Developing Applications with Google
Cloud Platform

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 applications.

Objectives

  • 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.

Audience

To get the most out of this course, participants should have:

  • 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

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

Course Outline

  • 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
  • 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 options to store application data
  • Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner
  • 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
  • Operations that can be performed on buckets and objects
  • Consistency model
  • Error handling
  • 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
  • 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
  • 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
  • Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API
  • Key concepts such as triggers, background functions, HTTP functions
  • Use cases
  • Developing and deploying functions
  • Logging, error reporting, and monitoring
  • Open API deployment configuration
  • Lab: Deploy an API for your application
  • 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
  • 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
  • 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

CLASS DETAILS

Duration: 3 Days

Delivery Method: Instructor-led

UPCOMING CLASSES

Mon 16

Developing Applications with Google Cloud Platform: 16-12-2019 : Sydney

December 16 @ 9:00 AMDecember 18 @ 5:00 PM

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