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google-cloud-solution-agentic-analytics-spark-knowledge-catalog

Generates architecture and deployment guidance for cross-cloud agentic analytics using Spark and Knowledge Catalog.

19.1k|1.5k|Updated Mar 31, 2026
Search Tags:#Google Cloud#multi-cloud#solution architecture#Data Governance#Apache Spark#knowledge catalog#agentic analytics
One-click install
npx skills add https://github.com/google/skills --skill google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Source: https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Command: npx skills add https://github.com/google/skills --skill google-cloud-solution-agentic-analytics-spark-knowledge-catalog

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Designing a governed, secure analytics architecture across data distributed in Google Cloud, other cloud providers, and on-premises systems is complex, requiring product selection, federation strategy, and governance decisions that are hard to get right manually.

Core Features & Use Cases

  • Requirements Discovery: Interactively gathers functional and non-functional requirements, resolves ambiguities, and produces an approved technical decomposition across four architectural layers.
  • Solution Architecture Generation: Recommends Google Cloud products (Knowledge Catalog, Managed Service for Apache Spark, Lakehouse for Apache Iceberg, AlloyDB), generates Mermaid architecture diagrams, design recommendations, and deployment guidance grounded in official documentation.
  • Validation and Packaging: Creates and runs validation plans with gcloud or curl commands, then consolidates everything into a solution-architecture-guide.md document.
  • Use Case: A data team with sales data in AWS S3 (Iceberg), PDFs in Cloud Storage, and operational data in AlloyDB uses this Skill to design a zero-copy federated analytics architecture with Knowledge Catalog governance and Spark processing.

Quick Start

Use this skill to design an agentic analytics architecture for my data spread across AWS S3, on-premises databases, and Google Cloud with proper governance.

Frequently Asked Questions about google-cloud-solution-agentic-analytics-spark-knowledge-catalog

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

FAQPage Schema
How do I design a cross-cloud analytics architecture on Google Cloud?▼

Use this Skill's four-phase workflow: requirements discovery, solution architecture, validation, and packaging. It recommends products like Managed Service for Apache Spark, Knowledge Catalog, and Lakehouse for Apache Iceberg, and generates diagrams and deployment guidance.

How to query data in AWS S3 from Google Cloud without copying it?▼

The Skill recommends zero-copy federation using Apache Iceberg REST catalogs with Spark, or BigQuery Omni, avoiding data transfer and egress fees. Cross-Cloud Interconnect provides the dedicated network connectivity between providers.

What is Knowledge Catalog used for in agentic analytics?▼

Knowledge Catalog (formerly Dataplex) provides central metadata governance, business glossaries, aspect types, and data lineage tracking. It grounds AI agents in trusted, certified data and helps trace PII leakage paths across systems.

Does this Skill support on-premises data sources?▼

Yes, the workflow explicitly covers data distributed across Google Cloud, other cloud providers, and on-premises systems. It recommends Cloud Interconnect for on-premises connectivity and federation mechanisms like remote query push-down.

What are the limitations of this architecture workflow?▼

The workflow requires interactive user approval at each phase, so it cannot run fully autonomously. It also depends on external resources like the Google Developer Knowledge MCP server and related Google Cloud skills for grounding recommendations.

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