BatchProcessing

Batch processing is a computing approach in which a group of jobs or data tasks is collected and executed together, usually without real-time user interaction. Jobs are submitted to a batch system, queued, and run according to a schedule or priority. Batch processing is optimal for high-volume, non-interactive workloads where processing can be performed in the background.

The concept originated on early mainframe systems in the mid-20th century, with punched cards and job control languages used to define task sequences. Overnight processing became common to maximize resource utilization. Over time batch systems gained capabilities such as dependency handling, error management, and automated monitoring.

Typical batch processing involves submitting jobs to a batch queue, a scheduler determining execution order, executing jobs on compute resources, and routing output to storage or reports. Batch jobs may read and write files, transform data, and write results to databases. Modern batch systems support dependencies, retries, and detailed logging to aid reliability and auditing.

Advantages include high throughput, efficient resource utilization, and automation of repetitive tasks. Disadvantages include added latency between submission and completion, limited interactivity, and complexity in scheduling and error handling.

Modern context and examples: batch processing remains foundational in data warehousing, ETL, and reporting, even as real-time and streaming systems evolve. Traditional examples include legacy mainframe batch jobs and cron-based schedules. In contemporary ecosystems, batch-style processing is implemented with frameworks such as Hadoop MapReduce and Apache Spark in batch mode, as well as cloud services like AWS Batch and Google Cloud Batch. Batch can be complemented by micro-batching or streaming for near-real-time results.