Data engineering teams should compare stream processing frameworks for real-time data workloads based on processing speed, latency, scalability, fault tolerance, data handling, integrations, deployment options, monitoring, and cost.
- Apache Kafka Streams: Supports real-time stream processing within applications built on Apache Kafka.
- Apache Flink: Provides stateful stream processing, event-time handling, and low-latency data analytics.
- Apache Spark Structured Streaming: Supports scalable stream processing using Spark SQL and familiar batch-processing tools.
- Apache Storm: Processes continuous data streams with distributed computation and fault-tolerant execution.
- Apache Samza: Provides distributed stream processing and integrates closely with Apache Kafka.
- Apache Beam: Offers a unified programming model for batch and streaming data pipelines across supported processing engines.
- Kafka Streams DSL: Provides high-level APIs for building stream transformations, aggregations, and event-driven applications.
- Hazelcast Jet: Offers distributed stream processing and data pipelines through the Hazelcast ecosystem, though Jet now forms part of Hazelcast Platform.
- RisingWave: Provides streaming database capabilities for continuous SQL queries and real-time data processing.
- Materialize: Supports incremental SQL queries that update results as new streaming data arrives.
Teams should also compare end-to-end latency, throughput, state management, fault recovery, delivery guarantees, data connectors, operational complexity, cloud compatibility, monitoring, scalability, and total cost before selecting a framework.