Serverless Data Processing with Dataflow: Foundations
Earned Jul 30, 2025 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jul 29, 2025 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jul 28, 2025 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jul 27, 2025 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jul 27, 2025 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 14, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jul 6, 2025 EDT
Google Cloud 数据工程简介
Earned Jun 30, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 24, 2025 EDT
Google Kubernetes Engine 使用入门
Earned May 20, 2025 EDT
从 BigQuery 数据中挖掘数据洞见
Earned May 18, 2025 EDT
使用 BigQuery 构建数据仓库
Earned May 15, 2025 EDT
Cloud Storage 使用入门
Earned May 13, 2025 EDT
Google Cloud 基础知识：核心基础设施
Earned May 12, 2025 EDT
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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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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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 Dataproc 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。
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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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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.
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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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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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欢迎学习“Google Kubernetes Engine 使用入门”课程。Kubernetes 是位于应用和硬件基础架构之间的软件层，如果您对 Kubernetes 感兴趣，那就来对地方了！Google Kubernetes Engine 将 Kubernetes 作为 Google Cloud 上的代管式服务提供给您使用。
本课程的目标是介绍 Google Kubernetes Engine（通常称为 GKE）的基础知识，以及将应用容器化并在 Google Cloud 中运行的方法。本课程首先介绍 Google Cloud 的基础知识，然后概述容器、Kubernetes、Kubernetes 架构以及 Kubernetes 操作。
Serverless Data Processing with Dataflow: Foundations
Earned Jul 30, 2025 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jul 29, 2025 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jul 28, 2025 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jul 27, 2025 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jul 27, 2025 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 14, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jul 6, 2025 EDT
Google Cloud 数据工程简介
Earned Jun 30, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 24, 2025 EDT
Google Kubernetes Engine 使用入门
Earned May 20, 2025 EDT
从 BigQuery 数据中挖掘数据洞见
Earned May 18, 2025 EDT
使用 BigQuery 构建数据仓库
Earned May 15, 2025 EDT
Cloud Storage 使用入门
Earned May 13, 2025 EDT
Google Cloud 基础知识：核心基础设施
Earned May 12, 2025 EDT