Model contracts and access controls in dbt

Introduction

Model contracts help ensure data quality by defining clear expectations for data models. Access controls keep data safe by limiting who can see or change it.

When you want to make sure your data models always have the right columns and data types.

When multiple people work on data models and you want to avoid mistakes.

When you need to protect sensitive data from unauthorized users.

When you want to track who can update or view specific data models.

When you want to automate checks to catch errors early in your data pipeline.

Syntax

dbt

version: 2
models:
  - name: your_model_name
    columns:
      - name: column_name
        tests:
          - not_null
          - unique
    access:
      grants:
        - select: [user_or_role]
        - insert: [user_or_role]

The version: 2 is required for dbt schema files.

Model contracts are defined in the schema.yml file alongside tests and access controls.

Examples

This example sets a contract that customer_id cannot be null or duplicated. Only users with analyst_role can select data from this model.

dbt

version: 2
models:
  - name: customers
    columns:
      - name: customer_id
        tests:
          - not_null
          - unique
    access:
      grants:
        - select: [analyst_role]

This example requires sale_amount to have values and allows the sales team to view data but only data engineers to insert new data.

dbt

version: 2
models:
  - name: sales
    columns:
      - name: sale_amount
        tests:
          - not_null
    access:
      grants:
        - select: [sales_team]
        - insert: [data_engineer]

Sample Program

This schema file defines a model contract for the orders model. It checks that order_id is unique and not null, and order_date is not null. It also controls access so only the reporting team can view data and only data admins can update it.

dbt

version: 2
models:
  - name: orders
    columns:
      - name: order_id
        tests:
          - not_null
          - unique
      - name: order_date
        tests:
          - not_null
    access:
      grants:
        - select: [reporting_team]
        - update: [data_admin]

Important Notes

Model contracts help catch data issues early by running tests automatically.

Access controls in dbt depend on your data warehouse permissions; dbt helps organize them but actual enforcement is done by the warehouse.

Keep your contracts and access controls updated as your data and team change.

Summary

Model contracts define rules for data quality in your dbt models.

Access controls limit who can see or change your data models.

Both help keep your data reliable and secure.

Practice

1. What is the main purpose of a model contract in dbt?

easy

A. To schedule when models run in dbt Cloud

B. To define rules that ensure data quality in models

C. To control who can access the dbt project files

D. To create visual dashboards from model data

Solution

1. Step 1: Understand what a model contract does

   A model contract sets rules that data must follow to be considered valid in dbt models.

2. Step 2: Compare options to the definition

   Only To define rules that ensure data quality in models describes defining rules for data quality, which matches the purpose of model contracts.

3. Final Answer:

   To define rules that ensure data quality in models -> Option B

4. Quick Check:

   Model contract = data quality rules [OK]

Hint: Model contracts = rules for data quality [OK]

Common Mistakes:

• Confusing access control with model contracts
• Thinking contracts schedule runs
• Mixing contracts with visualization tools

2. Which of the following is the correct syntax to define a model contract in a dbt model's schema.yml file?

easy

A. models:
- name: my_model
access_control:
users:
- admin

B. models:
- name: my_model
schedule:
cron: '0 0 * * *'

C. models:
- name: my_model
contract:
columns:
- name: id
tests:
- not_null

D. models:
- name: my_model
dashboard:
enabled: true

Solution

1. Step 1: Identify the syntax for model contracts in schema.yml

   Model contracts are defined under the contract key with column tests inside schema.yml.

2. Step 2: Match options to this syntax

   models: - name: my_model contract: columns: - name: id tests: - not_null correctly shows contract with columns and tests. Others show unrelated keys like access_control, schedule, or dashboard.

3. Final Answer:

   models: - name: my_model contract: columns: - name: id tests: - not_null -> Option C

4. Quick Check:

   Contract syntax = models: - name: my_model contract: columns: - name: id tests: - not_null [OK]

Hint: Look for 'contract' key with column tests in schema.yml [OK]

Common Mistakes:

• Confusing access control syntax with contract syntax
• Using schedule or dashboard keys incorrectly
• Missing indentation in YAML

3. Given this dbt model contract snippet in schema.yml:

models:
  - name: sales
    contract:
      columns:
        - name: order_id
          tests:
            - not_null
            - unique
        - name: amount
          tests:
            - not_null

What happens if the order_id column contains duplicate values when the model runs?

medium

A. The model run is skipped automatically

B. The model runs successfully but logs a warning

C. The model ignores the duplicates and completes without error

D. The model run fails because the contract test for uniqueness fails

Solution

1. Step 1: Understand contract tests behavior

   Contract tests like unique enforce data rules strictly. If violated, the model run fails.

2. Step 2: Apply to duplicate order_id values

   Since order_id must be unique, duplicates cause the test to fail, stopping the model run.

3. Final Answer:

   The model run fails because the contract test for uniqueness fails -> Option D

4. Quick Check:

   Contract test fail = model run fails [OK]

Hint: Contract tests fail stop model runs immediately [OK]

Common Mistakes:

• Thinking model logs warning but continues
• Assuming duplicates are ignored
• Believing model run is skipped automatically

4. You wrote this access control in your dbt_project.yml:

access_control:
  - role: analyst
    models:
      - sales
      - customers
  - role: viewer
    models:
      - sales

But analysts report they cannot see the customers model. What is the likely error?

medium

A. The access_control block is not supported in dbt_project.yml

B. The role name 'analyst' is misspelled

C. The models list must be under each model, not role

D. The customers model does not exist in the project

Solution

1. Step 1: Check where access controls are defined in dbt

   dbt does not support an access_control block in dbt_project.yml. Access controls are managed outside or via external tools.

2. Step 2: Analyze other options

   Role spelling or model existence might cause issues, but the main problem is unsupported syntax in the config file.

3. Final Answer:

   The access_control block is not supported in dbt_project.yml -> Option A

4. Quick Check:

   Access control block unsupported = The access_control block is not supported in dbt_project.yml [OK]

Hint: Access controls not set in dbt_project.yml [OK]

Common Mistakes:

• Trying to set access control inside dbt_project.yml
• Assuming role names cause access issues
• Not verifying model existence

5. You want to enforce that only users with the 'data_engineer' role can modify the finance model, and that the model must always have a non-null transaction_id. How can you combine model contracts and access controls to achieve this?

hard

A. Define a model contract in schema.yml for transaction_id not null, and manage access control via your data platform's permissions for 'data_engineer' role

B. Add an access control block in schema.yml restricting 'finance' model to 'data_engineer', and add a contract test for transaction_id

C. Use a dbt macro to check user role inside the model SQL and raise error if not 'data_engineer', plus contract test for transaction_id

D. Set transaction_id as primary key in the database and assign 'data_engineer' role ownership on the model

Solution

1. Step 1: Use model contracts for data quality

   Define a contract test in schema.yml to ensure transaction_id is not null, enforcing data quality.

2. Step 2: Manage access control outside dbt

   dbt does not handle user permissions directly; access control is managed by your data platform or warehouse permissions for the 'data_engineer' role.

3. Final Answer:

   Define a model contract in schema.yml for transaction_id not null, and manage access control via your data platform's permissions for 'data_engineer' role -> Option A

4. Quick Check:

   Contracts for data rules + external access control = Define a model contract in schema.yml for transaction_id not null, and manage access control via your data platform's permissions for 'data_engineer' role [OK]

Hint: Use contracts for data, platform for access control [OK]

Common Mistakes:

• Trying to set access control inside dbt schema.yml
• Using macros to check user roles inside SQL
• Assuming dbt manages user permissions directly