Batch processing refers to running large groups of tasks or data operations together rather than handling them one at a time. Instead of responding to individual requests in real time, the system gathers input—often files, logs, or database rows—and processes them in scheduled jobs known as batches. These jobs typically run on servers, containers, or distributed systems. Batch workflows often rely on queues, data pipelines, or orchestration tools to manage dependencies and timing.
Why it matters
Batch processing is efficient and cost-effective when working with high volumes of data or complex computations that don’t need immediate results. Systems like billing runs, report generation, or nightly database updates all rely on batches. Because batch jobs operate offline, they free up your primary application server to focus on interactive workloads handled through APIs.
Examples
A system that processes millions of log entries nightly to produce analytics reports uses batch processing. Exporting user data to a CSV file is another common batch task. For more detail on related workflows, see the lesson Batch vs Streaming.