使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
概览
Google Cloud Cortex Framework 提供参考架构、可部署的解决方案和打包的实施服务,可帮助您开始数据和 AI 云之旅。快速设计、构建和部署数据与 AI 解决方案所需的一切。
Cortex Framework 专注于解决特定问题,并为营销、销售、供应链、制造、财务和可持续发展等业务领域提供预建解决方案。如需了解详情,请参阅数据源和工作负载。
Google Cloud 集成
Cortex Framework 基于 Google Cloud 工具构建,可提供统一的环境来管理您的整个数据历程。下图描述了 Cortex Framework 如何使用各种组件来提供统一的平台,以存储、管理和分析来自各种数据源的数据:

图 1。Google Cloud Cortex Framework 技术堆栈。
多个工具负责从各种来源提取、转换和加载 (ETL) 数据到数据库中,以便日后进行可视化和分析。您可以根据业务需求使用以下部分或全部功能:
- 数据存储
- Cloud Storage:用于存储来自其他数据源的数据。
- BigQuery:用于存储和分析大型数据集的无服务器托管数据仓库。Cortex Framework 使用 BigQuery 存储原始数据、转换后的数据和报告数据。
- Secret Manager:用于存储密码、API 密钥和证书等敏感信息的安全存储服务。Cortex Framework 使用 Secret Manager 来保护您的敏感数据,并确保在数据和 AI 项目中以负责任的方式使用这些数据。
- 数据集成和处理
- Cloud Build:可自动构建、测试和部署软件的服务。Cortex Framework 使用 Cloud Build 自定义和部署预构建的解决方案。
- Dataflow:用于构建和运行数据流水线的代管式服务。Cortex Framework 使用 Dataflow 自动执行某些数据注入、转换和加载任务。
- Cloud Composer:用于工作流的托管式编排服务。Cortex Framework 使用 Cloud Composer 来管理和调度复杂的数据流水线。
- 数据转换和分析
- BigQuery:用于在 BigQuery 中构建和部署数据集的无服务器环境。Cortex Framework 使用 BigQuery 进行数据转换。
- Dataproc:用于大规模数据处理的托管式 Hadoop 和 Spark 服务。虽然对于 Cortex Framework 的预建解决方案来说不太常见,但 Dataproc 可用于满足自定义数据处理需求。
- Looker:用于数据探索和可视化的商业智能平台。Cortex Framework 与 Looker 集成,可提供用户友好的信息中心和报告。
- 机器学习和 AI
- Vertex AI:用于构建、训练、部署和管理模型的统一平台。Cortex Framework 可能会在其解决方案中使用预构建的 Vertex AI 组件。
Data Foundation
Cortex Framework 数据基础定义了数据的结构和组织方式,以确保一致性并促进不同应用之间的数据分析。Cortex Framework 通过其数据基础简化了数据管理、精简了开发流程,并为各种业务领域提供了预构建的解决方案。Data Foundation 包含有助于注入、转换和加载数据的工具和服务。
营销、运营和可持续发展等业务领域都可以从 Cortex Framework 中受益。它包含可用于收集、分析和使用数据的预定义工作负载。数据可以来自各种数据源,例如 Salesforce Marketing Cloud、某些 Google 平台(如 Google Ads 和 CM360)、TikTok、Meta、SAP 等。如需了解详情,请参阅数据源和工作负载。
部署
如需了解 Cortex Framework 部署说明,请参阅以下指南:
支持
如有任何请求或问题,您可以在我们的支持渠道中创建新工单,直接与 Cortex Framework 团队联系:
- 前往我们的支持渠道,创建新的支持请求。
- 可选:添加也应接收更新的用户的电子邮件地址。
- 点击创建,向我们的团队提交工单。
如有其他任何问题,请与 Cortex Framework 团队联系。
如需详细了解我们的支持渠道,请查看以下可用资源:
后续步骤
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-08-18。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-08-18。"],[[["\u003cp\u003eGoogle Cloud Cortex Framework offers pre-built solutions and reference architectures to quickly implement data and AI solutions for diverse business areas like Marketing, Sales, Supply Chain, Finance, and Sustainability.\u003c/p\u003e\n"],["\u003cp\u003eCortex Framework integrates various Google Cloud tools, including BigQuery, Dataflow, Cloud Composer, and Vertex AI, to provide a unified environment for managing the entire data lifecycle, from storage and processing to transformation, analysis, and ML.\u003c/p\u003e\n"],["\u003cp\u003eThe framework's Data Foundation defines the structure and organization of data, ensuring consistency and streamlining data analysis across applications, also enabling efficient data management, ingestion, transformation, and loading.\u003c/p\u003e\n"],["\u003cp\u003eCortex Framework supports data ingestion from multiple sources such as Salesforce Marketing Cloud, Google platforms like Ads and CM360, TikTok, Meta, and SAP, helping collect, analyze, and use data across varied business domains.\u003c/p\u003e\n"],["\u003cp\u003eThe deployment process is detailed through guides, including a quickstart demo, and outlines production environment setup from establishing workloads to executing the final deployment, with support contact provided for any requests or issues.\u003c/p\u003e\n"]]],[],null,["# Overview\n========\n\nGoogle Cloud Cortex Framework provides reference architectures, deployable solutions, and\npackaged implementation services to kickstart your\n[Data and AI Cloud](/data-cloud) journey. Everything you need to rapidly\ndesign, build, and deploy data and AI solutions for your business.\n\nCortex Framework focuses on solving specific problems and offers pre built\nsolutions for business areas like Marketing, Sales, Supply Chain, Manufacturing,\nFinance, and Sustainability. For more information, see\n[Data sources and workloads](/cortex/docs/data-sources-and-workloads).\n\n### Google Cloud integration\n\nCortex Framework builds on top of Google Cloud tools to provide a\nunified environment for managing your entire data journey. The following diagram\ndescribes how Cortex Framework uses various\ncomponents to provide a unified platform for storing, managing, and analyzing\ndata from diverse data sources:\n\n[](/static/cortex/docs/images/cortex_architecture.png)\n**Figure 1**. Google Cloud Cortex Framework Technical Stack.\n\nMultiple tools are responsible for extracting, transforming, and\nloading (ETL) data from various sources into the database for later\nvisualization and analysis. Depending on the needs of your business, you\ncan use some of the following:\n\n- **Data Storage**\n - [Cloud Storage](/storage/docs): for storing data from other data sources.