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Stream Processing
Distributed Event Streaming Platform
★ 4.8
Streaming SQL Database
★ 3.8
pip install confluent-kafkaN/A — PostgreSQL extensionpip install confluent-kafkaN/A — PostgreSQL extensionPython 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 PipelineDB via standard `psycopg2` connections — defining continuous views in SQL that automatically aggregate incoming rows. A Python ingestion process writes events to PipelineDB streams using standard INSERT statements, and the continuous view query results update in real time, making precomputed aggregates instantly available for dashboards.
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