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GCP News - 2026-08-07

2026-08-07
最終更新: 2026-08-27 21:31:31 JST

Google Cloud Release Notes

August 07, 2026

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AlloyDB for PostgreSQL

Feature

You can now sync tables from BigQuery into your AlloyDB instance, either as a one-time operation or on a periodic schedule. This feature (in Preview) lets you enable operational analytics that benefit from low-latency, transactional access to your data lake.

For more information, see Sync BigQuery data to AlloyDB.

Feature

AlloyDB integration with BigQuery lets you connect your operational and analytical data through real-time data access (lakehouse federation), periodic data synchronization, and one-time table syncs. These features are in Preview.

For more information, see Choose how to access BigQuery data from AlloyDB.

Cloud Billing

Feature

New filter and group-by option available in Cloud Billing Reports

In Billing Reports, Cloud Billing has added the Originating products filter and Group by to provide additional options that let you analyze and understand your costs. Originating products are Google Cloud products that cause usage in another product. For example, Gemini Enterprise is an originating product when it causes usage in the Gemini Enterprise app.

To help you track and analyze your AI spend, the Originating products dimension is used in the following ways:

For more information, see the following resources:

Cloud Data Fusion

Change

The Cloud SQL for MySQL and Cloud SQL for PostgreSQL plugins, version 1.11.14, are available in Cloud Data Fusion version 6.10.x. Version 1.12.5 of these plugins is available in Cloud Data Fusion version 6.11.x.

This release includes the following feature:

  • You can now configure transaction isolation levels in the Cloud SQL for MySQL and Cloud SQL for PostgreSQL plugins. This configuration applies to both batch sources and sinks, providing more precise control over data consistency and database locking (PLUGIN-1779).

Change

The Windows Share Copy Action plugin version 2.12.5 is available in Cloud Data Fusion version 6.9.1 and later. Version 2.13.2 of this plugin is available in Cloud Data Fusion version 6.11.0 and later.

This release includes the following change:

  • Added support for the modern SMBv2 and SMBv3 protocols to improve connection performance and reliability, while maintaining backward compatibility with pipelines that use SMBv1 (PLUGIN-1960).

Cloud SQL for PostgreSQL

Change

Newly created instances configured with high availability (HA) now have Knowledge Catalog (formerly Dataplex Universal Catalog) enabled by default.

Cloud SQL for PostgreSQL instances running on PostgreSQL version 14.0 or later send updates and metadata to Knowledge Catalog in near real-time.

You can either verify enablement or disable the feature using the Google Cloud console.

For more information, see Near real-time.

Cloud SQL for SQL Server

Change

Newly created instances configured with high availability (HA) now have Knowledge Catalog (formerly Dataplex Universal Catalog) enabled by default.

Cloud SQL for SQL Server instances send updates and metadata to Knowledge Catalog to help support data discovery.

You can either verify enablement or disable the feature using the Google Cloud console.

For more information, see Manage your Cloud SQL resources using Knowledge Catalog.

Confidential VM

Feature

The accelerator-optimized g4-standard-48 machine type for securely running AI and ML workloads is generally available (GA), with the following specifications:

  • 5th Generation AMD EPYC Turin processor
  • AMD SEV
  • 1 NVIDIA RTX PRO 6000 GPU

Cortex Framework

Announcement

Release 7.0.1

Fixed

  • Resolved an issue where running the uv run cortex-build command in Windows PowerShell or Windows Command Prompt resulted in a Could not auto-import local builder warning and an Invalid builder type NoneType for category ... error.

  • Improved Dataform quota management in uv run cortex-deploy script.

Gemini Enterprise

Change

Gemini Enterprise: Experimental agent telemetry aligned with OpenTelemetry generative AI semantic conventions

Gemini Enterprise agents emit richer, standards-aligned telemetry based on the OpenTelemetry generative AI semantic conventions, in addition to the existing stable telemetry. Trace spans and Cloud Logging entries include standardized gen_ai.* attributes (for example, gen_ai.agent.name, gen_ai.conversation.id, gen_ai.usage.input_tokens, and gen_ai.input.messages) that describe agent, model, and tool activity.

Prompt and response message content appears in these attributes only when your observability settings allow logging of prompt inputs and response outputs; otherwise it is redacted or omitted. OpenTelemetry classifies these conventions as Development status, so this telemetry is experimental and subject to change.

For more information, see Access traces and spans.

Feature

Gemini Enterprise: Custom MCP server data stores

You can connect your custom Model Context Protocol (MCP) server with Gemini Enterprise to securely access your company's private data, custom internal tools, and MCP-compliant third-party systems.

