Why incremental models save time and cost in dbt - The Real Reasons

The Big Idea

What if you could update huge data sets in minutes instead of hours?

The Scenario

Imagine you have a huge pile of data that grows every day. Every time you want to update your reports, you run the whole process from the start, even for data you already processed.

The Problem

This means waiting a long time, using lots of computer power, and sometimes making mistakes by reprocessing old data. It feels like painting the entire wall every time you want to fix a small spot.

The Solution

Incremental models let you update only the new or changed data. Instead of redoing everything, you add just the fresh pieces. This saves time, reduces cost, and makes your work faster and smarter.

Before vs After

✗ Before

select * from big_table

✓ After

select * from big_table where updated_at > (select max(updated_at) from target_table)

What It Enables

It makes data updates quick and efficient, so you can focus on insights instead of waiting for processing.

Real Life Example

A company updating daily sales reports only processes the new sales data each day, instead of recalculating all past sales, saving hours of work and computing costs.

Key Takeaways

Manual full data reloads waste time and resources.

Incremental models update only new or changed data.

This approach saves time, reduces cost, and speeds up data workflows.