Description

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 pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

Learning 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

Prerequisites

• Completed Google Cloud Fundamentals: Core Infrastructure (GCF-CI) or have equivalent experience
• Working knowledge of Node.js, Python, or Java
• Basic proficiency with command-line tools and Linux operating system environments

Who Should Attend

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

Course Outline

Module 1: Best Practices for Application Development

Topics:

• 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

Objectives:

• Design and develop secure, scalable, reliable, loosely coupled application components and microservices.
• Understand how to rearchitect applications for the cloud.

Activities:

• Module quiz

Module 2 – Getting Started with Google Cloud Development

Topics:

• 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

Objectives:

• Identify different Google Cloud services for hosting applications and scripts.

Activities:

• 1 demo, 1 lab,1 quiz

Module 3 – Overview of Data Storage Options

Topics:

• 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

Objectives:

• Choose the appropriate data storage option for application data.

Activities:

• 1 demo, 1 quiz

Module 4 – Best Practices for Using Datastore

Topics:

• 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

Objectives:

• Bulk-load data into Firestore by using Dataflow
• Understand best practices related to queries, built in and composite indexes, inserting and deleting data (batch data operations), and transactions error handling.

Activities:

• 2 demos, 1 lab, 1 quiz

Module 5 – Performing Operations on Buckets and Objects

Topics:

• 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

Objectives:

• Understand Cloud Storage concepts.
• Differentiate between strongly consistent and eventually consistent operations.
• Access Cloud Storage through request endpoints.
• Use object composition to upload an object in parallel.
• Use truncated exponential backoff to deal with network failures.

Activities:

• 2 demos, 1 quiz

Module 6 – Best Practices for Using Cloud Storage

Topics:

• 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

Objectives:

• Understand how to name buckets for static websites and other uses, how to name objects (from an access distribution perspective, and performance considerations.

Activities:

• 1 lab and 1 quiz

Module 7 – Handling Authentication and Authorization

Topics:

• 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

Objectives:

• Implement federated identity management

Activities:

• 1 lab and 1 quiz

Module 8 – Using Pub/Sub to Integrate Components of Your Application

Topics:

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

Objectives:

• Understand Pub/Sub topics, publishers, and subscribers.
• Understand pull and push subscriptions.
• Explore use cases for Pub/Sub.

Activities:

• 1 lab, 1 quiz

Module 9 – Adding Intelligence to Your Application

Topics:

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

Objectives:

• Explore pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language API.

Activities:

• 1 quiz

Module 10 – Using Cloud Functions for Event-Driven Processing

Topics:

• 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

Objectives:

• Use Cloud Functions for event-driven processing.

Activities:

• 1 demo, 1 lab, 1 quiz

Module 11 – Managing APIs with Cloud Endpoints

Topics:

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

Objectives:

• Understand OpenAPI deployment configuration.

Activities:

• 1 lab, 1 quiz

Module 12 – Deploying Applications

Topics:

• 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

Objectives:

• Understand how to create and store container images.
• Create repeatable deployments with deployment configuration and templates.

Activities:

• 1 demo, 1 lab, 1 quiz

Module 13 – Compute Options for Your Application

Topics:

• 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

Objectives:

• Explore considerations for choosing a compute option for your application or service.

Activities:

• 1 quiz

Module 14 – Debugging, monitoring, and Tuning Performance

Topics:

• 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

Objectives:

• Debug an application error by using Cloud Debugger and Error Reporting.
• Use Cloud Monitoring and Cloud Trace to trace a request across services, observe, and optimize performance.

Activities:

• 1 demo, 2 labs, 1 quiz

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