This feature is turned off by default. To enable it, an Organization Policy Administrator must remove the organization constraint. This feature is generally available (GA).

For more information, see:

Managed Service for Apache Spark

Announcement

New Managed Service for Apache Spark (formerly Google Cloud Serverless for Apache Spark) subminor runtime versions:

  • 1.2.85
  • 2.2.85
  • 2.3.38

Notes:

  • Apache Spark upgraded to 3.5.3 in 2.2 runtime.

  • Apache Gluten upgraded to 1.6 in 2.3 runtime.

Change

Managed Service for Apache Spark latest image and runtime versions:

  • Configured spark.scheduler.listenerbus.exitTimeout to 30s.

Google Cloud Blog (AI & ML)

Your agentic summer: No-cost lessons from Google experts to build and scale agents

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I’ve talked to developers, IT leaders, and builders who all ask the same question: How do we actually get agents into production? 

The answer isn't theoretical — it's hands-on. Whether it’s designing a system that allows your agents to interact with external data sources while maintaining strict security guardrails or creating self-optimizing supply chain workflows or whatever you can think up, we’ve got you covered.

That’s why we’ve designed a path to help you take your AI ideas from a rough sketch to fully autonomous agents running in production. This summer, you can harness the same frameworks and approaches used by Google experts to build and scale agents — entirely at no cost. 

Powered by Gemini Enterprise Agent Ready (GEAR), these hands-on labs and courses give you the blueprints and tools you need to deploy agents that ship.

 Find your roadmap to future-proof your skills this summer, starting here.

1. Intro to AI Agents: Build a foundational understanding of how autonomous agents can redefine productivity. 

2. Agent Fundamentals: Go under the hood of autonomous intelligence. Learn decision models and execution loops to deploy adaptive agents over rigid automation. 

3. Enterprise Agents and Use Cases: Discover how AI agents drive real business impact. Map agents directly to corporate KPIs, solve operational bottlenecks, and utilize no-code to high-code frameworks.

4. Create Your First Gemini Enterprise Application skill badge: Earn a skill badge that proves you can create an app with Gemini Enterprise. You will master capabilities like deep research agents, multi-agent ideation, and Gemini Notebook for focused analysis.

5. Human-Centered AI: Keep humanity at the core of automation. Learn to strategically balance machine speed with human intuition for successful orchestration. 

6. Agentic Strategy: Discover, Design, and Prototype: Prototype high-impact AI projects with zero code. Leverage Google’s transformation framework, map user journeys and build functional retail prototypes.

7. Orchestrate Multi-Agent Workflows with Gemini Enterprise skill badge: Demonstrate your ability to manage multiple agents powered by Gemini Enterprise with a skill badge. This skill badge shows that you can unify data across first- and third-party sources, develop multimedia marketing materials, and fully automate complex business actions across disjointed systems.

8. Engineer AI Agents with Agent Development Kit (ADK) skill badge: Build production-grade agents using expert developer tools. Earn a skill badge that proves you can perform live search grounding, build structured JSON schemas, and manage ADK pipelines.

9. Add Currency Tools to an Agent Using MCP: Connect your LLMs to external systems in just 20 minutes. Securely bridge agents with live external databases and deploy via CLI.

10. Manage Agent Memory and State: Give your agents a memory. Move beyond single-query replies and use session states with the ADK to build highly personalized, deeply contextual agents.

11. Create Agent Skills with Google: Infuse domain expertise into custom skills. Minimize AI unpredictability and build reusable workflows that optimize agent performance.

12. AgentOps: Operationalize AI Agents on Google Cloud: Harden your prototypes and scale safely to production. Implement observability, proactive monitoring dashboards, and robust CI/CD security.

Test your skills at the summertime Hackathon

Keep moving with agents! The All Things Agentic Hackathon is officially live.

The next leap in AI won't build itself — it needs you. Step up to the challenge with Gemini 3.5 and Google Cloud and deploy autonomous agents that do the heavy lifting in the background. Build what’s next, show the world what you can do, and compete for $180,000 in prizes, cash, and credits

Submissions are open from August 3, 2026 to August 31, 2026. Register here. 

Join GEAR today

Don't wait for the summer to pass you by. Get hands-on with the tools, earn real-world credentials, and build in-demand skills, with confidence.

Ready to level up your agentic skills? Check out our two newest learning paths: Build High-Performance Multi-Agent Systems and Govern and Secure Enterprise Agents. 

Learn more → Join GEAR today and start building.

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