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Developing Applications with Google Cloud

SS Course: 56030

Course Overview

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Learn how to design, develop, and deploy applications that seamlesslyintegrate 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.                                                                  

Scheduled Classes

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02/08/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

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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.
  • This course teaches participants the following skills:

Outline

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Viewing outline for:
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 (GCE)
  • Google Kubernetes Engine (GKE)
  • App Engine flexible environment
  • Cloud Functions
  • Cloud Dataflow
  • Cloud Run
  • Lab: Deploying your application on App Engine flexible environment
  • Application Performance Management Tools
  • Use Cloud Monitoring and Cloud Trace to trace a request across services, observe, and optimize performance.
  • 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
  • Debug an application error by using Cloud Debugger and Error Reporting.
  • 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 using Cloud Firestore in Datastore mode for:
  • Queries
  • Built-in and composite indexes
  • Inserting and deleting data (batch operations)
  • Transactions
  • Error handling
  • Bulk-loading data into Cloud Firestore 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
  • Understand Pub/Sub topics, publishers, and subscribers.
  • 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

Prerequisites

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Completed Google Cloud Fundamentals or have equivalent experience
  • Working knowledge of Node.js
  • Basic proficiency with command-line tools and Linux operating system environments
  • To get the most benefit from this course, participants should have the following prerequisites:

    Who Should Attend

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

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