🔄 Streaming Services¶

Real-time data processing and event-driven architecture services for streaming analytics.

🎯 Service Overview¶

Streaming services enable real-time data processing, event ingestion, and event-driven architectures. These services handle continuous data streams with low latency and high throughput requirements.

graph LR
    subgraph "Event Sources"
        IoT[IoT Devices]
        Apps[Applications]
        APIs[APIs & Services]
        Logs[System Logs]
    end

    subgraph "Streaming Services"
        EventHubs[Azure Event Hubs<br/>Event Ingestion]
        StreamAnalytics[Azure Stream Analytics<br/>Stream Processing]
        EventGrid[Azure Event Grid<br/>Event Routing]
    end

    subgraph "Destinations"
        DataLake[Data Lake<br/>Storage]
        CosmosDB[Cosmos DB<br/>Real-time Data]
        PowerBI[Power BI<br/>Live Dashboards]
        Functions[Azure Functions<br/>Event Handlers]
    end

    IoT --> EventHubs
    Apps --> EventHubs
    APIs --> EventGrid
    Logs --> StreamAnalytics

    EventHubs --> StreamAnalytics
    EventGrid --> Functions

    StreamAnalytics --> DataLake
    StreamAnalytics --> CosmosDB
    StreamAnalytics --> PowerBI
    EventGrid --> Functions

🚀 Service Cards¶

📨 Azure Event Hubs¶

Big data streaming platform and event ingestion service for millions of events per second.

🔥 Key Strengths¶

Massive Scale: Ingest millions of events per second
Kafka Compatible: Drop-in replacement for Apache Kafka
Auto-scaling: Automatically adjust to traffic patterns
Global Distribution: Multi-region event streaming

📊 Core Capabilities¶

Event Streaming Basics - Fundamental concepts
Kafka Compatibility - Migration from Kafka
Capture to Storage - Automatic archival
Schema Registry - Schema management

🎯 Best For¶

High-volume event ingestion
IoT device telemetry
Application logging and monitoring
Kafka migration scenarios

💰 Pricing Model¶

Standard: Throughput Units (TU) + ingress/egress
Dedicated: Dedicated Capacity Units (CU) for isolation
Premium: Enhanced performance and security

📖 Full Documentation →

⚡ Azure Stream Analytics¶

Real-time analytics service for streaming data with SQL-based queries.

🔥 Key Strengths¶

SQL-based: Familiar SQL syntax for stream processing
Serverless: No infrastructure management required
Built-in ML: Anomaly detection and machine learning
Edge Support: Deploy to IoT Edge devices

📊 Core Capabilities¶

Stream Processing Basics - Core concepts
Windowing Functions - Time-based aggregations
Anomaly Detection - Built-in ML features
Edge Deployments - IoT Edge processing

🎯 Best For¶

Real-time analytics and dashboards
IoT device analytics
Fraud detection systems
Operational monitoring

💰 Pricing Model¶

Streaming Units (SU): Compute capacity pricing
Edge: Per device licensing
Pay-as-you-go: Hourly billing

📖 Full Documentation →

🌐 Azure Event Grid¶

Event routing service for building reactive, event-driven applications.

🔥 Key Strengths¶

Serverless: Pay-per-event pricing model
Rich Filtering: Content-based event routing
Reliable Delivery: Built-in retry and dead letter queues
Azure Integration: Native events from all Azure services

📊 Core Capabilities¶

Event-driven Architecture - Design patterns
System Topics - Built-in Azure events

🎯 Best For¶

Event-driven application architectures
Serverless workflow automation
System integration and decoupling
Reactive microservices

💰 Pricing Model¶

Pay-per-operation: $0.60 per million operations
No minimum fees: True pay-as-you-use
Advanced features: Additional costs for premium features

📖 Full Documentation →

📊 Service Comparison¶

Feature Matrix¶

Feature	Event Hubs	Stream Analytics	Event Grid
Primary Purpose	Event Ingestion	Stream Processing	Event Routing
Throughput	Very High (millions/sec)	Medium (SU-based)	High
Processing Logic	❌ None	✅ SQL-based	❌ Routing Only
Kafka Compatible	✅ Yes	❌ No	❌ No
Built-in Analytics	❌ No	✅ Advanced	❌ No
Event Filtering	❌ Limited	✅ SQL-based	✅ Advanced
Schema Registry	✅ Yes	❌ No	❌ No
Serverless Option	❌ No	✅ Yes	✅ Yes
Edge Deployment	❌ No	✅ Yes	❌ No
Dead Letter Queues	❌ No	❌ No	✅ Yes
Cost Model	TU/CU-based	SU-based	Per-operation

Use Case Recommendations¶

📈 Real-time Analytics Dashboard¶

Architecture: Event Hubs → Stream Analytics → Power BI

Primary: Stream Analytics for processing
Supporting: Event Hubs for ingestion
Pattern: Lambda Architecture

🏭 IoT Device Monitoring¶

Architecture: IoT Devices → Event Hubs → Stream Analytics → Alerts

Primary: Event Hubs for high-volume ingestion
Supporting: Stream Analytics for real-time analysis
Pattern: Streaming Architectures

