원하는 방식의 Google Cloud 교육을 살펴보세요.

Google Cloud에서 개발자를 대상으로 한 980개 이상의 학습 활동을 선택할 수 있는 포괄적인 카탈로그를 설계했습니다. 이 카탈로그는 개발자가 선택할 수 있는 다양한 활동 형식으로 구성되어 있습니다. 짧은 분량의 개별 실습 또는 동영상, 문서, 실습, 퀴즈로 구성된 멀티 모듈 과정 중에서 선택하세요. 실습에서는 실제 클라우드 리소스에 대한 임시 사용자 인증 정보를 제공하므로 실제 리소스를 사용하여 Google Cloud를 알아볼 수 있습니다. 이수한 과정의 배지를 획득하고 Google Cloud 성과를 정의, 추적, 측정하세요.

필터링 기준
모두 지우기
  • 배지
  • 형식
  • 언어

결과 1187개
  1. 실습 추천

    Configure Device Settings for Users on ChromeOS

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

  2. 실습 추천

    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. 실습 추천

    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. 실습 추천

    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. 실습 추천

    Build and Configure an Integration using Application Integration

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

  6. 실습 추천

    Create a report in Looker Studio

    Use Looker Studio to build a report.

  7. 실습 추천

    Collect, process, and store data in BigQuery

    Create and import data in BigQuery

  8. 실습 추천

    Manage a partitioned table in BigQuery

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

  9. 실습 추천

    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. 실습 추천

    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