Developing Applications with Google Cloud Platform

Upon completion of the Developing Applications with Google Cloud Platform course, students will be able to:

• Use best practices for application development.
• Choose the appropriate data storage option for application data.
• Implement federated identity management.
• Develop loosely coupled application components or microservices.
• Integrate application components and data sources.
• Debug, trace, and monitor applications.
• Perform repeatable deployments with containers and deployment services.
• Choose the appropriate application runtime environment; use Google Kubernetes Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex.

• Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform

• Completed Google Cloud Platform Fundamentals or have equivalent experience
• Working knowledge of Node.js
• Basic proficiency with command-line tools and Linux operating system environments

Best Practices for Application Development

• Code and environment management
• Design and development of secure, scalable, reliable, loosely coupled application components and microservices
• Continuous integration and delivery
• Re-architecting applications for the cloud

Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK

• How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
• Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials

Overview of Data Storage Options

• Overview of options to store application data
• Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner

Best Practices for Using Google Cloud Datastore

• Best practices related to the following:
  • Queries
  • Built-in and composite indexes
  • Inserting and deleting data (batch operations)
  • Transactions
  • Error handling
• Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow
• Lab: Store application data in Cloud Datastore

Performing Operations on Buckets and Objects

• Operations that can be performed on buckets and objects
• Consistency model
• Error handling

Best Practices for Using Google Cloud Storage

• Naming buckets for static websites and other uses
• Naming objects (from an access distribution perspective)
• Performance considerations
• Setting up and debugging a CORS configuration on a bucket
• Lab: Store files in Cloud Storage

Handling Authentication and Authorization

• Cloud Identity and Access Management (IAM) roles and service accounts
• User authentication by using Firebase Authentication
• User authentication and authorization by using Cloud Identity-Aware Proxy
• Lab: Authenticate users by using Firebase Authentication

Using Google Cloud Pub/Sub to Integrate Components of Your Application

• Topics, publishers, and subscribers
• Pull and push subscriptions
• Use cases for Cloud Pub/Sub
• Lab: Develop a backend service to process messages in a message queue

Adding Intelligence to Your Application

• Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API

Using Google Cloud Functions for Event-Driven Processing

• Key concepts such as triggers, background functions, HTTP functions
• Use cases
• Developing and deploying functions
• Logging, error reporting, and monitoring

Managing APIs with Google Cloud Endpoints

• Open API deployment configuration
• Lab: Deploy an API for your application

Deploying an Application by Using Google Cloud Container Builder, Google Cloud Container Registry, and Google Cloud Deployment Manager

• Creating and storing container images
• Repeatable deployments with deployment configuration and templates
• Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments

Execution Environments for Your Application

• Considerations for choosing an execution environment for your application or service:
  • Google Compute Engine
  • Kubernetes Engine
  • App Engine flexible environment
  • Cloud Functions
  • Cloud Dataflow
• Lab: Deploying your application on App Engine flexible environment

Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver

• Stackdriver Debugger
• Stackdriver Error Reporting
• Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting
• Stackdriver Logging
• Key concepts related to Stackdriver Trace and Stackdriver Monitoring. Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance