In this lesson, you will learn how to transition from resource-heavy full-table refreshes to performant incremental updates using dbt.
From Full-Load to Incremental
In many legacy data pipelines, you likely have "monolithic" SQL scripts that truncate and reload an entire table every time they run. While simple, this becomes prohibitively expensive and slow as your datasets grow. Incremental refactoring is the practice of modifying these scripts so that they only process the "delta"—the new or updated rows—since the last successful run.
The Strategy
To refactor a legacy model, you need to tell dbt two things: how to filter for new data and how to handle existing data. By using the is_incremental() macro, you can wrap your filter logic to ensure it only executes during incremental runs.
Imagine a legacy orders table that currently uses:
SELECT * FROM raw.orders
To refactor this to an incremental model, you structure it like this:
{{ config(materialized='incremental') }}
SELECT *
FROM {{ ref('raw_orders') }}
{% if is_incremental() %}
-- Only pull rows updated since the last run
WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}
Why This Matters
When you move to an incremental strategy, you stop scanning gigabytes (or terabytes) of historical data every time you run your pipeline. Instead, you only process the latest partition. This achieves two critical goals:
- Warehouse Efficiency: You drastically reduce compute costs by limiting the amount of data processed per run.
- Reduced Latency: Because you are processing smaller chunks of data, your models finish faster, ensuring your downstream dashboards are updated with fresh data without putting unnecessary strain on your database cluster.
When refactoring, always ensure your source data has an appropriate watermark column (like updated_at or created_at) to reliably identify new records. If your legacy model includes complex joins or window functions, you may need to perform these calculations on the incremental window and merge them into your target table to avoid data inconsistencies.
Key Takeaway: Incremental refactoring transforms inefficient full-load scripts into cost-effective, high-performance pipelines by processing only the newest data partitions.