Spark Streaming vs. IBM Event Streams
A side-by-side comparison based on G2 review data.
| Metric | Spark Streaming | IBM Event Streams |
|---|---|---|
| Overall rating | 4.2 | 4.3 |
| Review count | 40 | 13 |
| Ease of use | 8.4 | 9.6 |
| Ease of setup | 7.9 | 9.5 |
| Quality of support | 8.8 | 8.6 |
| Meets requirements | 9.1 | 8.6 |
| NPS | 39.473684 | 58.333333 |
| Small Business | 30.8% | 23.1% |
| Mid-Market | 43.6% | 23.1% |
| Enterprise | 25.6% | 53.8% |
| Total page views | 74 | 78 |
| Total LLM citations | 66 | 169 |
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.
IBM Event Streams
IBM Event Streams is a high-throughput, fault-tolerant, event streaming solution. Powered by Apache Kafka, it provides access to enterprise data through event streams, enabling businesses to unlock insights from historical data, and identify and take action on situations in real time and at scale.
- User Interface
- Ease of Use