Spark Streaming vs. Confluent
A side-by-side comparison based on G2 review data.
| Metric | Spark Streaming | Confluent |
|---|---|---|
| Overall rating | 4.2 | 4.4 |
| Review count | 40 | 117 |
| Ease of use | 8.4 | 8.6 |
| Ease of setup | 7.9 | 8.3 |
| Quality of support | 8.8 | 8.5 |
| Meets requirements | 9.1 | 8.9 |
| NPS | 39.473684 | 59.292035 |
| Small Business | 30.8% | 34.2% |
| Mid-Market | 43.6% | 29.9% |
| Enterprise | 25.6% | 35.9% |
| Total page views | 74 | 757 |
| Total LLM citations | 66 | 7,445 |
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.
Confluent
Today’s customers expect every digital experience to be immediate, connected, and personalized. That takes more than data at rest - it takes trusted data in motion. Confluent is the complete Data Streaming Platform for keeping data in motion from the moment business change occurs through processing, governance, and serving. Built by the original creators of Apache Kafka®, Confluent connects applications, services, and systems; processes streams in real time with Apache Flink®; and serves trusted, always-current data wherever it is needed across cloud, hybrid, and self-managed environments.
- Streaming
- Scaling
- Data Integration
- Easy Integrations
- Features
- User Interface