Streaming (data)
Processing data continuously as it arrives rather than in batches. Event streams from Kafka or AWS Kinesis allow automations to process millions of small events per second essential for real-time inventory, pricing, and fraud detection.
Expert insight
Streaming data processing (acting on data continuously as it arrives, rather than in periodic batches) suits genuinely real-time use cases but adds meaningful complexity around out-of-order events and exactly-once processing guarantees, worth confirming the use case actually needs streaming before taking on that complexity over simpler batch processing.
How PURIST uses this
This concept is built into every automation we deploy.
When PURIST builds your automation, Streaming (data) is not an optional consideration it is part of the production standard. Our workflows are tested against edge cases, monitored 24/7, and built to handle what happens when things don't go as expected.
Every client workflow we deploy in the Architecture category is designed with this principle in mind from day one not added as an afterthought.
Complexity level
Technical term used in production automation systems.
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