Desarrollo de Aplicaciones con Android AND-X01

Certified Linux systems administrators can work proficiently to design, install, configure, and manage a system installation.

They will have an understanding of key concepts such as networking, storage, security, maintenance, logging and monitoring, application lifecycle, troubleshooting, API object primitives and the ability to establish basic use-cases for end users.

Modulo 1: Introduction to Linux (LFS101x)

Modulo 2: Introducción a Linux (LF-UPV-101x)

Modulo 3: Introduction to DevOps and Site Reliability Engineering (LFS162x)

Modulo 4: Introduction to Cloud Infrastructure Technologies (LFS151x)

Modulo 5: A Beginner’s Guide to Open Source Software Development (LFD102)

Modulo 6: Open Source Licensing Basics for Software Developers (LFC191)

Modulo 7: Secure Software Development: Requirements, Design, and Reuse (LFD104x)

Modulo 8: Secure Software Development: Implementation (LFD105x)

Modulo 9: Secure Software Development: Verification and More Specialized Topics (LFD106x)

Modulo 10: Fundamentals of Open Source IT and Cloud Computing (LFS200)

Modulo 11: Linux Foundation Certified IT Associate (LFCA)

Modulo 12: Essentials of Linux System Administration (LFS201)

Modulo 13: Linux Foundation Certified System Administrator (LFCS)

• Documentación Oficial de The linux Foundation de Linux Foundation Certified System Administrator (LFCS)

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

A CHFD should demonstrate the knowledge to develop and maintain client applications and smart contracts using the latest Fabric programming model.

Such a developer must also be able to:
• - package and deploy Fabric applications and smart contracts, perform end-to-end Fabric application life-cycle and smart contract management
• - program in Node.js

Passing a performance-based exam demonstrates the candidate's ability to perform challenging real world tasks under time constraints.

Modulo 1: Blockchain: Understanding Its Uses and Implications (LFS170x)

Modulo 2: Introduction to Hyperledger Blockchain Technologies (LFS171x)

Modulo 3: Introduction to Hyperledger Sovereign Identity Blockchain Solutions: Indy, Aries & Ursa (LFS172x)

Modulo 4: Becoming a Hyperledger Aries Developer (LFS173x)

Modulo 5: Hyperledger Besu Essentials: Creating a Private Blockchain Network (LFS176x)

Modulo 6: Hyperledger Fabric for Developers (LFD272)

Modulo 7: Certified Hyperledger Fabric Developer (CHFD)

• Documentación Oficial de The linux Foundation deCertified Hyperledger Fabric Developer (CHFD)

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

The CHFA will be able to effectively build a secure Hyperledger Fabric network for commercial deployment. Additionally, the CHFA will be able to install, configure, operate, manage, and troubleshoot the nodes on that network. Passing a performance-based exam demonstrates the candidate's ability to perform challenging real world tasks under time constraints.

Modulo 1: Blockchain: Understanding Its Uses and Implications (LFS170x)

Modulo 2: Introduction to Hyperledger Blockchain Technologies (LFS171x)

Modulo 3: Introduction to Hyperledger Sovereign Identity Blockchain Solutions: Indy, Aries & Ursa (LFS172x)

Modulo 4: Becoming a Hyperledger Aries Developer (LFS173x)

Modulo 5: Hyperledger Besu Essentials: Creating a Private Blockchain Network (LFS176x)

Modulo 6: Hyperledger Fabric for Developers (LFD272)

Modulo 7: Certified Hyperledger Fabric Developer (CHFD)

Modulo 8: Hyperledger Fabric Administration (LFS272)

Modulo 9: Certified Hyperledger Fabric Administrator (CHFA)

• Documentación Oficial de The Certified Hyperledger Fabric Administrator (CHFA)

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

JSNSD certification demonstrates the ability to perform tasks in a real world-type environment, giving employers confidence that the certificant possesses a broad range of skills around JavaScript and related technologies. Passing a performance-based exam demonstrates the candidate's ability to perform challenging real world tasks under time constaints.

Modulo 1:

Modulo 2:

Modulo 3:

Modulo 4:

Modulo 1: Introduction to Open Source Development, Git, and Linux (LFD201)

Modulo 2: FDC3: Interoperability for the Financial Desktop (LFS177x)

Modulo 3: Introduction to WebAssembly (LFD133x)

Modulo 4: WebAssembly Actors: From Cloud to Edge (LFD134x)

Modulo 5: Introduction to Node.js (LFW111x)

Modulo 6: Node.js Application Development (LFW211)

Modulo 7: OpenJS Node.js Application Developer (JSNAD)

Modulo 8: Node.js Services Development (LFW212)

Modulo 9: OpenJS Node.js Services Developer (JSNSD)

• Documentación Oficial de The OpenJS Node.js Services Developer (JSNSD)

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

• Recall fundamental cloud terminology
• Identify Google Cloud products and solutions that support digital transformation
• Explain how cloud technology and data can be leveraged to innovate within organizations
• Identify key change patterns and Google Cloud products for infrastructure and application modernization
• Describe contributing factors to the success of cloud operations and security

Modulo 1: Introduction to Digital Transformation with Google Cloud

• Define fundamental terms such as data, cloud technology, and digital transformation
• Use customer use cases to demonstrate how cloud technology is transforming businesses
• Recognize the Google Cloud solution pillars and use cases for each
• Describe key principles that help to scale an innovation mindset across an organization

Modulo 2: Innovating with Data and Google Cloud

• Describe the role of data in digital transformation and the importance of a data-driven culture
• Identify common Google Cloud solutions for data management
• Identify common Google Cloud solution for smart analytics
• Identify Google Cloud’s solutions for Machine Learning and AI

Modulo 3: Infrastructure and Application Modernization with Google Cloud

• Describe the key benefits of infrastructure and app modernization with reference to cloud technology
• Differentiate between virtual machines, containers, and Kubernetes
• Explain the Google Cloud solutions that help businesses to better manage their systems, such as App Engine and Google Kubernetes Engine
• Explain how Google Cloud solutions support app modernization and simplify API management.

Modulo 4: Understanding Google Cloud Security and Operations

• Describe financial governance in the cloud and Google Cloud's recommended best practices for effective cloud cost management
• Define a shared responsibility model for optimal cloud security
• Explain how IT teams and business leaders can use principles from DevOps and SRE to increase cloud operational efficiency
• Identify Google Cloud solutions for cloud resource monitoring and application performance management

• 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

• Explain each layer of the Anthos technology stack and the problems it is designed to address
• Explain each layer of the Anthos technology stack and the problems it is designed to address
• Create, connect and manage Anthos clusters from multiple deployment environments
• Load-balance clusters running in Google Cloud
• Describe automating policy and security at scale with Config Management.

Modulo 1: Introducing Anthos

• Overview
• Anthos technology stack
• Anthos components
• Billing

Modulo 2: Anthos Environments

• Containers and GKE review
• Anthos for centrally managed clusters
• Creating and managing Anthos clusters
• Anthos clusters on AWS
• Anthos clusters on Azure

Modulo 3: Multi-cluster Concepts on Anthos

• Anthos fleets
• Fleet networking
• Multi-cluster Service
• Multi-cluster Gateway

Modulo 4: Managing Configuration with Anthos

• Challenges with configuration management
• Anthos Config Management
• Config Sync
• Hierarchy Controller
• Policy Controller
• Config Connector
• Blueprints

Modulo 5: Introducing Anthos Service Mesh

• Introduction to Anthos Service Mesh
• Architecture
• Installation
• Life of a request in the mesh
• Mesh telemetry and instrumentation
• Anthos Service Mesh dashboards
• Anthos Service Mesh pricing and support

Modulo 6: Anthos Service Mesh Routing

• Networking and service discovery
• Anthos Service Mesh API resources
• Network resilience and testing

Modulo 7: Securing Network Traffic with Anthos Service Mesh

• Security across services
• Authentication and encryption
• Service authentication in the mesh
• End-user authentication in the mesh
• Authorization in the mesh
• Bonus: Employee authentication and authorization in the mesh

Modulo 8: Multi-cluster Networking with Anthos Service Mesh

• Fleet networking
• Single network east-west routing
• Multiple network east-west routing
• North-south routing

Modulo 9: Introduction to Anthos Clusters on Bare Metal

• Overview
• Use cases for clusters on-premises
• Anthos on bare metal specifics
• Components and services
• Support
• Billing

Modulo 10: Planning and Building the Admin Cluster

• Architecture
• Resource requirements
• Setting up the admin workstation
• Building the admin cluster
• Troubleshooting the admin bootstrap process
• Additional architectural options

Modulo 11: Adding and Configuring User Clusters

• Building the user cluster
• Enabling authentication
• Deploying applications
• Configuring storage

Modulo 12: Operating and Managing Clusters

• Observability
• Logging
• Monitoring
• Operations
• Security

Modulo 13: Application Migration with Anthos

• Modernization vision
• Workload discovery and migration assessment
• Migrating applications to containers

Modulo 14: Modern CI/CD for Anthos

• CI/CD in Google Cloud
• CI/CD in a private network
• CI/CD on-premises and multi-cloud environments
• Securing the software supply chain
• Deploying 3rd party software

Modulo 15: Serverless Computing Solutions on Anthos

• Overview of serverless computing solutions and their associated Anthos components
• Install Cloud Run for Anthos
• Running serverless workloads on Anthos

• 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

• Arquitectos de nube, administradores y personal de SysOps/DevOps.
• Personas que utilicen Google Cloud para crear nuevas soluciones o para integrar sistemas existentes, entornos de aplicaciones e infraestructura con Google Cloud.

• Comprender cómo funcionan los contenedores de software.
• Comprender la arquitectura de Kubernetes.
• Comprender la arquitectura de Google Cloud.
• Comprender cómo funciona la red de pods en Google Kubernetes Engine.
• Crear y administrar clústeres de Kubernetes Engine con Google Cloud Console y los comandos gcloud/kubectl.
• Iniciar, revertir y exponer trabajos en Kubernetes.
• Administrar el control de acceso mediante Kubernetes RBAC e IAM.
• Administrar las políticas de seguridad y de red de los pods.
• Utilizar Secrets y ConfigMaps para aislar las credenciales de seguridad y los artefactos de configuración.
• Comprender las opciones de Google Cloud para los servicios de almacenamiento administrado.
• Supervisar las aplicaciones que se ejecutan en Google Kubernetes Engine.

Módulo 1: Introduction to Google Cloud

• Temas:
  • Use the Google Cloud Console
  • Use Cloud Shell
  • Define Cloud Computing
  • Identify Google Cloud Compute Services
  • Understand Regions and Zones
  • Understand the Cloud Resource Hierarchy
  • Administer your Google Cloud Resources
• Objetivos:
  • Identify Google Cloud services and their function.
  • Choose the right Google Cloud services to create your own Cloud solution

Módulo  2: Containers and Kubernetes in Google Cloud

• Temas:
  • Create a Container Using Cloud Build
  • Store a Container in Container Registry
  • Understand the Relationship Between Kubernetes and Google Kubernetes Engine (GKE)
  • Understand how to Choose Among Google Cloud Compute Platforms
• Objetivos:
  • Create a Container using Cloud Build.
  • Store a Container in Container Registry.
  • Compare and Contrast Kubernetes and GKE features.

Módulo 3: Kubernetes Architecture

• Temas:
  • Understand the Architecture of Kubernetes: Pods, Namespaces
  • Understand the Control-plane Components of Kubernetes
  • Create Container Images using Cloud Build
  • Store Container Images in Container Registry
  • Create a Kubernetes Engine Cluster
• Objetivos:
  • Conceptualize the Kubernetes Architecture.
  • Deploy a Kubernetes Cluster using GKE.
  • Deploy Pods to a GKE Cluster.
  • View and Manage Kubernetes Objects.
  • Conceptualize the Migrate for Anthos process

Módulo 4: Kubernetes Operations

• Temas:
  • The Kubectl Command
• Objetivos:
  • Work with the Kubectl Command.
  • Inspect the Cluster and Pods.
  • View a Pod’s Console Output.
  • Sign in to a Pod Interactively.

Módulo 5: Deployment, Jobs, and Scaling

• Temas:
  • Deployments
  • Ways to Create Deployments
  • Services and Scaling
  • Updating Deployments
  • Rolling Updates
  • Blue/Green Deployments
  • Canary Deployments
  • Managing Deployments
  • Jobs and CronJobs
  • Parallel Jobs
  • CronJobs
  • Cluster Scaling
  • Downscaling
  • Node Pools
  • Controlling Pod Placement
  • Affinity and Anti-Affinity
  • Pod Placement Example
  • Taints and Tolerations
  • Getting Software into your Cluster
• Objetivos:
  • Create and Use Deployments.
  • Create and Run Jobs and CronJobs.
  • Scale Clusters Manually and Automatically.
  • Configure Node and Pod Affinity.
  • Get Software into your Cluster with Helm Charts and Kubernetes Marketplace.

Módulo 6: GKE Networking

• Temas:
  • Introduction
  • Pod Networking
  • Services
  • Finding Services
  • Service Types and Load Balancers
  • How Load Balancers Work
  • Ingress Resource
  • Container-Native Load Balancing
  • Network Security
• Objetivos:
  • Create Services to expose applications that are running within Pods.
  • Use load balancers to expose Services to external clients.
  • Create Ingress resources for HTTP(S) load balancing.
  • Leverage container-native load balancing to improve Pod load balancing.
  • Define Kubernetes network policies to allow and block traffic to Pods.

