### Batch ETL

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.