Configuring sources in YAML in dbt - Try It Yourself

Configuring sources in YAML

📖 Scenario: You are working on a data project using dbt. You want to tell dbt where your raw data lives by configuring sources in a YAML file. This helps dbt understand your data tables before you transform them.

🎯 Goal: Learn how to write a YAML configuration for a source in dbt. You will create a source with a name, specify the database and schema, and list the tables inside it.

📋 What You'll Learn

Create a YAML file with a source configuration

Define a source named raw_data

Set the database to analytics_db

Set the schema to public

Add a table named customers with a description

Add a table named orders with a description

💡 Why This Matters

🌍 Real World

In real data projects, configuring sources in YAML helps document and manage raw data tables before transforming them.

💼 Career

Data engineers and analysts use source configurations in dbt to build reliable and maintainable data pipelines.

1

Create the basic source structure

Create a YAML file and write a source configuration with the name raw_data. Set the database to analytics_db and the schema to public. Do not add any tables yet.

dbt

# Write the source configuration with name raw_data, database analytics_db, and schema public

Hint

Start with sources: then add a list item with name, database, and schema.

2

Add the first table to the source

Add a table named customers inside the tables list of the raw_data source. Add a description: Customer details table.

dbt

version: 2
sources:
  - name: raw_data
    database: analytics_db
    schema: public
    tables:
      # Add the customers table with description here

Hint

Inside tables:, add a list item with name: customers and description.

3

Add the second table to the source

Add another table named orders inside the tables list of the raw_data source. Add a description: Orders placed by customers.

dbt

version: 2
sources:
  - name: raw_data
    database: analytics_db
    schema: public
    tables:
      - name: customers
        description: "Customer details table"
      # Add the orders table with description here

Hint

Add a second list item under tables: with the orders table and its description.

4

Display the complete YAML source configuration

Print the complete YAML source configuration for raw_data with both tables customers and orders and their descriptions.

dbt

# Print the YAML source configuration below

Hint

Print the YAML text exactly as configured in previous steps.

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'