Módulo 7: Persistent Data and Storage

• Temas:
  • Volumes
  • Volume Types
  • The PersistentVolume Abstraction
  • More on PersistentVolumes
  • StatefulSets
  • ConfigMaps
  • Secrets
• Objetivos:
  • Use Secrets to isolate security credentials.
  • Use ConfigMaps to isolate configuration artifacts.
  • Push out and roll back updates to Secrets and ConfigMaps.
  • Configure Persistent Storage Volumes for Kubernetes Pods.
  • Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts.

Módulo 8: Access Control and Security in Kubernetes and Kubernetes Engine

• Temas:
  • Understand Kubernetes Authentication and Authorization
  • Define Kubernetes RBAC Roles and Role Bindings for Accessing Resources in Namespaces
  • Define Kubernetes RBAC Cluster Roles and ClusterRole Bindings for
  • Accessing Cluster-scoped Resources
  • Define Kubernetes Pod Security Policies
  • Understand the Structure of IAM
  • Define IAM roles and Policies for Kubernetes Engine Cluster Administration
• Objetivos:
  • Define IAM roles and policies for GKE.
  • Define Kubernetes RBAC roles and role bindings.
  • Define Kubernetes pod security policies.

Módulo 9: Logging and Monitoring

• Temas:
  • Use Cloud Monitoring to monitor and manage availability and performance
  • Locate and inspect Kubernetes logs
  • Create probes for wellness checks on live applications
• Objetivos:
  • Create forensic logs for systems monitoring.
  • Monitor your system performance from different vantage points.
  • Create probes for wellness checks on live applications.

Módulo 10: Using Google Cloud Managed Storage Services from Kubernetes Applications

• Temas:
  • Understand Pros and Cons for Using a Managed Storage Service Versus
  • Self-managed Containerized Storage
  • Enable Applications Running in GKE to Access Google Cloud Storage Services
  • Understand Use Cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and BigQuery from within a Kubernetes Application
• Objetivos:
  • Understand use cases for Cloud Storage within a Kubernetes application.
  • Understand use cases for Cloud SQL and Cloud Spanner within a Kubernetes application.
  • Understand use cases for Datastore within a Kubernetes application
  • Understand use cases for Cloud Bigtable within a Kubernetes application.

Módulo 11: Logging and Monitoring

• Temas:
  • CI/CD overview
  • CI/CD for Google Kubernetes Engine
  • CI/CD Examples
• Objetivos:
  • Manage application code in a source repository that can trigger code changes to a continuous delivery pipeline.
  • Create a continuous delivery pipeline using Cloud Build and start it manually or automatically with a code change.
  • Implement a canary deployment that hosts two versions of your application in production for release testing.

Documentación oficial para el curso Architecting with Google Kubernetes Engine.

• Formador certificado por Google Cloud.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Explore and put into practice API design, development and management concepts
• Understand the value of API-first development and how to apply it
• Describe the fundamentals of REST API design
• Describe API products, API product strategies, and how to publish APIs to a developer portal
• Understand the role of user authentication and authorization and the importance of API security
• Understand the out-of-the-box platform capabilities for implementing mediation, traffic management, caching, and fault handling
• Describe the value and use of API analytics
• Understand the deployment options for the Apigee platform

Modulo 1: Apigee Overview

• Understand the placement and role of API management in modern application development
• Define logical components and organizational structure of Apigee API platform
• Differentiate between Apigee flexible deployment models
• Explain the API lifecycle

Modulo 2: API-First and OpenAPI Specifications

• Describe the fundamentals of REST API design
• Understand the value of API-first development and how to apply it.
• Discuss OpenAPI specifications and their use in the context of API-first development

Modulo 3: API Proxies

• Define the building blocks of APIs and API proxies
• Describe how APIs proxies work and how capabilities such as flows, policies, route rules, environment groups, and target servers play a role
• Describe how APIs are exposed
• Describe how API proxies connect to backend systems

Modulo 4: API Products

• Define API products and API product strategies
• Understand the role of developers, applications, and API keys in API management
• Describe the API publication process
• Understand API responses and status codes for REST APIs

Modulo 5: Authentication, Authorization, and OAuth

• Discuss the importance of API security
• Understand the value of application identity
• Understand the role of user authentication and authorization
• Gain deep understanding of OAuth (access tokens, refresh tokens, common patterns for all grant types) and its application in the context of API design and management
• Discuss federated identity and the use of JSON Web Tokens in API proxies

Modulo 6: Content, Transport, and Internal Security

• Explore platform capabilities for protecting against content-based attacks
• Discuss transport security and how to protect the connection between Apigee and backend services
• Understand how to protect sensitive data using KVMs, data masking, and private variables

Modulo 7: Mediation

• Understand the out-of-the-box platform capabilities for implementing mediation and fault handling
• Describe implementation patterns and policies for JSON, XML, and SOAP
• Understand extensibility options using Service Callouts, JavaScript, and Java
• Explore development practices and capabilities used to reuse, share, and enforce execution of flows and policies

• 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

• Identify the purpose and value of Apigee API Platform
• Gain detailed understanding of Apigee API Platform architecture and recommended practices for topology design
• Gain detailed understanding of Apigee terminology and logical organizational structures
• Interact with Apigee API Platform and solve scenarios by leveraging recommended practices
• Understand capabilities and practices to secure, manage and scale the platform
• Understand and put in practice installation and upgrade processes

Modulo 1: Fundamentals

• Familiarize with Apigee product capabilities
• Familiarize with Google Cloud, Kubernetes and Anthos concepts
• Discuss REST fundamentals

Modulo 2: Architecture

• Discuss Apigee hybrid architecture
• Gain an understanding of the Apigee technology stack
• Discuss Apigee terminology and organizational structure
• Familiarize with the Apigee API

Modulo 3: Installation and Pla orm Operation

• Discuss installation process
• Discuss installation prerequisites
• Gain detail understanding of and practice installation process
• Gain an understanding of common operational capabilities and practices
• Discuss backup and restore strategy

Modulo 4: Deployment and Environment Management

• Familiarize with API proxy concept and deployment process
• Familiarize with API tracing and debugging
• Discuss Developer Portal solutions
• Gain understanding of and practice capabilities available for environment management

Modulo 5: Security

• Discuss infrastructure security
• Discuss data storage and encryption
• Discuss hybrid access management capabilities and RBAC

Modulo 6: Capacity Planning and Scaling

• Discuss capacity planning considerations
• Gain understanding of and practice capabilities to scale up/down runtime components

Modulo 7: Upgrade

• Familiarize with the upgrade process
• Discuss the rollback process

Modulo 8: Logging and Monitoring

• Familiarize with logging capabilities
• Understand metrics generated and captured by the system
• Discuss Apigee analytics
• Discuss monitoring and troubleshooting techniques
• Familiarize with the Apigee support process

• 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

• Identify the purpose and value of the Apigee API Platform
• Gain a detailed understanding of the Apigee API Platform for Private Cloud architecture and the recommended practices for topology design
• Gain a detailed understanding of the Apigee terminology and logical organizational structure
• Interact with the Apigee API Platform and solve scenarios by leveraging recommended practices
• Understand the capabilities and practices used to secure, manage and scale the platform
• Learn  and practice the installation and upgrade  of the Apigee API Platform for Private Cloud

Modulo 1: Architecture

• Familiarize with Apigee product capabilities and its place in theenterprise architecture
• Gain an understanding of the Apigee technology stack
• Gain a basic understanding of Apache Cassandra capabilities and how they are leveraged by the Apigee platform
• Gain a detailed understanding of Apigee terminology and organizational structure
• Explore topology design options

Modulo 2: Installation

• Discuss installation planning
• Understand installation dependencies
• Learn and practice the installation process
• Discuss post installation tasks and recurring  activities recommended for the platform
• Learn how to uninstall the platform

Modulo 3: Developer Portal installation

• Discuss the role of the Apigee Developer Portal
• Discuss recommended practices for hosting the Developer Portal
• Discuss the software distribution and installation process

Modulo 4: Management

• Discuss platform operations capabilities and recommended practices
• Discuss the role of the Management API and how it is leveraged by the platform
• Gain an understanding of the API deployment process
• Gain an understanding of Apigee analytics
• Discuss infrastructure operations capabilities and recommended practices

Modulo 5: Security

• Discuss Apigee capabilities to secure API traffic
• Understand how to configure TLS and mTLS for critical path components and user facing interfaces
• Learn how to configure single sign-on (SSO)

Modulo 6: Upgrade

• Discuss upgrade planning and recommended practices
• Discuss upgrade prerequisites and dependencies
• Learn and practice the upgrade process
• Discuss rollback scenarios and the rollback process

Modulo 7: Capacity Planning and Scaling

• Understand scenarios and recommended practices for capacity planning
• Understand how to scale up/down physical and logical capacity
• Understand how to add individual components, extend clusters and add new regions

Modulo 8: Monitoring

• Discuss recommended practices for monitoring the platform
• Understand how to monitor individual components
• Discuss the role of synthetic transactions monitoring
• Discuss recommended practices for API monitoring

Modulo 9: Troubleshooting

• Discuss failure scenarios and troubleshooting practices
• Understand the Apigee support model

• 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

Desarrolladores de la nube, desarrolladores de API, clientes y socios.

• Obtener un conocimiento detallado de Cloud Run, la plataforma informática completamente administrada de Google Cloud para implementar y escalar aplicaciones en contenedores de forma rápida y segura.
• Escribir y migrar código utilizando tus lenguajes favoritos (Go, Python, Java, Ruby, Node.js y más).
• Comunicación segura de servicio a servicio basada en identidades de servicio y otorgar a las aplicaciones solo los permisos que necesitan.
• Aprender a crear aplicaciones de alta disponibilidad con baja latencia para el usuario final, a nivel mundial.
• Obtener información sobre cómo conectarse y conservar datos en las ofertas de bases de datos administradas en Google Cloud.
• Comprender cómo la abstracción de toda la gestión de la infraestructura crea una experiencia de desarrollador sencilla.

Módulo 1: Introducing Application Development with Cloud Run

Temas:

This module gives a general overview of Cloud Run. If you’re new to Cloud Run (or even to Google Cloud), this will be a great introduction.

Objetivos:

• A general understanding of Cloud Run
• Understand how how high availability, low end-user latency and developer productivity are important architectural drivers for web based applications today
• Understand the advantages of serverless on Google Cloud.

Módulo 2: Understanding Cloud Run

Temas:

• You can use any language, any library and any binary. Cloud Run expects your app (in a container image) to listen on a port and respond to HTTP requests.
• Use a docker repository on Artifact Registry to store your images: Cloud Run only deploys from there.
• Cloud Run uses autoscaling to handle all incoming requests
• Pay for use pricing model
• No background tasks: Container lifetime is only guaranteed while handling requests
• There is no persistent storage: Store data downstream
• Cloud Run is portable (containers and Knative)

Objetivos:

• Understand Container Images and Containers
• Understand how Cloud Run is different from an always-on server
• Implement the deployment of a container image to Cloud Run (hands-on lab)
• Understand auto-scaling and on-demand containers

Módulo 3: Building Container Images

Temas:

• The contents of a container image (deep dive)
• There are two ways to build container images
  • Buildpacks (hands-off)
  • Docker (you’re in control)
• Cloud Run supports both source-based and a container image based workflow
• The most important considerations of building a secure container image

Objetivos:

• Deeply understand what is inside a container image
• Package an application into a container image with Buildpacks (hands-on lab activity)
• Understand that Dockerfiles are a lower-level and more transparent alternative to Buildpacks

Módulo 4: Building Container Images

Temas:

• Container lifecycle
  • Idle vs serving
  • Shutdown lifecycle hook
• Cold starts
  • Min instances
• Container readiness
• The service resource and what it describes
• Configuring memory limits and CPU allocation
• Deploying a new revision
• Traffic steering (tagging, gradual rollouts)

Objetivos:

• Understand the advantages of the shutdown lifecycle hook
• Understand how to avoid request queuing
• Implement new versions of an application (hands-on lab activity)
• Implement gradual traffic migration (hands-on lab activity)

Módulo 5: Configuring Service Identity and Authorization

Temas:

• Cloud IAM
  • Service account, policy binding, roles, types of members, resource hierarchy (in practice)
  • Service accounts
  • Cloud Run IAM roles
• Cloud Run
  • Default service account
  • Risks of using the default service account

Objetivos:

• Understand that every action on a Cloud resource is actually an API call
• Understand how and why to limit the permissions in your Cloud Run service to only specific and necessary API calls
• Understand the process needed to make the default permissions of a Cloud API more secure
• Use the client libraries to call other Google Cloud services (hands-on lab activity)

Módulo 6: Serving Requests

Temas:

• Custom Domains
• Global Load Balancer
  • URL Map
  • Frontend
  • Backend services
• Benefits and drawbacks of GLB over custom domain
• Types of GLB Backends
• Multi-region load balancing
• Multi-regional applications challenges
• Cloud CDN

Objetivos:

• Use Cloud CDN to improve the reliability and performance of an application
• Use path-based routing to combine multiple applications on one domain
• Route incoming requests to the Cloud Run service closest to clients

