Serverless Data Processing with Dataflow: Foundations
Earned Jul 30, 2025 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Jul 29, 2025 EDT
在 Google Cloud 為機器學習 API 準備資料
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 中的資料工程簡介
Earned Jun 30, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 24, 2025 EDT
開始使用 Google Kubernetes Engine
Earned May 20, 2025 EDT
從 BigQuery 資料取得深入分析結果
Earned May 18, 2025 EDT
透過 BigQuery 建構資料倉儲
Earned May 15, 2025 EDT
開始使用 Cloud Storage
Earned May 13, 2025 EDT
Google Cloud 基礎知識：核心基礎架構
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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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。
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完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Dataproc 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 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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在本課程中，您會學到 Google Cloud 上的資料工程、資料工程師的角色與職責，以及這些內容如何對應至 Google Cloud 提供的服務。您也將瞭解處理資料工程難題的許多方法。
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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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歡迎參加「開始使用 Google Kubernetes Engine」課程。Kubernetes 是位於應用程式和硬體基礎架構之間的軟體層。如果您對這項技術感興趣，這堂課程可以滿足您的需求。有了 Google Kubernetes Engine，您就能在 Google Cloud 中以代管服務的形式使用 Kubernetes。
本課程的目標在於介紹 Google Kubernetes Engine (常簡稱為 GKE) 的基本概念，以及如何將應用程式容器化，以便在 Google Cloud 中執行。課程首先會初步介紹 Google Cloud，隨後簡介容器、Kubernetes、Kubernetes 架構和 Kubernetes 作業。
Serverless Data Processing with Dataflow: Foundations
Earned Jul 30, 2025 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Jul 29, 2025 EDT
在 Google Cloud 為機器學習 API 準備資料
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 中的資料工程簡介
Earned Jun 30, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 24, 2025 EDT
開始使用 Google Kubernetes Engine
Earned May 20, 2025 EDT
從 BigQuery 資料取得深入分析結果
Earned May 18, 2025 EDT
透過 BigQuery 建構資料倉儲
Earned May 15, 2025 EDT
開始使用 Cloud Storage
Earned May 13, 2025 EDT
Google Cloud 基礎知識：核心基礎架構
Earned May 12, 2025 EDT