Incremental loading is a cornerstone of modern data warehousing. It relies on identifying changes in source data, often through timestamps, change data capture (CDC) mechanisms, or versioning. Only these changed records are extracted, transformed, and loaded into the data warehouse. This strategy minimizes processing time and resource consumption, making it ideal for operational reporting and near real-time analytics. However, it requires robust mechanisms to track changes accurately.

A full load, also known as a complete refresh, replaces all existing data in the target table with the data from the source. While simple to implement, it's generally only practical for small datasets or for initial data loading. For large, frequently updated datasets, a full load can lead to significant downtime and consume excessive processing power and network bandwidth. It's often used for dimension tables that change infrequently or for initial population of a data warehouse.

Slowly Changing Dimensions (SCDs) are crucial for maintaining historical accuracy in dimensional models. Different types of SCDs exist: Type 1 overwrites the old value, Type 2 adds a new row with a new key and effective dates, and Type 3 uses a 'previous value' column. The choice of SCD type depends on how you need to track historical changes for analysis. For instance, tracking customer address changes might require SCD Type 2 to analyze sales by previous addresses.

Loading data in batches is a common optimization technique. Instead of committing each record individually, data is collected into larger batches, and then a single commit operation is performed for the entire batch. This significantly reduces the overhead of database transactions, leading to higher throughput. The optimal batch size often needs to be determined through performance testing, as too small a batch can still have high overhead, while too large a batch might consume excessive memory or lead to longer transaction times.