Unbounded streams of data (eg. a Kafka Stream) are costly to store, have high data volumes, and will never finish loading, making it challenging to process and analyze. The TimeWindower transformer logically breaks up high-volume, unbounded data streams into groups using processing time (the time data arrives at TimeWindower) or event time (a timestamp on data representing the time an event happened). Once in groups, the data is ready for analysis and filtering.
TimeWindower can group high-volume data streams over short periods. For example, data incoming from a sensor at 2000 messages per second can be broken up into 30-second windows. Alternatively, TimeWindower can also group low-volume data streams over long periods, like data coming in at one message per second being windowed into 10-minute intervals.
Learn more about how to sort through raw, unbounded data streams in FME with Community tutorials like Getting Started with Stream Processing in FME, Filtering Unbounded Data Streams, Summarize Unbounded Data Streams, and Detecting Incidents in Unbounded Data Streams.
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