🔗 Event-driven Microservices¶

Architecture: Services → Event Grid → Functions/Logic Apps

Primary: Event Grid for service decoupling
Supporting: Azure Functions for event handling
Pattern: Streaming Architectures

📊 Stream Processing Pipeline¶

Architecture: Data Sources → Event Hubs → Stream Analytics → Storage

Primary: Stream Analytics for transformation
Supporting: Event Hubs for buffering
Pattern: Streaming Architectures

🎯 Common Architecture Patterns¶

Lambda Architecture with Streaming Services¶

graph TB
    Sources[Data Sources] --> EventHubs[Event Hubs]

    EventHubs --> StreamAnalytics[Stream Analytics<br/>Speed Layer]
    EventHubs --> DataFactory[Data Factory<br/>Batch Layer]

    StreamAnalytics --> CosmosDB[Cosmos DB<br/>Real-time Views]
    DataFactory --> DataLake[Data Lake<br/>Batch Views]

    CosmosDB --> ServingLayer[Serving Layer]
    DataLake --> ServingLayer

    ServingLayer --> PowerBI[Power BI]
    ServingLayer --> Applications[Applications]

Event-Driven Architecture¶

graph LR
    subgraph "Event Publishers"
        Service1[Service A]
        Service2[Service B]
        Azure[Azure Services]
    end

    subgraph "Event Infrastructure"
        EventGrid[Event Grid<br/>Event Router]
        EventHubs[Event Hubs<br/>Event Store]
    end

    subgraph "Event Consumers"
        Functions[Azure Functions]
        LogicApps[Logic Apps]
        StreamAnalytics[Stream Analytics]
    end

    Service1 --> EventGrid
    Service2 --> EventGrid
    Azure --> EventGrid

    EventGrid --> Functions
    EventGrid --> LogicApps
    EventGrid --> EventHubs

    EventHubs --> StreamAnalytics

🚀 Getting Started Recommendations¶

🆕 New to Streaming¶

Start with: Azure Stream Analytics
Why: SQL-based, serverless, easy to learn
Next: Add Event Hubs for higher throughput
Pattern: Simple stream processing pipeline

📊 Analytics-Focused¶

Start with: Event Hubs + Stream Analytics
Why: Purpose-built for analytics workloads
Next: Integrate with Power BI and Data Lake
Pattern: Real-time analytics dashboard

🏗️ Architecture-Focused¶

Start with: Event Grid
Why: Event-driven architecture foundation
Next: Add Event Hubs for high-volume scenarios
Pattern: Event-driven microservices

🏭 IoT-Focused¶

Start with: Event Hubs + Stream Analytics
Why: Optimized for IoT scenarios
Next: Add Edge deployments
Pattern: IoT analytics pipeline

💰 Cost Optimization Strategies¶

Event Hubs Cost Optimization¶

Right-size throughput units based on actual usage
Use auto-inflate to handle traffic spikes efficiently
Consider dedicated clusters for predictable high-volume workloads
Optimize partition count based on consumer parallelism

Stream Analytics Cost Optimization¶

Use appropriate streaming unit size for your workload
Implement auto-scaling to adjust to demand
Optimize query complexity to reduce SU requirements
Use temporal aggregations to reduce processing overhead

Event Grid Cost Optimization¶

Implement efficient filtering to reduce unnecessary operations
Use system topics instead of custom topics where possible
Optimize event schema to minimize payload size
Implement proper error handling to avoid retry costs

📖 Detailed Cost Guide →

🔒 Security Best Practices¶

Authentication & Authorization¶

Azure AD Integration: Use managed identities where possible
Shared Access Signatures: Implement least-privilege access
RBAC: Apply role-based access control
Network Security: Use private endpoints and VNet integration

Data Protection¶

Encryption in Transit: TLS 1.2 for all connections
Encryption at Rest: Azure Storage Service Encryption
Key Management: Azure Key Vault for secret management
Data Masking: Implement data anonymization where needed

📖 Security Guide →

📊 Monitoring & Observability¶

Key Metrics to Monitor¶

Event Hubs Metrics¶

Incoming Messages: Message ingestion rate
Outgoing Messages: Message consumption rate
Throttled Requests: Capacity utilization
Capture Backlog: Archive processing status

Stream Analytics Metrics¶

SU Utilization: Resource consumption
Input/Output Events: Processing throughput
Watermark Delay: Processing latency
Runtime Errors: Processing health

Event Grid Metrics¶

Published Events: Event publication rate
Delivered Events: Successful delivery rate
Failed Deliveries: Error rate monitoring
Dead Letter Events: Failed event tracking

📖 Monitoring Guide →

🔧 Integration Scenarios¶

With Analytics Services¶

Integration Scenarios: Real-time to batch processing
Streaming Architectures: Stream processing patterns
Real-time Analytics: Complete solutions

With Storage Services¶

Storage Services Guide: Integration with storage
Integration Scenarios: Implementation guides
Best Practices: Optimization guidance

📖 All Integration Scenarios →

📚 Learning Resources¶

🎓 Getting Started¶

📖 Advanced Topics¶

🔧 Code Examples¶

Last Updated: 2025-01-28
Services Documented: 3
Coverage: Complete