Event Hubs Architecture

Azure Event Hubs is a highly scalable data streaming platform and event ingestion service. It can capture millions of events per second so you can develop and deploy event-driven applications and solutions.

Core Components

The Event Hubs architecture is designed for high throughput and low latency, enabling the ingestion and processing of vast amounts of event data. The primary components are:

Namespaces

An Event Hubs namespace is a logical container for Event Hubs instances. A namespace provides a unique scope name for your Event Hubs. When you create an Event Hubs namespace, you are creating a PaaS (platform-as-a-service) cloud application that is accessible via the Event Hubs protocol.

• Each namespace is globally unique within Azure.
• Namespaces are regional, meaning they are deployed within a specific Azure region.
• You can create multiple Event Hubs within a single namespace.

Event Hubs

An Event Hub is the entity within a namespace that data streams are sent to and consumed from. An Event Hub is a named entity that acts as a channel. You can think of it as a specific topic or stream of events.

• Events are organized and stored in partitions.
• Events are typically ordered within a partition.
• Events are retained for a configurable duration.

Partitions

Partitions are the fundamental unit of parallelism in Event Hubs. An Event Hub consists of one or more partitions. Event Hubs uses partitions to allow consumers to scale out. When data is sent to an Event Hub, it is appended to one of the partitions.

• The number of partitions is chosen when the Event Hub is created and cannot be changed later.
• Events with the same partition key are consistently sent to the same partition.
• Consumers can read events from partitions independently, enabling parallel processing.

Consumer Groups

A consumer group is a logical view of an Event Hub. Each consumer group allows a separate instance of an application to read from the Event Hub independently, without impacting other consumer groups.

• Multiple consumer groups can be created for a single Event Hub.
• Each consumer group tracks its own position (offset) in the event stream.
• This enables different applications or services to process the same events concurrently for different purposes (e.g., real-time analytics, archival, fraud detection).

Data Flow and Processing

The typical data flow in Event Hubs involves producers sending events and consumers reading them:

1. Producers: Applications or services that generate event data. They send these events to a specific Event Hub within a namespace.
2. Ingestion: Event Hubs receives the events and appends them to the appropriate partitions based on the partition key or round-robin distribution.
3. Storage: Events are stored in partitions for a configured retention period.
4. Consumers: Applications or services that process the event data. They join a consumer group and read events from the partitions.
5. Processing: Consumers process the events for various use cases, such as real-time analytics, data warehousing, or triggering downstream workflows.

Scalability and Durability

Event Hubs is designed with scalability and durability as core principles:

• Throughput: Event Hubs offers high throughput with dedicated capacity units (Throughput Units or Processing Units) that can be scaled up or down.
• Partitioning: The partitioning strategy allows for horizontal scaling of both ingestion and consumption.
• Durability: Event Hubs ensures data durability by replicating data across multiple storage locations within the Azure region.
• Availability: Event Hubs provides high availability through its underlying Azure infrastructure.

Integration with Other Azure Services

Event Hubs integrates seamlessly with other Azure services to build comprehensive event-driven architectures:

• Azure Functions: Trigger functions based on incoming events from Event Hubs for serverless processing.
• Azure Stream Analytics: Process and analyze event streams in real-time using a SQL-like query language.
• Azure Databricks: Perform complex big data analytics on event streams.
• Azure Data Lake Storage: Archive event data for long-term storage and batch processing using Event Hubs Capture.
• Azure Logic Apps: Orchestrate workflows triggered by Event Hubs events.