Cloud Computing

Elevate your applications with Firestore’s new advanced query engine

A hallmark of a valuable database is how easy it is to query the data inside, so that developers can build tailored and complex user experiences in an application. Last week marked a significant evolution for Firestore, Google Cloud’s enterprise-grade, scalable document database, with the debut of an advanced query engine designed to help you […]

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Getting Started with Gemini 3: Hello World with Gemini 3 Flash

The Gemini 3 family of models, our most intelligent yet, features state-of-the-art reasoning to help you learn, build, and plan anything. Within this family, Gemini 3 Flash is designed for speed and efficiency, offering high performance in complex reasoning, multimodal understanding, agentic, and “vibe coding” tasks with Flash-level latency and cost. This blend of powerful reasoning and efficiency

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How Palo Alto Networks built a multi tenant scalable Unified Data Platform

Enterprises across the world are processing significant amounts of data. Palo Alto Networks processes thousands of firewall logs, telemetry signals and threat events every second across its product portfolio. To support this scale, Palo Alto Networks had 30,000 individual data pipelines, each with its own operational load. And while this single tenant architecture model worked

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Cloud CISO Perspectives: Practical guidance on building with SAIF

Welcome to the first Cloud CISO Perspectives for January 2026. Today, Tom Curry and Anton Chuvakin, from Google Cloud’s Office of the CISO, share our new report on using Google’s Secure AI Framework with Google Cloud capabilities and services to build boldly and responsibly with AI. As with all Cloud CISO Perspectives, the contents of

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Introducing BigQuery managed and SQL-native inference for open models

BigQuery provides access to a variety of LLMs for text and embedding generation, including Google’s Gemini models, Google-managed models from partners like Anthropic and Mistral. Using Gemini models and Google-managed partner models in BigQuery is simple — just create the model with the foundation model name and run inference directly in SQL queries. Today, we

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