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

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

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

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 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

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

Stream Processing

Apache Flink

Stream Processing Framework

★ 4.7

Apache-2.0

pip install apache-flink

Apache Spark Streaming

Scalable Stream Processing

★ 4.6

Apache-2.0

pip install pyspark

Side-by-Side Comparison

Apache Flink

Apache Spark Streaming

Apache Flink

Apache Spark Streaming

Best For

✓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

✓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 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

✓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

•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

•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

•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

•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

pip install apache-flink

pip install pyspark

Install

pip install apache-flink

pip install pyspark

Rating

★ 4.7

★ 4.6

Rating

★ 4.7

★ 4.6

Key Features

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

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 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.

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 Flink details →View Apache Spark Streaming details →

Side-by-Side Comparison

Apache Flink

Apache Spark Streaming

Apache Flink

Apache Spark Streaming

Best For

✓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

✓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 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

✓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

•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

•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

•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

•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

pip install apache-flink

pip install pyspark

Install

pip install apache-flink

pip install pyspark

Rating

★ 4.7

★ 4.6

Rating

★ 4.7

★ 4.6

Key Features

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

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