Módulo 7: Using Inbound and Outbound Access Control

Temas:

• Ingress settings
• Cloud Armor
• Using Cloud IAM to protect services
  • Understand how authenticated requests (IAM + OIDC tokens) work (builds on Module 5)
• VPC, VPC Access Connector
• Egress settings

Objetivos:

• Connecting your project to resources with a private IP
• Implementing controls to prevent outbound traffic to dangerous or unwanted hosts
• Implementing filters for inbound traffic using content-based rules
• Implementing controlled access to only specific service accounts

Módulo 8: Persisting Data

Temas:

• Understanding why you need to store data externally when running a workload on Cloud Run.
• Connect with Cloud SQL from Cloud Run
  • Understand how it works (managed Cloud SQL Proxy)
• Managing concurrency as a way to safeguard performance (understand why and when)
• Connecting with Memorystore
• VPC Connector
  • Challenges with scaling Memorystore (throughput)
• Briefly introduce Cloud Storage, Firestore and Cloud Spanner, while reinforcing how the client libraries use the built-in service account to connect (Module 5 is prerequisite knowledge).
• Multi-region data storage (and what Spanner and Firestore can do for you)

Objetivos:

• Understand how to connect your application with Cloud SQL to store relational data
• Use a VPC Connector to reach a private Memorystore instance
• Understand how to connect with Cloud Storage, Spanner and Firestore

Módulo 9: Implementing Service-to-Service Communication

Temas:

• Understanding Cloud Pub/Sub
  • Understanding topics, push subscriptions
  • Idempotency (Handling retries and at-least-once invocation)
    • Event ID, design for resume, or use a lease
  • Handling undeliverable messages
• How to asynchronously schedule a background task on a different service
• Cloud Tasks, and when to choose it over Cloud Pub/Sub
• Benefits of using Pub/Sub to pass messages over making sync RPC requests
• Learn about services in Google Cloud with a built-in integration to push events to Pub/Sub (Cloud Build, Artifact Registry, Cloud Storage, IOT
• Core, BigQuery)
• Cloud Scheduler to invoke services on a schedule.
• CloudEvents
• EventArc, and how to consume Audit logs
  • What to expect now, and how EventArc will develop over time

Objetivos:

• Using Cloud Pub/Sub to send messages between services
• Discovering the URL of other Cloud Run services
• Receiving events from other Google Cloud services
• Processing background tasks asynchronously

Módulo 10: Orchestrating and Automating Serverless Workflows

Temas:

• Conceptual overview of Cloud Workflows
• Invoking and passing parameters
• Understand steps and jumps
• Defining, using and passing values with variables
• Using the switch statement to add logic
• Workflow visualization
• Calling HTTPS endpoints
• Calling an authenticated Cloud Run service
• Example: polling API for completion

Objetivos:

• Understand the capabilities of Cloud Workflows
• Learn how to model a simple workflow with steps and conditional jumps
• Integrating Cloud Run with Cloud Workflows
• Understand how to invoke workflows

Documentación oficial para el curso Application Development with Cloud Run.

• Formador certificado por Google Cloud.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Define what Google Contact Center AI is
• Explain how Dialogflow can be used in contact center applications
• Describe how natural language understanding (NLU) is used to enable Dialogflow conversations
• Implement a chat virtual agent
• Implement a voice virtual agent
• Describe options for storing parameters and fulfilling user requests
• Deploy a virtual agent to production
• Identify best practices for design and deployment of virtual agents
• Identify key aspects, such as security and compliance in the context of contact centers.

Modulo 0: Course Overview

Modulo 1: Overview of Contact Center AI

• Define what Contact Center AI (CCAI) is and what it can do for contact centers
• Identify each component of the CCAI Architecture: Speech Recognition Dialogflow, Speech Synthesis, Agent Assist, and Insights
• Describe the role each component plays in a CCAI solution

Modulo 2: Google Implementation Methodology

• Identify the stages of the Google Implementation Methodology
• Enumerate the key activities of each implementation stage
• Acknowledge how to use Google's support assets for Partners

Modulo 3: Conversational Experiences

• List the basic principles of a conversational experience
• Explain the role of conversation virtual agents in a conversational experience
• Articulate how STT (speech to text) can determine the quality of a conversational experience
• Demonstrate and test how speech adaptation can improve the speech recognition accuracy of the agent
• Recognize the different NLU (natural language understanding) and NLP(natural language processing) techniques and the role they play on conversational experiences
• Explain the different elements of a conversation (intents, entities, etc)
• Use sentiment analysis to help with the achievement of a higher-quality conversational experience
• Improve conversational experiences by choosing different TTS voices(Wavenet vs Standard)
• Modify the speed and pitch of a synthesized voice
• Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage

Modulo 4: Fundamentals of building conversations with Dialogflow

• Identify user roles and their journeys
• Write personas for virtual agents and users
• Model user-agent interactions
• List the basics elements of the Dialogflow user interface
• Build a virtual agent to handle identified user journeys
• Train the NLU model through the Dialogflow console
• Define and test intents for a basic agent
• Train the agent to handle expected and unexpected user scenarios
• Recognize the different types of entities and when to use them
• Create entities
• Define and test entities on a basic agent
• Implement slot filling using the Dialogflow UI
• Describe when Mega Agent might be used

Modulo 5: Maintaining context in a conversation

• Create follow up intentsRecognize the scenarios in which context should be used
• Identify the possible statuses of a context (active versus inactive context)
• Implement dialogs using input and output contexts

Modulo 6: Moving from Chat agent to Voice agent

• Describe two ways that the media type changes the conversation
• Configure the telephony gateway for testing
• Test a basic voice agent
• Modify the voice of the agent
• Show how the different media types can have different responses
• Consider the modifications needed when moving to production
• Be aware of the telephony integration for voice in a production environment

Modulo 7: Taking actions with fulfillment

• Define the role of fulfillment with respect to CCAI
• Characterize what needs to be collected in order to fulfill a request
• Identify existing backend systems on the customer infrastructure
• Use Firestore to store mappings returned from functions
• Appreciate that the interaction with customers' data storages will vary based on the customer's data warehouses
• Implement fulfillment using Cloud Functions
• Implement fulfillment using Python on AppEngine
• Describe the use of Apigee for application deployment

Modulo 8: Testing and Logging

• Debug a virtual agent by testing intent accuracy
• Debug fulfillment by testing the different functions and integrations with backend systems through API calls
• Implement version control to achieve more scalable collaboration
• Log conversations using Cloud Logging
• Recognize ways that audits can be performed

Modulo 9: Environment Management

• Create Draft and Published versions of your virtual agent
• Create environments where your virtual agent will be published
• Load a saved version of your virtual agent to Draft
• Change which version is loaded to an environment

Modulo 10: Intelligent Assistance for Live Agents

• Recognize use cases where Agent Assist adds value
• Identify, collect, and curate documents for knowledge base construction
• Set up knowledge bases
• Describe how FAQ Assist works
• Describe how Document Assist works
• Describe how the Agent Assist UI works
• Describe how Dialogflow Assist works
• Describe how Smart Reply works
• Describe how real-time entity extraction works

Modulo 11: Integrating a virtual agent with third parties

• Use the Dialogflow API to programmatically create and modify the virtual agent
• Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN
• Replace existing head intent detection on IVRs with Dialogflow intents
• Describe virtual agent integration with Google Assistant
• Describe virtual agent integration with messaging platforms
• Describe virtual agent integration with CRM platforms (eg. Salesforce and Zendesk)
• Describe virtual agent integration with enterprise communication platforms(eg. Genesys, Avaya, Cisco, and Twilio)
• Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design
• Incorporate IVR features in the virtual agent.

Modulo 12: Drawing insights from call recordings

• Analyze audio recordings using the Speech Analytics Framework (SAF)

Modulo 13: Methods of compliance with federal regulations

• Describe two ways that security can be implemented on a Contact CenterAI integration
• Identify current compliance measures and scenarios where compliance is needed

Modulo 14: Best practices for virtual agents

• Convert pattern matching and decision trees to smart conversational design
• Recognize situations that require escalation to a human agent
• Support multiple platforms, devices, languages, and dialects
• Use Diagflow’s built-in analytics to assess the health of the virtual agent
• Perform agent validation through the Dialogflow UI
• Monitor conversations and Agent Assist
• Institute a DevOps and version control framework for agent development and maintenance
• Consider enabling spell correction to increase the virtual agent's accuracy

Modulo 15: Course Summary

• Recapitulate was covered during this course

• 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

• Define Google CCAI
• Explain how Dialogflow can be used in Contact Center applications
• Implement a virtual agent using Dialogflow CX
• Read and write data from Firestore using Cloud Functions
• Use Dialogflow tools and cloud logging for troubleshooting
• Describe how to manage virtual agent environments
• Identify general best practices for virtual agents
• Identify key aspects such as security and compliance in the context of contact centers
• Analyze audio recordings using the Speech Analytics Framework (SAF)
• Recognize use cases where Agent Assist adds value.

Modulo 1: Overview of Contact Center AI

• Define what Contact Center AI (CCAI) is and what it can do for contact centers
• Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI
• Describe the role each component plays in a CCAI solution

Modulo 2: Conversational Experiences

• List the basic principles of a conversational experience
• Explain the role of Conversation virtual agents in a conversation experience
• Articulate how STT (Speech to Text) can determine the quality of aconversation experience
• Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent
• Recognize the different NLU (Natural Language Understanding) andNLP (Natural Language Processing) techniques and the role they play on conversation experiences

Modulo 3: Fundamentals of Designing Conversations

• Identify user roles and their journeys
• Write personas for virtual agents and users
• Model user-agent interactions

Modulo 4: Dialogflow Product Options

• Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX)
• Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX
• Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX
• List the basic elements of the Dialogflow user interface

Modulo 5: Course Review

• Review what was covered in the course as relates to the objectives

Modulo 6: Fundamentals of building conversations with Dialogflow CX

• List the basic elements of the Dialogflow CX User Interface
• Create entities
• Create intents and form fill entities in training phrases
• Train the NLU model through the Dialogflow console
• Build a basic virtual agent to handle identified user journeys

Modulo 7: Scaling with standalone flows

• Recognize the scenarios in which standalone flows can help scale your virtual agent
• Implement a flow that uses other flows

Modulo 8: Using route groups for reusable routes

• Define the concept of route groups with respect to Dialogflow CX
• Create a route group
• Recognize the scenarios in which route groups should be used
• Identify the possible scope of a route group
• Implement a flow that uses a route group

Modulo 9: Course Review

• Review what was covered in the course as relates to the objectives

Modulo 10: Testing and logging

• Use Dialogflow tools for troubleshooting
• Use Google Cloud tools for debugging your virtual agent
• Review logs generated by virtual agent activity
• Recognize ways an audit can be performed

Modulo 11: Taking Actions with Fulfillment

• Characterize the role of fulfillment with respect to Contact Center AI Implement a virtual agent using Dialogflow ES
• Use Cloud Firestore to store customer data
• Implement fulfillment using Cloud Functions to read and write Firestore data
• Describe the use of Apigee for application deployment

Modulo 12: Integrating Virtual Agents

• Describe how to use the Dialogflow API to programmatically create and modifythe virtual agent
• Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN
• Describe how to replace existing head intent detection on IVRs with Dialogflow intents
• Describe virtual agent integration with Google Assistant
• Describe virtual agent integration with messaging platforms
• Describe virtual agent integration with CRM platforms (such as Salesforceand Zendesk)
• Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio)
• Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design
• Describe how to incorporate IVR features in the virtual agent

Modulo 13: Course Review

• Review what was covered in the course as relates to the objectives

Modulo 14: Environment Management

• Create Draft and Published versions of your virtual agent
• Create environments where your virtual agent will be published
• Load a saved version of your virtual agent to Draft
• Change which version is loaded to an environment

Modulo 15: Drawing Insights from Recordings with SAF

• Analyze audio recordings using the Speech Analytics Framework (SAF)

Modulo 16: Intelligence Assistance for Live Agents

• Recognize use cases where Agent Assist adds value
• Identify, collect and curate documents for knowledge base construction
• Describe how to set up knowledge bases
• Describe how FAQ Assist works
• Describe how Document Assist works
• Describe how the Agent Assist UI worksDescribe how Dialogflow Assist works
• Describe how Smart Reply works
• Describe how Real-time entity extraction works

Modulo 17: Compliance and Security

• Describe two ways security can be implemented on a CCAI integration
• Identify current compliance measures and scenarios where compliance is needed

Modulo 18: Best Practices

• Convert pattern matching and decision trees to smart conversational design
• Recognize situations that require escalation to a human agen
• Support multiple platforms, devices, languages, and dialects
• Use Diagflow’s built-in analytics to assess the health of the virtual agent
• Perform agent validation through the Dialogflow UI
• Monitor conversations and Agent Assist
• Institute a DevOps and version control framework for agent development and maintenance
• Consider enabling spell correction to increase the virtual agent's accuracy

Modulo 19: Implementation Methodology

• Identify the stages of the Google Enterprise Sales Process
• Describe the Partner role in the Enterprise Sales Process
• Detail the steps in a Contact Center AI project using Google’s ESP
• Describe the key activities of the Implementation Phase in ESP
• Locate and understand how to use Google's support assets for Partners

