Deploying and Managing Windows Workloads on Google Cloud

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Duración del curso
7 horas
Lugar de impartición
Madrid / Online
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Modalidad
Aula Virtual, Presencial
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Fecha de inicio
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Acerca del curso

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 pretrained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

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  • 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

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

Documentación Oficial de Google Cloud - Deploying and Managing Windows Workloads on Google Cloud

  • Formador Certificado por GCP
  • Más de 5 años de experiencia profesional
  • Más de 4 años de experiencia docente
  • Profesional activo en empresas del sector IT

Modulo 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

Modulo 2: Getting Started with Google Cloud Development

  • Overview of Google Cloud services for apps and scripts:
    • Google Cloud APIs
    • Cloud SDK
    • Cloud Client Libraries
    • Cloud Shell
    • Cloud Code

Modulo 3: Overview of Data Storage Options

  • Overview of options to store application data
  • Use cases for Cloud Storage, Firestore, Cloud Bigtable, Cloud SQL, and Cloud Spanner
  • Demo: Connecting Securely to a Cloud SQL Database

Modulo 4: Best Practices for Using Datastore

  • Queries
  • Built-in and composite indexes
  • Inserting and deleting data (batch operations)
  • Transactions
  • Error handling

Modulo 5: Performing Operations on Buckets and Objects

  • Cloud Storage concepts
  • Consistency model
  • Demo: Explore Cloud Storage
  • Request endpoints
  • Composite objects and parallel uploads
  • Truncated exponential backoff
  • Demo: Enable CORS Configuration in Cloud Storage

Modulo  6: Best Practices for Using Cloud Storage

  • Naming buckets for static websites and other uses
  • Naming objects (from an access distribution perspective)
  • Performance considerations
  • Lab: Storing Image and Video Files in Cloud Storage

Modulo 7: Handling Authentication and Authorization

  • Identity and Access Management (IAM) roles and service accounts
  • User authentication by using Firebase Authentication
  • User authentication and authorization by using Identity-Aware Proxy
  • Lab: Adding User Authentication to your Application

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

  • Topics, publishers, and subscribers
  • Pull and push subscriptions
  • Use cases for Pub/Sub
  • Lab: Developing a Backend Service

Modulo 9: Adding Intelligence to Your Application

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

Modulo 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
  • Demo: Invoke Cloud Functions Through Direct Request-response
  • Lab: Processing Pub/Sub Data using Cloud Functions

Modulo 11: Managing APIs with Cloud Endpoints

  • Open API deployment configuration
  • Lab: Deploying an API for the Quiz Application

Modulo 12: Deploying Applications

  • Creating and storing container images
  • Repeatable deployments with deployment configuration and templates
  • Demo: Exploring Cloud Build and Cloud Container Registry
  • Lab: Deploying the Application into Kubernetes Engine

Modulo 13: Compute Options for Your Application

  • Considerations for choosing a compute option for your application or service:
    • Compute Engine
    • Google Kubernetes Engine (GKE)
    • Cloud Run
    • Cloud Functions
  • Platform comparisons
  • Comparing App Engine and Cloud Run

Modulo 14: Debugging, Monitoring, and Tuning Performance

  • Google Cloud’s operations suite
  • Managing performance
  • Lab: Debugging Application Errors
  • Logging
  • Monitoring and tuning performance
  • Identifying and troubleshooting performance issues
  • Lab: Harnessing Cloud Trace and Cloud Monitoring

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