Spark Streaming vs. Amazon Kinesis Data Firehose
A side-by-side comparison based on G2 review data.
| Metric | Spark Streaming | Amazon Kinesis Data Firehose |
|---|---|---|
| Overall rating | 4.2 | 4.2 |
| Review count | 40 | 25 |
| Ease of use | 8.4 | 8.7 |
| Ease of setup | 7.9 | 8.8 |
| Quality of support | 8.8 | 8.6 |
| Meets requirements | 9.1 | 8.8 |
| NPS | 39.473684 | 38.095238 |
| Small Business | 30.8% | 20.0% |
| Mid-Market | 43.6% | 44.0% |
| Enterprise | 25.6% | 36.0% |
| Total page views | 74 | 66 |
| Total LLM citations | 66 | 188 |
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.
Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose is the easiest way to reliably load real-time streams into data lakes, warehouses, and analytics services. Kinesis Data Firehose can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration.