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Application Development - Python

Advanced Labs 6H 40M translation missing: ar.helpers.classrooms_helper.credit

In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.

Quest Outline

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App Dev: Setting up a Development Environment - Python

In this lab, you will provision a Google Compute Engine virtual machine and install software libraries for software development.

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App Dev: Storing Application Data in Cloud Datastore - Python

In this lab, you will review the case study application, an online Quiz. You will store application data for the Quiz application in Cloud Datastore.

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App Dev: Storing Image and Video Files in Cloud Storage - Python

In this lab, you add images and video to an application. You store files as objects in a Cloud Storage bucket.

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App Dev: Adding User Authentication to your Application - Python

In this lab, you will enhance the online Quiz application to use Firebase authentication.

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App Dev: Developing a Backend Service - Python

In this lab, you will enhance the online Quiz application by developing a backend service to process user feedback and save scores.

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App Dev: Deploying the Application into App Engine Flexible Environment - Python

In this lab, you will deploy the quiz application into App Engine flexible environment, leveraging App Engine features including instances, versions, and traffic splitting.

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App Dev: Deploying the Application into Kubernetes Engine - Python

In this lab, you will deploy the quiz application into Kubernetes Engine, leveraging Google Cloud Platform resources including Container Builder and Container Registry, and Kubernetes resources including Deployments, Pods, and Services.

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