Why if/else logic in models in dbt? - Purpose & Use Cases

The Big Idea

What if you could stop rewriting your data rules every time something changes?

The Scenario

Imagine you have a big spreadsheet with sales data from different regions. You want to calculate commissions, but the rules change depending on the region. You try to write separate formulas for each case manually.

The Problem

Manually writing separate formulas for each condition is slow and confusing. It's easy to make mistakes, and updating the rules means changing many places. This leads to errors and wasted time.

The Solution

Using if/else logic in dbt models lets you write clear, simple rules that automatically apply the right calculation based on conditions. This keeps your code clean and easy to update.

Before vs After

✗ Before

SELECT sales, region, CASE WHEN region = 'East' THEN sales * 0.1 ELSE sales * 0.05 END AS commission FROM sales_data;

✓ After

{{
  config(materialized='table')
}}

SELECT
  sales,
  region,
  CASE
    WHEN region = 'East' THEN sales * 0.1
    ELSE sales * 0.05
  END AS commission
FROM {{ ref('sales_data') }};

What It Enables

You can build flexible, maintainable models that adapt to complex business rules without rewriting code everywhere.

Real Life Example

A company uses if/else logic in dbt models to calculate different tax rates for products sold in various countries, automatically adjusting as tax laws change.

Key Takeaways

If/else logic helps handle different cases clearly in your data models.

It reduces errors and makes updates easier.

It supports building smarter, adaptable data pipelines.

Practice

1. In dbt models, which SQL construct is commonly used to add if/else logic for creating new columns?

easy

A. WHILE LOOP

B. FOR LOOP

C. CASE WHEN ... THEN ... ELSE ... END

D. FUNCTION CALL

Solution

1. Step 1: Understand conditional logic in SQL

   SQL uses CASE statements to check conditions and return values accordingly.

2. Step 2: Identify the correct syntax for if/else logic

   CASE WHEN ... THEN ... ELSE ... END is the standard way to implement if/else logic in SQL and dbt models.

3. Final Answer:

   CASE WHEN ... THEN ... ELSE ... END -> Option C

4. Quick Check:

   CASE statement = if/else logic [OK]

Hint: Remember: CASE is SQL's if/else [OK]

Common Mistakes:

• Confusing loops with conditional logic
• Using function calls instead of CASE
• Trying to use procedural code in SQL

2. Which of the following is the correct syntax for a CASE statement in a dbt model to check if a column score is greater than 50?

easy

A. CASE WHEN score THEN 'Pass' ELSE 'Fail' END

B. CASE score > 50 THEN 'Pass' ELSE 'Fail'

C. IF score > 50 THEN 'Pass' ELSE 'Fail' END

D. CASE WHEN score > 50 THEN 'Pass' ELSE 'Fail' END

Solution

1. Step 1: Review correct CASE syntax

   The correct syntax starts with CASE WHEN condition THEN result ELSE other_result END.

2. Step 2: Check each option for syntax errors

   CASE WHEN score > 50 THEN 'Pass' ELSE 'Fail' END correctly uses CASE WHEN score > 50 THEN 'Pass' ELSE 'Fail' END. Others miss WHEN, END, or have wrong structure.

3. Final Answer:

   CASE WHEN score > 50 THEN 'Pass' ELSE 'Fail' END -> Option D

4. Quick Check:

   CASE WHEN + condition + THEN + result + ELSE + result + END [OK]

Hint: CASE must have WHEN and END keywords [OK]

Common Mistakes:

• Omitting WHEN keyword
• Missing END at the end
• Using IF instead of CASE in SQL

3. Given the following dbt model SQL snippet, what will be the value of status for a row where score = 75?

SELECT
  score,
  CASE
    WHEN score >= 90 THEN 'Excellent'
    WHEN score >= 70 THEN 'Good'
    ELSE 'Average'
  END AS status
FROM scores_table

medium

A. 'Good'

B. NULL

C. 'Average'

D. 'Excellent'

Solution

1. Step 1: Check conditions in order for score = 75

   First condition: score >= 90? No (75 < 90). Second condition: score >= 70? Yes (75 >= 70).

