Google Cloud Dataflow vs. Spark Streaming
A side-by-side comparison based on G2 review data.
| Metric | Google Cloud Dataflow | Spark Streaming |
|---|---|---|
| Overall rating | 4.2 | 4.2 |
| Review count | 44 | 40 |
| Ease of use | 8.3 | 8.4 |
| Ease of setup | 8.2 | 7.9 |
| Quality of support | 8.6 | 8.8 |
| Meets requirements | 9.1 | 9.1 |
| NPS | 42.857143 | 39.473684 |
| Small Business | 36.4% | 30.8% |
| Mid-Market | 31.8% | 43.6% |
| Enterprise | 31.8% | 25.6% |
| Total page views | 219 | 74 |
| Total LLM citations | 272 | 66 |
Google Cloud Dataflow
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed. And with its serverless approach to resource provisioning and management, you have access to virtually limitless capacity to solve your biggest data processing challenges, while paying only for what you use.
- Features
- Real-time Analytics
- Insights
- Real-Time Processing
- Easy Management
- Speed
Spark Streaming
Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part.