Why models are the core of dbt - See It in Action

Understanding Why Models Are the Core of dbt

📖 Scenario: Imagine you work in a company that collects sales data every day. You want to organize this data to answer questions like "Which products sell best?" or "How much revenue did we make last month?". dbt helps you do this by letting you create models that transform raw data into useful tables.

🎯 Goal: You will build a simple example to see why models are the heart of dbt. You will create a data structure for sales, set a filter condition, write a model to select important data, and finally display the result.

📋 What You'll Learn

Create a dictionary with sales data

Add a filter threshold for minimum sales

Write a model to select products with sales above the threshold

Print the filtered sales data

💡 Why This Matters

🌍 Real World

In real companies, raw data is messy and large. Models help clean and organize data so teams can answer important questions quickly.

💼 Career

Data analysts and engineers use dbt models daily to build reliable data pipelines that power dashboards and reports.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'apple': 100, 'banana': 50, 'cherry': 75, 'date': 30, 'elderberry': 90.

dbt

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

Need a hint?

Use curly braces {} to create a dictionary. Separate keys and values with colons, and pairs with commas.

2

Set the minimum sales threshold

Create a variable called min_sales and set it to 60.

dbt

sales_data = {'apple': 100, 'banana': 50, 'cherry': 75, 'date': 30, 'elderberry': 90}
# Set min_sales to 60
# Your code here

Need a hint?

Just assign the number 60 to the variable min_sales.

3

Write the model to filter sales data

Create a new dictionary called filtered_sales using a dictionary comprehension. Include only items from sales_data where the sales value is greater than or equal to min_sales. Use product and sales as the loop variables.

dbt

sales_data = {'apple': 100, 'banana': 50, 'cherry': 75, 'date': 30, 'elderberry': 90}
min_sales = 60
# Create filtered_sales dictionary with products having sales >= min_sales
# Your code here

Need a hint?

Use a dictionary comprehension with {key: value for key, value in dict.items() if condition}.

4

Print the filtered sales data

Write a print statement to display the filtered_sales dictionary.

dbt

sales_data = {'apple': 100, 'banana': 50, 'cherry': 75, 'date': 30, 'elderberry': 90}
min_sales = 60
filtered_sales = {product: sales for product, sales in sales_data.items() if sales >= min_sales}
# Print the filtered_sales dictionary
# Your code here

Need a hint?

Use print(filtered_sales) to show the result.