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CI/CD Pipeline for Cloud Data Platform

Automated, tested deployments for a cloud data platform.

Bitbucket Pipelines for Cloud Composer, Dataflow and dbt, with unit tests and data quality checks in CI and automated dev/prod environments.

NDA 2024

IT & Data Cross-functional Platform Reliability & Performance
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Pipeline scope

  1. 01Bitbucket PipelinesCI/CD for Cloud Composer, Dataflow and dbt projects
  2. 02Tests in CIUnit tests and data quality checks fail the build before production
  3. 03EnvironmentsAutomated deployment to dev/prod from feature branches

Documentation and team training included.

Challenge

No automated deployments, no tests in the deployment flow and manual development-environment setup.

Delivered

A CI/CD pipeline in Bitbucket Pipelines for Cloud Composer, Dataflow and dbt, with tests in CI.

Enabled

Automated dev/prod deployments, fewer errors and faster onboarding.

Challenge

The customer's data engineering team faced three connected problems that compounded each other:

  • No automated deployments for Cloud Composer / Dataflow and dbt - every change shipped by hand
  • No unit tests or data quality checks in the deployment flow - regressions surfaced in production
  • Manual setup of development environments - slow onboarding, inconsistent dev/prod parity

The combination meant slow iteration, high error rates, and growing technical debt in the data platform.

Approach

We implemented a CI/CD pipeline using Bitbucket Pipelines covering Cloud Composer, Dataflow, and dbt projects. The pipeline integrates unit tests and data quality checks into the CI flow - broken code and broken data both fail the build before they reach production.

We standardized development-environment setup with automated deployment to dev/prod environments from feature branches. Minimal-manual-setup processes for testing reduced onboarding friction and kept environments and release processes consistent. Comprehensive documentation and team training closed the loop, so the engineering team can extend the pipeline themselves as the platform grows.

The platform ran Airflow in Cloud Composer for orchestration, Dataflow jobs deployed from templates in Google Cloud Storage for ingestion, Python jobs on Google Compute Engine VMs for processing, BigQuery as the data warehouse and QlikSense for reporting. Transformations were being migrated to dbt. The project was completed in Q4 2024.

  1. 01

    Build

    A CI/CD pipeline in Bitbucket Pipelines covering Cloud Composer, Dataflow and dbt projects.

  2. 02

    Test

    Unit tests and data quality checks integrated into the CI flow.

  3. 03

    Deploy

    Automated deployment to dev/prod environments from feature branches.

  4. 04

    Hand over

    Documentation and team training, so the team can extend the pipeline.

CI/CD workflow

Conceptual CI/CD workflow based on the project scope - not the customer’s exact system architecture.

Conceptual CI/CD workflow based on the project scope - not the customer's exact system architecture.
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Outcomes

Qualitative project outcomes; no measured savings are claimed.

  • Fully automated deployments - manual effort dramatically reduced

  • Enhanced data quality through integrated unit tests and validation in CI

  • Streamlined development environment setup - fewer errors, faster onboarding

  • Improved agility - faster iteration and deployment of data workflows

  • Engineering team empowered with documentation and training to maintain and extend the pipeline

  • Foundation for safe, scalable platform growth

Technology

Bitbucket Pipelines Cloud Composer (Airflow) Dataflow dbt BigQuery Google Cloud Storage (Dataflow templates) Python on Compute Engine VMs QlikSense

Solution areas: Data Foundations

Related case studies

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