Azure Event Hubs is a robust, cloud-based data streaming platform that enables real-time event ingestion and processing at scale. It acts as the crucial entry point for event-driven systems, adeptly managing millions of events every second with minimal delay.

Key Components of Event Hub

Producer

To understand how Event Hubs operates, we first need to look at the Producer, which is responsible for supplying the data. This can come from various sources, such as:

- Streaming data
- Logs
- Files
- Media
- Weather Data
- IoT sensor data, among others.

Event Hub

Next, we have the core component that processes all this incoming data: Event Hub itself. This is the main processing entity that organizes the data into multiple Partitions. Partitions act like highway lanes—adding more can enhance throughput. You can have a maximum of 32 partitions within a single Event Hub.

It’s important to note that you’re not restricted to just one Event Hub; multiple Event Hubs can be created when more advanced processing is necessary. These can be grouped under a Namespace, which serves as a logical collection of Event Hubs.

Consumers

After data is processed, Consumers step in to utilize this information. Consumers can be any application needing access to the data or various Azure services, such as:

- Azure Stream Analytics
- Azure Data Explorer

Similar to Partitions, each Event Hub can support multiple Consumers within a Consumer Group. In turn, multiple Consumer Groups can exist simultaneously.

Important Considerations

1. Within the Same Consumer Group
In any given Consumer Group, each Partition can be accessed by only one Consumer at a time. For example:
- If Consumer-1 from Group-A is reading from Partition-0, Consumer-2 from Group-A cannot access Partition-0 concurrently.
- If Consumer-1 stops, then Partition-0 can be reassigned to Consumer-2 within Group-A.

2. Across Different Consumer Groups
Different Consumer Groups can access Partitions simultaneously without restrictions. Event Hubs (or Kafka) treats each group as an independent subscription to the same event stream. For instance:
- Consumer-1 from Group-A and Consumer-3 from Group-B can both read from Partition-0 at the same time without interfering.
- They each maintain their own offset/checkpoint independently.

In summary, Azure Event Hubs provides a scalable and efficient way to process real-time data, with a structured approach to producing, processing, and consuming events.

For more info visit the content source: Real-Time Data Streaming with Azure Event Hubs