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 [blocked].
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
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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?
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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.
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Step 3: Generate component decomposition: Generate a technical decomposition of the components of the workload.
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Step 4: Ask for confirmation: Ask the user to confirm whether the generated technical decomposition matches their workload requirements.
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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
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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
- Build a borderless open data lakehouse
- Implement agentic analytics workflows for distributed data
- Analytics Hybrid and Multicloud Pattern
- Google Cloud multi-regional deployment archetype
- Network segmentation and connectivity for distributed applications in Cross-Cloud Network
- Patterns for Connecting Other Cloud Service Providers with Google Cloud
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.
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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 [blocked].
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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).
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Step 4: Generate design recommendations: Generate design guidance based on the guidelines in references/design_recommendations.md [blocked].
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Step 5: Draft solution architecture: Compile the requirements, technical decomposition, product mapping, architecture diagram, and design recommendations into a single Markdown file named
solution-architecture-guide.md, based on the template in assets/output-template.md [blocked]. -
Step 6: Request review: Present the generated solution architecture to the user and request their feedback or approval.
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Step 7: Iterate: If the user requests changes, generate an updated solution architecture and repeat steps 2-6 until the user approves the solution architecture.
Phase 3: Implementation plan
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Step 1: Retrieve relevant implementation resources:
- Build a Multicloud Open Data Lakehouse with Agentic AI
- Terraform Registry documentation for biglake_iceberg_catalog
- Create an Apache Iceberg table with metadata in Lakehouse runtime catalog
- Accelerate Spark batch workloads and sessions with Lightning Engine
- Create data agents
Important: Use these resources as the technical foundation for the IaC and deployment instructions you generate in the remaining steps of this phase.
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Step 2: Identify deployment prerequisites: Document prerequisites for the deployment, including the following:
- Projects and billing associations
- Required Google Cloud APIs
- Required IAM permissions
- Any other prerequisites
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Step 3: Generate Infrastructure as Code (IaC): Generate code (e.g., Terraform) and deployment scripts to automate the provisioning of the proposed Google Cloud resources.
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Step 4: Write deployment instructions: Draft sequential, step-by-step deployment instructions to execute the IaC and initialize the workload components.
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Step 5: Request review: Present the generated deployment instructions to the user for feedback and confirmation.
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Step 6: Iterate: If the user requests changes, generate an updated implementation plan and repeat steps 2-5 until the user approves the implementation plan.
Phase 4: Solution validation
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Step 1: Retrieve relevant verification resources (optional): If the resources from Phase 3 are not already in your context, retrieve the same implementation resources as the starting point for the validation checks and verification scripts that you generate in this phase.
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Step 2: Define validation checks: Outline validation steps to verify that the deployed infrastructure meets the workload requirements:
- Deployment dry-run: Commands like
terraform planto preview changes. - Connectivity and routing: Verification of network paths, load balancer routing, and service endpoints.
- Security policies: Verification of restricted access, firewall rules, and IAM enforcement.
- Deployment dry-run: Commands like
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Step 3: Generate verification scripts: Draft lightweight scripts or command-line instructions (e.g. using
curlorgcloud) that the user can run to perform these validation checks. -
Step 4: Compile validation report: Document the validation steps, verification scripts, and expected outcomes in a single Markdown file.
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Step 5: Conduct validation and finalize: Assist the user in executing the validation checks and troubleshooting any deployment issues. After the solution is validated successfully, request final approval from the user.
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Step 6: Iterate: If the user requests changes, then generate an updated validation plan and repeat steps 2-5 until the user approves the validation plan.
