# Azure CLI example to check version az --versionHistory and Growth of Azure
# Check Azure account info az account showAzure Global Infrastructure
az account list-locationsAzure Regions and Availability Zones
az vm create --location eastus --name myVM --image UbuntuLTS --generate-ssh-keysAzure Portal and CLI Basics
az group create --name myResourceGroup --location eastusAzure Subscriptions and Billing
az consumption usage list --subscription mySubscriptionIdUnderstanding Azure Resource Manager (ARM)
az deployment group create --resource-group myResourceGroup --template-file azuredeploy.jsonAzure Marketplace Overview
az vm image list --publisher MicrosoftWindowsServerIntroduction to Azure Support Plans
# Support plans managed via portal, no direct CLI commandAzure Service Level Agreements (SLAs)
# SLA details available on Azure website
az vm create --resource-group myResourceGroup --name myVM --image UbuntuLTS --admin-username azureuser --generate-ssh-keysAzure VM Scale Sets
az vmss create --resource-group myResourceGroup --name myScaleSet --image UbuntuLTS --instance-count 3Azure App Service
az webapp create --resource-group myResourceGroup --plan myAppServicePlan --name myWebApp --runtime "PYTHON|3.8"Azure Kubernetes Service (AKS)
az aks create --resource-group myResourceGroup --name myAKSCluster --node-count 3 --enable-addons monitoringAzure Functions (Serverless)
az functionapp create --resource-group myResourceGroup --consumption-plan-location eastus --runtime python --name myFunctionApp --storage-account mystorageaccountAzure Batch
# Azure Batch managed via SDK or portal; CLI limitedAzure Container Instances (ACI)
az container create --resource-group myResourceGroup --name myContainer --image mcr.microsoft.com/azuredocs/aci-helloworldAzure Dedicated Hosts
az vm host create --resource-group myResourceGroup --name myDedicatedHost --platform-fault-domain 1VM Images and Extensions
az vm extension set --resource-group myResourceGroup --vm-name myVM --name CustomScriptExtension --publisher Microsoft.Compute --settings '{"commandToExecute":"echo Hello World"}'
Azure Compute Pricing and Cost Managementaz consumption usage list --resource-group myResourceGroup
// Upload blob using Azure SDK (Python)
from azure.storage.blob import BlobServiceClient
client = BlobServiceClient.from_connection_string("")
container = client.get_container_client("mycontainer")
container.upload_blob("file.txt", b"Hello, Azure Blob!")
Azure Files// Mount Azure File Share (Linux CLI) sudo mount -t cifs //Azure Disks.file.core.windows.net/ /mnt/azurefiles -o vers=3.0,username= ,password= ,dir_mode=0777,file_mode=0777
// Create managed disk (Azure CLI)
az disk create --resource-group myResourceGroup --name myDisk --size-gb 128 --sku Premium_LRS
Azure Queue Storage
// Send message to queue (Python SDK)
from azure.storage.queue import QueueClient
queue = QueueClient.from_connection_string("", "myqueue")
queue.send_message("Hello, Queue!")
Azure Table Storage
// Insert entity into Table Storage
from azure.data.tables import TableClient
table = TableClient.from_connection_string("", "mytable")
entity = {"PartitionKey": "pk1", "RowKey": "rk1", "Name": "Azure"}
table.create_entity(entity)
Storage Account Types and Tiers
// Create storage account (Azure CLI)
az storage account create --name mystorageacct --resource-group myResourceGroup --sku Standard_LRS --kind StorageV2
Data Redundancy Options
// Enable geo-redundancy (conceptual)
az storage account update --name mystorageacct --resource-group myResourceGroup --sku Standard_GRS
Storage Security and Access Control
// Generate SAS token (Python SDK)
from azure.storage.blob import generate_blob_sas, BlobSasPermissions
sas_token = generate_blob_sas(account_name, container_name, blob_name, permission=BlobSasPermissions(read=True), expiry=datetime.utcnow() + timedelta(hours=1))
Storage Performance Optimization
// Example: Set blob tier to Cool
container.set_blob_tier("file.txt", "Cool")
Azure Data Lake Storage Gen2
// Create ADLS Gen2 filesystem (Azure CLI)
az storage fs create --account-name mystorageacct --name myfilesystem
// Create VNet with Azure CLI
az network vnet create --name myVNet --resource-group myResourceGroup --address-prefix 10.0.0.0/16
Subnets and IP Addressing
// Add subnet to VNet
az network vnet subnet create --address-prefix 10.0.1.0/24 --name mySubnet --vnet-name myVNet --resource-group myResourceGroup
Azure Load Balancer
// Create load balancer (Azure CLI)
az network lb create --resource-group myResourceGroup --name myLoadBalancer --sku Standard --frontend-ip-name myFrontEnd --backend-pool-name myBackEndPool
Azure Application Gateway
// Create Application Gateway (conceptual)
az network application-gateway create --name myAppGateway --resource-group myResourceGroup --sku Standard_v2 --capacity 2 --vnet-name myVNet --subnet mySubnet
Azure Traffic Manager
// Create Traffic Manager profile
az network traffic-manager profile create --name myTrafficManager --resource-group myResourceGroup --routing-method Performance --unique-dns-name mydns
Azure VPN Gateway
// Create VPN gateway (Azure CLI)
az network vnet-gateway create --name myVpnGateway --public-ip-address myPublicIP --resource-group myResourceGroup --vnet myVNet --gateway-type Vpn --vpn-type RouteBased --sku VpnGw1
Azure ExpressRoute
// ExpressRoute configuration is done via portal or partner
Network Security Groups (NSGs)
// Create NSG and add rule (Azure CLI)
az network nsg create --resource-group myResourceGroup --name myNSG
az network nsg rule create --nsg-name myNSG --resource-group myResourceGroup --name AllowHTTP --protocol tcp --direction inbound --priority 100 --source-address-prefixes '*' --destination-port-ranges 80 --access allow
Azure Firewall
// Deploy Azure Firewall (Azure CLI)
az network firewall create --name myFirewall --resource-group myResourceGroup --location eastus
Azure DNS and Private Link// Create private endpoint (Azure CLI) az network private-endpoint create --name myPrivateEndpoint --resource-group myResourceGroup --vnet-name myVNet --subnet mySubnet --private-connection-resource-id
// Example: Create an AAD user via Azure CLI az ad user create --display-name "John Doe" --user-principal-name john@example.com --password "StrongP@ssw0rd"User and Group Management
// Add user to a group via Azure CLI az ad group member add --group "Developers" --member-idRole-Based Access Control (RBAC)
// Assign contributor role to user on a resource group az role assignment create --assignee john@example.com --role Contributor --resource-group MyResourceGroupConditional Access Policies
// Sample PowerShell to create conditional access policy
New-AzureADMSConditionalAccessPolicy -DisplayName "Block Access from Untrusted Locations" -Conditions @{SignInRiskLevels=@("high")} -GrantControls @{BuiltInControls=@("block")}
Multi-Factor Authentication (MFA)
// Enable MFA for a user (via portal or PowerShell)
// PowerShell example to enable MFA (conceptual)
Set-MsolUser -UserPrincipalName john@example.com -StrongAuthenticationRequirements @(@{RelyingParty="*";State="Enabled"})
Managed Identities// Assign system-assigned managed identity to VM az vm identity assign --resource-group MyResourceGroup --name MyVMAzure AD Connect and Hybrid Identity
// Azure AD Connect setup done via GUI; sync status can be checked with: // PowerShell example Get-ADSyncSchedulerPrivileged Identity Management (PIM)
// Assign eligible role using Azure Portal or PowerShell (conceptual) // Example PowerShell Enable-AzureADPrivilegedRoleManagementIdentity Protection with AI
// Risk detection example via Graph API GET https://graph.microsoft.com/v1.0/identityProtection/riskyUsersAccess Reviews and Governance
// Create access review via Microsoft Graph API (conceptual) POST /identityGovernance/accessReviews/definitions
// Create Azure SQL Database via CLI az sql db create --resource-group MyResourceGroup --server myserver --name mydb --service-objective S0Azure Cosmos DB
// Create Cosmos DB account with SQL API az cosmosdb create --name mycosmosdb --resource-group MyResourceGroup --kind GlobalDocumentDBAzure Database for MySQL
// Create MySQL server az mysql server create --resource-group MyResourceGroup --name mymysqlserver --location eastus --admin-user admin --admin-password StrongP@ssw0rdAzure Database for PostgreSQL
// Create PostgreSQL server az postgres server create --resource-group MyResourceGroup --name mypgserver --location eastus --admin-user admin --admin-password StrongP@ssw0rdAzure Synapse Analytics
// Create Synapse workspace az synapse workspace create --name mysynapse --resource-group MyResourceGroup --storage-account mystorageaccount --file-system myfilesystemAzure SQL Managed Instance
// Create managed instance example az sql mi create --name mymanagedinstance --resource-group MyResourceGroup --location eastus --admin-user admin --admin-password StrongP@ssw0rdAzure Cache for Redis
// Create Redis cache az redis create --name myredis --resource-group MyResourceGroup --location eastus --sku Basic --vm-size c0Database Migration Service
// Start migration project (conceptual) // Use Azure Portal or CLI extensionsDatabase Security Best Practices
// Enable advanced threat protection for SQL DB az sql db threat-policy update --resource-group MyResourceGroup --server myserver --database mydb --state EnabledMonitoring and Scaling Databases
// Enable performance monitoring az monitor metrics alert create --name HighCPUAlert --resource-group MyResourceGroup --scopes /subscriptions/.../resourceGroups/.../providers/Microsoft.Sql/servers/myserver/databases/mydb --condition "avg CPU > 80"
// List available Azure AI services via Azure CLI
az cognitiveservices list --output table
Azure Cognitive Services// Example: Analyze sentiment with Text Analytics API (Python) from azure.ai.textanalytics import TextAnalyticsClient from azure.core.credentials import AzureKeyCredential client = TextAnalyticsClient(endpoint="https://Azure Machine Learning Studio.cognitiveservices.azure.com/", credential=AzureKeyCredential(" ")) response = client.analyze_sentiment(documents=["I love using Azure AI!"]) print(response[0].sentiment)
// Launch Azure ML Studio from portal or CLI (no direct CLI command)
# Use https://ml.azure.com/ for visual model building
Automated Machine Learning (AutoML)
// Start AutoML experiment using Azure ML SDK (Python)
from azureml.train.automl import AutoMLConfig
automl_config = AutoMLConfig(task='classification', primary_metric='accuracy', training_data=train_data, label_column_name='label')
AI-Powered Bots with Azure Bot Service// Create a bot using Azure CLI az bot create --resource-group MyResourceGroup --name MyBot --kind webapp --location eastus --appidAzure Form Recognizer--password
// Analyze form with Form Recognizer (Python SDK) from azure.ai.formrecognizer import DocumentAnalysisClient client = DocumentAnalysisClient(endpoint="Speech and Language Services", credential=AzureKeyCredential(" ")) poller = client.begin_analyze_document_from_url("prebuilt-layout", " ") result = poller.result() for page in result.pages: print(page.tables)
// Convert speech to text with Speech SDK (Python) from azure.cognitiveservices.speech import SpeechConfig, SpeechRecognizer speech_config = SpeechConfig(subscription="Custom Vision and Face API", region=" ") recognizer = SpeechRecognizer(speech_config=speech_config) result = recognizer.recognize_once() print(result.text)
// Train Custom Vision model (Python SDK)
from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
trainer = CustomVisionTrainingClient("", endpoint="")
project = trainer.create_project("MyProject")
Responsible AI and Ethics
