Back to Lobby

Developing Applications with Google Cloud Platform

CPD300 - Version:1
In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native application
Intended audience
Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform
Expand All
  • Module 1: 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
  • Module 2: 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
  • Module 3: 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
  • Module 4: 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
  • Module 5: Performing Operations on Buckets and Objects

    • Operations that can be performed on buckets and objects
    • Consistency model
    • Error handling
  • Module 6: 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
  • Module 7: 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
  • Module 8: 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
  • Module 9: Adding Intelligence to Your Application

    • Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API
  • Module 10: 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
  • Module 11: Managing APIs with Google Cloud Endpoints

    • Open API deployment configuration
    • Lab: Deploy an API for your application
  • Module 12: 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
  • Module 13: 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
  • Module 14: 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
  • 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
  • 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 source
  • 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.
Contact Us


Related Courses