Why sources define raw data contracts in dbt

Introduction

Raw data contracts help teams agree on what data looks like before using it. This avoids confusion and errors later.

When multiple teams share the same data source and need clear expectations.

When you want to catch data problems early before analysis.

When building automated data pipelines that depend on consistent data formats.

When onboarding new team members who need to understand data structure quickly.

When tracking changes in data sources over time to avoid breaking reports.

Syntax

dbt

sources:
  - name: raw_data_source
    tables:
      - name: raw_table
        freshness:
          warn_after: {count: 24, period: hour}
          error_after: {count: 48, period: hour}
        loaded_at_field: updated_at
        description: "Raw data contract for source table"

The sources block defines where raw data comes from.

Freshness settings help monitor if data is up-to-date.

Examples

Defines a source named sales_db with a table orders.

dbt

sources:
  - name: sales_db
    tables:
      - name: orders
        description: "Contract for raw orders data"

Sets freshness rules and a timestamp field to track data loading time.

dbt

sources:
  - name: marketing_data
    tables:
      - name: leads
        freshness:
          warn_after: {count: 12, period: hour}
          error_after: {count: 24, period: hour}
        loaded_at_field: load_time

Sample Program

This example shows a raw data contract for an ecommerce customers table. It sets rules to warn if data is older than 6 hours and error if older than 12 hours.

dbt

version: 2

sources:
  - name: ecommerce_raw
    description: "Raw data contract for ecommerce source"
    tables:
      - name: customers
        description: "Customer raw data with expected fields"
        freshness:
          warn_after: {count: 6, period: hour}
          error_after: {count: 12, period: hour}
        loaded_at_field: last_updated

# This YAML defines a raw data contract in dbt for the ecommerce customers table.
# It helps ensure data freshness and documents expectations.

Important Notes

Raw data contracts are written in YAML files in dbt projects.

They help automate data quality checks and documentation.

Summary

Raw data contracts define clear expectations for source data.

They help catch data issues early and keep teams aligned.

In dbt, raw data contracts are defined using sources in YAML files.

Practice

1. Why do we define raw data contracts in dbt sources?

easy

A. To set clear expectations for the raw data coming into the system

B. To speed up the data loading process

C. To automatically fix data errors

D. To create visual reports from raw data

Solution

1. Step 1: Understand the purpose of raw data contracts

   Raw data contracts define what the incoming data should look like, such as expected columns and types.

2. Step 2: Identify the main benefit in dbt context

   They help teams know what to expect and catch issues early, not speed up loading or fix errors automatically.

3. Final Answer:

   To set clear expectations for the raw data coming into the system -> Option A

4. Quick Check:

   Raw data contracts = clear data expectations [OK]

Hint: Raw data contracts = clear data rules for sources [OK]

Common Mistakes:

• Thinking contracts speed up data loading
• Assuming contracts fix data automatically
• Confusing contracts with reporting tools

2. Which of the following is the correct way to define a source in a dbt YAML file for raw data contracts?

easy

A. source:
name: raw_data
table: users
columns:
- id
tests: [not_null, unique]

B. sources:
- name: raw_data
tables:
- name: users
columns:
- name: id
tests: [not_null, unique]

C. sources:
raw_data:
users:
columns:
- id
tests: [not_null, unique]

D. source:
- raw_data
- users
- columns:
- id
tests: [not_null, unique]

Solution

1. Step 1: Recall dbt source YAML structure

   Sources are defined under sources: as a list with name and tables keys.

2. Step 2: Match correct indentation and keys

   sources: - name: raw_data tables: - name: users columns: - name: id tests: [not_null, unique] correctly uses sources list, name, tables, and columns with tests.

3. Final Answer:

   sources: - name: raw_data tables: - name: users columns: - name: id tests: [not_null, unique] -> Option B

4. Quick Check:

   dbt source YAML = list with name, tables, columns [OK]

Hint: Sources use list with name and tables keys in YAML [OK]

Common Mistakes:

• Using singular 'source' instead of 'sources'
• Incorrect indentation or missing keys
• Listing columns without proper nesting

3. Given this source definition in dbt YAML:

sources:
  - name: raw_sales
    tables:
      - name: transactions
        columns:
          - name: transaction_id
            tests: [not_null, unique]
          - name: amount
            tests: [not_null]

What happens if a transaction has a null amount when running dbt tests?

medium

A. The test will pass because nulls are allowed by default

B. The test will be skipped for the 'amount' column

C. dbt will automatically fill null amounts with zero

D. The test will fail, alerting that a null value exists in 'amount'

Solution

1. Step 1: Understand the 'not_null' test in dbt

   The 'not_null' test checks that no null values exist in the specified column.

2. Step 2: Predict test behavior on null data

   If a null value exists in 'amount', the 'not_null' test will fail and alert the user.

3. Final Answer:

   The test will fail, alerting that a null value exists in 'amount' -> Option D

4. Quick Check:

   'not_null' test fails on nulls [OK]

Hint: 'not_null' test fails if any nulls found [OK]

Common Mistakes:

• Thinking dbt fills nulls automatically
• Assuming tests pass by default
• Believing tests skip columns with nulls

4. You wrote this source YAML in dbt:

sources:
  - name: raw_data
    tables:
      - name: customers
        columns:
          - name: customer_id
            tests: not_null, unique

When running dbt, you get a syntax error. What is the problem?

medium

A. The tests list should be inside square brackets [ ]

B. The 'columns' key should be 'column'

C. The 'tables' key should be a dictionary, not a list

D. The source name cannot be 'raw_data'

Solution

1. Step 1: Check YAML syntax for tests

   Tests must be listed as a YAML list inside square brackets or as a list with dashes.

2. Step 2: Identify the error in tests format

   Writing tests: not_null, unique is invalid YAML; it should be tests: [not_null, unique].

3. Final Answer:

   The tests list should be inside square brackets [ ] -> Option A

4. Quick Check:

   Tests need brackets or dashes in YAML [OK]

Hint: Tests must be a list with brackets or dashes [OK]

Common Mistakes:

• Writing tests as comma-separated string without brackets
• Using wrong key names like 'column' instead of 'columns'
• Misunderstanding list vs dictionary in YAML

5. You want to ensure your raw data source in dbt matches a strict contract: every 'order_id' must be unique and not null, and 'order_date' must be present and in date format. How should you define this in your source YAML to catch issues early?

hard

A. Define the source with columns and add tests only for 'order_id' as unique, ignoring 'order_date'

B. Define the source with columns but no tests; rely on downstream models to catch errors

C. Define the source with columns 'order_id' and 'order_date' and add tests: 'order_id' with [not_null, unique], 'order_date' with [not_null, accepted_values] for dates

D. Define the source with columns and add tests for 'order_date' only, ignoring 'order_id'

Solution

1. Step 1: Identify required tests for 'order_id'

   To ensure uniqueness and no nulls, use tests [not_null, unique] on 'order_id'.

2. Step 2: Define tests for 'order_date'

   To ensure presence and valid dates, use [not_null] and a test like 'accepted_values' or a custom test for date format.

3. Step 3: Combine tests in source YAML

   Include both columns with their respective tests to catch issues early at the source level.

4. Final Answer:

   Define the source with columns 'order_id' and 'order_date' and add tests: 'order_id' with [not_null, unique], 'order_date' with [not_null, accepted_values] for dates -> Option C

5. Quick Check:

   Raw data contracts include all critical tests [OK]

Hint: Test all critical columns with not_null and uniqueness [OK]

Common Mistakes:

• Skipping tests on important columns
• Relying on downstream models for raw data validation
• Not testing data formats like dates