Preparing for your Professional Data Engineer Journey
Earned Dec 30, 2025 EST
在 BigQuery 中使用 Gemini 模型
Earned Dec 26, 2025 EST
Build Streaming Data Pipelines on Google Cloud
Earned Dec 23, 2025 EST
使用 Gemini in BigQuery 提高效率
Earned Dec 22, 2025 EST
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Dec 22, 2025 EST
使用 Dataplex 构建数据网格
Earned Dec 3, 2025 EST
使用 BigQuery 构建数据仓库
Earned Dec 2, 2025 EST
Serverless Data Processing with Dataflow: Operations
Earned Dec 1, 2025 EST
Serverless Data Processing with Dataflow: Foundations
Earned Nov 29, 2025 EST
Build Batch Data Pipelines on Google Cloud
Earned Nov 27, 2025 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Nov 25, 2025 EST
从 BigQuery 数据中挖掘数据洞见
Earned Nov 18, 2025 EST
Google Cloud 数据工程简介
Earned Oct 19, 2025 EDT
Google Cloud: Prompt Engineering Guide
Earned Jul 25, 2025 EDT
在 Google Cloud 上部署 Kubernetes 应用
Earned Jun 11, 2025 EDT
在 Google Cloud 上实施云安全基础措施
Earned Jun 11, 2025 EDT
构建安全的 Google Cloud 网络
Earned Apr 27, 2025 EDT
Google Cloud 重要基础设施：核心服务
Earned Apr 27, 2025 EDT
在 Google Cloud 上设置应用开发环境
Earned Apr 16, 2025 EDT
Google Cloud 重要基础设施：基础
Earned Apr 14, 2025 EDT
Google Cloud 基础知识
Earned Mar 15, 2025 EDT
Google Cloud 基础知识：核心基础设施
Earned Feb 26, 2025 EST
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.
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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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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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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完成入门技能徽章课程使用 Dataplex 构建数据网格，展示以下方面的技能：使用 Dataplex 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 Dataplex 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。
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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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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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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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.
在本课程中，您将了解 Google Cloud 数据工程、数据工程师的角色和职责，以及相关的 Google Cloud 产品和服务。您还将了解如何应对数据工程挑战。
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Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.
Preparing for your Professional Data Engineer Journey
Earned Dec 30, 2025 EST
在 BigQuery 中使用 Gemini 模型
Earned Dec 26, 2025 EST
Build Streaming Data Pipelines on Google Cloud
Earned Dec 23, 2025 EST
使用 Gemini in BigQuery 提高效率
Earned Dec 22, 2025 EST
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Dec 22, 2025 EST
使用 Dataplex 构建数据网格
Earned Dec 3, 2025 EST
使用 BigQuery 构建数据仓库
Earned Dec 2, 2025 EST
Serverless Data Processing with Dataflow: Operations
Earned Dec 1, 2025 EST
Serverless Data Processing with Dataflow: Foundations
Earned Nov 29, 2025 EST
Build Batch Data Pipelines on Google Cloud
Earned Nov 27, 2025 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Nov 25, 2025 EST
从 BigQuery 数据中挖掘数据洞见
Earned Nov 18, 2025 EST
Google Cloud 数据工程简介
Earned Oct 19, 2025 EDT
Google Cloud: Prompt Engineering Guide
Earned Jul 25, 2025 EDT
在 Google Cloud 上部署 Kubernetes 应用
Earned Jun 11, 2025 EDT
在 Google Cloud 上实施云安全基础措施
Earned Jun 11, 2025 EDT
构建安全的 Google Cloud 网络
Earned Apr 27, 2025 EDT
Google Cloud 重要基础设施：核心服务
Earned Apr 27, 2025 EDT
在 Google Cloud 上设置应用开发环境
Earned Apr 16, 2025 EDT
Google Cloud 重要基础设施：基础
Earned Apr 14, 2025 EDT
Google Cloud 基础知识
Earned Mar 15, 2025 EDT
Google Cloud 基础知识：核心基础设施
Earned Feb 26, 2025 EST