Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. This course uses lectures, demos, and hands-on labs to show you how to use Google Cloud services and pretrained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

What you'll learn

• 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

Who this course is for

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

Prerequisite

 Completed Google Cloud Fundamentals: Core Infrastructure or have equivalent experience

• Working knowledge of Node.js, Python, or Java
• Basic proficiency with command-line tools and Linux operating system environments

Course Content

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

Getting Started with Google Cloud Development

• Overview of Google Cloud services for apps and scripts:
• Google Cloud APIs
• Cloud SDK
• Cloud Client Libraries
• Cloud Shell
• Cloud Code
• Demo: Google APIs Explorer
• Lab: Setting up a Development Environment

Overview of Data Storage Options

• Overview of options to store application data
• Use cases for Cloud Storage, Firestore, Cloud Bigtable, Cloud SQL, and Cloud Spanner
• Demo: Connecting Securely to a Cloud SQL Database

Best Practices for Using Datastore

• Best practices related to using Firestore in Datastore mode for:
  • Queries
  • Built-in and composite indexes
  • Inserting and deleting data (batch operations)
  • Transactions
  • Error handling
• Demo: Explore Datastore
• Demo: Use Dataflow to Bulk-load Data into Datastore
• Lab: Storing Application Data in Datastore

Performing Operations on Buckets and Objects

• Cloud Storage concepts
• Consistency model
• Demo: Explore Cloud Storage
• Request endpoints
• Composite objects and parallel uploads
• Truncated exponential backoff
• Demo: Enable CORS Configuration in Cloud Storage

Best Practices for Using Cloud Storage

• Naming buckets for static websites and other uses
• Naming objects (from an access distribution perspective)
• Performance considerations
• Lab: Storing Image and Video Files in Cloud Storage

Handling Authentication and Authorization

• Identity and Access Management (IAM) roles and service accounts
• User authentication by using Firebase Authentication
• User authentication and authorization by using Identity-Aware Proxy
• Lab: Adding User Authentication to your Application

Using Pub/Sub to Integrate Components of Your Application

• Topics, publishers, and subscribers
• Pull and push subscriptions
• Use cases for Pub/Sub
• Lab: Developing a Backend Service

Adding Intelligence to Your Application

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

Using 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
• Demo: Invoke Cloud Functions Through Direct Request-response
• Lab: Processing Pub/Sub Data using Cloud Functions

Managing APIs with Cloud Endpoints

• Open API deployment configuration
• Lab: Deploying an API for the Quiz Application

Deploying Applications

• Creating and storing container images
• Repeatable deployments with deployment configuration and templates
• Demo: Exploring Cloud Build and Cloud Container Registry
• Lab: Deploying the Application into Kubernetes Engine

Compute Options for Your Application

• Considerations for choosing a compute option for your application or service:
  • Compute Engine
  • Google Kubernetes Engine (GKE)
  • Cloud Run
  • Cloud Functions
• Platform comparisons.
  • Comparing App Engine and Cloud Run

Debugging, Monitoring, and Tuning Performance

• Google Cloud’s operations suite
• Managing performance
• Lab: Debugging Application Errors
• Logging
• Monitoring and tuning performance
• Identifying and troubleshooting performance issues
• Lab: Harnessing Cloud Trace and Cloud Monitoring