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在 Google Cloud 控制台中运用您的技能

Sean Leggett

成为会员时间：2021

青铜联赛

7095 积分
Getting Started with Terraform for Google Cloud Earned Oct 14, 2024 EDT
Google Cloud 基礎知識：核心基礎架構 Earned Oct 11, 2024 EDT
為 Looker 資訊主頁和報表準備資料 Earned Aug 28, 2024 EDT
Analyzing and Visualizing Data in Looker Earned Aug 18, 2024 EDT
Building Batch Data Pipelines on Google Cloud Earned Jul 25, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Mar 9, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals Earned Feb 14, 2023 EST

This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure. Learners will get hands-on practice building and managing Google Cloud resources using Terraform.

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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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完成「為 Looker 資訊主頁和報表準備資料」技能徽章入門課程， 即可證明您具備下列技能：可篩選、排序和 pivot 資料、合併不同的 Looker 探索結果， 還能使用函式和運算子建構 Looker 資訊主頁和報表，取得資料分析結果和圖表。 技能徽章是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務上的精熟技能，代表您已通過測驗， 能在互動式實作環境中應用相關知識。完成這個技能徽章課程 和結業評量挑戰實驗室，即可取得徽章 並與他人分享。

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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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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