Micro-batching
Micro-batching is a data processing technique that divides large datasets or continuous data streams into small, fixed-size batches for efficient processing. It is commonly used in distributed computing and real-time analytics systems to balance latency and throughput by processing data in manageable chunks rather than individually or in large batches. This approach helps optimize resource utilization, improve fault tolerance, and enable near-real-time processing in systems like Apache Spark Streaming or Apache Flink.
Developers should learn micro-batching when building or working with real-time data processing systems, such as streaming analytics, ETL pipelines, or machine learning inference, where low latency and high throughput are critical. It is particularly useful in scenarios like financial transaction monitoring, IoT data aggregation, or log processing, as it allows for incremental updates and reduces the risk of system overload compared to processing each data point individually or in large, infrequent batches.