Developing Applications with Google Cloud

Application developers who want to create native cloud applications or redesign existing applications that will run on the Google Cloud.

This course teaches participants the following skills:

• Use best practices for application development.
  
• Select the appropriate data storage option for the application data.
  
• Implementing Federated Identity Management
  
• Develop offline application components or microservices
  
• Integrate application components and data sources
  
• Debug, track, and monitor applications.
  
• Perform repeatable deployments with containers and deployment services
  
• Choose the right application runtime environment, use Google Kubernetes Engine as your runtime environment, and then move to a non-operational solution with Google App Engine Flex
   
  

The course includes presentations, demonstrations and hands-on labs.

Module 1: Best Practices for Application Development

• Code and environmental management
  
• Design and development of safe, scalable, reliable and non-connected 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 the Google Cloud Client Libraries, the Google Cloud SDK, and the Google Firebase SDK
  
• Lab: Set up Google client libraries, the Google Cloud SDK, and the Firebase SDK on a Linux instance and set up application credentials

Module 3: Overview of data storage options

• Summary of options for storing 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

• Good practices related to the following:
  
• Consultations
  
• Integrated and composite indices
  
• Inserting and Deleting Data (Batch Operations)
  
• Transactions
  
• Error handling
  
• Massive data loading into the cloud data store using Google Cloud Dataflow
  
• Lab: Store application data in the Cloud Datastore

Module 5: Performing operations on cubes and objects

• Operations that can be performed on cubes and objects
  
• Consistency Model
  
• Error handling

Module 6: Best Practices for Using Google Cloud Storage

• Naming cubes for static websites and other uses
  
• Naming objects (from an access distribution perspective)
  
• Performance considerations
  
• Configuration and debugging of a CORS configuration in a cube
  
• Lab: Store files in cloud storage

Module 7: Handling of authentication and authorization

• Cloud identity and access management (IAM) service accounts and features
  
• Firebase Authentication
  
• User authentication and authorization using the Cloud Identity-Aware Proxy
  
• Lab: User authentication using Firebase Authentication

Module 8: Using Google Cloud Pub/Sub to integrate components of your application

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

Module 9: Adding Intelligence to your Application

Overview of the pre-formed machine learning APIs, such as the Cloud Vision API and the Cloud Natural Language Processing API.

• Module 10: Using Google Cloud Features for Event-Based Processing
  
• Key concepts such as triggers, background functions, HTTP functions
  
• Use cases
  
• Development and deployment of functions
  
• Logging, error reporting and monitoring

Module 11: Managing APIs with Google Cloud Endpoints

• Open API implementation settings
  
• Lab: Implement an API for your application

Module 12: Deploying an Application Using Google Cloud Container Builder, Google Cloud Container Registry, and Google Cloud Deployment Manager

• Creating and storing container images
  
• Repeatable implementations with implementation configuration and templates
  
• Lab: Use Deployment Manager to deploy a web application to flexible Google App Engine production and testing environments.

Module 13: Running environments for your application

• Considerations for choosing an execution environment for your application or service:
  
• Google Calculation Engine
  
• Kubernetes Engine
  
• Flexible App Engine environment
  
• Cloud Features
  
• Cloud data flow
  
• Lab: Implementing your application in a flexible App Engine environment

Module 14: Debugging, Monitoring and Tuning Performance Using Google Stackdriver

• Stackdriver Debugger
  
• Stackdriver Error Report
  
• Lab: Debugging an application error using the Stackdriver Debugger and bug reporting
  
• Stackdriver Registration
  
• Key concepts related to Stackdriver Trace and Stackdriver Monitoring.
• Lab: Use Stackdriver monitoring and Stackdriver Trace to track a request across all services, observe and optimize performance.
  

To get the maximum benefit from this course, participants should have the following prerequisites:

• Complete Google Cloud  fundamentals or equivalent experience
  
• Practical knowledge of Node.js
  
• Basic command of command line tools and Linux operating system environments