Creating Your First Azure Function: Step-by-Step for IT Administrators

Last updated January 14, 2026 ~26 min read 20 views
serverless azure azure functions function app azure cli powershell vscode managed identity application insights event grid service bus storage account rbac bicep infrastructure as code devops cicd monitoring logging security
Creating Your First Azure Function: Step-by-Step for IT Administrators

Serverless in Azure is often introduced as “write code, don’t manage servers,” but IT administrators and system engineers quickly discover the more useful framing: you do manage an application platform—identity, networking, observability, deployment, and cost controls—just with a different set of levers. Azure Functions sits in that sweet spot for event-driven automation, lightweight APIs, and integration glue between Azure services.

This article is a practical, operations-aware walkthrough for creating your first Function App and deploying an initial function. The goal is not just to “make it run,” but to make it run in a way you can support: consistent configuration, least-privilege access, clear logs and metrics, and repeatable deployment. Along the way, you’ll see multiple real-world admin scenarios woven into the steps so you can map the mechanics to typical enterprise needs.

What Azure Functions is (and what you actually deploy)

Azure Functions is a serverless compute service for running small units of code in response to events. The key point is that you don’t deploy “a function” in isolation—you deploy it within a Function App, which is the Azure resource that hosts one or more functions.

A Function App is roughly analogous to a web app container: it has a runtime, configuration settings (app settings), identity settings, networking settings, and scale behavior. Inside it, each function is a named entry point with a trigger (what starts it) and optional bindings (how it reads/writes data).

Understanding this layering up front helps with decisions you’ll make later:

  • You manage scaling, identity, networking, and configuration at the Function App level.
  • You manage triggers, bindings, and code at the function level.
  • You manage shared dependencies and deployment artifacts at the project/repo level.

That separation becomes important when you add a second or third function for related automation. For example, a common pattern is to put multiple “IT automation” functions (cleanup jobs, event-driven remediation, webhook handlers) into one Function App so they share identity, logging, and a deployment pipeline.

Core building blocks: triggers, bindings, and app settings

Before building anything, it helps to define three terms that determine how your function behaves in production.

A trigger defines the event that starts your function. Common triggers include HTTP (webhook/API), Timer (cron-like schedule), Storage Queue, Service Bus, Event Grid, and Event Hub. The trigger choice affects security model (e.g., HTTP auth), throughput characteristics, and error handling.

A binding is a declarative connection to an input or output. For example, your function can be triggered by a Service Bus message and output to a Storage Table or a queue. Not all teams use bindings heavily; some prefer explicit SDK calls for control. But bindings are useful when you want minimal code for common integration patterns.

App settings are key/value settings on the Function App. They are exposed to the runtime as environment variables. This is where connection strings, endpoint URLs, and feature flags typically live. In production, you should treat app settings as configuration managed outside the code, and prefer Azure-managed authentication over storing secrets.

These concepts show up repeatedly later when you secure access (managed identity vs connection strings), set up monitoring, and move from local development to Azure.

Choosing your hosting plan: Consumption, Premium, or Dedicated

Your hosting plan selection affects startup behavior, scaling, and networking options. The three common models for Azure Functions are:

Consumption plan scales automatically and bills per execution and resources used. It is often the default choice for low-to-moderate workloads and spiky event-driven automation. Operationally, the most discussed behavior is cold start—a function that hasn’t run recently may take longer to start. Cold start impact varies by language/runtime and dependencies.

Premium plan adds features such as more predictable warm instances, VNET integration options depending on configuration, and better performance for some workloads. It costs more but is a common choice for enterprise functions that must meet latency expectations or require specific networking patterns.

Dedicated (App Service) plan runs functions on reserved App Service compute. It is useful when you already have an App Service plan and want to host functions alongside web apps, or when you need more explicit control over compute resources.

For your first function, Consumption is typically the fastest path to learning. In enterprise environments, you may still prototype in Consumption and then move to Premium or Dedicated once latency and networking requirements are validated.

Picking a runtime and development workflow

Azure Functions supports multiple runtimes/languages. For IT administrators and system engineers, the most common are:

  • PowerShell for Azure automation tasks and administrative workflows.
  • C# (.NET) for strongly typed integrations and larger codebases.
  • JavaScript/TypeScript (Node.js) for webhooks, API glue, and JSON-heavy event processing.
  • Python for data processing and scripting workflows.

