Google Dataflow vs Azure Stream Analytics
| Dimension | Google Cloud Dataflow | Azure Stream Analytics |
|---|---|---|
| Programming Model | Apache Beam SDKs (Java, Python, Go) for batch + streaming; user-defined transforms. | SQL-like declarative language with optional JavaScript/C# custom code. |
| Execution | Fully managed runner that autosizes workers; horizontal scaling per pipeline. | Managed streaming engine; scale by adjusting Streaming Units (SUs). |
| Latency Profile | Supports true streaming + windowed batch; latency depends on watermark configuration. | Optimised for sub-second event processing with windowing and reference data joins. |
| Ecosystem Integration | Native hooks into BigQuery, Pub/Sub, Cloud Storage, Vertex AI. | Tight integration with Event Hubs, IoT Hub, Azure Data Explorer, Synapse. |
| Custom Code | Rich transformation logic via Beam libraries, stateful processing, side inputs/outputs. | Custom functions limited to JavaScript/C# UDFs; complex logic often pushed to Azure Functions/Data Explorer. |
Selecting a Service
Choose Dataflow when you need portable pipelines, complex event-time processing, or the ability to run the same Beam code on other runners (Flink, Spark, on-prem). Dataflow shines for hybrid batch + streaming ETL and ML feature pipelines.
Choose Stream Analytics when teams prefer SQL, work primarily inside Azure Event Hubs/IoT Hub ecosystems, and require managed low-latency dashboards without heavy custom code.
Operational Considerations
- Scaling: Dataflow autoscaling reacts to backlog; tune maximum workers. Stream Analytics requires manual SU adjustments or autoscale rules.
- Testing: Use Beam unit tests with DirectRunner for Dataflow; Stream Analytics has local testing via Visual Studio/VS Code.
- Cost: Dataflow bills per vCPU-hour and memory; Stream Analytics charges per SU-hour. Model workloads to avoid overprovisioning.
Minimal Examples
Dataflow (Python)
import apache_beam as beam
with beam.Pipeline() as p:
(p | beam.io.ReadFromPubSub(topic="projects/.../topics/input")
| beam.Map(lambda msg: msg.decode("utf-8"))
| beam.io.WriteToText("gs://bucket/output"))
Stream Analytics (SQL)
SELECT System.Timestamp AS window_end,
AVG(temperature) AS avg_temp
INTO OutputTable
FROM InputStream TIMESTAMP BY event_time
GROUP BY TumblingWindow(minute, 5);