Teams should compare stream processing frameworks based on latency, throughput, state management, fault tolerance, scalability, integrations, developer experience, deployment options, and cost. For a detailed overview, explore Stream Processing Frameworks for Real-Time Data Workloads.
- Apache Flink: Supports low-latency processing, advanced event-time handling, stateful workloads, and fault-tolerant streaming.
- Apache Kafka Streams: Provides lightweight stream processing directly within Kafka-based applications.
- Spark Structured Streaming: Combines streaming with Spark's batch processing and DataFrame ecosystem.
- Apache Beam: Provides a unified programming model that can run across multiple processing engines and cloud runners. ([Apache Beam][1])
- Apache Storm: Supports distributed real-time processing with topology-based stream workflows.
- Apache Samza: Provides stateful stream processing and strong integration with partitioned event streams.
- Google Cloud Dataflow: Offers managed stream and batch processing based on the Apache Beam model.
- Materialize: Uses streaming SQL and incremental computation to maintain real-time data views.
- RisingWave: Provides SQL-based stream processing with continuously updated materialized views.
- Hazelcast Platform: Supports low-latency in-memory stream processing and distributed data workflows.
Teams should also compare processing latency, throughput, state handling, exactly-once guarantees, recovery, connectors, scalability, deployment complexity, monitoring, security, developer support, and total cost before selecting a framework.
For more details, review the stream processing frameworks discussion before creating a shortlist.
Conclusion
The right framework depends on workload complexity, latency requirements, infrastructure, and team expertise.
Teams should compare performance, state management, integrations, scalability, reliability, and cost.
Testing shortlisted frameworks with representative real-time workloads can help identify a suitable solution.