Configuring sources in YAML in dbt

Introduction

We use YAML to tell dbt where to find our raw data tables. This helps dbt understand and organize the data before we work with it.

When you want to tell dbt about the raw tables in your database.

When you need to document your data sources clearly for your team.

When you want to track freshness or test the quality of your source data.

When you want to reference raw tables in your dbt models easily.

Syntax

dbt

version: 2
sources:
  - name: source_name
    database: your_database
    schema: your_schema
    tables:
      - name: table_name
        description: 'Description of the table'
        freshness:
          warn_after:
            count: 24
            period: hour
        tests:
          - unique:
              column_name: id
          - not_null:
              column_name: id

The version: 2 line is required for dbt to read the YAML correctly.

Indentation is important in YAML. Use 2 spaces per level.

Examples

This example defines a source named sales_db with one table customers.

dbt

version: 2
sources:
  - name: sales_db
    database: analytics
    schema: raw
    tables:
      - name: customers
        description: 'Customer details table'

This example adds freshness settings to warn if data is older than 12 hours.

dbt

version: 2
sources:
  - name: marketing_data
    database: marketing
    schema: public
    tables:
      - name: campaigns
        freshness:
          warn_after:
            count: 12
            period: hour

This example adds tests to check that the transactions table has unique and non-null values.

dbt

version: 2
sources:
  - name: finance
    database: finance_db
    schema: reports
    tables:
      - name: transactions
        tests:
          - unique:
              column_name: id
          - not_null:
              column_name: id

Sample Program

This YAML config tells dbt about the orders table in the ecommerce source. It includes a description, freshness check to warn if data is older than 6 hours, and tests to ensure data quality.

dbt

version: 2
sources:
  - name: ecommerce
    database: analytics_db
    schema: raw_data
    tables:
      - name: orders
        description: 'Raw orders data from ecommerce platform'
        freshness:
          warn_after:
            count: 6
            period: hour
        tests:
          - unique:
              column_name: id
          - not_null:
              column_name: id

Important Notes

Always keep your YAML files well-indented to avoid errors.

Use descriptive names and descriptions to help your team understand the data.

Run dbt source freshness and dbt test to check your source configurations.

Summary

YAML files tell dbt where to find raw data tables.

You can add descriptions, freshness rules, and tests to sources.

Proper source configuration helps keep data organized and reliable.

Practice

1. What is the main purpose of configuring sources in a dbt YAML file?

easy

A. To write SQL queries for data transformation

B. To tell dbt where to find raw data tables

C. To create dashboards for data visualization

D. To schedule dbt runs automatically

Solution

1. Step 1: Understand the role of source configuration

   Source configuration in dbt YAML files defines where raw data tables are located in the database.

2. Step 2: Differentiate from other dbt tasks

   Writing SQL queries and scheduling runs are done elsewhere, not in source YAML files.

3. Final Answer:

   To tell dbt where to find raw data tables -> Option B

4. Quick Check:

   Source config = raw data location [OK]

Hint: Sources define raw table locations in YAML [OK]

Common Mistakes:

• Confusing source config with SQL model code
• Thinking sources schedule runs
• Assuming sources create visualizations

2. Which of the following is the correct syntax to define a source in a dbt YAML file?

easy

A. source:
name: raw_data
table:
- customers

B. sources:
name: raw_data
tables:
- customers

C. sources:
- name: raw_data
tables:
- name: customers

D. source:
- raw_data:
tables:
- customers

Solution

1. Step 1: Recall correct YAML source structure

   The correct syntax uses 'sources' as a list with 'name' and nested 'tables' list, each with a 'name'.

2. Step 2: Compare options to syntax

   sources: - name: raw_data tables: - name: customers matches the correct indentation and keys exactly.