This guide uses PowerShell because it aligns well with admin-driven automation and is easy to reason about in operational contexts. The same infrastructure steps (resource group, storage, monitoring, identity) apply regardless of language.

You’ll build locally using the Azure Functions Core Tools and deploy using the Azure CLI. For many admins, this is a comfortable middle ground: you get a repeatable local environment and a clear deployment story without requiring a full CI/CD setup on day one.

Prerequisites and local tooling

To follow the steps end-to-end, you need:

  • An Azure subscription with permissions to create resource groups and resources.
  • Azure CLI installed and authenticated.
  • Azure Functions Core Tools installed.
  • Visual Studio Code (recommended) with the Azure Functions extension.

On Windows, PowerShell is typically available by default; on macOS/Linux, PowerShell (pwsh) can be installed.

Verify Azure CLI access and subscription context first. This reduces surprises later when resources end up in an unexpected subscription or tenant.

az login
az account show
az account list --output table

# If needed:

az account set --subscription "<subscription-id-or-name>"

Then verify Functions Core Tools:

bash
func --version

If you’re in a controlled enterprise environment, align these tool versions with what your team supports (for example, pinned versions in a developer workstation baseline). Consistency matters when you later add a deployment pipeline.

Step 1: Define a small but realistic first function

A good first function is one that has a clear operational purpose and integrates with your existing practices. Rather than “Hello World,” aim for something you could actually keep.

In this walkthrough, you’ll create an HTTP-triggered function that performs a simple administrative check: it accepts a JSON payload and returns structured output (including validation results). This sounds basic, but it mirrors real webhook patterns: ticketing integrations, monitoring alerts, and chatops commands.

This starting point also sets you up for the scenarios later in the article:

  1. A webhook from an on-call system calling an Azure Function to validate payloads and route events.
  2. A scheduled maintenance function (Timer trigger) using the same Function App identity and monitoring.
  3. An event-driven integration using Event Grid or Service Bus that reuses your configuration patterns.

By choosing a function that returns structured JSON and uses app settings, you’ll exercise the mechanics that matter most in production.

Step 2: Create an Azure resource group

Start by creating a dedicated resource group for the function resources. A resource group is the management boundary for lifecycle and RBAC. Keeping function-related resources together (Function App, storage, monitoring) makes teardown, access reviews, and cost tracking easier.

Set some variables (use Bash here; PowerShell equivalents are shown afterwards).

bash

# Bash variables

LOCATION="eastus"
RG="rg-func-first-demo"

az group create --name "$RG" --location "$LOCATION"

If you prefer PowerShell:

powershell
$Location = "eastus"
$RG = "rg-func-first-demo"

az group create --name $RG --location $Location

In real environments, naming conventions usually encode environment (dev/test/prod), region, and ownership. If your org uses Azure Policy, you may also need mandatory tags. Add them now if required.

Step 3: Create the supporting Storage account

Azure Functions requires a Storage account for runtime operations (for example, managing triggers and checkpoints). Even if your function doesn’t explicitly use Storage bindings, the platform uses it.

Create a general-purpose v2 storage account. Storage account names must be globally unique and lowercase.

bash

# Create a unique suffix

SUFFIX=$(date +%s)
STORAGE="stfuncfirst$SUFFIX"

az storage account create \
  --name "$STORAGE" \
  --resource-group "$RG" \
  --location "$LOCATION" \
  --sku Standard_LRS \
  --kind StorageV2

Operationally, this storage account becomes part of the function’s reliability story. Treat it as a production dependency: apply your organization’s baseline (secure transfer required, minimum TLS, logging if mandated). Some settings can be enforced via policy; others you may configure explicitly depending on your standards.

Step 4: Create the Function App

Now create the Function App resource that will host your function code. You’ll specify:

  • The Function App name (globally unique).
  • The runtime (PowerShell).
  • The storage account.
  • The hosting plan type.

