Why project structure scales with team size in dbt - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a dbt model that selects all columns from the source table.

dbt

select * from [1]

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

Atable('raw_data')

Bsource('raw', 'table')

Cref('source_table')

Ddataset('source')

Attempts:

3 left

💡 Hint

Common Mistakes

Using ref() instead of source() for raw tables.

Using incorrect function names like table() or dataset().

In dbt, source() is used to refer to raw data tables defined in the sources.yml file.

2fill in blank

medium

Complete the code to create a model that references another model named 'customers'.

dbt

select * from [1]

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

Aref('customers')

Bsource('raw', 'customers')

Ctable('customers')

Ddataset('customers')

Attempts:

3 left

💡 Hint

Common Mistakes

Using source() instead of ref() for models.

Using non-dbt functions like table().

Use ref() to reference other dbt models within your project.

3fill in blank

hard

Fix the error in the dbt model code to correctly filter customers with more than 5 orders.

dbt

select customer_id, count(order_id) as order_count
from [1]
group by customer_id
having order_count > 5

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

Adataset('orders')

Bsource('raw', 'orders')

Ctable('orders')

Dref('orders')

Attempts:

3 left

💡 Hint

Common Mistakes

Using source() for models instead of ref().

Using non-dbt functions like table().

Use ref() to reference the 'orders' model, which is needed for aggregation.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps model names to their descriptions if the description exists.

dbt

model_descriptions = {model: [1] for model, [2] in models.items() if description}

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

Adescription

Bdesc

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for both key and value.

Using undefined variable names.

The comprehension uses description as the value and desc as the variable name for descriptions.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of models with more than 100 rows.

dbt

large_models = { [1]: [2] for [3], [2] in models.items() if [2]['row_count'] > 100 }

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

Amodel_name

Bmodel_info

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up key and value variable names.

Using inconsistent variable names in the loop.

The dictionary comprehension uses model_name as the key and model_info as the value, iterating over model_name, model_info.

Practice

1. Why is having a clear project structure important when the dbt team grows larger?

easy

A. It helps keep work organized and reduces confusion among team members.

B. It makes the project run faster on the computer.

C. It allows the team to use more complex SQL queries.

D. It automatically fixes errors in the data models.

Solution

1. Step 1: Understand team collaboration needs

   As team size increases, many people work on the same project, so organization is key to avoid confusion.

2. Step 2: Recognize the role of project structure

   A clear structure helps separate work, making it easier to manage and understand for everyone.

3. Final Answer:

   It helps keep work organized and reduces confusion among team members. -> Option A

4. Quick Check:

   Clear structure = organized teamwork [OK]

Hint: Think about teamwork and avoiding confusion [OK]

Common Mistakes:

• Confusing project speed with team organization
• Assuming structure fixes errors automatically
• Believing complexity equals better structure

2. Which of the following is the correct way to organize models in a dbt project for a growing team?

easy

A. Separate models into folders by their purpose or domain.

B. Use random folder names to avoid conflicts.

C. Write all models in a single SQL file.

D. Put all models in one folder to keep things simple.

Solution

1. Step 1: Review folder organization best practices

   Separating models by purpose or domain helps teams work independently without conflicts.

2. Step 2: Evaluate options

   Keeping all models in one place or random names causes confusion and conflicts.

3. Final Answer:

   Separate models into folders by their purpose or domain. -> Option A

4. Quick Check:

   Folder by purpose = less conflict [OK]

Hint: Group models by what they do, not all together [OK]

Common Mistakes:

• Thinking one folder is simpler for many people
• Ignoring the importance of clear naming
• Believing random names help avoid conflicts

3. Given this dbt project structure:

models/
  staging/
    customers.sql
    orders.sql
  marts/
    sales.sql
    finance.sql

What is the main benefit of this structure for a team?

medium

A. It forces everyone to edit the same files to avoid duplication.

B. It hides the staging models from the team.

C. It makes the project run faster by splitting files.

D. It allows team members to work on staging and marts independently.

Solution

1. Step 1: Analyze folder roles

   Staging folder holds raw data models; marts folder holds business logic models.

2. Step 2: Understand team workflow

   Separating these allows different team members to work on different layers without interfering.

3. Final Answer:

   It allows team members to work on staging and marts independently. -> Option D

4. Quick Check:

   Separate folders = independent work [OK]

Hint: Think about who edits which folder [OK]

Common Mistakes:

• Assuming splitting files speeds up project execution
• Believing all team members must edit same files
• Thinking staging models are hidden

4. A dbt team notices frequent merge conflicts because everyone edits models in the same folder. What is the best fix?

medium

A. Delete unused models to reduce files.

B. Ask everyone to edit only one model file.

C. Move models into separate folders by feature or domain.

D. Use random file names to confuse conflicts.

Solution

1. Step 1: Identify cause of conflicts

   Editing many models in one folder causes overlapping work and merge conflicts.

2. Step 2: Apply project structure best practice

   Separating models by feature or domain reduces overlap and conflicts.

3. Final Answer:

   Move models into separate folders by feature or domain. -> Option C

4. Quick Check:

   Separate folders reduce conflicts [OK]

Hint: Separate work areas to avoid editing clashes [OK]

Common Mistakes:

• Thinking deleting files solves conflicts
• Believing random names help avoid conflicts
• Assuming one file for all is better

5. Your dbt team has grown to 10 members working on different business areas. How should you organize the project structure to scale efficiently?

hard

A. Keep all models in one folder but use prefixes in file names.

B. Create separate folders for each business area and assign team members accordingly.

C. Let each member create their own folder with no naming rules.

D. Merge all models into a single large SQL file to simplify.

Solution

1. Step 1: Consider team size and work areas

   With many members, organizing by business area reduces overlap and confusion.

2. Step 2: Evaluate options for scalability

   Separate folders with clear ownership help manage changes and onboarding.

3. Step 3: Avoid poor practices

   Single folder or random folders cause conflicts; one big file is unmanageable.

4. Final Answer:

   Create separate folders for each business area and assign team members accordingly. -> Option B

5. Quick Check:

   Folder per area = scalable teamwork [OK]

Hint: Group by business area for clear ownership [OK]

Common Mistakes:

• Using one folder with prefixes causes conflicts
• Random folders confuse team members
• One big file is hard to maintain