• 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

• Define Google CCAI
• Explain how Dialogflow can be used in Contact Center applications
• Implement a virtual agent using Dialogflow ES
• Read and write data from Firestore using Cloud Functions
• Use Dialogflow tools and cloud logging for troubleshooting
• Describe how to manage virtual agent environments
• Identify general best practices for virtual agents
• Identify key aspects such as security and compliance in the context of contact centers
• Analyze audio recordings using the Speech AnalyticsFramework (SAF)
• Recognize use cases where Agent Assist adds value

Modulo 1: Overview of Contact Center AI

• Define what Contact Center AI (CCAI) is and what it can do for contact centers
• Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI
• Describe the role each component plays in a CCAI solution

Modulo 2: Conversational Experiences

• List the basic principles of a conversational experience
• Explain the role of Conversation virtual agents in a conversation experience
• Articulate how STT (Speech to Text) can determine the quality of aconversation experience
• Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent
• Recognize the different NLU (Natural Language Understanding) andNLP (Natural Language Processing) techniques and the role they play on conversation experiences

Modulo 3: Fundamentals of Designing Conversations

• Identify user roles and their journeys
• Write personas for virtual agents and users
• Model user-agent interactions

Modulo 4: Dialogflow Product Options

• Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX)
• Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX
• Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX
• List the basic elements of the Dialogflow user interface

Modulo 5: Course Review

• Review what was covered in the course as relates to the objectives

Modulo 6: Fundamentals of building conversations with Dialogflow ES

• List the basic elements of the Dialogflow CX User Interface.• List the basic elements of the Dialogflow CX User Interface
• Build a virtual agent to handle identified user journeys
• Train the NLU model through the Dialogflow console
• Define and test intents for a basic agent
• Train the agent to handle expected and unexpected user scenarios
• Recognize the different types of entities and when to use them
• Create entities
• Define and test entities on a basic agent
• Implement slot filling using the Dialogflow UI
• Describe when Mega Agent might be used
• Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ

Modulo 7: Maintaining Context in a Conversation

• Create follow-up intents
• Recognize the scenarios in which context should be used
• Identify the possible statuses of a context (active versus inactive context)
• Implement dialogs using input and output contexts

Modulo 8: Moving From Chat to Voice Virtual Agent

• Describe two ways that the media type changes the conversation
• Configure the telephony gateway for testing
• Test a basic voice agent
• Modify the voice of the agent
• Show how the different media types can have different responses
• Consider the modifications needed when moving to production
• Be aware of the telephony integration for voice in a production environment

Modulo 9: Course Review

• Review what was covered in the course as relates to the objectives

Modulo 10: Testing and logging

• Use Dialogflow tools for troubleshooting
• Use Google Cloud tools for debugging your virtual agent
• Review logs generated by virtual agent activity
• Recognize ways an audit can be performed

Modulo 11: Taking Actions with Fulfillment

• Characterize the role of fulfillment with respect to Contact Center AI
• Implement a virtual agent using Dialogflow ES
• Use Cloud Firestore to store customer data
• Implement fulfillment using Cloud Functions to read and write Firestore data
• Describe the use of Apigee for application deployment

Modulo 12: Integrating Virtual Agents

• Quiz - IVR Features
  Quiz - Contact Center AI integration points
• Quiz - Common Platforms of

Modulo 13: Course Review

• Review what was covered in the course as relates to the objectives

Modulo 14: Environment Management

• Create Draft and Published versions of your virtual agent
• reate environments where your virtual agent will be published
• Load a saved version of your virtual agent to Draft
• Change which version is loaded to an environment

Modulo 15: Drawing Insights from Recordings with SAF

• Analyze audio recordings using the Speech Analytics Framework (SAF)

Modulo 16: Intelligence Assistance for Live Agents

• Recognize use cases where Agent Assist adds value
• Identify, collect and curate documents for knowledge base construction
• Describe how to set up knowledge bases
• Describe how FAQ Assist works
• Describe how Document Assist works
• Describe how the Agent Assist UI works
• Describe how Dialogflow Assist works
• Describe how Smart Reply works
• Describe how Real-time entity extraction works

Modulo 17: Compliance and Security

• Describe two ways security can be implemented on a CCAI integration
• Identify current compliance measures and scenarios where compliance is needed

Modulo 18: Best Practices

• Convert pattern matching and decision trees to smart conversational design
• Recognize situations that require escalation to a human agent
• Support multiple platforms, devices, languages, and dialects
• Use Diagflow’s built-in analytics to assess the health of the virtual agen
• Perform agent validation through the Dialogflow UI
• Monitor conversations and Agent Assist
• Institute a DevOps and version control framework for agent development and maintenance
• Consider enabling spell correction to increase the virtual agent's accuracy

Modulo 19: Implementation Methodology

• Identify the stages of the Google Enterprise Sales Process
• Describe the Partner role in the Enterprise Sales Process
• Detail the steps in a Contact Center AI project using Google’s ESP
• Describe the key activities of the Implementation Phase in ESP
• Locate and understand how to use Google's support assets for Partners

Modulo 20: Course Review

• Review what was covered in the course as relates to the objectives

• 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

Este curso está dirigido a ingenieros de datos y analistas de datos.

• Identificar la necesidad de integración de datos.
• Comprender las capacidades que Cloud Data Fusion proporciona como plataforma de integración de datos.
• Identificar casos de uso para la posible implementación de Cloud Data Fusion.
• Conocer los componentes principales de Cloud Data Fusion.
• Diseñar y ejecutar pipelines de procesamiento de datos por lotes y en tiempo real.
• Trabajar con Wrangler para construir transformaciones de datos.
• Utilizar conectores para integrar datos de diversas fuentes y formatos.
• Configurar el entorno de ejecución; supervisar y solucionar problemas de ejecución de canalizaciones.
• Comprender la relación entre los metadatos y el linaje de los datos.

Módulo 1: Introduction to data integration and Cloud Data Fusion

Contenidos:

• Data integration: what, why, challenges.
• Data integration tools used in industry.
• User personas.
• Introduction to Cloud Data Fusion.
• Data integration critical capabilities.
• Cloud Data Fusion UI components.

Objetivos:

• Understand the need for data integration.
• List the situations/cases where data integration can help businesses.
• List the available data integration platforms and tools.
• Identify the challenges with data integration.
• Understand the use of Cloud Data Fusion as a data integration platform.
• Create a Cloud Data Fusion instance.
• Familiarize with core framework and major components in Cloud Data Fusion.

Módulo 2: Building pipelines

Contenidos:

• Cloud Data Fusion architecture.
• Core concepts.
• Data pipelines and directed acyclic graphs (DAG).
• Pipeline Lifecycle.
• Designing pipelines in Pipeline Studio.

Objetivos:

• Understand Cloud Data Fusion architecture.
• Define what a data pipeline is.
• Understand the DAG representation of a data pipeline.
• Learn to use Pipeline Studio and its components.
• Design a simple pipeline using Pipeline Studio.
• Deploy and execute a pipeline.

Módulo 3: Designing complex pipelines

Contenidos:

• Branching, Merging and Joining.
• Actions and Notifications.
• Error handling and Macros.
• Pipeline Configurations, Scheduling, Import and Export.

Objetivos:

• Perform branching, merging, and join opeations.
• Execute pipeline with runtime arguments using macros.
• Work with error handlers.
• Execute pre- and post-pipeline executions with help of actions and notifications.
• Schedule pipelines for execution.
• Import and export existing pipelines.

Módulo 4: Pipeline execution environment

Contenidos:

• Schedules and triggers.
• Execution environment: Compute profile and provisioners.
• Monitoring pipelines.

Objetivos:

• Understand the composition of an execution environment.
• Configure your pipeline’s execution environment, logging, and Understand concepts like compute profile and provisioner.

• Create a compute profile.
• Create pipeline alerts.
• Monitor the pipeline under execution.

Módulo 5: Building Transformations and Preparing Data with Wrangler

Contenidos:

• Wrangler.
• Directives.
• User-defined directives.

Objetivos:

• Understand the use of Wrangler and its main components.
• Transform data using Wrangler UI.
• Transform data using directives/CLI methods.
• Create and use user-defined directives.

Módulo 6: Connectors and streaming pipelines

Contenidos:

• Understand the data integration architecture.
• List various connectors.
• Use the Cloud Data Loss Prevention (DLP) API.
• Understand the reference architecture of streaming pipelines.
• Build and execute a streaming pipeline.

Objetivos:

• Connectors.
• DLP.
• Reference architecture for streaming applications.
• Building streaming pipelines.

Módulo 7: Metadata and data lineage

Contenidos.

• Metadata.
• Data lineage.

Objetivos:

• List types of metadata.
• Differentiate between business, technical, and operational metadata.
• Understand what data lineage is.
• Understand the importance of maintaining data lineage.
• Differentiate between metadata and data lineage.

Módulo 8: Summary

Documentación oficial para el curso Data Integration with Cloud Data Fusion.

• Formador certificado por Google Cloud.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Derive insights from data using the analysis and visualization tools on Google Cloud Platform
• Load, clean, and transform data at scale with GoogleCloud Dataprep
• Explore and Visualize data using Google Data Studio
• Troubleshoot, optimize, and write high performance queries
• Practice with pre-built ML APIs for image and text understanding
• Train classification and forecasting ML models using SQLwith BQML

Modulo 1: Introduction to Data on the Google Cloud Platform

Modulo 2: Big Data Tools Overview

Modulo 3: Exploring your Data with SQL

Modulo 4: Google BigQuery Pricing

Modulo 5: Cleaning and Transforming your Data

Modulo 6: Storing and Exporting Data

Modulo 7: Ingesting New Datasets into Google BigQuery

Modulo 8: Data Visualization

Modulo 9: Joining and Merging Datasets

Modulo 10: Advanced Functions and Clauses

Modulo 11: Schema Design and Nested Data Structures

Modulo 12: More Visualization with Google Data Studio

Modulo 13: Optimizing for Performance

Modulo 14: Data Access

Modulo 15: Notebooks in the Cloud

Modulo 16: How Google does Machine Learning

Modulo 17: Applying Machine Learning to your Datasets (BQML)

• 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

Este curso está destinado a desarrolladores que sean responsables de:

• Extracción, carga, transformación, limpieza y validación de datos.
• Diseño de pipelines y arquitecturas para el procesamiento de datos.
• Integración de capacidades de análisis y machine learning en canalizaciones (pipelines) de datos.
• Consulta de conjuntos de datos, visualización de resultados de consultas y creación de informes.

• Diseñar y crear sistemas de procesamiento de datos en Google Cloud.
• Procesar datos por lotes y de transmisión mediante la implementación de canalizaciones (pipelines) de datos de escalado automático en Dataflow.
• Obtener información empresarial a partir de conjuntos de datos extremadamente grandes con BigQuery.
• Aprovechar los datos no estructurados con las APIs de Spark y ML en Dataproc.
• Habilitar conocimientos instantáneos a partir de la transmisión de datos.
• Comprender las APIs de ML y BigQuery ML, y aprender a usar AutoML para crear modelos potentes sin codificación.

Módulo 1: Introduction to Data Engineering

Temas:

• Explore the role of a data engineer
• Analyze data engineering challenges
• Introduction to BigQuery
• Data lakes and data warehouses
• Transactional databases versus data warehouses
• Partner effectively with other data teams
• Manage data access and governance
• Build production-ready pipelines
• Review Google Cloud customer case study

Objetivos:

• Understand the role of a data engineer
• Discuss benefits of doing data engineering in the cloud
• Discuss challenges of data engineering practice and how building data pipelines in the cloud helps to address these
• Review and understand the purpose of a data lake versus a data warehouse, and when to use which

Módulo 2: Building a Data Lake

Temas:

• Introduction to data lakes
• Data storage and ETL options on Google Cloud
• Building a data lake using Cloud Storage
• Securing Cloud Storage
• Storing all sorts of data types
• Cloud SQL as a relational data lake

Objetivos:

• Understand why Cloud Storage is a great option for building a data lake on Google Cloud
• Learn how to use Cloud SQL for a relational data lake

Módulo  3: Building a Data Warehouse

Temas:

• The modern data warehouse
• Introduction to BigQuery
• Getting started with BigQuery
• Loading data
• Exploring schemas
• Schema design
• Nested and repeated fields
• Optimizing with partitioning and clustering

Objetivos:

• Discuss requirements of a modern warehouse
• Understand why BigQuery is the scalable data warehousing solution on Google Cloud
• Understand core concepts of BigQuery and review options of loading data into BigQuery

Módulo  4: Introduction to Building Batch Data Pipelines

Temas:

• EL, ELT, ETL
• Quality considerations
• How to carry out operations in BigQuery
• Shortcomings
• ETL to solve data quality issues

Objetivos:

• Review different methods of loading data into your data lakes and warehouses: EL, ELT, and ETL
• Discuss data quality considerations and when to use ETL instead of EL and ELT

Módulo 5: Executing Spark on Dataproc

Temas:

• The Hadoop ecosystem
• Run Hadoop on Dataproc
• Cloud Storage instead of HDFS
• Optimize Dataproc

Objetivos:

• Review the parts of the Hadoop ecosystem
• Learn how to lift and shift your existing Hadoop workloads to the cloud using Dataproc
• Understand considerations around using Cloud Storage instead of HDFS for storage
• Learn how to optimize Dataproc jobs