This example uses the Consumption plan.

bash
FUNCAPP="func-first-demo-$SUFFIX"

az functionapp create \
  --name "$FUNCAPP" \
  --resource-group "$RG" \
  --consumption-plan-location "$LOCATION" \
  --runtime powershell \
  --functions-version 4 \
  --storage-account "$STORAGE"

A few notes that matter later:

The --functions-version 4 parameter selects the Functions v4 runtime line (the current standard for many languages). Runtime support and end-of-life timelines do change over time, so in production you should periodically review runtime versions.

The Function App will have a default hostname like https://<appname>.azurewebsites.net. You can later front it with API Management, a gateway, or private networking depending on requirements.

Step 5: Add Application Insights for logging and metrics

For IT operations, observability is not optional. Azure Functions integrates with Application Insights (part of Azure Monitor) to capture logs, requests, failures, and performance data.

Create an Application Insights resource and connect it to the Function App. The newer workspace-based approach uses a Log Analytics workspace. To keep the example straightforward and widely compatible, you can create Application Insights and let Azure manage the connection.

bash
APPINSIGHTS="appi-func-first-demo-$SUFFIX"

az monitor app-insights component create \
  --app "$APPINSIGHTS" \
  --location "$LOCATION" \
  --resource-group "$RG" \
  --application-type web

Then configure the Function App to use it. Depending on your environment and the CLI version, you may set the connection string. Retrieve it and apply as an app setting.

bash
AI_CONN=$(az monitor app-insights component show \
  --app "$APPINSIGHTS" \
  --resource-group "$RG" \
  --query connectionString -o tsv)

az functionapp config appsettings set \
  --name "$FUNCAPP" \
  --resource-group "$RG" \
  --settings "APPLICATIONINSIGHTS_CONNECTION_STRING=$AI_CONN"

This step is a practical example of why app settings matter: you can wire up monitoring without touching code. Later, you’ll use the same mechanism for endpoint configuration and feature flags.

Step 6: Create the function project locally (PowerShell)

With the platform resources ready, you’ll create a local Functions project and an HTTP-triggered function.

Create a folder, initialize the project, and add the function:

bash
mkdir func-first-demo
cd func-first-demo

# Initialize a PowerShell Functions project

func init --worker-runtime powershell

# Create an HTTP-triggered function

func new --name ValidateWebhook --template "HTTP trigger"

This generates the project structure and a function folder (for example, ValidateWebhook/). The entry point for PowerShell functions is typically run.ps1, and metadata is in function.json.

Spend a minute reviewing function.json. It defines the trigger and bindings. For an HTTP trigger, you’ll see an httpTrigger input binding and an http output binding.

Because admins often maintain automation repositories, it’s worth adopting basic repo hygiene immediately: initialize Git, add a .gitignore, and commit the initial structure. Even if you later move to a different runtime, the habit of versioning function code is foundational.

Step 7: Implement a useful HTTP endpoint (not just Hello World)

Open ValidateWebhook/run.ps1 and implement payload validation and structured output. This example validates required fields and returns an appropriate HTTP status code.

powershell
using namespace System.Net

param($Request, $TriggerMetadata)

# Expect JSON input with fields: source, severity, message

$body = $null
try {
    $body = $Request.Body
} catch {
    $body = $null
}

$errors = New-Object System.Collections.Generic.List[string]

if (-not $body) {
    $errors.Add("Missing JSON body")
} else {
    if (-not $body.source)   { $errors.Add("Missing field: source") }
    if (-not $body.severity) { $errors.Add("Missing field: severity") }
    if (-not $body.message)  { $errors.Add("Missing field: message") }
}

if ($errors.Count -gt 0) {
    $response = [pscustomobject]@{
        ok      = $false
        errors  = $errors
        example = @{ source = "monitoring"; severity = "warning"; message = "Disk usage high" }
    }

    Push-OutputBinding -Name Response -Value ([HttpResponseContext]@{
        StatusCode = [HttpStatusCode]::BadRequest
        Body       = ($response | ConvertTo-Json -Depth 4)
        Headers    = @{ "Content-Type" = "application/json" }
    })
    return
}

$response = [pscustomobject]@{
    ok       = $true
    received = @{ source = $body.source; severity = $body.severity; message = $body.message }
    tsUtc    = (Get-Date).ToUniversalTime().ToString("o")
}

Push-OutputBinding -Name Response -Value ([HttpResponseContext]@{
    StatusCode = [HttpStatusCode]::OK
    Body       = ($response | ConvertTo-Json -Depth 4)
    Headers    = @{ "Content-Type" = "application/json" }
})

This function is deliberately simple but operationally relevant. Many IT webhook flows fail due to inconsistent payloads. A validation function like this can sit behind an alerting system, standardize what downstream systems receive, and return clear errors early.

