[DEPRECATED] Building Advanced Codeless Pipelines on Cloud Data Fusion
Earned Apr 6, 2022 EDT
设置 Google Cloud 网络
Earned Apr 4, 2022 EDT
Google Developer Essentials
Earned Apr 4, 2022 EDT
DEPRECATED IoT in the Google Cloud
Earned Apr 3, 2022 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned Apr 2, 2022 EDT
用 Document AI 实现大规模自动数据采集
Earned Apr 2, 2022 EDT
DEPRECATED Anthos Service Mesh
Earned Apr 2, 2022 EDT
VM Migration
Earned Apr 1, 2022 EDT
DEPRECATED Network Performance and Optimization
Earned Mar 30, 2022 EDT
Baseline: Deploy & Develop
Earned Mar 30, 2022 EDT
云工程
Earned Mar 29, 2022 EDT
[DEPRECATED] Deploying Applications
Earned Mar 29, 2022 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Mar 28, 2022 EDT
面向机器学习的 BigQuery
Earned Mar 28, 2022 EDT
Managing Cloud Infrastructure with Terraform
Earned Mar 28, 2022 EDT
基准：基础架构
Earned Mar 26, 2022 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Mar 26, 2022 EDT
DEPRECATED Google Cloud's Operations Suite
Earned Mar 25, 2022 EDT
在 Google Cloud 中使用 Kubernetes
Earned Mar 25, 2022 EDT
DEPRECATED Cloud Architecture
Earned Mar 23, 2022 EDT
在 Cloud Data Fusion 上构建无代码流水线
Earned Feb 24, 2022 EST
Getting Started with Apache Kafka and Confluent Platform on Google Cloud
Earned Jan 29, 2022 EST
Deprecated Kubernetes Solutions
Earned Jan 19, 2022 EST
DEPRECATED Windows on Google Cloud
Earned Jan 17, 2022 EST
基准：数据、机器学习和 AI
Earned Jan 17, 2022 EST
Understanding LookML in Looker
Earned Jan 16, 2022 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Jan 16, 2022 EST
Sports Analytics: Pitch Perfect BigQuery
Earned Jan 15, 2022 EST
用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL
Earned Jan 14, 2022 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned Jan 14, 2022 EST
Google Cloud 基础知识：核心基础设施
Earned Jan 13, 2022 EST
Scientific Data Processing
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DEPRECATED Applied Data: Blockchain
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Data Science on Google Cloud
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DEPRECATED BigQuery for Marketing Analysts
Earned Jan 10, 2022 EST
Serverless Data Processing with Dataflow: Foundations
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Google Cloud Big Data and Machine Learning Fundamentals
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Preparing for your Professional Data Engineer Journey
Earned Jan 8, 2022 EST
DEPRECATED Create Conversational AI Agents with Dialogflow CX
Earned Dec 11, 2021 EST
DEPRECATED Application Development - Python
Earned Jul 27, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Jul 24, 2020 EDT
DEPRECATED BigQuery for Data Analysis
Earned Jul 24, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Jul 24, 2020 EDT
Data Catalog Fundamentals
Earned Jul 24, 2020 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jul 24, 2020 EDT
[DEPRECATED] Data Engineering
Earned Jul 24, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Jul 23, 2020 EDT
Cloud SQL
Earned Jul 22, 2020 EDT
为 Compute Engine 实现云负载均衡
Earned Jul 22, 2020 EDT
Google Cloud 基础知识
Earned Jul 22, 2020 EDT
本课程探讨 BigQuery 中用于减轻 AI 幻觉的检索增强生成 (RAG) 解决方案。BigQuery 引入了 RAG 工作流，其中涵盖了创建嵌入、搜索向量空间和生成更优质的回答。本课程解释了这些步骤背后的概念原理，以及这些步骤在 BigQuery 中的实际实施过程。学完本课程后，学员将能够使用 BigQuery 和生成式 AI 模型（如 Gemini）以及嵌入模型来构建 RAG 流水线，以解决在具体情况下遇到的 AI 幻觉问题。
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本课程能让机器学习从业者掌握评估生成式和预测式 AI 模型的基本工具、方法和最佳实践。要确保机器学习系统在实际运用中提供可靠、准确、高效的结果，做好模型评估至关重要。
学员将深入了解各项评估指标、方法及如何在不同模型类型和任务中适当应用这些指标和方法。课程将着重介绍生成式 AI 模型带来的独特挑战，并提供有效解决这些挑战的策略。通过利用 Google Cloud 的 Vertex AI Platform，学员可学习如何在模型选择、优化和持续监控工作中实施卓有成效的评估流程。
此课程将探索如何使用 AI 功能套件 Gemini in BigQuery 为“数据到 AI”工作流提供助力。其中涉及到的功能包括数据探索和准备、代码生成和问题排查，以及工作流发现和可视化。此课程包含概念解释、真实使用场景以及实操实验等内容，可帮助数据从业者提升效率并加快流水线开发速度。
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Digital transformation is a critical journey for modern organizations, and establishing a strong baseline in cloud computing is the first step toward driving meaningful innovation. Digital Transformation with Google Cloud introduces the core technologies and strategic frameworks that help organizations modernize their operations. This course explores fundamental cloud concepts, global network infrastructure, and the shared responsibility model to help leaders navigate their path to the cloud with confidence.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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本课程回顾了 Model Armor 的基本安全功能，并让您能够使用该服务。您将了解与 LLM 相关的安全风险，以及 Model Armor 如何保护您的 AI 应用。
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Build AI agents that can leverage enterprise databases using the MCP Toolbox for Databases. You will define secure database interaction tools, and implement intelligent querying capabilities (leveraging vector embeddings, structured queries).
