Batch ETL refers to extracting, transforming, and loading data in large batches at scheduled intervals rather than in real time. It is a foundational approach in data warehousing and BI systems.

In batch ETL:

• Data is extracted from source systems

• Transformed using business logic

• Loaded into a warehouse or analytics store

• Typically runs hourly, daily, or weekly

Batch ETL is commonly used for:

• Financial reporting

• Sales analytics

• Historical trend analysis

• Compliance reporting

• Executive dashboards

Tools like Fivetran, Airbyte, Talend, Informatica, dbt, and Airflow are widely used for batch ETL workflows.

The benefits include:

• Predictable performance

• Lower operational complexity

• Cost efficiency

• Easier debugging

The downside is data latency. Insights are only as fresh as the last batch run.

Most modern analytics stacks use a hybrid approach: batch ETL for core reporting and streaming pipelines for real-time use cases.

Batch ETL remains critical because many business decisions do not require real-time data. It balances stability, cost, and scalability.