Azure Event Hubs for Java: Scalable Event Streaming & Analytics

Event Hubs provides big data streaming for telemetry, logs, and real-time analytics at massive scale. With native Kafka protocol support and reactive programming patterns through Project Reactor, the Java SDK enables building high-throughput event systems that process millions of events per second.

This skill empowers AI assistants to integrate event streaming into Java applications — from IoT platforms ingesting device telemetry to financial systems processing market data and web applications capturing user behavior analytics.

What This Skill Does

The Azure Event Hubs SDK for Java delivers event publishing with batching and partitioning, distributed consumption via event processor, checkpoint management for fault tolerance, reactive patterns with Project Reactor, and Kafka compatibility for ecosystem integration.

Key capabilities include batch event sending for throughput optimization, partition key support for ordering guarantees, consumer groups for parallel processing, automatic load balancing across consumers, checkpoint storage in Blob Storage, and both synchronous and asynchronous clients.

Getting Started

Add Event Hubs dependencies:

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-messaging-eventhubs</artifactId>
</dependency>
<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-messaging-eventhubs-checkpointstore-blob</artifactId>
</dependency>

Create producer and send events:

EventHubProducerAsyncClient producer = new EventHubClientBuilder()
    .fullyQualifiedNamespace("<namespace>.servicebus.windows.net")
    .eventHubName("telemetry")
    .credential(new DefaultAzureCredentialBuilder().build())
    .buildAsyncProducerClient();

EventDataBatch batch = producer.createBatch().block();
batch.tryAdd(new EventData("Event data"));
producer.send(batch).block();

Process events with automatic checkpointing:

EventProcessorClient processor = new EventProcessorClientBuilder()
    .fullyQualifiedNamespace("<namespace>.servicebus.windows.net")
    .eventHubName("telemetry")
    .consumerGroup("$Default")
    .checkpointStore(new BlobCheckpointStore(...))
    .processEvent(context -> {
        System.out.println("Event: " + context.getEventData().getBodyAsString());
        context.updateCheckpoint();
    })
    .buildEventProcessorClient();

processor.start();

Key Features

Reactive Publishing with Project Reactor for non-blocking event sending. Event Processor handles distributed consumption with automatic partition assignment. Checkpoint Management enables exactly-once semantics through Azure Blob Storage. Partition Keys ensure ordering within logical groupings. Kafka Wire Protocol allows using Kafka clients seamlessly.

When to Use

Use for IoT telemetry ingestion, application log aggregation, financial transaction streams, user activity analytics, and real-time data pipelines feeding Stream Analytics or Spark. Avoid for request/response messaging or when strong ACID transactions are required.

Related Skills

• azure-identity-java - Authentication
• reactor-core - Reactive programming
• kafka-clients - Kafka protocol compatibility

Source

Maintained by Microsoft. View on GitHub