Tech Matchups: Azure Event Hubs vs Azure Data Factory
Overview
Picture your data pipeline as a galactic relay, where streams and batches race to their destinations. Azure Event Hubs, launched in 2014, is the hyperspace courier—a high-throughput event streaming platform, used by 20% of Azure’s streaming customers (2024).
Azure Data Factory, introduced in 2015, is the orbital scheduler—a managed ETL service for batch data movement, powering 25% of Azure’s data integration workloads.
Both are data titans, but their roles differ: Event Hubs excels in real-time streaming, while Data Factory orchestrates scheduled ETL. They’re vital for apps from IoT to analytics, balancing speed with structure.
Section 1 - Data Ingestion and Setup
Event Hubs captures streams—example: create a hub:
Data Factory builds pipelines—example: create a pipeline:
Event Hubs ingests real-time events (e.g., 1M IoT messages/sec) with AMQP or Kafka protocols. Data Factory schedules batch transfers (e.g., 100GB/day) with 100+ connectors. Event Hubs is streaming-focused, Data Factory batch-focused.
Scenario: Event Hubs processes live sensor data; Data Factory migrates CRM data. Choose by data flow.
Section 2 - Performance and Scalability
Event Hubs scales with Throughput Units—example: 10 TU for 10M events/sec with ~1ms latency. Scales to 1TB/day with partitioning.
Data Factory scales with Integration Runtimes—example: 100 activities for 10TB/day with ~1min latency. Scales via parallel execution.
Scenario: Event Hubs streams 1M live events; Data Factory processes 100TB nightly. Event Hubs excels in speed, Data Factory in volume—pick by timing.
Section 3 - Cost Models
Event Hubs is per Throughput Unit—example: 1 TU (~$0.028/hour) costs ~$20/month. Free tier includes 1M events/month.
Data Factory is per activity—example: 1,000 activities (~$1/1,000) cost ~$1. Data movement (~$0.25/hour) adds costs. No free tier.
Practical case: Event Hubs suits live streams; Data Factory fits scheduled ETL. Event Hubs is stream-based, Data Factory activity-based—optimize by workload.
Section 4 - Use Cases and Ecosystem
Event Hubs excels in real-time—example: process 1M IoT events for analytics. Data Factory shines in ETL—think 100TB data warehouse loads.
Ecosystem-wise, Event Hubs integrates with Stream Analytics; Data Factory with Synapse. Event Hubs is event-driven, Data Factory pipeline-driven.
Practical case: Event Hubs powers a dashboard; Data Factory builds a data lake. Choose by processing needs.
Section 5 - Comparison Table
| Aspect | Event Hubs | Data Factory |
|---|---|---|
| Type | Streaming | Batch ETL |
| Performance | ~1ms | ~1min |
| Cost | ~$0.028/TU-hour | ~$1/1,000 activities |
| Scalability | 1TB/day | 100TB/day |
| Best For | Real-time | Scheduled ETL |
Event Hubs suits live streams; Data Factory excels in batch ETL. Choose by data timing.
Conclusion
Azure Event Hubs and Data Factory are data integration powerhouses with distinct strengths. Event Hubs delivers high-throughput, real-time event streaming for dynamic apps like IoT or live analytics, ideal for low-latency needs. Data Factory orchestrates scheduled, large-scale ETL pipelines for data lakes or warehouses, perfect for batch processing. Consider data flow (streaming vs. batch), latency (milliseconds vs. minutes), and ecosystem integration.
For real-time analytics, Event Hubs shines; for data migration, Data Factory delivers. Pair Event Hubs with Stream Analytics or Data Factory with Synapse for optimal results. Test both—Event Hubs’ free tier or Data Factory’s pay-as-you-go make prototyping easy.