Why is_incremental() macro in dbt? - Purpose & Use Cases

The Big Idea

What if you could update huge datasets in seconds instead of hours?

The Scenario

Imagine you have a huge table with millions of rows, and every day you need to add only the new data. Doing this by hand means reloading the entire table each time, which takes forever and wastes resources.

The Problem

Manually reloading all data is slow and costly. It can cause errors like duplicates or missing updates. It also makes your data pipeline fragile and hard to maintain.

The Solution

The is_incremental() macro in dbt lets you write models that only add or update new data. This saves time and avoids mistakes by running just the needed changes.

Before vs After

✗ Before

SELECT * FROM big_table -- reload all data every time

✓ After

SELECT * FROM source_table WHERE updated_at > (SELECT MAX(updated_at) FROM target_table) -- only new data

What It Enables

It enables fast, reliable data updates that keep your warehouse fresh without wasting time or resources.

Real Life Example

A sales team updates daily transactions. Using is_incremental(), they add only new sales each day, so reports are always up-to-date and fast to generate.

Key Takeaways

Manually reloading data is slow and error-prone.

is_incremental() runs only new or changed data, saving time.

This makes data pipelines efficient and reliable.