All news with #google cloud tag

🚀 Google Cloud was recognized as a Leader in the 2026 Gartner Magic Quadrant for Cloud-Native Application Platforms for the third consecutive year, reflecting its focus on developer-centric, application-first capabilities. The platform unifies serverless, containerized, and agentic deployment options and integrates generative AI tools for rapid prototyping, one-click deployments, and managed MCP servers. Google highlights features like the Gemini Enterprise Agent Runtime, Application Design Center, Antigravity orchestration, and Cloud Run enhancements for secure, scalable agent and app hosting.

🔒 Google Cloud announced the preview of quantum-safe key import for software-based keys in Cloud KMS, extending its post-quantum offerings including quantum-safe digital signatures and KEMs. The feature uses hybrid public key encryption (HPKE) to wrap keys in a quantum-resistant envelope during transit, mitigating store-now, decrypt-later risks. The import workflow integrates with existing Cloud KMS APIs and supports client-side wrapping via libraries like Tink and OpenSSL, with options for X-Wing, ML-KEM-768, or ML-KEM-1024 and AES-256-GCM for symmetric wrapping.

🔍 This post outlines ten essential questions startups must address when moving from AI prototype to production on Google Cloud. It contrasts Google AI Studio for rapid prototyping with the Gemini Enterprise Agent Platform for enterprise controls, and emphasizes sequencing migration before you have real users. The article highlights operational pitfalls—API key leaks, IAM ownership gaps, and quota 429s—and provides practical checklist items, role guidance, and mitigation strategies including regional endpoints, retries, and consumption models.

🔍 AlloyDB's ScaNN index now supports a four-level tree (preview) to scale vector search to over 10 billion vectors while preserving low latency and high recall. As a managed PostgreSQL-compatible service, AlloyDB pairs Google's infrastructure with an analytical engine optimized for agentic AI workloads. The new architecture reduces compute intensity through hierarchical partitioning and improves memory efficiency via balanced tree shapes and sampling optimization, targeting <= 51 ms p95 latency at 95% recall.

🚀 This technical guide explains Google Cloud’s Managed Service for Apache Spark, contrasting traditional managed clusters with serverless deployment modes and execution models (interactive sessions and batches). It covers resource and cost optimization techniques including history-based autotuning, tuning cores/memory, dynamic allocation caps, and shuffle partition sizing. The article also demonstrates integrated troubleshooting using Gemini Cloud Assist to diagnose runtime failures and generate resilient PySpark fixes.

🛠️ The blog explains how legacy Apache Hive Metastores become bottlenecks as enterprises scale their data lakes and adopt multiple query engines. It introduces the Google Cloud Lakehouse runtime catalog, a serverless metadata registry built on the Apache Iceberg REST Catalog specification that supports both legacy Hive tables and modern table formats. The post outlines common pain points — scaling, governance, and operational TCO — and describes a migration path that extracts Hive table definitions and registers them into the serverless catalog. The result is unified governance, zero-data-copy access across engines, and reduced operational overhead.

🗂️ Box and Google Cloud are integrating Gemini Multimodal Embeddings 2 into Box's Agentic Platform to extend RAG beyond text and enable unified search and reasoning across documents, images, tables, and charts. This integration preserves spatial and visual structure, supports crossmodal retrieval, and bridges heterogeneous formats like .docx, .xlsx, .pdf, and .pptx. The combined system targets use cases in finance, healthcare, and enterprise auditing by enabling layout-aware embeddings, cross-file synthesis, and visual-to-text auditing.

🧭 This post describes Google's Governance Agent project that automates metadata propagation using column-level lineage, Knowledge Catalog, and BigQuery. It explains how the agent propagates descriptions, glossary terms, policy tags, and trust scores from upstream sources while applying confidence thresholds and conservative grounding. The project provides both a Gradio dashboard and a CLI to support steward review and automated pipelines, and emphasizes that automation is meant to reduce repetitive work, not remove steward oversight.

🔍 This article demonstrates a hybrid streaming pattern that pairs lightweight, CPU-based inference with downstream generative AI agents using Google Dataflow and the Agent Development Kit (ADK). It outlines an Apache Beam pipeline that filters routine events locally and routes only complex cases to a Gemini-backed agent for multi-step remediation, reducing API costs, latency, and quota exhaustion. The approach preserves a static DAG while enabling dynamic runtime branching for targeted automation.

🤖 Deutsche Bank partnered with Google Cloud to build an agentic AI-driven resilience platform that modernizes regulatory tabletop exercises and incident analysis. The platform ingests architecture, logs, data flows, and telemetry to generate context-aware scenarios, audit-ready evidence, and structured session records. Using Gemini Enterprise Agent Platform, LangGraph, and Google ADK, the bank achieves both deterministic, traceable execution and adaptive investigation. This approach supports continuous, regulator-aligned resilience across complex, distributed systems.

