Why governance ensures data trust in dbt

Introduction

Governance helps make sure data is correct and safe. This builds trust so people can use data with confidence.

When sharing data across teams in a company

When making important decisions based on data

When tracking data changes over time

When ensuring data follows rules and laws

When preventing mistakes or misuse of data

Syntax

dbt

-- Example of a dbt model with governance comments
-- This model selects clean customer data
select
  id,
  name,
  email
from raw.customers
where email is not null

dbt models are SQL files that transform raw data.

Adding comments helps explain data rules and checks.

Examples

This model filters out customers without emails to keep data clean.

dbt

-- Model with data quality test
select
  id,
  name,
  email
from raw.customers
where email is not null

Descriptions help users understand what the data means.

dbt

-- Adding a description in dbt schema.yml
models:
  - name: customers
    description: "Cleaned customer data with valid emails"

Sample Program

This example shows a dbt model that filters out customers without emails. The schema.yml file adds descriptions and tests to ensure data quality. These governance steps help users trust the data.

dbt

-- dbt model: models/customers.sql
select
  id,
  name,
  email
from raw.customers
where email is not null

-- dbt schema.yml snippet
models:
  - name: customers
    description: "Cleaned customer data with valid emails"
    tests:
      - not_null:
          column_name: email
      - unique:
          column_name: id

Important Notes

Governance includes documentation, testing, and access control.

dbt makes it easy to add tests that catch data problems early.

Clear descriptions help everyone understand data purpose and limits.

Summary

Governance builds trust by making data reliable and understandable.

dbt supports governance with tests and documentation.

Good governance helps teams use data safely and confidently.