Data engineering teams should compare stream processing frameworks based on processing speed, latency, scalability, fault tolerance, data handling, integrations, deployment options, monitoring, and cost.
- Apache Flink: Supports stateful stream processing, event-time handling, and low-latency data analytics.
- Apache Kafka Streams: Provides real-time stream processing within applications built on Apache Kafka.
- Apache Spark Structured Streaming: Supports scalable stream processing using Spark SQL and familiar batch-processing tools.
- Apache Storm: Processes continuous data streams through 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 pipelines across supported processing engines.
- 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.
- Hazelcast Platform: Offers distributed computing, in-memory data processing, and stream processing capabilities.
- Amazon Kinesis Data Analytics: Supports real-time data processing and analytics through AWS streaming services.
Teams should also compare processing latency, throughput, state management, fault recovery, delivery guarantees, data connectors, cloud compatibility, monitoring, scalability, operational complexity, and total cost before selecting a framework.