Naming conventions at scale in dbt - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a model with a proper naming convention prefix.

dbt

models:
  - name: [1]_sales_data
    description: "Model for sales data"

Drag options to blanks, or click blank then click option'

Astg

Btmp

Cint

Draw

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raw' prefix for transformed data

Using 'tmp' which is usually for temporary or intermediate models

The prefix stg stands for staging, which is a common naming convention for cleaned raw data models.

2fill in blank

medium

Complete the code to set a naming convention for incremental models.

dbt

models:
  - name: [1]_customer_updates
    config:
      materialized: incremental

Drag options to blanks, or click blank then click option'

Atmp

Braw

Cstg

Dint

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raw' prefix for incremental models

Using 'stg' which is for staging models

The prefix int is commonly used for intermediate or incremental models in dbt naming conventions.

3fill in blank

hard

Fix the error in the naming convention for a temporary model.

dbt

models:
  - name: [1]_temp_results
    config:
      materialized: table

Drag options to blanks, or click blank then click option'

Astg

Btmp

Cint

Draw

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raw' or 'stg' prefixes for temporary models

Not matching the prefix with the materialization type

The prefix tmp is used for temporary models, which are often materialized as tables for intermediate processing.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps model names to their prefixes based on naming conventions.

dbt

model_prefixes = {model: [1] for model in models if model.startswith([2])}

Drag options to blanks, or click blank then click option'

Amodel.split('_')[0]

B'stg_'

C'int_'

Dmodel.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using uppercase conversion instead of splitting

Filtering with wrong prefix string

The code extracts the prefix from the model name by splitting at the underscore and filters models starting with 'stg_'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps model names to their suffixes if they end with '_data' and have a 'raw' prefix.

dbt

filtered_models = {model: model[1] for model in models if model.startswith([2]) and model.endswith([3])}

Drag options to blanks, or click blank then click option'

A[4:]

B'raw_'

C'_data'

D[:4]

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect slice indices

Using wrong prefix or suffix strings

The code extracts the suffix after the prefix by slicing from index 4 onward, filters models starting with 'raw_' and ending with '_data'.

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