Why sources define raw data contracts in dbt - Quick Recap

Recall & Review

beginner

What is a raw data contract in the context of data sources?

A raw data contract is an agreement or set of rules that defines the expected structure, format, and quality of raw data coming from a source before it is processed or transformed.

beginner

Why do data teams define raw data contracts for sources?

To ensure data consistency, reliability, and to catch errors early by setting clear expectations about the data's format and quality before it enters the transformation pipeline.

intermediate

How do raw data contracts help in data transformation workflows?

They act as a checkpoint that validates incoming data, preventing bad or unexpected data from causing errors downstream in transformations or analyses.

intermediate

What might happen if raw data contracts are not defined for sources?

Without raw data contracts, data inconsistencies or errors can go unnoticed, leading to incorrect analysis, broken pipelines, and loss of trust in data.

beginner

Give an example of a rule that might be included in a raw data contract.

An example rule could be: "The column 'user_id' must always be a non-null integer," ensuring that every record has a valid user identifier.

What is the main purpose of defining raw data contracts for sources?

A To create visualizations directly from raw data

B To speed up data transformation by skipping validation

C To store data in a compressed format

D To ensure data meets expected structure and quality before processing

Raw data contracts set expectations for data format and quality to catch issues early.

Which of the following is NOT a benefit of raw data contracts?

A Improved data reliability

B Early detection of data errors

C Automatic data visualization

D Clear communication of data expectations

Raw data contracts do not create visualizations; they focus on data quality and structure.

What could happen if raw data contracts are missing?

A Data will automatically be corrected

B Data pipelines may break due to unexpected data

C Data will be faster to process without checks

D Data will be encrypted

Without contracts, unexpected data can cause errors in pipelines.

A raw data contract might specify that a column must be:

A Non-null and of a specific data type

B Encrypted

C Randomly generated

D Always null

Contracts define expected data types and nullability to ensure data quality.

In dbt, why is defining raw data contracts important before transformations?

A To avoid errors and maintain trust in transformed data

B To skip testing transformed data

C To reduce storage costs

D To create dashboards automatically

Contracts help catch issues early, ensuring transformations work on clean data.

Explain why defining raw data contracts for sources is important in a data pipeline.

Think about what happens if bad data enters your system.

Describe what kind of rules might be included in a raw data contract.

Consider how you would check if data is 'correct' before using it.

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