On-demand activities

Google Cloud 根據您的需求規劃了全方位的課程內容,內含超過 980 項學習活動,並涵蓋多種活動型態,您可自由選擇。您可以選擇簡短的個別研究室,或是包含影片、文件、研究室和測驗的多單元課程。在研究室中,您可以透過臨時憑證實際使用雲端資源,直接累積 Google Cloud 實作經驗。完成課程可獲得徽章,讓您輕鬆掌握、追蹤及評估自己的 Google Cloud 學習成果!

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1187 条结果
  1. 实验 精选

    Configure Device Settings for Users on ChromeOS

    In this lab, you'll configure device settings for Users on ChromeOS.

  2. 实验 精选

    Secure Software Supply Chain: Create and Use Cloud Workstations

    In this lab, you will use Cloud Workstations to create a workstation configuration and launch a new workstation instance from that configuration.

  3. 实验 精选

    Building Batch Pipelines in Cloud Data Fusion

    This lab will teach you how to use the Pipeline Studio in Cloud Data Fusion to build an ETL pipeline. Pipeline Studio exposes the building blocks and built-in plugins for you to build your batch pipeline, one node at a time. You will also use the Wrangler plugin to build and apply transformations to your data that…

  4. 实验 精选

    Online Data Migration to Cloud Spanner using Striim

    In this lab you will learn how to migrate a Cloud SQL for MySQL database to Cloud Spanner using Google Cloud's data migration partner, Striim.

  5. 实验 精选

    Build and Configure an Integration using Application Integration

    Learn the core concepts, functionalities, and best practices of Application Integration

  6. 实验 精选

    Create a report in Looker Studio

    Use Looker Studio to build a report.

  7. 实验 精选

    Collect, process, and store data in BigQuery

    Create and import data in BigQuery

  8. 实验 精选

    Manage a partitioned table in BigQuery

    Manage a partitioned table and use filters to reduce data examined in BigQuery

  9. 实验 精选

    Set up a SIEM forwarder for Windows on Docker

    In this lab, you configure a SIEM forwarder on a Windows VM using a standard Docker image. You use labels to add searchable metadata to the logs to optimize analytical capabilities.

  10. 实验 精选

    Machine Learning with Spark on Google Cloud Dataproc

    In this lab you will learn how to implement logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset