Naming conventions at scale in dbt - Time & Space Complexity

Time Complexity: Naming conventions at scale

O(n)

Understanding Time Complexity

When working with many dbt models, naming conventions help organize and find things quickly.

We want to understand how the effort to manage names grows as the number of models increases.

Scenario Under Consideration

Analyze the time complexity of the following dbt naming convention check.

    {% for model in graph.nodes.values() %}
      {% if not model.name.startswith('stg_') and model.resource_type == 'model' %}
        {{ exceptions.raise_compiler_error('Model name must start with stg_') }}
      {% endif %}
    {% endfor %}
    

This code loops over all models and checks if their names follow the 'stg_' prefix rule.

Identify Repeating Operations

Look for repeated actions in the code.

• Primary operation: Looping over all models in the project.
• How many times: Once for each model, so as many times as there are models.

How Execution Grows With Input

As the number of models grows, the checks grow too.

Input Size (n)	Approx. Operations
10	10 checks
100	100 checks
1000	1000 checks

Pattern observation: The number of checks grows directly with the number of models.

Final Time Complexity

Time Complexity: O(n)

This means the time to check naming grows in a straight line as you add more models.

Common Mistake

[X] Wrong: "Checking names only once is enough, no matter how many models there are."

[OK] Correct: Each model needs its own check, so the total work grows with the number of models.

Interview Connect

Understanding how checks scale helps you design dbt projects that stay manageable as they grow.

Self-Check

"What if we grouped models by folder and checked naming only once per folder? How would the time complexity change?"

Practice

1. Why is it important to use consistent prefixes like stg_, int_, and fct_ in dbt projects?

easy

A. They make the SQL code run faster.

B. They help organize models by their role, making the project easier to understand.

C. They automatically create indexes in the database.

D. They allow dbt to skip testing models.

Solution

1. Step 1: Understand the purpose of prefixes in dbt

   Prefixes like stg_ (staging), int_ (intermediate), and fct_ (fact) indicate the role of each model.

2. Step 2: Recognize how prefixes help in large projects

   Using these prefixes organizes models logically, making it easier to find and maintain them.

3. Final Answer:

   They help organize models by their role, making the project easier to understand. -> Option B

4. Quick Check:

   Consistent prefixes = better organization [OK]

Hint: Remember: prefixes show model roles clearly [OK]

Common Mistakes:

• Thinking prefixes affect performance
• Assuming prefixes create database indexes
• Believing prefixes skip tests

2. Which of the following is the correct way to name a staging model for customer data in dbt?

easy

A. stgCustomerData

B. StgCustomerData

C. STG_CUSTOMER_DATA

D. stg_customer_data

Solution

1. Step 1: Recall dbt naming style guidelines

   dbt recommends lowercase letters and underscores to separate words for readability.

2. Step 2: Evaluate each option

   stg_customer_data uses lowercase and underscores, matching the recommended style.

3. Final Answer:

   stg_customer_data -> Option D

4. Quick Check:

   Lowercase + underscores = correct naming [OK]

Hint: Use lowercase and underscores for dbt model names [OK]

Common Mistakes:

• Using uppercase letters
• Using camelCase instead of underscores
• Starting with uppercase letters

3. Given the following dbt model names, which one is a fact table according to naming conventions?

stg_orders
int_customer_info
fct_sales_summary
stg_products

medium

A. stg_orders

B. int_customer_info

C. fct_sales_summary

D. stg_products

Solution

1. Step 1: Identify prefixes and their meanings

   stg_ means staging, int_ means intermediate, fct_ means fact table.

2. Step 2: Match the prefix to the fact table

   Only fct_sales_summary starts with fct_, indicating a fact table.

3. Final Answer:

   fct_sales_summary -> Option C

4. Quick Check:

   Prefix fct_ = fact table [OK]

Hint: Fact tables start with fct_ prefix [OK]

Common Mistakes:

• Confusing staging and fact prefixes
• Ignoring prefix meanings
• Choosing intermediate models as fact tables

4. You see a dbt model named fct_customer_orders but it contains only raw data loading logic without aggregations. What is the likely issue?

medium

A. The model name prefix fct_ is incorrect for raw data; it should be stg_.

B. The model should be renamed to int_customer_orders because it is intermediate.

C. The model name is fine; raw data can be in fact tables.

D. The model should be deleted because it is redundant.

Solution

1. Step 1: Understand prefix meanings

   fct_ is for fact tables, which usually contain aggregated or transactional data, not raw loads.

2. Step 2: Match model content to prefix

   Since the model only loads raw data, stg_ (staging) prefix is more appropriate.

3. Final Answer:

   The model name prefix fct_ is incorrect for raw data; it should be stg_. -> Option A

4. Quick Check:

   Raw data = stg_ prefix [OK]

Hint: Match prefix to model content type [OK]

Common Mistakes:

• Assuming any prefix works for any model
• Thinking raw data belongs in fact tables
• Renaming without checking model logic

5. In a large dbt project, you want to create a naming convention that helps new team members quickly identify model types and their dependencies. Which approach best supports this goal?

hard

A. Use consistent prefixes like stg_, int_, fct_, keep names lowercase with underscores, and document the naming rules.

B. Use random prefixes for variety, mix uppercase and lowercase, and avoid documentation to encourage exploration.

C. Name all models with the project name prefix only, ignoring model roles and dependencies.

D. Use only numbers as prefixes to indicate model order, without descriptive names.

Solution

1. Step 1: Identify best practices for naming at scale

   Consistent prefixes and lowercase with underscores improve clarity and readability.

2. Step 2: Recognize importance of documentation

   Documenting naming rules helps new team members understand conventions and dependencies quickly.

3. Final Answer:

   Use consistent prefixes like stg_, int_, fct_, keep names lowercase with underscores, and document the naming rules. -> Option A

4. Quick Check:

   Consistency + documentation = easier onboarding [OK]

Hint: Combine clear prefixes with documentation [OK]

Common Mistakes:

• Ignoring documentation
• Using inconsistent naming styles
• Choosing unclear or numeric-only prefixes