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Stream Processing
Python Stream Processing
★ 4.5
Distributed Event Streaming Platform
★ 4.8
pip install faust-streamingpip install confluent-kafkapip install faust-streamingpip install confluent-kafkaFaust 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.
Python data engineers use `confluent-kafka-python` or `kafka-python` to produce events to topics and consume them in real-time. A common pattern is a Faust or plain consumer loop that reads messages, transforms them with pandas or Pydantic, and writes results to a database or another topic. Kafka is the backbone of event-driven data architectures in Python shops.
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