Why dbt transformed data transformation workflows - See It in Action

Why dbt Transformed Data Transformation Workflows

📖 Scenario: Imagine you work in a company where data is collected from many sources. Before you can analyze it, you need to clean and organize it. This process is called data transformation. Traditionally, this was slow and hard to manage. Then, a tool called dbt changed how teams transform data.

🎯 Goal: You will create a simple example to understand how dbt helps transform data step-by-step, making workflows easier and clearer.

📋 What You'll Learn

Create a dictionary with raw data representing sales

Add a configuration variable to set a sales threshold

Use a comprehension to filter sales above the threshold

Print the filtered sales data

💡 Why This Matters

🌍 Real World

Companies collect data from many places. They need to clean and organize it before making decisions. dbt helps teams write clear, reusable steps to transform data quickly and safely.

💼 Career

Data analysts and engineers use dbt to build reliable data pipelines. Understanding how to filter and transform data is a key skill for these roles.

1

Create raw sales data dictionary

Create a dictionary called raw_sales with these exact entries: 'store1': 100, 'store2': 250, 'store3': 75, 'store4': 300

dbt

# Create the raw_sales dictionary with exact store sales values
# Your code here

Need a hint?

Use curly braces to create a dictionary and separate each store and sales value with a colon.

2

Set sales threshold configuration

Create a variable called sales_threshold and set it to 150

dbt

raw_sales = {'store1': 100, 'store2': 250, 'store3': 75, 'store4': 300}
# Create sales_threshold variable and set it to 150
# Your code here

Need a hint?

Just assign the number 150 to the variable sales_threshold.

3

Filter sales above threshold using comprehension

Create a new dictionary called filtered_sales using a dictionary comprehension that includes only items from raw_sales where the sales value is greater than sales_threshold

dbt

raw_sales = {'store1': 100, 'store2': 250, 'store3': 75, 'store4': 300}
sales_threshold = 150
# Create filtered_sales dictionary with stores having sales > sales_threshold
# Your code here

Need a hint?

Use a dictionary comprehension with for store, sales in raw_sales.items() and an if condition.

4

Print the filtered sales data

Write a print statement to display the filtered_sales dictionary

dbt

raw_sales = {'store1': 100, 'store2': 250, 'store3': 75, 'store4': 300}
sales_threshold = 150
filtered_sales = {store: sales for store, sales in raw_sales.items() if sales > sales_threshold}
# Print the filtered_sales dictionary
# Your code here

Need a hint?

Use print(filtered_sales) to show the result.