Staging, intermediate, and marts pattern in dbt - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Purpose of the Staging Layer in dbt

In the Staging, intermediate, and marts pattern used in dbt, what is the main purpose of the staging layer?

A To clean and standardize raw source data for easier use in later models

B To create final business reports and dashboards directly from raw data

C To aggregate data into summary tables for quick analysis

D To store backup copies of raw data without transformation

💡 Hint

Think about what you do first when you get messy data from different sources.

The staging layer is where raw data is cleaned and standardized. This makes it easier for intermediate and marts layers to work with consistent data.

❓ Predict Output

intermediate

Output of a dbt Model SQL Query in the Intermediate Layer

Given this dbt model SQL in the intermediate layer:

select
  user_id,
  count(order_id) as total_orders
from {{ ref('stg_orders') }}
group by user_id

What will be the output schema of this model?

dbt

select
  user_id,
  count(order_id) as total_orders
from {{ ref('stg_orders') }}
group by user_id

A Columns: user_id (int), total_orders (int) with one row per order

B Columns: order_id (int), total_orders (int) with one row per order

C Columns: user_id (int), total_orders (int) with one row per user

D Columns: user_id (int), order_id (int) with one row per order

💡 Hint

Look at the GROUP BY clause and the aggregation function.

The query groups rows by user_id and counts orders per user, so output has one row per user with total_orders count.

❓ data_output

advanced

Resulting DataFrame Shape After Running a Mart Model

Consider a mart model that joins the intermediate user orders model with a user demographics model on user_id. If the intermediate model has 1000 users and the demographics model has 950 users, what is the number of rows in the resulting mart model after an inner join?

A 1000 rows, because all users from the intermediate model are kept

B 50 rows, because only users missing in demographics are kept

C 1950 rows, because rows from both models are combined

D 950 rows, because only users present in both models are kept

💡 Hint

Remember what an inner join does to rows when keys don't match.

An inner join keeps only rows with matching user_id in both tables, so the smaller count (950) determines the output rows.

🔧 Debug

advanced

Identifying the Error in a dbt Model Reference

Look at this dbt model SQL snippet:

select * from {{ ref('intermediate_user_orders') }}

When running dbt, it fails with an error saying the referenced model does not exist. What is the most likely cause?

A The model name in ref is misspelled or does not match any model file

B The SQL syntax is invalid because of missing semicolon

C The {{ ref() }} function cannot be used inside select statements

D The model file is missing a schema declaration

💡 Hint

Check the spelling and existence of the referenced model.

The error usually means the model name inside ref() does not match any existing model file name in the project.

🚀 Application

expert

Choosing the Correct Layer for a New Data Transformation

You have raw sales data coming from multiple sources with inconsistent date formats and missing values. You want to create a model that standardizes dates and fills missing values before calculating monthly sales totals. According to the staging, intermediate, and marts pattern, where should you place the model that standardizes dates and fills missing values?

A In the marts layer, because it creates final reports

B In the staging layer, because it prepares raw data for further use

C Outside dbt, because data cleaning should not be done in dbt

D In the intermediate layer, because it aggregates data for analysis

💡 Hint

Think about where raw data is first cleaned and standardized.

Data cleaning like standardizing dates and filling missing values belongs in the staging layer to prepare raw data for later transformations.

Practice

1. What is the main purpose of the staging layer in the dbt pattern Staging, intermediate, and marts?

easy

A. To create final reports and dashboards

B. To aggregate data for business metrics

C. To clean and prepare raw data for further transformations

D. To store backup copies of raw data

Solution

1. Step 1: Understand the role of staging

   The staging layer is where raw data is cleaned and standardized to make it ready for analysis.

2. Step 2: Differentiate from other layers

   Intermediate and marts layers perform transformations and aggregations, while staging focuses on cleaning.

