize platforms on Azure, this session reshapes how you think about reliability. What DR by Design Actually Means, a Technical Overview Bhavana frames the Resilience Control Platform as five chapters: architecture and service boundaries, orchestration with Durable Functions, the Cosmos DB data layer, identity across multiple user realms, and the resilience playbook itself. The platform has four moving parts: A portal where operators define and monitor scenarios. An orchestration engine that executes long-running workflows. Cosmos DB as a shared persistence layer. Downstream infrastructure APIs the engine acts on. The big “DR by Design” idea is that resiliency isn’t bolted on later. It’s a property of every choice from boundaries upward. As Bhavana puts it, you stop designing for “fail and recover” and start designing for “fail and continue.” Users don’t know (or care) which region runs their workflow; they just need it to run reliably, consistently, and without interruption. How It Works, Under the Hood Bhavana’s team made several deliberate design moves worth borrowing. Arm’s-length service boundaries. Version one had the portal and orchestration engine tightly coupled with shared dependency injection and a shared database context. It felt clean until they tried to operate it. Now the two services talk over REST contracts, each with its own dependencies. Yes, that means a bit of duplicated code. What they gained, independent deployments, isolated failures, and clear ownership, more than paid for it. The right runtime for the workload. The portal is a session-driven web app, so it lives on App Service. The orchestration engine bursts on demand and runs workflows for minutes (sometimes hours), so it’s built on Durable Functions. Forcing both into one model would have looked simpler on paper and been worse in practice. Accept Fast, Process Asynchronously. Clicking Execute returns a 202 immediately. The orchestrator does the heavy lifting in the background and updates status in Cosmos DB. The portal just reflects progress. Users never wait on long workflows. Durable Functions patterns that actually scale. Three lessons stood out: The Monitor pattern replaces busy polling with durable timers. The orchestrator wakes up, checks status, and goes back to sleep without holding compute. Orchestrators are state machines, not scripts. Calling DateTime.UtcNow inside an orchestrator produces non-deterministic replay and random production failures. The fix is to use the orchestration context for time and IDs. Continue-as-new keeps replay history bounded. Long-running orchestrations otherwise spend more time replaying history than doing real work. Cosmos DB designed around access, not org charts. Partitioning by tenant feels logical and creates hotspots the moment one tenant gets busy. The team partitions by entity (each plan owns its partition) and uses hierarchical keys combining plan ID and execution ID. They also lean on TTL for data lifecycle so completed records expire automatically, no cleanup jobs required. Identity as an execution boundary. Corporate users authenticate through Microsoft Entra with OpenID Connect. Operations users come in through a federated WS-Federation system. Instead of forking the app, the team built home realm discovery at the front door, normalized everything into a single identity model behind it, and added custom middleware in the Azure Functions isolated worker model to extract, validate, enrich, and fail-fast on every token. Authorization is config-driven so every endpoint gets the same treatment. Multi-region from day one. The full stack (portal, engine, APIs, supporting services) runs in parallel across regions, fronted by Azure Front Door as the global entry point. Health probes drive automatic regional failover with no human in the loop. Cosmos DB single-write with automatic failover. Multi-write looks attractive on a slide and introduces real conflict-resolution complexity. The team chose one primary write region plus a replica with automatic failover. The Cosmos SDK detects region unavailability and routes requests to the promoted region without application code changes. Idempotency from day zero. Once you have retries (and Front Door, the SDK, and your clients all retry), every operation has to be safe to run more than once. Client-provided IDs, Cosmos conflict detection (a 409 means “already succeeded”), and idempotent orchestration events make sure the same outcome lands no matter how many times a signal arrives. Real-World Value, Use Cases, ROI, Scenarios What does this buy you in practice? Scenario validation under stress without compromising production. The platform is built to proactively validate and govern system behavior at scale. Long-running workflows that survive everything. Host restarts, transient downstream errors, regional failovers, none of them lose work in flight. Predictable cost. Durable timers and continue-as-new mean you stop paying for compute that’s only waiting. Operability at scale. Independent services, clean contracts, and centralized identity all mean a smaller cognitive load when something breaks at 2 a.m. Honest tradeoffs. Single-write Cosmos loses theoretical write latency in the second region and gains predictable behavior, no conflict ambiguity, and far easier debugging