// Monitor model fairness using Azure ML interpretability tools (conceptual)
# Use Azure ML SDK’s interpretability package to assess model bias
AI Model Deployment and Monitoring
// Deploy model endpoint with Azure ML CLI
az ml model deploy --name my-endpoint --model my-model:1 --ic inferenceconfig.yml --dc deploymentconfig.yml
// Create new Azure DevOps project via CLI
az devops project create --name MyProject
Azure Repos (Git)
// Clone Azure Repo
git clone https://dev.azure.com/organization/project/_git/repository
Azure Pipelines for CI/CD
// Run pipeline via CLI
az pipelines run --name MyPipeline
Azure Boards and Work Items
// Create work item using Azure CLI
az boards work-item create --title "Bug Fix" --type "Bug"
Infrastructure as Code (IaC) with ARM Templates
// Deploy ARM template via Azure CLI
az deployment group create --resource-group MyResourceGroup --template-file azuredeploy.json
Azure CLI and PowerShell
// List Azure VMs using CLI
az vm list --output table
Azure SDKs and APIs
// Python example to list storage accounts
from azure.mgmt.storage import StorageManagementClient
client = StorageManagementClient(credentials, subscription_id)
accounts = client.storage_accounts.list()
for account in accounts:
print(account.name)
Monitoring with Azure Application Insights
// Enable Application Insights for an app (Azure CLI)
az monitor app-insights component create --app MyAppInsights --location eastus --resource-group MyResourceGroup
Azure DevTest Labs
// Create a DevTest Lab using CLI
az lab create --resource-group MyResourceGroup --name MyDevTestLab
Security in DevOps (DevSecOps)
// Integrate security scanning in Azure Pipeline YAML (snippet)
- task: SecurityScanner@1
inputs:
scanType: 'staticAnalysis'
# Example: Enable Security Center standard tier via Azure CLI
az security pricing create --name default --tier Standard
Azure Sentinel (SIEM)
# Connect data source to Azure Sentinel (PowerShell example)
Connect-AzAccount
Set-AzSentinelDataConnector -WorkspaceName "MyWorkspace" -ResourceGroupName "MyRG" -ConnectorType "AzureActiveDirectory"
Security Best Practices
# Enable MFA for Azure AD users (PowerShell)
Install-Module MSOnline
Connect-MsolService
Set-MsolUser -UserPrincipalName user@domain.com -StrongAuthenticationRequirements @(@{RelyingParty="*";State="Enabled"})
Azure Key Vault
# Create a Key Vault and add a secret
az keyvault create --name MyVault --resource-group MyResourceGroup --location eastus
az keyvault secret set --vault-name MyVault --name "DbPassword" --value "MySecret123"
Data Encryption at Rest and in Transit
# Enable storage encryption (default for Azure Storage)
az storage account create --name mystorageaccount --resource-group MyResourceGroup --sku Standard_LRS --encryption-services blob
Threat Protection and Incident Response
# Trigger a Logic App playbook on threat detection (pseudocode)
# logicApp.trigger_on_security_alert()
Compliance Certifications
# List compliance certifications (Azure portal or REST API)
# az rest --method get --uri https://management.azure.com/providers/Microsoft.Compliance/standards?api-version=2021-07-01
Security Automation and Orchestration
# Create a Logic App for automated response (pseudocode)
# logicApp.create_trigger_and_action()
Network Security with AI
# Configure Network Security Group rule
az network nsg rule create --resource-group MyRG --nsg-name MyNSG --name AllowSSH --protocol Tcp --direction Inbound --priority 1000 --source-address-prefixes '*' --source-port-ranges '*' --destination-port-ranges 22 --access Allow
Secure Access and Zero Trust Model
# Enable Conditional Access Policy (Azure AD example)
# az rest --method post --uri https://graph.microsoft.com/beta/identity/conditionalAccess/policies --body '{...}'
# Enable Azure Monitor on a VM
az monitor metrics alert create --name CpuAlert --resource-group MyRG --scopes /subscriptions/xxx/resourceGroups/MyRG/providers/Microsoft.Compute/virtualMachines/MyVM --condition "avg Percentage CPU > 80" --window-size 5m --evaluation-frequency 1m
Log Analytics and Querying
# Example Kusto Query Language (KQL) query
Heartbeat | summarize count() by Computer
Alerts and Action Groups
# Create an action group with email receiver
az monitor action-group create --resource-group MyRG --name MyActionGroup --action email Admin admin@example.com
Azure Automation and Runbooks
# Start a runbook
az automation runbook start --resource-group MyRG --automation-account-name MyAccount --name MyRunbook
Azure Cost Management and Billing
# Create a budget with alert
az consumption budget create --amount 500 --category cost --time-grain monthly --name MyBudget --resource-group MyRG
Azure Policy and Blueprints
# Assign policy to enforce tag on resources
az policy assignment create --name 'RequireTag' --scope /subscriptions/xxx/resourceGroups/MyRG --policy 'tagPolicyDefinitionId'
Resource Tagging and Organization
# Add tag to a resource
az tag create --name Environment --value Production
Backup and Disaster Recovery
# Enable backup for a VM
az backup protection enable-for-vm --resource-group MyRG --vault-name MyVault --vm MyVM
Azure Service Health and Advisories
# View service health events (Azure CLI)
az resource show --ids /subscriptions/xxx/resourceGroups/MyRG/providers/Microsoft.ResourceHealth/events
Governance and Resource Locks
# Create a read-only lock
az lock create --name ReadOnlyLock --lock-type ReadOnly --resource-group MyRG --resource-name MyResource --resource-type Microsoft.Compute/virtualMachines
from azure.iot.hub import IoTHubRegistryManager
connection_string = "HostName=example.azure-devices.net;SharedAccessKeyName=..."
registry_manager = IoTHubRegistryManager(connection_string)
devices = registry_manager.get_devices()
print(devices)
# IoT Central uses web portal; example ARM template deployment:
az deployment group create --resource-group MyResourceGroup --template-file iot-central-template.json
// Sample Azure Sphere CLI to monitor devices
azsphere device show-attached
iotedge list iotedge deploy
az iot dps enrollment create --dps-name MyDPS --device-id MyDevice --allocation-policy "Hashed"
SELECT deviceId, AVG(temperature) as avgTemp
INTO output
FROM iotInput TIMESTAMP BY eventTime
GROUP BY deviceId, TumblingWindow(minute, 5)
client = DigitalTwinsClient(endpoint, credential)
client.create_digital_twin("building1", digital_twin_model)
blob_client.upload_blob(data, overwrite=True)
az ml model deploy -n edgeModel --target iot-edge-device
az security iot-monitoring list
az migrate project create --resource-group MyRG --name MyProject
az migrate migrate-vm --project MyProject --vm-name MyVM
az dms create --resource-group MyRG --name MyDMS --location eastus
az connectedmachine connect --resource-group MyRG --name MyMachine --location eastus
az stack registration create --resource-group MyRG --name MyStack
az network vpn-connection create --name MyVPN --resource-group MyRG --vnet-gateway1 MyGateway --local-gateway2 LocalGateway
Start-ADSyncSyncCycle -PolicyType Delta
az costmanagement query --scope /subscriptions/{subscriptionId} --timeframe MonthToDate
# Document migration plan with milestones and rollback strategies
az advisor recommendation list --resource-group MyRG
az backup vault create --resource-group myResourceGroup --name myBackupVault --location eastusAzure Site Recovery
az site-recovery vault create --resource-group myResourceGroup --name myRecoveryVault --location eastusGeo-Redundancy and Replication
az storage account create --name mystorageacct --resource-group myResourceGroup --location eastus --sku Standard_GRSFailover Strategies
# Trigger planned failover using CLI (simplified) az site-recovery replication-failover --name myVMFailover --resource-group myResourceGroupTesting and Validation
az site-recovery test-failover --resource-group myResourceGroup --name myTestFailoverCompliance and Auditing
az monitor activity-log list --resource-group myResourceGroup --filter "eventChannels eq 'Admin'"Recovery Time Objectives (RTO)
# RTO monitored through SLA reports and monitoring toolsRecovery Point Objectives (RPO)
# Configurable in backup policies via Azure Portal or CLIDR Planning and Automation
az automation runbook create --resource-group myResourceGroup --name myDRRunbook --type PowerShellCase Studies and Best Practices
# Documentation and playbooks available in Azure docs and GitHub
az network ddos-protection create --resource-group myResourceGroup --name myDdosProtectionPlan --location eastusApplication Security Groups
az network asg create --resource-group myResourceGroup --name myAppSecurityGroup --location eastusWeb Application Firewall (WAF)
az network application-gateway waf-policy create --resource-group myResourceGroup --name myWafPolicyAzure Bastion
az network bastion create --resource-group myResourceGroup --name myBastionHost --location eastus --vnet-name myVnet --subnet bastionSubnetPrivate Endpoints and Service Endpoints
az network private-endpoint create --resource-group myResourceGroup --name myPrivateEndpoint --vnet-name myVnet --subnet mySubnet --private-connection-resource-id /subscriptions/.../resourceGroups/.../providers/Microsoft.Storage/storageAccounts/mystorageacct --group-id blobNetwork Virtual Appliances (NVAs)
# Deploy NVAs via Azure Marketplace or ARM templatesAzure Firewall Manager
az network firewall-manager policy create --name myFirewallPolicy --resource-group myResourceGroupSecurity Policies and Threat Intelligence
az security threat-protection create --name myThreatProtection --resource-group myResourceGroupAI for Network Threat Detection
# Integrate Azure Sentinel for AI-powered network threat detection az sentinel workspace create --resource-group myResourceGroup --workspace-name mySentinelWorkspaceNetwork Performance Optimization
az network traffic-manager profile create --name myTrafficManager --resource-group myResourceGroup --routing-method Performance --unique-dns-name myapp
// View cost details via Azure CLI
az costmanagement query --scope /subscriptions/{subscriptionId} --timeframe MonthToDate
// Create a budget alert (Azure CLI) az consumption budget create --category cost --amount 1000 --time-grain monthly --name MyBudget --resource-group MyRG
// Purchase reserved instance via Azure Portal (no CLI support currently)
// Get right-sizing recommendations az advisor recommendation list --category Cost
// Create Spot VM (Azure CLI) az vm create --name MySpotVM --resource-group MyRG --priority Spot --max-price -1
// Add tag to a resource az resource tag --tags Project=Analytics --resource-group MyRG --resource-name MyVM --resource-type Microsoft.Compute/virtualMachines
// Schedule VM shutdown using Azure Automation (conceptual) // Runbook triggers VM stop at 7 PM daily
// Enable anomaly detection (conceptual) // Configured in Azure portal under Cost Management
// Example: Get Advisor recommendations az advisor recommendation list --category Cost
// Create Azure Policy to enforce tagging az policy definition create --name 'require-tags' --rules 'policy.json' --mode All
// Create Synapse workspace (Azure CLI) az synapse workspace create --name MyWorkspace --resource-group MyRG --storage-account MyStorage
// Create ADF pipeline (conceptual JSON)
{
"name": "CopyPipeline",
"activities": [ ... ]
}
// Define Stream Analytics job (conceptual) // Input: IoT Hub; Output: Power BI
// Create Databricks workspace az databricks workspace create --name MyDBWorkspace --resource-group MyRG --location eastus
// Call Cognitive Services API (Python)
import requests
response = requests.post('https://api.cognitive.microsoft.com/...', data=mydata)
// Train ML model using Synapse notebook (PySpark) // from azureml.core import Workspace, Experiment ...
