google cloud developer

This class is designed to help IT professionals prepare for the Google Professional Cloud Developer certification exam.

This Track Includes:

Developing Applications with Google Cloud – 3 Days


3 Days


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.


This course teaches participants the following skills:

  • 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 Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine flexible environment.

Course Outline:

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

Module 1: 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.

Module 2: Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK

  • How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK.
  • Lab: Set up Google Client Libraries, Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials.

Module 3: Overview of Data Storage Options

  • Overview of options to store application data.
  • Use cases for Google Cloud Storage, Cloud Firestore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner.

Module 4: Best Practices for Using Cloud Firestore

  • 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.

Module 5: Performing Operations on Cloud Storage

  • Operations that can be performed on buckets and objects.
  • Consistency model.
  • Error handling.

Module 6: Best Practices for Using Cloud Storage

  • 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.

Module 7: Handling Authentication and Authorization

  • 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.

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

  • 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.

Module 9: Adding Intelligence to Your Application

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

Module 10: 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.

Module 11: Managing APIs with Cloud Endpoints

  • Open API deployment configuration.
  • Lab: Deploy an API for your application.

Module 12: Deploying Applications

  • 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.

Module 13: Execution Environments for Your Application

  • 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.

Module 14: Debugging, Monitoring, and Tuning Performance

  • Application Performance Management Tools.
  • 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.

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


Familiarity with application development, systems operations, Linux operating systems, and data analytics/machine learning is helpful in understanding the technologies covered.


Google Cloud Certified – Professional Cloud Developer

Below is a Course Schedule for this:



Duration: 4 Days

Course Fee

Course Fee$2250.00
SME (Company Sponsored) – All Singaporean and Permanent Resident Employee$
Singapore Citizens aged 40 years old and above$
Singapore Citizen and Permanent Resident aged 21 years old and above$

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