Developing Application with Google Cloud

In this course, application developers will learn to design, develop, and deploy apps that seamlessly integrate the components of the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn to use pre-trained GCP services and machine learning APIs to create secure, scalable, and intelligent native cloud applications.


In this course, participants will learn the following skills:
  • Use the best practices for application development.
  • Choose the data storage option appropriate for your application data.
  • Implement federated identity management.
  • Develop application components or lightly coupled microservices.
  • Integrate application components and data sources.
  • Debug, track and monitor applications.
  • Carry out repeatable implementations with containers and implementation services.
  • Choose the appropriate application environment.
  • Use Google Kubernetes Engine as a performance environment and change to an independent environment solution with Google App Engine Flex.


This class is intended for the following audience:
  • Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform


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

  • Finalización de Google Cloud Fundamentals: Core Infrastructure o experiencia equivalente.
  • Experiencia práctica de Node.js
  • Competencia básica en herramientas de línea de comandos y entornos de sistema operativo Linux.
  • Experiencia en operaciones del sistema, incluida la implementación y gestión de aplicaciones, en las instalaciones o en un entorno de nube pública.


24 hours (3 days)


Check the next open public class in our enrollment page.
If you are interested in a private training class for your company, contact us.
Developing Applications with Google Cloud dependencies with other courses and certifications
Developing Applications with Google Cloud dependencies with other courses and certifications

Course Outline

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