Azure Event Hubs

Key Components and Flow

This diagram illustrates a common event processing architecture leveraging Azure Event Hubs. The flow typically involves:

• Event Sources: Applications, devices, or services that generate and send events.
• Azure Event Hubs: A highly scalable, real-time data streaming service that ingests millions of events per second. It acts as a buffer and gateway for event data.
• Event Consumers: Applications or services that read and process events from Event Hubs. This can include real-time analytics, data warehousing, or custom processing logic.
• Processing & Analytics: Services like Azure Stream Analytics, Azure Functions, or custom applications that transform, analyze, and act upon the event data.
• Data Storage: Destinations for processed data, such as Azure Data Lake Storage, Azure SQL Database, Azure Cosmos DB, or other storage solutions.
• Monitoring & Alerting: Tools to observe the system's health, performance, and to trigger alerts for anomalies.

The architecture is designed for high throughput, low latency, and fault tolerance, making it suitable for a wide range of real-time data scenarios.

Example Technologies Used

• Event Ingestion: Azure Event Hubs SDKs (e.g., .NET, Java, Python), Kafka integration.
• Real-time Processing: Azure Stream Analytics, Azure Functions with Event Hubs triggers, Apache Spark Streaming.
• Data Storage: Azure Data Lake Storage Gen2, Azure Blob Storage, Azure Cosmos DB, Azure SQL Database.
• Monitoring: Azure Monitor, Application Insights.