Why packages accelerate dbt development - See It in Action

Why packages accelerate dbt development

📖 Scenario: Imagine you are working on a data project using dbt (data build tool). You want to build data models faster and avoid repeating common code. Using dbt packages helps you reuse tested code from others, saving time and effort.

🎯 Goal: You will create a simple example to see how using a dbt package can speed up your data transformations by reusing existing models.

📋 What You'll Learn

Create a dictionary called local_models with two models: 'sales' and 'customers' with their row counts.

Create a variable called package_models with two models from a package: 'sales_summary' and 'customer_segments' with their row counts.

Combine local_models and package_models into a new dictionary all_models using dictionary unpacking.

Print the all_models dictionary to show all models available after adding the package.

💡 Why This Matters

🌍 Real World

In real data projects, dbt packages let you reuse tested data transformations, saving time and reducing errors.

💼 Career

Data analysts and engineers use dbt packages to accelerate building reliable data models and pipelines.

1

Create local dbt models dictionary

Create a dictionary called local_models with these exact entries: 'sales': 1000 and 'customers': 500 representing row counts.

dbt

# Create the local_models dictionary with sales and customers models
# Your code here

Need a hint?

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

2

Create package models dictionary

Create a dictionary called package_models with these exact entries: 'sales_summary': 100 and 'customer_segments': 50 representing row counts from the package.

dbt

local_models = {'sales': 1000, 'customers': 500}
# Create the package_models dictionary with sales_summary and customer_segments models
# Your code here

Need a hint?

Similar to step 1, create another dictionary for package models.

3

Combine local and package models

Create a new dictionary called all_models by combining local_models and package_models using dictionary unpacking syntax.

dbt

local_models = {'sales': 1000, 'customers': 500}
package_models = {'sales_summary': 100, 'customer_segments': 50}
# Combine local_models and package_models into all_models
# Your code here

Need a hint?

Use {**dict1, **dict2} to merge two dictionaries in Python.

4

Print all available models

Print the all_models dictionary to display all models available after adding the package.

dbt

local_models = {'sales': 1000, 'customers': 500}
package_models = {'sales_summary': 100, 'customer_segments': 50}
all_models = {**local_models, **package_models}
# Print the all_models dictionary
# Your code here

Need a hint?

Use print(all_models) to show the combined dictionary.