A look at 8 top stream processing platforms

Stream processing is the real-time or near-real-time processing of data "in motion." It enables querying and analyzing continuous data streams, reacting to critical events within a brief timeframe (usually milliseconds). Event streaming platforms like Apache Kafka enable the flow of data between back-end apps and services. Key use cases for stream processing include real-time fraud detection & payments, IoT sensor data, real-time dashboards, log, traffic, and network monitoring, context-aware online advertising & user behavior tracking, geofencing, and vehicle tracking, and cybersecurity. Popular stream processing platforms include Apache Spark, Apache Kafka Streams, Apache Flink, Spring Cloud Data Flow, Amazon Kinesis, Google Cloud Dataflow, Apache Pulsar, and IBM Streams. Each platform has its strengths and weaknesses, so it's essential to carefully analyze them before choosing the right one for a specific use case.