Batch Processing Systems
Batch processing systems are computing architectures designed to process large volumes of data in discrete, scheduled jobs rather than in real-time. They handle data in batches, often collected over time, and execute operations like data transformation, aggregation, and analysis offline. This approach is efficient for non-urgent, high-throughput tasks where latency is not critical, such as generating reports or updating databases.
Developers should learn batch processing systems when dealing with large-scale data processing tasks that don't require immediate results, such as nightly ETL (Extract, Transform, Load) pipelines, log analysis, or batch analytics. It's essential for scenarios where data accumulates over time and needs periodic processing, like in financial systems for end-of-day transactions or in e-commerce for inventory updates. Using batch processing improves resource efficiency by optimizing compute and storage usage during off-peak hours.