Micro-batch Processing
Micro-batch processing is a data processing paradigm that divides streaming data into small, fixed-size batches for near-real-time processing. It bridges the gap between traditional batch processing (large, periodic jobs) and pure stream processing (continuous, event-by-event), offering low-latency results with fault tolerance and scalability. This approach is commonly implemented in distributed data processing frameworks to handle high-throughput data streams efficiently.
Developers should learn micro-batch processing when building applications requiring near-real-time analytics, such as fraud detection, IoT sensor monitoring, or real-time dashboard updates, where latency of seconds to minutes is acceptable. It is particularly useful in scenarios where data arrives continuously but processing benefits from batching for efficiency, consistency, and integration with existing batch-oriented systems, as seen in Apache Spark Streaming or cloud data pipelines.