Offline Processing
Offline processing is a computing paradigm where data is processed in batches or scheduled tasks without requiring real-time user interaction or immediate responses. It involves collecting, storing, and analyzing data at a later time, often for tasks like data transformation, reporting, or machine learning model training. This approach contrasts with online processing, which handles requests immediately as they occur.
Developers should learn offline processing for handling large-scale data workloads that don't require instant results, such as generating daily reports, performing ETL (Extract, Transform, Load) operations, or training complex machine learning models. It's essential in scenarios where processing can be deferred to optimize resource usage, reduce costs, or manage system load during off-peak hours, commonly used in data warehousing, analytics, and batch job systems.