during failovers. That’s usually the right trade. In short, the platform behaves the same on a quiet Tuesday and during a regional outage. That’s the whole point. Getting Started You don’t need to build the Resilience Control Platform tomorrow. You can start applying these patterns this week. Map your service boundaries honestly. If two services share a DI container or database context, decouple them behind a REST contract. Pick runtimes by workload, not by consistency. Interactive UI on App Service; long-running orchestrations on Durable Functions. Adopt the 202-Accepted pattern for anything that could take more than a couple of seconds. Audit your Durable orchestrators for DateTime.UtcNow, Guid.NewGuid, and direct HTTP calls. Move them into activities, use the orchestration context for time and IDs, and apply continue-as-new on long loops. Revisit your Cosmos partition keys against actual access patterns and enable TTL for transient data. Stand up a second region behind Azure Front Door, enable Cosmos DB automatic failover, and make every write operation idempotent with client-provided IDs. Resources Reliability design principles, Azure Well-Architected Framework Durable Orchestrations overview Azure Durable Functions documentation Azure Functions documentation Hierarchical partition keys in Azure Cosmos DB Azure Cosmos DB documentation Azure Front Door documentation Microsoft Entra ID documentation Keep Learning at the Summit Catch the full Microsoft Azure Infra Summit 2026 session playlist here Cheers! Pierre RomanAzure Function App — Queue-Based Architecture for Long-Running Sync Jobs
The Problem: HTTP Triggers and Long-Running Jobs Don't Mix Here's a situation you've probably run into: you have a job that needs to loop over dozens of Azure resources, call APIs, and do real work. You wrap it in an HTTP-triggered Azure Function so it can be called on demand. It works great and after a few minutes, the caller gets a 504 Gateway Timeout. The 230-second limit is enforced by Azure Front Door / the platform load balancer. It cannot be overridden by app settings or host configuration. Any HTTP trigger that runs longer than ~3.5 minutes will timeout for the caller. In our case, the job iterates over 30+ Azure subscriptions — for each one it switches context, lists resources, and triggers image imports. Total runtime: anywhere from 2 to 10 minutes depending on how many ACRs need updating. Way over the limit. The Solution: Decouple Request from Execution via a Queue The fix is clean once you see it: the HTTP trigger shouldn't do the work — it should just accept the work and hand it off. That's what a queue is for. The flow splits into two independent phases: Request phase — The HTTP trigger validates the caller (JWT + app role check), packages the job parameters into a queue message, and returns 202 Accepted. This takes under 3 seconds. Execution phase — A Queue Trigger picks up the message and runs the actual sync. No HTTP connection involved, so there's no timeout. On a Dedicated (P-series) plan, execution time is unlimited. Approach What the caller gets Result HTTP trigger → run sync inline Waits for the full job to complete 504 TIMEOUT after 230 seconds HTTP trigger → Queue → Queue Trigger 202 Accepted immediately NO TIMEOUT job runs as long as needed 🤸♀️There's an added bonus - Reliability in Azure Queue Storage: Azure Storage Queues give you automatic retry out of the box. If the job crashes halfway through, the message becomes visible again after a visibility timeout and the Queue Trigger picks it up for a retry — up to 5 attempts before the message is moved to the poison queue. No retry logic to write 🤸♀️. Locking Down the Endpoint Since the HTTP trigger is the public entry point, it needs solid auth. We layer two things: ⭐Use EasyAuth for the "is this a real Entra ID token?" check, and a custom App Role for the "is this person allowed to trigger syncs?" check. These are independent concerns and should stay that way. Layer What it does How EasyAuth (Entra ID) Rejects requests without a valid Entra ID Bearer token — before your code even runs Configured at the Function App level via the Authentication blade App Role check Validates that the token contains the SyncJob.Execute role — only assigned users/SPs can trigger the job Decoded in the function code from the JWT roles claim Managed Identity Authenticates the Function App to Azure APIs (no credentials in code) Connect-AzAccount -Identity — identity assigned via RBAC One gotcha worth knowing: when using v2 tokens (which is the default with modern App Registrations), the aud claim in the token is the raw App ID GUID — not the api:// prefixed URI. You need to explicitly add both forms to your allowedAudiences in EasyAuth, otherwise valid tokens get rejected. APP_ID="<your-app-id>" TENANT_ID="<your-tenant-id>" FUNCTION_APP_URL="https://<your-function-app>.azurewebsites.net" # Interactive login (device code flow — works from any terminal) az login --tenant "${TENANT_ID}" \ --scope "api://${APP_ID}/.default" \ --use-device-code TOKEN=$(az account get-access-token \ --scope "api://${APP_ID}/.default" \ --query accessToken -o tsv) # Trigger the sync — returns 202 immediately curl -s -X POST "${FUNCTION_APP_URL}/api/SyncContainerRegistryHttpTrigger" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" Passing