// Connect Power BI to Azure Synapse (conceptual) // Use DirectQuery or Import mode
// Sentiment analysis example (Python) from azure.ai.textanalytics import TextAnalyticsClient client = TextAnalyticsClient(endpoint, credential) response = client.analyze_sentiment(documents)
// Enable Azure Synapse workspace firewall az synapse workspace firewall-rule create --name AllowMyIP --workspace-name MyWorkspace --resource-group MyRG --start-ip-address 1.2.3.4 --end-ip-address 1.2.3.4
// Enable diagnostic settings for Synapse
az monitor diagnostic-settings create --resource MyWorkspace --resource-group MyRG --logs '[{"category": "SynapseSqlRequests", "enabled": true}]' --workspace MyLogWorkspace
// Simple blockchain data structure example (Python)
class Block:
def __init__(self, prev_hash, data):
self.prev_hash = prev_hash
self.data = data
Azure Blockchain Service Architecture
# Azure CLI to create blockchain member (example)
az blockchain member create --name member1 --resource-group rg --service-name blockchainService
Setting up Consortium Networks
# Define consortium policy via Azure Portal or ARM templates
Smart Contract Development and Deployment
// Sample Solidity contract snippet
pragma solidity ^0.8.0;
contract SimpleStorage {
uint storedData;
function set(uint x) public { storedData = x; }
function get() public view returns (uint) { return storedData; }
}
Identity and Access Control on Blockchain
// Example: assign roles with Azure AD
az role assignment create --assignee user@example.com --role "Blockchain Contributor"
Integrating Blockchain with Azure Logic Apps
# Logic Apps trigger example
When a new transaction is confirmed, send an email notification
Monitoring and Managing Blockchain Nodes
# Azure Monitor for blockchain node metrics
az monitor metrics list --resource blockchainNodeResourceId
Blockchain Security Best Practices
// Example: Enable multi-sig in smart contracts
// Requires multiple signatures for critical transactions
Use Cases: Supply Chain, Finance, Healthcare
// Example: Supply chain tracking smart contract logs
Future Trends and Azure Quantum Integration
// Quantum-safe encryption algorithms being researched
// Basic qubit state representation (conceptual)
|ψ⟩ = α|0⟩ + β|1⟩ where α² + β² = 1
Azure Quantum Ecosystem
# Azure Quantum workspace creation
az quantum workspace create --name myQuantumWorkspace --resource-group rg --location westus
Quantum Development Kit and Q# Language
// Q# example: simple quantum operation
operation HelloQuantum() : Result {
using (q = Qubit()) {
H(q);
return M(q);
}
}
Quantum Algorithms on Azure
// Grover’s algorithm Q# pseudo-code snippet
// Amplifies probability of correct solution
Integration with Classical Azure Services
// Calling Q# from C#
using Microsoft.Quantum.Simulation.Simulators;
using (var sim = new QuantumSimulator())
{
var result = HelloQuantum.Run(sim).Result;
Console.WriteLine(result);
}
Use Cases: Optimization, Cryptography
// Optimization problem example solved with quantum variational algorithms
Quantum Security Considerations
// Example: Post-quantum cryptography research
Running Hybrid Quantum-Classical Workloads
// Hybrid workflow example using Q# and Python SDK
Quantum Simulation and Debugging Tools
// Q# simulator run and debugging commands
Roadmap and Future of Azure Quantum
// Planned features include scalable quantum hardware and enhanced SDKs
// Example: AWS Lambda function (Python)
def handler(event, context):
return {"statusCode": 200, "body": "Hello, Serverless!"}
Azure Functions Deep Dive
// Example: Azure Function (JavaScript)
module.exports = async function (context, req) {
context.res = { body: "Hello from Azure Functions!" };
}
Durable Functions for State Management
// Durable function orchestrator example (JavaScript)
const df = require("durable-functions");
module.exports = df.orchestrator(function* (context) {
const result = yield context.df.callActivity("SayHello", "world");
return result;
});
Event-Driven Architectures with Event Grid
// Example: Subscribing Azure Function to Event Grid event
// In Azure Portal: configure Event Grid subscription targeting function endpoint
Logic Apps and Workflow Automation
// Sample Logic App JSON trigger snippet
{
"triggers": {
"Recurrence": {
"type": "Recurrence",
"recurrence": { "frequency": "Hour", "interval": 1 }
}
}
}
Integrating Serverless with API Management// Example: Policy snippet to validate JWT token in API ManagementSecurity and Identity in Serverless
// Example: Azure Function with managed identity access to Key Vault
const { DefaultAzureCredential } = require("@azure/identity");
const credential = new DefaultAzureCredential();
// Use credential to fetch secrets securely
Monitoring and Troubleshooting Serverless Apps
// Example: Log custom event in Azure Function (C#)
log.LogInformation("Processing ETL job started at {time}", DateTime.UtcNow);
Cost Optimization for Serverless Workloads
// Example: Setting timeout and memory for AWS Lambda (serverless.yml)
functions:
etlHandler:
handler: handler.etl
timeout: 30
memorySize: 512
Scaling and Performance Considerations
// Example: AWS Lambda provisioned concurrency config (AWS CLI)
aws lambda put-provisioned-concurrency-config --function-name etlHandler --provisioned-concurrent-executions 5
// Sample: Creating a search service (Azure CLI)
az search service create --name mysearch --resource-group mygroup --sku basic
Setting up Search Indexes and Data Sources
// Example: Define an index schema (JSON snippet)
{
"name": "products-index",
"fields": [
{ "name": "id", "type": "Edm.String", "key": true },
{ "name": "name", "type": "Edm.String", "searchable": true },
{ "name": "category", "type": "Edm.String" }
]
}
Integrating AI Enrichment Pipelines
// Example: AI enrichment skillset JSON snippet
{
"skills": [
{ "@odata.type": "#Microsoft.Skills.Vision.OcrSkill", "name": "OCRSkill", "inputs": [], "outputs": [] }
]
}
Querying and Search APIs
// Example: Search query with filter (REST)
GET https://[service-name].search.windows.net/indexes/products-index/docs?search=*&$filter=category eq 'electronics'&api-version=2021-04-30-Preview
Security and Access Control
// Example: Regenerate admin API key (Azure CLI)
az search admin-key renew --service-name mysearch --resource-group mygroup
Scalability and Performance Tuning
// Example: Scale replicas (Azure CLI)
az search service update --name mysearch --resource-group mygroup --replica-count 3
Multi-Language and Synonym Support
// Example: Define synonym map (JSON snippet)
{
"name": "synonym-map",
"synonyms": "tv, television\ncellphone, mobile phone"
}
Custom Skills and Extensions
// Example: Custom skill REST API call (conceptual)
POST /skillsets/my-custom-skillset?api-version=2021-04-30-Preview
{ "skills": [ { "uri": "https://myfunction.azurewebsites.net/api/custom-skill" } ] }
Use Cases in E-commerce, Healthcare, and More
// Example: Faceted search query for e-commerce products
GET /indexes/products/docs?search=*&facet=category,count:10&api-version=2021-04-30-Preview
Monitoring and Analytics for Search Services
// Example: Enable diagnostic logging (Azure CLI)
az monitor diagnostic-settings create --resource /subscriptions/.../providers/Microsoft.Search/searchServices/mysearch --name search-logs --logs '[{"category":"SearchQueryLogs","enabled":true}]'
Azure Media Services is a cloud-based platform for video streaming and media processing. It provides tools for encoding, live and on-demand streaming, content protection, and media analytics, enabling scalable and secure delivery of media content globally.
// Create Media Services client (Python SDK)
from azure.identity import DefaultAzureCredential
from azure.mgmt.media import AzureMediaServices
credential = DefaultAzureCredential()
client = AzureMediaServices(credential, subscription_id)
Azure Media Services supports video encoding to adaptive bitrate formats for smooth playback on various devices. It also manages streaming endpoints for live and on-demand content delivery.
// Submit an encoding job (conceptual)
job = client.jobs.create(resource_group, account_name, transform_name, job_name, parameters)
Media Services enables creating live streaming workflows with real-time ingestion, encoding, and broadcasting. On-demand workflows handle stored content streaming with dynamic packaging and delivery.
// Start live event via CLI
az ams live-event start --resource-group rg --account-name account --live-event-name liveEvent1
DRM technologies like PlayReady and Widevine protect video content from unauthorized access. Azure Media Services integrates DRM licensing and encryption for secure delivery across platforms.
// Configure content protection policy (conceptual JSON)
{
"contentKeyPolicy": {
"drm": ["PlayReady", "Widevine"]
}
}
Azure Media Services integrates AI capabilities such as video indexing, transcription, and face detection to generate metadata and insights that enhance content discoverability and user engagement.
// Submit video to Azure Video Indexer API (conceptual)
POST https://api.videoindexer.ai/{location}/Accounts/{accountId}/Videos
Analytics tools monitor streaming quality, viewer engagement, and playback metrics. This data helps optimize media workflows and improve user experience by identifying bottlenecks or failures.
// Retrieve media analytics metrics (conceptual)
metrics = client.metrics.get(resource_group, account_name, parameters)
Azure Media Services integrates seamlessly with Azure CDN and edge networks, caching media close to users worldwide to reduce latency and improve streaming performance.
// Link Media Services endpoint with Azure CDN
az cdn endpoint create --origin media-service-endpoint --profile-name profile --name endpoint
The service scales automatically to handle variable workloads and delivers media globally using Azure’s vast data center footprint, ensuring availability and low latency worldwide.
// Autoscale live events (conceptual)
az ams live-event update --scale units=5
Security best practices include encrypting media content, restricting access with Azure Active Directory, regularly patching resources, and monitoring for unusual activity to protect media assets.
// Assign RBAC roles to users
az role assignment create --assignee user@example.com --role "Media Services Contributor" --scope /subscriptions/{subscriptionId}/resourceGroups/{rg}
Azure Media Services supports various industries including broadcasting live events, e-learning training videos, and entertainment streaming, providing robust, scalable solutions for diverse media delivery needs.
// Deploy media pipeline for e-learning content (conceptual)
deploy_media_pipeline("training-videos")
Azure Logic Apps is a cloud service to automate workflows and integrate apps, data, and services through prebuilt connectors and triggers. It enables creation of scalable, event-driven automation without code.
// Create Logic App via Azure CLI
az logic workflow create --resource-group rg --name logicApp1 --definition @workflow.json
Connectors link Logic Apps to various services like Office 365, Salesforce, or Azure Storage. Building workflows involves chaining actions and triggers using these connectors to automate complex business processes.
// Example trigger: HTTP request
{
"type": "Request",
"kind": "Http",
"inputs": {
"schema": {}
}
}
Azure Event Grid is an event routing service supporting reactive, event-driven architectures. It routes events from sources like resource changes to handlers such as Logic Apps, Functions, or third-party services with high reliability.
// Create Event Grid subscription
az eventgrid event-subscription create --name sub1 --source-resource-id /subscriptions/.../resourceGroups/rg/providers/Microsoft.Storage/storageAccounts/sa --endpoint https://logicapp-url
Event-driven automation enables reactive workflows such as sending alerts on resource changes, auto-scaling, or triggering data processing pipelines when new data arrives, improving efficiency and responsiveness.
