Processing large volumes of data in scheduled groups rather than continuously in real time.
In batch processing, data is collected over a period of time and then processed all at once — nightly, weekly, or on another schedule. This approach is efficient for large workloads that do not require immediate results and allows systems to process data during off-peak hours.
Example: A provincial open data portal refreshes its datasets every night at 2 a.m. All the day's new records are collected, validated, transformed, and published in one batch run. Users see updated data each morning rather than in real time.
Batch processing is contrasted with real-time or streaming processing, where data is handled as soon as it arrives. Many open data publishing workflows use batch processing because the data does not change frequently enough to justify continuous processing.