Spark Streaming vs. Akka
A side-by-side comparison based on G2 review data.
| Metric | Spark Streaming | Akka |
|---|---|---|
| Overall rating | 4.2 | 4.2 |
| Review count | 40 | 30 |
| Ease of use | 8.4 | 8.6 |
| Ease of setup | 7.9 | 0.0 |
| Quality of support | 8.8 | 8.3 |
| Meets requirements | 9.1 | 8.8 |
| NPS | 39.473684 | 43.478261 |
| Small Business | 30.8% | 40.0% |
| Mid-Market | 43.6% | 30.0% |
| Enterprise | 25.6% | 30.0% |
| Total page views | 74 | 217 |
| Total LLM citations | 66 | 635 |
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.
Akka
Akka is a platform to build and run apps that are elastic, agile, and resilient. Industry titans and disruptors rely on Akka when application responsiveness must be guaranteed. Engineering teams use a simple SDK and powerful libraries to build transactional, durable, and real-time services that distribute logic and data together. Operations are fully automated in serverless and BYOC environments, and a self-hosted option is available.