Batch Loading

Batch loading is a data processing technique where multiple data items are grouped together and processed as a single unit, rather than individually. It is commonly used in database operations, file transfers, and data pipelines to improve efficiency by reducing overhead, such as network latency or transaction costs. This approach optimizes resource usage and throughput, especially when handling large volumes of data.

Also known as: Bulk Loading, Batch Processing, Bulk Insert, Batch Job, Batch Operations

Why learn Batch Loading?

Developers should use batch loading when dealing with high-volume data operations where individual processing would be inefficient, such as in ETL (Extract, Transform, Load) processes, bulk database inserts, or data synchronization tasks. It is particularly valuable in scenarios like data warehousing, log aggregation, or batch job scheduling, where it minimizes system load and improves performance by amortizing fixed costs over multiple items.

Learning Resources

• docs: Batch Processing Patterns
• docs: Batch Loading in Databases
• docs: Batch Processing with Apache Spark
• video: Batch vs Stream Processing Explained
• course: Data Engineering Fundamentals: Batch and Stream Processing