// Trigger Logic App on blob upload
az eventgrid event-subscription create --source-resource-id /.../storageAccounts/sa --endpoint https://logicapp-url --event-types Microsoft.Storage.BlobCreated
Security measures include using managed identities, IP restrictions, authentication tokens, and encrypting data in transit to protect Logic Apps and Event Grid event flows from unauthorized access.
// Assign managed identity to Logic App
az logic workflow update --name logicApp1 --resource-group rg --set identity.type=SystemAssigned
Logic Apps support built-in error handling and customizable retry policies for transient failures, ensuring workflow resilience by automatically retrying failed actions or triggering compensating steps.
// Define retry policy in Logic App JSON
"retryPolicy": {
"type": "exponential",
"count": 4,
"interval": "PT5S"
}
Logic Apps can invoke Azure Functions for custom code execution within workflows, extending functionality beyond connectors to handle complex logic, data transformation, or integration scenarios.
// Call Azure Function from Logic App
{
"type": "Function",
"inputs": {
"functionName": "ProcessData"
}
}
Azure provides monitoring tools to track Logic App runs, diagnose failures, and analyze performance via Azure Monitor and Application Insights, helping maintain reliable workflows.
// Query Logic App runs with Azure Monitor Logs
AzureDiagnostics | where ResourceType == "LOGICAPPS_WORKFLOWS"
Managing costs involves understanding pricing models based on triggers, actions, and event volumes. Optimizing workflows to reduce unnecessary runs and batching events can help lower expenses.
// Estimate Logic Apps cost with Azure Pricing Calculator
https://azure.microsoft.com/en-us/pricing/calculator/
Azure Logic Apps and Event Grid support hybrid environments by integrating on-premises systems with cloud services using connectors, gateways, and secure event routing, enabling seamless workflows across environments.
// Configure on-premises data gateway (conceptual)
az data-gateway create --name gateway1 --resource-group rg
Security automation uses tools and scripts to detect, analyze, and respond to cyber threats automatically. It reduces manual workload, speeds incident response, and improves consistency in managing security events across systems.
# Example: Python script to automate alert notification
def send_alert(message):
# Code to send alert email or SMS
pass
Azure Sentinel Playbooks are collections of automated workflows using Logic Apps. They orchestrate incident responses by integrating with various services to investigate, remediate, or escalate security incidents.
# Example: Logic App trigger in Azure Sentinel Playbook
{
"type": "Microsoft.Logic/workflows",
"properties": {
"definition": {
"triggers": { "When_a_response_to_an_Azure_Sentinel_alert_is_triggered": {} }
}
}
}
AI models analyze logs and network data to identify unusual patterns indicating security incidents. Automated detection enables faster identification of threats and reduces false positives through machine learning.
# Pseudo code for anomaly detection in security logs
model = train_anomaly_detector(security_logs)
alerts = model.predict(new_logs)
Security Orchestration, Automation, and Response (SOAR) tools integrate multiple security products into unified workflows. Automating incident triage and remediation accelerates response times and improves coordination.
# Example: SOAR platform API call to quarantine device
soar.quarantine_device(device_id="1234")
Threat intelligence feeds provide up-to-date information on known malicious IPs, domains, and malware. Automating ingestion into security tools enhances detection capabilities and helps prevent attacks proactively.
# Example: Fetch threat intel feed
import requests
response = requests.get("https://threatintel.example.com/feed")
process_feed(response.json())
Automated remediation executes predefined actions such as blocking IPs, isolating hosts, or resetting credentials. This reduces risk exposure and minimizes manual effort in containing incidents.
# Auto-block IP example
def block_ip(ip_address):
firewall.block(ip_address)
Automation tools aid in collecting logs, running queries, and visualizing attack paths during investigations. Automated threat hunting uncovers hidden threats by continuously scanning data for suspicious activities.
# Example: Automated search for suspicious processes
results = security_tool.search("process_name:*malware*")
Automating compliance reports ensures timely and accurate documentation of security incidents and controls. This facilitates audits and helps maintain adherence to regulatory requirements.
# Generate compliance report example
def generate_report(data):
# Format and export report
pass
Best practices include defining clear workflows, testing automation regularly, incorporating human oversight, and maintaining up-to-date threat intelligence. These ensure effective, safe, and reliable automation.
# Example: Validate automation rules before deployment
def validate_rules(rules):
for rule in rules:
if not rule.is_valid():
raise Exception("Invalid rule detected")
Future trends include increased AI sophistication for predictive threat detection, autonomous incident response, integration of behavioral analytics, and improved collaboration between AI and human analysts.
# Future: AI-powered continuous risk scoring pseudocode
while True:
risk_score = ai_model.evaluate(system_data)
if risk_score > threshold:
trigger_response()
Cloud native architecture embraces microservices, containerization, and dynamic orchestration. It focuses on scalability, resilience, and continuous delivery by leveraging cloud platform capabilities for rapid app development and deployment.
# Example: Dockerfile for a cloud native app
FROM node:16-alpine
WORKDIR /app
COPY . .
RUN npm install
CMD ["node", "server.js"]
Azure Kubernetes Service (AKS) orchestrates containerized microservices, providing automated scaling, load balancing, and rolling updates. Microservices architecture promotes modularity, fault isolation, and faster development cycles.
# Deploy app on AKS (kubectl commands)
kubectl apply -f deployment.yaml
kubectl expose deployment myapp --type=LoadBalancer --port=80
Service mesh provides communication, security, and observability between microservices. API gateways manage external client requests, handle authentication, rate limiting, and routing to microservices.
# Example Istio virtual service YAML snippet
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
name: my-service
spec:
hosts:
- "*"
http:
- route:
- destination:
host: my-service
Containerization packages apps and dependencies consistently. DevOps pipelines automate build, test, and deployment, enabling continuous integration and continuous delivery (CI/CD) for rapid, reliable updates.
# Sample GitHub Actions workflow for container build and push
name: Build and Push
on: [push]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- run: docker build -t myapp .
- run: docker push myapp
Serverless components like Azure Functions and AWS Lambda let developers run code without managing servers. Integrating these with cloud native apps enhances scalability and reduces infrastructure overhead.
# Azure Function example in Python
import logging
import azure.functions as func
def main(req: func.HttpRequest) -> func.HttpResponse:
logging.info('Python HTTP trigger processed a request.')
return func.HttpResponse("Hello from Azure Functions!")
Monitoring and logging tools track app health, performance, and errors. Tools like Azure Monitor and Application Insights provide real-time telemetry to help diagnose issues and optimize user experience.
# Example: Enable Application Insights in Azure
az monitor app-insights component create --app myapp-ai --location eastus --resource-group myRG
Security includes identity management, secure APIs, and secrets management. Compliance requires audit logging and policy enforcement. Tools like Azure Key Vault and Azure Policy help secure and govern apps.
# Example: Store secret in Azure Key Vault
az keyvault secret set --vault-name myVault --name "DbPassword" --value "P@ssw0rd123"
Azure DevOps pipelines automate build, test, and deployment workflows. CI/CD ensures quick, reliable delivery of cloud native applications with version control and environment consistency.
# Sample Azure DevOps YAML pipeline snippet
trigger:
- main
pool:
vmImage: 'ubuntu-latest'
steps:
- script: npm install
- script: npm test
- script: az webapp deploy --name myapp
Cloud native apps scale horizontally with auto-scaling, load balancing, and failover. Resilience is built via redundancy, circuit breakers, and graceful degradation to ensure uptime and performance under load.
# Kubernetes Horizontal Pod Autoscaler example
kubectl autoscale deployment myapp --min=2 --max=10 --cpu-percent=80
Real-world cloud native apps include Netflix’s microservices architecture, Spotify’s deployment pipelines, and Shopify’s containerized infrastructure. These demonstrate the benefits of scalability, rapid development, and operational agility.
# Pseudo code: Deploying a microservice pipeline
git push origin main
ci_pipeline.run()
deploy_to_k8s()
monitor_traffic()
SAP on Azure offers a cloud platform optimized to run SAP workloads with scalability, security, and integration capabilities. It supports SAP HANA, S/4HANA, and other SAP applications with Azure’s global infrastructure, allowing enterprises to modernize and reduce infrastructure complexity.
// Example: Deploy SAP HANA VM using Azure CLI az vm create --resource-group MyResourceGroup --name MySapVm --image sap-hana-2
Proper infrastructure planning includes sizing VMs, networking setup, storage configuration, and high throughput IOPS. It ensures SAP systems meet performance SLAs and can handle peak workloads efficiently.
// Example: Configure premium storage for SAP disks az vm disk attach --vm-name MySapVm --disk MyPremiumDisk --new
Azure provides options like Availability Zones and paired regions to ensure SAP systems remain highly available and recoverable during failures or disasters. Backup strategies and replication support business continuity.
// Example: Configure VM in availability zone az vm create --zone 1 --name HA-SAP-VM --image sap-hana-2
SAP workloads can integrate with Azure services like Azure Data Factory, Logic Apps, and Azure Functions to extend functionality, automate processes, and enrich SAP data with cloud analytics.
// Example: Trigger Azure Function from SAP event (pseudocode)
function onSapEvent(event) {
azureFunction.invoke(event);
}
Best practices include network segmentation, encryption of data at rest and in transit, role-based access control, and continuous monitoring to protect SAP workloads from threats.
// Example: Enable Azure Disk Encryption on VM az vm encryption enable --resource-group MyResourceGroup --name MySapVm
Optimizing SAP performance on Azure involves tuning VM sizes, storage types, network latency, and SAP application parameters to ensure responsiveness and throughput.
// Example: Resize VM for better performance az vm resize --resource-group MyResourceGroup --name MySapVm --size Standard_E64s_v3
Azure Backup and native SAP tools are combined to create consistent backup and restore plans, minimizing data loss and downtime in case of failures.
// Example: Create backup policy for SAP VMs az backup policy create --resource-group MyResourceGroup --vault-name MyBackupVault --name SapBackupPolicy
Azure Monitor and SAP Solution Manager enable continuous performance and health monitoring with alerting and diagnostics to quickly resolve issues.
// Example: Enable Azure Monitor on VM az monitor diagnostic-settings create --resource-group MyResourceGroup --name SapMonitoring --vm MySapVm
Tools like Azure Migrate and Database Migration Service support SAP system migration from on-premises or other clouds to Azure, ensuring minimal downtime and data integrity.
// Example: Start Azure Migrate assessment az migrate project create --resource-group MyResourceGroup --name SapMigrateProject
Various industries use SAP on Azure for supply chain, finance, and manufacturing optimization, leveraging cloud agility and SAP’s enterprise-grade capabilities.
// Example: Reference case study link
console.log("See https://azure.microsoft.com/en-us/solutions/sap/ for examples.");
Predictive maintenance uses AI to forecast equipment failures before they occur, reducing downtime and maintenance costs. It analyzes sensor data and historical patterns to schedule maintenance proactively rather than reactively.
// Example: Simple predictive maintenance alert pseudocode
if (sensor.reading > threshold) {
alert("Potential equipment failure detected.");
}
Data collection involves integrating IoT sensors to gather real-time machine data such as temperature, vibration, and pressure, which serve as inputs for AI models.
