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Stream Processing
Distributed Event Streaming Platform
★ 4.8
Scalable Stream Processing
★ 4.6
pip install confluent-kafkapip install pysparkpip install confluent-kafkapip install pysparkPython 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.
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.
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