Batch Processing Platforms
Batch processing platforms are computing systems designed to process large volumes of data in discrete, scheduled batches rather than in real-time streams. They handle data-intensive workloads by executing jobs on stored datasets, often using distributed computing frameworks to scale across clusters. These platforms are essential for tasks like ETL (Extract, Transform, Load), data warehousing, and large-scale analytics where latency is not critical.
Developers should learn batch processing platforms when building data pipelines for analytics, reporting, or machine learning that require processing terabytes or petabytes of historical data efficiently. They are ideal for use cases like nightly report generation, data aggregation for dashboards, or training ML models on large datasets, as they optimize resource usage and handle fault tolerance in distributed environments.