Better Data Engineering Part 6: CI/CD, Testing & Observability

Why?

Because a pipeline that “works on my machine” is not a pipeline — it’s a liability.

Introduction

dbt gives you structure, but production‑grade data engineering requires:

• automated testing
• automated deployments
• monitoring
• observability
• governance

Let’s break down the essentials.

Rule 1: Use CI/CD for Every Pull Request

A proper CI/CD pipeline should:

• install dbt
• run dbt compile
• run dbt build on changed models
• run tests
• block merges if tests fail

This prevents broken logic from reaching production.

Rule 2: Test Early, Test Often

Testing is not optional.

Start with:

• schema tests
• relationship tests
• accepted values
• uniqueness

Then add:

• custom tests
• business logic tests
• freshness tests

Your warehouse becomes trustworthy by design.

Rule 3: Add Observability to Your Stack

Observability answers:

• What broke?
• Why did it break?
• Where did it break?
• Who is impacted?

Use tools like:

• Elementary
• Great Expectations
• dbt artifacts + dashboards
• warehouse query history

Observability is not a luxury — it’s a requirement.

Rule 4: Automate Documentation

Documentation should:

• update automatically
• reflect lineage
• include tests
• include descriptions

dbt docs + CI/CD = always up‑to‑date documentation.

Rule 5: Treat Your Data Platform Like Software

Adopt engineering best practices:

• version control
• code reviews
• modularity
• naming conventions
• reproducible environments
• automated deployments

This is how you build production‑grade data systems.

---

This concludes the series — but more advanced topics are coming soon.