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在本课程中，您将学习如何使用 Google 智能体开发套件构建复杂的多智能体系统。您将构建搭载工具的智能体，利用父子层级关系和工作流进行连接，以此定义它们的交互方式。您将在本地运行智能体，将其部署到 Vertex AI Agent Engine 并作为托管式智能体流运行，基础设施决策和资源扩缩则由 Agent Engine 处理。请注意，这些实验基于此产品的预发布版本。在进行维护更新时，这些实验可能会出现一些延迟。
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在本课程中，您将学习如何使用 Google 的可移植 UI 工具包 Flutter 来开发应用，并将开发的应用与 Google 的生成式 AI 模型家族 Gemini 相集成。您还将练习使用 Vertex AI Agent Builder，这是 Google 为构建和管理 AI 智能体及应用而提供的平台。
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AI 推理是指利用经过训练的机器学习模型，通过应用其学习到的模式，对新的、未见过的数据进行预测的过程。本课程专为有兴趣在 Cloud Run 上快速部署 AI 推理服务的开发者、数据科学家和机器学习工程师而设计。对于熟悉基于云的无服务器应用部署解决方案，但可能没有使用 Google Cloud 无服务器产品运行 AI 推理经验的学员来说，本课程非常有用。
本课程包含使用 GPU 部署 AI 推理模型，以及将生成式 AI 应用与数据存储服务集成的示例。
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通过使用生成式 AI，提升网站导航体验，从而为您的用户提供更好的搜索体验。在本课程中，您将学习如何通过 Vertex AI Search 为您的网站用户提供生成式搜索体验，使他们能够发现网站提供的内容。作为网站编辑者，您还将学习如何使用生成式 AI 快速且高效地翻译内容，并根据建议对内容进行改进。
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生成式 AI 应用可以提供大语言模型 (LLM) 问世前几乎不可能实现的全新用户体验。作为应用开发者，您要如何利用生成式 AI 在 Google Cloud 上构建更具吸引力且功能强大的应用？
在本课程中，您将了解生成式 AI 应用，以及如何利用提示设计和检索增强生成 (RAG) 技术，构建使用 LLM 的强大应用。您将了解可用于生产用途且适合生成式 AI 应用的架构，并构建一个基于 LLM 和 RAG 的聊天应用。
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在本课程中，您将了解 Gemini（Google Cloud 推出的一款依托生成式 AI 的协作工具）如何帮助您使用 Google 产品和服务开发、测试、部署和管理应用。在 Gemini 的协助下，您可以学习如何开发和构建 Web 应用、修复应用中的错误、开发测试和查询数据。您可以通过实操实验了解如何利用 Gemini 来改进软件开发生命周期 (SDLC)。
Duet AI 已更名为 Gemini，这是我们的新一代模型。
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完成“开始用 Gemini Code Assist 进行应用开发”课程，即可获得技能徽章。在本课程中，您将学习如何利用 Google AI 编码助理的强大功能。
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AI 智能体代表着超越传统大语言模型 (LLM) 的重大转变：AI 智能体不再仅仅只是生成基于文本的解决方案，更能自主行动来执行这些方案。
本课程将介绍 AI 智能体的基础知识、AI 智能体与 LLM API 的区别，以及 AI 智能体在现实世界中的价值所在。本课程基于 Google 的智能体白皮书，将为您提供必要的理论基础知识，以助您编写首行智能体代码 — 非常适合希望从自主、目标导向行为（而不仅仅是文本生成）的角度理解 AI 系统的开发者、架构师和技术决策者。
加入社区论坛，提出问题并参与讨论。
本课程专为各个级别的开发者打造，您将学到 Gemini Code Assist 的核心特性和功能。Gemini Code Assist 依托 AI 技术，可协助您在 Google Cloud 上进行应用开发。从智能代码建议、自动补全、实时错误检测到重构辅助，您将发现 Gemini Code Assist 如何显著提升开发效率和代码质量，帮助您节省宝贵时间，专注于更具价值和趣味性的任务。
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In this course, you will learn how to centralize diverse sources like PDFs, web pages, and even audio files into a single, intelligent workspace. You will learn to chat with your documents to find specific information, generate instant summaries, and verify answers with AI-powered citations.
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本课程全面概述了 Google Cloud 的智能体平台，包括 Vertex AI Agent Builder、Gemini Enterprise、Conversational Agents 和智能体开发套件。学员将了解每项产品的独特功能，区分具体应用场景的最佳解决方案，并获得有关创建搜索和聊天应用的基础知识。
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了解 AI 智能体如何为业务带来实际影响。您将把智能体类型关联到您的关键绩效指标 (KPI)，并探索能够解决实际瓶颈的用例。然后，您将了解 Gemini Enterprise 如何帮助您构建和编排合适的智能体，其范围涵盖从无代码到高代码的各种解决方案。
在本课程中，您将了解 Google Cloud 中依托生成式 AI 技术的协作工具 Gemini 如何帮助开发者构建应用。您将学习如何向 Gemini 输入提示，让其为您解释代码、推荐 Google Cloud 服务并为您的应用生成代码。您将通过实操实验体验 Gemini 对应用开发工作流的改进作用。
Duet AI 已更名为 Gemini，这是我们的新一代模型。
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完成“利用氛围编程和 MCP 构建 Cloud 智能应用”课程，即可获得技能徽章。在本课程中，您将学习如何利用 Google 的 AI 编码助理和 MCP 服务器的强大功能。
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使用 Google 的智能体开发套件 (ADK) 构建、配置和运行您的第一个 AI 智能体，将您对智能体的理解转化为实际应用。
在本实操课程中，您将设置一个完整的 ADK 开发环境，使用 Python 代码和 YAML 配置两种方式创建智能体，并通过多个界面运行智能体。您还将学习定义智能体行为的核心参数，将您在课程 1 中学到的知识应用到实际代码中。
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This structured course is for developers interested in building intelligent agents using the Agent Development Kit (ADK). It combines hands-on experience, core concepts, and practical application, to provide a comprehensive guide to using ADK. You can also join our community of Google Cloud experts and peers to ask questions, collaborate on answers, and connect with the Googlers making the products you use every day.
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This course is for developers interested in learning how to use TPUs for inference—from architecture to deployment, and how to solve common implementation challenges.