Módulo 6: Serverless Data Processing with Dataflow

Temas:

• Introduction to Dataflow
• Why customers value Dataflow
• Dataflow pipelines
• Aggregating with GroupByKey and Combine
• Side inputs and windows
• Dataflow templates
• Dataflow SQL

Objetivos:

• Understand how to decide between Dataflow and Dataproc for processing data pipelines
• Understand the features that customers value in Dataflow
• Discuss core concepts in Dataflow
• Review the use of Dataflow templates and SQL

Módulo 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

Temas:

• Building batch data pipelines visually with Cloud Data Fusion
• Components
• UI overview
• Building a pipeline
• Exploring data using Wrangler
• Orchestrating work between Google Cloud services with Cloud Composer
• Apache Airflow environment
• DAGs and operators
• Workflow scheduling
• Monitoring and logging

Objetivos:

• Discuss how to manage your data pipelines with Data Fusion and Cloud Composer
• Understand Data Fusion’s visual design capabilities
• Learn how Cloud Composer can help to orchestrate the work across multiple Google Cloud services

Módulo 8: Introduction to Processing Streaming Data

Temas:

Process Streaming Data

Objetivos:

• Explain streaming data processing
• Describe the challenges with streaming data
• Identify the Google Cloud products and tools that can help address streaming data challenges

Módulo 9: Serverless Messaging with Pub/Sub

Temas:

• Introduction to Pub/Sub
• Pub/Sub push versus pull
• Publishing with Pub/Sub code

Objetivos:

• Describe the Pub/Sub service
• Understand how Pub/Sub works
• Gain hands-on Pub/Sub experience with a lab that simulates real-time streaming sensor data

Módulo 10: Dataflow Streaming Features

Temas:

• Steaming data challenges
• Dataflow windowing

Objetivos:

• Understand the Dataflow service
• Build a stream processing pipeline for live traffic data
• Demonstrate how to handle late data using watermarks, triggers, and accumulation

Módulo 11: High-Throughput BigQuery and Bigtable Streaming Features

Temas:

• Streaming into BigQuery and visualizing results
• High-throughput streaming with Cloud Bigtable
• Optimizing Cloud Bigtable performance

Objetivos:

• Learn how to perform ad hoc analysis on streaming data using BigQuery and dashboards
• Understand how Cloud Bigtable is a low-latency solution
• Describe how to architect for Bigtable and how to ingest data into Bigtable
• Highlight performance considerations for the relevant services

Módulo  12: Advanced BigQuery Functionality and Performance

Temas:

• Analytic window functions
• Use With clauses
• GIS functions
• Performance considerations

Objetivos:

• Review some of BigQuery’s advanced analysis capabilities
• Discuss ways to improve query performance

Módulo 13: Introduction to Analytics and AI

Temas:

• What is AI?
• From ad-hoc data analysis to data-driven decisions
• Options for ML models on Google Cloud

Objetivos:

• Understand the proposition that ML adds value to your data
• Understand the relationship between ML, AI, and Deep Learning
• Identify ML options on Google Cloud

Módulo 14: Prebuilt ML Model APIs for Unstructured Data

Temas:

• Unstructured data is hard
• ML APIs for enriching data

Objetivos:

• Discuss challenges when working with unstructured data
• Learn the applications of ready-to-use ML APIs on unstructured data

Módulo 15: Big Data Analytics with Notebooks

Temas:

• What’s a notebook?
• BigQuery magic and ties to Pandas

Objetivos:

• Introduce Notebooks as a tool for prototyping ML solutions
  Learn to execute BigQuery commands from Notebooks

Módulo 16: Production ML Pipelines with Kubeflow

Temas:

• Ways to do ML on Google Cloud
• Vertex AI Pipelines
• AI Hub

Objetivos:

• Describe options available for building custom ML models
• Understand the use of tools like Vertex AI Pipelines

Módulo 17: Custom Model Building with SQL in BigQuery ML

Temas:

• BigQuery ML for quick model building
• Supported models

Objetivos:

• Learn how to create ML models by using SQL syntax in BigQuery
• Demonstrate building different kinds of ML models using BigQuery ML

Módulo 18: Custom Model Building with AutoML

Temas:

• Why AutoML?
• AutoML Vision
• AutoML NLP
• AutoML tables

Objetivos:

• Explore various AutoML products used in machine learning
• Learn to use AutoML to create powerful models without coding

Documentación oficial para el curso Google Cloud Big Data and Machine Learning Fundamentals.

• Formador certificado por Google Cloud.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

Desarrolladores de aplicaciones que deseen crear aplicaciones nativas de la nube o rediseñar aplicaciones existentes que sean ejecutadas en Google Cloud.

• Utilizar las mejores prácticas para el desarrollo de aplicaciones.
• Seleccionar la opción de almacenamiento de datos adecuada para los datos de la aplicación.
• Implementar la gestión de identidades federadas.
• Desarrollar microservicios o componentes de aplicación poco acoplados.
• Integrar componentes de aplicaciones y fuentes de datos.
• Depurar, rastrear y monitorear aplicaciones.
• Realizar implementaciones repetibles con contenedores y servicios de implementación.
• Seleccionar el entorno de tiempo de ejecución de la aplicación adecuado.

Módulo 1: Best Practices for Application Development

Temas:

• 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

Objetivos:

• Design and develop secure, scalable, reliable, loosely coupled application components and microservices.
• Understand how to rearchitect applications for the cloud.

Módulo 2: Getting Started with Google Cloud Development

Temas:

• Overview of Google Cloud services for apps and scripts:
  • Google Cloud APIs
  • Cloud SDK
  • Cloud Client Libraries
  • Cloud Shell
  • Cloud Code
• Demo: Google APIs Explorer
• Lab: Setting up a Development Environment

Objetivos:

Identify different Google Cloud services for hosting applications and scripts.

Módulo 3: Overview of Data Storage Options

Temas:

• 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

Objetivos:

Choose the appropriate data storage option for application data.

Módulo 4: Best Practices for Using Datastore

Temas:

• Best practices related to using Firestore in Datastore mode for:
  • Queries
  • Built-in and composite indexes
  • Inserting and deleting data (batch operations)
  • Transactions
  • Error handling
• Demo: Explore Datastore
• Demo: Use Dataflow to Bulk-load Data into Datastore
• Lab: Storing Application Data in Datastore

Objetivos:

• Bulk-load data into Firestore by using Dataflow.
• Understand best practices related to queries, built in and composite indexes, inserting and deleting data (batch operations), and transactions error handling.

Módulo 5: Performing Operations on Buckets and Objects

Temas:

• 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

Objetivos:

• Understand Cloud Storage concepts.
• Differentiate between strongly consistent and eventually consistent operations.
• Access Cloud Storage through request endpoints.
• Use object composition to upload an object in parallel.
• Use truncated exponential backoff to deal with network failures.

Módulo 6: Best Practices for Using Cloud Storage

Temas:

• 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

Objetivos:

Understand how to name buckets for static websites and other uses, how to name objects (from an access distribution perspective, and performance considerations.

Módulo 7: Handling Authentication and Authorization

Temas:

• 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

Objetivos:

Implement federated identity management.

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

Temas:

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

Objetivos:

• Understand Pub/Sub topics, publishers, and subscribers.
• Understand pull and push subscriptions.
• Explore use cases for Pub/Sub.

Módulo 9: Adding Intelligence to Your Application

Temas:

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

Objetivos:

Explore pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language API.

Módulo 10: Using Cloud Functions for Event-Driven Processing

Temas:

• 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

Objetivos:

Use Cloud Functions for event-driven processing.

Módulo 11: Managing APIs with Cloud Endpoints

Temas:

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

Objetivos:

Understand OpenAPI deployment configuration.

Módulo 12: Deploying Applications

Temas:

• 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

Objetivos:

• Understand how to create and store container images.
• Create repeatable deployments with deployment configuration and templates.

Módulo 13: Compute Options for Your Application

Temas:

• 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

Objetivos:

Explore considerations for choosing a compute option for your application or service.

Módulo 14: Debugging, Monitoring, and Tuning Performance

Temas:

• 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

Objetivos:

• Debug an application error by using Cloud Debugger and Error Reporting.
• Use Cloud Monitoring and Cloud Trace to trace a request across services, observe, and optimize performance.

Documentación oficial para el curso Developing Applications with Google Cloud.

• Formador certificado por Google Cloud.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Cloud Solutions Architects, DevOps Engineers.
• Personas que utilicen Google Cloud para crear nuevas soluciones o para integrar sistemas existentes, entornos de aplicación e infraestructura, con un enfoque en Compute Engine.

• Configurar redes VPC y máquinas virtuales.
• Administrar Identity and Access Management para recursos.
• Implementar servicios de almacenamiento de datos en Google Cloud.
• Administrar y examinar la facturación de los recursos de Google Cloud.
• Supervisar los recursos mediante los servicios de Google Cloud.
• Conectar tu infraestructura a Google Cloud.
• Configurar balanceadores de carga y escalado automático para instancias de VM.
• Automatizar la implementación de los servicios de infraestructura de Google Cloud.
• Aprovechar los servicios administrados en Google Cloud.

Módulo 1: Introduction to Google Cloud

• List the different ways of interacting with Google Cloud
• Use the Cloud Console and Cloud Shell
• Create Cloud Storage buckets
• Use the Google Cloud Marketplace to deploy solutions

Módulo 2: Virtual Networks

• List the VPC objects in Google Cloud
• Differentiate between the different types of VPC networks
• Implement VPC networks and firewall rules
• Implement Private Google Access and Cloud NAT

Módulo 3: Virtual Machines

• Recall the CPU and memory options for virtual machines
• Describe the disk options for virtual machines
• Explain VM pricing and discounts
• Use Compute Engine to create and customize VM instances

Módulo 4: Cloud IAM

• Describe the Cloud IAM resource hierarchy
• Explain the different types of IAM roles
• Recall the different types of IAM members
• Implement access control for resources using Cloud IAM

Módulo 5: Storage and Database Services

• Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable
• Choose a data storage service based on your requirements
• Implement data storage services

Módulo 6: Resource Management

• Describe the cloud resource manager hierarchy
• Recognize how quotas protect Google Cloud customers
• Use labels to organize resources
• Explain the behavior of budget alerts in Google Cloud
• Examine billing data with BigQuery

Módulo 7: Resource Monitoring

• Describe the services for monitoring, logging, error reporting, tracing, and debugging
• Create charts, alerts, and uptime checks for resources with Cloud Monitoring
• Use Cloud Debugger to identify and fix errors

Módulo 8: Interconnecting Networks

• Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud
• Determine which Google Cloud interconnect or peering service to use in specific circumstances
• Create and configure VPN gateways
• Recall when to use Shared VPC and when to use VPC Network Peering

Módulo 9: Load Balancing and Autoscaling

• Recall the various load balancing services
• Determine which Google Cloud load balancer to use in specific circumstances
• Describe autoscaling behavior
• Configure load balancers and autoscaling

Módulo 10: Infrastructure Automation

• Automate the deployment of Google Cloud services using Deployment Manager or Terraform
• Outline the Google Cloud Marketplace

Módulo 11: Managed Services

• Describe the managed services for data processing in Google Cloud

Documentación oficial para el curso Architecting with Google Compute Engine.

• Formador certificado por Google Cloud.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Arquitectos de nube.
• Arquitectos de soluciones.
• Personas que diseñan soluciones para infraestructuras de nube.

• Conocer cómo proteger las conexiones del punto de enlace de la red virtual privada (VPC) de Amazon Simple Storage Service (Amazon S3) en un entorno de laboratorio.
• Conocer las mejores prácticas para decidir qué servicios utilizar y cómo implementar la administración de permisos.
• Crear diseños híbridos de AWS destinados a atender los aumentos de tráfico y optimizar el trabajo remoto para garantizar la conformidad de los niveles 2 o 3 de seguridad FIPS 140-2.
• Aprender sobre soluciones y productos disponibles para diseñar una infraestructura híbrida.

