Batch processing is handling a large group of tasks together in a single run rather than one at a time. You let the work pile up, then process the whole stack at a set moment, often overnight when systems are quiet and nobody is waiting on the result.
Think of doing laundry. You don’t run the machine for one sock; you wait until the basket is full and wash a load. The cost of running the machine is roughly the same whether it holds one item or thirty, so a full load is far more efficient. Batch processing applies that logic to data: collect a day of orders, then invoice them all at once. It often runs on a scheduled job kicked off by a cron job at a fixed time.
The trade-off is timing. A batch run that happens at midnight means nothing from today is processed until then. For jobs where that delay is fine, batching is cheaper and simpler than reacting to every single event. For anything urgent, you reach for real-time handling instead. Batches also need a plan for the day they go wrong. If a nightly run of 10,000 records dies on record 6,000, what happens next? A naive job restarts from the top and processes the first 6,000 twice, creating duplicate invoices. A well-built one records where it stopped, so the morning rerun picks up at 6,001 and finishes clean. Think of marking your page in a long book; you do not start chapter one again every time you sit down. The other quiet risk is the batch that silently shrinks. A feed that normally brings 8,000 orders suddenly brings 30, and nobody notices because the run still finished without an error. So a serious batch job checks its own size and shape, and shouts when the numbers look wrong instead of cheerfully processing almost nothing.
At TopDevs we use batch processing for the heavy, non-urgent work like reporting and data syncs, freeing real-time capacity for the things a client actually needs answered right now.
