Borderless open data lakehouse: agentic AI architecture on Google Cloud
Design a multi-cloud Iceberg + BigQuery data lakehouse (Cloud Storage, S3, Azure Blob) with the Gemini agent platform — discovery, architecture, Terraform, validation
- What
- Design a multi-cloud Iceberg + BigQuery data lakehouse (Cloud Storage, S3, Azure Blob) with the Gemini agent platform — discovery, architecture, Terraform, validation
- Cost
- Free
- Needs
- a Google Cloud account with billing enabled (deploying the designed solution provisions real multi-cloud infrastructure and incurs real cloud costs); a data workload to architect — the skill itself needs nothing to read
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — Google's official solution skill for designing a borderless open data lakehouse: a 4-phase agent workflow. Phase 1, requirements discovery — interview the workload (data sources, metadata federation, security, analytical requirements, expected AI-agent queries) and produce a confirmed technical decomposition. Phase 2, solution design — map components to Google Cloud products using the repo's reference docs, draw a Mermaid architecture diagram (ingestion vs serving subsystems, Managed Spark as the shared ETL bridge), and compile a solution-architecture-guide.md. Phase 3, implementation plan — document prerequisites (projects, billing, APIs, IAM), generate Terraform IaC, write step-by-step deployment instructions. Phase 4, validation — dry-runs, connectivity and security checks, verification scripts, and a validation report. Covers Lakehouse for Apache Iceberg, BigQuery data agents, Cross-Cloud Interconnect, and the Gemini Enterprise Agent Platform across Cloud Storage, AWS S3 and Azure Blob. Honest caveats: enterprise-grade design methodology — deploying what it produces provisions real multi-cloud infrastructure, so bring a Google Cloud account with billing enabled (real cloud costs); expects the agent to fetch live Google Cloud documentation to ground its guidance; explicitly not for single-cloud warehouses. Apache-2.0 licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a Google Cloud account with billing enabled (deploying the designed solution provisions real multi-cloud infrastructure and incurs real cloud costs); a data workload to architect — the skill itself needs nothing to read Install "Borderless open data lakehouse: agentic AI architecture on Google Cloud" for me. It gives my agent Google's official 4-phase workflow for a borderless open data lakehouse: requirements discovery and technical decomposition, solution design with product mapping and Mermaid architecture diagrams, an implementation plan with Terraform IaC generation and deployment instructions, and validation with dry-runs and verification scripts — across Lakehouse for Apache Iceberg, BigQuery data agents, Cross-Cloud Interconnect, Managed Spark and the Gemini Enterprise Agent Platform (Cloud Storage, S3, Azure Blob). Apache-2.0 licensed. Repository: https://github.com/google/skills/blob/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via the secure vault, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "google-cloud-solution-agentic-ai-borderless-data-lakehouse". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. describe the workload to architect and confirm the Phase 1 decomposition; set up the GCP project and billing before Phase 3). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.