Why sources define raw data contracts in dbt - The Real Reasons

The Big Idea

What if you could stop chasing data errors and start trusting your data every day?

The Scenario

Imagine you receive data files from different teams every day. Each file has different formats, missing columns, or unexpected changes. You try to process them manually by checking each file before using it.

The Problem

This manual checking is slow and tiring. You often miss changes or errors, causing your reports to be wrong. Fixing these mistakes later wastes time and causes frustration.

The Solution

Defining raw data contracts means setting clear rules about what the data should look like before you use it. This helps catch problems early and keeps your data clean and reliable automatically.

Before vs After

✗ Before

if 'date' in data.columns and data['date'].notnull().all():
    process(data)

✓ After

sources('raw_data').expect_columns(['date', 'id', 'value']).expect_not_null('date')

What It Enables

It lets you trust your data pipeline and focus on analysis, not fixing data problems.

Real Life Example

A marketing team defines a contract for customer data to ensure every record has an ID and signup date. This prevents errors in campaign reports caused by missing or wrong data.

Key Takeaways

Manual data checks are slow and error-prone.

Raw data contracts set clear expectations for incoming data.

They help catch issues early and keep data reliable.

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