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Understanding Real-time Stream Processing Real-time stream processing differs from batch processing by handling continuous, unbounded data streams with low latency. Unlike batch jobs that operate on complete datasets, stream processing applies computations to data as it arrives, often using incremental updates for efficiency. Key Characteristic ...

Parallelism and Operator Chains The parallelism of a specific operator is defined by the number of its subtasks. A data flow containing parallel subtasks is known as a parallel stream, requiring multiple stream partitions to distribute the workload. Generally, the parallelism of a Flink program is determined by the maximum parallelism among all ...

Message middleware enables reliable, synchronous or asynchronous communication between distributed applications using message queues and transmission protocols. It facilitates platform-agnostic data exchange and supports system integration through decoupled, scalable communication models. Apache Kafka is a distributed event streaming platform r ...