Módulo 1: Reviewing Architecting Concepts

• Group Exercise: Review Architecting on AWS core best practices
• Lab 1: Securing Amazon S3 VPC Endpoint Communications

Módulo 2: Single to Multiple Accounts

• AWS Organizations for multi-account access and permissions
• AWS SSO to simplify access and authentication across AWS accounts and third-party services
• AWS Control Tower
• Permissions, access, and authentication

Módulo 3: Hybrid Connectivity

• AWS Client VPN authentication and control
• AWS Site-to-Site VPN
• AWS Direct Connect for hybrid public and private connections
• Increasing bandwidth and reducing cost
• Basic, high, and maximum resiliency
• Amazon Route 53 Resolver DNS resolution

Módulo 4: Specialized Infrastructure

• AWS Storage Gateway solutions
• On-demand VMware Cloud on AWS
• Extending cloud infrastructure services with AWS Outposts
• AWS Local Zones for latency-sensitive workloads
• Your 5G network with and without AWS Wavelength

Módulo 5: Connecting Networks

• Simplifying private subnet connections
• VPC isolation with a shared services VPC
• Transit Gateway Network Manager and VPC Reachability Analyzer
• AWS Resource Access Manager
• AWS PrivateLink and endpoint services
• Lab 2: Configuring Transit Gateways

Módulo 6: Containers

• Container solutions compared to virtual machines
• Docker benefits, components, solutions architecture, and versioning
• Container hosting on AWS to reduce cost
• Managed container services: Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (Amazon EKS)
• AWS Fargate
• Lab 3: Deploying an Application with Amazon EKS on Fargate

Módulo 7: Continuous Integration/Continuous Delivery (CI/CD)

• CI/CD solutions and impact
• CI/CD automation with AWS CodePipeline
• Deployment models
• AWS CloudFormation StackSets to improve deployment management

Módulo 8: High Availability and DDoS Protection

• Common DDoS attacks layers
• AWS WAF
• AWS WAF web access control lists (ACLs), real-time metrics, logs, and security automation
• AWS Shield Advanced services and AWS DDoS Response Team (DRT) services
• AWS Network Firewall and AWS Firewall Manager to protect accounts at scale

Módulo 9: Securing Data

• What cryptography is, why you would use it, and how to use it
• AWS KMS
• AWS CloudHSM architecture
• FIPS 140-2 Level 2 and Level 3 encryption
• Secrets Manager

Módulo 10: Large-Scale Data Stores

• Amazon S3 data storage management including storage class, inventory, metrics, and policies
• Data lake vs. data warehouse: Differences, benefits, and examples
• AWS Lake Formation solutions, security, and control
• Lab 4: Setting Up a Data Lake with Lake Formation

Módulo 11: Large-Scale Applications

• What edge services are and why you would use them
• Improve performance and mitigate risk with Amazon CloudFront
• Lambda@Edge
• AWS Global Accelerator: IP addresses, intelligent traffic distribution, and health checks
• Lab 5: Migrating an On-Premises NFS Share Using AWS DataSync and Storage Gateway

Módulo 12: Optimizing Cost

• On-premises and cloud acquisition/deprecation cycles
• Cloud cost management tools including reporting, control, and tagging
• Examples and analysis of the five pillars of cost optimization

Módulo 13: Migrating Workloads

• Business drivers and the process for migration
• Successful customer practices
• The 7 Rs to migrate and modernize
• Migration tools and services from AWS
• Migrating databases and large data stores
• AWS Schema Conversion Tool (AWS SCT)

Módulo 14: Capstone Project

• Use the Online Course Supplement (OCS) to review use cases, investigate data, and answer architecting design questions about Transit Gateway, hybrid connectivity, migration, and cost optimization

Documentación oficial del curso Advanced Architecting on AWS.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Todo aquel que desee obtener la certificación AWS Certified Solutions Architect Associate.
• Clientes y socios de AWS que tengan entre seis y doce meses de experiencia con AWS, incluida una sólida formación y experiencia en arquitectura.
• Personas que desempeñen roles de arquitecto de soluciones o arquitecto de la nube que diseñen servicios o arquitecturas en la nube de AWS.
• Personas que no hayan realizado el curso Architecting on AWS en los últimos seis meses aproximadamente.

• Tome decisiones sobre arquitecturas basadas en los principios y las mejores prácticas de AWS.
• Usar los servicios de AWS para hacer que su infraestructura sea escalable, confiable y de alta disponibilidad
• Usar AWS Managed Services para lograr mayor flexibilidad y resiliencia en una infraestructura.
• Aumentar la eficiencia de una infraestructura basada en AWS para incrementar el rendimiento y reducir costos.
• Utilizar el Well-Architected Framework para mejorar las arquitecturas con las soluciones de AWS.
• Navegar por la logística del proceso de evaluación, la estructura del examen y los tipos de preguntas.
• Identificar cómo se relacionan las preguntas con los conceptos de arquitectura de AWS.
• Interpretar los conceptos evaluados mediante preguntas de examen.

Architecting on AWS

• Módulo 1: Introduction
• Módulo 2: The Simplest Architectures
• Hands-On Lab 1: Hosting a Static Website
• Módulo 3: Adding a Compute Layer
• Módulo 4: Adding a Database Layer
• Hands-On Lab 2: Deploying a Web Application on AWS
• Módulo 5: Networking in AWS Part 1
• Hands-On Lab 3: Creating a Virtual Private Cloud
• Módulo 6: Networking in AWS Part 2
• Módulo 7: AWS Identity and Access Management (IAM)
• Módulo 8: Elasticity, High Availability, and Monitoring
• Hands-On Lab 4: Creating a Highly Available Environment
• Módulo 9: Automation
• Hands-On Lab 5: Automating Infrastructure Deployment with AWS CloudFormation
• Módulo 10: Caching
• Módulo 11: Building Decoupled Architectures
• Módulo 12: Microservices and Serverless Architectures
• Hands-On Lab 6: Implementing a Serverless Architecture with AWS Managed Services
• Módulo 13: RTP/RPO and Backup Recovery Setup
• Módulo 14: Optimizations and Review

Preparación para el examen de certificación AWS Certified Solutions Architect Associate

• Módulo 0: The Exam
• Módulo 1: Design Resilient Architectures
• Módulo 2: Design Performant Architectures
• Módulo 3: Specify Secure Applications and Architectures
• Módulo 4: Design Cost-Optimized Architectures
• Módulo 5: Define Operationally Excellent Architectures
• Additional deep dive of AWS services
• Quiz #1
• Practice exam: AWS Certified Solutions Architect Associate
• Quiz #2
• Wrap-up

Documentación oficial del curso intensivo de preparación examen AWS Certified Solutions Architect Associate.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Ingenieros de datos.
• Arquitectos de soluciones.
• Ingenieros de red.
• Ingenieros de infraestructura que se preparen para realizar el examen de certificación AWS Certified Advanced Networking Specialty.

• Navegar por el examen AWS Certified Advanced Networking Specialty.
• Comprender los conceptos avanzados de redes en AWS para poder diseñar marcos de redes bien diseñados para las cargas de trabajo en Amazon Virtual Private Cloud (Amazon VPC).
• Conectar centros de datos en las instalaciones a Amazon VPC (AWS Direct Connect, VPN de AWS), que le permitan a AWS funcionar como una extensión del centro de datos.
• Aprovechar la automatización de la red para acelerar la implementación de cargas de trabajo y la migración de aplicaciones.
• Practicar las mejores prácticas de seguridad y solución de problemas de redes.
• Prepararte para el examen de certificación AWS Certified Advanced Networking Specialty.

• Módulo 1: Testing (center) information and expectations
• Módulo 2: Exam overview and structure
• Módulo 3: Content domains and question breakdown
• Módulo 4: Topics and concepts within content domains
• Módulo 5: Question structure and interpretation techniques
• Módulo 6: Practice exam questions

Documentación oficial del curso preparación de examen AWS Certified Advanced Networking Specialty.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Ingenieros de plataformas de datos.
• Administradores de bases de datos.
• Arquitectos de soluciones.

• Identificar tus fortalezas y debilidades en cada dominio del examen para que sepas en qué concentrarte cuando estudies para el examen.
• Describir los temas y conceptos técnicos que componen cada uno de los dominios del examen.
• Resumir la logística y mecánica del examen y sus preguntas.
• Usar estrategias efectivas para estudiar y tomar el examen.
• Prepararte para el examen de certificación AWS Certified Database Specialty.

Módulo 0: Course introduction

Módulo 1: Domain 1 – Workload-Specific Database Design

• Select the appropriate database services for specific types of data and workloads
• 1.2 Determine strategies for disaster recovery and high availability
• 1.3 Design database solutions for performance, compliance, and scalability
• 1.4 Compare the costs of database solutions
• Multiple question interactive domain review

Módulo 2: Domain 2 – Deployment and Migration

• 2.1 Automate database solution deployments
• 2.2 Determine data preparation and migration strategies
• 2.3 Execute and validate data migration
• Multiple question interactive domain review

Módulo 3: Domain 3 – Management and Operations

• 3.1 Determine maintenance tasks and processes
• 3.2 Determine backup and restore strategies
• 3.3 Manage the operational environment of a database solution
• Multiple question interactive domain review

Módulo 4: Domain 4 – Monitoring and Troubleshooting

• 4.1 Determine monitoring and alerting strategies
• 4.2 Troubleshoot and resolve common database issues
• 4.3 Optimize database performance
• Multiple question interactive domain review

Módulo 5: Domain 5 – Database Security

• 5.1 Encrypt data at rest and in transit
• 5.2 Evaluate auditing solutions
• 5.3 Determine access control and authentication mechanisms
• 5.4 Recognize potential security vulnerabilities within database solutions

Multiple question interactive domain review

Módulo 6: AWS Exam Preparation

• 20 sample exam questions

Módulo 7: Course Review and Wrap-Up

Documentación oficial del curso preparación de examen AWS Certified Database Specialty.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Ingenieros de plataformas de datos.
• Arquitectos de datos.
• Científicos de datos.
• Analistas de datos.
• Arquitectos de soluciones.

• Saber orientarse a lo largo del proceso evaluativo.
• Comprender la estructura del examen y los tipos de preguntas.
• Identificar cómo las preguntas se relacionan con los conceptos de análisis de datos de AWS.
• interpretar los conceptos que se evalúan con cada pregunta del examen.
• Prepararte para el examen de certificación AWS Certified Data Analytics Specialty.

Módulo 0: Introduction and Overview

Módulo 1: Domain 1 – Data Collection Systems

• Determine the operational characteristics of the collection system
• Select a collection system that handles the frequency, volume, and source of data
• Select a collection system that addresses the key properties of data, such as order, format, and compression
• Multiple question interactive domain review

Módulo 2: Domain 2 – Storage and Data Management Concerns

• Determine the operational characteristics of the storage system for analytics
• Determine data access and retrieval patterns
• Select an appropriate data layout, schema, structure, and format
• Define a data lifecycle based on usage patterns and business requirements
• Determine the appropriate system for cataloging data and managing metadata
• Multiple question interactive domain review

Módulo 3: Domain 3 – Data Processing Solutions

• Determine appropriate data processing solution requirements
• Design a solution for transforming and preparing data for analysis
• Automate and operationalize a data processing solution
• Multiple question interactive domain review

Módulo 4: Domain 4 – Analysis and Visualization of Analytical Data

• Determine the operational characteristics of the analysis and visualization solution
• Select the appropriate data analysis solution for a given scenario
• Select the appropriate data visualization solution for a given scenario
• Multiple question interactive domain review

Módulo 5: Domain 5 – Security of the Data Analysis System

• Select the appropriate authentication and authorization mechanisms
• Apply data protection and encryption techniques
• Apply data governance and compliance controls
• Multiple question interactive domain review

Módulo 6: AWS Exam Preparation

• Sample exam questions

Módulo 7: Course Review and Wrap-Up

Documentación oficial del curso de preparación de examen AWS Certified Data Analytics Specialty.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

Conocer la metodología Lean Six Sigma Yellow Belt

Modulo 1: ¿Qué es Six Sigma?

Modulo 2: Historia y aplicación de Six Sigma

Modulo 3: Otros métodos de calidad y mejora de procesos

Modulo 4: Conceptos Lean

Modulo 5: Conceptos básicos de Six Sigma

Modulo 6: Acercamiento al Problema

Modulo 7: ¿Qué es un Proceso?

Modulo 8: Calidad

Modulo 9: Selección de los proyectos adecuados

Modulo 10: Gestión básica de equipos Six Sigma

Modulo 11: Introducción a DMAIC y DMADV

• Documentación Oficial de Lean Six Sigma Yellow Belt

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

• Modo de Fallos y Análisis de Efectos
• Gestión de proyectos y equipos
• Probabilidad y Teorema del Límite Central
• Distribuciones estadísticas
• Estadísticas descriptivas
• Cómo realizar pruebas de hipótesis básicas
• Eliminación de residuos y Kaizen
• Gráficos de control básicos

Modulo 1: ¿Qué es Six Sigma?

Modulo 2: Historia y aplicación de Six Sigma

Modulo 3: Otros métodos de calidad y mejora de procesos

Modulo 4: Conceptos Lean

Modulo 5: Conceptos básicos de Six Sigma

Modulo 6: Abordando el Problema

Modulo 7: ¿Qué es un Proceso?

Modulo 8: Calidad

Modulo 9: Selección de los proyectos correctos

Modulo 10: Gestión básica de equipos Six Sigma

Modulo 11: Introducción a DMAIC y DMADV

• Definir
• Medida
• Analizar
• Mejorar
• Control

Modulo 12: Análisis Gráfico Intermedio

Modulo 13: Distribuciones de probabilidad normal

Modulo 14: Correlación y regresión

Modulo 15: Distribuciones de probabilidad no normales

Modulo 16: Evaluación de la hipótesis

Modulo 17: Tamaño de la muestra

Modulo 18: Gráficos de control avanzados

Modulo 19: Aplicando Estadísticas a procesos de Negocios a través de Six Sigma

• Documentación Oficial de Lean Six Sigma Green Belt

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

• Habilidades avanzadas de gestión de proyectos y equipos
• Conocimiento de la amplia lista de herramientas de lluvia de ideas y proyectos de Six Sigma
• Estadísticas intermedias a avanzadas
• Comprensión de otros programas de calidad y mejora de procesos, incluidos Lean y Total Quality Management
• Habilidad para diseñar procesos
• Capacidades avanzadas para diagramar procesos, incluidos diagramas de flujo y mapas de flujo de valor
• Uso de software para realizar análisis, como Excel o Minitab

Modulo 1: ¿Qué es Six Sigma?