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This course is designed for developers looking to build an optimized AI inference stack on Google Cloud. Whether you’re working with GPUs or TPUs, you’ll explore the fundamental components of an inference stack, learn design principles for maximizing performance and reliability, and explore practical techniques to take your workloads from 0 to 1.
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In this course, you learn to analyze and choose the right database for your needs, to effectively develop applications on Google Cloud. You explore relational and NoSQL databases, dive into Cloud SQL, AlloyDB, and Spanner, and learn how to align database strengths with your application requirements, including those of generative AI. Gain hands-on experience configuring Vector Search and migrating applications to the cloud.
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This Databases course consists of a series of advanced-level labs designed to validate your proficiency in migrating and managing Google Cloud databases. Each lab presents a set of the required tasks that you must complete with minimal assistance. The labs in this course have replaced the previous L300 Data Management Challenge Lab. If you have already completed the Challenge Lab as part of your L300 accreditation requirement, it will be carried over and count towards your L300 status. You must score 80% or higher for each lab to complete this course, and fulfill your CEPF L300 Database requirement. For technical issues with a Challenge Lab, please raise a Buganizer ticket using this CEPF Buganizer template: go/cepfl300labsupport
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Gemini Enterprise 结合了 Google 在搜索和 AI 领域的专长。它是一款强大的工具，让员工只需通过一个搜索栏，就能从文档库、邮件、聊天消息、工单系统及其他数据源中查找具体信息。Gemini Enterprise 助理还能帮助进行头脑风暴、开展研究、生成文档大纲并执行其他操作，比如邀请同事参加某个日历活动。因此它能加快知识型工作的进度并提升协作效率。（请注意，Gemini Enterprise 以前称为 Google Agentspace，本课程中可能会提及以前的产品名称。）
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The learning path offers a deep dive into Google Cloud's data processing solutions, including:
Dataflow
Pub/Sub
Managed Service for Apache Kafka
BigQuery Engine for Apache Flink
You'll learn how to leverage these tools to build, deploy, and troubleshoot efficient and scalable data pipelines for both batch and streaming data processing needs.
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本课程介绍了 AI 可解释性和透明度的相关概念，探讨了 AI 透明度对于开发者和工程师的重要性。同时探索了有助于在数据和 AI 模型中实现可解释性和透明度的实用方法及工具。
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本课程致力于为您提供所需的知识和工具，让您能够了解 MLOps 团队在部署和管理生成式 AI 模型以及探索 Vertex AI 如何帮助 AI 团队简化 MLOps 流程时面临的独特挑战，并帮助您在生成式 AI 项目中取得成功。
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本课程介绍了 Responsible AI 的概念和 AI 原则，还介绍了在 AI/机器学习实践中识别公平性与偏见以及减少偏见的实用技巧，同时探索了使用 Google Cloud 产品和开源工具来实施 Responsible AI 最佳实践的实用方法和工具。
在本次课程中，探索 AI 赋能的搜索技术、工具和应用。学习利用向量嵌入的语义搜索、融合语义和关键字的混合搜索方法，以及检索增强生成 (RAG) 技术，以打造基于事实的 AI 智能体，尽可能减少 AI 幻觉。获取 Vertex AI Vector Search 实战经验，打造您自己的智能搜索引擎。
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This learner pack introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Goals
Identify the purpose and value of Google Cloud Data Platform
Learn about batch and streaming data pipelines
Build data lake and data warehouse
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
Brought to you by the CLS Tech Specialization Team (gcc-enablement-tech@). Share your request/feedback on go/learningpacks-feedback!
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随着企业对人工智能和机器学习的应用越来越广泛，以负责任的方式构建这些技术也变得更加重要。但对很多企业而言，真正践行 Responsible AI 并非易事。如果您有意了解如何在组织内践行 Responsible AI，本课程正适合您。
本课程将介绍 Google Cloud 目前如何践行 Responsible AI，以及从中总结的最佳实践和经验教训，便于您以此为框架构建自己的 Responsible AI 方法。
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完成 Introduction to Generative AI、Introduction to Large Language Models 和 Introduction to Responsible AI 三门课程，赢取技能徽章。通过最终测验，即表明您理解了生成式 AI 的基本概念。
技能徽章是由 Google Cloud 颁发的数字徽章，旨在认可您对 Google Cloud 产品与服务的了解程度。公开您的个人资料并将技能徽章添加到您的社交媒体个人资料中，以此来分享您获得的成就。
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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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完成入门技能徽章课程使用 Knowledge Catalog 构建数据网格，展示以下方面的技能：使用 Knowledge Catalog 构建数据网格， 以在 Google Cloud 上实现数据安全、治理和发现。您将在 Knowledge Catalog 中练习和测试自己在标记资产、分配 IAM 角色和评估数据质量方面的技能。
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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Managed Service for Apache Spark 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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MongoDB Atlas provided customers a fully managed, database-as-a-service on Google’s data cloud that is unmatched in speed, scale, and security—all with AI built in. Modern database systems, including MongoDB, have been a big step forward—giving businesses a more flexible, scalable, and developer-friendly alternative to legacy relational databases. But there is an even bigger payoff with a solution such as MongoDB Atlas a fully managed, database-as-a-service (DBaaS) offering. It is an approach that gives businesses all of the advantages of a modern, scalable, highly available database, while freeing IT to focus on high-value activities.
In this course you will learn about Cloud Spanner. You will get an introduction to Cloud Spanner, contrasting it with other Database products to understand when and how to use Spanner to solve your relational database needs at scale. You will learn how to create and manage Spanner databases using various tools on Google Cloud, learn to optimize relational schemas with Spanner’s distributed database model in mind, interact with your Spanner databases using the Spanner APIs, integrate Spanner with your applications, and learn how to use other Google tools for administering Spanner databases and managing your data.
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This learning pack is designed to have hands-on experience on Google Cloud data solutions.
Goals
Plan, execute, test, and monitor simple and complex enterprise database migrations to Google Cloud
Choose an appropriate Google Cloud database, migrate SQL Server databases and run Oracle databases on Google Cloud bare metal
Recognize and overcome the challenges of moving data to prevent data loss, preserve data integrity, and minimize downtime
Evaluate on-premises database architectures and plan migrations to make the business case for moving databases to Google Cloud
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
Brought to you by the CLS Tech Specialization Team (gcc-enablement-tech@). Share your request/feedback on go/learningpacks-feedback!
