When should I use Apache Samza instead of Apache Storm?

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 Apache Storm instead of Apache Samza?

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

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 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Largely superseded by Apache Flink and Spark Structured Streaming for all new projects.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

Apache Samza vs Apache Storm: Key Differences for Python Data Engineering

Stream Processing

Apache Samza

Distributed Stream Processing Framework

★ 4.0

Apache-2.0

N/A — Java-based

Apache Storm

Real-Time Computation System

★ 4.4

Apache-2.0

pip install streamparse

Side-by-Side Comparison

Apache Samza

Apache Storm

Apache Samza

Apache Storm

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

✓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

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

✓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

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

•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

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

•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

License

Apache-2.0

License

Apache-2.0

Install

N/A — Java-based

pip install streamparse

Install

N/A — Java-based

pip install streamparse

Rating

★ 4.0

★ 4.4

Rating

★ 4.0

★ 4.4

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

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

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.

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.

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 Apache Storm details →

Side-by-Side Comparison

Apache Samza

Apache Storm

Apache Samza

Apache Storm

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

✓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

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

✓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

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

•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

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

•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

License

Apache-2.0

License

Apache-2.0

Install

N/A — Java-based

pip install streamparse

Install

N/A — Java-based

pip install streamparse

Rating

★ 4.0

★ 4.4

Rating

★ 4.0

★ 4.4

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

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

How Python Data Engineers Use These Tools