🔒 Google Cloud published a staged post-quantum migration roadmap on August 12, splitting work into three risk domains from its quantum threat model. The provider targets mitigating store-now-decrypt-later (SNDL) risks by end of 2027, with signature hardening and key management agility running to end of 2028. Several services already support hybrid NIST-standardized ML-KEM and related primitives, while others (Cloud VPN, Private CA, Cloud HSM) phase in through 2028. Google warns hardware replacement cycles may extend some transitions beyond 2029.

🚀 Gemini in Google Cloud's Database Migration Service streamlines conversion of stored procedures, triggers, and functions from proprietary dialects like PL/SQL and T-SQL into PostgreSQL PL/pgSQL. The service analyzes full schema context, provides side-by-side diffs and inline explanations, and enforces IAM-bound security. Teams can validate, edit, and deploy converted code within a single console to shorten migration timelines.

🔒 Google Cloud publishes an updated roadmap to migrate its infrastructure and services to post-quantum cryptography by 2029, addressing risks like Store Now, Decrypt Later and signature forgery. The plan prioritizes API endpoints, load balancers, Cloud KMS, and key management while collaborating on standards such as NIST and IETF. Google outlines domain-specific timelines through 2027–2028 and emphasizes shared responsibilities with customers for operational readiness.

📱 Google Cloud announced the public preview of Developer Device Platform (DDP), a fully managed service offering on-demand access to real physical devices and high-concurrency virtual emulators. DDP provides interactive debugging via Device Streaming and parallel CI/CD testing via Device Run, enabling faster iteration, smarter sharding, and auto-retries. The platform supports integration with coding agents and will integrate with Android Studio and CLI, charging users on a pay-per-minute public preview model.

🔍 Malachyte applies attention-based neural networks and LLM-inspired sequence modeling to address the retail "cold start" problem, updating user vectors in real time to personalize search and product pages. By streaming every interaction through Managed Service for Apache Kafka into Bigtable and combining multimodal embeddings, the platform refines predictions and privacy-friendly personalization within 100 milliseconds. Built on Google Cloud's AI stack, the solution leverages GKE, GCE, and Cloud Pub/Sub to enable continuous learning across retailers.

🔒 Google Cloud outlines its proactive, shared-fate approach to detect and contain emerging threats across AI workloads, cryptomining, credential exposure, supply chain attacks, and account takeover. The post describes detection signals, tailored containment actions like granular throttling and localized identity isolation, and escalation paths including targeted suspensions. It highlights integrations such as GitHub Secret Scanning and details observability tools like Cloud Abuse Event Logging, Cloud Audit Logging, and billing alerts.

🔒 Enterprises with strict compliance and sovereignty needs often keep data on-premises, risking missed AI advances. Recent research of over 1,400 IT leaders found 48% prioritize infrastructure with data residency and local security controls, and 52% now use hybrid cloud to combine public cloud power with local data control. Google Distributed Cloud (GDC) delivers on-premises AI, optimized infrastructure, and a choice of Gemini or open models to enable secure, sovereign AI.

🔒 Google Cloud and MLCommons’ MedPerf use Confidential Computing to benchmark medical AI on real patient data while preserving privacy. The collaboration runs evaluations inside hardware-isolated Trusted Execution Environments, extending protection across CPUs and GPUs with A3 VMs and NVIDIA H100 GPUs. This approach enables federated evaluation for initiatives like Federated Tumor Segmentation, revealing site-specific performance gaps and improving trust in clinical AI.

🔍 Mirendil will leverage Google Cloud’s AI Hypercomputer, combining TPU accelerators and NVIDIA full-stack AI infrastructure to support model pre-training and post-training workloads. Google Cloud partnered closely with Mirendil on design and deployment across compute, storage, networking, and control planes. Managed training clusters run in Gemini Enterprise Agent Platform, and Mirendil is already live with TPU v5P chips while NVIDIA systems come online soon.

🚀 UiPath re-architected its infrastructure to support agentic AI and high-scale intelligent document processing by moving from isolated clusters to a shared Google Cloud GPU fleet. The company balances A3 (NVIDIA H100) instances for training with G4 (NVIDIA RTX Pro 6000) instances for inference, using Google Cloud AI Hypercomputer and Dynamic Workload Scheduler to secure predictable capacity. This shared-fleet approach maximizes utilization, reduces costs, and lets engineering teams focus on model performance rather than infrastructure.