Parameters Through the Queue One nice property of this pattern: the queue message is just JSON, so you can pass whatever parameters the job needs. In our case, we pass a subscriptionFilter wildcard so callers can target a subset of subscriptions without touching any code. The parameter travels the full chain: HTTP body → queue message → Queue Trigger → PowerShell script parameter. Here's how each step handles it. Step 1 — HTTP Trigger reads the body and enqueues the message using the Push-OutputBinding output binding. Azure Functions wires the binding to the queue automatically — no SDK call needed: param($Request, $TriggerMetadata) # ... decode the JWT, check role assignment $queuePayload = @{ triggeredBy = $decoded.Payload.upn ?? $decoded.Payload.oid triggeredAt = (Get-Date -Format 'o') subscriptionFilter = if ($body.subscriptionFilter) { $body.subscriptionFilter } else { "*" } } | ConvertTo-Json -Compress Push-OutputBinding -Name QueueMessage -Value $queuePayload Push-OutputBinding -Name Response -Value ([HttpResponseContext]@{ StatusCode = [System.Net.HttpStatusCode]::Accepted Body = @{ message = "Sync job queued. Check Azure Monitor logs for execution status." } }) ⭐Push-OutputBinding is how Azure Functions PowerShell workers write to output bindings (queues, blobs, HTTP responses…). The binding name QueueMessage maps to the queue defined in function.json — the runtime handles serialisation and delivery. Step 2 — Queue Trigger passes the filter to the script as a named parameter: param($QueueItem, $TriggerMetadata) Write-Host "Triggered SyncContainerRegistry via Storage Queue. Payload: $QueueItem" $subscriptionFilter = if ($QueueItem.subscriptionFilter) { $QueueItem.subscriptionFilter } else { "*" } $SubscriptionFilter = $subscriptionFilter . "$PSScriptRoot/../SyncContainerRegistry/run.ps1" Step 3 — Long running job with the filter as parameter: param($Timer) if (-not $SubscriptionFilter) { $SubscriptionFilter = "*" } $subscriptions = Get-AzSubscription | Where-Object { $_.Name -like $SubscriptionFilter } foreach ($subscription in $subscriptions) { Set-AzContext -SubscriptionId $subscription.Id | Out-Null # ... do the work } Targeting a subset of subscriptions # Sync all subscriptions (default — omit the body) curl -s -X POST "${FUNCTION_APP_URL}/api/SyncContainerRegistryHttpTrigger" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" # Sync only subscriptions matching a pattern curl -s -X POST "${FUNCTION_APP_URL}/api/SyncContainerRegistryHttpTrigger" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" \ -d '{"subscriptionFilter": "*project-alpha*"}' ⭐PowerShell's -like operator uses * as a wildcard anywhere in the string. The pattern *project-alpha* matches sub-mycompany-project-alpha-prd, sub-mycompany-project-alpha-dev, etc. A pattern without a leading * only matches from the start of the string — keep this in mind when naming subscriptions. Pushing a Message Directly via PowerShell You can also push a message straight to the queue without going through the HTTP trigger — useful for testing, scripting, or bypassing the auth layer in a controlled environment. Connect-AzAccount # or -Identity for a Managed Identity context $storageAccount = "<your-storage-account>" $queueName = "sync-job-queue" # Build the payload — same shape the HTTP trigger produces $payload = @{ triggeredBy = $env:USERNAME triggeredAt = (Get-Date -Format 'o') subscriptionFilter = "*project-alpha*" # or "*" for all } | ConvertTo-Json -Compress # Get a queue client via the connected account (no key needed) $ctx = New-AzStorageContext -StorageAccountName $storageAccount -UseConnectedAccount $queue = Get-AzStorageQueue -Name $queueName -Context $ctx $queue.QueueClient.SendMessage($payload) ⭐ -UseConnectedAccount authenticates via the current Connect-AzAccount session — no storage key required, as long as your identity has the Storage Queue Data Message Sender role on the storage account. The Queue Message The HTTP trigger packages the caller identity and filter into a simple JSON payload before enqueuing. The Queue Trigger reads it back as a deserialised PowerShell object — no manual JSON parsing needed. { "triggeredBy": "user@company.com", "triggeredAt": "2026-06-01T11:03:55.570+02:00", "subscriptionFilter": "*project-alpha*" } Design Decisions at a Glance Decision Choice Why Async execution Azure Storage Queue HTTP trigger has a hard 230s timeout. The sync job takes 2–10 minutes. The queue decouples acceptance from execution — and gives us retry for free. Authentication EasyAuth + App Role No credentials in code. Access is controlled via Entra ID app roles — revocable per user without touching infrastructure. Azure identity Managed Identity No secrets to rotate or store. The Function App authenticates to Azure APIs using its platform-assigned identity. Job parameter Wildcard filter via queue payload Lets callers target any subscription subset without code changes. The filter travels through the queue — the Queue Trigger just passes it along. Hosting plan Dedicated (P-series) Consumption plan caps function execution at 10 minutes. A Dedicated plan has no execution time limit — essential when the job can run longer. See you in the Cloud JamesdldEnhancing Data Security and Digital Trust in the Cloud using Azure Services.