// Example: Reading sensor data stream
const sensorData = iotHub.receiveData("machine123");
ML models are trained on historical sensor and failure data to predict remaining useful life or failure probabilities. Techniques include regression, classification, and time series analysis.
// Example: Train simple regression model (pseudocode) model.train(trainingData.features, trainingData.labels);
Azure IoT Hub collects device data while AI services like Azure Machine Learning provide model training, deployment, and real-time inference capabilities, enabling end-to-end predictive maintenance solutions.
// Example: Deploy model on Azure ML
azureML.deployModel("predictive-maintenance-model");
Real-time dashboards and alerts notify operators immediately of anomalies or predicted failures, allowing timely intervention and minimizing unexpected downtime.
// Example: Setup alert on anomaly detection
if (anomalyScore > 0.8) {
sendAlert("Maintenance needed soon!");
}
Power BI visualizes sensor data, maintenance schedules, and predictions in intuitive dashboards, helping stakeholders understand machine health trends and plan maintenance.
// Example: Power BI embed code snippet (pseudocode) powerBI.embedReport(reportId, elementId);
Azure Automation orchestrates workflows triggered by AI predictions, such as ordering parts or scheduling technician visits, reducing manual overhead.
// Example: Runbook trigger on alert
automationClient.startRunbook("ScheduleMaintenance");
Securing sensor data with encryption, access controls, and compliance with privacy laws ensures data integrity and protects sensitive operational information.
// Example: Encrypt data in transit using TLS iotHub.configureTLS(true);
Models are deployed in production and regularly retrained with new data to maintain accuracy as equipment or conditions change.
// Example: Schedule retraining job weekly
scheduler.scheduleJob("retrainModel", "0 0 * * 0");
Predictive maintenance reduces unplanned downtime, lowers maintenance costs, and extends asset lifespan, resulting in significant ROI and operational efficiency gains.
// Example: Calculate cost savings
const savings = downtimeHours * hourlyCost;
console.log(`Estimated savings: $${savings}`);
Azure Maps provides geospatial APIs and SDKs for map rendering, routing, and spatial analytics on the Microsoft Azure platform. It allows developers to build location-aware applications with rich map visuals and spatial data integration, supporting global scale and high availability.
// Basic HTML to load Azure Maps SDK
<script src="https://atlas.microsoft.com/sdk/javascript/mapcontrol/2/atlas.min.js"></script>
<div id="myMap" style="width: 600px; height: 400px;"></div>
Azure Maps supports rendering vector and raster maps, with interactive controls like zoom and pan. Custom layers enable adding markers, polygons, and routes to visualize spatial data effectively.
// JavaScript example: Initialize map
var map = new atlas.Map('myMap', {
center: [-122.33, 47.6],
zoom: 10,
authOptions: { authType: 'subscriptionKey', subscriptionKey: 'YourKey' }
});
Geocoding converts addresses to geographic coordinates, while routing APIs provide directions and travel time estimations. Azure Maps offers REST APIs to perform these operations for logistics, navigation, and location services.
// Sample REST request to Azure Maps Geocoding API
GET https://atlas.microsoft.com/search/address/json?api-version=1.0&query=1 Microsoft Way, Redmond, WA&subscription-key=YourKey
Spatial analytics process geographic data to identify patterns, while geofencing triggers actions when devices enter or exit predefined areas. Azure Maps supports these features for real-time monitoring and event-driven workflows.
// Pseudo code for geofence event
if (device_location within geofence_area) {
trigger_alert();
}
Azure Maps integrates with IoT services to track device locations in real time. This enables scenarios like fleet management, asset tracking, and dynamic geospatial visualization linked with live data streams.
// Example: Visualize real-time IoT device location on map
map.markers.add(new atlas.HtmlMarker({
position: [lon, lat],
htmlContent: '<div>Device</div>'
}));
Azure Maps uses subscription keys and Azure Active Directory for authentication and access control. Security best practices include key rotation, role-based access, and network restrictions to protect map services and data.
// Azure CLI: Create Azure Maps account with RBAC
az resource create --resource-group MyResourceGroup --resource-type "Microsoft.Maps/accounts" --name MyMapsAccount --location eastus --properties "{}"
Developers can customize map appearance by applying custom styles and adding layers such as traffic, weather, or business data. This enhances the user experience by tailoring maps to specific application needs.
// Add traffic flow layer (JavaScript)
map.layers.add(new atlas.layer.TileLayer({
source: new atlas.source.TileSource({
url: "https://atlas.microsoft.com/traffic/flow/tiles/{z}/{x}/{y}?api-version=1.0&subscription-key=YourKey"
})
}));
To optimize performance, Azure Maps supports tile caching, efficient API calls, and minimizes data transfer. Lazy loading and asynchronous requests ensure fast rendering and responsiveness in client applications.
// Example: Enable tile caching (conceptual)
map.setOptions({ tileCacheSize: 1000 });
Azure Maps powers use cases like route optimization in logistics, location-based marketing in retail, and urban planning in smart cities. These applications benefit from real-time spatial insights and rich map visualizations.
// Pseudo example: Calculate optimized route for delivery
var route = calculateRoute(deliveryPoints);
displayRouteOnMap(route);
Azure Maps provides monitoring tools to track API usage, performance, and errors. Usage analytics help manage costs and improve service reliability by identifying patterns and anomalies.
// Azure Portal: View Azure Maps usage metrics dashboard
// CLI to get usage info (conceptual)
az monitor metrics list --resource MyMapsAccount --metric-names TotalRequests
Azure API Management enables organizations to create, publish, secure, and monitor APIs. It acts as a gateway that controls API traffic, enforces policies, and provides developer engagement through portals.
// Create API Management service (Azure CLI)
az apim create --name myapim --resource-group mygroup --location eastus --publisher-email admin@example.com --publisher-name MyCompany
APIs can be designed, imported, and published within Azure API Management. It supports REST, SOAP, and GraphQL, allowing developers to quickly expose backend services to consumers.
// Add API from OpenAPI spec (Azure CLI)
az apim api import --resource-group mygroup --service-name myapim --api-id myapi --specification-url https://example.com/openapi.json
API Management supports multiple authentication methods including OAuth 2.0, JWT tokens, and subscription keys. Policies enable enforcing security rules to protect backend services from unauthorized access.
// Example policy snippet enforcing subscription key
<inbound>
<validate-subscription-key />
</inbound>
To prevent abuse, API Management allows configuring rate limits and throttling policies, controlling how many calls a client can make over time, improving API reliability and protecting backend systems.
// Rate limit policy example
<rate-limit calls="1000" renewal-period="60" />
The built-in developer portal offers interactive API documentation, testing tools, and subscription management, helping developers understand and consume APIs effectively.
// Portal customization is done via Azure Portal UI or REST APIs.
// Example: Publish documentation markdown file to portal (conceptual)
Azure API Management integrates with Azure Monitor to provide detailed analytics on API usage, latency, errors, and health, empowering teams to optimize API performance and troubleshoot issues.
// View metrics in Azure Portal or query via CLI
az monitor metrics list --resource /subscriptions/{subId}/resourceGroups/{rg}/providers/Microsoft.ApiManagement/service/{serviceName} --metric-names Calls
APIs can be versioned to manage changes without disrupting consumers. Lifecycle management features allow retiring old versions, creating new ones, and controlling access accordingly.
// Create a new API version (Azure CLI)
az apim api revision create --resource-group mygroup --service-name myapim --api-id myapi --api-version 2
Azure API Management integrates with Azure Logic Apps and Functions, enabling automation and serverless execution in workflows triggered by API calls, thus extending functionality and reducing backend complexity.
// Example: Call Azure Function via API Management
GET https://myapim.azure-api.net/myfunction?code=function_key
Deploying API Management across multiple regions improves availability and reduces latency for global users. It supports active-active configurations with automatic failover capabilities.
// Azure CLI example to add region to APIM service
az apim update --name myapim --resource-group mygroup --add additionalRegions regionName=eastus2
Azure API Management offers multiple pricing tiers based on throughput, features, and scale. Organizations select plans to balance cost with performance and functionality, optimizing their API strategy budget.
// View pricing tiers and details at Azure Portal pricing page
// Example: Select Developer tier for testing and evaluation
AI enhances application monitoring by automating anomaly detection, performance trend analysis, and predictive diagnostics. By analyzing vast log and telemetry data, AI models identify patterns and predict failures before they impact users, improving uptime and user experience.
// Example: Pseudo AI anomaly detection in monitoring logs
function detectAnomalies(logData) {
// Apply ML model to identify abnormal patterns
return anomalies;
}
Azure Monitor provides AI-powered analytics, smart alerts, and automated remediation. Features like Metric Alerts with dynamic thresholds and integration with Azure Logic Apps enable intelligent and automated incident management.
// Azure CLI: Create dynamic threshold alert
az monitor metrics alert create --name "DynamicCPUAlert" --resource-group myRG --scopes myVMId --condition "avg Percentage CPU > dynamic_threshold()" --description "Alert on dynamic CPU usage spikes"
Application Insights integrates AI to analyze application performance and exceptions, offering root cause analysis and smart diagnostics to reduce troubleshooting time and improve reliability.
// Sample Application Insights query for AI-driven insights
requests
| summarize avg(duration) by bin(timestamp, 1h)
| render timechart
AI models analyze metrics and logs to detect anomalies and automatically correlate related events to find root causes, accelerating incident resolution and minimizing downtime.
// Example: Anomaly detection with Azure Cognitive Services
detectAnomaly(metricData) {
// Call Azure Anomaly Detector API
// Return detected anomalies
}
Machine learning models applied to log data identify patterns, forecast trends, and detect security threats. This empowers proactive operations and faster detection of critical issues.
// Sample Kusto query for ML-based log analysis
Heartbeat
| summarize count() by Computer, bin(TimeGenerated, 1h)
| extend is_anomaly = series_decompose_anomalies(count_)
AI-powered systems trigger alerts based on anomaly detection and automatically initiate incident response workflows, reducing manual intervention and accelerating remediation.
// Sample logic app trigger on alert
trigger OnAnomalyDetected() {
// Send notifications, create tickets, trigger remediation scripts
}
Dashboards present AI-driven insights like anomaly scores, predictive trends, and health metrics in intuitive visuals, enabling faster decision-making and easier monitoring.
// Power BI example: visualize anomaly scores
// Connect Power BI to Azure Monitor logs
// Create charts highlighting anomalies over time
AI integrations in DevOps automate monitoring, testing, and deployment decisions, improving pipeline efficiency and reliability through predictive analytics and anomaly detection.
// Example: AI alert integration with Jenkins pipeline
pipeline {
stages {
stage('Monitor') {
steps {
script {
if (detectAnomalies(buildLogs)) {
error('Anomaly detected, failing build')
}
}
}
}
}
}
AI analyzes user interactions and behavior patterns to identify unusual activities, improve UX, and detect fraud, supporting security and personalized experiences.
// Sample ML pseudo-code for user behavior anomaly detection
analyzeUserBehavior(data) {
// Train model on normal behavior
// Flag deviations as suspicious
}
Implement AI monitoring incrementally, start with critical systems, ensure data quality, and combine AI alerts with human expertise. Use cases span proactive incident management, capacity planning, and security threat detection.
// Example: Combining AI alerts with manual review
function alertHandler(alert) {
if (alert.severity > threshold) {
notifyEngineer(alert);
}
}
Secure container images by scanning for vulnerabilities, using minimal base images, signing images, and storing them in trusted registries. Regular updates and patching reduce attack surface.