In a real environment, you might extend it by verifying an HMAC signature header (common for webhook security) or mapping incoming severities to a normalized schema used by your ticketing system.

Step 8: Run the function locally and test it

Start the local host:

bash
func start

You’ll see a local URL for the HTTP trigger (typically http://localhost:7071/api/ValidateWebhook). Test it with curl.

A failing request (missing fields):

bash
curl -s -X POST \
  http://localhost:7071/api/ValidateWebhook \
  -H "Content-Type: application/json" \
  -d '{"source":"monitoring"}' | jq .

A valid request:

bash
curl -s -X POST \
  http://localhost:7071/api/ValidateWebhook \
  -H "Content-Type: application/json" \
  -d '{"source":"monitoring","severity":"warning","message":"Disk usage high"}' | jq .

Local execution is more than a developer convenience. For IT admins, it is a fast way to validate runtime prerequisites and logic before you introduce cloud variables like identity and networking.

Step 9: Deploy to Azure using Azure CLI

With the function working locally, publish it to the Function App you created earlier.

bash
func azure functionapp publish "$FUNCAPP"

This packages your project and deploys it to the Function App. Once complete, retrieve the function URL. You can list functions and keys using Azure CLI, but be mindful: keys are secrets. For initial testing, you might use the default function key, then switch to better authentication patterns later.

List functions:

bash
az functionapp function list \
  --name "$FUNCAPP" \
  --resource-group "$RG" \
  -o table

If you use function-level authorization, you’ll need a key to call it. One approach is to get the default key:

bash
KEY=$(az functionapp keys list \
  --name "$FUNCAPP" \
  --resource-group "$RG" \
  --query functionKeys.default -o tsv)

echo "$KEY"

Call the deployed function:

bash
curl -s -X POST \
  "https://$FUNCAPP.azurewebsites.net/api/ValidateWebhook?code=$KEY" \
  -H "Content-Type: application/json" \
  -d '{"source":"monitoring","severity":"warning","message":"Disk usage high"}' | jq .

At this point, you’ve created and deployed your first Azure Function end-to-end. The remaining sections focus on what IT administrators usually need immediately after the first successful call: authentication hardening, configuration management, and integration with Azure services.

Understanding authentication options for HTTP-triggered functions

HTTP-triggered functions can be exposed to the public internet, which means you must be intentional about authentication. Azure Functions supports several patterns, and the “right” one depends on whether you’re building an internal automation endpoint, a partner-facing webhook, or a public API.

The simplest model is function keys (shared secrets). They’re easy for quick tests but tend to sprawl operationally: key rotation, safe storage, and accidental exposure become concerns.

For enterprise use, common approaches include:

  • Azure AD authentication (Microsoft Entra ID) via built-in authentication/authorization (often called “Easy Auth”). This is strong for internal callers and service-to-service authentication.
  • Fronting your function with API Management and enforcing policies (JWT validation, IP restrictions, rate limits).
  • Using private endpoints and restricting inbound access so only private network callers can reach it.

Because this is a “first function” guide, you won’t implement every option. Instead, you’ll harden the foundation by setting up managed identity for outbound access and by treating HTTP keys as a temporary bootstrap method.

Step 10: Enable managed identity for the Function App

A managed identity is an identity in Microsoft Entra ID tied to an Azure resource. It allows your function to authenticate to Azure services without storing credentials in code or app settings.

Enable a system-assigned managed identity on the Function App:

bash
az functionapp identity assign \
  --name "$FUNCAPP" \
  --resource-group "$RG"

Retrieve the principal ID (useful for role assignments):

bash
PRINCIPAL_ID=$(az functionapp identity show \
  --name "$FUNCAPP" \
  --resource-group "$RG" \
  --query principalId -o tsv)

echo "$PRINCIPAL_ID"

This identity becomes the cornerstone for secure integrations: reading from Key Vault (if you use it), sending to Service Bus, writing to Storage, or calling Azure Resource Manager.

