Batch processing is a data processing method where data is collected over a period of time and processed together as a group, rather than being processed immediately as it arrives. It is one of the oldest and most widely used approaches in data analytics and BI.

In batch processing, jobs typically run on a schedule, hourly, daily, or weekly. For example:

• Daily sales aggregation

• Nightly data warehouse refresh

• Weekly financial reporting

• Monthly billing calculations

Batch processing is commonly used in ETL and ELT pipelines. Data is extracted from source systems, transformed in bulk, and loaded into a data warehouse. Tools like Airflow, dbt, Fivetran, and custom SQL jobs rely heavily on batch workflows.

The main advantages of batch processing are:

• Simplicity

• Cost efficiency

• Predictable workloads

• Easier error handling

• Suitable for large historical datasets

Because batch jobs run at fixed times, they can take advantage of lower-cost compute resources and avoid constant processing overhead.

However, batch processing has limitations:

• Data is not real-time

• Insights can be delayed

• Errors may go unnoticed until the next run

• Not suitable for time-sensitive use cases

This is why batch processing is often contrasted with stream processing, which handles data in real time. In practice, most organizations use a hybrid approach: batch for core reporting and streaming for critical operational metrics.

From a BI standpoint, batch processing works well for dashboards that don’t require minute-level freshness, such as finance, strategy, or executive reporting.

Batch processing remains foundational in analytics because it balances reliability, scalability, and cost. Even as real-time systems grow, batch workflows continue to power the majority of BI use cases.