Enhancing Data Security and Digital Trust in the Cloud by Implementing Client-Side Encryption (CSE) using Azure Apps, Azure Storage and Azure Key Vault. Think of Client-Side Encryption (CSE) as a strategy that has proven to be most effective in augmenting data security and modern precursor to traditional approaches. CSE can provide superior protection for your data, particularly if an authentication and authorization account is compromised.Azure Database for MySQL triggers for Azure Functions (Public Preview)
Developers can now accelerate development time and focus only on the core business logic of their applications, for developing event-driven applications with Azure Database for MySQL as the backend data store. We are excited to announce that you can now invoke an Azure Function based on changes to an Azure Database for MySQL table. This new capability is made possible through the Azure Database for MySQL triggers for Azure Functions, now available in public preview. Azure Database for MySQL triggers The Azure Database for MySQL trigger uses change tracking functionality to monitor a MySQL table for changes and trigger a function when a row is created or updated enabling customers to build highly-scalable event-driven applications. Similar to the Azure Database for MySQL Input and Output bindings for Azure Functions, a connection string for the MySQL database is stored in the application settings of the Azure Function to trigger the function when a change is detected on the tables. Note: In public preview, Azure Database for MySQL triggers for Azure Functions are available only for dedicated and premium plan of Azure Functions To enable change tracking on an existing Azure Database for MySQL table to use trigger bindings for an Azure Function, it is necessary to alter the table structure, for example, enabling change tracking on an employees data table: ALTER TABLE employees ADD COLUMN az_func_updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP; Azure Database for MySQL trigger uses the 'az_func_updated_at' and column's data to monitor the table for any changes on which change tracking is enabled. Changes are then processed in the order that they were made, with the oldest changes being processed first. Important: If changes to multiple rows are made at once, then the exact order they're sent to the function is determined on the ascending order of the az_func_updated_at and the primary key columns. If multiple changes are made to a row in-between an iteration, then only the latest changes for that particular rows are considered. The following example demonstrates a C# function that is triggered when changes occur in the employees table. The MySQL trigger uses attributes for the table name and the connection string. using System.Collections.Generic; using Microsoft.Azure.WebJobs; using Microsoft.Azure.WebJobs.Extensions.MySql; using Microsoft.Extensions.Logging; namespace EmployeeSample.Function { public static class EmployeesTrigger { [FunctionName(nameof(EmployeesTrigger))] public static void Run( [MySqlTrigger("Employees", "MySqlConnectionString")] IReadOnlyList<MySqlChange<Employee>> changes, ILogger logger) { foreach (MySqlChange<Employee> change in changes) { Employee employee= change. Item; logger.LogInformation($"Change operation: {change.Operation}"); logger.LogInformation($"EmployeeId: {employee.employeeId}, FirstName: {employee.FirstName}, LastName: {employee.LastName}, Company: {employee. Company}, Department: {employee. Department}, Role: {employee. Role}"); } } } } Join the preview and share your feedback! We are eager for you to try out the new Azure Database for MySQL triggers for Azure Functions and build highly scalable event-driven and serverless applications. For more information refer https://aka.ms/mysqltriggers about using MySQL triggers for all the supported programming frameworks with detailed step-by-step instructions If you have any feedback or questions about the information provided above, please leave a comment below or email us at AskAzureDBforMySQL@service.microsoft.com. Thank you!Ignite 2025: Advancing Azure Database for MySQL with Powerful New Capabilities
At Ignite 2025, we’re introducing a wave of powerful new capabilities for Azure Database for MySQL, designed to help organizations modernize, scale, and innovate faster than ever before. From enhanced high availability and seamless serverless integrations to AI-powered insights and greater flexibility for developers, these advancements reflect our commitment to delivering a resilient, intelligent data platform. Join us as we unveil what’s next for MySQL on Azure - and discover how industry leaders are already building the future with confidence. Enhanced Failover Performance with Dedicated SLB for High-Availability Servers We’re excited to announce the General Availability of Dedicated Standard Load Balancer (SLB) for HA-enabled servers in Azure Database for MySQL. This enhancement introduces a dedicated SLB to High Availability configurations for servers created with public access or private link. By managing the MySQL data traffic path, SLB eliminates the need for DNS updates during failover, significantly reducing failover time. Previously, failover relied on DNS changes, which caused delays due to DNS TTL (30 seconds) and client-side DNS caching. What’s new with GA: The FQDN consistently resolves to the SLB IP address before and after failover. Load-balancing rules automatically route traffic to the active node. Removes DNS cache dependency, delivering faster failovers. Note: This feature is not supported for servers using private access with VNet integration. Learn more Build serverless, event-driven apps at scale – now GA with Trigger Bindings for Azure Functions We’re excited to announce the General Availability of Azure Database for MySQL Trigger bindings for Azure Functions, completing the full suite of Input, Output, and Trigger capabilities. This feature lets you build real-time, event-driven applications by automatically invoking Azure Functions when MySQL table rows are created or updated - eliminating custom polling and boilerplate code. With native support across multiple languages, developers can now deliver responsive, serverless solutions that scale effortlessly and accelerate innovation. Learn more Enable AI agents to query Azure Database for MySQL using Azure MCP Server We’re excited to announce that Azure MCP Server now supports Azure Database for MySQL, enabling AI agents to query and manage MySQL data using natural language through the open Model Context Protocol (MCP). Instead of writing SQL, you can simply ask questions like “Show the number of new users signed up in the last week in appdb.users grouped by day.”, all secured with