What is dbt - Hands-On Activity

What is dbt

📖 Scenario: Imagine you work in a team that manages data for a company. You want to make sure the data is clean, organized, and easy to use for reports and analysis.

🎯 Goal: Learn what dbt is and how it helps transform raw data into useful information by writing simple code.

📋 What You'll Learn

Understand the basic purpose of dbt

Create a simple dbt model file

Configure a dbt project variable

Write a SQL select statement inside a dbt model

Display the transformed data output

💡 Why This Matters

🌍 Real World

Companies use dbt to automate and document their data transformations, making data reliable and easy to analyze.

💼 Career

Data analysts and engineers use dbt to build and maintain data pipelines that power business decisions.

1

Create a simple dbt model file

Create a dbt model file named my_first_model.sql with a SQL query that selects all columns from a table called raw_data.

dbt

-- Write a SQL select statement in my_first_model.sql
-- Your code here

Hint

dbt models are just SQL files with select statements.

2

Add a configuration variable

In the same my_first_model.sql file, add a dbt config block at the top to set materialized='table'.

dbt

{{ config(materialized='table') }}
select * from raw_data
-- Your code here

Hint

Use {{ config(materialized='table') }} at the top of your model file.

3

Write core transformation logic

Modify the SQL query in my_first_model.sql to select only the id and value columns from raw_data where value is greater than 10.

dbt

{{ config(materialized='table') }}
-- Select id and value where value > 10
select id, value from raw_data where value > 10
-- Your code here

Hint

Use a where clause to filter rows.

4

Display the transformed data output

Print the final SQL query from my_first_model.sql to show the transformed data selection.

dbt

print("{{ config(materialized='table') }}\nselect id, value from raw_data where value > 10")
# Your code here

Hint

Use a print statement to show the SQL code.

Practice

1. What is the main purpose of dbt in data projects?

easy

A. To transform raw data into clean, organized tables using SQL

B. To store large amounts of raw data without changes

C. To create visual dashboards directly from raw data

D. To replace databases with a new storage system

Solution

1. Step 1: Understand dbt's role in data transformation

   dbt is designed to help transform raw data into clean tables using SQL.

2. Step 2: Compare options with dbt's function

   Options A, B, and D describe storage or visualization, which are not dbt's main tasks.

3. Final Answer:

   To transform raw data into clean, organized tables using SQL -> Option A

4. Quick Check:

   dbt = data transformation tool [OK]

Hint: Remember dbt transforms data with SQL, not stores or visualizes [OK]

Common Mistakes:

• Confusing dbt with a database system
• Thinking dbt creates dashboards
• Assuming dbt only stores raw data

2. Which of the following is the correct way to define a model in dbt using SQL?

easy

A. CREATE MODEL my_model AS SELECT * FROM raw_data;

B. SELECT * FROM raw_data WHERE date > '2023-01-01';

C. dbt run SELECT * FROM raw_data;

D. INSERT INTO my_model SELECT * FROM raw_data;

Solution

1. Step 1: Identify how dbt models are written

   dbt models are SQL SELECT statements saved as files; no CREATE MODEL or INSERT commands are used.

2. Step 2: Check each option's syntax

   SELECT * FROM raw_data WHERE date > '2023-01-01'; is a valid SELECT query, suitable for a dbt model. Options A, C, and D use incorrect or unsupported syntax in dbt.

3. Final Answer:

   SELECT * FROM raw_data WHERE date > '2023-01-01'; -> Option B

4. Quick Check:

   dbt model = SQL SELECT query [OK]

Hint: dbt models are just SELECT queries saved as files [OK]

Common Mistakes:

• Using CREATE or INSERT statements in dbt models
• Trying to run dbt commands inside SQL files
• Confusing dbt syntax with database commands

3. Given this dbt model SQL code:

SELECT user_id, COUNT(*) AS orders_count FROM orders GROUP BY user_id

What will be the output of this model?

medium

A. A table with each user_id and their total number of orders

B. A list of all orders without grouping

C. An error because GROUP BY is missing

D. A table with user_id and order details for each order

Solution

1. Step 1: Analyze the SQL query

   The query selects user_id and counts orders grouped by user_id, summarizing orders per user.

2. Step 2: Determine the output structure

   The output will be a table listing each user_id with their total orders count, not detailed orders or errors.

3. Final Answer:

   A table with each user_id and their total number of orders -> Option A

4. Quick Check:

   GROUP BY user_id = orders count per user [OK]

Hint: GROUP BY aggregates data by user_id for counts [OK]

Common Mistakes:

• Thinking the query returns all order details
• Assuming missing GROUP BY causes error here
• Confusing COUNT(*) with listing rows

4. You wrote this dbt model SQL:

SELECT user_id, SUM(order_amount) FROM orders

When you run dbt, you get an error. What is the likely cause?

medium

A. SELECT statement must include WHERE clause

B. SUM() function is not allowed in dbt

C. Table orders does not exist

D. Missing GROUP BY clause for user_id

Solution

1. Step 1: Check SQL aggregation rules

   When using SUM(order_amount) with user_id, SQL requires GROUP BY user_id to group data properly.

2. Step 2: Identify error cause

   Missing GROUP BY causes SQL error; SUM() is valid, table existence or WHERE clause are unrelated here.

3. Final Answer:

   Missing GROUP BY clause for user_id -> Option D

4. Quick Check:

   Aggregation needs GROUP BY user_id [OK]

Hint: Use GROUP BY with aggregation functions like SUM() [OK]

Common Mistakes:

• Thinking SUM() is invalid in dbt
• Assuming WHERE clause is mandatory
• Ignoring SQL aggregation rules

5. You want to create a dbt model that shows total sales per product category but only for categories with sales over 1000. Which SQL code correctly achieves this?

hard

A. SELECT category, SUM(sales) AS total_sales FROM sales_data WHERE sales > 1000 GROUP BY category

B. SELECT category, SUM(sales) AS total_sales FROM sales_data WHERE SUM(sales) > 1000 GROUP BY category

C. SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING SUM(sales) > 1000

D. SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category WHERE total_sales > 1000

Solution

1. Step 1: Understand filtering on aggregated data

   Filtering on SUM(sales) requires HAVING clause after GROUP BY, not WHERE.

2. Step 2: Evaluate each option's correctness

   SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING SUM(sales) > 1000 uses HAVING with SUM(sales) > 1000 correctly. Options A, B, and C misuse WHERE or HAVING clauses.

3. Final Answer:

   SELECT category, SUM(sales) AS total_sales FROM sales_data GROUP BY category HAVING SUM(sales) > 1000 -> Option C

4. Quick Check:

   Use HAVING to filter aggregated results [OK]

Hint: Use HAVING, not WHERE, to filter after aggregation [OK]

Common Mistakes:

• Using WHERE to filter aggregated sums
• Placing WHERE after GROUP BY
• Confusing HAVING and WHERE clauses