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This learning pack is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
Goals
Plan, execute, test, and monitor simple and complex enterprise database migrations to Google Cloud
Choose an appropriate Google Cloud database, migrate SQL Server databases and run Oracle databases on Google Cloud bare metal
Recognize and overcome the challenges of moving data to prevent data loss, preserve data integrity, and minimize downtime
Evaluate on-premises database architectures and plan migrations to make the business case for moving databases to Google Cloud
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
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The Google Cloud Rapid Migration & Modernization Program (RaMP) is a holistic, end-to-end migration/modernization program that helps customers & partners leverage expertise and best practices, lower risk, control costs, and simplify a customer's path to cloud success. This course will give an overview of the program and some of the tools and best practices available to support customer migrations & modernizations.
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Cloud technology on its own only provides a fraction of the true value to a business; When combined with data–lots and lots of it–it has the power to truly unlock value and create new experiences for customers.
In this course, you'll learn what data is, historical ways companies have used it to make decisions, and why it is so critical for machine learning. This course also introduces learners to technical concepts such as structured and unstructured data. database, data warehouse, and data lakes. It then covers the most common and fastest growing Google Cloud products around data.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。
这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
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This learner pack introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Goals
Identify the purpose and value of Google Cloud Data Platform
Learn about batch and streaming data pipelines
Build data lake and data warehouse
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
Brought to you by the CLS Tech Specialization Team (gcc-enablement-tech@). Share your request/feedback on go/learningpacks-feedback!
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This learning pack is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
Goals
Plan, execute, test, and monitor simple and complex enterprise database migrations to Google Cloud
Choose an appropriate Google Cloud database, migrate SQL Server databases and run Oracle databases on Google Cloud bare metal
Recognize and overcome the challenges of moving data to prevent data loss, preserve data integrity, and minimize downtime
Evaluate on-premises database architectures and plan migrations to make the business case for moving databases to Google Cloud
You can find all of our technical learning packs on go/techlearningpacks and industry learning packs on go/industrylearningpacks.
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完成“创建和管理 Cloud SQL for PostgreSQL 实例”这一入门级的技能徽章课程，展示您在以下方面的技能： 迁移、配置和管理 Cloud SQL for PostgreSQL 实例及数据库。
It’s no secret today that data is growing rapidly and considered the most critical asset of any organization. NetApp and Google Cloud play an instrumental role in enabling you to optimally store, protect and govern your data. With NetApp Cloud Manager and NetApp Cloud Volumes ONTAP data storage technology that utilizes Google Cloud compute, storage and networking infrastructure, you can easily manage storage operations and meet the requirements of any workload. In this course, you get hands-on practice on using NetApp Cloud Manager and Cloud Volumes ONTAP and learn about the capabilities delivered such as multi-protocol data access, built-in storage efficiencies and data protection features, remote caching and more.
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Flex your Google Clout! Each week unlocks a new cloud puzzle. How fast can you find the solution? Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
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In this quest you will get hands-on experience writing infrastructure as code with Terraform.
Flex your Google Clout! Each day unlocks a new cloud puzzle. Complete all five and you’ll earn the inaugural Google Cloud badge! Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
Earn a skill badge by completing the Explore Machine Learning Models with Explainable AI quest, where you will learn how to do the following using Explainable AI: build and deploy a model to an AI platform for serving (prediction), use the What-If Tool with an image recognition model, identify bias in mortgage data using the What-If Tool, and compare models using the What-If Tool to identify potential bias. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest and the final assessment challenge lab to receive a skill badge that you can share with your network.
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Google Cloud 结算和费用管理基础知识系列课程包含两部分内容， 本课是第二课。本课程最适合担任财务和/或 IT 职务 且负责优化所在组织的云基础设施的人员。 在这门课程中，您将学习多种控制和优化 Google Cloud 成本的方式， 包括设置预算和提醒、管理配额限制以及充分利用 承诺使用折扣。在实操实验中，您将练习使用各种 工具控制和优化 Google Cloud 成本，或者影响技术 团队应用成本优化最佳实践。
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完成入门级技能徽章课程“从 BigQuery 数据中挖掘数据洞见”，展示您在以下方面的技能： 编写 SQL 查询、查询公共表、将示例数据加载到 BigQuery 中、 在 BigQuery 中使用查询验证器排查常见的语法错误，以及通过连接到 BigQuery 数据在 Data Studio 中 创建报告。
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Many traditional enterprises use legacy systems and apps that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems and investing in new products and services. This course explores these challenges and offers solutions to overcome them by using cloud technology.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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完成用 Google Data Cloud 共享数据技能徽章课程，赢取技能 徽章。您将获得使用 Google Cloud 数据共享合作伙伴 的实操经验，这些合作伙伴拥有专有数据集， 客户可将其用于自己的分析应用场景。客户订阅这些数据集，可在自己的 平台上查询，然后使用自己的数据集加以扩充，并使用自己的可视化 工具，用于面向客户的信息中心。
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This quest introduces you to Vault and teaches you how to secure, store, and tightly control access to tokens, passwords, certificates, and encryption keys to protect secrets and other sensitive data.
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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets.
This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.
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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps.
We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views.
After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.
Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.
After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
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Cloud Logging is a fully managed service that performs at scale. It can ingest application and system log data from thousands of VMs and, even better, analyze all that log data in real time. In this fundamental-level Quest, you learn how to store, search, analyze, monitor, and alert on log data and events from Google Cloud. The labs in the Quest give you hands-on practice using Cloud Logging to maximize your learning experience and provide insight on how you can use Cloud Logging to your own Google Cloud environment.
