Developing Applications with Google Cloud

Developing Applications with Google Cloud

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.

Objetivos

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 Flex

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
15 horas

  • Dificultad 50% 50%
  • Nivel alcanzado 80% 80%

Dirigido a

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

Conocimientos requeridos

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

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

Temario

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 Cloud ​Datastore

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
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 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 ​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 ​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
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

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