Suresh S
Member since 2022
Bronze League
800 points
Build Batch Data Pipelines on Google Cloud
Earned Nov 7, 2022 EST
Build Streaming Data Pipelines on Google Cloud
Earned Nov 3, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Sep 23, 2022 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Jun 20, 2022 EDT
在 Compute Engine 導入 Cloud Load Balancing
Earned Mar 26, 2022 EDT
Build a Certification Study Guide: ACE Exam Prep
Earned Mar 26, 2022 EDT
彈性的 Google Cloud 基礎架構：資源調度與自動化
Earned Mar 21, 2022 EDT
可靠的 Google Cloud 基礎架構：設計與程序
Earned Mar 20, 2022 EDT
重要的 Google Cloud 基礎架構：基本概念
Earned Mar 17, 2022 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Mar 7, 2022 EST
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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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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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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完成「在 Compute Engine 導入 Cloud Load Balancing」技能徽章入門課程，即可證明您具備下列技能： 在 Compute Engine 建立及部署虛擬機器， 以及設定網路和應用程式負載平衡器。
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Learn how to use Gemini Notebook to create a personalized study guide for the Associate Cloud Engineer certification exam.
You'll review Gemini Notebook features, create a notebook in Gemini Notebook , and learn how to use a study guide to practice for a certification exam.
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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務。這堂課結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，包括安全地建立互連網路、負載平衡、自動調度資源、基礎架構自動化，以及代管服務。
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這堂課程可讓參加人員瞭解如何使用確實有效的設計模式，在 Google Cloud 中打造相當可靠且效率卓越的解決方案。這堂課程接續了「設定 Google Compute Engine 架構」或「設定 Google Kubernetes Engine 架構」課程的內容，並假設參加人員曾實際運用上述任一課程涵蓋的技術。這堂課程結合了簡報、設計活動和實作研究室，可讓參加人員瞭解如何定義業務和技術需求，並在兩者之間取得平衡，設計出相當可靠、可用性高、安全又符合成本效益的 Google Cloud 部署項目。
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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務，尤其側重於 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，例如網路、虛擬機器和應用程式服務等基礎架構元件。您會瞭解如何透過控制台和 Cloud Shell 使用 Google Cloud。另外，您也能瞭解雲端架構師的職責、基礎架構設計方法，以及具備虛擬私有雲 (VPC)、專案、網路、子網路、IP 位址、路徑和防火牆規則的虛擬網路設定。
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「Google Cloud 基礎知識：核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室，介紹並比較 Google Cloud 的多種運算和儲存服務，同時提供重要的資源和政策管理工具。
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