\n - [BigQuery](/bigquery/docs): serverless, managed data warehouse for storing and analyzing large datasets. Cortex Framework uses BigQuery for storing raw, transformed, and reporting data.\n - [Secret Manager](/secret-manager/docs/overview): secure storage service for sensitive information like passwords, API keys, and certificates. Cortex Framework uses Secret Manager to protect your sensitive data and ensure its responsible use within your data and AI projects.\n- **Data Integration and Processing**\n - [Cloud Build](/build/docs/overview): service that automates building, testing, and deploying your software. Cortex Framework uses Cloud Build for customizing and deploying pre built solutions.\n - [Dataflow](/dataflow/docs): managed service for building and running data pipelines. Cortex Framework uses Dataflow to automate certain data ingestion, transformation, and loading tasks.\n - [Cloud Composer](/composer/docs): managed orchestration service for workflows. Cortex Framework uses Cloud Composer to manage and schedule complex data pipelines.\n- **Data Transformation and Analysis**\n - [BigQuery](/bigquery/docs): serverless environment for building and deploying datasets within BigQuery. Cortex Framework uses BigQuery for data transformations.\n - [Dataproc](/dataproc/docs): managed Hadoop and Spark service for large-scale data processing. While less common for Cortex Framework's pre built solutions, Dataproc could be used for custom data processing needs.\n - [Looker](/looker/docs): business intelligence platform for data exploration and visualization. Cortex Framework integrates with Looker to provide user-friendly dashboards and reports.\n- **ML and AI**\n - [Vertex AI](/vertex-ai/docs): unified platform for building, training, deploying, and managing models. Cortex Framework might use prebuilt Vertex AI components within its solutions.\n\nData Foundation\n---------------\n\nThe Cortex Framework Data Foundation defines the structure and organization of the data to\nensure consistency and facilitate data analysis across different applications.\nCortex Framework simplifies data management, streamlines development,\nand offers prebuilt solutions for various business domains through its Data Foundation.\nData Foundation incorporates tools and services that help\ningest, transform, and load data.\n\nMarketing, operations, and sustainability are all business areas that can be\nbenefited from Cortex Framework. It includes predefined workloads\nthat can be used to collect, analyze, and use data. Data can come\nfrom various data sources such as Salesforce Marketing Cloud, some Google\nplatforms (like Google Ads and CM360), TikTok, Meta, SAP, and more.\nFor more information, see the [Data sources and workloads](/cortex/docs/data-sources-and-workloads).\n\nDeployment\n----------\n\nFor Cortex Framework deployment instructions, see the following guides:\n\n- **Quickstart Demo** : a [quickstart demo](/cortex/docs/quickstart-demo) to test the Cortex Framework set up process with sample data with in just a few clicks.\n- **Deployment steps** : after reading the [prerequisites](/cortex/docs/deployment-prerequisites) for Cortex Framework Data Foundation deployment, follow the steps for deployment in production environments:\n 1. [Establish workloads](/cortex/docs/deployment-step-one).\n 2. [Clone repository](/cortex/docs/deployment-step-two).\n 3. [Determine integration mechanism](/cortex/docs/deployment-step-three).\n 4. [Set up components](/cortex/docs/deployment-step-four).\n 5. [Configure deployment](/cortex/docs/deployment-step-five).\n 6. [Execute deployment](/cortex/docs/deployment-step-six).\n\nSupport\n-------\n\nIn case of any requests or issues, you can reach out directly to the\nCortex Framework team by creating a new ticket in our support channel:\n\n1. Go to our [support channel](https://issuetracker.google.com/issues/new?component=1702583) to create a new support case.\n2. *Optional*: Add the email addresses of people that should also receive updates.\n3. Submit the ticket to our team by clicking **Create**.\n\nFor all other questions, contact the\n[Cortex Framework team](mailto:cortex-support@google.com).\n\nFor more information about our support channel, take a look at the available\nresources:\n\n- [User's guide](https://developers.google.com/issue-tracker)\n- [FAQ](https://developers.google.com/issue-tracker/references/faq)\n- [Terms of Service](https://developers.google.com/issue-tracker/#terms_of_service)\n\nWhat's next?\n------------\n\n- See [Cortex Framework Data Foundation](https://github.com/GoogleCloudPlatform/cortex-data-foundation) repository in GitHub.\n- For information about data sources and workloads available in Cortex Framework, see [Data sources and workloads](/cortex/docs/data-sources-and-workloads)."]]