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完成在 Google Cloud 上使用 Machine Learning API 课程，赢取高级技能徽章。 在本课程中，您将了解以下机器学习和 AI 技术的基本功能： Cloud Vision API、Cloud Translation API 和 Cloud Natural Language API。
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Cloud technology is a powerful asset, and when paired with data, it becomes a catalyst for innovation and enhanced customer experiences. Exploring Data Transformation with Google Cloud examines how organizations can leverage the cloud to make their data more accessible, actionable, and valuable.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.
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Workspace is Google's collaborative applications platform, delivered from Google Cloud. In this introductory-level course you will get hands-on practice with Workspace’s core applications from a user perspective. Although there are many more applications and tool components to Workspace than are covered here, you will get experience with the primary apps: Gmail, Calendar, Sheets and a handful of others. Each lab can be completed in 10-15 minutes, but extra time is provided to allow self-directed free exploration of the applications.
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This course demonstrates the power of integrating Google Cloud services and tools with Workspace applications - like using Node.js to build a survey bot, the Natural Language API to recognize sentiment in a Google Doc, and building a chat bot with Apps Script.
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Twelve years ago Lily started the Pet Theory chain of veterinary clinics, and has been expanding rapidly. Now, Pet Theory is experiencing some growing pains: their appointment scheduling system is not able to handle the increased load, customers aren't receiving lab results reliably through email and text, and veteranerians are spending more time with insurance companies than with their patients. Lily wants to build a cloud-based system that scales better than the legacy solution and doesn't require lots of ongoing maintenance. The team has decided to go with serverless technology. For the labs in the Google Cloud Run Serverless Quest, you will read through a fictitious business scenario in each lab and assist the characters in implementing a serverless solution. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google…
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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.
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This is the second of two Quests of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.
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通过 DevOps 获得 竞争优势。DevOps 是一种组织和文化运动，旨在加快软件交付速度， 提高服务可靠性，并在软件利益相关方之间 建立共享所有权。在本课程中，您将学习如何使用 Google Cloud 提高 软件交付能力，确保较高的速度、稳定性、可用性和安全性。 DevOps Research and Assessment 已加入 Google Cloud。您的团队表现如何？参加 这个包含 5 道选择题的测验，来一探究竟吧！
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The Data Lake Modernization course aims to prepare you to lead a Data Lake Modernization engagement through discovery & qualification through the technical considerations & cost modelling. The training is designed to educate on the Migration Journey, Data Lifecycle, Costing & Hands on Technical execution. At the end of the training you will have a deeper understanding of the Data Lake ecosystem, modernizing and migrating to GCP and hands-on experience of building data ingestion, processing & analytics pipelines on GCP.
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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.
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In this course, you will receive technical training for Enterprise Data Warehouses solutions using BigQuery based on the best practices developed internally by Google’s technical sales and services organizations. The course will also provide guidance and training on key technical challenges that can arise when migrating existing Enterprise Data Warehouses and ETL pipelines to Google Cloud. You will get hands-on experience with real migration tasks, such as data migration, schema optimization, and SQL Query conversion and optimization. The course will also cover key aspects of ETL pipeline migration to Dataproc as well as using Pub/Sub, Dataflow, and Cloud Data Fusion, giving you hands-on experience using all of these tools for Data Warehouse ETL pipelines.
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This content is deprecated. Please see the latest version of the course, here.
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This course focuses on how you can bring your on-premises data lakes and workloads to Google Cloud to unlock cost savings and scale.
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This course further explores SQL Server on Google Cloud.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.
完成中级技能徽章课程通过 BigQuery ML 创建机器学习模型，展示您在以下方面的技能： 使用 BigQuery ML 创建和评估机器学习模型，以执行数据预测。
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安全是 Google Cloud 服务绝不妥协的核心原则，为此， Google Cloud 开发了特定工具，为您的所有项目提供 安全与身份保障。本入门课程中，您将了解 用于管理用户和虚拟机账号的 Google Cloud Identity and Access Management (IAM) 服务 并进行实操练习。您还将通过 配置 VPC 和 VPN 获取网络安全方面的实践经验，并了解有哪些工具可用于 抵御安全威胁和防止数据泄露。
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完成构建安全的 Google Cloud 网络课程，赢取技能徽章。在此课程中，您将了解与网络有关的众多 资源，以便在 Google Cloud 上构建、扩缩和保护自己的应用。
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完成使用 Google Cloud Managed Service for Prometheus 来监控环境这一技能徽章课程，赢取技能徽章。在此课程中，您将学习如何使用 Google Cloud Managed Service for Prometheus 监控 Kubernetes。
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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services.
This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
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This advanced-level Quest builds on its predecessor Quest, and offers hands-on practice on the more advanced data integration features available in Cloud Data Fusion, while sharing best practices to build more robust, reusable, dynamic pipelines. Learners get to try out the data lineage feature as well to derive interesting insights into their data’s history.
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完成设置 Google Cloud 网络课程，赢取技能徽章， 您将了解如何在 Google Cloud Platform 上执行基本的网络组建和管理任务 - 创建自定义网络、添加子网防火墙规则，然后创建虚拟机并测试 虚拟机之间相互通信时的延迟时间。
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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.
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In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.
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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.
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完成用 Document AI 实现大规模自动数据采集课程，赢取入门级技能徽章。在本课程中，您将学习如何使用 Document AI 提取、处理和采集数据。
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This intermediate-level quest is unique among Qwiklabs quests. These labs have been curated to give operators hands-on practice with Anthos—a new, open application modernization platform on Google Cloud. Anthos enables you to build and manage modern hybrid applications. Tasks include: installing service mesh, collecting telemetry, and securing your microservices with service mesh policies. This quest is composed of labs targeted to teach you everything you need to know to introduce service mesh, and Anthos, into your next hybrid cloud project.
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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.
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If you want to take your Google Cloud networking skills to the next level, look no further. This course is composed of labs that cover real-life use cases and it will teach you best practices for overcoming common networking bottlenecks. From getting hands-on practice with testing and improving network performance, to integrating high-throughput VPNs and networking tiers, Network Performance and Optimization is an essential course for Google Cloud developers who are looking to double down on application speed and robustness.
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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.
