Application Development - Python
Advanced 7 Steps 6h 40m 45 积分
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.
PrerequisitesAs this Quest relies heavily on the Python programming language, the student should be an experienced programmer with Python. This Quest requires prior hands-on experience with GCP computing and storage services. It is recommended that the student have at least earned a Badge by completing the hands-on labs in the Cloud Architecture and/or the Baseline: Deploy and Develop Quests before beginning.
In this lab, you will provision a Google Compute Engine virtual machine and install software libraries for software development.
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.
In this lab, you add images and video to an application. You store files as objects in a Cloud Storage bucket.
In this lab, you will enhance the online Quiz application to use Firebase authentication.
In this lab, you will enhance the online Quiz application by developing a backend service to process user feedback and save scores.
In this lab, you will deploy the quiz application into App Engine flexible environment, leveraging App Engine features including instances, versions, and traffic splitting.
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.