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

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-data-lakehouse.md.txt)
• [Implement agentic analytics workflows for distributed data](https://docs.cloud.google.com/architecture/agentic-ai-cross-cloud-analytics.md.txt)
• [Analytics Hybrid and Multicloud Pattern](https://docs.cloud.google.com/architecture/hybrid-multicloud-patterns-and-practices/analytics-hybrid-multicloud-pattern.md.txt)
• [Google Cloud multi-regional deployment archetype](https://docs.cloud.google.com/architecture/deployment-archetypes/multiregional.md.txt)
• [Network segmentation and connectivity for distributed applications in Cross-Cloud Network](https://docs.cloud.google.com/architecture/ccn-distributed-apps-design/connectivity.md.txt)
• [Patterns for Connecting Other Cloud Service Providers with Google Cloud](https://docs.cloud.google.com/architecture/patterns-for-connecting-other-csps-with-gcp.md.txt)

*Important*: Use the content that you retrieve from Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase.

• [ ] **Step 2: Map components to Google Cloud products**: For each component in

the confirmed technical decomposition, identify the appropriate Google Cloud products and features, based on the guidelines in [references/product_mapping.md](references/product_mapping.md).

• [ ] **Step 3: Create architecture diagram**: Create an architecture diagram

that shows the components, their relationships, and data/control flows.

• The diagram must be in the Mermaid format:

https://github.com/mermaid-js/mermaid.

• The diagram must show a clear distinction between the products in the

data ingestion subsystem and the serving subsystem.

• The diagram must show Managed Service for Apache Spark as a shared

component for ETL/ingestion processing, bridging the data ingestion and serving subsystems (distinct from interactive IDE analytics workflows).

• [ ] **Step 4: Generate design recommendations**: Generate design guidance

based on the guide