// Example: Using Trivy to scan images
trivy image myapp:latest
Network Policies control traffic flow between pods, namespaces, and external endpoints to enforce least privilege communication and prevent lateral movement of threats within clusters.
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: deny-all
namespace: default
spec:
podSelector: {}
policyTypes:
- Ingress
- Egress
AKS security includes enabling role-based access control (RBAC), integrating with Azure Active Directory, using managed identities, and enabling network policies and private clusters.
// Azure CLI to enable AKS RBAC and AAD integration
az aks create --resource-group myRG --name myAKSCluster --enable-aad --enable-rbac
Manage container access with Kubernetes RBAC, service accounts, and integration with cloud IAM systems to restrict permissions and follow the principle of least privilege.
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: default
name: pod-reader
rules:
- apiGroups: [""]
resources: ["pods"]
verbs: ["get", "watch", "list"]
Store sensitive data like API keys and passwords securely using Azure Key Vault. Kubernetes can integrate via CSI drivers or Azure AD pod-managed identities for secure secret retrieval.
// Example: Using Azure Key Vault Provider for Secrets Store CSI Driver
apiVersion: secrets-store.csi.x-k8s.io/v1
kind: SecretProviderClass
metadata:
name: azure-kvname
spec:
provider: azure
parameters:
usePodIdentity: "true"
keyvaultName: "myKeyVault"
objects: |
array:
- |
objectName: secret1
objectType: secret
Monitor containers and clusters at runtime to detect suspicious behavior using tools like Falco or Azure Defender, enabling quick threat detection and response.
// Example: Install Falco on Kubernetes
helm install falco falcosecurity/falco
Implement auditing for Kubernetes API calls, enforce security benchmarks (like CIS), and maintain logs for compliance. Tools like Azure Policy help automate governance.
// Enable audit logging in Kubernetes
apiVersion: audit.k8s.io/v1
kind: Policy
rules:
- level: Metadata
Automate scanning container images and cluster configurations as part of CI/CD pipelines to detect vulnerabilities early and enforce security policies.
// Example: Integrate Clair for image scanning in pipeline
clairctl analyze myapp:latest
Establish playbooks and automated workflows for responding to security incidents in containers, including containment, investigation, and recovery to minimize damage.
// Conceptual incident response script
function handleIncident(alert) {
isolatePod(alert.pod);
collectForensics(alert.pod);
notifyTeam(alert);
}
Follow best practices such as minimal base images, regular patching, role-based access control, secrets management, network segmentation, and continuous monitoring to secure containerized workloads effectively.
// Summary best practice checklist
const containerSecurityBestPractices = [
"Use minimal images",
"Scan images regularly",
"Implement RBAC",
"Manage secrets securely",
"Monitor runtime behavior"
];
# Registering a new Purview account via Azure CLI az purview account create --resource-group myResourceGroup --name myPurviewAccount --location eastusData Catalog and Classification
# Example: Scan data source to catalog data (via REST API) curl -X POST "https://myPurviewAccount.purview.azure.com/catalog/api/scanning/scans" -H "Authorization: BearerMetadata Management"
# Access metadata through Purview SDK or REST APIs for integrationData Lineage Tracking
# Example lineage query via REST API or Azure portal visualizationPolicy and Access Control
# Assign roles to users/groups with Azure RBAC for Purview resources az role assignment create --assignee user@example.com --role "Purview Data Curator" --scope /subscriptions/.../resourceGroups/myResourceGroup/providers/Microsoft.Purview/accounts/myPurviewAccountIntegration with Azure Data Services
# Example: Enable scanning of Azure Data Lake Storage Gen2 with Purview az purview scanning scan create --account-name myPurviewAccount --resource-group myResourceGroup --scan-name myScan --datasource-name myDataLakeSourceCompliance and Regulatory Reporting
# Compliance reports accessed through Purview portal or Power BI integrationAI-Powered Data Discovery
# AI-driven classification is automatic upon data scanning within PurviewAutomating Governance Workflows
# Example: Trigger Logic App on classification changes for governance automationBest Practices for Data Governance
# Establish scheduled scans and audits using Azure Automation or Data Factory
az security assessment list --resource-group myResourceGroupAI-Powered Threat Detection
# Integrate Azure Sentinel with built-in AI analytics for threat detection az sentinel alert-rule list --resource-group myResourceGroupSecurity Playbooks with Azure Sentinel
az logic workflow create --resource-group myResourceGroup --name myPlaybook --definition @playbook.jsonAutomated Response and Remediation
def remediate_threat(threat_id):
# Call Azure REST API or SDK to isolate resource
pass
Using Machine Learning for Fraud Detectionfrom azureml.core import Workspace, Model # Use ML pipeline for fraud detectionIntegration with Third-Party Security Tools
# Configure connectors in Azure Sentinel to ingest external security logsContinuous Security Monitoring
az monitor metrics list --resource-group myResourceGroup --resource myResource --metric "SecurityAlerts"Security Policy Automation
az policy assignment create --name "Require MFA" --scope /subscriptions/... --policy "Require MFA Policy Definition"Reporting and Compliance Automation
az security regulatory-compliance-assessment list --resource-group myResourceGroupFuture Trends in Security Automation
# Conceptual: AI agent auto-updates firewall rules based on threat intel
if threat_detected():
auto_update_firewall()
// Example: Initialize Azure ML Workspace
from azureml.core import Workspace
ws = Workspace.from_config()
print("Workspace loaded:", ws.name)
Model Development and Versioning
// Register a model with versioning
from azureml.core.model import Model
model = Model.register(workspace=ws, model_path="model.pkl", model_name="myModel")
print("Model registered:", model.name, model.version)
Automated Model Training Pipelines
// Define a simple pipeline step
from azureml.pipeline.steps import PythonScriptStep
train_step = PythonScriptStep(name="Train Model", script_name="train.py", compute_target="cpu-cluster")
Continuous Integration/Continuous Delivery for ML
// Example: Trigger pipeline on Git push (conceptual)
trigger_pipeline_on_commit(repo="gitrepo", branch="main")
Model Deployment Strategies
// Deploy model as web service
service = Model.deploy(ws, "myservice", [model], deployment_config)
service.wait_for_deployment(show_output=True)
Monitoring Models in Production
// Enable model monitoring (conceptual)
monitoring.enable(model_name="myModel")
Managing Model Drift and Retraining
// Trigger retrain if drift detected
if monitoring.detect_drift():
pipeline.submit("retrain_pipeline")
Governance and Compliance in MLOps
// Set role-based access control (RBAC)
workspace.set_permissions(user="data_scientist", role="Contributor")
Integration with Azure DevOps
// Trigger Azure ML pipeline from Azure DevOps YAML
trigger:
- main
jobs:
- job: RunMLPipeline
steps:
- script: az ml pipeline run --file pipeline.yml
Best Practices and Case Studies
// Case study: Automated retraining improved accuracy by 15%
// Copy data from Blob to Synapse SQL pool
az synapse data-integration pipeline create --file ingest_pipeline.json
Data Transformation and ETL
// Run Spark SQL for transformation
spark.sql("SELECT * FROM raw_data WHERE status='active'").write.saveAsTable("clean_data")
Managing Data Warehouses
// Pause SQL pool to save cost
az synapse sql pool pause --name mySqlPool --resource-group myResourceGroup
Using Apache Spark in Synapse
// Start Spark session in Synapse notebook
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SynapseSpark").getOrCreate()
Real-Time Data Processing
// Stream data from Event Hub to Synapse (conceptual)
stream = eventhub.readStream()
stream.writeStream.format("synapse").start()
Data Security and Access Control
// Grant user access to Synapse workspace
az synapse role assignment create --workspace-name myWorkspace --assignee user@domain.com --role "Synapse Contributor"
Performance Tuning
// Create partitioned table in Synapse SQL
CREATE TABLE sales_data (id INT, amount FLOAT) WITH (DISTRIBUTION = HASH(id), HEAP);
Integration with Power BI
// Connect Power BI to Synapse SQL pool (conceptual)
powerbi.connect("synapse_sql_pool_connection_string")
Monitoring and Troubleshooting
// View Synapse workspace metrics in Azure Portal
Best Practices for Data Engineering
// Example: Automate pipeline deployment
az synapse pipeline create --workspace-name myWorkspace --file pipeline.json
// Pseudocode: Collect customer data data = collect_customer_data() insights = analyze_with_ai(data) print(insights)Data Collection and Integration
// Example: Integrate data from APIs
api_data = fetch_api_data('crm')
merged_data = merge_datasets(api_data, web_data)
Customer Segmentation with AI// Using KMeans for segmentation from sklearn.cluster import KMeans segments = KMeans(n_clusters=4).fit_predict(customer_features) print(segments)Predictive Analytics for Customer Behavior
// Train model to predict churn model.fit(X_train, y_train) churn_preds = model.predict(X_test)Personalization and Recommendations
// Example recommendation generation recommendations = recommender.get_recommendations(user_id) print(recommendations)Integration with Marketing Automation
// Trigger campaign based on segment
if user_segment == 'high_value':
marketing_automation.launch_campaign('VIP_offer')
Privacy and Compliance Considerations
// Pseudocode for consent check
if user.has_consented():
process_data()
else:
anonymize_data()
Visualization and Reporting// Generate report with visualization tool report = create_dashboard(data) report.show()Customer Journey Analytics
// Track customer events timeline journey = track_customer_journey(user_id) print(journey)Use Cases and Success Stories
// Summary of results
print("Customer retention improved by 25% after AI adoption.")