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在众多课程中，本入门课程独具特色。 这些实验经过精心设计，旨在让 IT 专业人员通过实践掌握 Google Cloud 认证 Associate Cloud Engineer 考核中的各项主题和服务内容。从 IAM 到网络组建和管理， 再到 Kubernetes Engine 部署，本课程将通过特定实验 检验您的 Google Cloud 知识掌握情况。请注意，虽然这些实操 实验有助于提升您的技能和能力，我们仍建议您同时查阅 考试指南和其他可用的备考资源。
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The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with Google Cloud.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.
Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This course will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Automate Deployment and Manage Traffic on a Google Cloud Network will give you the practical experience needed so you can start building robust networks right away.
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Learn the ins and outs of Google Cloud's operations suite, an important service for generating insights into the health of your applications. It provides a wealth of information in application monitoring, report logging, and diagnoses. These labs will give you hands-on practice with and will teach you how to monitor virtual machines, generate logs and alerts, and create custom metrics for application data. It is recommended that the students have at least earned a Badge by completing the Google Cloud Essentials. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this course, enroll in and finish the challenge lab at the end of the Monitor and Log with Google Cloud Operations Suite to receive an exclusive Google Cloud digital badge.
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Kubernetes 是最受欢迎的容器编排系统， Google Kubernetes Engine 专为支持 Google Cloud 中的托管式 Kubernetes 部署 而设计。在本高级课程中，您将亲自动手配置 Docker 映像、容器，并部署功能完备的 Kubernetes Engine 应用。 此课程将帮助您掌握在工作流中集成容器编排所需的 实用技能。 想要参加实操实验室挑战赛， 展示您的技能并检验所学知识？完成本课程后，不妨继续参与这项额外的 实验室挑战赛，赢得 Google Cloud 专属数字徽章。 该挑战赛位于在 Google Cloud 上部署 Kubernetes 应用课程的结尾处。
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This fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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本课程提供 Cloud Data Fusion 的实操练习。Cloud Data Fusion 是云原生的 无代码数据集成平台。受益于此，ETL 开发者、数据工程师和分析师 可以利用预构建的转换工具和连接器，轻松构建和 部署流水线，而无需操心编码问题。本课程首先提供了 一个快速入门实验，让学习者熟悉 Cloud Data Fusion 界面。接下来， 学员将尝试运行批处理和实时流水线，并使用内置的 Wrangler 插件对数据执行一些有趣的转换。
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Organizations around the world rely on Apache Kafka to integrate existing systems in real time and build a new class of event streaming applications that unlock new business opportunities. Google and Confluent are in a partnership to deliver the best event streaming service based on Apache Kafka and to build event driven applications and big data pipelines on Google Cloud Platform. In this course, you will first learn how to deploy and create a streaming data pipeline with Apache Kafka, then try out the different functionalities of the Confluent Platform.
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Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 8 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
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Google Cloud is committed to supporting Windows workloads in its frameworks and services. In this advanced-level quest, you will get hands-on practice running many of the popular Windows services on Google Cloud. For example, you will learn how to instantiate Microsoft SQL databases, cloud tools for Powershell on Google Cloud Platform frameworks.
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大数据、机器学习和人工智能是当今计算领域的热门话题， 但这些领域的专业性很强，因而很难找到 入门资料。幸运的是，Google Cloud 在这些领域提供了方便用户使用的服务， 通过本入门级课程，您可以 开始学习使用 BigQuery、Cloud Speech API 和 Video Intelligence 等工具。
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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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In this introductory level Quest you will gain practical experience on the fundamentals of sports data science using BigQuery. Start your journey by creating a soccer dataset in BigQuery by importing CSV and JSON files. Harness the power of BigQuery with sophisticated SQL analytical concepts, including using BigQuery ML to train an expected goals model on the soccer event data and evaluate the impressiveness of World Cup goals.
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完成用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL 这一入门级的技能徽章课程，展示您在以下方面的技能： 使用 Database Migration Service 中提供的不同作业类型和连接选项，将 MySQL 数据迁移到 Cloud SQL； 以及在运行 Database Migration Service 作业时 迁移 MySQL 用户数据。
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This course offers hands-on practice with migrating MySQL data to Cloud SQL using Database Migration Service. You start with an introductory lab that briefly reviews how to get started with Cloud SQL for MySQL, including how to connect to Cloud SQL instances using the Cloud Console. Then, you continue with two labs focused on migrating MySQL databases to Cloud SQL using different job types and connectivity options available in Database Migration Service. The course ends with a lab on migrating MySQL user data when running Database Migration Service jobs.
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“Google Cloud 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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Earn a skill badge by completing the Create Conversational AI Agents with Dialogflow CX quest, where you will learn how to create a conversational virtual agent, including how to: define intents and entities, use versions and environments, create conversational branching, and use IVR features. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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Want to learn the core SQL and visualization skills of a Data Analyst? Interested in how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.
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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.