Real-world scenario 1: Webhook validation and ticket routing

Imagine you operate a monitoring platform that fires alerts to multiple destinations: email, chat, and a ticketing system. A common pain point is inconsistent payload structure across different sources (VM alerts, container alerts, custom app alerts). You end up with brittle parsing logic in each downstream system.

A lightweight Azure Function can act as a normalization and validation layer. The monitoring system calls your HTTP endpoint, the function validates required fields, and then either rejects malformed alerts (400) or forwards normalized alerts onward.

Operationally, the value is that you centralize validation logic and logging. When an upstream team changes a payload, you see the error in one place (Application Insights), and you can respond without hunting across multiple integrations.

You now have the scaffolding for that pattern: an HTTP-triggered function, structured JSON responses, and Application Insights wired in. The next step for that scenario would typically be adding an output integration (for example, enqueueing the validated alert to Service Bus), which you’ll build toward later.

Step 11: Use app settings for environment-specific configuration

As soon as you have more than one environment (dev/test/prod), hard-coded values become the fastest way to create outages. Azure Functions is designed for externalized configuration via app settings.

Add an app setting that controls behavior, such as a “minimum severity” threshold.

bash
az functionapp config appsettings set \
  --name "$FUNCAPP" \
  --resource-group "$RG" \
  --settings "MIN_SEVERITY=warning"

Then update your function to read it. In PowerShell functions, app settings are available as environment variables.

powershell
$minSeverity = $env:MIN_SEVERITY
if (-not $minSeverity) { $minSeverity = "warning" }

You can use this to implement a simple filter (for example, drop info events). Even if you don’t implement the full filter now, the point is to establish the pattern: behavior is configured through app settings, not code changes.

In mature environments, these app settings are often managed through IaC (Bicep/Terraform) and release pipelines so changes are audited.

Step 12: Secure secrets the right way (avoid connection strings when possible)

Many Azure service integrations historically relied on connection strings stored in app settings. That still works, but it increases secret-handling burden. For IT administrators, that burden shows up as rotation processes, incident response when secrets leak, and compliance exceptions.

Prefer these approaches:

  • Managed identity + RBAC when the target service supports Microsoft Entra ID authorization (common for Service Bus, Storage, Key Vault).
  • Key Vault references in app settings when you must use secrets (so secrets aren’t stored directly in the Function App configuration).

Even if your first function doesn’t need secrets, adopting this stance early prevents you from building a “quick demo” that later becomes production with poor security posture.

Step 13: Integrate with Azure Storage using managed identity (practical pattern)

A common first integration is writing audit records to Blob Storage. For example, your webhook validator might archive accepted payloads for later analysis. Storage supports Microsoft Entra ID authorization in many scenarios, but implementing it correctly requires two parts:

  1. Assign the Function App identity an RBAC role on the storage account.
  2. Use an SDK/approach that authenticates using that identity.

Grant the Function App identity permission to write blobs. A common role is Storage Blob Data Contributor (scope it narrowly—prefer container scope when practical, but account scope is acceptable for a first build).

bash
STORAGE_ID=$(az storage account show \
  --name "$STORAGE" \
  --resource-group "$RG" \
  --query id -o tsv)

az role assignment create \
  --assignee-object-id "$PRINCIPAL_ID" \
  --assignee-principal-type ServicePrincipal \
  --role "Storage Blob Data Contributor" \
  --scope "$STORAGE_ID"

In PowerShell, you can use Az.Accounts + Az.Storage modules to authenticate with managed identity, but inside Azure Functions you need to be careful about module availability and cold-start overhead. Many teams instead use REST calls with an access token, or choose a runtime with first-class managed identity support for the SDK they want.

For a first function, it’s often enough to understand the platform pattern: managed identity + RBAC. You can then pick the implementation approach that matches your language/runtime and operational constraints.

Step 14: Add a Timer-triggered function for scheduled operations

Now that your Function App exists and is monitored, it’s useful to add a second function that covers a different operational need: scheduled maintenance.

This reflects a common enterprise pattern: one Function App hosting a small suite of related automation functions that share identity and monitoring.