Microsoft Entra authentication for enterprise-grade security. This integration delivers a unified, secure interface for building intelligent, context-aware workflows across Azure services - accelerating insights and automation. Learn more Greater networking flexibility with Custom Port Support Custom port support for Azure Database for MySQL is now generally available, giving organizations the flexibility to configure a custom port (between 25001 and 26000) during new server creation. This enhancement streamlines integration with legacy applications, supports strict network security policies, and helps avoid port conflicts in complex environments. Supported across all network configurations - including public access, private access, and Private Link - custom port provisioning ensures every new MySQL server can be tailored to your needs. The managed experience remains seamless, with all administrative capabilities and integrations working as before. Learn more Streamline migrations and compatibility with Lower Case Table Names support Azure Database for MySQL now supports configuring lower_case_table_names server parameter during initial server creation for MySQL 8.0 and above, ensuring seamless alignment with your organization’s naming conventions. This setting is automatically inherited for restores and replicas, and cannot be modified. Key Benefits: Simplifies migrations by aligning naming conventions and reducing complexity. Enhances compatibility with legacy systems that depend on case-insensitive table names. Minimizes support dependency, enabling faster and smoother onboarding. Learn more Unlock New Capabilities with Private Preview Features at Ignite 2025 We’re excited to announce that you can now explore two powerful capabilities in early access - Reader Endpoint for seamless read scaling and Server Rename for greater flexibility in server management. Scale reads effortlessly with Reader Endpoint (Private Preview) We’re excited to announce that the Reader Endpoint feature for Azure Database for MySQL is now ready for private preview. Reader Endpoint provides a dedicated read-only endpoint for read replicas, enabling automatic connection-based load balancing of read-only traffic across multiple replicas. This simplifies application architecture by offering a single endpoint for read operations, improving scalability and fault tolerance. Azure Database for MySQL supports up to 10 read replicas per primary server. By routing read-only traffic through the reader endpoint, application teams can efficiently manage connections and optimize performance without handling individual replica endpoints. Reader endpoints continuously monitor the health of replicas and automatically exclude any replica that exceeds the configured replication lag threshold or becomes unavailable. To enroll in the preview, please submit your details using this form. Limitations During Private Preview: Only performance-based routing is supported in this preview. Certain settings such as routing method and the option to attach new replicas to the reader endpoint can only be configured at creation time. Only one reader endpoint can be created per replica group. Including the primary server as a fallback for read traffic when no replicas are available is not supported in this preview. Get flexibility in server management with Server Rename (Private Preview) We’re excited to announce the Private Preview of Server Rename for Azure Database for MySQL. This feature lets you update the name of an existing MySQL server without recreating it, migrating data, or disrupting applications - making it easier to adopt clear, consistent naming. It provides a near zero-downtime path to a new hostname of the server. To enroll in the preview, please submit your details using this form. Limitations During Private Preview: Primary server with read replicas: Renaming a primary server that has read replicas keeps replication healthy. However, the SHOW SLAVE STATUS output on the replicas will still display the old primary server's name. This is a display inconsistency only and does not affect replication. Renaming is currently unsupported for servers using Customer Managed Key (CMK) encryption or Microsoft Entra Authentication (Entra Id). Real-World Success: Azure Database for MySQL Powers Resilient Applications at Scale Factorial Factorial, a leading HR software provider, uses Azure Database for MySQL alongside Azure Kubernetes Service to deliver secure, scalable HR solutions for thousands of businesses worldwide. By leveraging Azure Database for MySQL’s reliability and seamless integration with cloud-native technologies, Factorial ensures high availability and rapid innovation for its customers. Learn more YES (Youth Employment Service) South Africa’s largest youth employment initiative, YES, operates at national scale by leveraging Azure Database for MySQL to deliver a resilient, centralized platform for real-time job matching, learning management, and career services - connecting thousands of young people and employers, and helping nearly 45 percent of participants secure permanent roles within six months. Learn more Nasdaq At Ignite 2025, Nasdaq will showcase how it uses Azure Database for MySQL - alongside Azure Database for PostgreSQL and other Azure products - to power a secure, resilient architecture that safeguards confidential data while unlocking new agentic AI capabilities. Learn more These examples demonstrate that Azure Database for MySQL is trusted by industry leaders to build resilient, scalable applications - empowering organizations to innovate and grow with confidence. We Value Your Feedback Azure Database for MySQL is built for scale, resilience, and performance - ready to support your most demanding workloads. With every update, we’re focused on simplifying development, migration, and management so you can build with confidence. Explore the latest features and enhancements to see how Azure Database for MySQL meets your data needs today and in the future. We welcome your feedback and invite you to share your experiences or suggestions at AskAzureDBforMySQL@service.microsoft.com Stay up to date by visiting What's new in Azure Database for MySQL, and follow us on YouTube | LinkedIn | X for ongoing updates. Thank you for choosing Azure Database for MySQL!Serverless MCP Agent with LangChain.js v1 — Burgers, Tools, and Traces 🍔