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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。
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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
使用 BigQuery 创建嵌入、向量搜索和 RAG
Earned May 18, 2026 EDT
利用 Vertex AI 实现机器学习运维 (MLOps)：模型评估
Earned May 18, 2026 EDT
利用 Gemini CLI 加快应用开发速度
Earned May 18, 2026 EDT
使用 BigQuery 机器学习推理功能
Earned May 1, 2026 EDT
面向数据科学家和分析师的 Gemini
Earned May 1, 2026 EDT
使用 Gemini 和 Streamlit 开发生成式 AI 应用
Earned Apr 17, 2026 EDT
部署多智能体架构
Earned Apr 15, 2026 EDT
使用 Gemini in BigQuery 提高效率
Earned Apr 9, 2026 EDT
Digital Transformation with Google Cloud
Earned Apr 8, 2026 EDT
Model Armor：保障 AI 部署安全
Earned Apr 8, 2026 EDT
Build AI Agents with Enterprise Databases
Earned Apr 7, 2026 EDT
使用智能体开发套件 (ADK) 和 Agent Engine 部署多智能体系统
Earned Apr 7, 2026 EDT
使用 Vertex AI 和 Flutter 构建生成式 AI 智能体
Earned Mar 27, 2026 EDT
利用 Cloud Run 部署和扩缩 AI 模型
Earned Mar 27, 2026 EDT
在 Google Cloud 上使用生成式 AI 进行网站现代化改造
Earned Mar 27, 2026 EDT
在 Google Cloud 上创建生成式 AI 应用
Earned Mar 26, 2026 EDT
适用于端到端 SDLC 的 Gemini
Earned Mar 26, 2026 EDT
开始用 Gemini Code Assist 进行应用开发
Earned Mar 24, 2026 EDT
智能体基础知识
Earned Mar 23, 2026 EDT
为组织配置 Gemini Code Assist
Earned Mar 20, 2026 EDT
借助 Gemini Code Assist 简化应用开发
Earned Mar 19, 2026 EDT
Unlock Insights with NotebookLM
Earned Mar 19, 2026 EDT
了解 Google Cloud 智能体
Earned Mar 13, 2026 EDT
企业智能体及其应用场景
Earned Mar 11, 2026 EDT
打造您的首个 Gemini Enterprise 应用
Earned Mar 11, 2026 EDT
Gemini Enterprise 简介
Earned Mar 11, 2026 EDT
适用于应用开发者的 Gemini
Earned Mar 10, 2026 EDT
利用氛围编程和 MCP 构建 Cloud 智能应用
Earned Mar 10, 2026 EDT
使用智能体开发套件 (ADK) 打造您的首个智能体
Earned Mar 10, 2026 EDT
Build intelligent agents with Agent Development Kit (ADK)
Earned Feb 24, 2026 EST
How to Use TPUs for Inference
Earned Jan 29, 2026 EST
Architecting an AI Inference Stack
Earned Jan 29, 2026 EST
Select a Google Cloud Database for Your Applications
Earned Sep 5, 2025 EDT
[CEPF L300 Course]: Databases
Earned Jul 29, 2025 EDT
借助 Gemini Enterprise 加速知识交流
Earned Mar 4, 2025 EST
GCC Tech Learning Packs - Streaming Analytics - SME Academy
Earned Jan 15, 2025 EST
面向开发者的 Responsible AI：可解释性和透明度
Earned Aug 14, 2024 EDT
适用于生成式 AI 的机器学习运维 (MLOps)
Earned Aug 13, 2024 EDT
面向开发者的 Responsible AI：公平性与偏见
Earned Aug 13, 2024 EDT
使用多模态 Gemini 和多模态 RAG 检查富文档
Earned Aug 13, 2024 EDT
矢量搜索和嵌入
Earned Jul 31, 2024 EDT
Feature Engineering
Earned Apr 16, 2024 EDT
GCC Technical Specialty Learning Packs - Data Analytics - Prerequisites
Earned Apr 2, 2024 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned Mar 5, 2024 EST
Generative AI Fundamentals - 简体中文
Earned Mar 5, 2024 EST
负责任的 AI 简介
Earned Mar 5, 2024 EST
Build Batch Data Pipelines on Google Cloud
Earned Feb 29, 2024 EST
使用 Knowledge Catalog 构建数据网格
Earned Feb 28, 2024 EST
在 Google Cloud 上为机器学习 API 准备数据
Earned Feb 27, 2024 EST
使用 BigQuery 构建数据仓库
Earned Feb 22, 2024 EST
Serverless Data Processing with Dataflow: Operations
Earned Feb 16, 2024 EST
Getting Started with MongoDB Atlas on Google Cloud
Earned Dec 12, 2023 EST
增强 Lakehouse 数据的元数据管理与数据发现能力
Earned Dec 11, 2023 EST
Understanding Cloud Spanner
Earned Nov 27, 2023 EST
GCC Technical Specialty Learning Packs - Data Management - Specialty
Earned Nov 21, 2023 EST
GCC Technical Specialty Learning Packs - Data Management - Prerequisites
Earned Nov 21, 2023 EST
Rapid Migration & Modernization Program
Earned Nov 21, 2023 EST
Innovating with Data and Google Cloud
Earned Oct 24, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Oct 19, 2023 EDT
Vertex AI Studio 简介
Earned Aug 23, 2023 EDT
创建图片标注模型
Earned Aug 23, 2023 EDT
编码器-解码器架构
Earned Aug 23, 2023 EDT
图像生成简介
Earned Aug 23, 2023 EDT
使用 Dataplex 整理和治理数据
Earned Jun 13, 2023 EDT
探索生成式 AI - Agent Platform
Earned May 17, 2023 EDT
Transformer 模型和 BERT 模型
Earned May 11, 2023 EDT
注意力机制
Earned May 11, 2023 EDT
大型语言模型简介
Earned May 11, 2023 EDT
生成式 AI 简介
Earned May 11, 2023 EDT
GCC Technical Specialty Learning Packs - Data Analytics - Prerequisites
Earned Jan 4, 2023 EST
GCC Technical Specialty Learning Packs - Data Management - Prerequisites
Earned Jan 4, 2023 EST
创建和管理 Cloud SQL for PostgreSQL 实例
Earned Jan 4, 2023 EST
创建和管理 AlloyDB 实例
Earned Oct 17, 2022 EDT
NetApp: Build, Protect and Govern your Data Infrastructure On Google Cloud
Earned Sep 8, 2022 EDT
#GoogleClout Set 5 (4/10)
Earned Sep 2, 2022 EDT
Creating Infrastructure on Google Cloud with Terraform
Earned Aug 18, 2022 EDT
创建和管理 Bigtable 实例
Earned Aug 18, 2022 EDT
#GoogleClout Set 2, 1/10
Earned Aug 12, 2022 EDT
创建和管理 Cloud Spanner 实例
Earned Aug 12, 2022 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI
Earned Aug 11, 2022 EDT
优化 Google Cloud 成本
Earned Aug 9, 2022 EDT
从 BigQuery 数据中挖掘数据洞见
Earned Jun 29, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Jun 27, 2022 EDT
用 Google Data Cloud 共享数据
Earned Jun 27, 2022 EDT
Using Vault on Google Cloud