Create a new function locally:

bash
func new --name NightlyMaintenance --template "Timer trigger"

Open the generated function.json for the timer function. You’ll see a schedule expression. Azure Functions timer triggers use a cron-like schedule. Adjust it to run once per day at 02:00 UTC for example.

Then implement minimal logic in NightlyMaintenance/run.ps1:

powershell
param($Timer)

$utcNow = (Get-Date).ToUniversalTime().ToString("o")
Write-Host "NightlyMaintenance executed at $utcNow"

# Example placeholder: emit a structured log line

$event = [pscustomobject]@{
    eventType = "maintenance"
    timeUtc   = $utcNow
    action    = "noop"
}
Write-Host ($event | ConvertTo-Json -Depth 3)

Run locally to confirm it loads (timer triggers can be tested locally by running the host; it will execute on schedule, and you can adjust to a short interval during development).

Publish again:

bash
func azure functionapp publish "$FUNCAPP"

The new function should appear in the Function App list. This illustrates a practical operational model: keep related automation in one place, deploy together, and observe together.

Real-world scenario 2: Scheduled cleanup of expired resources

A timer-triggered function is often used for “quiet maintenance” that doesn’t deserve a full VM or an always-on job: deleting expired blobs, pruning old log files, closing stale Service Bus sessions, or validating that required tags exist on new resources.

For example, suppose your organization requires a dataClassification tag on storage accounts. You could run a nightly function that queries resources and reports violations. The key operational advantage is that the job runs in a managed platform with built-in logging and scaling behavior, rather than relying on a workstation scheduled task or an ad-hoc VM.

In practice, you’d pair this with managed identity and Azure Resource Manager APIs, plus a clear alerting path when violations are found. The point here is that the same Function App you used for a webhook can also host a scheduled governance check, sharing identity and telemetry.

Step 15: Understand scaling and concurrency from an ops perspective

Azure Functions scaling is one of the main reasons teams choose it, but scaling behavior varies by trigger type and plan.

With HTTP triggers on the Consumption plan, instances can scale out based on load. For queue-based triggers (Storage Queue, Service Bus), scaling is influenced by queue depth and message processing rate.

Two practical implications for admins:

First, idempotency matters. Idempotency means repeated processing of the same event does not produce incorrect results. In distributed systems, retries happen—due to transient errors, timeouts, or downstream throttling. If your function creates tickets, sends emails, or modifies resources, build in deduplication or checks.

Second, downstream limits matter. Your function can scale faster than the system it calls. If you integrate with an API that rate-limits, you may need to buffer work (Service Bus/Queue) and control concurrency in code.

Even for a first function, keep these in mind as you decide whether an HTTP trigger should directly do work or should enqueue work for asynchronous processing.

Step 16: Add an asynchronous path with Service Bus (integration pattern)

A common enterprise pattern is: HTTP trigger validates and authenticates, then enqueues a message to Service Bus. A separate function processes messages reliably and can scale based on queue depth.

You can implement the full pattern later, but it’s useful to understand the moving parts now:

  • Service Bus namespace and queue.
  • RBAC for send/receive.
  • App settings for queue name and fully qualified namespace.
  • Triggered function using Service Bus trigger.

In production, Service Bus is often chosen over Storage queues when you need features like dead-lettering, sessions, or more robust enterprise messaging semantics.

Because Service Bus setup can be policy-driven in many organizations, coordinate with your messaging standards. The important takeaway is that Azure Functions integrates well with queue-based decoupling, and your first HTTP function can become the front door to a more resilient pipeline.

Step 17: Networking considerations: public endpoints, IP restrictions, and private access

By default, an HTTP-triggered function on Consumption is reachable over the public internet. That’s not automatically wrong, but it requires intentional controls.

If the function is intended for internal automation only, consider these patterns:

Use built-in authentication (Entra ID) to ensure only authorized identities can call it. This is usually the first control to add because it doesn’t require network plumbing.

Use access restrictions (IP allowlist) when callers come from known egress IPs. This can be effective for partner integrations or fixed corporate NATs, but it can be brittle if IPs change.

For higher security, use private networking patterns (often Premium plan features) so the function is reachable only from private address space. This is common when functions interact with private endpoints or must not be publicly accessible.

