When should I use Apache Storm instead of Redpanda?

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 Redpanda instead of Apache Storm?

Kafka-compatible event streaming with lower latency and simpler operations without ZooKeeper or JVM. Teams wanting Kafka producer and consumer semantics with reduced operational overhead. High-throughput streaming workloads where Kafka's JVM GC pauses or ZooKeeper are pain points

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Largely superseded by Apache Flink and Spark Structured Streaming for all new projects.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

Smaller ecosystem and fewer enterprise integrations than the mature Apache Kafka community. Some advanced Kafka features and Confluent Platform plugins have no Redpanda equivalent. Self-hosted C++ binary; managed cloud options are fewer than Confluent or MSK

Apache Storm vs Redpanda: Key Differences for Python Data Engineering

Stream Processing

Apache Storm

Real-Time Computation System

★ 4.4

Apache-2.0

pip install streamparse

Redpanda

Modern Streaming Platform

★ 4.6

BSL-1.1 (Community) / Apache-2.0 (core)

pip install confluent-kafka

Side-by-Side Comparison

Apache Storm

Redpanda

Apache Storm

Redpanda

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

✓Kafka-compatible event streaming with lower latency and simpler operations without ZooKeeper or JVM
✓Teams wanting Kafka producer and consumer semantics with reduced operational overhead
✓High-throughput streaming workloads where Kafka's JVM GC pauses or ZooKeeper are pain points

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

✓Kafka-compatible event streaming with lower latency and simpler operations without ZooKeeper or JVM
✓Teams wanting Kafka producer and consumer semantics with reduced operational overhead
✓High-throughput streaming workloads where Kafka's JVM GC pauses or ZooKeeper are pain points

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

•Smaller ecosystem and fewer enterprise integrations than the mature Apache Kafka community
•Some advanced Kafka features and Confluent Platform plugins have no Redpanda equivalent
•Self-hosted C++ binary; managed cloud options are fewer than Confluent or MSK

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

•Smaller ecosystem and fewer enterprise integrations than the mature Apache Kafka community
•Some advanced Kafka features and Confluent Platform plugins have no Redpanda equivalent
•Self-hosted C++ binary; managed cloud options are fewer than Confluent or MSK

License

Apache-2.0

BSL-1.1 (Community) / Apache-2.0 (core)

License

Apache-2.0

BSL-1.1 (Community) / Apache-2.0 (core)

Install

pip install streamparse

pip install confluent-kafka

Install

pip install streamparse

pip install confluent-kafka

Rating

★ 4.4

★ 4.6

Rating

★ 4.4

★ 4.6

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

Redpanda

1Kafka-compatible API requiring no ZooKeeper or JVM dependency
2Written in C++ for 10x lower latency and higher throughput than Apache Kafka
3Single binary deployment simplifying operations compared to Kafka's multi-component setup
4Built-in schema registry and Kafka Connect compatibility
5Tiered storage for cost-effective long-term event log retention

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.

Redpanda

Python data engineers use Redpanda as a drop-in replacement for Apache Kafka, using the same kafka-python and confluent-kafka Python clients without code changes. Redpanda is adopted for real-time event streaming pipelines where lower operational complexity and better performance are priorities. It is used in data platforms for decoupling microservices, buffering high-throughput data streams, and feeding downstream processors like Flink or Spark Streaming.

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Apache Kafka vs Apache Spark Streaming

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Apache Samza vs Apache Kafka

Individual Tool Pages

View Apache Storm details →View Redpanda details →

Side-by-Side Comparison

Apache Storm

Redpanda

Apache Storm

Redpanda

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

✓Kafka-compatible event streaming with lower latency and simpler operations without ZooKeeper or JVM
✓Teams wanting Kafka producer and consumer semantics with reduced operational overhead
✓High-throughput streaming workloads where Kafka's JVM GC pauses or ZooKeeper are pain points

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

✓Kafka-compatible event streaming with lower latency and simpler operations without ZooKeeper or JVM
✓Teams wanting Kafka producer and consumer semantics with reduced operational overhead
✓High-throughput streaming workloads where Kafka's JVM GC pauses or ZooKeeper are pain points

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

•Smaller ecosystem and fewer enterprise integrations than the mature Apache Kafka community
•Some advanced Kafka features and Confluent Platform plugins have no Redpanda equivalent
•Self-hosted C++ binary; managed cloud options are fewer than Confluent or MSK

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

•Smaller ecosystem and fewer enterprise integrations than the mature Apache Kafka community
•Some advanced Kafka features and Confluent Platform plugins have no Redpanda equivalent
•Self-hosted C++ binary; managed cloud options are fewer than Confluent or MSK

License

Apache-2.0

BSL-1.1 (Community) / Apache-2.0 (core)

License

Apache-2.0

BSL-1.1 (Community) / Apache-2.0 (core)

Install

pip install streamparse

pip install confluent-kafka

Install

pip install streamparse

pip install confluent-kafka

Rating

★ 4.4

★ 4.6

Rating

★ 4.4

★ 4.6

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

Redpanda

1Kafka-compatible API requiring no ZooKeeper or JVM dependency
2Written in C++ for 10x lower latency and higher throughput than Apache Kafka
3Single binary deployment simplifying operations compared to Kafka's multi-component setup
4Built-in schema registry and Kafka Connect compatibility
5Tiered storage for cost-effective long-term event log retention

How Python Data Engineers Use These Tools