When should I use Apache Storm instead of Faust?

Real-time computation on unbounded streams with guaranteed message processing semantics. Legacy real-time processing systems built before Flink and Spark Streaming became dominant. Low-latency stream topologies (spouts and bolts) where sub-second processing is critical

When should I use Faust instead of Apache Storm?

Python-native stream processing with a Kafka Streams-inspired API using async Python. Building real-time Python agents that consume and produce Kafka topics without leaving Python. Teams who want Kafka stream processing without learning a JVM-based framework

What are the main weaknesses of Apache Storm?

Largely superseded by Apache Flink and Spark Structured Streaming for all new projects. JVM-based; Python support via Streamparse is a second-class experience with limitations. No built-in stateful processing — managing state requires external systems like Redis or HBase

What are the main weaknesses of Faust?

Limited to Kafka as the messaging backend; not a general-purpose stream processing engine. Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads. Less active maintenance since Robinhood open-sourced it; community support has declined

Apache Storm vs Faust: Key Differences for Python Data Engineering

Stream Processing

Apache Storm

Real-Time Computation System

★ 4.4

Apache-2.0

pip install streamparse

Faust

Python Stream Processing

★ 4.5

BSD-3-Clause

pip install faust-streaming

Side-by-Side Comparison

Apache Storm

Faust

Apache Storm

Faust

Best For

✓Real-time computation on unbounded streams with guaranteed message processing semantics
✓Legacy real-time processing systems built before Flink and Spark Streaming became dominant
✓Low-latency stream topologies (spouts and bolts) where sub-second processing is critical

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

Best For

✓Real-time computation on unbounded streams with guaranteed message processing semantics
✓Legacy real-time processing systems built before Flink and Spark Streaming became dominant
✓Low-latency stream topologies (spouts and bolts) where sub-second processing is critical

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

Weaknesses

•Largely superseded by Apache Flink and Spark Structured Streaming for all new projects
•JVM-based; Python support via Streamparse is a second-class experience with limitations
•No built-in stateful processing — managing state requires external systems like Redis or HBase

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

Weaknesses

•Largely superseded by Apache Flink and Spark Structured Streaming for all new projects
•JVM-based; Python support via Streamparse is a second-class experience with limitations
•No built-in stateful processing — managing state requires external systems like Redis or HBase

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

License

Apache-2.0

BSD-3-Clause

License

Apache-2.0

BSD-3-Clause

Install

pip install streamparse

pip install faust-streaming

Install

pip install streamparse

pip install faust-streaming

Rating

★ 4.4

★ 4.5

Rating

★ 4.4

★ 4.5

Key Features

Apache Storm

1Real-time stream processing with guaranteed message processing semantics
2Topology-based architecture defining spouts (sources) and bolts (processors)
3Horizontal scalability by adding workers to running topologies
4At-least-once and exactly-once processing guarantees via transactional topologies
5Integration with Kafka, HDFS, HBase, and other ecosystem components

Faust

1Python-native stream processing framework built on Kafka
2Stateful processing with RocksDB-backed tables for aggregations
3Async agents defined as `@app.agent` coroutines for each topic
4Cron-style timers and windowed aggregations built in
5Type annotations on stream records enable schema validation at runtime

How Python Data Engineers Use These Tools

Apache Storm

Python data engineers use Apache Storm via the streamparse library to write Storm topologies in Python. Storm is used for real-time event processing, fraud detection, and IoT telemetry pipelines where data must be processed and acted upon within milliseconds. While Kafka Streams and Flink have largely superseded Storm in new builds, it remains in production at organisations processing high-velocity event streams.

Faust

Faust lets Python data engineers write Kafka stream processors entirely in async Python — defining an agent as a coroutine that processes messages from a topic and produces results to another. Engineers use Faust tables for stateful aggregations (e.g., running counts or session windows) that persist across restarts via RocksDB.

More Stream Processing Comparisons

Stream Processing

Apache Flink vs Apache Kafka

Stream Processing

Apache Storm vs Apache Kafka

Stream Processing

Faust vs Apache Kafka

Stream Processing

Apache Kafka vs Apache Spark Streaming

Stream Processing

Apache Kafka vs Redpanda

Stream Processing

Apache Samza vs Apache Kafka

Individual Tool Pages

View Apache Storm details →View Faust details →

Side-by-Side Comparison

Apache Storm

Faust

Apache Storm

Faust

Best For

✓Real-time computation on unbounded streams with guaranteed message processing semantics
✓Legacy real-time processing systems built before Flink and Spark Streaming became dominant
✓Low-latency stream topologies (spouts and bolts) where sub-second processing is critical

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

Best For

✓Real-time computation on unbounded streams with guaranteed message processing semantics
✓Legacy real-time processing systems built before Flink and Spark Streaming became dominant
✓Low-latency stream topologies (spouts and bolts) where sub-second processing is critical

✓Python-native stream processing with a Kafka Streams-inspired API using async Python
✓Building real-time Python agents that consume and produce Kafka topics without leaving Python
✓Teams who want Kafka stream processing without learning a JVM-based framework

Weaknesses

•Largely superseded by Apache Flink and Spark Structured Streaming for all new projects
•JVM-based; Python support via Streamparse is a second-class experience with limitations
•No built-in stateful processing — managing state requires external systems like Redis or HBase

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

Weaknesses

•Largely superseded by Apache Flink and Spark Structured Streaming for all new projects
•JVM-based; Python support via Streamparse is a second-class experience with limitations
•No built-in stateful processing — managing state requires external systems like Redis or HBase

•Limited to Kafka as the messaging backend; not a general-purpose stream processing engine
•Lower throughput than Flink or Kafka Streams for high-volume, latency-sensitive workloads
•Less active maintenance since Robinhood open-sourced it; community support has declined

License

Apache-2.0

BSD-3-Clause

License

Apache-2.0

BSD-3-Clause

Install

pip install streamparse

pip install faust-streaming

Install

pip install streamparse

pip install faust-streaming

Rating

★ 4.4

★ 4.5

Rating

★ 4.4

★ 4.5

Key Features

Apache Storm

1Real-time stream processing with guaranteed message processing semantics
2Topology-based architecture defining spouts (sources) and bolts (processors)
3Horizontal scalability by adding workers to running topologies
4At-least-once and exactly-once processing guarantees via transactional topologies
5Integration with Kafka, HDFS, HBase, and other ecosystem components

Faust

1Python-native stream processing framework built on Kafka
2Stateful processing with RocksDB-backed tables for aggregations
3Async agents defined as `@app.agent` coroutines for each topic
4Cron-style timers and windowed aggregations built in
5Type annotations on stream records enable schema validation at runtime

How Python Data Engineers Use These Tools