When should I use Apache Samza instead of Faust?

Stateful stream processing tightly integrated with Apache Kafka and durable local state stores. Teams in the LinkedIn or Hadoop ecosystem needing large, persistent local state per partition. Long-running stateful jobs where RocksDB-backed local state provides fast key-value access

When should I use Faust instead of Apache Samza?

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 Samza?

Tightly coupled to Kafka and YARN; difficult to deploy and operate outside that ecosystem. Smaller community than Flink with fewer tutorials, integrations, and managed service options. Python API is minimal — primarily a Java and Scala framework with limited Python support

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 Samza vs Faust: Key Differences for Python Data Engineering

Stream Processing

Apache Samza

Distributed Stream Processing Framework

★ 4.0

Apache-2.0

N/A — Java-based

Faust

Python Stream Processing

★ 4.5

BSD-3-Clause

pip install faust-streaming

Side-by-Side Comparison

Apache Samza

Faust

Apache Samza

Faust

Best For

✓Stateful stream processing tightly integrated with Apache Kafka and durable local state stores
✓Teams in the LinkedIn or Hadoop ecosystem needing large, persistent local state per partition
✓Long-running stateful jobs where RocksDB-backed local state provides fast key-value access

✓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

✓Stateful stream processing tightly integrated with Apache Kafka and durable local state stores
✓Teams in the LinkedIn or Hadoop ecosystem needing large, persistent local state per partition
✓Long-running stateful jobs where RocksDB-backed local state provides fast key-value access

✓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

•Tightly coupled to Kafka and YARN; difficult to deploy and operate outside that ecosystem
•Smaller community than Flink with fewer tutorials, integrations, and managed service options
•Python API is minimal — primarily a Java and Scala framework with limited Python support

•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

•Tightly coupled to Kafka and YARN; difficult to deploy and operate outside that ecosystem
•Smaller community than Flink with fewer tutorials, integrations, and managed service options
•Python API is minimal — primarily a Java and Scala framework with limited Python support

•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

N/A — Java-based

pip install faust-streaming

Install

N/A — Java-based

pip install faust-streaming

Rating

★ 4.0

★ 4.5

Rating

★ 4.0

★ 4.5

Key Features

Apache Samza

1Stateful stream processing framework built on Kafka and YARN
2Local state stores per partition for fault-tolerant stateful computations
3Exactly-once semantics with Kafka changelog-backed state recovery
4Flexible windowing: tumbling, sliding, and session windows
5High-level Streams API and low-level Task API for custom processing

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 Samza

Python data engineers interact with Apache Samza primarily through its REST API or by bridging Python logic into Samza jobs via subprocess calls. More commonly, Python pipelines produce events to Kafka topics that Samza jobs consume for stateful aggregation — Python handles data ingestion and enrichment while Samza manages low-latency stateful stream processing at scale.

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 Samza details →View Faust details →

Side-by-Side Comparison

Apache Samza

Faust

Apache Samza

Faust

Best For

✓Stateful stream processing tightly integrated with Apache Kafka and durable local state stores
✓Teams in the LinkedIn or Hadoop ecosystem needing large, persistent local state per partition
✓Long-running stateful jobs where RocksDB-backed local state provides fast key-value access

✓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

✓Stateful stream processing tightly integrated with Apache Kafka and durable local state stores
✓Teams in the LinkedIn or Hadoop ecosystem needing large, persistent local state per partition
✓Long-running stateful jobs where RocksDB-backed local state provides fast key-value access

✓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

•Tightly coupled to Kafka and YARN; difficult to deploy and operate outside that ecosystem
•Smaller community than Flink with fewer tutorials, integrations, and managed service options
•Python API is minimal — primarily a Java and Scala framework with limited Python support

•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

•Tightly coupled to Kafka and YARN; difficult to deploy and operate outside that ecosystem
•Smaller community than Flink with fewer tutorials, integrations, and managed service options
•Python API is minimal — primarily a Java and Scala framework with limited Python support

•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

N/A — Java-based

pip install faust-streaming

Install

N/A — Java-based

pip install faust-streaming

Rating

★ 4.0

★ 4.5

Rating

★ 4.0

★ 4.5

Key Features

Apache Samza

1Stateful stream processing framework built on Kafka and YARN
2Local state stores per partition for fault-tolerant stateful computations
3Exactly-once semantics with Kafka changelog-backed state recovery
4Flexible windowing: tumbling, sliding, and session windows
5High-level Streams API and low-level Task API for custom processing

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