AI agents that can actually do stuff (not just chat) are the fun part nowadays, but wiring them cleanly into real APIs, keeping things observable, and shipping them to the cloud can get... messy. So we built a fresh end‑to‑end sample to show how to do it right with the brand new LangChain.js v1 and Model Context Protocol (MCP). In case you missed it, MCP is a recent open standard that makes it easy for LLM agents to consume tools and APIs, and LangChain.js, a great framework for building GenAI apps and agents, has first-class support for it. You can quickly get up speed with the MCP for Beginners course and AI Agents for Beginners course. This new sample gives you: A LangChain.js v1 agent that streams its result, along reasoning + tool steps An MCP server exposing real tools (burger menu + ordering) from a business API A web interface with authentication, sessions history, and a debug panel (for developers) A production-ready multi-service architecture Serverless deployment on Azure in one command ( azd up ) Yes, it’s a burger ordering system. Who doesn't like burgers? Grab your favorite beverage ☕, and let’s dive in for a quick tour! TL;DR key takeaways New sample: full-stack Node.js AI agent using LangChain.js v1 + MCP tools Architecture: web app → agent API → MCP server → burger API Runs locally with a single npm start , deploys with azd up Uses streaming (NDJSON) with intermediate tool + LLM steps surfaced to the UI Ready to fork, extend, and plug into your own domain / tools What will you learn here? What this sample is about and its high-level architecture What LangChain.js v1 brings to the table for agents How to deploy and run the sample How MCP tools can expose real-world APIs Reference links for everything we use GitHub repo LangChain.js docs Model Context Protocol Azure Developer CLI MCP Inspector Use case You want an AI assistant that can take a natural language request like “Order two spicy burgers and show me my pending orders” and: Understand intent (query menu, then place order) Call the right MCP tools in sequence, calling in turn the necessary APIs Stream progress (LLM tokens + tool steps) Return a clean final answer Swap “burgers” for “inventory”, “bookings”, “support tickets”, or “IoT devices” and you’ve got a reusable pattern! Sample overview Before we play a bit with the sample, let's have a look at the main services implemented here: Service Role Tech Agent Web App ( agent-webapp ) Chat UI + streaming + session history Azure Static Web Apps, Lit web components Agent API ( agent-api ) LangChain.js v1 agent orchestration + auth + history Azure Functions, Node.js Burger MCP Server ( burger-mcp ) Exposes burger API as tools over MCP (Streamable HTTP + SSE) Azure Functions, Express, MCP SDK Burger API ( burger-api ) Business logic: burgers, toppings, orders lifecycle Azure Functions, Cosmos DB Here's a simplified view of how they interact: There are also other supporting components like databases and storage not shown here for clarity. For this quickstart we'll only interact with the Agent Web App and the Burger MCP Server, as they are the main stars of the show here. LangChain.js v1 agent features The recent release of LangChain.js v1 is a huge milestone for the JavaScript AI community! It marks a significant shift from experimental tools to a production-ready framework. The new version doubles down on what’s needed to build robust AI applications, with a strong focus on agents. This includes first-class support for streaming not just the final output, but also intermediate steps like tool calls and agent reasoning. This makes building transparent and interactive agent experiences (like the one in this sample) much more straightforward. Quickstart Requirements GitHub account Azure account (free signup, or if you're a student, get free credits here) Azure Developer CLI Deploy and run the sample We'll use GitHub Codespaces for a quick zero-install setup here, but if you prefer to run it locally, check the README. Click on the following link or open it in a new tab to launch a Codespace: Create Codespace This will open a VS Code environment in your browser with the repo already cloned and all the tools installed and ready to go. Provision and deploy to Azure Open a terminal and run these commands: # Install dependencies npm install # Login to Azure azd auth login # Provision and deploy all resources azd up Follow the prompts to select your Azure subscription and region. If you're unsure of which one to pick, choose East US 2 . The deployment will take about 15 minutes the first time, to create all the necessary resources (Functions, Static Web Apps, Cosmos DB, AI Models). If you're curious about what happens under the hood, you can take a look at the main.bicep file in the infra folder, which defines the infrastructure as code for this sample. Test the MCP server While the deployment is running, you can run the MCP server and API locally (even in Codespaces) to see how it works. Open another terminal and run: npm start This will start all services locally, including the Burger API and the MCP server, which will be available at http://localhost:3000/mcp . This may take a few seconds, wait until you see this message in the terminal: 🚀 All services ready 🚀 When these services are running without Azure resources provisioned, they will use in-memory data instead of Cosmos DB so you can experiment freely with the API and MCP server, though the agent won't be functional as it requires a LLM resource. MCP tools The MCP server exposes the following tools, which the agent can use to interact with the burger ordering system: Tool Name Description get_burgers Get a list of all burgers in the menu get_burger_by_id Get a specific burger by its ID get_toppings Get a list of all toppings in the menu get_topping_by_id Get a specific topping by its ID get_topping_categories Get a list of all topping categories get_orders Get a list of all orders in the system get_order_by_id Get a specific order by its ID place_order Place a new order with burgers (requires userId , optional nickname ) delete_order_by_id Cancel an order if it has not yet been started (status must be pending , requires userId ) You can test these tools using the MCP Inspector. Open another terminal and run: npx -y @modelcontextprotocol/inspector Then open the URL printed in the terminal in your browser and connect using these settings: Transport: Streamable HTTP URL: http://localhost:3000/mcp Connection Type: Via Proxy (should be default) Click on Connect, then try listing the tools first, and run get_burgers tool to get the menu info. Test the Agent Web App After the deployment is completed, you can run the command npm run env to print the URLs of the deployed services. Open the Agent Web App URL in your browser (it should look like https://<your-web-app>.azurestaticapps.net ). You'll first be greeted by an authentication page, you can sign in either with your GitHub or Microsoft account and then you should be able to access the chat interface. From there, you can start asking any question or use one of the suggested prompts, for example try asking: Recommend me an extra spicy burger . As the agent processes your request, you'll see the response streaming in real-time, along with the intermediate steps and tool calls. Once the response is complete, you can also unfold the debug panel to see the full reasoning chain and the tools that were invoked: Tip: Our agent service also sends detailed tracing data using OpenTelemetry. You can explore these either in Azure Monitor for the deployed service, or locally using an OpenTelemetry collector. We'll cover this in more detail in a future post. Wrap it up Congratulations, you just finished spinning up a full-stack serverless AI agent using LangChain.js v1, MCP tools, and Azure’s serverless platform. Now it's your turn to dive in the code and extend it for your use cases! 