Apache Kafka vs. Spark Streaming
A side-by-side comparison based on G2 review data.
| Metric | Apache Kafka | Spark Streaming |
|---|---|---|
| Overall rating | 4.5 | 4.2 |
| Review count | 132 | 40 |
| Ease of use | 8.8 | 8.4 |
| Ease of setup | 8.3 | 7.9 |
| Quality of support | 8.2 | 8.8 |
| Meets requirements | 9.0 | 9.1 |
| NPS | 72.440945 | 39.473684 |
| Small Business | 22.7% | 30.8% |
| Mid-Market | 31.1% | 43.6% |
| Enterprise | 46.2% | 25.6% |
| Total page views | 388 | 74 |
| Total LLM citations | 1,699 | 66 |
Apache Kafka
Apache Kafka is an open-source distributed event streaming platform developed by the Apache Software Foundation. It is designed to handle real-time data feeds with high throughput and low latency, making it ideal for building data pipelines, streaming analytics, and integrating data across various systems. Kafka enables organizations to publish, store, and process streams of records in a fault-tolerant and scalable manner, supporting mission-critical applications across diverse industries.
- Scalability
- Real-time Data
- Performance Efficiency
- Performance
- Reliability
- Easy Integrations
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.