Can a batch run be started manually?

Yes. Scheduling is common but not required by the concept.

Is micro-batching simply the same as a daily batch job?

Both group work, but a streaming engine may retain incremental state and offsets across micro-batches. Its semantics need separate attention.

Does parallel processing remove the batch boundary?

No. Many workers can cooperate on one defined batch.

AI Glossary / Data engineering and architecture / Beginner

Batch processing

Definition

Batch processing runs a defined collection of work or data as a job, usually without requiring a person to interact with each individual item. In data engineering, it commonly processes a bounded dataset and produces results when the run completes. A batch may start on a schedule, after an event or through an explicit request.

What it means

A nightly report can process the day's completed orders as one batch. A one-time archive conversion can process a fixed list of files. Both are batch jobs even though one repeats and the other does not.

Batch does not mean slow, old-fashioned or necessarily overnight. A small batch can finish quickly, while a large parallel job can use many workers. The defining idea is bounded work, not duration.

Streaming processing instead maintains processing over arriving data. The boundary is not simply whether a timer starts the work: some streaming engines can run on a trigger and process currently available data incrementally. Product terminology and state-management behavior therefore matter.

How it works

A batch run identifies its input, applies the required transformations and writes output. An orchestrator or job scheduler may manage dependencies, resource allocation, retries and completion status. Large jobs can divide the input into partitions for parallel execution.

The input boundary should be reproducible. A file manifest, table snapshot or explicitly selected period can establish what belongs to a run. Reading a changing folder without a defined cutoff can make two attempts process different data.

Output handling is equally important. A failed attempt may have written some results before stopping. A retry needs a strategy such as replacing a run-specific output, writing through a suitable transactional mechanism or deduplicating by stable identifiers. Automatic retries alone do not guarantee duplicate-free results.

A completed computation also needs validation. Check expected partitions, row counts or other workload-specific invariants before announcing a dataset as ready. The job's exit status is useful evidence, but it may not reveal missing input or incorrect business logic.

Three practical examples

A company produces a daily activity report from a defined snapshot of completed events.
An AI team generates embeddings for a versioned collection of documents and records which model and input versions produced them.
A media archive converts a manifest of recordings into a new format, retrying failed items while preserving a clear completion record.

Think of it this way

Think of sending a numbered tray of forms through a processing desk. The tray defines what belongs to the job, and a completion sheet records which forms were accepted, rejected or still need attention.

The analogy is limited because computing jobs can run across many machines and partially retry. Define both membership and completion. A tray labeled finished should not conceal forms that never arrived at the desk or outputs that were written twice.

Worked example

Processing time is only part of freshness

Suppose an invented reporting job runs once an hour, beginning exactly at the hour, and takes eight minutes. It processes events available at its start. Assume no queue delay, no late arrivals and immediate publication when the job finishes.

An event arriving just before 10:00 can appear in the 10:08 result, after roughly eight minutes. An event arriving just after 10:00 waits until the 11:00 run and appears around 11:08, after almost 68 minutes.

The eight-minute runtime is therefore not an eight-minute maximum freshness guarantee. The scheduling wait must also be included. Late arrivals, unavailable resources and failed runs can extend the delay further.

If users require a shorter delay, the team can evaluate more frequent bounded runs or a streaming design. The choice depends on cost, state, corrections and freshness requirements.

Illustrative case study

A fictional research team processes 1,000 documents into an analysis table. A run writes results for 700 documents and then fails. The scheduler retries the entire input, appending another 1,000 outputs. A dashboard now reports 1,700 processed documents.

The team introduces stable document and run identifiers, defines how retry attempts replace or reconcile prior outputs and validates the expected input coverage before publishing results. It keeps failed-item information separately so that a partial success is not presented as a complete dataset.

Batch reliability involves the whole workflow. Retrying computation is useful, but the destination's write semantics and the completion checks determine whether the final result is correct.

Common misconceptions and mistakes

Batch processing always rereads all historical data. A job can explicitly select a bounded partition or incremental input; the selection and tracking design determine its scope.
A batch must execute on one machine. Partitioned work can run in parallel across many resources.
Success means every intended business record was processed. Missing inputs or faulty selection rules can survive an otherwise successful execution.

Business use

Batch processing is suitable when work can be grouped and the required result does not need to update continuously for every arriving event. It can simplify repeatable analytics, bulk conversion and large offline AI jobs.

Define the delivery deadline and data-freshness requirement separately from the schedule. Record the input boundary, transformation version, output location and completion criteria. Plan how late records, corrections and reruns affect previously published results.

For AI workloads, preserve model and prompt versions where relevant, alongside source identifiers and rejected-item reasons. A batch label alone does not make outputs reproducible if inputs or model behavior change between attempts. Reconcile what was requested, what completed and what remains pending before using the results downstream.

Check your understanding

An hourly job takes five minutes and reads inputs only at its start. Can an event arriving just after a run starts wait almost 65 minutes for its result under ideal conditions?

Answer: Yes. It waits nearly an hour for the next run, then five minutes for processing. Runtime alone does not describe freshness.

Related terms

Partition
Orchestration
Batch inference
Streaming processing
bounded dataset
job scheduler
retry
idempotency
data freshness

Learn next

Read input snapshots and retry-safe output design. Draw one batch run from its input cutoff to publication, then simulate a failure after half the output is written and explain how recovery avoids duplicates.

Sources

Apache Beam, Basics of the Beam model: https://beam.apache.org/documentation/basics/. Primary explanation of bounded data and batch pipelines.

AWS, Components of AWS Batch: https://docs.aws.amazon.com/batch/latest/userguide/batch_components.html. Primary example of jobs, queues and compute environments.

Databricks, Batch vs. streaming data processing: https://docs.databricks.com/aws/en/data-engineering/batch-vs-streaming. Primary product-specific discussion of processing semantics. The timing and research examples are original illustrations.

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