Spark Streaming vs. IBM StreamSets
A side-by-side comparison based on G2 review data.
| Metric | Spark Streaming | IBM StreamSets |
|---|---|---|
| Overall rating | 4.2 | 4.0 |
| Review count | 40 | 115 |
| Ease of use | 8.4 | 8.6 |
| Ease of setup | 7.9 | 8.3 |
| Quality of support | 8.8 | 8.3 |
| Meets requirements | 9.1 | 8.4 |
| NPS | 39.473684 | 29.20354 |
| Small Business | 30.8% | 23.7% |
| Mid-Market | 43.6% | 33.3% |
| Enterprise | 25.6% | 43.0% |
| Total page views | 74 | 297 |
| Total LLM citations | 66 | 1,104 |
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 StreamSets
IBM StreamSets is a robust streaming data integration tool for hybrid, multi-cloud environments that enables real-time decision making. It allows ingestion and in-flight transformation of structured, unstructured, and semi-structured data from streaming sources, and reliably delivers trusted data into diverse destinations. Flexible deployment options promote security, cost-effectiveness and performance. With several pre-built connectors, an intuitive no-code/low-code interface, and automatic adaptability to data drifts, StreamSets accelerates data pipeline operationalization.
- Ease of Use
- User Interface
- Data Pipelining
- Data Management
- Integrations
- Implementation Ease