😎 And don't forget to azd down once you're done to avoid any unwanted costs. Going further This was just a quick introduction to this sample, and you can expect more in-depth posts and tutorials soon. Since we're in the era of AI agents, we've also made sure that this sample can be explored and extended easily with code agents like GitHub Copilot. We even built a custom chat mode to help you discover and understand the codebase faster! Check out the Copilot setup guide in the repo to get started. You can quickly get up speed with the MCP for Beginners course and AI Agents for Beginners course. If you like this sample, don't forget to star the repo ⭐️! You can also join us in the Azure AI community Discord to chat and ask any questions. Happy coding and burger ordering! 🍔
HOW TO: "If cell contains specific text display the immediate next word after it"
I have an excel file with each individual cell filled with data as it follows: Alejandro - GREEN Daniel - RED Sebastian - BLUE What I have been trying to do is to use a formula to extract the upper case value thats after the "-" immediate to the specific name of the person. At the moment I have the formula: =IF(COUNTIF(F3:F20,"*"&"Daniel"&"*"),"Yes","No") Which would simply return "Yes" if the cell contains the name Daniel, what I dont know how to do is to replace the "Yes" for a formula that would give me the "RED" in return. I know the formulas Left, Mid and Right are probably the way to go but since the separating character appears 3 times in a single cell (it being "-" I THINK) I have no idea how to stablish to use only the one after the specific name, and return only the very first word next to it (RED in case of Daniel). For this I have the formula =MID(M26,FIND("-",M26)+2,3) Which would give me GRE (3 characters only, dont know how to make it dynamic length) and would only return the very first entry as opposed to it being the one after the specific name, kinda close but no luck yet :/ Is this even possible? Edit: I attach my original file, I didnt before because the formula get so convoluted with other stuff I tried to keep it simple, basically its the formula on column O where i would replace the ":D" with the formula that im asking about.Strategic Solutions for Seamless Integration of Third-Party SaaS
Modern systems must be modular and interoperable by design. Integration is no longer a feature, it’s a requirement. Developers are expected to build architectures that connect easily with third-party platforms, but too often, core systems are designed in isolation. This disconnect creates friction for downstream teams and slows delivery. At Microsoft, SaaS platforms like SAP SuccessFactors and Eightfold support Talent Acquisition by handling functions such as requisition tracking, application workflows, and interview coordination. These tools help reduce costs and free up engineering focus for high-priority areas like Azure and AI. The real challenge is integrating them with internal systems such as Demand Planning, Offer Management, and Employee Central. This blog post outlines a strategy centered around two foundational components: an Integration and Orchestration Layer, and a Messaging Platform. Together, these enable real-time communication, consistent data models, and scalable integration. While Talent Acquisition is the use case here, the architectural patterns apply broadly across domains. Whether you're embedding AI pipelines, managing edge deployments, or building platform services, thoughtful integration needs to be built into the foundation, not bolted on later.Azure Database for MySQL bindings for Azure Functions (General Availability)
We’re thrilled to announce the general availability (GA) of Azure Database for MySQL Input and Output bindings for Azure Functions—a powerful way to build event-driven, serverless applications that seamlessly integrate with your MySQL databases. Key Capabilities With this GA release, your applications can use: Input bindings that allow your function to retrieve data from a MySQL database without writing any connection or query logic. Output bindings that allow your function to insert or update data in a MySQL table without writing explicit SQL commands. In addition you can use both the input and output bindings in the same function to read-modify-write data patterns. For example, retrieve a record, update a field, and write it back—all without managing connections or writing SQL. These bindings are fully supported for both in-process and isolated worker models, giving you flexibility in how you build and deploy your Azure Functions. How It Works Azure Functions bindings abstract away the boilerplate code required to connect to external services. With the MySQL Input and Output bindings, you can now declaratively connect your serverless functions to your Azure Database for MySQL database with minimal configuration. You can configure these bindings using attributes in C#, decorators in Python, or annotations in JavaScript/Java. The bindings use the MySql.Data.MySqlClient library under the hood and support Azure Database for MySQL Flexible Server. Getting Started To use the bindings, install the appropriate NuGet or npm package: # For isolated worker model (C#) dotnet add package Microsoft.Azure.Functions.Worker.Extensions.MySql # For in-process model (C#) dotnet add package Microsoft.Azure.WebJobs.Extensions.MySql Then, configure your function with a connection string and binding metadata. Full samples for all the supported programming frameworks are available in our github repository. Here is a sample C# in-process function example where you want to retrieve a user by ID, increment their login count, and save the updated record back to the MySQL database for lightweight data transformations, modifying status fields or updating counters and timestamps. public class User { public int Id { get; set; } public string Name { get; set; } public int LoginCount { get; set; } } public static