When should I use Faust instead of Apache Flink?

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

When should I use Apache Flink instead of Faust?

Stateful stream processing with exactly-once guarantees and low latency at scale. Real-time analytics, complex event processing, and windowed aggregations on event streams. Unified batch and streaming pipelines with a consistent API and shared execution engine

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

What are the main weaknesses of Apache Flink?

JVM-based with a complex deployment model involving JobManager, TaskManager, and checkpointing. Python API (PyFlink) lags behind the Java and Scala APIs in feature coverage and maturity. Steep learning curve for stateful operators, watermarks, checkpointing, and savepoint management

Faust vs Apache Flink: Key Differences for Python Data Engineering

Stream Processing

Faust

Python Stream Processing

★ 4.5

BSD-3-Clause

pip install faust-streaming

Apache Flink

Stream Processing Framework

★ 4.7

Apache-2.0

pip install apache-flink

Side-by-Side Comparison

Faust

Apache Flink

Faust

Apache Flink

Best For

✓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

✓Stateful stream processing with exactly-once guarantees and low latency at scale
✓Real-time analytics, complex event processing, and windowed aggregations on event streams
✓Unified batch and streaming pipelines with a consistent API and shared execution engine

Best For

✓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

✓Stateful stream processing with exactly-once guarantees and low latency at scale
✓Real-time analytics, complex event processing, and windowed aggregations on event streams
✓Unified batch and streaming pipelines with a consistent API and shared execution engine

Weaknesses

•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

•JVM-based with a complex deployment model involving JobManager, TaskManager, and checkpointing
•Python API (PyFlink) lags behind the Java and Scala APIs in feature coverage and maturity
•Steep learning curve for stateful operators, watermarks, checkpointing, and savepoint management

Weaknesses

•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

•JVM-based with a complex deployment model involving JobManager, TaskManager, and checkpointing
•Python API (PyFlink) lags behind the Java and Scala APIs in feature coverage and maturity
•Steep learning curve for stateful operators, watermarks, checkpointing, and savepoint management

License

BSD-3-Clause

Apache-2.0

License

BSD-3-Clause

Apache-2.0

Install

pip install faust-streaming

pip install apache-flink

Install

pip install faust-streaming

pip install apache-flink

Rating

★ 4.5

★ 4.7

Rating

★ 4.5

★ 4.7

Key Features

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

Apache Flink

1True streaming engine processing events one at a time with low latency
2Exactly-once state consistency guarantees using distributed checkpointing
3Unified batch and stream processing API in a single framework
4Stateful computations with managed state backends (RocksDB, memory)
5SQL support via Flink SQL for stream-relational queries

How Python Data Engineers Use These Tools

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.

Apache Flink

Python data engineers use Apache Flink via PyFlink to build real-time streaming pipelines for fraud detection, real-time analytics, and complex event processing. Flink SQL enables engineers to write streaming queries in familiar SQL syntax, joining Kafka streams with database lookups in real time. Flink is preferred over Spark Streaming for use cases requiring low latency (sub-second) processing and stateful computations across unbounded event streams.

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 Faust details →View Apache Flink details →

Side-by-Side Comparison

Faust

Apache Flink

Faust

Apache Flink

Best For

✓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

✓Stateful stream processing with exactly-once guarantees and low latency at scale
✓Real-time analytics, complex event processing, and windowed aggregations on event streams
✓Unified batch and streaming pipelines with a consistent API and shared execution engine

Best For

✓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

✓Stateful stream processing with exactly-once guarantees and low latency at scale
✓Real-time analytics, complex event processing, and windowed aggregations on event streams
✓Unified batch and streaming pipelines with a consistent API and shared execution engine

Weaknesses

•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

•JVM-based with a complex deployment model involving JobManager, TaskManager, and checkpointing
•Python API (PyFlink) lags behind the Java and Scala APIs in feature coverage and maturity
•Steep learning curve for stateful operators, watermarks, checkpointing, and savepoint management

Weaknesses

•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

•JVM-based with a complex deployment model involving JobManager, TaskManager, and checkpointing
•Python API (PyFlink) lags behind the Java and Scala APIs in feature coverage and maturity
•Steep learning curve for stateful operators, watermarks, checkpointing, and savepoint management

License

BSD-3-Clause

Apache-2.0

License

BSD-3-Clause

Apache-2.0

Install

pip install faust-streaming

pip install apache-flink

Install

pip install faust-streaming

pip install apache-flink

Rating

★ 4.5

★ 4.7

Rating

★ 4.5

★ 4.7

Key Features

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

Apache Flink

1True streaming engine processing events one at a time with low latency
2Exactly-once state consistency guarantees using distributed checkpointing
3Unified batch and stream processing API in a single framework
4Stateful computations with managed state backends (RocksDB, memory)
5SQL support via Flink SQL for stream-relational queries

How Python Data Engineers Use These Tools