dbt Contracts Explained: Enforce Reliable Data Models

If you’ve ever worked on a growing data project, you’ve probably faced this headache: you build a solid dbt model, someone upstream changes a column name or data type, and suddenly your pipeline breaks, your dashboard shows nonsense, and you’re stuck debugging. That’s where data contracts come in.

In the world of software, contracts define clear rules between systems. Now, the data world is catching up with dbt, leading the charge. With the introduction of model contracts in dbt, we finally have a way to formalize expectations about our data structures, right at the modeling layer. But what does that mean?

It means you can now declare and enforce rules for what your data models should look like:

• Which columns must exist
• What data types should they have
• Whether or not they can be null

And dbt will enforce those rules before your models are built. That’s right, you get faster feedback, tighter control, and less fire-fighting.

This blog is your beginner-friendly guide to everything you need to know about dbt contracts, also known as contract dbt, data contracts dbt, and model contracts dbt. We’ll walk through how they work, why they matter, how to use them, and how to avoid common mistakes.

What is a Data Contract in dbt?

Okay, so what exactly is a data contract in dbt?

At its core, a data contract is like a promise: your dbt model promises that the table it creates will follow a specific structure. That includes:

• The exact columns that should exist
• What data type should each column be
• Whether those columns can be null or not

And dbt isn’t just taking your word for it, it enforces this structure when you build your models.

Let’s break that down.

Let us look at a simple analogy, shall we?

Think of your dbt model like a bakery. Your data contract is the recipe: it says, “this cake must have 3 layers, 1 chocolate topping, and no nuts.” If someone suddenly adds sprinkles (or removes a layer), dbt throws up a red flag and says, “Whoa, that’s not the cake we agreed on!”

This helps keep your data predictable and your downstream tools happy.

Where Do You Define a Contract?

In dbt, contracts are defined inside the model’s .yml file (not in the .sql query itself). Here’s the structure:

models:
  - name: customer_orders
    config:
      contract:
        enforced: true
    columns:
      - name: customer_id
        data_type: string
        description: "Unique ID for each customer"
      - name: order_date
        data_type: date
        description: "Date when the order was placed"

That tells dbt: “Only build this model if it will produce exactly these columns with these data types.”

But, how is this different from a dbt test?

Great question! Tests in dbt check your data (e.g., no nulls, unique IDs, valid values).
Contracts, on the other hand, check your schema before data even gets written to your table. They’re more proactive than reactive.

Why Should You Use Contracts in dbt Projects?

I know, you might be wondering, “Do I really need dbt contracts? Aren’t my dbt models already working fine?” Let’s talk about what adding a dbt contract brings to the table.

1. Protect against schema drift

One of the most common problems in any data pipeline is schema drift, i.e., when the schema of upstream tables changes over time. For example:

• A data producer renames customer_id to cust_id
• A field like order_date suddenly allows NULL values
• A numeric column switches from INTEGER to FLOAT

Without contracts, your dbt models would happily build with the wrong structure until something downstream breaks. However, with contracts, dbt will catch the problem immediately, before the bad data even touches your production models.

2. Faster feedback & fewer surprises

When a model build fails because of a contract violation, you find out instantly. This means:

• Less time spent debugging invisible breakages
• More confidence every time you run dbt build

3. Better Collaboration Across Teams

As more teams and people touch your data ecosystem, clear expectations become crucial.
Data contracts are like a shared agreement between every stakeholder and the downstream application/users.

When the structure is clearly defined and enforced, everyone works with the same assumptions. No more guessing what columns should exist or what types they should be.

4. Scaling with Confidence

If you’re thinking about scaling your dbt project, adding more models, sources, contributors, or even moving toward a data mesh approach, contracts become non-negotiable.

They let you treat your models like products: stable, predictable, and version-controlled.

Without contracts, scaling your data platform is like building a skyscraper on sand.
With contracts, you’re laying down concrete foundations.

Let me quickly tell you a real story here.

One team I worked with had a critical model that fed revenue reports. One day, a tiny upstream change, a decimal precision tweak, caused a 7% discrepancy in reported numbers. No one noticed for two weeks. However, a simple dbt contract would have caught it during the first build.

Setting Up a Model Contract in dbt (Step-by-Step Guide)

Alright, now for the fun part, actually setting up a model contract in dbt!
If you’ve written a .yml file for your models before, you’re already halfway there.

Let’s walk through it together:

Step 1: Enable Contract Enforcement

First, you must tell dbt that you want to enforce a contract for a specific model.

You do this by adding a config block inside your model’s YAML file (schema.yml or wherever you define your models).

Here’s the basic structure:

models:
  - name: customer_orders
    config:
      contract:
        enforced: true

This enforces that the table must match your column definitions exactly.

Step 2: Define Your Columns

Next, list all the columns you expect in your model, along with their:

• Name
• Data type
• Description (Optional but recommended)

Example:

models:
  - name: customer_orders
    config:
      contract:
        enforced: true
    columns:
      - name: customer_id
        data_type: string
        description: "Unique ID for each customer"
      - name: order_date
        data_type: date
        description: "Date when the order was placed"
      - name: total_amount
        data_type: numeric
        description: "Total value of the order in USD"

The data_type should match what your database (Snowflake, BigQuery, Redshift, etc)

Step 3: Align Your SQL Model to the Contract

Inside your model’s .sql file, make sure your SELECT matches the contract exactly.

Example model file (customer_orders.sql):

select
    customer_id,
    order_date,
    total_amount
from {{ ref('raw_orders') }}

When you run dbt build, dbt will validate your model’s output against the contract before writing the table.

How does dbt Enforce Data Contracts?

Understanding how enforcement works will help you troubleshoot faster and use contracts smarter. Let’s break it down step by step, how dbt actually checks all this behind the scenes:

Step 1: Validation at Build Time

When you run a dbt command like dbt build or dbt run, dbt doesn’t just execute your SQL.
It first checks your model’s output against the contract you defined in your YAML.

Specifically, dbt verifies:

• Are all required columns present?
• Do the data types match exactly (based on your target warehouse’s rules)?
• Does the nullability (whether a column can be NULL or not) align with the contract, if specified?

If anything is missing or mismatched, dbt will fail the build immediately and catch issues before data lands in your target tables.

Step 2: Warehouses Play a Role Too

While dbt handles most of the validation, your database warehouse also enforces certain things based on how dbt defines the output table. For example:

• In Snowflake, dbt creates a table with column types and NULL/NOT NULL settings based on the contract.
• In BigQuery, it similarly defines table schemas and can throw errors if incoming data doesn’t match.

So, contracts are a combo effort: dbt sets the rules, and your warehouse helps police them.

What Happens When Something Breaks?

If dbt detects a contract violation, it will stop the model build and throw a ContractViolationException.

Example error message:

dbt.exceptions.ContractViolationException:
Model 'customer_orders' does not match its defined contract.
Expected column 'total_amount' of type 'numeric', but got 'string' instead.

This is good! It means you’re finding issues early, before they become major downstream problems.

Conclusion

dbt data contracts help ensure your models always produce the correct structure, making your pipelines more reliable and easier to maintain. By enforcing contracts smartly, starting with critical models and combining them with good testing practices, you build a future-proof, trustworthy data platform. With just a bit of extra setup, contracts give you major protection against unexpected errors and broken dashboards.

Looking to simplify your dbt transformations even further? Try Hevo Transformer, our native dbt Core integration that lets you transform data at scale with minimal setup and maximum control.

Frequently Asked Questions (FAQs)