2. Step 2: Determine which condition applies first

   The first true condition is WHEN score >= 70 THEN 'Good', so status = 'Good'.

3. Final Answer:

   'Good' -> Option A

4. Quick Check:

   75 >= 70 = 'Good' [OK]

Hint: Check CASE conditions top to bottom [OK]

Common Mistakes:

• Ignoring order of CASE conditions
• Choosing 'Excellent' incorrectly
• Assuming ELSE applies before WHEN

4. Identify the error in this dbt model snippet:

SELECT
  user_id,
  CASE
    WHEN age > 18 THEN 'Adult'
    WHEN age <= 18 THEN 'Minor'
    ELSE 'Unknown'
FROM users

medium

A. Missing END keyword for CASE statement

B. Incorrect comparison operator in WHEN clauses

C. ELSE clause cannot be used in CASE

D. Missing comma after user_id

Solution

1. Step 1: Check CASE statement syntax

   CASE statements must end with END keyword to close the block.

2. Step 2: Verify presence of END keyword

   The snippet lacks END after ELSE 'Unknown', causing syntax error.

3. Final Answer:

   Missing END keyword for CASE statement -> Option A

4. Quick Check:

   CASE must end with END [OK]

Hint: Always close CASE with END [OK]

Common Mistakes:

• Forgetting END keyword
• Misplacing ELSE clause
• Assuming ELSE is optional

5. You want to create a new column category in your dbt model based on sales amount: if sales > 1000, category is 'High'; if sales between 500 and 1000 inclusive, category is 'Medium'; otherwise, 'Low'. Which CASE statement correctly implements this logic?

hard

A. CASE WHEN sales > 1000 THEN 'High' WHEN sales > 500 THEN 'Medium' ELSE 'Low' END

B. CASE WHEN sales > 1000 THEN 'High' WHEN sales >= 500 AND sales <= 1000 THEN 'Medium' ELSE 'Low' END

C. CASE WHEN sales > 500 AND sales <= 1000 THEN 'Medium' WHEN sales > 1000 THEN 'High' ELSE 'Low' END

D. CASE WHEN sales > 1000 THEN 'High' ELSE WHEN sales >= 500 THEN 'Medium' ELSE 'Low' END

Solution

1. Step 1: Analyze the conditions and their order

   Check sales > 1000 first for 'High', then sales between 500 and 1000 inclusive for 'Medium', else 'Low'.

2. Step 2: Verify correct syntax and condition order

   CASE WHEN sales > 1000 THEN 'High' WHEN sales >= 500 AND sales <= 1000 THEN 'Medium' ELSE 'Low' END correctly orders conditions (highest first) and uses proper inclusive range with AND. CASE WHEN sales > 500 AND sales <= 1000 THEN 'Medium' WHEN sales > 1000 THEN 'High' ELSE 'Low' END misses inclusive lower bound (sales=500 fails) and suboptimal order. CASE WHEN sales > 1000 THEN 'High' WHEN sales > 500 THEN 'Medium' ELSE 'Low' END misses inclusive lower bound. CASE WHEN sales > 1000 THEN 'High' ELSE WHEN sales >= 500 THEN 'Medium' ELSE 'Low' END has syntax error with ELSE WHEN.

3. Final Answer:

   CASE WHEN sales > 1000 THEN 'High' WHEN sales >= 500 AND sales <= 1000 THEN 'Medium' ELSE 'Low' END -> Option B

4. Quick Check:

   Order and range checks correct in CASE WHEN sales > 1000 THEN 'High' WHEN sales >= 500 AND sales <= 1000 THEN 'Medium' ELSE 'Low' END [OK]

Hint: Check ranges carefully and order conditions top-down [OK]

Common Mistakes:

• Wrong order of conditions causing misclassification
• Syntax error using ELSE WHEN instead of WHEN
• Missing inclusive range checks