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

SKILL.md

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.

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

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.

 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.

 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 guidelines in
references/design_recommendations.md.

 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.

 Step 6: Request review: Present the generated solution architecture to
the user and request their feedback or approval.

 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

 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.

 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

 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.

 Step 4: Write deployment instructions: Draft sequential, step-by-step
deployment instructions to execute the IaC and initialize the workload
components.

 Step 5: Request review: Present the generated deployment instructions
to the user for feedback and confirmation.

 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

 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.

 Step 2: Define validation checks: Outline validation steps to verify
that the deployed infrastructure meets the workload requirements:

Deployment dry-run: Commands like terraform plan to 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.

 Step 3: Generate verification scripts: Draft lightweight scripts or
command-line instructions (e.g. using curl or gcloud) 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.

 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.

 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.