When should I use Apache Samza instead of Apache Spark Streaming?

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 Spark Streaming instead of Apache Samza?

Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API. Teams already using PySpark who want to add streaming with minimal new tooling or concepts. Combining historical batch data with real-time streams in a single unified Spark job

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 Spark Streaming?

Micro-batch architecture introduces latency compared to true streaming engines like Flink. Legacy DStream API is deprecated; migration to Structured Streaming is required for new work. JVM overhead and cluster management complexity remain significant operational challenges

Apache Samza vs Apache Spark Streaming: Key Differences for Python Data Engineering

Stream Processing

Apache Samza

Distributed Stream Processing Framework

★ 4.0

Apache-2.0

N/A — Java-based

Apache Spark Streaming

Scalable Stream Processing

★ 4.6

Apache-2.0

pip install pyspark

Side-by-Side Comparison

Apache Samza

Apache Spark Streaming

Apache Samza

Apache Spark Streaming

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

License

Apache-2.0

License

Apache-2.0

Install

N/A — Java-based

pip install pyspark

Install

N/A — Java-based

pip install pyspark

Rating

★ 4.0

★ 4.6

Rating

★ 4.0

★ 4.6

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 Spark Streaming

1Micro-batch and continuous streaming modes on the Spark engine
2Exactly-once semantics with checkpointing and write-ahead logs
3Unified API with Spark batch — same DataFrame operations on streams
4Native Kafka, Kinesis, S3, and Delta Lake source/sink support
5Watermarking for handling late-arriving data in event-time windows

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 Spark Streaming

Python data engineers use Spark Structured Streaming via PySpark to process high-volume Kafka streams at scale. A streaming job reads a Kafka topic as a DataFrame, applies transformations (filtering, aggregations, joins with static data), and writes results continuously to Delta Lake or a database — using the same PySpark syntax as batch jobs.

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 Spark Streaming details →

Side-by-Side Comparison

Apache Samza

Apache Spark Streaming

Apache Samza

Apache Spark Streaming

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

License

Apache-2.0

License

Apache-2.0

Install

N/A — Java-based

pip install pyspark

Install

N/A — Java-based

pip install pyspark

Rating

★ 4.0

★ 4.6

Rating

★ 4.0

★ 4.6

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 Spark Streaming

1Micro-batch and continuous streaming modes on the Spark engine
2Exactly-once semantics with checkpointing and write-ahead logs
3Unified API with Spark batch — same DataFrame operations on streams
4Native Kafka, Kinesis, S3, and Delta Lake source/sink support
5Watermarking for handling late-arriving data in event-time windows

How Python Data Engineers Use These Tools