What is Batch Processing? - Definition & Meaning

Definition

Batch processing is the processing of large volumes of data in groups (batches) at scheduled times, rather than in real time per event. It is ideal for non-urgent tasks such as reporting, model training, and large ETL jobs.

Technical explanation

Batch processing collects data over a period and processes it in one run. Benefits: more efficient resource use (bulk optimization), lower complexity than stream processing, and simpler error handling. Tools: Apache Spark for distributed batch jobs, AWS Batch, Apache Airflow for orchestration, and cron for simple scheduling. Batch pipelines typically consist of extract-transform-load (ETL) steps. Lambda architecture combines batch with stream for both historical and real-time insights. For ML, model training is often run as an overnight batch job.

How AVARC Solutions applies this

AVARC Solutions builds batch pipelines for ETL, reporting, and ML training. We use Airflow or Prefect for orchestration and Spark or DuckDB for large datasets. For clients, we design hybrids where batch handles overnight processing and streaming feeds real-time dashboards where needed.

Practical examples

• A retail company running batch jobs every night to aggregate sales data and update inventory forecasts.
• An ML pipeline retraining a recommendation model weekly on full customer interaction data.
• A data warehouse filled via daily ETL batches from source systems for reporting.

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