Vertex AI is now Gemini Enterprise Agent Platform! We are currently updating our content to reflect this change. Please bear with us if you encounter naming inconsistencies during this transition.
使用 BigQuery ML 為預測模型進行資料工程
Earned Jun 15, 2025 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Jun 11, 2025 EDT
在 BigQuery 使用 Gemini 模型
Earned Jun 11, 2025 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jun 1, 2025 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jun 1, 2025 EDT
Build Batch Data Pipelines on Google Cloud
Earned May 30, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned May 30, 2025 EDT
Google Cloud 中的資料工程簡介
Earned May 30, 2025 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 29, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned May 25, 2025 EDT
完成 運用 Cloud Run 開發無伺服器應用程式 技能徽章中階課程， 即可證明您具備下列技能：整合 Cloud Run 和 Cloud Storage 以管理資料、 使用 Cloud Run 和 Pub/Sub 架構可復原的非同步系統、 使用 Cloud Run 建構 REST API 閘道，以及在 Cloud Run 建構及部署服務。
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 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 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.
運用 Cloud Run 開發無伺服器應用程式
Earned Sep 29, 2025 EDT
在 Google Cloud 部署 Kubernetes 應用程式
Earned Sep 29, 2025 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Sep 28, 2025 EDT
透過 BigQuery 建構資料倉儲
Earned Jun 16, 2025 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Jun 15, 2025 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Jun 11, 2025 EDT
在 BigQuery 使用 Gemini 模型
Earned Jun 11, 2025 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jun 1, 2025 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jun 1, 2025 EDT
Build Batch Data Pipelines on Google Cloud
Earned May 30, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned May 30, 2025 EDT
Google Cloud 中的資料工程簡介
Earned May 30, 2025 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 29, 2025 EDT
Preparing for your Professional Data Engineer Journey
Earned May 25, 2025 EDT