--- name: google-cloud-solution-agentic-ai-borderless-data-lakehouse description: >- 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. --- # 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. Note that underlying APIs, Terraform resources, and IAM roles often retain their legacy identifiers.
Legacy Name Updated Name Notes / API Identifiers
Vertex AI Gemini Enterprise Agent Platform Used for AI serving and agentic workflows.
BigLake Lakehouse for Apache Iceberg Used for central metadata and governance. Terraform resources (e.g., google_biglake_table, google_biglake_repository) and APIs still use biglake.
Dataproc Serverless Managed Service for Apache Spark Used for distributed processing. APIs and IAM roles still use dataproc.
Dataplex Knowledge Catalog Used for active context and governance.
cross-cloud, multicloud, multi-cloud borderless Rephrase all occurrences to borderless, with the exception of the product Cross-Cloud Interconnect.
## 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 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? 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. 3. **Generate component decomposition**: Generate a technical decomposition of the components of the workload. 4. **Ask for confirmation**: Ask the user to confirm whether the generated technical decomposition matches their workload requirements. 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 1. **Retrieve relevant Google Cloud documentation**: - [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. 2. **Map components to Google Cloud products**: Explain to the user that the solution consists of two subsystems: - The **data ingestion subsystem** ingests data from external sources and uses a central lakehouse to unify and process fragmented databases into a unified data profile in Google Cloud. - The **serving subsystem** lets users query an AI assistant and a data analysis agent to analyze the consolidated data. For each component in the confirmed technical decomposition, identify the appropriate Google Cloud products and features, based on the following guidance: - **Data ingestion subsystem components**: - **Central metadata and governance** - **Recommended primary product**: Lakehouse for Apache Iceberg - **Alternative product1**: Dataproc Metastore - **Pros**: Better for legacy open-source heavy pipelines. - **Cons**: Can have lower performance for borderless federation and Apache Iceberg. - **Processing engine** - **Recommended primary product**: Managed Service for Apache Spark with Lightning Engine - **Alternative product1**: BigQuery - **Pros**: Allows querying data in place on external clouds using standard SQL, reducing data movement. - **Cons**: Less flexible than Spark for highly complex, programmatic transformations or custom code. - **Alternative product2**: Dataflow - **Pros**: Powerful for complex, unified batch or stream ETL. - **Cons**: Requires learning the Apache Beam programming model and managing a Cloud Storage bucket for job error logs. - **Internal data storage** - **Recommended primary product**: Cloud Storage using Apache Iceberg format or Apache Parquet format. - **Alternative product1**: BigQuery storage - **Pros**: Provides high performance for native BigQuery queries. - **Cons**: Less portable for other open-source processing engines compared to open formats on Cloud Storage. - **Alternative product1**: Cloud Storage - **Pros**: Eliminates borderless egress fees and latency if you choose to consolidate your workload to a single cloud. - **Security** - **Recommended primary product**: Use Secret Manager to securely hold authentication credentials for federated REST catalogs. Manage direct storage object access using BigQuery Cloud Resource connections and runtime credential vending. - **Alternative product1**: Cloud Key Management Service (KMS) - **Pros**: Provides hardware-backed key management for encryption. - **Cons**: Not designed to store plain-text secrets like API tokens. - **Borderless networking** - **Recommended primary product**: Cross-Cloud Interconnect - **Alternative product1**: Cloud VPN (HA VPN) - **Pros**: Offers lower costs during low-traffic periods. - **Cons**: Can have higher latency and lower bandwidth compared to dedicated Cross-Cloud Interconnect. - **Serving subsystem components**: - **AI serving and agentic workflows**: - **Recommended primary product**: BigQuery data agent with Antigravity CLI - **Alternative product1**: Gemini Enterprise Agent Platform - **Pros**: Provides built-in orchestration, native enterprise grounding, and managed chat UIs. - **Cons**: Offers less granular control over the prompt loop, and can be more expensive than a lightweight MCP server. - **Alternative product2**: Google Cloud Data Agent Kit - **Pros**: Optimized for data practitioners, data engineers, and data scientists to manage the data lifecycle and perform interactive analysis directly within their IDE. - **Cons**: Designed for developer-centric workflows rather than serving production end-to-end business applications. 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 use component labels and groupings consistent with the official Google Cloud architecture icons. - 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, bridging the data ingestion and serving subsystems. 4. **Generate design recommendations**: Generate design guidance based on the following Google Cloud best practices and recommendations: - **Security, privacy, and compliance**: - To create and manage permissions for Google Cloud resources, use Google's Identity and Access Management (IAM) service. - To securely access raw data in Cloud Storage, use BigQuery Cloud resource connections. - To securely manage borderless external catalog authentication, use the security standard of the source platform to choose between Lakehouse Iceberg REST catalog federation with Secret Manager and OIDC token federation. - To delegate storage access and apply fine-grained IAM policies at the table level for tables stored in Cloud Storage, enable credential vending for your Lakehouse for Apache Iceberg runtime catalog. - To enforce the principle of least privilege, manage access through system-managed identities. - Use Cloud NAT and Cloud Router to enable resources in private subnets to securely access external cloud sources without exposure to the public internet. - Use Sensitive Data Protection to discover, classify, and de-identify sensitive data in prompts, responses, and data lake files using masking or encryption. - For more information about security, privacy, and compliance in Google Cloud, see