Skill detail

google-cloud-solution-agentic-ai-borderless-data-lakehouse

>- Guides agents to discover requirements and design a governed, secure borderless open data lakehouse with agentic AI integration. Use when designing a multi-product architecture that connects data silos to AI agents, joining data across clouds, or running federated queries across Google Cloud and external data sources, including on-premises or other cloud providers. Don't use for simple single-cloud data warehouses or non-AI workloads.

Why we recommend it: Discovers requirements and designs a governed borderless open data lakehouse with agentic AI integration across clouds, including ingestion, serving, and federated queries.

Needs (stated): Credentials

MatchPossibleRecommended for Engineering

Sourcegoogle/skillsExternal source

Reported installsNot reportedPopularity signal only

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SKILL.md

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---
name: google-cloud-solution-agentic-ai-borderless-data-lakehouse
metadata:
  category: MultiProductSolutions
description: >-
  Discovers requirements and designs a borderless open data lakehouse using
  Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting
  multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob),
  establishing ingestion and AI serving subsystems, configuring Cross-Cloud
  Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data
  agents. Don't use for single-cloud data warehouses, or when the focus is on
  Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows
  (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
---

# Borderless open data lakehouse agentic AI system

Follow this workflow to help users design and implement a custom multi-product
solution in the cloud for a given workload, use case, or requirement.

## Product Renaming & Terminology

When generating solution designs, architecture diagrams, and documentation, use
the updated Google Cloud product names. For details on legacy vs. updated
product names and terminology, see
[references/product_renaming.md](references/product_renaming.md).

## Workflow

The solution design and implementation workflow consists of the following
phases:

- **Phase 1: Requirements discovery and analysis**: Analyze the workload's
  requirements, constraints, dependencies, and current state.
- **Phase 2: Solution design**: Build a technology stack, architecture, and
  deployment configuration for the workload based on Google Cloud design best
  practices and recommendations.
- **Phase 3: Implementation plan**: Generate automation
  and instructions to deploy the solution.
- **Phase 4: Solution validation**: Validate that the deployment meets the
  requirements of the workload.

### Phase 1: Requirements discovery and analysis

- [ ] **Step 1: Discover requirements**: Understand the functional and
  non-functional requirements, business goals, and current state (if any) of the
  workload, including its architecture, dependencies, and constraints. Use the
  following questions to guide the requirements discovery process:
   - What are your primary data sources?
   - How do you manage and federate metadata across your data sources?
   - What are your security and credential management requirements?
   - What are the analytical and computational requirements to join and
     transform this borderless data?
   - What types of natural language prompts or user queries do you expect AI
     agents or end-users to execute against this data?

- [ ] **Step 2: Identify components**: Based on the requirements analysis,
  identify the components of the workload and their relationships. Also identify
  any borderless components, hybrid components, or on-prem components that the
  solution needs to integrate with.

- [ ] **Step 3: Generate component decomposition**: Generate a technical
  decomposition of the components of the workload.

- [ ] **Step 4: Ask for confirmation**: Ask the user to confirm whether the
  generated technical decomposition matches their workload requirements.

- [ ] **Step 5: Iterate**: If the user requests changes, then generate an
  updated technical decomposition, and ask the user to confirm the changes.
  Continue iterating until the user confirms the technical decomposition.

### Phase 2: Solution design

- [ ] **Step 1: Retrieve relevant Google Cloud documentation**: Use available
  search or fetch tools to read the content of the following Google Cloud
  documentation to ground the guidance that you generate in the remaining
  steps of this phase before proceeding.
   - [Build hybrid and borderless architectures using Google Cloud](https://docs.cloud.google.com/architecture/hybrid-multicloud-patterns/one-page-view.md.txt)
   - [Build a borderless open data lakehouse](https://docs.cloud.google.com/architecture/agentic-ai-build-multicloud-open-

Read the full source on GitHub

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