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gcp-pipeline-resource-provisioning

Provision GCP data pipeline resources from a deployment.yaml.

161|37|Updated Apr 2, 2026
Search Tags:#automation#gcp#bigquery#data-pipelines#provisioning#dataproc#deployment.yaml
One-click install
npx skills add https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack --skill gcp-pipeline-resource-provisioning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gcp-pipeline-resource-provisioning
Source: https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack/tree/main/skills/gcp-pipeline-resource-provisioning
Command: npx skills add https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack --skill gcp-pipeline-resource-provisioning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates declarative resource creation and provisioning for data pipelines across Google Cloud resources (BigQuery, Dataform, Dataproc, and DTS), reducing manual configuration drift and enabling repeatable deployments via a central deployment.yaml.

Core Features & Use Cases

  • Declarative provisioning for a suite of GCP data resources (BigQuery datasets/tables, Dataform repositories and workflows, Dataproc clusters, and DTS transfer configurations).
  • Environment-aware deployments across dev, staging, and prod through a single deployment.yaml with consistent labels and governance.
  • Validation and deployment workflow guidance to ensure secure, auditable resource provisioning without embedding secrets in code.

Quick Start

Create or update deployment.yaml at the repository root to provision resources across environments.

Frequently Asked Questions about gcp-pipeline-resource-provisioning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate provisioning for GCP data pipelines across dev, staging, and prod?▼

Automate GCP data pipeline provisioning by defining BigQuery, Dataform, Dataproc, and DTS resources declaratively in a deployment.yaml. This ensures consistent, auditable configurations across environments without manual setup.

What is the best way to prevent configuration drift when deploying BigQuery datasets and Dataproc clusters?▼

Prevent configuration drift by using a central deployment.yaml for declarative resource provisioning. This approach applies consistent labels and governance across all GCP pipeline environments.

How do I provision Dataform repositories and DTS transfer configurations without embedding secrets in code?▼

Provision Dataform and DTS resources securely by following the deployment workflow validation, which ensures auditable resource creation without embedding secrets directly in your code.

Do I need an existing deployment.yaml to provision GCP data pipeline resources?▼

Yes, you need an existing deployment.yaml at your repository root. The provisioning workflow reads this file to declaratively provision and validate resources across environments.

Can I manage BigQuery, Dataproc, and Dataform deployments through a single configuration file?▼

Yes, you can manage deployments for BigQuery, Dataproc, and Dataform through a single deployment.yaml, applying consistent configurations and labels across dev, stage, and prod projects.

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