https://cloud.google.com/architecture/framework/perspectives/ai-ml/security.md.txt - **Reliability**: - To ensure reliable performance and minimize data transfer costs over borderless networks, use Cross-Cloud Interconnect to establish a private connection between Google Cloud and other cloud providers, avoiding the unpredictable latency of the public internet. - Configure Dedicated or Partner Interconnect with redundant connections in different edge availability domains and locations. - Use Cloud DNS routing policies to route traffic to regional load balancers, enabling automatic failover to alternative regions during outages. - For more information about reliability in Google Cloud, see https://cloud.google.com/architecture/framework/perspectives/ai-ml/reliability.md.txt - **Operational excellence**: - Place external connections and routing in a dedicated transit VPC to serve as a shared, modular connectivity service for other VPCs and simplify network administration. - Use Terraform as the Infrastructure as Code (IaC) tool to define and automate the provisioning of heterogeneous resources, ensuring consistency across environments. - Configure bidirectional DNS forwarding between Cloud DNS and external DNS servers to enable seamless name resolution across hybrid environments. - For more information about operational excellence in Google Cloud, see https://cloud.google.com/architecture/framework/perspectives/ai-ml/operational-excellence.md.txt - **Cost optimization**: - To save costs and overhead associated with building and maintaining CDC pipelines, use BigQuery federated queries to AlloyDB to query your data directly. - In cases where latency is not critical, use the Standard Network Service Tier for outbound internet traffic to reduce network egress costs. - For more information about cost optimization in Google Cloud, see https://cloud.google.com/architecture/framework/perspectives/ai-ml/cost-optimization.md.txt - **Performance efficiency**: - To mitigate AI model hallucinations, ground your model on the unified data profile to enforce business definitions and statistical validation. - To perform exact-match filters to operational databases, use BigQuery federated queries. - To offload memory-heavy and complex data manipulation, such as vectorized borderless joins, use Managed Service for Apache Spark with Lightning Engine. - Use Cross-Cloud Interconnect to establish high-bandwidth, low-latency private connections to other CSPs to maximize throughput for distributed applications. - Configure redundant intra-regional network connections in an active/active design using Equal Cost Multi-Path (ECMP) routing to aggregate bandwidth. - Select Google Cloud regions that are geographically close to the external CSP's regions to minimize latency and improve data transfer performance. - For more information about performance efficiency in Google Cloud, see https://cloud.google.com/architecture/framework/perspectives/ai-ml/performance-efficiency.md.txt - **Sustainability**: - To maximize compute efficiency and reduce energy consumption, use Managed Service for Apache Spark with Lightning Engine to handle massive joins and complex windowing. - Use BigQuery Omni to query data in place on AWS or Azure (Amazon S3 or Azure Blob Storage) to avoid resource-intensive borderless data replication. - For more information about sustainability considerations, see the https://docs.cloud.google.com/architecture/framework/sustainability/printable.md.txt. 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`. 6. **Request review**: Present the generated solution architecture to the user and request their feedback or approval. 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 1. **Retrieve relevant implementation resources**: - [Build a Multicloud Open Data Lakehouse with Agentic AI](https://codelabs.developers.google.com/next26/multicloud-lakehouse) - [Terraform Registry documentation for biglake_iceberg_catalog](https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/biglake_iceberg_catalog) - [Create an Apache Iceberg table with metadata in Lakehouse runtime catalog](https://docs.cloud.google.com/managed-spark/docs/guides/spark-workloads-with-bigquery-metastore.md.txt) - [Accelerate Spark batch workloads and sessions with Lightning Engine](https://docs.cloud.google.com/managed-spark/docs/guides/lightning-engine-serverless.md.txt) - [Create data agents](https://docs.cloud.google.com/bigquery/docs/create-data-agents.md.txt) *Important*: Use these resources as the technical foundation for the IaC and deployment instructions you generate in the remaining steps of this phase. 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 3. **Generate Infrastructure as Code (IaC)**: Generate code (e.g., Terraform) and deployment scripts to automate the provisioning of the proposed Google Cloud resources. 4. **Write deployment instructions**: Draft sequential, step-by-step deployment instructions to execute the IaC and initialize the workload components. 5. **Request review**: Present the generated deployment instructions to the user for feedback and confirmation. 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 1. **Retrieve relevant verification resources**: - [Build a Multicloud Open Data Lakehouse with Agentic AI](https://codelabs.developers.google.com/next26/multicloud-lakehouse) - [Create and run Connectivity Tests](https://docs.cloud.google.com/network-intelligence-center/docs/connectivity-tests/how-to/running-connectivity-tests.md.txt) - [Create an Apache Iceberg table with metadata in Lakehouse runtime catalog](https://docs.cloud.google.com/managed-spark/docs/guides/spark-workloads-with-bigquery-metastore.md.txt) - [Accelerate Spark batch workloads and sessions with Lightning Engine](https://docs.cloud.google.com/managed-spark/docs/guides/lightning-engine-serverless.md.txt) *Important*: Use these resources and their verification patterns as the starting point for the validation checks and verification scripts you generate in the remaining steps of this phase. 2. **Define validation checks**: Outline validation steps to verify that the deployed infrastructure meets the workload's 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. 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. 4. **Compile validation report**: Document the validation steps, verification scripts, and expected outcomes in single Markdown file. 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. 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. ## Supporting Links - [Build hybrid and multicloud 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)