GOOGLE CLOUD NEXT 2026: AGENTS MEET A BORDERLESS CROSS-CLOUD LAKEHOUSE
Google rolled out an agent-first control plane and a cross-cloud lakehouse with auto-tagged storage and a new knowledge catalog. At Google Cloud Next, live cov...
Google rolled out an agent-first control plane and a cross-cloud lakehouse with auto-tagged storage and a new knowledge catalog.
At Google Cloud Next, live coverage highlights a "Cross-Cloud Lakehouse" billed as borderless, plus a new Knowledge Catalog, Smart Storage with instant tagging and zero manual data engineering, and a Deep Research Agent TechRadar. Workspace Intelligence also hit GA for richer context across documents and mail.
Analysts frame the shift as control plane wars: Gemini moves from model to runtime and governance hub, with BigQuery positioned as a "reasoning surface" rather than just storage WebProNews. The strategy is to orchestrate policies, memory, identity, and evaluations over multi-step agents.
The broader push lands as Google formalizes the agent platform it has teased for years The New Stack, while also pitching faster "AI defense" cycles to keep up with attackers The New Stack.
A cross-cloud lakehouse plus a governance-centric agent runtime could collapse ETL, cataloging, and orchestration into one fabric.
Auto-tagging and a unified catalog promise less manual data plumbing and clearer lineage, critical for regulated data.
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terminal
Stand up a two-cloud POC with the Cross-Cloud Lakehouse; measure query latency, pushdown behavior, and egress costs on real workloads.
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terminal
Evaluate Smart Storage auto-tagging precision/recall on PII and domain labels; compare to your current data catalog and DLP rules.
Legacy codebase integration strategies...
- 01.
Map existing lakes and marts into Knowledge Catalog; check metadata parity, lineage fidelity, and policy enforcement gaps.
- 02.
Pilot an agent that reads legacy systems and writes to BigQuery or neutral object storage, validating governance hooks and auditability.
Fresh architecture paradigms...
- 01.
Design agent-first pipelines using Gemini as the runtime with Vertex AI orchestration, and keep data in place via federated queries.
- 02.
Adopt zero-ETL patterns and define data contracts early; let the catalog drive discovery, ACLs, and schema evolution.
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