Earned Jun 24, 2022 EDT
Exploring and Preparing your Data with BigQuery
Earned Jun 23, 2022 EDT
Achieving Advanced Insights with BigQuery
Earned Jun 23, 2022 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Jun 23, 2022 EDT
Cloud Logging
Earned Jun 17, 2022 EDT
在 Google Cloud 上使用 Machine Learning API
Earned Jun 14, 2022 EDT
Exploring Data Transformation with Google Cloud
Earned Jun 12, 2022 EDT
开发 Google Cloud 网络
Earned Jun 10, 2022 EDT
Advanced ML: ML Infrastructure
Earned Jun 9, 2022 EDT
Google Workspace Essentials
Earned Jun 9, 2022 EDT
Workspace: Add-ons
Earned Jun 8, 2022 EDT
Google Cloud Run Serverless Workshop
Earned Jun 8, 2022 EDT
Cloud Development
Earned Jun 8, 2022 EDT
Data Science on Google Cloud: Machine Learning
Earned Jun 7, 2022 EDT
DevOps 基础知识
Earned Jun 7, 2022 EDT
Data Lake Modernization (Hadoop) on GCP
Earned May 27, 2022 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned May 27, 2022 EDT
Data Warehousing for Partners: Data Warehouse Migration with BigQuery
Earned May 20, 2022 EDT
Google Cloud Platform Fundamentals: Core Infrastructure
Earned May 19, 2022 EDT
Data Lake Modernization on Google Cloud
Earned May 18, 2022 EDT
Inside Track: SQL Server - Advanced
Earned May 17, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 17, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned May 17, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned May 17, 2022 EDT
DEPRECATED Exploring APIs
Earned May 13, 2022 EDT
在 Looker 中构建 LookML 对象
Earned May 11, 2022 EDT
在 Looker 中应用高级 LookML 概念
Earned May 10, 2022 EDT
为 Looker 信息中心和报告准备数据
Earned May 9, 2022 EDT
通过 BigQuery ML 创建机器学习模型
Earned Apr 22, 2022 EDT
安全与身份基础知识
Earned Apr 12, 2022 EDT
构建安全的 Google Cloud 网络
Earned Apr 11, 2022 EDT
使用 Google Cloud Managed Service for Prometheus 来监控环境
Earned Apr 11, 2022 EDT
[Deprecated]Enterprise Database Migration
Earned Apr 9, 2022 EDT
[DEPRECATED] Building Advanced Codeless Pipelines on Cloud Data Fusion
Earned Apr 6, 2022 EDT
设置 Google Cloud 网络
Earned Apr 4, 2022 EDT
Google Developer Essentials
Earned Apr 4, 2022 EDT
DEPRECATED IoT in the Google Cloud
Earned Apr 3, 2022 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned Apr 2, 2022 EDT
用 Document AI 实现大规模自动数据采集
Earned Apr 2, 2022 EDT
DEPRECATED Anthos Service Mesh
Earned Apr 2, 2022 EDT
VM Migration
Earned Apr 1, 2022 EDT
DEPRECATED Network Performance and Optimization
Earned Mar 30, 2022 EDT
Baseline: Deploy & Develop
Earned Mar 30, 2022 EDT
云工程
Earned Mar 29, 2022 EDT
[DEPRECATED] Deploying Applications
Earned Mar 29, 2022 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Mar 28, 2022 EDT
面向机器学习的 BigQuery
Earned Mar 28, 2022 EDT
Managing Cloud Infrastructure with Terraform
Earned Mar 28, 2022 EDT
基准：基础架构
Earned Mar 26, 2022 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Mar 26, 2022 EDT
DEPRECATED Google Cloud's Operations Suite
Earned Mar 25, 2022 EDT
在 Google Cloud 中使用 Kubernetes
Earned Mar 25, 2022 EDT
DEPRECATED Cloud Architecture
Earned Mar 23, 2022 EDT
在 Cloud Data Fusion 上构建无代码流水线
Earned Feb 24, 2022 EST
Getting Started with Apache Kafka and Confluent Platform on Google Cloud
Earned Jan 29, 2022 EST
Deprecated Kubernetes Solutions
Earned Jan 19, 2022 EST
DEPRECATED Windows on Google Cloud
Earned Jan 17, 2022 EST
基准：数据、机器学习和 AI
Earned Jan 17, 2022 EST
Understanding LookML in Looker
Earned Jan 16, 2022 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Jan 16, 2022 EST
Sports Analytics: Pitch Perfect BigQuery
Earned Jan 15, 2022 EST
用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL
Earned Jan 14, 2022 EST
Migrating MySQL data to Cloud SQL using Database Migration Service
Earned Jan 14, 2022 EST
Google Cloud 基础知识：核心基础设施
Earned Jan 13, 2022 EST
Scientific Data Processing
Earned Jan 13, 2022 EST
DEPRECATED Applied Data: Blockchain
Earned Jan 12, 2022 EST
Data Science on Google Cloud
Earned Jan 11, 2022 EST
DEPRECATED BigQuery for Marketing Analysts
Earned Jan 10, 2022 EST
Serverless Data Processing with Dataflow: Foundations
Earned Jan 10, 2022 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jan 10, 2022 EST
Preparing for your Professional Data Engineer Journey
Earned Jan 8, 2022 EST
DEPRECATED Create Conversational AI Agents with Dialogflow CX
Earned Dec 11, 2021 EST
DEPRECATED Application Development - Python
Earned Jul 27, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Jul 24, 2020 EDT
DEPRECATED BigQuery for Data Analysis
Earned Jul 24, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Jul 24, 2020 EDT
Data Catalog Fundamentals
Earned Jul 24, 2020 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jul 24, 2020 EDT
[DEPRECATED] Data Engineering
Earned Jul 24, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Jul 23, 2020 EDT
Cloud SQL
Earned Jul 22, 2020 EDT
为 Compute Engine 实现云负载均衡
Earned Jul 22, 2020 EDT
Google Cloud 基础知识
Earned Jul 22, 2020 EDT