The operational decision here should be driven by threat model and caller identity. A webhook from a SaaS monitoring platform might require a public endpoint with strong authentication; an internal job called from Azure Automation could be locked down to private access.

Step 18: Logging practices that help operations (structured logs)

When a function fails in production, your first question is usually: “What happened, and how many times?” Application Insights captures logs, but the quality of your logs determines how quickly you can answer.

Prefer structured logging: log JSON-like objects or consistent key/value pairs rather than free-form strings. You already started doing that in the timer function by emitting a JSON object.

For the HTTP function, you can log an event object that includes a correlation ID. If a caller provides a request ID header, propagate it. Otherwise, generate one. This makes it easier to trace across systems.

In PowerShell, use Write-Host for logs captured by the platform, but keep them concise and consistent. Avoid logging secrets or full payloads if they can contain sensitive data. If you must log payloads for debugging, consider redaction and implement an opt-in debug flag via app settings.

Step 19: Deployment approaches: zip deploy, run-from-package, and pipelines

Your initial deployment used func azure functionapp publish, which is a convenient wrapper for packaging and deploying.

In more controlled environments, you’ll usually move to one of these approaches:

Run-from-package (a deployment model where the app runs from a package file) improves deployment consistency and reduces the risk of partial file updates.

CI/CD pipelines (GitHub Actions, Azure DevOps) provide repeatable builds, environment promotion, and approvals.

Even if you don’t implement a pipeline in your first iteration, you can structure your repository now so the transition is easy:

  • Keep configuration out of code (use app settings).
  • Use parameterized templates for infrastructure.
  • Tag releases and maintain a changelog.

If you later adopt Infrastructure as Code, consider codifying the Function App, storage, and monitoring resources in Bicep or Terraform. That shift turns your first function from a one-off artifact into something you can recreate reliably.

Step 20: Infrastructure as Code with Bicep (practical starter)

To make this guide operationally complete, it’s worth seeing what a minimal IaC definition looks like, even if you created resources by CLI initially.

Below is a simplified Bicep example for a Function App on Consumption with a storage account and app settings. Treat it as a starting point, and adapt it to your standards (tags, diagnostics settings, naming conventions).

bicep
param location string = resourceGroup().location
param functionAppName string
param storageAccountName string

resource storage 'Microsoft.Storage/storageAccounts@2023-01-01' = {
  name: storageAccountName
  location: location
  sku: {
    name: 'Standard_LRS'
  }
  kind: 'StorageV2'
  properties: {
    supportsHttpsTrafficOnly: true
    minimumTlsVersion: 'TLS1_2'
  }
}

resource plan 'Microsoft.Web/serverfarms@2023-01-01' = {
  name: '${functionAppName}-plan'
  location: location
  sku: {
    name: 'Y1'
    tier: 'Dynamic'
  }
}

resource site 'Microsoft.Web/sites@2023-01-01' = {
  name: functionAppName
  location: location
  kind: 'functionapp'
  identity: {
    type: 'SystemAssigned'
  }
  properties: {
    serverFarmId: plan.id
    siteConfig: {
      appSettings: [
        {
          name: 'AzureWebJobsStorage'
          value: 'DefaultEndpointsProtocol=https;AccountName=${storage.name};EndpointSuffix=${environment().suffixes.storage};AccountKey=${listKeys(storage.id, storage.apiVersion).keys[0].value}'
        }
        {
          name: 'FUNCTIONS_EXTENSION_VERSION'
          value: '~4'
        }
        {
          name: 'FUNCTIONS_WORKER_RUNTIME'
          value: 'powershell'
        }
      ]
    }
    httpsOnly: true
  }
}

This example intentionally shows an account key being used for the required runtime storage setting. That is a platform requirement in many configurations. For other dependencies (your own storage containers, Service Bus, Key Vault), you should still prefer managed identity.

If your organization requires private endpoints, diagnostics settings, or customer-managed keys, your IaC will expand accordingly. The value of introducing IaC early is not complexity; it’s repeatability.

Step 21: Operational guardrails: timeouts, retries, and idempotency

Functions are often used for automation that touches critical systems. Guardrails prevent minor hiccups from turning into incident pages.

