Kafka Streams
A client library for Apache Kafka used to build stream processing applications. It provides lightweight stream processing capabilities and supports windowed aggregation and state stores.
Timeline (last 90 days)
Streaming feature computation is usually implemented on stream processing engines (such as Flink, Spark Structured Streaming or Kafka Streams), whose core is to define features as continuous aggregations over unbounded event streams.
Delivering up-to-date features within millisecond-level latency requires building streaming computation pipelines (such as Flink or Kafka Streams) and making trade-offs among storage cost, computation latency, and data freshness.
All Facts (2)
Streaming feature computation is usually implemented on stream processing engines (such as Flink, Spark Structured Streaming or Kafka Streams), whose core is to define features as continuous aggregations over unbounded event streams.
50%UnverifiedDelivering up-to-date features within millisecond-level latency requires building streaming computation pipelines (such as Flink or Kafka Streams) and making trade-offs among storage cost, computation latency, and data freshness.
50%