Why Event Hubs?

Modern applications and IoT systems often generate massive volumes of telemetry, logs, and streaming data. Traditional messaging systems can’t handle this scale efficiently. Azure Event Hubs is designed for big data ingestion, enabling real-time and batch processing of millions of events per second.

1. Event Hubs Overview

Definition:
A big data streaming ingestion service that collects, buffers, and processes large volumes of telemetry and event data.

Key Features:

• Handles millions of events per second.

• Supports real-time and batch processing.

• Integrates with Azure Stream Analytics, Databricks, Synapse, and Functions.

• Retains data for a configurable period (default 1–7 days, up to 90 days in premium tiers).

2. Core Concepts

• Partitions

  • Data is split into multiple partitions.

  • Events with the same partition key go to the same partition (order preserved).

• Consumer Groups

  • Multiple independent views of the event stream.

  • Example: one consumer group for real-time dashboards, another for batch analytics.

• Capture

  • Automatically archive streaming data to Blob or Data Lake.

3. Event Hubs Tiers

• Basic/Tier → entry-level, limited features.

• Standard Tier → multiple consumer groups, higher throughput.

• Dedicated Tier → isolated cluster, massive scale, SLA-backed.

• Premium Tier → predictable latency, enhanced security.

4. Best Use Cases

• IoT telemetry ingestion (sensor data, devices).

• Website clickstream logging.

• Application performance monitoring.

• Fraud detection and anomaly monitoring.

• Streaming analytics pipelines.

Example Enterprise Scenario

A ride-sharing platform requires:

• Processing millions of driver GPS updates per minute.

• Sending real-time ride assignment data to matching algorithms.

• Storing telemetry data for offline analysis in Data Lake.

Correct design:

• Use Event Hubs for ingesting GPS events.

• Create multiple consumer groups (one for real-time ride matching, one for analytics).

• Enable Event Hubs Capture to push data into Data Lake.

Confusion Buster

• Event Hubs vs Event Grid

  • Event Hubs = data streams (telemetry, continuous).

  • Event Grid = discrete events (blob uploaded, resource created).

• Event Hubs vs Service Bus

  • Event Hubs = big data, telemetry, millions/sec.

  • Service Bus = enterprise-grade transactions, order, DLQ.

• Partition Key vs Consumer Group

  • Partition Key = ensures order for related events.

  • Consumer Group = independent view for different consumers.

Exam Tips

• “Which Azure service ingests millions of telemetry events per second?” → Event Hubs.

• “Which feature ensures ordering for related events in Event Hubs?” → Partition Key.

• “Which feature allows independent readers of the same event stream?” → Consumer Groups.

• “Which feature archives streaming data to Blob or Data Lake?” → Capture.

What to Expect in the Exam

• Direct Q: “Which Azure service is best for IoT telemetry ingestion?” → Event Hubs.

• Scenario Q: “Company needs to stream website clicks in real time for dashboards.” → Event Hubs + Stream Analytics.

• Scenario Q: “App requires capturing raw telemetry in Data Lake for offline analysis.” → Event Hubs Capture.

• Trick Q: “Event Grid can process millions of telemetry events per second like Event Hubs.” → False.