Course overview
In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.
Learning outcomes
This course teaches participants the following skills:
- Connaître les bonnes pratiques relatives au développement d’applications
- Choisir la solution de stockage adaptée pour les données des applications
- Mettre en œuvre la gestion des identités fédérées
- Développer des microservices et des composants d’application faiblement couplés
- Intégrer les composants d’une application et les sources de données
- Déboguer, tracer et surveiller des applications
- Effectuer des déploiements reproductibles à l’aide de conteneurs et de services de déploiement
- Choisir l’environnement d’exécution adapté à l’application, utiliser Google Kubernetes Engine en tant qu’environnement d’exécution, puis passer à une solution no-ops avec l’environnement flexible Google App Engine
Prerequisites
To get the most out of this course, participants should have:
- Avoir des connaissances en développement d’application
Target audience
This course is intended for the following participants:
Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform
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, Google 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, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner
Module 4: Best Practices for Using Google Cloud Datastore
Best practices related to the following: Queries Built-in and composite indexes Inserting and deleting data (batch operations) Transactions Error handling 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
Module 5: Performing Operations on Buckets and Objects
Operations that can be performed on buckets and objects Consistency model Error handling
Module 6: Best Practices for Using Google 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 Google Cloud 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 Google 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 Google Cloud Endpoints
Open API deployment configuration Lab: Deploy an API for your application
Module 12: Deploying an Application by Using Google Cloud Cloud Build, Google Cloud Container Registry, and Google Cloud Deployment Manager
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 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
Module 14: Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver
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