Deep dive into CI/CD practices for data engineering teams using Databricks and Google Cloud, covering packaging, pipelines, security, and practical implementation roadmap.
In Part 1, we described the target state for a modern data engineering team and how to structure people and roles so work gets done. In Part 2, we go deep on the engine that keeps that target state healthy day after day: CI/CD.
CI/CD stands for Continuous Integration and Continuous Delivery. In simple terms, CI/CD is the habit of packaging, testing, and deploying changes automatically and reliably. When CI/CD is in place, you reduce human error, move faster, and keep production stable—even while multiple teams are shipping new pipelines and features.
This article explains not just what to set up on Databricks + Google Cloud, but also why each choice matters, with practical examples and a step-by-step rollout plan.
Tested changes get packaged and deployed to dev, then staging, then production, using repeatable steps.
A column gets renamed upstream, and jobs break. CI catches this early with schema tests, before production fails at 2 a.m.
Multiple squads push code. CI/CD keeps environments consistent so one team's change doesn't silently break another team's job.
The business wants new data and features quickly. CI/CD gives you both speed (automated steps) and safety (tests and approvals).
Auditors and FinOps want to know who changed what, when, and how. CI/CD plus version control makes this easy.
Mental model: CI/CD is the paved road your teams drive on. It's smoother and safer than everyone taking their own side streets.
Five key components that work together to create a robust, secure, and scalable CI/CD pipeline
Databricks Asset Bundles (DABs) package your notebooks, libraries, and configuration into a single, consistent unit. Think of a DAB as a shipping box that carries your pipeline from dev to prod without repacking.
The same bundle that worked in dev runs in prod with only environment variables changed—no hand edits.
Configuration lives next to code, reducing mismatch errors.
Re-deploy a known-good bundle if something goes wrong.
In dev, a job writes to
dev_sales.silver.orders. In prod, it writes to
prod_sales.silver.orders. The same bundle switches targets by reading an environment
variable.
The automation engine that runs your CI/CD steps
If your code lives in GitHub and you want tight integration and simple setup.
If you prefer a GCP-native service and want deeper Google Cloud integrations.
The computer follows the same steps every time.
Approvals and environment protection rules ensure sensitive steps are controlled.
Engineers spend time on features, not on manual deployments.
Declare and manage infrastructure through code
Environments (dev/stage/prod) are clones, not hand-built cousins.
Infra changes are reviewed via PR like code.
If something breaks, re-apply state to restore a known configuration.
Non-human accounts for pipeline access
A service principal is a non-human account your pipelines use to talk to Databricks. OAuth provides tokens for that access.
Deployments do not depend on personal accounts
Actions are clearly attributed to "CI/CD bot"
Principal gets only the roles it needs
Push code → run unit tests, linting, and schema checks. Problems surface immediately.
DABs get created by the pipeline with correct configs. No manual repacking.
Dev → Staging → Prod, with automated checks and approvals.
Secrets in GCP Secret Manager; CI uses WIF; Databricks uses service principals.
Code, infra, configs, and data-model definitions in version control.
Clear ownership and accountability across the CI/CD pipeline
Owns the paved road—DAB templates, Terraform modules, cluster policies, and CI standards.
Gives squads a secure, fast, and consistent way to ship.
Pipeline Automation
Builds and maintains CI/CD pipelines, runners, and deployment logic.
Ensures automation is reliable and secure.
Domain Owners
Own their domain's pipelines end-to-end using the paved road.
They deliver business value while following common standards.
Standards & Coherence
Defines standards for environments, naming, lineage, and non-functional requirements (NFRs).
Keeps the platform coherent as teams scale.
Release Train Engineer
Coordinates release cadence across teams; manages dependencies and change windows.
Avoids collisions (e.g., three squads changing the same table this week).
Learn from common mistakes to build a more robust CI/CD pipeline
Let our experts help you implement these strategies and build a world-class, scalable data platform.
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