Why governance ensures data trust in dbt - See It in Action

Why governance ensures data trust

📖 Scenario: Imagine you work in a company where many teams use data to make decisions. Sometimes, the data is confusing or wrong, and people don't trust it. To fix this, the company wants to organize how data is handled and checked. This is called data governance.

🎯 Goal: You will create a simple dbt project that shows how data governance helps keep data clean and trusted. You will set up a data source, add a rule to check data quality, and then see the results that prove the data is trustworthy.

📋 What You'll Learn

Create a source configuration for a sales table

Add a test to check that sales amounts are never negative

Write a dbt model that selects only valid sales

Run the model and show the filtered results

💡 Why This Matters

🌍 Real World

Companies use data governance to make sure data is accurate and reliable before teams use it for decisions.

💼 Career

Data analysts and engineers use dbt to build trusted data pipelines that enforce governance rules and improve data trust.

1

Set up the sales data source

Create a source configuration in sources.yml for a table called sales in the raw schema. The source name should be raw_data. Include the table name and schema exactly as given.

dbt

# Create a source configuration for the sales table in sources.yml
# Your code here

Need a hint?

Use the sources key with a list containing a source named raw_data. Inside it, set schema to raw and add a table named sales.

2

Add a test to check sales amounts

In the same sources.yml file, add a test under the sales table to ensure the column amount is never negative. Use the built-in dbt test not_null and a custom test for amount >= 0.

dbt

version: 2
sources:
  - name: raw_data
    schema: raw
    tables:
      - name: sales
        # Add tests for amount column here
# Your code here

Need a hint?

Under the sales table, add a columns list. For the amount column, add not_null and a test with expression: amount >= 0.

3

Create a model to select valid sales

Create a dbt model file called valid_sales.sql. Write a SQL query that selects all columns from the source raw_data.sales where amount is greater than or equal to zero.

dbt

-- Create a model valid_sales.sql
-- Select all columns from source raw_data.sales where amount >= 0
-- Your code here

Need a hint?

Use the source function with 'raw_data' and 'sales'. Add a where clause to keep rows with amount >= 0.

4

Run the model and show the results

Run the dbt model valid_sales and print the resulting rows. Use print() to display the output of the model query.

dbt

# Run the valid_sales model and print the results
# Your code here

Need a hint?

Print the column headers and example rows that show only sales with non-negative amounts.