Why production dbt needs automation - See It in Action

Why Production dbt Needs Automation

📖 Scenario: Imagine you work in a company that uses dbt (data build tool) to transform raw data into clean, useful tables for reports and dashboards. In production, these transformations must run smoothly and reliably every day without manual work.

🎯 Goal: You will create a simple simulation to understand why automating dbt runs in production is important. You will set up data, configure a schedule, run transformations automatically, and see the output.

📋 What You'll Learn

Create a list of dbt models with their names and statuses

Set a configuration variable for the automation schedule

Write a loop to simulate running dbt models automatically based on the schedule

Print the results of the automated dbt runs

💡 Why This Matters

🌍 Real World

In real companies, dbt automations run data transformations regularly without manual work. This keeps data fresh and reliable for business decisions.

💼 Career

Data engineers and analysts use dbt automation to ensure production data pipelines run smoothly, saving time and reducing errors.

1

Set up the dbt models list

Create a list called dbt_models with these dictionaries exactly: {'name': 'customers', 'status': 'pending'}, {'name': 'orders', 'status': 'pending'}, and {'name': 'products', 'status': 'pending'}.

dbt

# Create the list dbt_models with the specified dictionaries
# Your code here

Need a hint?

Use a list with three dictionaries. Each dictionary has keys 'name' and 'status'.

2

Set the automation schedule

Create a variable called automation_schedule and set it to the string 'daily' to represent how often dbt runs should happen automatically.

dbt

dbt_models = [
    {'name': 'customers', 'status': 'pending'},
    {'name': 'orders', 'status': 'pending'},
    {'name': 'products', 'status': 'pending'}
]
# Set automation_schedule to 'daily'
# Your code here

Need a hint?

Just assign the string 'daily' to automation_schedule.

3

Simulate automated dbt runs

Use a for loop with the variable model to go through dbt_models. Inside the loop, change each model's 'status' to 'success' to simulate a successful automated run.

dbt

dbt_models = [
    {'name': 'customers', 'status': 'pending'},
    {'name': 'orders', 'status': 'pending'},
    {'name': 'products', 'status': 'pending'}
]
automation_schedule = 'daily'
# Use a for loop to update each model's status to 'success'
# Your code here

Need a hint?

Loop over dbt_models and set model['status'] = 'success' inside the loop.

4

Print the automated run results

Write a print statement to display the dbt_models list after automation to show all models with their updated statuses.

dbt

dbt_models = [
    {'name': 'customers', 'status': 'pending'},
    {'name': 'orders', 'status': 'pending'},
    {'name': 'products', 'status': 'pending'}
]
automation_schedule = 'daily'
for model in dbt_models:
    model['status'] = 'success'
# Print the dbt_models list
# Your code here

Need a hint?

Use print(dbt_models) to show the updated list.