Introduction to Data Loads - Full & Incremental

In the context of data integration and data warehousing, full load and incremental load are two different methods of updating a database or data warehouse with new or changed data. Here’s a detailed explanation of the differences between the two:

Full Load 

Definition: A full load, also known as a destructive load, involves completely replacing the existing data in a target table or dataset with the new data.

Key Characteristics:

1. Complete Replacement: All existing data in the target table is deleted or overwritten, and the new data is loaded afresh.
2. Simpler Process: The process is straightforward because it does not need to consider changes or differences between the new and old data.
3. Potential for Data Loss: There is a risk of losing data if the load process fails after the existing data has been deleted but before the new data is fully loaded.

Use Cases:

• Initial data load when creating the Data Model.
• Scenarios where data changes completely.
• Simple data migrations.
• Capturing record deletions for APIs that don’t support them.

Incremental Load 

Definition: An incremental load, also known as a delta load, involves updating the target table or dataset by loading only the new or changed data since the last load.

Key Characteristics:

1. Partial Update: Only new, modified, or deleted records are processed and loaded into the target table.
2. Efficiency: More efficient in terms of resource consumption (CPU, memory, and disk usage) because it processes only a subset of the total data.
3. Frequency: Suitable for frequent updates, especially in environments where data changes regularly but only a portion of the data is affected.
4. Harder to be Accurate: Some APIs (such as SuccessFactors) don’t track deletes, which can mean that Incremental Data Loads might need to be supplemented by Full Loads

Use Cases:

• Maintaining a data warehouse or data mart where only incremental changes need to be applied.
• Real-time or near-real-time data synchronization.
• Scenarios with large datasets where full loads are impractical due to time or resource constraints.

Comparison Table

By understanding these differences, organizations can choose the appropriate loading strategy based on their specific requirements, data volume, and update frequency.