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Stream Processing
Python Stream Processing
★ 4.5
Streaming SQL Database
★ 3.8
pip install faust-streamingN/A — PostgreSQL extensionpip install faust-streamingN/A — PostgreSQL extensionFaust 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 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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