Dataflow Architecture
Dataflow architecture is a computing model where program execution is driven by the availability of data rather than a sequential control flow. It represents computations as a directed graph of nodes (operators) and edges (data streams), enabling parallel and distributed processing. This approach is foundational for stream processing systems, big data pipelines, and event-driven applications.
Developers should learn dataflow architecture when building real-time analytics, ETL pipelines, or IoT systems that require low-latency processing of continuous data streams. It's essential for implementing scalable, fault-tolerant systems in frameworks like Apache Flink or Apache Beam, where data-driven execution optimizes resource usage and handles high-throughput scenarios efficiently.