// Azure CLI example to view policies az policy definition listManaging Subscriptions and Resource Groups
// Create resource group az group create --name MyResourceGroup --location eastusAzure Policy Implementation
// Assign built-in policy az policy assignment create --name 'EnforceTagging' --policy 'RequireTagPolicy' --scope /subscriptions/xxxxBlueprints for Environment Standardization
// Create blueprint (conceptual CLI) az blueprint create --name 'EnvironmentBlueprint' --resource-group MyResourceGroupCompliance Manager Overview
// Access Compliance Manager via Azure Portal // No CLI support yetAudit and Reporting Tools
// Create log analytics workspace az monitor log-analytics workspace create --resource-group MyResourceGroup --workspace-name MyWorkspaceManaging Risk with Azure Security Center
// Enable Security Center standard tier az security pricing create --name Default --tier StandardGovernance for Multi-Cloud Environments
// Connect resource to Azure Arc az connectedmachine connect --resource-group MyResourceGroup --name MyMachineRole-Based Access Control (RBAC) Best Practices
// Assign Reader role az role assignment create --assignee user@contoso.com --role Reader --scope /subscriptions/xxxxAutomating Governance with Azure Automation
// Create runbook (conceptual) az automation runbook create --resource-group MyResourceGroup --automation-account MyAutomation --name MyRunbook --type PowerShell
// Train Custom Vision model with Python SDK
from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
trainer = CustomVisionTrainingClient("", endpoint="")
project = trainer.create_project("MyCustomVisionProject")
Language Understanding (LUIS)
// Create LUIS app via CLI (conceptual)
az cognitiveservices account create --name myLUISaccount --resource-group myResourceGroup --kind LUIS
Speech-to-Text and Text-to-Speech Customization
// Customize speech recognition model
# Use Speech Studio portal or Speech SDK to train custom models
Translator Service and Multilingual Support
// Translate text using Translator Text API (Python)
import requests
endpoint = "https://api.cognitive.microsofttranslator.com/translate?api-version=3.0&to=fr"
headers = {'Ocp-Apim-Subscription-Key': '', 'Content-type': 'application/json'}
body = [{'Text': 'Hello, how are you?'}]
response = requests.post(endpoint, headers=headers, json=body)
print(response.json())
Knowledge Mining with Cognitive Search
// Create search index with Azure CLI
az search service create --name mysearchservice --resource-group myResourceGroup --sku basic
AI for Document Understanding// Analyze document layout with Form Recognizer SDK from azure.ai.formrecognizer import DocumentAnalysisClient client = DocumentAnalysisClient(endpoint="Custom Neural Voice and Ethical Use", credential=AzureKeyCredential(" ")) poller = client.begin_analyze_document_from_url("prebuilt-layout", " ") result = poller.result() for table in result.tables: print(table.cells)
// Neural Voice creation requires Azure Speech Studio portal setup
# Ethical use must be reviewed before deployment
Security and Privacy in Cognitive Services
// Enable encryption in Cognitive Services resource
az cognitiveservices account update --name mycogservice --resource-group myResourceGroup --assign-identity
Performance Optimization
// Example: Use batch translation to reduce API calls
# Send multiple texts in one request to Translator API
Integrating Cognitive Services with Azure Functions
// Azure Function (Python) calling Cognitive Services
import azure.functions as func
import requests
def main(req: func.HttpRequest) -> func.HttpResponse:
response = requests.post("", headers={"Ocp-Apim-Subscription-Key": ""})
return func.HttpResponse(response.text)
// Create a Synapse pipeline via Azure Portal or REST API
# No direct CLI example; use Synapse Studio for orchestration design
Data Movement Activities
// Example: Copy data activity in pipeline JSON
{
"name": "CopyBlobToSQL",
"type": "Copy",
"inputs": [ { "referenceName": "BlobDataset", "type": "DatasetReference" } ],
"outputs": [ { "referenceName": "SqlDataset", "type": "DatasetReference" } ]
}
Control Flow and Parameters
// Use If Condition activity example in JSON
{
"name": "IfCondition",
"type": "IfCondition",
"expression": "@equals(pipeline().parameters.runType, 'Full')",
"ifTrueActivities": [ /* activities */ ],
"ifFalseActivities": []
}
Triggering Pipelines and Scheduling
// Create schedule trigger with Azure CLI
az synapse trigger create --name MyScheduleTrigger --workspace-name MyWorkspace --type ScheduleTrigger --interval 1 --frequency Hour
Debugging and Monitoring Pipelines
// View pipeline runs in Synapse Studio UI
# Use portal or REST API for detailed monitoring
Integration Runtime Management
// Create self-hosted integration runtime via CLI (conceptual)
az synapse integration-runtime create --name MySelfHostedIR --workspace-name MyWorkspace --resource-group MyResourceGroup --type SelfHosted
Custom Activities with Azure Functions
// Pipeline activity calling Azure Function example (JSON snippet)
{
"name": "AzureFunctionActivity",
"type": "AzureFunctionActivity",
"typeProperties": { "functionName": "ProcessDataFunction" }
}
Handling Failures and Retries
// Set retry policy in activity JSON
{
"name": "CopyData",
"type": "Copy",
"policy": {
"retry": 3,
"retryIntervalInSeconds": 30
}
}
Security in Pipeline Execution
// Assign managed identity to pipeline for secure access
az synapse managed-identity assign --workspace-name MyWorkspace --resource-group MyResourceGroup
Use Cases and Best Practices
// Modular pipeline design example (conceptual)
# Use pipeline templates and parameterization for reusability
# Azure CLI example: Create AVD host pool
az desktopvirtualization hostpool create --resource-group MyRG --name MyHostPool --location eastus --validation-environment false --custom-rdp-property ""
Deployment Architecture
# Pseudocode: Deploy session hosts in host pool
# az desktopvirtualization sessionhost create --hostpool-name MyHostPool --resource-group MyRG --name SessionHost1
User Profile Management
# Example FSLogix profile container configuration (PowerShell)
Set-ItemProperty -Path "HKLM:\SOFTWARE\FSLogix\Profiles" -Name "Enabled" -Value 1
Security and Compliance in AVD
# Enable Azure AD Conditional Access for AVD (pseudocode)
# az ad conditional-access policy create --name "AVD Policy" --conditions ...
Scaling and Load Balancing
# Auto-scale schedule example (pseudocode)
# schedule auto-scale to add session hosts at 8am, remove at 6pm
Integration with Microsoft 365
# Example: Enable Teams optimization in AVD (registry)
Set-ItemProperty -Path "HKLM:\SOFTWARE\Policies\Microsoft\Teams" -Name "AVDOptimization" -Value 1
Monitoring and Diagnostics
# Query user session counts in Log Analytics
DesktopVirtualizationConnections | summarize count() by SessionHost
Cost Optimization Strategies
# Example: Schedule shutdown of idle session hosts (pseudocode)
# Azure Automation Runbook to stop VMs at night
Application Delivery and Management
# Publish remote app example (Azure CLI)
az desktopvirtualization application group application add --resource-group MyRG --application-group-name MyAppGroup --name "Word" --resource-type RemoteApp --command-line "winword.exe"
Troubleshooting and Support
# Collect AVD diagnostics logs (pseudocode)
# az monitor diagnostic-settings create ...
# Example: Create custom connector via Azure CLI (pseudocode)
# az logic workflow custom-connector create --resource-group MyRG --name MyConnector --spec ./swagger.json
API Management Integration
# Example: Link Logic App to API Management (pseudocode)
# az apim api create --service-name MyAPIM --resource-group MyRG --path logicapp --api-id mylogicapp
B2B and Enterprise Integration Patterns
# Example: Configure EDI partner agreement (pseudocode)
# az logic integration-account agreement create ...
Error Handling and Compensation Logic
# Example: Configure retry policy
{
"type": "RetryPolicy",
"retryCount": 3,
"retryInterval": "PT10S"
}
Stateful vs Stateless Workflows
# Example: Define stateful workflow in Logic Apps JSON (snippet)
"definition": {
"stateful": true,
...
}
Event-Driven Architecture with Logic Apps
# Example: Trigger on Event Grid event
{
"type": "EventGridTrigger",
"inputs": {}
}
Integration with On-Premises Systems
# Example: Configure On-Premises Data Gateway (pseudocode)
# gateway.create --name MyGateway --resource-group MyRG
Security Best Practices
# Assign managed identity to Logic App
az logic workflow update --resource-group MyRG --name MyLogicApp --set identity.type=SystemAssigned
Performance Tuning
# Adjust concurrency control in Logic Apps
{
"concurrency": {
"runs": 10
}
}
Case Studies and Scenarios
# Case study: Automate order processing using Logic Apps (pseudocode)
# LogicApp.trigger_on_new_order()
# LogicApp.call_SAP_API()
trigger:
- main
pool:
vmImage: 'ubuntu-latest'
steps:
- script: echo "Hello, Azure DevOps!"
stages:
- stage: Build
jobs:
- job: BuildJob
steps:
- script: echo "Building..."
- stage: Deploy
dependsOn: Build
jobs:
- job: DeployJob
steps:
- script: echo "Deploying..."
steps:
- task: AzureResourceManagerTemplateDeployment@3
inputs:
deploymentScope: 'Resource Group'
templateLocation: 'Linked artifact'
csmFile: 'azuredeploy.json'
# Example: Set pipeline permissions in Azure DevOps portal
# Assign roles: Reader, Contributor, Administrator
- task: PublishBuildArtifacts@1
inputs:
PathtoPublish: '$(Build.ArtifactStagingDirectory)'
ArtifactName: 'drop'
publishLocation: 'Container'
- script: dotnet test MyProject.Tests.csproj --logger trx
- script: kubectl apply -f deployment.yaml
- task: AzureFunctionApp@1
inputs:
azureSubscription: 'MySubscription'
appType: 'functionAppLinux'
appName: 'my-function-app'
# View pipeline run results and test coverage in Azure DevOps UI
# Configure pre-deployment approvals in Azure DevOps release pipeline settings
# Use pipeline templates for reuse
# Example:
# - template: build.yml
az aks create --resource-group MyResourceGroup --name MyAKSCluster --node-count 3 --enable-addons monitoring --generate-ssh-keys
az aks nodepool add --resource-group MyResourceGroup --cluster-name MyAKSCluster --name nodepool2 --node-count 2
kubectl get svc
kubectl apply -f networkpolicy.yaml
az aks update --resource-group MyResourceGroup --name MyAKSCluster --enable-aad
az aks enable-addons --resource-group MyResourceGroup --name MyAKSCluster --addons monitoring
kubectl apply -f pvc.yaml
- task: Kubernetes@1
inputs:
connectionType: 'Azure Resource Manager'
azureSubscriptionEndpoint: 'MySubscription'
azureResourceGroup: 'MyResourceGroup'
kubernetesCluster: 'MyAKSCluster'
command: 'apply'
useConfigurationFile: true
configuration: 'manifests/deployment.yaml'
az aks scale --resource-group MyResourceGroup --name MyAKSCluster --node-count 5
kubectl autoscale deployment myapp --min=2 --max=10 --cpu-percent=80
az aks upgrade --resource-group MyResourceGroup --name MyAKSCluster --kubernetes-version 1.24.0
kubectl logs pod/myapp-pod
kubectl describe pod myapp-pod
// Create Recovery Services vault (CLI) az backup vault create --resource-group MyRG --name MyBackupVault --location eastus
// Create backup policy (conceptual JSON)
{
"schedulePolicy": {"scheduleRunFrequency": "Daily", "scheduleRunTimes": ["02:00"]},
"retentionPolicy": {"retentionDuration": "30 days"}
}
// Enable VM backup az backup protection enable-for-vm --resource-group MyRG --vault-name MyBackupVault --vm MyVM --policy-name DefaultPolicy
// Enable Site Recovery for VM (CLI) az backup protection enable-for-vm --resource-group MyRG --vault-name MyBackupVault --vm MyVM
// Adjust replication frequency in Site Recovery policy (conceptual)
{
"replicationFrequencyInSeconds": 60
}
// Recovery plan failover timing configured in Azure portal
// Test failover (CLI) az site-recovery recovery-plan test-failover --resource-group MyRG --vault-name MyBackupVault --recovery-plan-name MyPlan --failover-direction PrimaryToRecovery
// Enable encryption on Recovery Services vault (conceptual) // Encryption is enabled by default using Azure-managed keys
// View backup costs via Azure Cost Management
az costmanagement query --scope /subscriptions/{subscriptionId} --timeframe MonthToDate
// Sample Azure Automation runbook trigger for failover (conceptual) // Runbook executes failover process for Site Recovery
// View security posture via Azure Portal https://portal.azure.com/#blade/Microsoft_Azure_Security/OverviewBlade
// Enable threat protection (CLI) az security auto-provisioning-setting update --name default --auto-provision "On"
// Enable vulnerability assessment extension on VM az vm extension set --publisher Microsoft.Azure.Security --name AzureSecurityLinux --vm-name MyVM --resource-group MyRG
