BatchVerarbeitungsEngines

Batch processing, sometimes written as Batchverarbeitung in German, is a computing paradigm in which work is collected into batches and executed without interactive user input. Jobs are submitted to a queue and run when resources are available. Output appears as discrete files or reports, and processing can be scheduled for off-peak times to optimize resource use. In modern systems, batch processing handles large-scale data transformations, archival tasks, and routine maintenance.

Operation and scheduling: A batch workflow consists of job definitions, input data, and a sequence of steps. A batch scheduler assigns jobs to compute resources, enforces dependencies, retries on failure, and logs results. Jobs may be independent or arranged as pipelines where the output of one step becomes the input of the next. Storage, data management, and permissions are central concerns, as is checkpointing to recover from partial failures.

History and scope: The concept originated with early mainframe computing and punched-card processing, where jobs were collected and executed in batches during off-hours. Over time, batch processing evolved with job-control languages and batch systems such as JCL on IBM systems, PBS, Slurm, and LSF, as well as cron-based scheduling in smaller environments. Modern implementations span on-premises data centers and cloud environments and can be integrated with streaming systems in hybrid architectures.

Applications and trade-offs: Batch processing is well suited for high-volume data transformations, end-of-day reconciliation, payroll, ETL pipelines, and report generation. Its advantages include predictable resource use, high throughput, and simplified error handling for large workloads. Disadvantages include higher latency for individual tasks and the need for careful scheduling and monitoring to prevent bottlenecks.