3. Final Answer:

   sources: - name: raw_data tables: - name: customers -> Option C

4. Quick Check:

   Correct YAML keys and indentation = sources: - name: raw_data tables: - name: customers [OK]

Hint: Look for 'sources' list with 'name' and 'tables' keys [OK]

Common Mistakes:

• Using singular 'source' instead of 'sources'
• Missing 'name' key for tables
• Incorrect indentation breaking YAML structure

3. Given this YAML snippet, what is the value of the 'loaded_at_field' for the source 'sales_data'?

sources:
  - name: sales_data
    tables:
      - name: transactions
        loaded_at_field: transaction_date

medium

A. transaction_date

B. transactions

C. loaded_at_field

D. sales_data

Solution

1. Step 1: Locate the 'loaded_at_field' key in YAML

   It is nested under the 'transactions' table inside the 'sales_data' source.

2. Step 2: Identify the value assigned

   The value assigned to 'loaded_at_field' is 'transaction_date'.

3. Final Answer:

   transaction_date -> Option A

4. Quick Check:

   loaded_at_field value = transaction_date [OK]

Hint: Find 'loaded_at_field' key's value under table [OK]

Common Mistakes:

• Confusing source name with field value
• Picking table name instead of field value
• Misreading YAML indentation levels

4. Identify the error in this source configuration YAML:

sources:
  - name: marketing_data
    tables:
      - name: leads
        freshness:
          warn_after:
            count: 12
            period: hours
          error_after:
            count: 1
            period: days

medium

A. 'warn_after' and 'error_after' counts are reversed

B. The indentation under 'freshness' is incorrect

C. The 'error_after' period should be less than 'warn_after'

D. The 'period' values must be singular strings

Solution

1. Step 1: Understand dbt freshness period syntax

   dbt freshness requires singular 'period' values like 'hour', 'day', 'minute'. Plural forms ('hours', 'days') are invalid and cause errors.

2. Step 2: Check the YAML periods

   'period: hours' and 'period: days' use plural, which dbt does not recognize.

3. Step 3: Rule out other options

   A: Counts logical (12 hours warn before 1 day/24 hours error). B: Indentation correct. C: Incorrect--error_after time must be *longer* than warn_after.

4. Final Answer:

   The 'period' values must be singular strings -> Option D

5. Quick Check:

   period: hour/day (singular only) [OK]

Hint: dbt freshness periods must be singular (hour, day) [OK]

Common Mistakes:

• Using plural periods ('hours', 'days')
• Incorrect YAML indentation
• Thinking error_after time should be shorter than warn_after

5. You want to add a test to ensure the 'email' column in the 'users' table source is never null. Which YAML snippet correctly adds this test?

hard

A. sources:
- name: app_data
tables:
- name: users
columns:
- name: email
tests:
- not_null

B. sources:
- name: app_data
tables:
- name: users
tests:
- column: email
test: not_null

C. sources:
- name: app_data
tables:
- users:
columns:
- email:
tests:
- not_null

D. sources:
- name: app_data
tables:
- name: users
columns:
- email
test: not_null

Solution

1. Step 1: Recall correct test syntax in source YAML

   Tests are added under 'columns' with 'name' and a 'tests' list containing test names.

2. Step 2: Check each option's structure

   sources: - name: app_data tables: - name: users columns: - name: email tests: - not_null correctly uses 'columns' list with 'name' and 'tests' list containing 'not_null'.

3. Step 3: Identify errors in other options

   sources: - name: app_data tables: - name: users tests: - column: email test: not_null uses wrong keys, sources: - name: app_data tables: - users: columns: - email: tests: - not_null has wrong nesting, sources: - name: app_data tables: - name: users columns: - email test: not_null uses 'test' instead of 'tests'.

4. Final Answer:

   sources: - name: app_data tables: - name: users columns: - name: email tests: - not_null -> Option A

5. Quick Check:

   Tests under columns with 'tests' list = sources: - name: app_data tables: - name: users columns: - name: email tests: - not_null [OK]

Hint: Tests go under columns with 'tests' list [OK]

Common Mistakes:

• Using 'test' instead of 'tests'
• Wrong nesting of columns and tests
• Misnaming keys like 'column' instead of 'name'