Dataflowet

Dataflowet is a term used to describe a data processing paradigm that emphasizes the flow of data between processing steps and the orchestration of those steps through events and triggers. In this view, computations are represented as a graph of operators, where nodes perform transformations and edges carry data records between stages. The goal is to unify streaming and batch processing under a single model, enabling continuous ingestion, transformation, and export of data with consistent semantics. Dataflowet architectures typically separate the logical dataflow from the execution engine, allowing scalable parallelism, backpressure handling, and fault tolerance.

Core components include data sources and sinks, transform operators, and a runtime or scheduler that manages task execution and data routing. Stateful operators, windowing, and watermarking enable time-based aggregations and event-time processing. Delivery guarantees such as exactly-once or at-least-once semantics are implemented through checkpointing, durable state, and recovery protocols. The programming model usually lets developers declare the graph and transforms, while the runtime handles scheduling, fault recovery, and optimization. This approach supports both streaming and batch processing, often through unified APIs and lazy evaluation to optimize execution plans.

Relation to other concepts: Dataflowet is influenced by dataflow programming, stream processing, ETL, and event-driven architectures. It shares goals with systems like Apache Beam, Apache Flink, and Apache Spark Structured Streaming, but is described as a generalized concept rather than a single project.

Applications include real-time analytics, data integration pipelines, ETL for data warehouses, sensor data processing in IoT, and event aggregation in event-sourced systems. Challenges include debugging distributed dataflow graphs, tuning latency, and managing state and operator failover.

See also: Dataflow programming, ETL, Event-driven architecture.