Why Model dependencies and parallelism in dbt? - Purpose & Use Cases

The Big Idea

What if your data projects could build themselves faster and without mistakes?

The Scenario

Imagine you have a big recipe book where each dish depends on ingredients prepared by other dishes. You try to cook everything step-by-step, waiting for one dish to finish before starting the next, even if some dishes don't depend on each other.

The Problem

This slow, one-by-one cooking wastes time. You might forget which dish needs which ingredient first, causing mistakes or redoing steps. It's hard to keep track and takes much longer than needed.

The Solution

Model dependencies and parallelism in dbt help you organize your recipes so you know exactly which dishes depend on others. dbt then cooks all independent dishes at the same time, saving you lots of time and avoiding confusion.

Before vs After

✗ Before

run model_a
depend on model_a: run model_b
run model_c after model_b

✓ After

dbt run --models model_a model_b model_c --threads 3

What It Enables

It lets you build complex data projects faster by running independent tasks at the same time while respecting their order.

Real Life Example

In a sales dashboard, you first prepare customer data, then sales data, and finally combine them. With model dependencies and parallelism, dbt runs customer and sales data models simultaneously if they don't depend on each other, speeding up the whole process.

Key Takeaways

Manual step-by-step runs are slow and error-prone.

Model dependencies map out what needs to run first.

Parallelism runs independent models at the same time, saving time.