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GCPDEVA - Developing Applications with Google Cloud Platform

Overview

Duration: 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.


Objectives

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.


Audience

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


Content

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


Prerequisites

To get the most benefit from this course, participants should have the following prerequisites:

  • Completed Google Cloud Platform Fundamentals or have equivalent experience
  • Working knowledge of Node.js, Python, or Java
  • Basic proficiency with command-line tools and Linux operating system environments


Certification

This course is not associated with any certification.

Schedule

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