BatchStreaming

BatchStreaming is a data processing paradigm that blends elements of batch processing and streaming. It processes unbounded data streams by grouping incoming data into small, fixed-size batches, or micro-batches, and applying batch-like computations to each batch. This approach seeks to reduce the latency of traditional batch jobs while maintaining the throughput and reproducibility of batch processing.

Data arrives continuously and is collected into micro-batches according to time, count, or both. Each batch is consumed by a batch engine, producing results that are emitted downstream. The system uses windowing and watermarking to handle late data and to define progress, preventing unbounded delays.

BatchStreaming sits on a spectrum between pure batch and true streaming. Many modern frameworks implement micro-batching to deliver low-latency streaming while preserving deterministic batch semantics. Traditional batch jobs remain useful for offline computation, whereas true streaming systems update results per event.

Use cases include real-time dashboards, stream ETL, anomaly detection, and incremental machine-learning pipelines that benefit from batch reprocessing. Advantages include improved latency relative to long-running batch, simpler correctness guarantees anchored in batch semantics, and the reuse of established batch-processing tools.

Challenges include selecting an appropriate batch size, tuning windowing and watermark strategies, and handling late-arriving data. Exactly-once processing, state management, and fault tolerance can be more complex than in pure batch or streaming systems. System design must balance latency, throughput, and resource usage.