// List security recommendations az security assessment list
// Connect Security Center to Sentinel (conceptual) // Configure workspace linkage in Azure Portal
// View alerts via Azure Portal or CLI az security alert list
// Create custom policy definition (conceptual) // az policy definition create --name customPolicy --rules policyRules.json
// View compliance dashboard in Azure Portal https://portal.azure.com/#blade/Microsoft_Azure_Security/ComplianceDashboardBlade
// Create automation rule (conceptual) // Use Azure Logic Apps to automate remediation
// Example: Assign least privilege IAM roles
az role assignment create --assignee user@example.com --role Reader --scope /subscriptions/{subscriptionId}
// Example: Connect data sources in Azure Sentinel via portal
// Setup log analytics workspace and enable data connectors
Setting up Data Connectors
// Enable Microsoft Defender data connector via Azure CLI
az sentinel data-connector create --resource-group rg --workspace-name ws --data-connector-id MicrosoftSecurityIncidentCreation
Creating Custom Analytics Rules
SecurityEvent
| where EventID == 4625
| summarize Count=count() by Account
| where Count > 5
Hunting Queries and Investigation
let SuspiciousLogins = SecurityEvent | where EventID == 4625 and Account !contains "admin";
SuspiciousLogins
Automated Playbooks
// Example: Logic App to disable user on suspicious activity
// Triggered by Sentinel alert, calls Azure AD API
Threat Intelligence Integration
// Import threat intelligence via TAXII feed connector in Sentinel
Incident Response Orchestration
// Sample incident creation via API
POST https://management.azure.com/.../incidents
Compliance and Audit Reporting
// Export compliance reports as workbooks in Sentinel portal
Machine Learning in Sentinel
// ML-based anomaly detection example query
Heartbeat
| extend anomaly_score = anomaly_detection_function(CounterValue)
| where anomaly_score > threshold
Scaling Sentinel for Enterprises
// Configure Sentinel workspace retention via CLI
az monitor log-analytics workspace update --retention-time 365
// Example: Define fairness metrics in ML model evaluation
from sklearn.metrics import classification_report
print(classification_report(y_true, y_pred))
Bias Detection and Mitigation
// Sample code for bias mitigation using re-weighting
# Adjust sample weights to balance classes during training
Transparency and Explainability
// SHAP explanation example
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
shap.summary_plot(shap_values, X)
Privacy and Data Protection
// Example: Data anonymization with hashing
import hashlib
hashed_id = hashlib.sha256(original_id.encode()).hexdigest()
Human-in-the-Loop AI
// Workflow: Model suggests, human approves or rejects decisions before action
Governance Frameworks
// Example: Documented AI model governance policy
Regulatory Considerations
// Conduct compliance checks and audits during AI project lifecycle
Building Ethical AI Solutions
// Use Azure Responsible AI tools for fairness and interpretability checks
Monitoring AI Systems for Fairness
// Periodic fairness evaluation using automated pipelines
Case Studies and Guidelines
// Reference: Microsoft Responsible AI Principles and Case Studies documentation
// Example: JSON snippet defining a telemetry in device template
{
"displayName": "Temperature",
"name": "temperature",
"schema": "double",
"unit": "Celsius"
}
Custom Rules and Actions
// Example: Rule condition JSON
{
"condition": "temperature > 75",
"actions": ["sendEmailNotification"]
}
Device Management and Monitoring
// Example: REST API call to list devices
GET https://{app_subdomain}.azureiotcentral.com/api/devices?api-version=1.0
Integration with Power BI
// Example: Export IoT Central data to Power BI via REST API
GET https://api.powerbi.com/v1.0/myorg/datasets
Security Best Practices
// Example: Using X.509 certificates for device authentication
// Device connects with certificate credentials stored securely
Scaling IoT Solutions
// Example: Configure message routing for scale
{
"endpoint": "EventHub",
"filter": "true"
}
Edge Computing Integration
// Example: Deploy module to IoT Edge device using Azure CLI
az iot edge set-modules --device-id MyEdgeDevice --hub-name MyIoTHub --content ./deployment.json
Data Export and Analytics
// Example: Configure data export to Blob Storage
{
"destination": {
"storageAccount": "myblobstorage"
}
}
Firmware Updates and OTA
// Example: Firmware update job creation API call
POST /firmwareUpdateJobs
{
"deviceIds": ["device1", "device2"],
"firmwareVersion": "1.0.2"
}
Industry Use Cases
// Example: Monitoring factory machines telemetry for predictive maintenance
// Example: Using Azure Cognitive Services with data anonymization
// Strip PII before sending data to APIs
Securing API Keys and Endpoints
// Example: Load API key from environment variable (Python)
import os
api_key = os.getenv('COGNITIVE_API_KEY')
Ethical Considerations
// Example: Review outputs for fairness before deployment
Handling Sensitive Data
// Example: Mask sensitive text before analysis
def mask_sensitive(text):
return text.replace("secret", "*****")
Accessibility Features
// Example: Use Speech SDK with subtitles enabled
// SpeechRecognition with real-time captioning
AI Model Fairness and Bias
// Example: Use fairness toolkits to evaluate model bias
User Consent and Transparency
// Example: Consent form before AI data collection
Logging and Auditing Use
// Example: Enable diagnostic logs for Cognitive Services
az monitor diagnostic-settings create --resource /subscriptions/... --logs '[{"category":"AuditLogs","enabled":true}]'
Incident Response for AI Services
// Example: Incident response workflow pseudocode
def incident_response():
identify_issue()
notify_team()
mitigate()
review_and_learn()
Continuous Improvement Practices
// Example: Regular retraining pipeline for AI models
Mapping Data Flows in Azure Data Factory allow visually designing data transformations at scale without writing code. They support complex ETL tasks such as joins, aggregations, and conditional splits that run on Spark clusters, providing a powerful way to build scalable data pipelines.
// Example: Create a data flow activity in ADF JSON pipeline
"activities": [{
"name": "MappingDataFlow1",
"type": "MappingDataFlow",
"typeProperties": { "dataFlow": { "referenceName": "MyDataFlow" } }
}]
Integration Runtimes (IR) provide compute infrastructure for running pipelines in Azure Data Factory. Managing IR includes scaling, configuring self-hosted or Azure IRs, and handling network and security settings to ensure optimal data movement and transformation.
// Create self-hosted IR via Azure CLI
az datafactory integration-runtime self-hosted create --factory-name myADF --resource-group myRG --name myIR
Parameterization enables passing dynamic values to datasets and pipelines, making pipelines reusable and flexible. Expressions allow logical and string manipulations in parameters, conditions, and activities to customize behavior.
// Example pipeline parameter usage
"parameters": { "fileName": { "type": "String" } },
"activities": [{
"type": "Copy",
"inputs": [{ "name": "InputDataset", "parameters": { "fileName": "@pipeline().parameters.fileName" } }]
}]
Azure Data Factory offers debugging features such as trigger runs, step-through activity runs, and data preview to validate and troubleshoot pipelines before production deployment, reducing runtime errors.
// Trigger debug run via Azure CLI
az datafactory pipeline run create --factory-name myADF --resource-group myRG --name myPipeline --parameters "{\"fileName\":\"test.csv\"}"
Triggers in ADF schedule pipeline execution. Types include schedule triggers (time-based), tumbling window triggers (periodic windows), and event-based triggers (blob or custom events), allowing flexible automation.
// Create schedule trigger via CLI
az datafactory trigger create --factory-name myADF --resource-group myRG --name dailyTrigger --type ScheduleTrigger --properties "{\"recurrence\":{\"frequency\":\"Day\",\"interval\":1}}"
Custom activities run code/scripts like Python, .NET, or batch scripts within pipelines. This extends ADF capabilities to custom logic or external systems integration beyond built-in connectors.
// Define custom activity in pipeline JSON
"activities": [{
"name": "RunPythonScript",
"type": "Custom",
"typeProperties": {
"command": "python process_data.py"
}
}]
Data lineage tracks the data flow path through transformations and datasets, helping audit and troubleshoot. ADF monitoring provides pipeline run details, activity status, and alerts for failures.
// Query pipeline run status via CLI az datafactory pipeline-run show --factory-name myADF --resource-group myRG --run-id
ADF security uses role-based access control (RBAC), managed identities, and private endpoints to protect pipelines and data sources, ensuring least privilege and secure data movement.
// Assign role to user on ADF resource
az role assignment create --assignee user@example.com --role "Data Factory Contributor" --scope /subscriptions/.../resourceGroups/myRG/providers/Microsoft.DataFactory/factories/myADF
Optimize pipeline performance by using parallel copy, partitioning, caching, and scaling Integration Runtime compute resources to reduce execution time and cost.
// Enable parallel copy in copy activity
"copyBehavior": "parallel"
Common use cases include data migration, ELT pipelines, batch processing, and hybrid data integration. Patterns such as incremental loads and orchestration pipelines help design maintainable solutions.
// Incremental copy example using watermark column
"source": {
"type": "SqlSource",
"query": "SELECT * FROM sales WHERE LastModified > @pipeline().parameters.lastWatermark"
}
Azure Cost Management provides granular breakdowns of resource costs by service, resource group, or tag. Detailed analysis helps identify cost drivers and optimize resource utilization.
// Query cost details via Azure CLI
az costmanagement query --scope /subscriptions/{subscriptionId} --type Usage --timeframe MonthToDate
Tagging resources with meaningful keys enables cost allocation by department, project, or environment. Consistent tagging supports reporting and budget management.
// Add tag to resource
az resource tag --tags Project=Finance Environment=Prod --resource-group myRG --name myVM --resource-type Microsoft.Compute/virtualMachines
Budgets set spending limits and send alerts when thresholds are reached, helping teams control cloud expenses proactively.
// Create budget with alert via CLI
az consumption budget create --amount 1000 --category cost --name "MonthlyBudget" --resource-group myRG --time-grain Monthly
Azure uses AI to forecast future costs based on historical usage patterns, enabling better financial planning and avoiding unexpected expenses.
// Access cost forecast in portal or via API (conceptual)
az consumption forecast show --scope /subscriptions/{subscriptionId}
Rightsizing analyzes resource utilization metrics to recommend scaling down or shutting off underused resources, optimizing cost efficiency without impacting performance.
// Get VM recommendations via Azure Advisor
az advisor recommendation list --category Performance --resource-group myRG
Managing Reserved Instances (RIs) helps save costs by committing to long-term usage. Tracking RI utilization and expiration ensures maximum savings and prevents overprovisioning.
// Purchase RI via Azure portal or CLI (conceptual)
// Track RI usage via Advisor
az advisor recommendation list --category Cost --resource-group myRG
Spot instances offer unused capacity at discounted rates for interruptible workloads. Savings Plans provide flexible pricing commitments. Both reduce costs when used appropriately.
// Deploy spot VM with CLI
az vm create --name spotVM --resource-group myRG --priority Spot --image UbuntuLTS
Azure Policy enables enforcing tagging, location, SKU, and cost-related policies. Governance ensures compliance with organizational spending rules.
// Create policy assignment to enforce tags
az policy assignment create --policy policyDefinitionId --scope /subscriptions/{subscriptionId}
Billing APIs allow programmatic access to detailed usage and cost data, enabling custom reporting, alerts, and integration with external financial systems.
// Call Azure Billing API (conceptual)
GET https://management.azure.com/subscriptions/{subscriptionId}/providers/Microsoft.Billing/billingAccounts
Azure Cost Management provides dashboards that visualize spending, forecast, and recommendations. These insights help optimize budgets and resource usage effectively.
// Export cost data to Power BI or Excel for analysis
az costmanagement export create --definition @exportDefinition.json