Modulo 2: Historia y aplicacion de Six Sigma

Modulo 3: Otros métodos de calidad y mejora de procesos

Modulo 4: Conceptos Lean

Modulo 5: Conceptos básicos de Six Sigma

Modulo 6: Abordando el Problema

Modulo 7: ¿Qué es un proceso?

Modulo 8: Calidad

Modulo 9: Seleccion de los proyectos correctos

Modulo 10: Gestion básica de equipos Six Sigma

Modulo 11: Introduccion a DMAIC y DMADV

• Definir
• Medida
• Analizar
• Mejorar
• Control

Modulo 12: Análisis Gráfico Intermedio

Modulo 13: Distribuciones de probabilidad normal

Modulo 14: Correlacion y regresion

Modulo 15: Distribuciones de probabilidad no normales

Modulo 16: Evaluacion de la hipotesis

Modulo 17: Tamaño de la muestra

Modulo 18: Gráficos de control avanzados

Modulo 19: Aplicacion de estadísticas a aplicaciones comerciales a través de Six Sigma

Modulo 20: Introduccion a Minitab

Modulo 21: Gráficos y herramientas de calidad en Minitab

Modulo 22: El menú Estadísticas en Minitab

Modulo 23: Análisis de varianza (ANOVA de 1 way)

Modulo 24: Diseño de experimentos

Modulo 25: Interacciones, factoriales multinivel y creacion de experimentos

Modulo 26: Lluvia de ideas y herramientas de mejora de procesos

Modulo 27: Mapas de procesos

Modulo 28: Mapeo de flujo de valor

• Documentación Oficial de Lean Six Sigma Black Belt

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

• Definir y configurar Looker, definiendo las capacidades que proporciona para trabajar con datos
• Conocimientos en los cuatro conceptos analíticos centrales en Looker (dimensiones,
  medidas, filtros, pivotes)
• Usar dimensiones, medidas, filtros y pivotes para analizar y
  visualizar datos
• Cree métricas avanzadas instantáneamente con cálculos de tablas
• Cree y comparta visualizaciones usando Looks y tableros
• Utilice carpetas y tableros en Looker para administrar y organizar
  contenido

Modulo 1: Introduccion a Looker

• Definir Looker y las capacidades que proporciona para trabajar con datos
• Navegar por la interfaz de usuario de Looker para acceder a los datos y las funcionalidades

Modulo 2: Explicar los cuatro conceptos analíticos centrales en Looker (dimensiones, medidas, filtros, pivotes)

• Utilizar dimensiones para acceder a atributos de datos
• Utilizar medidas para agregar atributos de datos
• Combinar dimensiones y medidas para un análisis y visualizacion de datos más completos
• Filtrar dimensiones y medidas para analizar un subconjunto de los datos
• Utilizar pivotes para reestructurar y agrupar datos

Modulo 3: Cálculos de tablas

• Como se utilizan los cálculos de tablas y las funciones de compensacion para trabajar con datos en Looker
• Tipos de cálculos de tablas y funciones de compensacion disponibles en Looker
• Crear nuevas métricas instantáneamente con cálculos de tablas y funciones de compensacion

Modulo 4: Looks y Dashboards

• Como crear y entregar Looks y tableros en Looker para compartir resultados con las partes interesadas
• Crear y compartir informes de visualizacion independientes llamados Looks
• Creear y compartir paneles para combinar múltiples visualizaciones para un tema o dominio comercial
• Agregar contenido nuevo a un tablero existente
• Agregar filtros a un Look o tablero para un análisis flexible de un subconjunto de los datos Entregue datos fuera de Looker para el acceso de otras partes interesadas o sistemas

Modulo 5: Gestion de contenido

• Utilizar carpetas en Looker para organizar el contenido para la navegabilidad y el descubrimiento
• Crear y comparta tableros para centralizar el contenido que vive en diferentes carpetas en una instancia de Looker

• 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

• Thoroughly understand how ML can be used to improve business processes and create new value
• Thoroughly understand how ML can be used to improve business processes and create new value
• Explore common machine learning use cases implemented by businesses
• Identify the requirements to carry out an ML project, from assessing feasibility, to data preparation, model training, evaluation, and deployment
• Define data characteristics and biases that affect the quality of ML models
• Recognize key considerations for managing ML projects, including data strategy, governance, and project teams
• Pitch a custom ML use case that can meaningfully impact your business

Modulo 1: Introduction

• Differentiate between AI, machine learning, and deep learning
• Differentiate between AI, machine learning, and deep learning
• Describe the high-level uses of ML to improve business processes or to  create new value
• Begin assessing the feasibility of ML use cases

Modulo 2: What is Machine Learning?

• Differentiate between supervised and unsupervised machine learning problem types
• Differentiate between supervised and unsupervised machine learning problem types
• Identify examples of regression, classification, and clustering problem statements
• Recognize the core components of Google’s standard definition for ML and considerations for each when carrying out an ML project

Modulo 3: Employing ML

• Describe the end-to-end process to carry out an ML project and considerations within each phase
• Describe the end-to-end process to carry out an ML project and considerations within each phase
• Practice pitching a custom ML problem statement that has the potential to meaningfully impact your business

Modulo 4: Discovering ML Use Cases

• Discover common machine learning opportunities in day-to-day business processes

Modulo 5: How to Be Successful at ML

• Identify the requirements for businesses to successfully use ML

Modulo 6: Summary

• Summarize key concepts and tools covered in the course content
• Summarize key concepts and tools covered in the course content
• Compete for best ML use case presentation based on creativity, originality, and feasibility

• 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

• Arquitectos de soluciones.
• Ingenieros de diseño de soluciones.
• Desarrolladores que quieran comprender la arquitectura de AWS.

• Identificar las prácticas básicas de arquitectura de AWS.
• Identificar servicios y características para crear soluciones de TI resistentes, seguras y de alta disponibilidad en la nube de AWS.
• Aprender el proceso de diseño de soluciones de TI óptimas mediante el AWS Well-Architected Framework, basado en escenarios de la vida real.
• Explorar los servicios de AWS para la seguridad de las cuentas, las redes, la informática, el almacenamiento, las bases de datos, el monitoreo, la automatización, los contenedores, la arquitectura sin servidor, los servicios de borde, y las copias de seguridad y recuperación.

Módulo 1: Architecting Fundamentals

• AWS services
• AWS infrastructure
• AWS Well-Architected Framework
• Hands-on lab: Explore and interact with the AWS Management Console and AWS Command Line Interface

Módulo 2: Account Security

• Principals and identities
• Security policies
• Managing multiple accounts

Módulo 3: Networking 1

• IP addressing
• VPC fundamentals
• VPC traffic security

Módulo 4: Compute

• Compute services
• EC2 instances
• Storage for EC2 instances
• Amazon EC2 pricing options
• AWS Lambda
• Hands-On Lab: Build your Amazon VPC infrastructure

Módulo 5: Storage

• Storage services
• Amazon S3
• Shared file systems
• Data migration tools

Módulo 6: Database Services

• Database services
• Amazon RDS
• Amazon DynamoDB
• Database caching
• Database migration tools
• Hands-on Lab: Create a database layer in your Amazon VPC infrastructure

Módulo 7: Monitoring and Scaling

• Monitoring
• Alarms and events
• Load balancing
• Auto scaling
• Hands-on Lab: Configure high availability in your Amazon VPC

Módulo 8: Automation

• AWS CloudFormation
• Infrastructure management

Módulo 9: Containers

• Microservices
• Containers
• Container services

Módulo 10: Networking 2

• VPC endpoints
• VPC peering
• Hybrid networking
• AWS Transit Gateway

Módulo 11: Serverless

• What is serverless?
• Amazon API Gateway
• Amazon SQS
• Amazon SNS
• Amazon Kinesis
• AWS Step Functions
• Hands-on Lab: Build a serverless architecture

Módulo 12: Edge Services

• Edge fundamentals
• Amazon Route 53
• Amazon CloudFront
• DDoS protection
• AWS Outposts
• Hands-On Lab: Configure an Amazon CloudFront distribution with an Amazon S3 origin

Módulo 13: Backup and Recovery

• Disaster planning
• AWS Backup
• Recovery strategies
• Hands-on Lab: Capstone lab – Build an AWS Multi-Tier architecture. Participants review the concepts and services learned in class and build a solution based on a scenario. The lab environment provides partial solutions to promote analysis and reflection. Participants deploy a highly available architecture. The instructor is available for consultation.

Documentación oficial del curso Architecting on AWS.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Explicar los costos de los servicios básicos de AWS
• Determinar y estimar costos asociados con las cargas de trabajo en la nube actuales y futuras
• Utilizar estrategias y prácticas recomendadas para reducir los costos de AWS
• Utilizar herramientas de AWS para administrar, monitorear, alertar y optimizar los gastos de AWS

Modulo 0: Couse Overview

• Course introduction

Modulo 1: Introduction to Cloud Financial Management

• Introduction to Cloud Financial Management
• Four pillars of Cloud Financial Management

Modulo 2: Resource Tagging

• Tagging resources
• Hands-On Lab: Cost optimization: Control Resource Consumption Using Tagging and AWS Config

Modulo 3: Pricing and Cost

• Fundamentals of pricing
• AWS Free Tier
• Volume discounts
• Savings plans and Reserved Instances
• Demonstration: AWS Pricing Calculator

Modulo 4: AWS Billing, Reporting, and Monitoring

• Understanding AWS invoices
• Reporting and planning
• AWS Cost Explorer
• AWS Budgets
• Demonstration: AWS Billing Console
• Demonstration: AWS Cost Explorer
• Demonstration: Trusted Advisor
• Hands-On Lab: Cost optimization: Deploy Ephemeral Environments Using Amazon EC2 Auto Scaling

Modulo 5: Architecting for Cost: Compute

• Evolution of compute efficiency
• Amazon EC2 right-sizing
• Purchasing options
• Architect for Amazon EC2 Spot Instance
• Impact of software licensing
• Demonstration: Compute Optimizer
• Demonstration: Spot Instance Advisor
• Hands-On Lab: Cost optimization: Right Size Amazon EC2 Instances Using Amazon CloudWatch Metrics

Modulo 6: Architecting for Cost: Networking

• Data transfer costs
• Understand data costs for different services
• How to triage network costs
• Hands-On Lab: Cost optimization: Reduce Data Transfer Costs Using Amazon CloudFront and Endpoints

Modulo 7: Architecting for Cost: Storage

• Amazon EBS cost, pricing, and best practices
• Amazon S3 cost, pricing, and best practices
• Amazon EFS cost, pricing, and best practices
• Hands-On Lab: Cost optimization: Reduce Storage Costs Using Amazon S3 Lifecycle Management

Modulo 8: Architecting for Cost: Databases

• Amazon RDS cost, pricing, and best practices
• Amazon Aurora cost, pricing, and best practices
• Amazon DynamoDB cost, pricing, and best practices
• Amazon ElastiCache cost, pricing, and best practices
• Amazon Redshift cost, pricing, and best practices

Modulo 9: Cost Governance

• Setting up AWS Organizations
• AWS Systems Manager
• Hands-On Lab: Cost optimization: Reduce Compute Costs Using AWS Instance Scheduler

Modulo 10: Course Summary

• Course review

• Documentación del curso AWS Cloud Financial Management for Builders

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

• Establezca una zona de aterrizaje con AWS Control Tower
• Configurar AWS Organizations para crear un entorno de varias cuentas
• Implementación de la administración de identidades mediante usuarios y grupos de AWS Single Sign-On (SSO)
• Federar el acceso mediante AWS SSO

Modulo 1: Governance at Scale

• Governance at scale focal points
• Business and Technical Challenges

Modulo 2: Governance Automation

• Multi-account strategies, guidance, and architecture
• Environments for agility and governance at scale
• Governance with AWS Control Tower
• Use cases for governance at scale

Modulo 3: Preventive Controls

• Enterprise environment challenges for developers
• AWS Service Catalog
• Resource creation
• Workflows for provisioning accounts
• Preventive cost and security governance
• Self-service with existing IT service management (ITSM) tools

Lab 1: Deploy Resources for AWS Catalog

Modulo 4: Detective Controls

• Operations aspect of governance at scale
• Resource monitoring
• Configuration rules for auditing
• Operational insights
• Remediation
• Clean up accounts

Lab 2: Compliance and Security Automation with AWS Config

Lab 3: Taking Action with AWS Systems Manager

Modulo 5: Resources

• Explore additional resources for security governance at scale

• Documentación del curso AWS Security Governance at Scale

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

• Identificar las características de Well-Architected Framework, los principios de diseño, los pilares de diseño y los usos comunes
• Aplicar los principios de diseño, los servicios clave y las mejores prácticas para cada pilar del Marco Bien Diseñado
• Utilice la herramienta bien diseñada para realizar revisiones bien diseñadas

Modulo 1: Well-Architected Introduction

• History of Well-Architected
• Goals of Well-Architected
• What is the AWS Well-Architected Framework?
• The AWS Well-Architected Tool

Modulo 2: Design Principles

• Operational Excellence
• Lab 1: Operational Excellence
• Reliability
• Lab 2: Reliability
• Security
• Lab 3: Security
• Performance Efficiency
• Lab 4: Performance Efficiency
• Cost Optimization
• Lab 5: Cost Optimization

