Apache Flink vs Apache Spark
A side-by-side comparison of two Data Stream Processing AI agents — to help you pick the right one.
Apache Flink
Apache Flink is a distributed stream processing framework designed for high-throughput, low-latency data processing. It supports both event-time processing and stateful computations, enabling real-time analytics and complex event-driven applications. Flink can also handle batch processing as a special case of stream processing.
Apache Spark
Apache Spark is a unified analytics engine for large-scale data processing, supporting batch processing, real-time stream processing, and machine learning. It provides in-memory computing capabilities to speed up iterative algorithms and interactive queries.
| Apache Flink | Apache Spark | |
|---|---|---|
| Category | Data Stream Processing | Data Stream Processing |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Skill level | Intermediate | Intermediate |
| Pricing | Open Source | Open Source |
Apache Flink: what it solves
It enables real-time data processing at scale, solving the challenge of analyzing and acting on streaming data with low latency and high accuracy.
Apache Spark: what it solves
Enables efficient processing of large-scale data workloads by distributing computations across clusters, reducing latency and improving throughput compared to traditional batch systems.