On-demand activities
Find the right on-demand learning activities for you. Labs are short learning activities that teach you a specific lesson by giving you direct, temporary, hands-on access to real cloud resources. Courses are longer activities, consisting of several modules made of videos, documents, hands-on labs and quizzes. Finally, quests are similar, but are usually shorter and contain only labs.
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Lab Featured Configure Device Settings for Users on ChromeOS
In this lab, you'll configure device settings for Users on ChromeOS.
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Lab Featured 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.
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Lab Featured 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…
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Lab Featured 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.
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Lab Featured Build and Configure an Integration using Application Integration
Learn the core concepts, functionalities, and best practices of Application Integration
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Lab Featured Manage a partitioned table in BigQuery
Manage a partitioned table and use filters to reduce data examined in BigQuery
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Lab Featured 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.
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Lab Featured 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