• Documentación del curso AWS Well-Architected Best Practices

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

• Implementar soluciones de AWS en un ecosistema de big data
• Utilizar Apache Hadoop en el contexto de Amazon EMR
• Identificar los componentes de un clúster de Amazon EMR, luego lanzarlo y configurar un clúster de Amazon EMR
• Utilizar marcos de programación habituales disponibles para Amazon EMR, entre otros, Hive, Pig y streaming

Modulo 1: Overview of Big Data

• • What is big data
  • The big data pipeline
  • Big data architectural principals

Modulo 2: Big Data ingestion and transfer

• Overview: Data ingestion
• Transferring data

Modulo 3: Big data streaming and Amazon Kinesis

• Stream processing of big data
• Amazon Kinesis
• Amazon Kinesis Data Firehose
• Amazon Kinesis Video Streams
• Amazon Kinesis Data Analytics
• Hands-on lab 1: Streaming and Processing Apache Server Logs Using Amazon Kinesis

Modulo 4: Big data storage solutions

• AWS data storage options
• Storage solutions concepts
• Factors in choosing a data store

Modulo 5: Big data processing and analytics

• Big data processing and analytics
• Amazon Athena
• Hands-on lab 2: Using Amazon Athena to Analyze Log Data

Modulo 6: Apache Hadoop and Amazon EMR

• Introduction to Amazon EMR and Apache Hadoop
• Best practices for ingesting data
• Amazon EMR
• Amazon EMR architecture
• Hands-on lab 3: Storing and Querying Data on Amazon DynamoDB

Modulo 7: Using Amazon EMR

• Developing and running your application
• Launching your cluster
• Handling output from your completed jobs

Modulo 8: Hadoop programming frameworks

• Hadoop frameworks
• Other frameworks for use on Amazon EMR
• Hands-on lab 4: Processing Server Logs with Hive on Amazon EMR

Modulo 9: Web interfaces on Amazon EMR

• Hue on Amazon EMR
• Monitoring your cluster
• Hands-on lab 5: Running Pig Scripts in Hue on Amazon EMR

Modulo 10: Apache Spark on Amazon EMR

• Apache Spark
• Using Spark
• Hands-on lab 6: Processing NY Taxi Data Using Apache Spark

Modulo 11: Using AWS Glue to automate ETL workloads

• What is AWS Glue?
• AWS Glue: Job orchestration

Modulo 12: Amazon Redshift and big data

• Data warehouses vs. traditional databases
• Amazon Redshift
• Amazon Redshift architecture

Modulo 13: Securing your Amazon deployments

• Securing your Amazon deployments
• Amazon EMR security overview
• AWS Identity and Access Management (IAM) overview
• Securing data
• Amazon Kinesis security overview
• Amazon DynamoDB security overview
• Amazon Redshift security overview

Modulo 14: Managing big data costs

• Total cost considerations for Amazon EMR
• Amazon EC2 pricing models
• Amazon Kinesis pricing models
• Cost considerations for Amazon DynamoDB
• Cost considerations and pricing models for Amazon Redshift
• Optimizing cost with AWS

Modulo 15: Visualizing and orchestrating big data

• Visualizing big data
• Amazon QuickSight
• Orchestrating a big data workflow
• Hands-on lab 7: Using TIBCO Spotfire to visualize data

Modulo 16: Big data design patterns

• Common architectures

Modulo 17: Course wrap-up

• What’s next?

• Documentación del curso Big Data on AWS

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

• Ingenieros de almacenes de datos.
• Ingenieros de plataformas de datos.
• Arquitectos y operadores que construyen y gestionan canalizaciones de análisis de datos.

• Comparar las características y ventajas de los almacenes de datos, los lagos de datos y las arquitecturas de datos modernas.
• Diseñar e implementar una solución analítica de almacén de datos.
• Identificar y aplicar las técnicas adecuadas, incluida la compresión, para optimizar el almacenamiento de datos.
• Seleccionar e implementar las opciones apropiadas para ingerir, transformar y almacenar datos.
• Elegir la instancia y los tipos de nodo, los clústeres, el escalado automático y la topología de red adecuados para un caso de uso empresarial concreto.
• Comprender cómo el almacenamiento y el procesamiento de datos afectan a los mecanismos de análisis y visualización necesarios para obtener información empresarial procesable.
• Proteger los datos en reposo y en tránsito.
• Supervisar las cargas de trabajo analíticas para identificar y solucionar problemas.
• Aplicar las mejores prácticas de gestión de costes.

A: Overview of Data Analytics and the Data Pipeline

• Data analytics use cases
• Using the data pipeline for analytics

Módulo 1: Using Amazon Redshift in the Data Analytics Pipeline

• Why Amazon Redshift for data warehousing?
• Overview of Amazon Redshift

Módulo 2: Introduction to Amazon Redshift

• Amazon Redshift architecture
• Interactive Demo 1: Touring the Amazon Redshift console
• Amazon Redshift features
• Practice Lab 1: Load and query data in an Amazon Redshift cluster

Módulo 3: Ingestion and Storage

• Ingestion
• Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
• Data distribution and storage
• Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
• Querying data in Amazon Redshift
• Practice Lab 2: Data analytics using Amazon Redshift Spectrum

Módulo 4: Processing and Optimizing Data

• Data transformation
• Advanced querying
• Practice Lab 3: Data transformation and querying in Amazon Redshift
• Resource management
• Interactive Demo 4: Applying mixed workload management on Amazon Redshift
• Automation and optimization
• Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster

Módulo 5: Security and Monitoring of Amazon Redshift Clusters

• Securing the Amazon Redshift cluster
• Monitoring and troubleshooting Amazon Redshift clusters

Módulo 6: Designing Data Warehouse Analytics Solutions

• Data warehouse use case review
• Activity: Designing a data warehouse analytics workflow

B: Developing Modern Data Architectures on AWS

• Modern data architectures

Documentación oficial del curso Building Data Analytics Solutions using Amazon Redshift.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Ingenieros de plataformas de datos.
• Arquitectos de soluciones.
• Profesionales de TI.

• Aplicar metodologías de lago de datos en el plan y diseño de un lago de datos.
• Articular los componentes y servicios requeridos para crear un lago de datos de AWS.
• Asegurar un lago de datos con los permisos adecuados.
• Incorporar, almacenar y transformar datos en lagos de datos.

Módulo 1: Introduction to data lakes

• Describe the value of data lakes
• Compare data lakes and data warehouses
• Describe the components of a data lake
• Recognize common architectures built on data lakes

Módulo 2: Data ingestion, cataloging, and preparation

• Describe the relationship between data lake storage and data ingestion
• Describe AWS Glue crawlers and how they are used to create a data catalog
• Identify data formatting, partitioning, and compression for efficient storage and query
• Lab 1: Set up a simple data lake

Módulo 3: Data processing and analytics

• Recognize how data processing applies to a data lake
• Use AWS Glue to process data within a data lake
• Describe how to use Amazon Athena to analyze data in a data lake

Módulo 4: Building a data lake with AWS Lake Formation

• Describe the features and benefits of AWS Lake Formation
• Use AWS Lake Formation to create a data lake
• Understand the AWS Lake Formation security model
• Lab 2: Build a data lake using AWS Lake Formation

Módulo 5: Additional Lake Formation configurations

• Automate AWS Lake Formation using blueprints and workflows
• Apply security and access controls to AWS Lake Formation
• Match records with AWS Lake Formation FindMatches
• Visualize data with Amazon QuickSight
• Lab 3: Automate data lake creation using AWS Lake Formation blueprints
• Lab 4: Data visualization using Amazon QuickSight

Módulo 6: Architecture and course review

• Post course knowledge check
• Architecture review
• Course review

Documentación oficial del curso Building Data Lakes on AWS.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.

• Evaluar la relación entre Amazon Redshift y otros sistemas de big data
• Evaluar casos de uso de cargas de trabajo de almacenamiento de datos y revisar implementaciones reales de servicios de análisis y datos de AWS como parte de una solución de almacenamiento de datos
• Elegir un tamaño y tipo de nodo de Amazon Redshift adecuado para sus necesidades relacionadas con datos. Comprender cuáles son las características de seguridad adecuadas para Amazon Redshift, como el cifrado, los permisos de IAM y los permisos para bases de datos

Modulo 1: Introduction to Data Warehousing

• Relational databases
• Data warehousing concepts
• The intersection of data warehousing and big data
• Overview of data management in AWS
• lab 1: Introduction to Amazon Redshift

Modulo 2: Introduction to Amazon Redshift

• Conceptual overview
• Real-world use cases
• lab 2: Launching an Amazon Redshift cluster

Modulo 3: Launching clusters

• Building the cluster
• Connecting to the cluster
• Controlling access
• Database security
• Load data
• lab 3: Optimizing database schemas

Modulo 4: Designing the database schema

• Schemas and data types
• Columnar compression
• Data distribution styles
• Data sorting methods

Modulo 5: Identifying data sources

• Data sources overview
• Amazon S3
• Amazon DynamoDB
• Amazon EMR
• Amazon Kinesis Data Firehose
• AWS Lambda Database Loader for Amazon Redshift
• lab 4: Loading real-time data into an Amazon Redshift database

Modulo 6: Loading data

• Preparing Data
• Loading data using COPY
• Maintaining tables
• Concurrent write operations
• Troubleshooting load issues
• lab 5: Loading data with the COPY command

Modulo 7: Writing queries and tuning for performance

• Amazon Redshift SQL
• User-Defined Functions (UDFs)
• Factors that affect query performance
• The EXPLAIN command and query plans
• Workload Management (WLM)
• lab 6: Configuring workload management

Modulo 8: Amazon Redshift Spectrum

• Amazon Redshift Spectrum
• Configuring data for Amazon Redshift Spectrum
• Amazon Redshift Spectrum Queries
• lab 7: Using Amazon Redshift Spectrum

Modulo 9: Maintaining clusters

• Audit logging
• Performance monitoring
• Events and notifications
• Lab 8: Auditing and monitoring clusters
• Resizing clusters
• Backing up and restoring clusters
• Resource tagging and limits and constraints
• lab 9: Backing up, restoring and resizing clusters

Modulo 10: Analyzing and visualizing data

• Power of visualizations
• Building dashboards
• Amazon QuickSight editions and features

• Documentación del curso Data Warehousing on AWS

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

• Desarrolladores de software.
• Arquitectos de soluciones.
• Profesionales de TI que quieran mejorar sus habilidades de desarrollo con los servicios de AWS.

• Crear una sencilla y completa aplicación en la nube con los kits de desarrollo de software de AWS (AWS SDK), la interfaz de línea de comandos (AWS CLI) y los IDE.
• Configurar los permisos de AWS Identity and Access Management (IAM) para respaldar un entorno de desarrollo.
• Utilizar múltiples patrones de programación en las aplicaciones para acceder a los servicios de AWS.
• Utilizar los AWS SDK para realizar operaciones CRUD (crear, leer, actualizar, eliminar) en los recursos de Amazon Simple Storage Service (Amazon S3) y Amazon DynamoDB.

Módulo 1: Course Overview

• Logistics
• Student resources
• Agenda
• Introductions

Módulo 2: Building a Web Application on AWS

• Discuss the architecture of the application you are going to build during this course
• Explore the AWS services needed to build your web application
• Discover how to store, manage, and host your web application

Módulo 3: Getting Started with Development on AWS

• Describe how to access AWS services programmatically
• List some programmatic patterns and how they provide efficiencies within AWS SDKs and AWS CLI
• Explain the value of AWS Cloud9

Módulo 4: Getting Started with Permissions

• Review AWS Identity and Access Management (IAM) features and components permissions to support a development environment
• Demonstrate how to test AWS IAM permissions
• Configure your IDEs and SDKs to support a development environment
• Demonstrate accessing AWS services using SDKs and AWS Cloud9

Lab 1: Configure the Developer Environment

Módulo 5: Getting Started with Storage

• Describe the basic concepts of Amazon S3
• List the options for securing data using Amazon S3
• Define SDK dependencies for your code
• Explain how to connect to the Amazon S3 service
• Describe request and response objects

Módulo 6: Processing Your Storage Operations

• Perform key bucket and object operations
• Explain how to handle multiple and large objects
• Create and configure an Amazon S3 bucket to host a static website
• Grant temporary access to your objects
• Demonstrate performing Amazon S3 operations using SDKs

Lab 2: Develop Solutions Using Amazon S3

Módulo 7: Getting Started with Databases

• Describe the key components of DynamoDB
• Explain how to connect to DynamoDB
• Describe how to build a request object
• Explain how to read a response object
• List the most common troubleshooting exceptions

Módulo 8: Processing Your Database Operations

• Develop programs to interact with DynamoDB using AWS SDKs
• Perform CRUD operations to access tables, indexes, and data
• Describe developer best practices when accessing DynamoDB
• Review caching options for DynamoDB to improve performance
• Perform DynamoDB operations using SDK

Lab 3: Develop Solutions Using Amazon DynamoDB

Módulo 9: Processing Your Application Logic

• Develop a Lambda function using SDKs
• Configure triggers and permissions for Lambda functions
• Test, deploy, and monitor Lambda functions

Lab 4: Develop Solutions Using AWS Lambda Functions