infra-gcp-data-platform-review

Review GCP data platform designs for BigQuery security and cost risks.

14|1|Updated May 5, 2026

Search Tags:#gcp #security review #cost optimization #iam #data governance #bigquery #vpc service controls

Ready to run

npx skills add https://github.com/ivanshamaev/de-agent-skills --skill infra-gcp-data-platform-review

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: infra-gcp-data-platform-review
Source: https://github.com/ivanshamaev/de-agent-skills/tree/main/group_skills/infra_dataops_group_skills/infra_gcp_data_platform_review
Command: npx skills add https://github.com/ivanshamaev/de-agent-skills --skill infra-gcp-data-platform-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you review a GCP data platform to reduce security gaps and control BigQuery and storage costs before problems reach production.

Core Features & Use Cases

BigQuery security review: validate dataset IAM, column-level security with policy tags, default encryption, and partition/filter requirements to prevent risky access patterns and expensive scans.
GCS data lake governance: enforce uniform bucket-level access, CMEK encryption, lifecycle policies, and public access prevention with example Terraform wiring to service accounts.
Platform architecture comparison & streaming readiness: compare Dataproc vs Dataflow vs Spark on GKE, and outline Pub/Sub streaming + Cloud Composer (managed Airflow) considerations.
Operational guardrails: cover Workload Identity for GKE, VPC Service Controls to limit exfiltration, and monitoring/cost optimization using INFORMATION_SCHEMA and reservations.

Quick Start

Ask the AI to produce a GCP review checklist and Terraform/SQL review points for your BigQuery datasets, GCS buckets, streaming pipeline, identity setup, and VPC Service Controls posture.

Frequently Asked Questions about infra-gcp-data-platform-review

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

FAQPage Schema

How do I review BigQuery dataset IAM and prevent unauthorized access to sensitive columns?▼

To review BigQuery security, validate dataset IAM roles and enforce column-level security using policy tags. This prevents risky access patterns and restricts unauthorized queries against sensitive data.

What is the best way to optimize BigQuery storage and query costs before production?▼

Optimize BigQuery costs by validating partition and clustering settings, and analyzing cost signals from INFORMATION_SCHEMA and reservations. This controls storage limits and reduces expensive full table scans.

How do I enforce GCS bucket governance and prevent public access?▼

Enforce GCS data lake governance by enabling uniform bucket-level access, configuring CMEK encryption, and applying lifecycle policies. This prevents public exposure and manages storage costs automatically.

Dataproc vs Dataflow vs Spark on GKE: how do I choose managed orchestration for my data platform?▼

Compare Dataproc, Dataflow, and Spark on GKE based on your streaming architecture and processing needs. Selecting the right managed orchestration ensures scalable pipeline execution and operational efficiency.

Do I need VPC Service Controls and Workload Identity to secure GKE data pipelines?▼

Yes, configuring Workload Identity for GKE and VPC Service Controls perimeter coverage limits data exfiltration. These guardrails secure your platform by isolating workloads and protecting sensitive resources.