Sela

Developing Applications with Google Cloud Platform

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

Topics

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:
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:
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

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