Compare theTop Stream Processing Frameworks
2024 Top Stream Processing Frameworks
Flink
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in clustered environments, performing computations in-memory at speed and at any scale. With its long history and active community support, Flink remains a top choice for organizations seeking to unlock insights from their streaming data sources.
Kafka Streams
Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the approach of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Pathway
Pathway is a data processing framework that handles streaming data in a way easily accessible to Python and AI developers. It is a light, next-generation technology developed since 2020, made available for download as a Python-native package from GitHub and as a Docker image on Dockerhub. Pathway handles advanced algorithms in deep pipelines, connects to data sources like Kafka and S3, and enables real-time ML model and API integration for new AI use cases. It is powered by Rust, while maintaining the joy of interactive development with Python. Pathway’s performance enables it to process millions of data points per second, scaling to multiple workers, while staying consistent and predictable. Pathway covers a spectrum of use cases between classical streaming and data indexing for knowledge management, bringing in powerful transformations, speed, and scale.
Spark Streaming
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Kinesis, or TCP sockets, and can be processed using complex algorithms that can be expressed with high-level functions like map, reduce, join and window. Finally, processed data can be pushed out to filesystems, databases, and live
| Stream Processing Frameworks | |||||
|---|---|---|---|---|---|
| Pathway | Flink | Flink + Redis | Flink + Druid | Spark / Databricks | |
| Data processing & transformation | |||||
| PUSH - data pipelines | |||||
| Batch - for SQL use cases | ✅ | ✅ | n/a | n/a | ✅ |
| Batch - for ML/AI use cases | ✅ | ✅🐌 | ✅ | ||
| Streaming / live data for SQL use cases | ✅ | ✅ | ✅ | ✅ | ⚠️2 |
| Streaming / live data for ML/AI use cases | ✅ | ❌ | ❌ | ❌ | ❌ |
| PULL - real-time request serving | |||||
| Basic (Real-time feature store) | ✅ | ❌ | ✅ | ✅ | ✅ |
| Advanced (Query API / on-demand API) | ✅ | ❌ | ❌ | ⚠️1 | ❌ |
| Development & deployment effort | |||||
| INTERACTIVE DEVELOPMENT - notebooks, data experimentation | |||||
| Batch / local data files | ✅ | ✅ | ❌ | ✅ | |
| Streaming | ✅ | ❌ | ❌ | ❌ | |
| DEPLOYMENT | |||||
| Tests and CI/CD: Local - in process, without cluster | ✅ | ❌ | ❌ | ❌ | ✅🐌 |
| Job management directly through containerized deployment (Kubernetes / Docker) | ✅ | ❌ | ❌ | ❌ | ❌ |
| Horizontal + vertical scaling | ✅ | ✅ | ✅ | ✅ | ✅ |
| Streaming consistency | |||||
| STREAMING CONSISTENCY | ✅ | 😠 | ❌ | ❌ | 😠 |
- ✅: Supported
- 😠: Consistency is limited to Eventual Consistency. See O'Reilly 2024 Streaming Databases Chapter 6 on Streaming Consistency for more context.
- 🐌:Not scalable (e.g., local single-threaded only) or posing blocking performance issues
- ⚠️1: External ML integration only (SQL-first processing)
- ⚠️2: Limited to a subset of SQL, limited JOIN complexity
