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Data Build Tool (dbt) has revolutionized how we transform data in the modern data stack. One of its most powerful features is incremental models, which significantly reduce build times and resource consumption. But with various strategies available, choosing the right one can be tricky. Let’s dive into the world of dbt incremental strategies and unlock their potential.
The Foundation: Full Refresh (Not Really Incremental, But Essential)
Before we delve into the incremental techniques, it’s crucial to understand the baseline: full refresh. While not an incremental strategy itself, it serves as a valuable comparison.
How it works: Every time you run a full refresh model, dbt completely recreates the target table. This means it ignores the if is_incremental() block and executes a CREATE OR REPLACE TABLE command.
When to use it: Full refresh is ideal for:
- Initial model creation.
- Schema changes that require a complete rebuild.
- Small datasets where build time isn’t a major concern.