class UpdateLoginCountFunction { [FunctionName("UpdateLoginCount")] public static async Task<IActionResult> Run( [HttpTrigger(AuthorizationLevel.Function, "post", Route = "user/{id}/login")] HttpRequest req, [MySql("SELECT * FROM users WHERE id = @id", CommandType = System.Data.CommandType.Text, Parameters = "@id={id}", ConnectionStringSetting = "MySqlConnectionString")] User user, [MySql("users", ConnectionStringSetting = "MySqlConnectionString")] IAsyncCollector<User> userCollector, ILogger log) { if (user == null) { return new NotFoundObjectResult("User not found."); } // Modify the user object user.LoginCount += 1; // Write the updated user back to the database await userCollector.AddAsync(user); return new OkObjectResult($"Login count updated to {user.LoginCount} for user {user. Name}."); } } Learn More Azure Functions MySQL Bindings Azure Functions Conclusion With input and output bindings for Azure Database for MySQL now generally available, building serverless apps on Azure with MySQL has never been simpler or more efficient. By eliminating the need for manual connection management and boilerplate code, these bindings empower you to focus on what matters most: building scalable, event-driven applications with clean, maintainable code. Whether you're building real-time dashboards, automating workflows, or syncing data across systems, these bindings unlock new levels of productivity and performance. We can’t wait to see what you’ll build with them. If you have any feedback or questions about the information provided above, please leave a comment below or email us at AskAzureDBforMySQL@service.microsoft.com. Thank you!How to Use Postgres MCP Server with GitHub Copilot in VS Code
GitHub Copilot has changed how developers write code, but when combined with an MCP (Model Copilot Protocol) server, it also connects your services. With it, Copilot can understand your database schema and generate relevant code for your API, data models, or business logic. In this guide, you'll learn how to use the Neon Serverless Postgres MCP server with GitHub Copilot in VS Code to build a sample REST API quickly. We'll walk through how to create an Azure Function that fetches data from a Neon database, all with minimal setup and no manual query writing. From Code Generation to Database Management with GitHub Copilot AI agents are no longer just helping write code—they’re creating and managing databases. When a chatbot logs a customer conversation, or a new region spins up in the Azure cloud, or a new user signs up, an AI agent can automatically create a database in seconds. No need to open a dashboard or call an API. This is the next evolution of software development: infrastructure as intent. With tools like database MCP servers, agents can now generate real backend services as easily as they generate code. GitHub Copilot becomes your full-stack teammate. It can answer database-related questions, fetch your database connection string, update environment variables in your Azure Function, generate SQL queries to populate tables with mock data, and even help you create new databases or tables on the fly. All directly from your editor, with natural language prompts. Neon has a dedicated MCP server that makes it possible for Copilot to directly understand the structure of your Postgres database. Let's get started with using the Neon MCP server and GitHub Copilot. What You’ll Need Node.js (>= v18.0.0) and npm: Download from nodejs.org. An Azure subscription (create one for free) Install either the stable or Insiders release of VS Code: Stable release Insiders release Install the GitHub Copilot, GitHub Copilot for Azure, and GitHub Copilot Chat extensions for VS Code Azure Functions Core Tools (for local testing) Connect GitHub Copilot to Neon MCP Server Create Neon Database Visit the Neon on Azure Marketplace page and follow the Create a Neon resource guide to deploy Neon on Azure for your subscription. Neon has free plan is more than enough to build proof of concept or kick off a new startup project. Install the Neon MCP Server for VS Code Neon MCP Server offers two options for connecting your VS Code MCP client to Neon. We will use the Remote Hosted MCP Server option. This is the simplest setup—no need to install anything locally or configure a Neon API key in your client. Add the following Neon MCP server configuration to your user settings in VS Code: { "mcp":{ "servers":{ "Neon":{ "command":"npx", "args":[ "-y", "mcp-remote", "https://mcp.neon.tech/sse" ] } } } } Click on Start on the MCP server. A browser window will open with an OAuth prompt. Just follow the steps to give your VS Code MCP client access to your Neon account. Generate an Azure Function REST API using GitHub Copilot Step 1: Create an empty folder (ex: mcp-server-vs-code) and open it in VS Code. Step 2: Open GitHub Copilot Chat in VS Code and switch to Agent mode. You should see the available tools. Step 3: Ask Copilot something like "Create an Azure function with an HTTP trigger”: Copilot will generate a REST API using Azure Functions in JavaScript with a basic setup to run the functions locally. Step 4: Next, you can ask to list existing Neon projects: Step 5: Try out different prompts to fetch the connection string for the chosen database and set it to the Azure Functions settings. Then ask to create a sample Customer table and so on. Or you can even prompt to create a new database branch on Neon. Step 6: Finally, you can prompt Copilot to update the Azure functions code to fetch data from the table: Combine the Azure MCP Server Neon MCP gives GitHub Copilot full access to your database schema, so it can help you write SQL queries, connect to the database, and build API endpoints. But when you add Azure MCP Server into the mix, Copilot can also understand your Azure services—like Blob Storage, Queues, and Azure AI. You can run both Neon MCP and Azure MCP at the same time to create a full cloud-native developer experience. For example: Use Neon MCP for serverless Postgres with branching and instant scale. Use Azure MCP to connect to other Azure services from the same Copilot chat. Even better: Azure MCP is evolving. In newer versions, you can spin up Azure Functions and other services directly from Copilot chat, without ever leaving your editor. Copilot pulls context from both MCP servers, which means smarter code suggestions and faster development. You can mix and match based on your stack and let Copilot help you build real, working apps in minutes. Final Thoughts With GitHub Copilot, Neon MCP server, and Azure Functions, you're no longer writing backend code line by line. It is so fast to build APIs. You're orchestrating services with intent. This is not the future—it’s something you can use today.