Timeouts are important because an HTTP caller may give up long before your function completes. If you expect long-running work, redesign toward asynchronous processing (enqueue then process).

Retries depend on trigger type. Queue-based triggers often retry automatically, and message systems may have dead-letter behavior. Your code should anticipate retries and handle duplicates safely.

Idempotency is easiest when you choose stable identifiers. For webhook events, that might be an event ID from the source system. Store processed IDs in a durable store if necessary, or design downstream actions to be safe when repeated.

These are not “advanced topics”; they are the difference between a useful automation function and one that creates intermittent problems.

Real-world scenario 3: Event-driven remediation using Event Grid

Consider a security operations scenario: you want to detect when certain resources are modified (for example, a Network Security Group rule is changed) and run a remediation workflow.

A typical architecture is:

  • Azure Activity logs or resource events flow into Event Grid.
  • An Azure Function subscribes to those events.
  • The function validates the event, checks policy rules, and either reverts the change or creates a ticket.

In this scenario, Azure Functions provides a small, focused execution environment that can scale with bursts of changes (for example, during a deployment). Your earlier investments—managed identity, structured logging, and app settings—carry directly into this use case.

Even if you don’t implement the full Event Grid path in your first project, it’s helpful to recognize the pattern: event-driven operations, not polling. It reduces latency, cost, and noise compared to cron jobs that scan for changes.

Step 22: Cost awareness and controlling spend

One reason Functions is popular is its cost model. Still, in production you should understand how cost accumulates and how to control it.

On the Consumption plan, you pay for executions and resource consumption. High-frequency triggers, large payloads, or inefficient code can increase cost. Timer triggers running too often are a common surprise.

Application Insights ingestion and retention can also be a significant cost driver. From an ops perspective, decide:

  • What log level you need by default.
  • How long you retain logs.
  • Whether you sample high-volume request telemetry.

Cost control isn’t just about saving money; it’s about making the platform predictable. For production Function Apps, it’s common to set budgets and alerts at the resource group or subscription scope.

Step 23: Validating your deployed setup in Azure Portal (what to check)

While most admins prefer automation, the Azure Portal is still useful for verification and quick inspection.

After deployment, validate:

You can see your functions listed under the Function App, and the HTTP trigger has a visible endpoint.

Application Insights shows incoming requests and traces. Verify you see both successful and failed requests so you know error paths are observable.

The Function App has a managed identity enabled, and the identity has the expected role assignments.

App settings reflect your configuration values (like MIN_SEVERITY). Confirm no secrets are accidentally stored in plaintext when you intended to use managed identity.

This portal verification step provides confidence that your CLI-based workflow is actually producing the resources and settings you expect.

Step 24: Hardening the HTTP endpoint for production use

If you plan to keep the HTTP endpoint, consider these production hardening steps as you evolve beyond the initial bootstrap.

Move from function keys to stronger auth for internal callers. Entra ID authentication is typically the cleanest approach for service-to-service calls within Azure.

Add request validation beyond JSON shape. Many webhook providers include signature headers; verify them using a shared secret stored in Key Vault or in a secure secret store referenced by app settings.

Implement rate limiting if you expect bursts or untrusted callers. This is often better handled at a gateway (API Management) than inside the function.

Be deliberate about what you return in error responses. In development, detailed errors help. In production, you want enough detail for callers to fix payloads without exposing internals.

These improvements build directly on the working function you already deployed. The platform mechanics don’t change; you’re simply tightening operational controls.

Step 25: Putting it together as a small operations service

By now, you have a Function App with:

  • An HTTP-triggered function suitable for webhook validation.
  • A Timer-triggered function suitable for scheduled maintenance.
  • Application Insights telemetry for both.
  • A managed identity to support secure outbound access.
  • A configuration pattern via app settings.

This is a realistic “first Azure Functions deployment” for an IT team. It can grow into a small operations service that handles event ingestion, governance checks, and lightweight automation.

If you continue expanding it, keep the operational through-line:

Treat the Function App as an application platform you own. Apply your baseline controls, track changes via code and pipelines, and keep integrations identity-driven rather than secret-driven. When you add new functions, make them idempotent and observable by default.

That approach is what turns a successful first deployment into a service your team can rely on.