Naming conventions at scale in dbt - Mini Project: Build & Apply

Naming conventions at scale

📖 Scenario: You work in a company where many data tables are created daily. To keep things clear and easy to find, you need to follow a naming rule for these tables.Imagine you have a list of raw data table names, and you want to create new names for cleaned tables by adding a prefix and suffix.

🎯 Goal: You will create a dictionary of raw table names, set a prefix and suffix, then generate new cleaned table names using these rules.

📋 What You'll Learn

Create a dictionary called raw_tables with exact keys and values

Create two variables called prefix and suffix with exact string values

Use a dictionary comprehension to create a new dictionary called cleaned_tables with new names

Print the cleaned_tables dictionary

💡 Why This Matters

🌍 Real World

In companies, consistent naming helps teams find and understand data tables quickly, especially when many tables exist.

💼 Career

Data engineers and analysts often apply naming conventions to keep data organized and maintainable at scale.

1

Create the raw tables dictionary

Create a dictionary called raw_tables with these exact entries: 'sales': 'sales_data', 'customers': 'customer_info', 'products': 'product_list'.

dbt

# Create the raw_tables dictionary with exact entries
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with keys and values separated by colons.

2

Set prefix and suffix variables

Create a variable called prefix and set it to the string 'cleaned_'. Then create a variable called suffix and set it to the string '_v1'.

dbt

raw_tables = {'sales': 'sales_data', 'customers': 'customer_info', 'products': 'product_list'}
# Create prefix and suffix variables
# Your code here

Need a hint?

Use simple assignment with = to set string values.

3

Create cleaned table names with dictionary comprehension

Use a dictionary comprehension to create a new dictionary called cleaned_tables. For each key and value in raw_tables.items(), the new value should be the prefix plus the original value plus the suffix.

dbt

raw_tables = {'sales': 'sales_data', 'customers': 'customer_info', 'products': 'product_list'}
prefix = 'cleaned_'
suffix = '_v1'
# Create cleaned_tables dictionary using dictionary comprehension
# Your code here

Need a hint?

Use {key: value for key, value in dict.items()} format for dictionary comprehension.

4

Print the cleaned tables dictionary

Write a print statement to display the cleaned_tables dictionary.

dbt

raw_tables = {'sales': 'sales_data', 'customers': 'customer_info', 'products': 'product_list'}
prefix = 'cleaned_'
suffix = '_v1'
cleaned_tables = {key: prefix + value + suffix for key, value in raw_tables.items()}
# Print the cleaned_tables dictionary
# Your code here

Need a hint?

Use print(cleaned_tables) to show the dictionary.