3. Final Answer:

   To clean and prepare raw data for further transformations -> Option C

4. Quick Check:

   Staging = cleaning raw data [OK]

Hint: Staging = clean raw data first [OK]

Common Mistakes:

• Confusing staging with marts layer
• Thinking staging creates final reports
• Assuming staging stores backups

2. Which of the following is the correct way to reference a model in dbt within the intermediate layer?

easy

A. SELECT * FROM stg_customers

B. SELECT * FROM {{ ref('stg_customers') }}

C. SELECT * FROM ref('stg_customers')

D. SELECT * FROM {{ source('stg_customers') }}

Solution

1. Step 1: Recall dbt model referencing syntax

   dbt uses the Jinja function ref() inside double curly braces to reference models.

2. Step 2: Identify correct syntax

   The correct syntax is {{ ref('model_name') }}, so {{ ref('stg_customers') }} is correct.

3. Final Answer:

   SELECT * FROM {{ ref('stg_customers') }} -> Option B

4. Quick Check:

   Use {{ ref('model') }} to reference models [OK]

Hint: Use {{ ref('model_name') }} to reference models [OK]

Common Mistakes:

• Omitting curly braces around ref()
• Using source() instead of ref() for models
• Calling ref() without curly braces

3. Given the following dbt model code in the intermediate layer:

SELECT
  customer_id,
  COUNT(order_id) AS total_orders
FROM {{ ref('stg_orders') }}
GROUP BY customer_id

What will be the output columns of this model?

medium

A. customer_id, total_orders

B. order_id, total_orders

C. customer_id, order_id

D. customer_id only

Solution

1. Step 1: Analyze SELECT statement

   The query selects customer_id and counts order_id as total_orders.

2. Step 2: Understand GROUP BY effect

   Grouping by customer_id means output will have one row per customer with total orders counted.

3. Final Answer:

   customer_id, total_orders -> Option A

4. Quick Check:

   Output columns = customer_id and total_orders [OK]

Hint: GROUP BY columns + aggregated columns appear in output [OK]

Common Mistakes:

• Expecting order_id as output column
• Missing aggregated column in output
• Confusing GROUP BY with WHERE clause

4. You wrote this dbt model in the marts layer:

SELECT
  customer_id,
  SUM(total_orders) AS total_orders
FROM {{ ref('int_customer_orders') }}
GROUP BY customer_id

But the model fails with an error about total_orders. What is the likely cause?

medium

A. Missing GROUP BY clause

B. SUM() cannot be used in marts layer

C. Incorrect use of {{ ref() }} syntax

D. The referenced model int_customer_orders does not have a column named total_orders

Solution

1. Step 1: Check referenced model columns

   The error suggests total_orders column is missing in int_customer_orders.

2. Step 2: Validate aggregation usage

   SUM() is valid in marts layer and GROUP BY is present, so those are not issues.

3. Final Answer:

   The referenced model int_customer_orders does not have a column named total_orders -> Option D

4. Quick Check:

   Column missing in referenced model causes error [OK]

Hint: Check referenced model columns before aggregating [OK]

Common Mistakes:

• Assuming SUM() is invalid in marts
• Forgetting GROUP BY with aggregation
• Misusing ref() syntax

5. You want to build a sales report in the marts layer that shows total sales per region. Your staging layer has raw sales data, and the intermediate layer calculates sales per store. Which is the best approach to build this report using the staging, intermediate, and marts pattern?

hard

A. Reference the intermediate sales per store model in the marts layer and aggregate sales by region

B. Directly query the raw sales data in the marts layer and group by region

C. Aggregate sales by region in the staging layer

D. Create the sales report in the intermediate layer without using marts

Solution

1. Step 1: Follow pattern responsibilities

   Staging cleans raw data, intermediate transforms data per store, marts create final aggregates like sales per region.

2. Step 2: Use intermediate model in marts

   Use the intermediate sales per store model as input to marts, then aggregate by region for the report.

3. Final Answer:

   Reference the intermediate sales per store model in the marts layer and aggregate sales by region -> Option A

4. Quick Check:

   Marts aggregate intermediate data for final reports [OK]

Hint: Build reports in marts using intermediate models [OK]

Common Mistakes:

• Skipping intermediate layer aggregation
• Aggregating raw data directly in marts
• Doing final aggregation in staging